World malaria report 2016

WORLD MALARIA REPORT

2016

World malaria report 2016. ISBN 978-92-4-151171-1 © World Health Organization 2016 Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercialShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/ igo). Under the terms of this licence, you may copy, redistribute and adapt the work for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this work, there should be no suggestion that WHO endorses any specific organization, products or services. The use of the WHO logo is not permitted. If you adapt the work, then you must license your work under the same or equivalent Creative Commons licence. If you create a translation of this work, you should add the following disclaimer along with the suggested citation: “This translation was not created by the World Health Organization (WHO). WHO is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition”. Any mediation relating to disputes arising under the licence shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization (http://www.wipo.int/amc/en/mediation/ rules). Suggested citation. World Malaria Report 2016. Geneva: World Health Organization; 2016. Licence: CC BY-NC-SA 3.0 IGO. Cataloguing-in-Publication (CIP) data. CIP data are available at http://apps.who.int/iris. Sales, rights and licensing. To purchase WHO publications, see http://apps.who.int/bookorders. To submit requests for commercial use and queries on rights and licensing, see http://www.who.int/about/licensing. Third-party materials. If you wish to reuse material from this work that is attributed to a third party, such as tables, figures or images, it is your responsibility to determine whether permission is needed for that reuse and to obtain permission from the copyright holder. The risk of claims resulting from infringement of any third-party-owned component in the work rests solely with the user. General disclaimers. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of WHO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by WHO in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by WHO to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall WHO be liable for damages arising from its use. Map production: WHO Global Malaria Programme and WHO Public Health Information and Geographic Systems. Design and layout: Alex Williamson (cover), designisgood.info and www.paprika-annecy.com Photo credits | pp. vi, x, 1, 6, 16, 23, 26, 34, 38, 56: © The Global Fund/John Rae | p. 86: © WHO/Sven Torfinn Please consult the WHO Global Malaria Programme website for the most up-to-date version of all documents (www.who.int/malaria) Printed in Switzerland

Contents Foreword Acknowledgements Abbreviations Key points 1. Global targets, milestones and indicators 2. Investments in malaria programmes and research iv vii xi xii 2 7 8 11 12 13 17 20 20 20 22 24 25 27 28 29 30 31 32 32 35 36 37 39 40 42 45 46 47 48 50 50 52 54 57 WORLD MALARIA REPORT 2016

2.1 Total expenditure for malaria control and elimination 2.2 Funding for malaria-related research 2.3 Malaria expenditure per capita for malaria control and elimination 2.4 Commodity procurement trends 3.1 Population at risk sleeping under an insecticide-treated mosquito net 3.2 Targeted risk group receiving ITNs 3.3 Population at risk protected by indoor residual spraying 3.4 Population at risk sleeping under an insecticide-treated mosquito net or protected by indoor residual spraying 3.5 Vector insecticide resistance 3.6 Pregnant women receiving three or more doses of intermittent preventive therapy 4.1 Children aged under 5 years with fever for whom advice or treatment was sought from a trained provider 4.2 Suspected malaria cases receiving a parasitological test 4.3 Suspected malaria cases attending public health facilities and receiving a parasitological test 4.4 Malaria cases receiving first-line antimalarial treatment according to national policy 4.5 ACT treatments among all malaria treatments 4.6 Parasite resistance 5.1 Health facility reports received at national level 5.2 Malaria cases detected by surveillance systems 6.1 Estimated number of malaria cases by WHO region, 2000–2015 6.2 Estimated number of malaria deaths by WHO region, 2000–2015 6.3 Parasite prevalence 6.4 Malaria case incidence rate 6.5 Malaria mortality rate 6.6 Malaria elimination and prevention of re-establishment 6.7 Malaria cases and deaths averted since 2000 and change in life expectancy 6.8 Economic value of reduced malaria mortality risk, estimated by full income approach

3. Preventing malaria

4. Diagnostic testing and treatment

5. Malaria surveillance systems 6. Impact

Conclusions References Annexes

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Foreword

Dr Margaret Chan Director-General World Health Organization The World Malaria Report, published annually by WHO, provides an in-depth analysis of progress and trends in the malaria response at global, regional and country levels. It is the result of a collaborative effort with ministries of health in affected countries and many partners around the world. Our 2016 report spotlights a number of positive trends, particularly in sub-Saharan Africa, the region that carries the heaviest malaria burden. It shows that, in many countries, access to disease-cutting tools is expanding at a rapid rate for those most in need. Children are especially vulnerable, accounting for more than two thirds of global malaria deaths. In 22 African countries, the proportion of children with a fever who received a malaria diagnostic test at a public health facility increased by 77% over the last 5 years. This test helps health providers swiftly distinguish between malarial and non-malarial fevers, enabling appropriate treatment. Malaria in pregnancy can lead to maternal mortality, anaemia and low birth weight, a major cause of infant mortality. WHO recommends intermittent preventive treatment in pregnancy, known as IPTp, for all pregnant women in sub-Saharan Africa living in areas of moderate-to-high transmission of malaria. The last 5 years have seen a five-fold increase in the delivery of three or more doses of IPTp in 20 African countries. Long-lasting insecticidal nets are the mainstay of malaria prevention. WHO recommends their use for all people at risk of malaria. Across sub-Saharan Africa, the proportion of people sleeping under treated nets has nearly doubled over the last 5 years. We have made excellent progress, but our work is incomplete. Last year alone, the global tally of malaria reached 212 million cases and 429 000 deaths. Across

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Africa, millions of people still lack access to the tools they need to prevent and treat the disease. In many countries, progress is threatened by the rapid development and spread of mosquito resistance to insecticides. Antimalarial drug resistance could also jeopardize recent gains. In 2015, the World Health Assembly endorsed the WHO Global Technical Strategy for Malaria, a 15-year malaria framework for all countries working to control and eliminate malaria. It sets ambitious but attainable goals for 2030, with milestones along the way to track progress. The Strategy calls for the elimination of malaria in at least 10 countries by the year 2020 – a target well within reach. According to this report, 10 countries and territories reported fewer than 150 locally-acquired cases of malaria. A further nine countries reported between 150 and 1000 cases. But progress towards other global targets must be accelerated. The report finds that less than half of the 91 malaria-affected countries are on track to achieve the 2020 milestones of a 40% reduction in case incidence and mortality. To speed progress towards our global malaria goals, WHO is calling for new and improved malaria-fighting tools. Greater investments are needed in the development of new vector control interventions, improved diagnostics and more effective medicines. WHO announced that the world’s first malaria vaccine would be piloted in three countries in sub-Saharan Africa. The vaccine, known as RTS,S, has been shown to provide partial protection against malaria in young children. It will be evaluated as a potential complement to the existing package of WHO-recommended malaria preventive, diagnostic and treatment measures. The need for more funding is an urgent priority. In 2015, malaria financing totalled US$ 2.9 billion. To achieve our global targets, contributions from both domestic and international sources must increase substantially, reaching US$ 6.4 billion annually by 2020. The challenges we face are sizeable but not insurmountable. Recent experience has shown that with robust funding, effective programmes and country leadership, progress in combatting malaria can be sustained and accelerated. The potential returns are well worth the effort. With all partners united, we can defeat malaria and improve the health of millions of people around the world.

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Acknowledgements We are very grateful to the numerous people who contributed to the production of the World Malaria Report 2016. The following people collected and reviewed data from malaria endemic countries: Ahmad Mureed and Fraidon Sediqi (Afghanistan); Lammali Karima (Algeria); Pedro Rafael Dimbu and Yava Luvundo Ricardo (Angola); Giovanini Coelho and Mario Zaidenberg (Argentina); Suleyman Mammadov (Azerbaijan); Anjan Kumar Saha (Bangladesh); Carlos Ayala and Kim Bautista (Belize); Dos Santos Hounkpe Bella (Benin); Tenzin Wangdi (Bhutan); Percy Halkyer and Raúl Marcelo Manjón Tellería (Bolivia [Plurinational State of]); Tjantilili Mosweunyane (Botswana); Oscar Mesones Lapouble and Cassio Roberto Leonel Peterka (Brazil); Yacouba Savadogo (Burkina Faso); Ndayizeye Félicien (Burundi); António Lima Moreira (Cabo Verde); Tol Bunkea (Cambodia); Kouambeng Celestin (Cameroon); Christophe Ndoua (Central African Republic); Mahamat Idriss Djaskano (Chad); Li Zhang (China); Gabriela Rey and Sandra Lorena Giron Vargas (Colombia); Astafieva Marina (Comoros); Youndouka Jean Mermoz (Congo); Liliana Jiménez Gutiérrez and Enrique Pérez-Flores (Costa Rica); Ehui Anicet and Parfait Katche (Côte d’Ivoire); Kim Yun Chol (Democratic People’s Republic of Korea); Joris Losimba Likwela (Democratic Republic of the Congo); Luz A. Mercedes and Hans Salas (Dominican Republic); César Díaz and Adriana Estefanía Echeverría Matute (Ecuador); Ahmed El-Taher Khater (Egypt); Jaime Enrique Alemán Escobar and Franklin Hernandez (El Salvador); Matilde Riloha (Equatorial Guinea); Selam Mihreteab and Selam Mihreteab (Eritrea); Hiwot Solomon Taffese (Ethiopia); Laure Garancher (French Guiana); Okome Nze Gyslaine (Gabon); Momodou Kalleh (Gambia); Constance Bart-Plange (Ghana); Jaime Juárez and Erica Chávez Vásquez (Guatemala); Nouman Diakite (Guinea); Jean Seme Fils Alexandre and Quacy Grant (Guyana); Darlie Antoine and Moussa Thior (Haiti); Engels Ilich Banegas Medina and Rosa Elena Mejía (Honduras); A.C. Dhariwal (India); M. Epid and Elvieda Sariwati (Indonesia); Leyla Faraji and Ahmad Raeisi (Iran [Islamic Republic of]); Muthana Ibrahim Abdul Kareem (Iraq); Khalil Kanani (Jordan); James Kiarie (Kenya); Almunther Alhasawi (Kuwait); Bouasy Hongvanthong (Lao People’s Democratic Republic); Najib Achi (Lebanon); Oliver J. Pratt (Liberia); Abdunnaser Ali El-Buni (Libya); Rakotorahalahy Andry Joeliarijaona (Madagascar); Austin Albert Gumbo (Malawi); Mohd Hafizi Bin Abdul Hamid (Malaysia); Diakalia Kone (Mali); Mohamed Lemine Ould Khairy (Mauritania); Anita Bahena, Ezequiel Díaz Pérez, Rosario García Suárez and Héctor Olguín Bernal (Mexico); Souad Bouhout (Morocco); Guidion Mathe (Mozambique); Aung Thi (Myanmar); Mwalenga Nghipumbwa (Namibia); Rajendra Mishra and Uttam Raj Pyakurel (Nepal); Martha Reyes and Aída Mercedes Soto Bravo (Nicaragua); Djermakoye Hadiza Jackou (Niger); Audu Bala Mohammed (Nigeria); Majed Al-Zadjali (Oman); Muhammad Suleman Memon (Pakistan); Margarita Ana Botello, José Lasso, Carlos Victoria and Fernando Vizcaíno (Panama); John Deli (Papua New Guinea); Miguel Angel Aragón and Cynthia Viveros (Paraguay); Mónica Guardo and Victor Alberto Laguna Torres (Peru); Raffy Deray (Philippines); Maha Hammam Alshamali (Qatar); Park Kyeongeun (Republic of Korea); Murindahabi Ruyange Monique vii

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(Rwanda); Jessica Da Veiga Soares (Sao Tome and Principe); Mohammed Hassan Al-Zahrani (Saudi Arabia); Medoune Ndiop (Senegal); Samuel Juana Smith (Sierra Leone); John Leaburi (Solomon Islands); Fahmi Essa Yusuf (Somalia); Bridget Shandukani (South Africa); H.D.B. Herath (Sri Lanka); Abd Alla Ahmed Ibrahim Mohammed (Sudan); Beatrix Jubithana and Juanita Malmberg (Suriname); Zulisile Zulu (Swaziland); Nipon Chinanonwait (Thailand); Maria do Rosiro de Fatima Mota (Timor-Leste); Tchadjobo Tchassama (Togo); Dhikrayet Gamara (Tunisia); Damian Rutazaana (Uganda); Mary John (United Arab Emirates); Anna Mahendeka (United Republic of Tanzania, [Mainland]); Abdul-wahid H. Al-mafazy (United Republic of Tanzania [Zanzibar]); Esau Nackett (Vanuatu); Angel Manuel Alvarez and Jesus Toro Landaeta (Venezuela [Bolivarian Republic of]); Nguyen Quy Anh (Viet Nam); Moamer Badi (Yemen); Mercy Mwanza Ingwe (Zambia); Busisani Dube and Wonder Sithole (Zimbabwe). The following WHO staff in regional and subregional offices assisted in the design of data collection forms; the collection and validation of data; and the review of epidemiological estimates, country profiles, regional profiles and sections: Birkinesh Amenshewa, Magaran Bagayoko, Steve Banza Kubenga and Issa Sanou (WHO Regional Office for Africa [AFRO]); Spes Ntabangana (AFRO/ Inter-country Support Team [IST] Central Africa); Khoti Gausi (AFRO/IST East and Southern Africa); Abderrahmane Kharchi Tfeil (AFRO/IST West Africa); Maria Paz Ade, Janina Chavez, Rainier Escalada, Valerie Mize, Roberto Montoya, Eric Ndofor and Prabhjot Singh (WHO Regional Office for the Americas [AMRO]); Hoda Atta, Caroline Barwa and Ghasem Zamani (WHO Regional Office for the Eastern Mediterranean [EMRO]); Elkhan Gasimov and Karen Taksoe-Vester (WHO Regional Office for Europe [EURO]); Eva-Maria Christophel (WHO Regional Office for South-East Asia [SEARO]); Rabindra Abeyasinghe, James Kelley, Steven Mellor and Raymond Mendoza (WHO Regional Office for the Western Pacific [WPRO]). Carol D’Souza and Jurate Juskaite (Global Fund to Fight AIDS, Tuberculosis and Malaria [Global Fund]) supplied information on financial disbursements from the Global Fund. Adam Wexler (Kaiser Family Foundation) provided information on financial contributions for malaria control from the United States of America. Julie Wallace (United States Agency for International Development) and Iain Jones (United Kingdom Department for International Development) reviewed financing data from their respective agencies. Jeremy Lauer (WHO Department of Health Systems Governance and Financing) edited the narrative on the economic valuation of malaria mortality reduction. John Milliner (Milliner Global Associates) provided information on long-lasting insecticidal nets delivered by manufacturers. Peter Gething (University of Oxford), Samir Bhatt (Imperial College, University of London) and the Malaria Atlas Project (MAP, www.map.ox.ac.uk) team, with the support of the Bill & Melinda Gates Foundation and the Medical Research Council (United Kingdom of Great Britain and Northern Ireland [United Kingdom]), produced estimates of insecticide-treated mosquito net (ITN) coverage for African countries using data from household surveys, ITN deliveries by manufacturers, ITNs distributed by national malaria control programmes (NMCPs), and ITN coverage indicators. They also produced estimates of Plasmodium falciparum parasite prevalence in sub-Saharan Africa. Catherine Moyes and Antoinette Wiebe (MAP) and Christen Fornadel (United States President’s Malaria Initiative) provided data on insecticide resistance and Anna Trett assisted with data compilation. Liliana Carvajal and Valentina Buj (United Nations Children’s Fund [UNICEF]) reviewed data and texts and made suggestions for improvement.

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Acknowledgements Michael Lynch, John Painter and Nelli Westercamp (United States Centers for Disease Control and Prevention) and Cristin Fergus (London School of Economics, University of London) provided data analysis and interpretation for sections on chemoprevention, diagnostic testing and treatment. Adam Bennett (Global Health Group), Donal Bisanzio and Peter Gething (MAP) and Thom Eisele (Tulane University) produced analysis of malaria treatment from household surveys. Li Liu (Johns Hopkins Bloomberg School of Public Health), Dan Hogan and Colin Mathers (WHO Department of Health Statistics and Information Systems) prepared estimates of malaria mortality in children aged under 5 years, on behalf of the Child Health Epidemiology Reference Group, and undertook calculations on life expectancy. The maps for country and regional profiles were produced by MAP’s ROADMAPII team; led by Mike Thorn, the team comprised Harry Gibson, Naomi Gray, Joe Harris, Andy Henry, Annie Kingsbury, Daniel Pfeffer and Jen Rozier. MAP is supported by the Bill & Melinda Gates Foundation and the Medical Research Council (United Kingdom). We are also grateful to: ■■

Melanie Renshaw (African Leaders Malaria Alliance [ALMA]), Trenton Ruebush (independent consultant) and Larry Slutsker (Program for Appropriate Technology in Health [PATH]), who graciously reviewed all sections and provided substantial comments for their improvement; Claudia Nannini (WHO) for legal review; Carlota Gui (WHO consultant) and Laurent Bergeron (WHO Global Malaria Programme) for the translation into Spanish and French, respectively, of the foreword and key points; Claude Cardot and the Designisgood team for the design and layout of the report; Paprika (Annecy, France) for generating Annex 4; Alex Williamson for the report cover; and Hilary Cadman and the Cadman Editing Services team for technical editing of the report.

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The production of the World Malaria Report 2016 was coordinated by Richard Cibulskis (WHO Global Malaria Programme). Laurent Bergeron (WHO Global Malaria Programme) provided programmatic support for overall management of the project. The World Malaria Report 2016 was produced by John Aponte (WHO consultant), Maru Aregawi, Laurent Bergeron, Richard Cibulskis, Jane Cunningham, Tessa Knox, Edith Patouillard, Pascal Ringwald, Silvia Schwarte, Saira Stewart and Ryan Williams, on behalf of the WHO Global Malaria Programme. We are grateful to our colleagues in the Global Malaria Programme who reviewed sections of the report and provided helpful comments: Pedro Alonso, Amy Barrette, Andrea Bosman, Gawrie Loku Galappaththy, Abdisalan Noor, Peter Olumese, Leonard Ortega, Camille Pillon, Charlotte Rasmussen, Vasee Sathiyamoorthy and David Schellenberg. We also thank Hiwot Taffese Negash and Simone Colairo-Valerio for administrative support. Funding for the production of this report was gratefully received from the Bill & Melinda Gates Foundation; Luxembourg’s Ministry of Foreign and European Affairs – Directorate for Development Cooperation and Humanitarian Affairs; the Spanish Agency for International Development Cooperation; the Swiss Agency for Development and Cooperation through a grant to the Swiss Tropical and Public Health Institute; and the United States Agency for International Development. WORLD MALARIA REPORT 2016

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Abbreviations ACT AIDS AIM AMFm ANC CDC CI cITN CRS DAC GDP Global Fund GTS HIV HRP2 IPTi IPTp IQR IRS ITN LLIN M&E NMCP OECD artemisinin-based combination therapy acquired immunodeficiency syndrome Action and investment to defeat malaria 2016–2030 Affordable Medicine Facility– malaria antenatal care Centers for Disease Control and Prevention confidence interval conventional insecticide-treated net creditor reporting system Development Assistance Committee gross domestic product Global Fund to Fight AIDS, Tuberculosis and Malaria Global Technical Strategy for Malaria 2016–2030 human immunodeficiency virus histidine rich protein 2 intermittent preventive treatment in infants intermittent preventive treatment in pregnancy interquartile range indoor residual spraying insecticide-treated mosquito net long-lasting insecticidal net monitoring and evaluation national malaria control programme Organisation for Economic Co-operation and Development P. PMI PPP RDT SDG SMC UI UN UNICEF USA USAID VSL WHO WTA Plasmodium President’s Malaria Initiative purchasing power parity rapid diagnostic test Sustainable Development Goal seasonal malaria chemoprevention uncertainty interval United Nations United Nations Children’s Fund United States of America United States Agency for International Development value of a statistical life World Health Organization willingness to accept

SP sulfadoxine-pyrimethamine

AQ amodiaquine

DDT dichloro-diphenyl-trichloroethane

Abbreviations of WHO regions and offices AFR AFRO AMR AMRO EMR EMRO EUR EURO SEAR SEARO WPR WPRO WHO African Region WHO Regional Office for Africa WHO Region of the Americas WHO Regional Office for the Americas WHO Eastern Mediterranean Region WHO Regional Office for the Eastern Mediterranean WHO European Region WHO Regional Office for Europe WHO South-East Asia Region WHO Regional Office for South-East Asia WHO Western Pacific Region WHO Regional Office for the Western Pacific

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Key points 1. Global targets, milestones and indicators ■■

The targets of the Global Technical Strategy for Malaria 2016–2030 (GTS) are, by 2030: to reduce malaria incidence and mortality rates globally by at least 90% compared with 2015 levels; to eliminate malaria from at least 35 countries in which malaria was transmitted in 2015; and to prevent re-establishment of malaria in all countries that are malaria free. For malaria, Target 3.3 of the Sustainable Development Goals (SDGs) – to end the epidemics of AIDS, TB, malaria and neglected tropical diseases (NTDs) by 2030 – is interpreted by WHO as the attainment of the GTS targets. To track progress of the GTS, the World Malaria Report 2016 presents information on 26 indicators. The World Malaria Report is produced by the WHO Global Malaria Programme, with the help of WHO regional and country offices, ministries of health in endemic countries and a broad range of other partners. The primary sources of information are reports from 91 endemic countries. This information is supplemented by data from nationally representative household surveys and databases held by other organizations.

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2. Investments in malaria programmes and research ■■

Total funding for malaria control and elimination in 2015 is estimated at US$ 2.9 billion, having increased by US$ 0.06 billion since 2010. This total represents just 46% of the GTS 2020 milestone of US$ 6.4 billion. Governments of endemic countries provided 32% of total funding in 2015, of which US$ 612 million was direct expenditures through national malaria control programmes (NMCPs) and US$ 332 million was expenditures on malaria patient care. The United States of America is the largest single international funder of malaria control activities, accounting for an estimated 35% of global funding in 2015, followed by the United Kingdom of Great Britain and Northern Ireland (16%), France (3.2%), Germany (2.4%), Japan (2.3%), Canada (1.7%), the Bill & Melinda Gates Foundation (1.2%) and European Union institutions (1.1%). About one half of this international funding (45%) is channelled through the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund). Spending on research and development for malaria was estimated at US$ 611 million in 2014 (the latest year for which data are available), increasing from US$ 607 million in 2010, and representing more than 90% of the GTS annual investment target of US$ 673 million.

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Countries with the highest number of malaria cases are furthest from the per capita spending milestones for 2020 set in the GTS.

3. Preventing malaria Vector control ■■

The proportion of the population at risk in sub-Saharan Africa sleeping under an insecticide-treated mosquito net (ITN) or protected by indoor residual spraying (IRS) is estimated to have risen from 37% in 2010 (uncertainty interval [UI]: 25–48%) to 57% in 2015 (UI: 44–70%). In sub-Saharan Africa, 53% of the population at risk slept under an ITN in 2015 (95% confidence interval [CI]: 50–57%), increasing from 30% in 2010 (95% CI: 28–32%), The rise in the proportion of people at risk sleeping under an ITN has been driven by an increase in the proportion of the population with access to an ITN (60% in 2015, 95% CI: 57–64%; 34% in 2010, 95% CI: 32–35%). The proportion of households with at least one ITN increased to 79% in 2015 (95% CI: 76–83); thus, a fifth of households where ITNs are the main method of vector control do not have access to a net. The proportion of households with sufficient ITNs for all household members was 42% (95% CI: 39–45%). IRS is generally used by NMCPs only in particular areas. The proportion of the population at risk protected by IRS declined from a peak of 5.7% globally in 2010 to 3.1% in 2015, and from 10.5% to 5.7% in sub-Saharan Africa. Reductions in IRS coverage may be attributed to cessation of spraying with pyrethroids, particularly in the WHO African Region. Of 73 malaria endemic countries that provided monitoring data for 2010 onwards, 60 reported resistance to at least one insecticide, and 50 reported resistance to two or more insecticide classes. Resistance to pyrethroids – the only class currently used in ITNs –is the most commonly reported. A WHO-coordinated five-country evaluation showed that ITNs still remained effective but there is still a need for new vector control tools. In 2015, 31% of eligible pregnant women received three or more doses of intermittent preventive treatment in pregnancy (IPTp) among 20 countries with sufficient data, a major increase from 6% in 2010.

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Intermittent preventive therapy in pregnancy ■■

4. Diagnostic testing and treatment Access to care ■■

Among 23 nationally representative surveys completed in sub-Saharan Africa between 2013 and 2015 (representing 61% of the population at risk), a median of 54% of febrile children aged under 5 years (interquartile range [IQR]: 41–59%) were taken to a trained provider.

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A higher proportion of febrile children sought care in the public sector (median: 42%, IQR: 31–50%) than in the private sector (median: 20%, IQR: 12–28%). A large proportion of febrile children were not brought for care (median: 36%, IQR: 26–42%). The proportion of febrile children who received a malaria diagnostic test was greater if they sought care in the public sector (median: 51%, IQR: 35–60%) than if the children sought care in the formal private sector (median: 40%, IQR: 28–57%) or in the informal private sector (median: 9%, IQR: 4–12%). The proportion receiving a test in the public sector has increased from 29% in 2010 (IQR: 19–46%). Data reported by NMCPs indicate that the proportion of suspected malaria cases receiving a parasitological test in the public sector increased from 40% of suspected cases in the WHO African Region in 2010 to 76% in 2015. This increase was primarily due to an increase in the use of rapid diagnostic tests (RDTs), which accounted for 74% of diagnostic testing among suspected cases in 2015. HRP2 deletions, which allow malaria parasites to evade detection by common RDTs, have been reported from more than 10 countries. Among 11 nationally representative household surveys conducted in sub-Saharan Africa from 2013 to 2015, the median proportion of children aged under 5 years with evidence of recent or current Plasmodium falciparum infection and a history of fever, who received any antimalarial drug, was 30% (IQR: 20–51%). The median proportion receiving an artemisinin-based combination therapy (ACT) was 14% (IQR: 5–45%). However, no clear conclusions can be drawn from these findings because the ranges associated with the medians are wide, indicating large variation among countries; in addition, the household surveys cover only a third of the population at risk in sub-Saharan Africa. Further investments are needed to better track malaria treatment at health facilities (through routine reporting systems and health facility surveys) and at community level to better understand the extent of barriers to accessing malaria treatment. The proportion of antimalarial treatments that are ACTs given to children with both a fever in the previous 2 weeks and a positive RDT at the time of survey increased from a median of 29% in 2010–2012 (IQR: 17–55%) to 80% in 2013–2015 (IQR: 29–95%). Antimalarial treatments were more likely to be ACTs if children sought treatment at public health facilities or via community health workers than if they sought treatment in the private sector. Plasmodium falciparum resistance to artemisinin has been detected in five countries in the Greater Mekong subregion. In Cambodia, high failure rates after treatment with an ACT have been detected for four different ACTs.

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Key points

5. Malaria surveillance systems ■■

The proportion of health facility reports received at national level exceeded 80% in 40 of the 47 countries that reported on this indicator. This indicator could not be calculated for 43 countries, either because the number of health facilities that were expected to report was not specified (two countries) or because the number of reports submitted was not stated (17 countries), or both (24 countries). A total of 23 countries received reports from private health facilities, but these comprised a minority of all reports received in these countries (median: 2.1%, IQR: 0.6–13%). In 2015, it is estimated that malaria surveillance systems detected 19% of cases that occur globally (UI: 16–21%). The bottlenecks in case detection vary by country and WHO region. In four WHO regions a large proportion of patients seek treatment in the private sector and these cases are not captured by existing surveillance systems. In three WHO regions a relatively low proportion of patients attending public health facilities also receive a diagnostic test. Case detection rates have improved since 2010 (10%), with most of the improvement being due to increased diagnostic testing in sub-Saharan Africa.

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6. Impact Parasite prevalence ■■

The proportion of the population at risk in sub-Saharan Africa who are infected with malaria parasites is estimated to have declined from 17% in 2010 to 13% in 2015 (UI: 11–15%). The number of people infected with malaria parasites in sub-Saharan Africa is estimated to have decreased from 131 million in 2010 (UI: 126–136 million) to 114 million in 2015 (UI: 99–130 million). Infection rates are higher in children aged 2–10 years, but most infected people are in other age groups. In 2015, an estimated 212 million cases of malaria occurred worldwide (UI: 148–304 million). Most of the cases in 2015 were in the WHO African Region (90%), followed by the WHO South-East Asia Region (7%) and the WHO Eastern Mediterranean Region (2%). About 4% of estimated cases globally are due to P. vivax, but outside the African continent the proportion of P. vivax infections is 41%. The incidence rate of malaria is estimated to have decreased by 41% globally between 2000 and 2015, and by 21% between 2010 and 2015.

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Of 91 countries and territories with malaria transmission in 2015, 40 are estimated to have achieved a reduction in incidence rates of 40% or more between 2010 and 2015, and can be considered on track to achieve the GTS milestone of a further reduction of 40% by 2020. Reductions in case incidence rates need to be accelerated in countries with high case numbers if the GTS milestone of a 40% reduction in case incidence rates by 2020 is to be achieved. In 2015, it was estimated that there were 429 000 deaths from malaria globally (UI: 235 000–639 000). Most deaths in 2015 are estimated to have occurred in the WHO African Region (92%), followed by the WHO South-East Asia Region (6%) and the WHO Eastern Mediterranean Region (2%). The vast majority of deaths (99%) are due to P. falciparum malaria. Plasmodium vivax is estimated to have been responsible for 3100 deaths in 2015 (range: 1800–4900), with 86% occurring outside Africa. In 2015, 303 000 malaria deaths (range: 165 000–450 000) are estimated to have occurred in children aged under 5 years, which is equivalent to 70% of the global total. The number of malaria deaths in children is estimated to have decreased by 29% since 2010, but malaria remains a major killer of children, taking the life of a child every 2 minutes. Malaria mortality rates are estimated to have declined by 62% globally between 2000 and 2015 and by 29% between 2010 and 2015. In children aged under 5 years, they are estimated to have fallen by 69% between 2000 and 2015 and by 35% between 2010 and 2015. Of 91 countries and territories with malaria transmission in 2015, 39 are estimated to have achieved a reduction of 40% or more in mortality rates between 2010 and 2015. A further 10 countries had zero indigenous deaths in 2015. If the GTS milestone of a 40% reduction in mortality rates is to be achieved by 2020, rates of mortality reduction must increase in countries with high numbers of deaths. Between 2000 and 2015, 17 countries eliminated malaria (i.e. attained zero indigenous cases for 3 years or more); six of these countries have been certified as malaria free by WHO. In progressing to malaria elimination, the 17 countries reported a median of 184 indigenous cases 5 years before attaining zero cases (IQR: 78–728) and a median of 1748 cases 10 years before attaining zero cases (IQR: 423–5731). In 2015, 10 countries and territories reported fewer than 150 indigenous cases and a further nine countries reported between 150 and 1000 indigenous cases. Thus, there appears to be a good prospect of attaining the GTS milestone of eliminating malaria from 10 countries by 2020.

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Key points

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Malaria has not been re-established in any of the countries that eliminated malaria between 2000 and 2015. Between 2001 and 2015, it is estimated that a cumulative 6.8 million fewer malaria deaths have occurred globally than would have occurred had incidence and mortality rates remained unchanged since 2000. The highest proportion of deaths was averted in the WHO African Region (94%). Of the estimated 6.8 million fewer malaria deaths between 2001 and 2015, about 6.6 million (97%) were for children aged under 5 years. Not all of the deaths averted can be attributed to malaria control efforts. Some progress is probably related to increased urbanization and overall economic development, which has led to improved housing and nutrition. As a consequence of reduced malaria mortality rates, particularly among children aged under 5 years, it is estimated that life expectancy at birth has increased by 1.2 years in the WHO African Region. This increase represents 12% of the total increase in life expectancy of 9.4 years seen in sub-Saharan Africa, from 50.6 years in 2000 to 60 years in 2015. Globally, reductions in malaria mortality have led to an increase in life expectancy of 0.26 years in malaria endemic countries, representing 5% of the overall gain of 5.1 years. Current methodologies suggest that the increased life-expectancy resulting from malaria mortality reductions observed between 2000 and 2015 can be valued at US$ 1810 billion in the WHO African Region (UI: US$ 1330–2480 billion), which is equivalent to 44% of the gross domestic product (GDP) of the affected countries in 2015. Globally, the malaria mortality reductions are valued at US$ 2040 billion (UI: US$ 1560–2700 billion), which is 3.6% of the total GDP of malaria affected countries. The economic value of longer life is expressed as a percentage of GDP to provide a convenient and well-known comparison, but is not meant to suggest that the value of longevity is itself a component of domestic output, or that the value of these gains enter directly into the national income accounts. Nonetheless, the comparison suggests that the value of the gains in life expectancy due to reductions in malaria mortality are substantial.

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Avant-propos

Dr Margaret Chan Directeur général de l’Organisation mondiale de la Santé (OMS) Le Rapport sur le paludisme dans le monde, publié chaque année par l’OMS, fournit une analyse détaillée des progrès et des tendances de la lutte contre le paludisme au niveau mondial, régional et national. Il s’agit là du produit d’un effort collaboratif entre les ministères de la Santé des pays endémiques et de nombreuses organisations partenaires dans le monde. Notre rapport 2016 met en lumière plusieurs tendances positives, notamment en Afrique subsaharienne où la maladie sévit le plus. Il indique que l’accès aux interventions préventives et thérapeutiques augmente rapidement parmi les populations qui en ont le plus besoin et ce, dans nombre de pays. Les enfants sont particulièrement vulnérables ; ils représentent plus des deux tiers des décès dus au paludisme dans le monde. Des enquêtes réalisées dans 22 pays africains montrent que le pourcentage d’enfants ayant été soumis à un test de diagnostic du paludisme au sein d’établissements de soins publics a augmenté de 77 % ces cinq dernières années. Ce test permet aux prestataires de santé de rapidement différencier les fièvres palustres des autres, ce qui garantit l’administration d’un traitement approprié. Le paludisme pendant la grossesse peut avoir des conséquences dramatiques : mortalité maternelle, anémie et enfants présentant un poids insuffisant à la naissance, une cause principale de mortalité néonatale. L’OMS recommande le traitement préventif intermittent pendant la grossesse (TPIp) à toutes les femmes enceintes d’Afrique subsaharienne vivant dans des zones de transmission modérée à élevée. Au cours des cinq dernières années, le taux d’administration d’au moins trois doses de TPIp a été multiplié par cinq dans 20 pays africains au total. Les moustiquaires imprégnées d’insecticide longue durée sont essentielles à la prévention du paludisme et l’OMS en recommande l’utilisation à l’ensemble de la population à risque. En Afrique subsaharienne, le pourcentage de la population dormant sous moustiquaire a quasiment doublé ces cinq dernières années. xviii

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Les progrès réalisés sont excellents, mais il reste beaucoup à faire. Pour la seule année 2015, les estimations font état de 212 millions de cas de paludisme et de 429 000 décès associés. En Afrique, la population n’ayant toujours pas accès aux outils nécessaires pour prévenir et traiter la maladie se compte par millions. Dans de nombreux pays, les progrès sont menacés par le développement et la propagation rapides de la résistance des moustiques aux insecticides. La résistance aux antipaludiques pourrait aussi mettre en péril les avancées récentes. En 2015, l’Assemblée mondiale de la Santé a approuvé la Stratégie technique mondiale de lutte contre le paludisme, un cadre opérationnel d’une durée de 15 ans pour tous les pays engagés dans le contrôle et l’élimination du paludisme. Cette stratégie définit des cibles ambitieuses et néanmoins réalisables pour 2030, avec des objectifs intermédiaires permettant un suivi des progrès. Cette stratégie vise à éliminer le paludisme dans au moins 10 pays d’ici à 2020, ce qui semble réalisable. Le présent rapport indique en effet que 10 pays et territoires ont rapporté moins de 150 cas de paludisme transmis localement, et que 9 autres en ont recensé entre 150 et 1 000. Néanmoins les progrès relatifs aux autres cibles mondiales doivent s’accélérer. D’après ce rapport, plus de la moitié des 91 pays endémiques ne sont pas en voie d’atteindre les objectifs de 40 % de réduction de l’incidence du paludisme et de la mortalité associée d’ici à 2020. Pour accélérer les progrès vers les cibles mondiales liées au paludisme, l’OMS demande expressément le développement de nouveaux outils antipaludiques et l’amélioration de l’arsenal existant. Des investissements plus importants sont nécessaires pour mettre au point de nouvelles interventions de lutte antivectorielle, des outils de diagnostic améliorés et des médicaments plus efficaces. Le mois dernier, l’OMS a annoncé la mise en place de projets pilotes dans trois pays d’Afrique subsaharienne concernant le premier vaccin antipaludique. Ce vaccin, RTS, S, a démontré une protection partielle contre le paludisme chez les jeunes enfants ; il sera évalué en tant qu’outil complémentaire à l’arsenal de mesures recommandées par l’OMS en matière de prévention, de diagnostic et de traitement du paludisme. Il est prioritaire et urgent d’augmenter le financement de la lutte contre le paludisme, estimé à US$ 2,9 milliards en 2015. Pour atteindre les cibles mondiales, les investissements nationaux et internationaux doivent en effet atteindre US$ 6,4 milliards par an d’ici 2020. Les obstacles face à nous ne sont ni négligeables ni insurmontables. L’expérience récente a démontré qu’avec des financements solides, des programmes efficaces et un leadership national fort, les progrès en matière de lutte contre le paludisme peuvent être maintenus et accélérés. Les perspectives de retour sur investissement sont séduisantes. Avec l’ensemble des partenaires réunis, nous pouvons vaincre le paludisme et améliorer la santé de millions de personnes dans le monde.

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Points essentiels

1.  Cibles, objectifs intermédiaires et indicateurs au niveau mondial ■■

Les cibles définies par la Stratégie technique mondiale de lutte contre le paludisme 2016-2030 (le « GTS ») pour 2030 sont les suivantes : réduire, au plan mondial, l’incidence du paludisme et la mortalité associée d’au moins 90 % par rapport à 2015, éliminer le paludisme dans au moins 35 pays où il y avait transmission en 2015 et empêcher la réapparition du paludisme dans tous les pays exempts. Concernant le paludisme, la cible 3.3 des Objectifs de développement durable, à savoir mettre fin à l’épidémie de sida, à la tuberculose, au paludisme et aux maladies tropicales négligées d’ici à 2030, est interprétée par l’Organisation mondiale de la Santé (OMS) comme l’atteinte des cibles du GTS. Pour suivre les progrès réalisés par rapport au GTS, le Rapport sur le paludisme dans le monde décrit les avancées réalisées par rapport à 26 indicateurs. Le Rapport sur le paludisme dans le monde est produit par le Programme mondial de lutte antipaludique créé par l’OMS, en collaboration avec les bureaux nationaux et régionaux de l’OMS, les ministères de la Santé des pays endémiques et de nombreuses organisations partenaires. Les principales sources de données sont les rapports émanant de 91 pays et territoires endémiques, complétées par des informations issues des enquêtes nationales réalisées auprès des ménages et des bases de données provenant d’autres organisations.

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2.  Investissements dans les programmes et la recherche antipaludiques ■■

En 2015, le financement mondial pour le contrôle et l’élimination du paludisme a été estimé à US$ 2,9 milliards, soit US$ 60 millions de plus qu’en 2010. Ce montant ne représente que 46 % de l’objectif intermédiaire fixé par le GTS à US$ 6,4 milliards pour 2020. Les gouvernements des pays endémiques ont contribué à hauteur de 32 % du total des financements en 2015, dont US$ 612 millions de dépenses directes par le biais des programmes nationaux de lutte contre le paludisme (PNLP) et US$ 332 millions en prise en charge des patients souffrant d’infections palustres. Avec une contribution estimée à 35 % du financement mondial de la lutte contre le paludisme en 2015, les États-Unis arrivent en tête des bailleurs de fonds individuels, suivis par le Royaume-Uni de Grande-Bretagne et d’Irlande du

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Nord (16 %), la France (3,2 %), l’Allemagne (2,4 %), le Japon (2,3 %), le Canada (1,7 %), la Fondation Bill & Melinda Gates (1,2 %) et les institutions de l’Union Européenne (1,1 %). Environ la moitié de ce financement international (45 %) transite par le Fonds mondial de lutte contre le sida, la tuberculose et le paludisme (Fonds mondial). ■■

Les dépenses en matière de recherche et de développement pour lutter contre le paludisme ont été estimées à US$ 611 millions en 2014 (l’année la plus récente pour laquelle des données sont disponibles), contre US$ 607 millions en 2010, ce qui représente plus de 90 % de l’objectif d’investissements annuels fixé à US$ 673 millions par le GTS. Les pays ayant le plus de cas de paludisme sont aussi ceux où les dépenses nationales (rapportées au nombre d’habitants) sont les plus éloignées de l’objectif défini par le GTS pour 2020.

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3. Prévention du paludisme Lutte antivectorielle ■■

En Afrique subsaharienne, le pourcentage de la population à risque dormant sous moustiquaire imprégnée d’insecticide (MII) ou ayant bénéficié de la pulvérisation intradomiciliaire d’insecticides à effet rémanent (PID) aurait augmenté de 37 % en 2010 (incertitude comprise entre 25 % et 48 %) à 57 % en 2015 (incertitude : 44 %-70 %). En Afrique subsaharienne, 53 % de la population à risque dort sous moustiquaire en 2015 (intervalle de confiance [IC] de 95 % : 50 %-57 %), contre 30 % en 2010 (IC de 95 % : 28 %-32 %). L’augmentation du pourcentage de la population à risque dormant sous MII est due à un accès accru aux moustiquaires (60 % en 2015, IC de 95 % : 57 %-64 % ; 34 % en 2010, IC de 95 % : 32 %-35 %). Le pourcentage des ménages possédant au moins une MII a augmenté, pour atteindre 79 % en 2015 (IC de 95 % : 76 %-83 %) ; en d’autres termes, un cinquième des ménages pour lesquels les MII sont le principal moyen de lutte antivectorielle n’ont pas accès à une moustiquaire. Le pourcentage des ménages avec un nombre de MII suffisant pour couvrir tous les membres du foyer s’élève à 42 % (IC de 95 % : 39 %-45 %). La PID est généralement utilisée par les PNLP dans des zones spécifiques uniquement. Le pourcentage de la population à risque protégée par PID a baissé, passant d’un pic de 5,7 % au niveau mondial en 2010 à 3,1 % en 2015, et de 10,5 % à 5,7 % en Afrique subsaharienne. La baisse de la couverture en PID peut être attribuée à l’arrêt de la pulvérisation à base de pyréthoïdes, en particulier dans la région Afrique de l’OMS. Sur 73 pays endémiques ayant communiqué des données de suivi à partir de 2010, 60 ont signalé une résistance à au moins une classe d’insecticides, et 50 à deux classes au moins. La résistance aux pyréthoïdes, la seule classe d’insecticides actuellement utilisée pour les MII, est la plus fréquente. Quand bien même une évaluation coordonnée par l’OMS dans cinq pays a montré que les moustiquaires étaient toujours efficaces, de nouveaux outils de lutte antivectorielle sont nécessaires.

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Traitement préventif intermittent pendant la grossesse ■■

Dans 20 pays disposant de données suffisantes, 31 % des femmes enceintes éligibles ont reçu au moins trois doses de traitement préventif intermittent pendant la grossesse (TPIp) en 2015, contre 6 % en 2010.

4. Diagnostic et traitement Accès aux soins ■■

Sur 23 enquêtes représentatives au niveau national et réalisées en Afrique subsaharienne entre 2013 et 2015 (représentant 61 % de la population à risque), une médiane de 54 % des enfants de moins de 5 ans ayant eu de la fièvre (écart interquartile [ÉI] : 41 %-59 %) ont été orientés vers un prestataire de santé formé. Le pourcentage des enfants fiévreux ayant sollicité des soins dans le secteur public est plus important que dans le secteur privé, à savoir une médiane de 42 % (ÉI : 31 %-50 %) contre 20 % (ÉI : 12 %-28 %). Le pourcentage d’enfants fiévreux n’ayant pas sollicité de soins est important (médiane de 36 %, ÉI : 26 %-42 %). Le pourcentage d’enfants fiévreux ayant été soumis à un test de diagnostic est plus important dans le secteur public (médiane de 51 %, ÉI : 35 %-60 %) que dans le secteur privé formel (médiane de 40 %, ÉI : 28 %-57 %) ou le secteur privé informel (médiane de 9 %, ÉI : 4 %-12 %). Le pourcentage d’enfants ayant été soumis à un test dans le secteur public est en augmentation, car il était de 29 % en 2010 (ÉI : 19 %-46 %). Les données rapportées par les PNLP indiquent que le pourcentage de cas suspectés de paludisme soumis à un test parasitologique dans le secteur public a augmenté de 40 % dans la région Afrique de l’OMS à 76 % en 2015. Cette hausse est principalement due à une plus grande utilisation des tests de diagnostic rapide (TDR) qui représentent 74 % des moyens de dépistage parmi les cas suspectés de paludisme en 2015. La suppression de la HRP2, permettant aux parasites du paludisme d’échapper à la détection par les tests de diagnostic rapide habituels, a été rapportée dans plus de 10 pays. Sur 11 enquêtes nationales réalisées auprès des ménages entre 2013 et 2015 en Afrique subsaharienne, le pourcentage médian des enfants de moins de 5 ans présentant, ou ayant récemment présenté une infection à Plasmodium (P.) falciparum avec des antécédents de fièvre et ayant reçu un médicament antipaludique s’élève à 30 % (ÉI : 20 %-51 %). Le pourcentage médian ayant reçu une combinaison thérapeutique à base d’artémisinine (ACT) est de 14 % (ÉI : 5 %-45 %). Ces résultats ne permettent néanmoins de tirer aucune conclusion précise ; en effet, les plages associées aux valeurs médianes sont larges, indiquant des écarts importants entre pays. Par ailleurs, ces enquêtes réalisées auprès des ménages ne couvrent qu’un tiers de la population à risque en Afrique subsaharienne.

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Points essentiels

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Des financements plus importants sont nécessaires pour mieux suivre l’accès au traitement antipaludique au niveau des établissements de soins (par le biais des systèmes de reporting de routine et des enquêtes auprès des établissements de soins) et au niveau communautaire et ce, dans le but de mieux mesurer l’ampleur des obstacles. Le pourcentage d’ACT parmi les traitements antipaludiques administrés aux enfants ayant eu de la fièvre dans les 2 semaines précédant l’enquête et eu un résultat positif au TDR au moment de l’enquête a augmenté d’une valeur médiane de 29 % en 2010-2012 (ÉI : 17 %-55 %) à 80 % en 2013-2015 (ÉI : 29 %-95 %). Le traitement antipaludique était plus susceptible d’être par ACT si les enfants sollicitaient des soins d’établissements de soins publics ou d’agents de santé communautaires que s’ils s’orientaient vers le secteur privé. La résistance du parasite Plasmodium falciparum à l’artémisinine a été détectée dans cinq pays de la sous-région du Grand Mékong. Au Cambodge, des taux d’échec au traitement ont été observés pour quatre types d’ACT.

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5. Systèmes de surveillance du paludisme ■■

Le pourcentage de rapports reçus au niveau national et provenant des établissements de soins a dépassé 80 % dans 40 des 47 pays ayant donné des informations sur cet indicateur. Cet indicateur n’a pas pu être calculé pour 43 pays et ce, pour différentes raisons : ou il n’était pas mentionné combien d’établissements de soins devaient rapporter (le cas pour 2 pays), ou le nombre de rapports soumis n’était pas indiqué (le cas pour 17 pays), ou les deux (24 pays). Au total, 23 pays ont reçu des rapports de la part des établissements de soins privés, mais ces rapports ne représentent qu’une minorité de tous les rapports reçus dans ces pays (valeur médiane : 2,1 %, ÉI : 0,6 %-13 %). En 2015, il est estimé que les systèmes de surveillance du paludisme ont détecté 19 % des cas au niveau mondial (incertitude : 16 %-21 %). Les obstacles au dépistage des cas ne sont pas les mêmes d’un pays et d’une région de l’OMS à l’autre. Dans quatre d’entre elles, une large proportion des patients sollicitent un traitement dans le secteur privé, et ces cas ne sont pas capturés par les systèmes de surveillance existants. Dans trois régions de l’OMS, une part relativement faible des patients se rendant dans des établissements de soins publics reçoivent un test de diagnostic. Le taux de dépistage des cas a augmenté depuis 2010 (10 %), principalement en raison de l’intensification du diagnostic en Afrique subsaharienne.

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6. Impact Prévalence parasitaire ■■

Le pourcentage d’infections palustres parmi la population à risque en Afrique subsaharienne est estimée en baisse, passant de 17 % en 2010 à 13 % en 2015 (incertitude : 11 %-15 %).

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En Afrique subsaharienne, le nombre de patients atteints d’infections palustres aurait diminué de 131 millions en 2010 (incertitude : 126-136 millions) à 114 millions en 2015 (incertitude : 99-130 millions). Le taux d’infection est plus élevé chez les enfants de 2 à 10 ans ; néanmoins la plupart des infections (74 %) concernent les tranches d’âge supérieures.

Incidence des cas ■■ Au niveau mondial, le nombre de cas de paludisme est estimé à 212 millions en 2015 (incertitude : 148-304 millions). ■■ En 2015, la plupart des cas (90 %) ont été enregistrés dans la région Afrique de l’OMS, loin devant la région Asie du Sud-Est (7 %) et la région Méditerranée orientale (2 %) de l’OMS. ■■ Les infections à P. vivax sont estimées responsables d’environ 4 % des cas de paludisme dans le monde mais, hors Afrique, cette proportion atteint 41 %. ■■ Au niveau mondial, l’incidence du paludisme aurait diminué de 41 % entre 2000 et 2015, et de 21 % entre 2010 et 2015. ■■ Entre 2010 et 2015, l’incidence du paludisme aurait diminué d’au moins 40 % dans 40 des 91 pays et territoires où la transmission du paludisme reste active en 2015. On peut donc considérer que ces pays et territoires sont en bonne voie pour atteindre une réduction de 40 % d’ici 2020, qui est un objectif intermédiaire du GTS. ■■ Pour atteindre cet objectif d’ici 2020, la baisse doit s’accélérer dans les pays où l’incidence du paludisme est la plus élevée. Mortalité ■■ ■■

Au niveau mondial, le nombre de décès dus au paludisme a été estimé à 429 000 en 2015 (incertitude : 235 000-639 000). En 2015, la plupart de ces décès sont survenus dans la région Afrique (92 %), loin devant la région Asie du Sud-Est (6 %) et la région Méditerranée orientale (2 %) de l’OMS. L’immense majorité (99 %) des décès sont dus au paludisme à P. falciparum. Les infections à P. vivax seraient à l’origine de 3 100 décès en 2015 (incertitude : 1 800-4 900), dont 86 % hors Afrique. En 2015, le nombre de décès dus au paludisme chez les enfants de moins de 5 ans a été estimé à 303 000 (incertitude : 165 000-450 000), soit 70 % du total mondial toutes tranches d’âge confondues. Ce nombre serait en baisse de 29 % depuis 2010 ; cependant, le paludisme reste l’une des principales causes de mortalité infantile, tuant un enfant toutes les deux minutes. Au niveau mondial, la mortalité liée au paludisme aurait diminué de 62 % entre 2000 et 2015, et de 29 % entre 2010 et 2015. Chez les enfants de moins de 5 ans, elle aurait chuté de 69 % entre 2000 et 2015, et de 35 % entre 2010 et 2015. Entre 2010 et 2015, la mortalité liée au paludisme aurait diminué d’au moins 40 % dans 39 des 91 pays et territoires où la transmission du paludisme reste active en 2015. Dix autres pays ont réduit à zéro le nombre de décès dus au paludisme indigène en 2015. Pour réduire la mortalité liée au paludisme d’au moins 40 % d’ici 2020 (objectif intermédiaire du GTS), la baisse doit s’accélérer dans les pays payant le plus lourd tribut à la maladie.

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Points essentiels

Élimination ■■

Entre 2000 et 2015, 17 pays ont éliminé le paludisme (c’est-à-dire réduit à zéro le nombre de cas indigènes pendant au moins trois ans) et 6 d’entre eux ont été certifiés exempts de paludisme par l’OMS. Sur la voie de l’élimination du paludisme, ces 17 pays ont rapporté une médiane de 184 cas indigènes cinq ans avant d’avoir réduit le nombre de cas à zéro (ÉI : 78-728) et une médiane de 1 748 cases dix ans auparavant (ÉI : 423-5 731). En 2015, 10 pays et territoires ont rapporté moins de 150 cas indigènes, et 9 autres pays en ont recensé entre 150 et 1 000. Il s’agit là de résultats encourageants vers l’atteinte de l’objectif intermédiaire de 2020, à savoir éliminer le paludisme dans au moins 10 pays. La transmission du paludisme n’est réapparue dans aucun des pays ayant éliminé cette maladie entre 2000 et 2015.

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Baisse de la mortalité liée au paludisme, augmentation de l’espérance de vie et valorisation économique ■■

Au total, 6,8 millions de décès dus au paludisme ont été évités au niveau mondial entre 2001 et 2015, par rapport aux chiffres que nous aurions enregistrés si les taux d’incidence et de mortalité étaient restés inchangés depuis 2000. La plupart des décès (94 %) ont été évités dans la région Afrique de l’OMS. Sur les 6,8 millions de décès dus au paludisme évités entre 2001 et 2015, environ 6,6 millions (97 %) l’ont été parmi les enfants de moins de 5 ans. Tous les décès évités ne sont pas liés aux efforts de lutte contre le paludisme ; une partie d’entre eux s’expliquent vraisemblablement par une urbanisation accrue et la croissance économique en général, à l’origine de l’amélioration des conditions de logements et d’une meilleure nutrition. Conséquence de la baisse de la mortalité due au paludisme, en particulier chez les enfants de moins de 5 ans, l’espérance de vie à la naissance aurait augmenté de 1,2 an dans la région Afrique de l’OMS. Cette hausse représente 12 % de l’augmentation de 9,4 ans de l’espérance de vie en Afrique subsaharienne, passée de 50,6 ans en 2000 à 60 ans en 2015. Au niveau mondial, la baisse du risque de mortalité due au paludisme aurait contribué à une augmentation de l’espérance de vie de 0,26 an dans les pays endémiques, soit 5 % des 5,1 ans gagnés au total. La baisse du risque de mortalité due au paludisme entre 2000 et 2015 et donc, les gains en termes d’espérance de vie, peuvent être valorisés à US$ 1 810 milliards dans la région Afrique de l’OMS (incertitude : US$ 1 330-2 480 milliards), soit 44 % du produit intérieur brut (PIB) des pays affectés en 2015. Au niveau mondial, la baisse du risque de mortalité due au paludisme est valorisée à US$ 2 040 milliards (incertitude : US$ 1 560-2 700 milliards), soit 3,6 % du total du PIB des pays affectés. Ces valeurs de bien-être économique sont exprimées en termes de pourcentage du PIB à titre comparatif ; elles ne sauraient laisser entendre que la valeur de la longévité est une composante de la richesse nationale produite, ni que la valeur de ces gains est directement intégrée dans le revenu national. Cette comparaison suggère seulement que la valeur économique attachée à la baisse de la mortalité due au paludisme est conséquente.

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Prefacio

Dra. Margaret Chan, Directora General Organización Mundial de la Salud El Informe Mundial sobre Paludismo, publicado anualmente por la Organización Mundial de la Salud (OMS), ofrece un análisis en profundidad del progreso y las tendencias en la respuesta al paludismo (o malaria) a nivel mundial, regional y nacional. Es el resultado de un continuo esfuerzo colaborativo entre los Ministerios de Salud de los países endémicos y numerosas organizaciones colaboradoras en todo el mundo. Nuestro informe 2016 destaca una serie de tendencias positivas, en particular, en el África subsahariana, la región que padece la mayor carga de paludismo. Esto demuestra que, en muchos países, el acceso a las intervenciones preventivas se está expandiendo a un ritmo acelerado entre las poblaciones más necesitadas. Los niños son especialmente vulnerables y representan más de dos tercios de las muertes por paludismo a nivel mundial. En 22 países africanos, la proporción de niños con fiebre que recibieron una prueba de diagnóstico de paludismo en un centro de salud público se incrementó un 77% en los últimos 5 años. Esta prueba ayuda a los proveedores de salud poder distinguir rápidamente entre paludismo y fiebres no palúdicas, permitiendo asistir con un tratamiento adecuado. El paludismo durante el embarazo puede causar mortalidad materna, anemia y recién nacidos con bajo peso al nacer, una de las principales causas de mortalidad infantil. La OMS recomienda el tratamiento preventivo intermitente durante el embarazo, conocido como el TPIe, para todas las mujeres embarazadas en el África subsahariana, que viven en zonas de transmisión moderada y alta. En los últimos 5 años, la tasa de administración de al menos tres dosis de TPIe se ha incrementado por cinco en 20 países africanos. Los mosquiteros (o toldillos) con insecticidas de larga duración siguen siendo uno de los pilares de la prevención del paludismo y la OMS recomienda su uso para toda población en riesgo de contraer la enfermedad. En el África subsahariana, la proporción de personas que duermen bajo mosquiteros tratados con insecticida se ha duplicado por poco en los últimos 5 años. Hemos hecho grandes progresos, pero nuestro trabajo sigue incompleto. Sólo en el último año, el recuento mundial del paludismo alcanzó los 212 millones de

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casos y 429 000 muertes. En África, millones de personas siguen sin acceso a las herramientas necesarias para prevenir y tratar la enfermedad. En muchos países, el progreso se ve amenazado por el rápido desarrollo y la propagación de la resistencia del mosquito a los insecticidas. La resistencia a los medicamentos antipalúdicos también podría poner en peligro los logros recientes. En 2015, la Asamblea Mundial de la Salud adoptó la Estrategia técnica mundial contra la malaria 2016-2030, un marco operacional para los próximos 15 años para todos los países que trabajan en el control y la eliminación del paludismo. Esta estrategia establece unos objetivos ambiciosos pero alcanzables para el 2030, con objetivos a corto y medio plazo que permiten hacer un seguimiento del progreso. La estrategia insta a la eliminación del paludismo en al menos 10 países para el año 2020: un objetivo a nuestro alcance. Según este informe, 10 países y territorios han registrado menos de 150 casos de paludismo autóctonos. Otros nueve países informaron entre 150 y 1000 casos. Pero el progreso hacia los otros objetivos mundiales debe ser acelerado. El informe llega a la conclusión de que menos la mitad de los 91 países afectados por el paludismo están en vías de alcanzar los objetivos a medio plazo de 2020, es decir, una reducción del 40% en el caso de incidencia y mortalidad. Para acelerar los progresos hacia nuestras metas a nivel mundial en relación con el paludismo, la OMS hace un llamamiento para nuevas y mejores herramientas para la lucha contra la enfermedad. Se necesitan mayores inversiones en el desarrollo de nuevas intervenciones de control vectorial, mejores diagnósticos y medicamentos más eficaces. El mes pasado, la OMS anunció que la primera vacuna contra el paludismo será pilotada en 3 países del África subsahariana. La vacuna, conocida como RTS,S ha demostrado proporcionar una protección parcial contra el paludismo en los más jóvenes. Será evaluada como un posible complemento al paquete de medidas y herramientas existentes recomendadas por la OMS en materia de prevención, diagnóstico y tratamiento. La necesidad de contar con más fondos es una prioridad urgente. Se estima que en 2015, la financiación para la lucha contra el paludismo superó los US$ 2,9 mil millones. Para lograr nuestras metas a nivel mundial, las contribuciones de fuentes nacionales e internacionales deben aumentar de manera considerable para poder alcanzar los US$ 6,4 mil millones anuales para el año 2020. Los retos a los que nos enfrentamos son considerables, pero no insuperables. La experiencia reciente ha demostrado que con una sólida financiación, programas eficaces y liderazgo de los países, el progreso en la lucha contra el paludismo puede ser sostenido y acelerado. Las ganancias potenciales bien valen el esfuerzo. Todos unidos, podemos derrotar al paludismo y mejorar la salud de millones de personas alrededor del mundo.

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Puntos clave 1. Metas mundiales, hitos e indicadores ■■

Las metas para el 2030 de la Estrategia técnica mundial contra la malaria 20162030 (en adelante referido como “el GTS”, por sus siglas en inglés de Global Technical Strategy for Malaria 2016-2030) consisten en: reducir a nivel mundial la incidencia de casos de paludismo (o malaria) y la mortalidad asociada en al menos un 90% en comparación con los datos de 2015; eliminar el paludismo en al menos 35 países en los que había transmisión en el 2015 y prevenir el restablecimiento del paludismo en todos los países que la han eliminado. Respecto al paludismo en los Objetivos de desarrollo sostenibles (ODS), la Meta 3.3 es poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas para el 2030 y es interpretado por la Organización mundial de la salud (OMS) como el logro de las metas del GTS. Para el seguimiento del progreso del GTS y de la Acción e inversión para vencer a la malaria 2016-2030 (AIM), la OMS y el programa Roll Back Malaria han definido conjuntamente una lista de 41 indicadores para utilizar a nivel mundial, nacional y subnacional. De entre ellos, 12 son considerados clave para monitorizar el GTS y el plan AIM a nivel mundial. El Informe mundial sobre el Paludismo tiene como objetivo informar acerca de los avances realizados cada año en estos 12 y una selección de otros indicadores. El Programa Mundial sobre Paludismo de la OMS produce el Informe mundial sobre Paludismo en colaboración con los equipos de las oficinas regionales y nacionales de la OMS, Ministerios de Salud de los países endémicos y un amplio número de organizaciones colaboradoras. Las principales fuentes de información son los informes procedentes de 91 países endémicos, complementados con datos procedentes de encuestas nacionales representativas y bases de datos mantenidas por otras organizaciones.

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2. Inversión en programas del paludismo e investigación ■■

En 2015, la financiación total para el control y eliminación del paludismo era aproximadamente de US$ 2,9 mil millones, US$ 60 millones más que en 2010. Esta cantidad no representa más que el 46% de la meta fijada por el GTS en US$ 6,4 mil millones para el 2020. Los gobiernos de países con paludismo endémico han contribuido con un 32% del total de la financiación en 2015, de los cuales US$ 612 millones han sido costes directos de los programas nacionales de control de malaria (PNCM) y US$ 332 millones han sido costes de tratamientos de pacientes con paludismo.

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Los Estados Unidos de América son el principal inversor internacional de fondos para las actividades destinadas al control del paludismo, con una contribución estimada del 35% de la financiación mundial para la lucha contra el paludismo en 2015, seguido por el Reino Unido de Gran Bretaña e Irlanda del Norte (16%), Francia (3,2%), Alemania (2,4%), Japón (2,3%), Canadá (1,7%), la fundación Bill & Melinda Gates (1,2%) y las instituciones de la Unión Europea (1,1%). Alrededor de la mitad de las inversiones internacionales (45%) son canalizadas a través del Fondo Mundial de lucha contra el sida, la tuberculosis y la malaria (Fondo Mundial). El gasto en investigación y desarrollo para la lucha contra el paludismo se ha estimado en US$ 611 millones en 2014 (el último año con datos disponibles), incrementando la cifra de US$ 607 millones en 2010, y representando más del 90% de la meta de la inversión anual fijada por el GTS en US$ 673 millones. Los países con el mayor número de casos de paludismo, son aquellos que están más alejados de la meta de gasto per cápita para el 2020 establecida por el GTS.

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3. Prevención del paludismo Control de vectores ■■

En el África subsahariana, el porcentaje de la población en riesgo de paludismo que duerme bajo un mosquitero tratado con insecticida (MTI) o protegido con el rociado residual intradomiciliario (RRI) se estima que habría incrementado de un 37% en 2010 (Intervalo de incertidumbre [II]:25%–48%) al 57% en 2015 (II: 44%–70%). Para los países en el África subsahariana donde los MTI son el principal método de intervención para el control vectorial, 53% de la población en riesgo duerme bajo un MTI en 2015 (Intervalo de confianza [IC] de 95%: 50%–57%), contra el 30% en 2010 (IC de 95%: 28%–32%). El crecimiento en el acceso a los MTI en los hogares (60% en 2015, IC de 95%: 57%–64%; 34% en 2010, IC de 95%: 32%–35%) ha logrado un gran aumento de la población en riesgo de paludismo que duerme bajo un MTI. El porcentaje de hogares con al menos un MTI ha aumentado, alcanzando el 79% en 2015 (IC de 95%: 76%–83%); por lo tanto, una quinta parte de los hogares donde los MTI son la principal herramienta para la lucha antivectorial no tienen acceso a una red tratada. El porcentaje de hogares con un número suficiente de MTI para todos los miembros del hogar se ha elevado a un 42% (IC de 95%: 39%–45%) El RRI es generalmente usado por los PNMC en zonas específicas. A nivel global, el porcentaje de la población en riesgo protegida por el RRI ha decaído de un máximo del 5,7% alcanzado en 2010 a un 3,1% en 2015, y de un 10,5% a un 5,7% en el África Subsahariana. La reducción en la cobertura del RRI podría ser atribuida al cese del rociamiento con piretroides, en particular en la zona regional de África de la OMS.

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De los 73 países endémicos que proporcionaron datos a partir del 2010 en adelante; 60 reportaron una resistencia de al menos un insecticida y 50 reportaron resistencia a dos o más clases de insecticida. La resistencia a los piretroides (la única clase de insecticida que se utiliza actualmente en los MTI) es la que se registra con más frecuencia. La última evaluación llevada a cabo en 5 países y bajo la coordinación de la OMS, llegó a la conclusión de que los MTI seguían siendo efectivos, sin embargo se siguen necesitando nuevas herramientas para el control vectorial. En los 20 países africanos con datos suficientes, 31% de las mujeres embarazadas elegibles recibieron tres o más dosis de tratamiento preventivo intermitente durante el embarazo (TPIe) en 2015, contra el 6% en 2010.

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4. Pruebas de diagnóstico y tratamiento Acceso al tratamiento ■■

En las 23 encuestas representativas a nivel nacional y realizadas en el África subsahariana entre 2013 y 2015 (representando el 61% de la población en riesgo), una mediana de 54% de niños febriles por debajo de los 5 años (Rango intercuartil [RI]: 41%–59%) fueron llevados a un proveedor de salud formado. El porcentaje de niños febriles que solicitó tratamiento en el sector público (mediana: 42%, RI: 31%–50%) fue más alto que en el sector privado (mediana: 20%, RI: 12%–28%). El porcentaje de niños febriles que no solicitaron tratamiento es importante (mediana: 36%, RI: 26%–42%) El porcentaje de niños febriles que tuvieron una prueba de diagnóstico del paludismo ha sido mayor si solicitaban tratamiento en el sector público (mediana: 51%, RI: 35%–60%) que si recurrían a un tratamiento en el sector privado formal (mediana: 40%, RI: 28%–57%) o el sector privado informal (mediana: 9%, RI: 4%–12%). El porcentaje de niños que tuvieron la prueba de diagnóstico en el sector público ha aumentado del 29% en 2010 (RI: 19%–46%). Los datos comunicados por los PNCM indican que el porcentaje de casos sospechosos de paludismo que tienen una prueba parasitológica en el sector público ha aumentado de un 40% de casos sospechosos en la región de África de la OMS en 2010 a un 76% en 2015. Este incremento es principalmente debido a una mayor utilización de los test de diagnóstico rápido (RDT, por sus siglas en inglés Rapid diagnostic tests), que contribuyeron al 74% de las pruebas de diagnóstico entre los casos sospechosos en 2015. En más de 10 países se han reportado deleciones del gen HRP2, lo cual permite a parásitos del paludismo evadir la detección por los test de diagnósticos más comunes.

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Puntos clave

Tratamiento ■■

Entre las 11 encuestas representativas a nivel nacional que fueron llevadas a cabo entre 2013 y 2015 en el África subsahariana, la proporción mediana de niños por debajo de los 5 años con evidencia de una infección de P. falciparum reciente o presente e historia de fiebre que recibieron algún medicamento antipalúdico se elevó a 30% (RI: 20%–51%). De mediana, el 14% (RI: 5%–45%) recibió una terapia combinada con artemisinina (TCA). Sin embargo, no pudo extraerse ninguna conclusión clara de estos resultados puesto que los rangos asociados a las medianas eran muy amplios, indicando una gran variedad entre los países, a lo que hay que añadir que las encuestas solo representaban un tercio de la población en riesgo en el África subsahariana. Son necesarias mayores inversiones para poder mejorar el seguimiento de los tratamientos en los centros de salud (a través de los sistemas rutinarios de reporte y de las encuestas a los centros de salud) y a nivel comunitario, para poder entender hasta qué punto existen barreras que impiden el acceso a un tratamiento contra el paludismo. El porcentaje de tratamientos antipalúdicos con TCA proporcionados a niños con fiebre en las últimas dos semanas y con un RDT positivo en el momento de la encuesta, aumentó de una mediana inicial de 29% entre 2010-2012 (RI: 17%–55%) al 80% en 2013-2015 (RI: 29%–95%). Los tratamientos antipalúdicos fueron más probables de ser TCA si los niños buscaban tratamiento en centros de salud pública o a través de trabajadores de salud de las comunidades, que si se dirigían al sector privado. Se ha detectado resistencia de P. falciparum a la artemisinina en cinco países de la subregión del Gran Mekong. En Camboya, altos índices de fracaso después de las TCA han sido detectados en cuatro diferentes.

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5. Sistemas de vigilancia del paludismo ■■

El porcentaje de informes recibidos a nivel nacional y procedente de los centros de salud superó el 80% en 40 de los 47 países que informaron sobre este indicador. Este indicador no pudo ser calculado en 43 países, por distintas razones: si bien porque no se especificó el número de centros de salud que se esperaba para poder informar (en 2 países) o bien porque no se especificó el número de informes entregados (en 17 países), o por último, con ambas situaciones (en 24 países). En total, 23 países recibieron informes de centros de salud privados, pero éstos representan una minoría de todos los informes recibidos (mediana: 2,1%, RI: 0,6%–13%). En 2015, se estima que los sistemas de vigilancia del paludismo detectan el 19% de los casos que ocurren a nivel mundial (II: 16%–21%). Los obstáculos que se hallan en la detección de casos varían según el país y la región de la OMS. En cuatro de las regiones de la OMS una gran proporción de pacientes solicitan tratamiento en el sector privado, y en sus casos no se

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contabiliza en los sistemas de vigilancia existentes. En tres de las regiones de la OMS una proporción relativamente baja de los pacientes que asisten a los centros de salud públicos reciben una prueba de diagnóstico. ■■

La tasa de detección de casos ha mejorado y aumentado su cifra desde 2010 (10%), principalmente debido al incremento del uso de las pruebas de diagnóstico en el África subsahariana.

6. Impacto Prevalencia del parásito que provoca el paludismo ■■

El porcentaje de las poblaciones en riesgo en el África subsahariana con infecciones por el parásito del paludismo ha descendido de un 17% calculado en 2010 a un 13% en 2015 (II: 11%–15%). En el África subsahariana, el número de personas infectadas por el parásito del paludismo ha descendido de 131 millones en 2010 (II: 126 – 136 millones) a 114 millones en 2015 (II: 99 – 130 millones. La tasa de infección es más alta en niños entre 2 y 10 años, aunque la mayor parte de las personas afectadas se encuentran en rangos de edades superiores. A nivel mundial, se calcularon 212 millones de casos de paludismo en 2015 (II: 148 – 304 millones). En 2015, la mayoría de los casos fueron registrados en la región de África de la OMS (90%), seguida de la región de Asia sudoriental (7%) y la región del Mediterráneo oriental (2%). Las infecciones por P. vivax son responsables de un 4% de los casos mundiales de paludismo, sin embargo fuera del continente africano el porcentaje de infecciones por P. vivax es de 41%. A nivel mundial, la tasa de incidencia de casos del paludismo ha disminuido un 41% entre 2000 y 2015, y un 21% entre 2010 y 2015. De los 91 países y territorios con transmisión de paludismo en 2015, se estima que 40 han alcanzado una reducción en las tasas de incidencia de 40% o más entre 2010 y 2015, y se puede considerar que están en el camino de alcanzar la meta del GTS de una reducción adicional del 40% para el 2020. Si se quiere alcanzar la meta del GTS en reducir de 40% la tasa de incidencia de casos para el año 2020, se debería acelerar la disminución de la tasa de incidencia de casos en países con un alto número de casos reportados. En 2015, se estimaron 429 000 muertes por paludismo en todo el mundo (II: 235 000 – 639 000). En 2015, se estimó que la mayoría de las muertes ocurrieron en la región de África de la OMS (92%), seguida de la región de Asia sudoriental de la OMS (6%) y la región del Mediterráneo oriental de la OMS (2%).

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La inmensa mayoría de las muertes (99%) por paludismo fueron debidas al P. falciparum. Se estima que P. vivax pudo haber sido el responsable de 3100 muertes en 2015 (rango: 1800 – 4900), 86% de ellas fuera de África. En 2015, el número estimado de muertes causadas por paludismo en niños menores de 5 años fue de 303 000 (rango: 165 000 – 450 000), el equivalente al 70% del total mundial. Se estima que el número de muertes ha disminuido un 29% desde 2010, aunque sigue siendo una de las principales causas de mortalidad infantil, acabando con la vida de un niño cada dos minutos. A nivel mundial, la tasa de mortalidad por paludismo habría disminuido un 62% entre 2000 y 2015, y un 29% entre 2010 y 2015. En niños menores de 5 años, habría disminuido un 69% entre 2000 y 2015, y en un 35% entre 2010 y 2015. Entre 2010 y 2015, la tasa de mortalidad por paludismo habría disminuido al menos un 40% en 39 de los 91 países y territorios con transmisión de paludismo activa en 2015. Otros 10 países no tuvieron muertes autóctonas en 2015. Si se quiere alcanzar la meta del GTS en reducir la tasa de la mortalidad en más de un 40% para el 2020, se debería acelerar la reducción de la tasa de mortalidad en países con un alto número de muertes. Entre 2000 y 2015, 17 países han eliminado el paludismo (es decir, que han reducido a cero los casos autóctonos en tres años o más) y entre los cuales, seis países han sido certificados por la OMS como libres de paludismo. En el progreso hacia la eliminación del paludismo, estos 17 países han reportado una media de 184 casos autóctonos cinco años antes de alcanzar los cero casos (RI: 78 – 728) y una mediana de 1748 casos en diez años antes de alcanzar los cero casos (RI: 423 – 5731). En 2015, 10 países y territorios reportaron menos de 150 casos autóctonos, y otros 9 países reportaron entre 150 y 1000 casos autóctonos. Por tanto, en perspectiva positiva, parecería que sería posible alcanzar la meta del GTS para el 2020 y eliminar el paludismo en 10 países. El paludismo no ha sido reintroducida en ninguno de los países que eliminaron esta enfermedad entre 2000 y 2015.

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Reducción de la mortalidad por paludismo, el incremento de la esperanza de vida y la evaluación económica ■■

Entre 2001 y 2015, se estima que un total acumulado de 6,8 millones de muertes por paludismo han sido evitadas a nivel mundial entre 2000 y 2015, en relación a la cifras que se hubiesen producido si la incidencia y las tasas de mortalidad se hubiesen mantenido inalteradas desde 2000. La mayoría de las muertes (94%) fueron evitadas en la región de África de la OMS. Del total estimado de 6,8 millones menos de muertes por paludismo entre 2001 y 2015, alrededor de 6,6 millones (97%) fueron entre niños menores de 5 años. No todas las muertes pueden ser atribuidas a los esfuerzos para controlar el paludismo. Parte del progreso es probable que esté relacionado con un

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incremento de la urbanización y de un desarrollo económico generalizado, lo que ha llevado a la mejora de la vivienda y la nutrición. ■■

Como consecuencia de la reducción de la tasa de mortalidad por paludismo, en particular, entre los niños menores de 5 años, se ha estimado que la esperanza de vida al nacer habría incrementado en más de 1,2 años en la región de África de la OMS. Este incremento representaría el 12% del aumento total de la esperanza de vida de 9,4 años en el África subsahariana, que ha pasado de 50,6 años en 2000 a 60 años en 2015. A nivel mundial, la reducción de la tasa de mortalidad por paludismo ha contribuido a un incremento en la esperanza de vida de 0,26 años en los países endémicos, siendo el 5% de los 5,1 años ganados en total. Los métodos de análisis actuales sugieren que el incremento en la esperanza de vida originados por la reducción de la mortalidad por paludismo observada entre los años 2000 y 2015 se puede valorar en US$ 1810 mil millones dentro de la región de África de la OMS (II: US$ 1330 – 2480 mil millones), lo que equivale al 45% del Producto Interior Bruto (PIB) de los países afectados en 2015. A nivel mundial, la reducción del riesgo de mortalidad debido al paludismo se valoriza en US$ 2040 mil millones (II: US$ 1560 – 2700 mil millones), siendo alrededor del 3,6% del PIB. Estos valores de bienestar económico se expresan en términos porcentuales del PIB a título comparativo, porque no pueden representar una parte actual de la riqueza producida ni dar a entender que pueden medir el mismo tipo de riqueza. Esta comparación sugiere únicamente que el valor económico que se atribuye a la disminución de la mortalidad por paludismo es substancial.

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1. Global targets, milestones and indicators Since 2000, substantial progress has been made in fighting malaria. According to the latest estimates, between 2000 and 2015, malaria case incidence was reduced by 41% and malaria mortality rates by 62% (see Section 6 of this report). At the beginning of 2016, malaria was considered to be endemic in 91 countries and territories, down from 108 in 2000 (Figure 1.1). Much of the change can be attributed to the wide-scale deployment of malaria control interventions (1). Despite this remarkable progress, malaria continues to have a devastating impact on people’s health and livelihoods. Updated estimates indicate that 212 million cases occurred globally in 2015, leading to 429 000 deaths, most of which were in children aged under 5 years in Africa. Recognizing the need to hasten progress in reducing the burden of malaria, WHO developed the Global Technical Strategy for Malaria 2016–2030 (GTS) (2), which sets out a vision for accelerating progress towards malaria elimination. The WHO strategy is complemented by the Roll Back Malaria advocacy plan, Action and investment to defeat malaria 2016–2030 (AIM) (3). Together, these documents emphasize the need for universal access to interventions for malaria prevention, diagnosis and treatment; that all countries1 should accelerate efforts towards malaria elimination; and that malaria surveillance should be a core intervention. The GTS and AIM also recognize the importance of innovation and research and a strong enabling environment, and share the same global targets for 2030 and the same milestones for 2020 and 2025, as shown in Table 1.1. The time frame of the GTS and AIM is aligned with that of the Sustainable Development Goals (SDGs) (4). For malaria, Target 3.3 of the SDGs – to end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, waterborne diseases, and other communicable diseases by 2030 – is interpreted as the attainment of the GTS and AIM targets. The indicator used to track progress of Target 3.3 is malaria case incidence. 1. In order to facilitate reading throughout the report, “countries” is used as a generic term referring to countries and areas or territories. The term “area” or “territory” is used only when mentioning one or more areas/territories in lists of specific countries.

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Figure 1.1 Countries endemic for malaria in 2000 and 2016.

Countries with 3 consecutive years of zero indigenous cases are considered to have eliminated malaria. No country in the WHO European region reported zero indigenous cases in 2015 but Tajikistan has not yet had 3 consecutive years of zero indigenous cases, its last case being reported in July 2014. Source: WHO database

Countries endemic for malaria, 2016 Countries not endemic for malaria, 2000

Countries endemic in 2000, no longer endemic in 2016 Not applicable

Table 1.1 Global targets for 2030 and milestones for 2020 and 2025. Source: (2) Milestones 2020 2025 Targets 2030

Goals

1. Reduce malaria mortality rates globally compared with 2015 2. Reduce malaria case incidence globally compared with 2015 3. Eliminate malaria from countries in which malaria was transmitted in 2015 4. Prevent re-establishment of malaria in all countries that are malaria free

≥40%

≥75%

≥90%

>40% At least 10 countries Re-establishment prevented

≥75% At least 20 countries Re-establishment prevented

≥90% At least 35 countries Re-establishment prevented

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Global targets, milestones and indicators

The GTS highlights a minimal set of 14 outcome and impact indicators against which progress in malaria control and elimination should be monitored, of which 12 are relevant at global level. The World Malaria Report 2016 aims to report on these global indicators, and a selection of other indicators as shown in Table 1.2. It also reports on the supply of key commodities to endemic countries (which influences the progress of malaria control and elimination programmes) (Section 2.4); the evolution of resistance to interventions by vectors and parasites (Sections 3.6 and 4.6, respectively). This year, the report also considers the gain in life expectancy that the reductions in malaria mortality have brought about, and the economic value society places on such changes (Section 6.7). The main text is followed by methods, regional profiles, country trends in selected indicators and data tables. Country profiles and methods are available online at http://www.who.int/malaria/ publications/world-malaria-report-2016/en/. The World Malaria Report is produced by the WHO Global Malaria Programme, with the help of WHO regional and country offices, ministries of health in endemic countries, and a broad range of other partners. The primary sources of information are reports from national malaria control programmes (NMCPs) in the 91 endemic countries. This information is supplemented by data from nationally representative household surveys (demographic and health surveys, malaria indicator surveys and multiple indicator cluster surveys) and databases held by other organizations: the Alliance for Malaria Prevention; the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund), the Organisation for Economic Co-operation and Development; Policy Cures; United Nations Children’s Fund (UNICEF); the US President’s Malaria Initiative; and WHO. A description of data sources and methods is provided in Annex 1.

Table 1.2 Indicators reviewed in World Malaria Report 2016. indicators in GTS are highlighted in light grey.

Indicators among minimal set of 14 recommended

Applicability of indicator by transmission setting Indicator High Inputs Low Elimination or prevention of re-establishment

Financing

1.1 1.2

Total malaria funding and expenditure per capita for malaria control and elimination Funding for malaria relevant research Proportion of population at risk that slept under an ITN the previous night Proportion of population with access to an ITN within their household Proportion of households with at least one ITN for every two people Proportion of households with at least one ITN Proportion of available ITNs used the previous night

● ● ● ● ● ● ●

● ●

● ●

Outcome

Vector control

2.1 2.2 2.3 2.4 2.5

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Applicability of indicator by transmission setting Indicator High Low Elimination or prevention of re-establishment

Vector control

2.6 2.7 2.8

Proportion of targeted risk group receiving ITNs Proportion of population at risk protected by IRS in the previous 12 months Proportion of population at risk sleeping under an ITN or living in house sprayed by IRS in the previous 12 months Proportion of pregnant women who received ≥3 doses of IPTp Proportion of pregnant women who received 2 doses of IPTp Proportion of pregnant women who received 1 dose of IPTp Proportion of pregnant women who attended ANC at least once Proportion of children under 5 with fever in the previous 2 weeks for whom advice or treatment was sought Proportion of patients with suspected malaria who received a parasitological test Proportion of children under 5 with fever in the previous 2 weeks who had a finger or heel stick Proportion of patients with confirmed malaria who received first-line antimalarial treatment according to national policy Proportion of treatments with ACTs (or other appropriate treatment according to national policy) among febrile children <5 Proportion of malaria cases detected by surveillance systems Proportion of expected health facility reports received Parasite prevalence: proportion of population with evidence of infection with malaria parasites Malaria case incidence: number and rate per 1000 persons per year Malaria mortality: number and rate per 100 000 persons per year Number of areas/countries that have newly eliminated malaria since 2015 Number of areas/countries that were malaria free in 2015 in which malaria has been re-established

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

●

Chemoprevention 3.1 3.2 3.3 3.4 Case detection Diagnostic testing 4.1 5.1 5.2 Treatment 6.1 6.2 Surveillance 7.1 7.2 Impact

●

●

● ●

● ●

Prevalence Incidence Mortality Elimination

8.1 9.1 10.1 11.1

●

●

● ●

Prevention of 12.1 re-establishment

●

Indicator highly relevant to setting

Indicator potentially relevant to setting

ACT, artemisinin-based combination therapy; ANC, antenatal care; GTS, Global Technical Strategy for Malaria 2016-2030; IPTp, intermittent preventive treatment in pregrancy; IRS, indoor residual spraying; ITN, insecticide-treated mosquito net WORLD MALARIA REPORT 2016

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2. Investments in malaria programmes and research Progress in reducing malaria incidence and mortality between 2000 and 2015 was made possible by large increases in the financing of malaria control and elimination programmes. Further progress in reducing malaria depends on increased investments in malaria programmes. The GTS estimated that annual investments in malaria control and elimination need to increase to US$ 6.4 billion per year by 2020 to meet the first milestone under that strategy of a 40% reduction in malaria incidence and mortality rates. The GTS also recognized that innovations in tools and approaches are needed to achieve its targets, and estimated that an additional US$ 674 million (range: US$ 530 million–832 million) would be required annually for malaria research and development. This section of the report examines recent trends in the financing of malaria programmes and of malaria research and development. It considers the indicators listed in Box 2.1. This section also considers the quantities of commodities delivered, because this provides insight into malaria expenditures, and because the availability of supplies is a key determinant of programme coverage.

Box 2.1 Indicators related to investments in malaria programmes and research >> Total expenditure for malaria control and elimination >> Funding for malaria research and development >> Expenditure per capita for malaria control and elimination

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Investments in malaria programmes and research

2.1 Total expenditure for malaria control and elimination Total funding for malaria control and elimination in 2015 is estimated at US$ 2.9 billion, rising just US$ 0.06 billion since 2010 and representing only 46% of the GTS 2020 milestone of US$ 6.4 billion (Figure 2.1). Funding for malaria increased year on year between 2005 and 2010, but subsequently fluctuated, with totals for 2014 and 2015 lower than 2013. Pledges at the Global Fund replenishment conference for funding in 2017–2019 increased by 8% compared with 2014–2016. However, total funding needs to increase by a substantially greater amount if the 2020 milestone is to be achieved. Governments of endemic countries provided 32% of total funding in 2015, of which US$ 612 million was direct expenditure through NMCPs and US$ 332 million was expenditure on patient service delivery care (Figure 2.2). Domestic government contributions are greatest in the WHO African Region (US$ 528 million), followed by the WHO Region of the Americas (US$ 202 million) and the WHO South-East Asia Region (US$ 92 million). Domestic governments accounted for the greatest share of funding for malaria in the WHO European Region (99%) and the WHO Region of the Americas (88%), but represented 50% or less in the other WHO regions. The level of domestic government financing reflects the size of the malaria burden in each region, and the willingness and ability of governments to tackle this burden. International funding accounts for most (68%) of the funding for malaria control and elimination programmes. Such funding may be provided direct to endemic countries through bilateral aid or through intermediaries such as the Global Fund, World Bank or other multilateral institutions (Figure 2.2). The United States of

Figure 2.1 Investments in malaria control activities by funding source, 2005–2015. Annual values have

been converted to constant 2015 US$ using the gross domestic product implicit price deflator from the USA in order to measure funding trends in real terms. Sources: ForeignAssistance.gov, Global Fund to Fight AIDS, Tuberculosis and Malaria, national malaria control programme reports, Organisation for Economic Co‑operation and Development (OECD) creditor reporting system, the World Bank Data Bank, WHO estimates of malaria cases and treatment seeking at public facilities, and WHO CHOICE unit cost estimates of outpatient visit and inpatient admission 4 Governments of endemic countries Global Fund USA UK World Bank Others

3 US$ (billions)

2

1

0

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Global Fund, Global Fund to Fight AIDS, Tuberculosis and Malaria; UK, United Kingdom of Great Britain and Northern Ireland; USA, United States of America

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Figure 2.2 Annual flow of funding for malaria control and elimination, 2014–2015. Sources of funds are

listed on the left and destination WHO regions on the right. Intermediaries through which much donor funding is channelled are shown in the middle. Sources: ForeignAssistance.gov, Global Fund to Fight AIDS, Tuberculosis and Malaria, national malaria control programme reports, Organisation for Economic Co‑operation and Development (OECD) creditor reporting system, the World Bank Data Bank, WHO estimates of malaria cases and treatment seeking at public facilities, and WHO CHOICE unit cost estimates of outpatient visit and inpatient admission

Government of endemic countries $944 m, 32%

Africa $ 2083 m, 70% USA $1048 m, 35%

UK $465 m, 16%

Americas $ 230 m, 8% Global Fund $911 m Eastern Mediterranean $122 m, 4% Europe $27 m, 1% South East Asia $207 m, 7% Western Pacific $102 m, 3% World Bank $74 m EU institutions, WHO, UNICEF $13 m Unspecified recipients $186 m, 6%

France $94 m, 3% Germany $72 m, 2% Japan $68 m, 2% Canada $51 m, 2% BMGF $36 m, 1% EU institutions $33 m, 1% Others $154 m, 5%

BMGF, Bill & Melinda Gates Foundation; EU, European Union; Global Fund, Global Fund to Fight AIDS, Tuberculosis and Malaria; UK, United Kingdom of Great Britain and Northern Ireland; UNICEF, United Nations Children’s Fund; USA, United States of America WORLD MALARIA REPORT 2016

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Investments in malaria programmes and research

America is the largest single international funder of malaria control activities; it accounted for an estimated 35% of total malaria funding in 2015 (including bilateral aid and contributions to intermediaries), followed by the United Kingdom of Great Britain and Northern Ireland (16%), France (3.2%), Germany (2.4%), Japan (2.3%), Canada (1.7%), the Bill & Melinda Gates Foundation (1.2%) and European Union institutions (1.1%). Contributions from other countries represented 5% of total funding. Nearly half of all international funding (45%) is channelled through the Global Fund. The Global Fund is responsible for a significant share of malaria funding in the WHO Eastern Mediterranean Region (62%), the WHO South-East Asia Region (45%) and the WHO Western Pacific Region (35%). In the WHO African Region, 25% of funding comes from domestic governments, 33% from the Global Fund and 29% from bilateral support from the United States Agency for International Development (USAID). Almost 90% of domestic funding is accounted for by health system spending (Figure 2.3). In contrast, more than half of the funding from the Global Fund and USAID is devoted to the delivery of preventive interventions. Around a sixth of Global Fund, and a third of USAID funding is spent on treatment. The progress of prevention and treatment programmes is therefore highly sensitive to variations in donor spending.

Figure 2.3 Malaria financing, 2013–2015, by type of expenditure. Health-system spending includes planning, monitoring and evaluation, communications and advocacy, supply management, training and human resources (apart from those used for the delivery of services). Prevention includes procurement and delivery of insecticide-treated mosquito nets, support of indoor residual spraying and delivery of intermittent preventive therapy in pregnancy. Treatment includes commodities and resources for service delivery such as human resources, infrastructure and equipment. Sources: Global Fund Enhanced Financial Reporting (EFR), USAID PMI malaria operational plans for 2013-2015 available at https://www. pmi.gov/resource-library/mops/fy-2016, national malaria control programme reports, WHO estimates of malaria cases and treatment seeking at public facilities, and WHO CHOICE unit cost estimates of outpatient visit and inpatient admission 6% 6% 17% 24% 15% 88% 32% Health systems Prevention Treatment

Governments of endemic countries

59% Global Fund 53%

USAID PMI

Global Fund, Global Fund to Fight AIDS, Tuberculosis and Malaria; PMI, President’s Malaria Initiative; USAID, United States Agency for International Development

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2.2 Funding for malaria-related research Spending on research and development for malaria rose from an estimated US$ 607 million in 2010 to US$ 611 million in 2014 (the latest year for which data are available). The 2014 total represents more than 90% of the GTS annual investment target of US$ 674 million (Figure 2.4). The largest research and development spending category was antimalarial medicines (35%), followed by vaccines (28%) and basic research (27%). Investments in diagnostics and vector-control tools were each estimated to account for only 3% of the 2014 spending. Public sector investors contributed to nearly half of total research and development funding in 2014, with the US National Institutes for Health and the US Department of Defence comprising 55% of this category (Figure 2.5). Philanthropic investment sources (primarily the Bill & Melinda Gates Foundation and the United Kingdom’s Wellcome Trust) accounted for 28% of the total. Private sector funding sources, namely pharmaceutical and biotechnology companies, accounted for 23% of total spending in 2014.

Search Tool. Policy Cures. https://gfinder.policycures.org/Public SearchTool/ Drugs Vector control Vaccines Diagnostics Basic research Unspecified

Figure 2.4 Funding for malaria-related research and development, 2010–2014. Source: Gfinder Public

Figure 2.5 Source of funding for malariarelated research and development, 2014. Source: Gfinder Public Search Tool. Policy Cures. https://gfinder.policycures.org/PublicSearchTool/

800

GTS annual target: US$ 674 million

Unspecified 0% Private sector 22% Public sector 50%

600

US$ (millions)

400

200

0

2010

2011

2012

2013

2014

Philanthropic 28%

GTS, Global Technical Strategy for Malaria 2016–2030

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Investments in malaria programmes and research

2.3 Malaria expenditure per capita for malaria control and elimination An analysis of malaria spending in relation to population at risk can help in assessing the adequacy of current funding levels. The composition and costs of malaria control and elimination programmes vary by setting. Based on resource need estimates from the GTS, countries with more than 1 million cases require a higher per capita spending (US$ 3.40) than those with between 10 000 and 1 million cases (US$ 2.50). Countries with fewer than 10 000 cases require the highest per capita spending (US$ 3.75) owing to the added cost of case-based surveillance, which becomes feasible with low case numbers. Countries with more than 1 million cases are furthest from the per capita spending milestones for 2020 set in the GTS (Figure 2.6). Countries with fewer than 10 000 cases are able to meet a greater proportion of funding requirements from domestic sources because of a lower total financial requirement (related to the lower number of cases) and generally higher gross national incomes.

Figure 2.6 Malaria financing per person at risk, 2013–2015, by estimated number of malaria cases, 2015. The solid bar shows the interquartile range among countries endemic for malaria in 2015, and the white line shows the

median. The 10th and 90th percentiles are shown as black cross-bars. Sources: ForeignAssistance.gov, Global Fund to Fight AIDS, Tuberculosis and Malaria, national malaria control programme reports, Organisation for Economic Co-operation and Development creditor (OECD) reporting system and the Data Bank of the World Bank 10 8 US$ per person at risk International Domestic Total 2020 global milestone

6 4 2 0

> 1 000 000 cases (33 countries)

10 000–1 000 000 cases (32 countries)

<10 000 cases (26 countries)

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2.4 Commodity procurement trends Insecticide-treated mosquito nets Between 2013 and 2015, a total of 510 million insecticide-treated mosquito nets (ITNs) were reported by manufacturers as having been delivered to countries in sub-Saharan Africa, which exceeds the minimum amount required to achieve universal access to an ITN in the household (491 million)1. More ITNs were delivered in 2014 (189 million) and 2015 (178 million) than in any previous year (Figure 2.7). Decreasing prices may have contributed to increased procurement, with the average procurement price falling from US$ 6.27 to US$ 4.36 per net between 2010 and 2014 (2015 prices). Six countries accounted for more than 50% of deliveries in sub-Saharan Africa (Nigeria, 93 million ITNs; Democratic Republic of the Congo, 61 million; Ethiopia, 45 million; Uganda, 28 million; Burkina Faso, 20 million and Kenya, 18 million). Outside sub-Saharan Africa, 73 million ITNs were delivered by manufacturers between 2013 and 2015, with more than half of those deliveries accounted for by five countries (India, 13 million ITNs; Indonesia, 9.3 million; Myanmar, 8.9 million; Cambodia, 4.3 million and Papua New Guinea, 4.1 million). Manufacturer deliveries are a forward indicator of in-country distribution and household coverage with ITNs. NMCP distributions to households lag the deliveries of ITNs to countries by an average of 0.5–1.0 years, and ITN coverage indicators, reviewed in Section 3 of this report, lag 3-year cumulative totals of manufacturer deliveries by about 1 year. A total of 128 million ITNs are projected to be delivered to countries in sub-Saharan Africa in 2016, based on shipments up to October 2016. The 3-year cumulative totals of manufacturer deliveries suggest that although ITN coverage will rise further in 2016 it may drop in 2017. 1. Based on the assumption that every household received the exact number of nets required for 100% access within households and that nets are retained for at least 3 years. In practice, ITNs are lost or replaced before 3 years, so the number of ITNs required to achieve universal access is greater.

Figure 2.7 Number of ITNs delivered by manufacturers and delivered by NMCPs 2009–2016. Data from NMCPs for 2016 and 2017 not yet available. Sources: Milliner Global Associates and NMCP reports 250 200 Number of ITNs (millions) Manufacturer deliveries: Outside Africa Sub-Saharan Africa NMCP deliveries: Outside Africa Sub-Saharan Africa

150 100 50 0

2009 2010

2010 2011

2011 2012

2012 2013

2013 2014

2014 2015

2015 2016

2016 2017

ITN, insecticide-treated mosquito net; NMCP, national malaria control programme

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Rapid diagnostic tests Sales of rapid diagnostic tests (RDTs) reported by manufacturers rose from 88 million globally in 2010 to 320 million in 2013, but fell to 270 million in 2015 (Figure 2.8). The decrease in sales was most pronounced in Asia, with sales of “falciparum only” tests falling from 22 million to less than 1 million between 2014 and 2015. In contrast, sales of “falciparum only” tests increased in Africa from 166 million to 179 million, whereas combination tests decreased from 89 million to 61 million between 2014 and 2015. The number of RDTs distributed by NMCPs, while following a similar trend to manufacturer sales before 2015, did not show the same dip in 2015. In sub-Saharan Africa, the numbers distributed rose from 165 million in 2014 to 179 million in 2015; outside Africa, they rose from 25 million to 28 million. Some of the difference in trends and levels may be due to incomplete reporting. The differences may also be due to the fact that RDT sales reported by manufacturers include both public and private health sectors, whereas RDTs distributed by NMCPs represent tests in the public sector only. Because of inconsistencies in how data are reported, it is not possible to establish how trends in each variable are linked over time. It is not known to what extent the 2015 decline in reported manufacturer RDT deliveries will affect the availability of diagnostic testing for patients with fever.

Figure 2.8 Number of RDTs sold by manufacturers and distributed by NMCPs, 2010–2015. Sources:

NMCP reports and data from manufacturers eligible for the WHO Foundation for Innovative New Diagnostics/US Centers for Disease Control and Prevention Malaria Rapid Diagnostic Test Product Testing Program 350 300 Number of RDTs (millions) Manufacturer deliveries Sub-Saharan Africa: P. falciparum only tests Combination tests Outside Africa: P. falciparum only tests Combination tests NMCP deliveries Sub-Saharan Africa Outside Africa

250 200 150 100 50 0

2010

2011

2012

2013

2014

2015

NMCP, national malaria control programme; RDT, rapid diagnostic test

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Artemisinin-based combination therapies The number of courses of artemisinin-based combination therapy (ACT) procured from manufacturers increased from 187 million in 2010 to a peak of 393 million in 2013, but subsequently fell to 311 million in 2015, of which 209 million were delivered to the public sector (Figure 2.9). The number of ACT treatments distributed by NMCPs to public sector health facilities also declined from 192 million in 2013 to 153 million in 2015. The discrepancy between manufacturer deliveries to the public sector and the number of courses distributed through public facilities can be accounted for, in part, by incomplete reporting by NMCPs. The WHO African Region accounted for 98% of all manufacturer deliveries in 2015 (in cases where the destination is known) and 97% of NMCP deliveries. In the WHO African Region, the number of ACT treatments distributed by NMCPs in the public sector (148 million) is now fewer than the number of malaria diagnostic tests provided (170 million) (Figure 2.10). The decreasing ratio of treatments to tests in the public sector (87:100 in 2015) is a reflection that more patients are receiving a diagnostic test before being treated. However, there is still scope for improvement in the ratio of treatments to tests, because this ratio should approximate the malaria test positivity rate of patients seeking treatment, which is generally 52% (or 0.52) across all countries in sub-Saharan Africa.

Figure 2.9 Number of ACT treatment courses delivered by manufacturers and distributed by NMCPs, 2010–2015. AMFm/GF indicates AMFm operated from 2010 to 2013, and GF co-payment mechanism from 2014. Sources: Companies eligible for procurement by WHO/United Nations Children’s Fund and NMCP reports Public sector Private sector - AMFm/GF Public sector - AMFm/GF Distributed by NMCPs

programme reports, WHO African Region, 2010–2015

Figure 2.10 Ratio of ACT treatment courses distributed to diagnostic tests performed (RDTs or microscopy), WHO African Region 2010-2015. Source: National malaria control

500 ACT treatment courses (millions)

Ratio of ACTs: tests undertaken and test positivity rate

3.00 2.50 2.00 1.50 1.00 0.50 0 Test positivity rate

Ratio of ACTs: tests undertaken

400 300 200 100 0 2010 2011 2012 2013 2014 2015

2010

2011

2012

2013

2014

2015

ACT, artemisinin-based combination therapy; AMFm, Affordable Medicines Facility–malaria; GF, Global Fund to Fight AIDS, Tuberculosis and Malaria; NMCP, national malaria control programme

ACT, artemisinin-based combination therapy; RDT, rapid diagnostic test

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3. Preventing malaria

Cases of malaria can be prevented by vector control (stopping mosquitoes from biting human beings), by chemoprevention (providing drugs that suppress infections) or, potentially, by vaccination. These prevention strategies are discussed below. Vector control The most commonly used methods to prevent mosquito bites are sleeping under an ITN and spraying the inside walls of a house with an insecticide – indoor residual spraying (IRS). Use of ITNs has been shown to reduce malaria incidence rates by 50% in a range of settings, and to reduce malaria mortality rates by 55% in children aged under 5 years in sub-Saharan Africa (5,6). Historical and programme documentation suggest a similar impact for IRS, but randomized trial data are limited (7). These two core vector-control interventions – use of ITNs and IRS – are considered to have made a major contribution to the reduction in malaria burden since 2000, with ITNs estimated to account for 50% of the decline in parasite prevalence among children aged 2–10 years in sub-Saharan Africa between 2001 and 2015 (1). In a few specific settings and circumstances, ITNs and IRS can be supplemented by larval source management (8) or other environmental modifications that reduce the suitability of environments as mosquito habitats or that otherwise restrict biting of humans.

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Preventing malaria

Chemoprevention In sub-Saharan Africa, intermittent preventive treatment of malaria in pregnancy (IPTp) with sulfadoxine-pyrimethamine (SP) has been shown to reduce maternal anaemia (7), low birth weight (1) and perinatal mortality (8). Intermittent preventive treatment in infants (IPTi) with SP provides protection against clinical malaria and anaemia (9); however, as of 2015, no countries have reported implementation of an IPTi policy. Seasonal malaria chemoprevention (SMC) with amodiaquine (AQ) plus SP (AQ+SP) for children aged 3–59 months reduces the incidence of clinical attacks and severe malaria by about 80% (10,11) and could avert millions of cases and thousands of deaths in children living in areas of highly seasonal malaria transmission in the Sahel subregion (12). As of 2015, 10 countries had adopted the policy (Burkina Faso, Chad, Gambia, Guinea, Guinea Bissau, Mali, Niger, Nigeria, Senegal and Togo). Vaccines A number of malaria vaccine research projects are underway (13). The only vaccine to have completed Phase 3 testing is RTS,S/AS01, which reduced clinical incidence by 39% and severe malaria by 31.5% among children aged 5–17 months who completed four doses. Following the positive scientific opinion of the European Medicines Authority under Article 58 ( 14 ), WHO recommended that RTS,S be implemented on a pilot scale in parts of three to five sub-Saharan African countries (15). The aim is to provide information on feasibility, safety and mortality impact, to guide recommendations on the potential wider scale use of this vaccine in 3–5 years’ time. The first phase of vaccination is expected to commence in 2018. RTS,S is being considered as a complementary malaria control tool in Africa that could potentially be added to, rather than replace, the core package of proven malaria preventive, diagnostic and treatment interventions. Indicators Ensuring universal access of populations at risk to preventive interventions is central to achieving the goals and milestones of the GTS. Accordingly, this section reviews the indicators listed in Box 3.1 to assess the extent to which universal access to interventions has been achieved. Use of ITNs is reported only for sub-Saharan Africa, where malaria vectors are most amenable to control with this intervention. Similarly, the analysis of IPTp is confined to sub-Saharan Africa, the region where it is applicable. The coverage of IPTi, SMC and vaccines is not reported given their current limited adoption.

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Box 3.1 Indicators related to preventing malaria Insecticide-treated mosquito nets >> >> >> >> >> >> Proportion of population at risk that slept under an ITN the previous night Proportion of population with access to an ITN within their household Proportion of households with at least one ITN for every two people Proportion of households with at least one ITN Proportion of existing ITNs used the previous night Proportion of targeted risk group receiving ITNs (antenatal and immunization clinic attenders)

Indoor residual spraying

Insecticide-treated mosquito nets and indoor residual spraying IRS in the previous 12 months

>> Proportion of population at risk protected by IRS in the previous 12 months >> Proportion of population at risk sleeping under an ITN or living in a house sprayed by >> >> >> >>

Intermittent preventive therapy in pregnancy

Proportion of pregnant women who received at least three doses of IPTp Proportion of pregnant women who received 2 doses of IPTp Proportion of pregnant women who received 1 dose of IPTp Proportion of pregnant women who attended antenatal care at least once

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3.1 Population at risk sleeping under an insecticide-treated mosquito net For countries in sub-Saharan Africa, it is estimated that 53% of the population at risk slept under an ITN in 2015 (95% confidence interval [CI]: 50–57%), increasing from 5% in 2005 and from 30% in 2010 (95% CI: 28–32%) (Figure 3.1). The rise in the proportion of the population sleeping under an ITN has been driven by increases in the proportion of the population that have access to an ITN in their house (in 2015 the proportion was 60%, 95% CI: 57–64%). The proportion sleeping under an ITN is generally close to the proportion with access to an ITN. Thus, while it continues to be important to encourage consistent ITN use among those who have access to a net, ensuring access to ITNs for those who do not have them is central to increasing overall use. The proportion of households with one or more ITNs increased to 79% in 2015 (95% CI: 76–83%). However, this means that a fifth of households do not have access to any nets. Moreover, the proportion of households with sufficient ITNs for all household members was just 42% (95% CI: 39–45%), substantially short of universal access (100%) to this preventive measure. This reiterates the need to ensure that all households receive sufficient nets so there is at least one for every two persons.

3.2 Targeted risk group receiving ITNs In addition to mass distribution campaigns, WHO recommends the continuous distribution of ITNs to all pregnant women attending antenatal care (ANC) and all infants attending child immunization clinics (17). Data reported by NMCPs indicate that, between 2013 and 2015, mass campaigns accounted for 86% of ITNs distributed in sub-Saharan Africa, while antenatal clinics accounted for 10% and immunization clinics for 4% (Figure 3.2). The number of ITNs distributed through antenatal and immunization clinics can be compared to the number of pregnant women attending ANC and the number of children receiving immunization, to determine the extent to which these channels are used for ITN delivery (18). Data reported by NMCPs in 2013–2015 indicate that 39% of pregnant women that attended ANC and 20% of children that attended immunization clinics received an ITN. Hence, these continuous distribution channels for ITNs appear to be underused. Some of the gap can be attributed to countries not yet adopting a policy to distribute ITNs through these channels; four countries that did not distribute ITNs through ANC clinics accounted for 10% of the 61% gap, and nine countries that did not distribute ITNs through immunization clinics accounted for 22% of the 80% gap.

3.3 Population at risk protected by indoor residual spraying NMCPs reported that 106 million people worldwide were protected by IRS in 2015; this figure includes 49 million people in the WHO African Region and 44 million people in the WHO South-East Asia Region (of whom >41 million are in India). The proportion of the population at risk protected by IRS declined globally from a peak of 5.7% in 2010 to 3.1% in 2015, with decreases seen in all WHO regions (Figure 3.3). The proportions of the population protected by IRS are low because IRS is generally used only in particular areas. Declining IRS coverage may be attributed to a change from pyrethroids to more expensive insecticide classes, although heavy reliance on pyrethroids continues particularly outside of the WHO African Region (Figure 3.4). Concurrent, sequential or mosaic use of insecticide classes with different modes of action is one component of a comprehensive insecticide resistance management strategy.

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Figure 3.1 Proportion of population at risk with access to an ITN and sleeping under an ITN, and proportion of households with at least one ITN and enough ITNs for all occupants, subSaharan Africa, 2005–2015. Source: Insecticidetreated mosquito net coverage model from Malaria Atlas Project (16) 100% Proportion of population at risk or households

Figure 3.2 Proportion of ITNs distributed through different delivery channels in sub-Saha­ ran Africa, 2013–2015. Source: National malaria control programme reports

80% 60% 40% 20% 0

Household with at least 1 ITN Population with access to an ITN in household Household with enough ITNs for all occupants Population sleeping under an ITN

Child immunization clinics, 4% Antenatal care clinics, 10%

Mass campaign, 86%

2005

2010

2015

ITN, insecticide-treated mosquito net

Figure 3.3 Proportion of the population at risk protected by IRS by WHO region, 2010–2015. Source: National malaria control programme reports 12% 10% Proportion of population at risk

Figure 3.4 Insecticide class used for indoor residual spraying 2010–2015. Source: National malaria control programme reports Pyrethroids only Pyrethroids and other insecticides Other insecticides only

Number of countries

8% 6% 4%

AFR AMR World SEAR EMR WPR

50 40 30 20 10 0 50 Number of countries 2010 2011

2012 2013 WHO African Region

2014

2015

2% 0

40 30 20 10 0 2010 2011 2012 2013 Other WHO regions 2014 2015

2010

2011

2012

2013

2014

2015

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; IRS, indoor residual spraying; SEAR, WHO South-East Asia Region; WPR, WHO Western Pacific Region

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Preventing malaria

3.4 Population at risk sleeping under an insecticide-treated mosquito net or protected by indoor residual spraying Combining data on the proportion of the population sleeping under an ITN with information on the proportion protected by IRS – and accounting for households that may receive both interventions – the proportion of the population in sub-Saharan Africa protected by vector control was estimated at 57% in 2015 (uncertainty interval [UI], 44–70%) compared with 37% in 2010 (UI, 25–48%) (Figure 3.5). The proportion exceeded 80% in three countries in 2015: Cabo Verde, Zambia and Zimbabwe.

Figure 3.5 Proportion of the population at risk protected by IRS or sleeping under an ITN in sub-Saharan Africa, 2010–2015. Sources: Insecticide-treated mosquito net coverage model from Malaria Atlas Project (16), national malaria control programme reports and further analysis by WHO ITN only ITN & IRS IRS only

100%

Proportion of population at risk

80%

60%

40%

20%

0

2010

2011

2012

2013

2014

2015

IRS, indoor residual spraying; ITN, insecticide-treated mosquito net

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Preventing malaria

3.5 Vector insecticide resistance Resistance of malaria vectors to the four insecticide classes currently used in ITNs and IRS threatens malaria prevention efforts. Of the 73 malaria endemic countries that provided monitoring data to WHO for 2010 onwards, 60 reported resistance to at least one insecticide in one malaria vector from one collection site, and 50 reported resistance to two or more insecticide classes. Resistance to pyrethroids – the only class currently used in ITNs – is the most commonly reported (Figure 3.6); in 2015, over three quarters of the countries monitoring this insecticide class reported resistance. However, the impact of pyrethroid resistance on ITN effectiveness is not yet well established. A WHO-coordinated five-country evaluation conducted in areas with pyrethroid-resistant malaria vectors did not find an association between malaria disease burden and levels of resistance, and showed that ITNs still provided personal protection (19). Nevertheless, evidence of geographical spread of resistance and intensification in some areas underscores the need to urgently take action to manage resistance and to reduce reliance on pyrethroids. Priority actions include establishing and applying national insecticide resistance monitoring and management plans in line with the WHO Global plan for insecticide resistance management in malaria vectors (GPIRM), released in 2012. New vector monitoring and control tools and approaches are also urgently required. WHO Test procedures for monitoring insecticide resistance in malaria vector mosquitoes were updated in November 2016 to include bioassays for resistance intensity and metabolic mechanisms. Information from national programmes and partners on insecticide resistance in malaria vectors is collated by WHO in a global database.

Network for Vector Resistance, Malaria Atlas Project, President’s Malaria Initiative (United States), scientific publications 50 Resistance reported Resistance not reported

Figure 3.6 Insecticide resistance and monitoring status for malaria endemic countries (2015), by insecticide class and WHO region, 2010–2015. Source: National malaria control programme reports, African Not monitored

40 Number of countries

30

20

10

0

AFR

AMR EMR

EUR

SEAR WPR

AFR

AMR EMR

EUR SEAR WPR AFR

AMR EMR

EUR SEAR WPR

AFR

AMR EMR

EUR SEAR WPR

Pyrethroids

Organochlorine (DDT)

Carbamates

Organophosphates

AFR, WHO African Region; AMR, WHO Region of the Americas; DDT, dichloro-diphenyl-trichloroethane; EMR, WHO Eastern Mediterranean Region; EUR, European Region; SEAR, WHO South-East Asia Region; WPR, WHO Western Pacific Region

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3.6 Pregnant women receiving three or more doses of intermittent preventive therapy It is estimated that, in 2015, among 20 countries that reported, 31% of eligible pregnant women (UI: 29–32%) received three or more doses of IPTp in 36 African countries that have adopted the policy – a large increase from the 18% receiving three or more doses in 2014 and 6% in 2010 (Figure 3.7). The proportion still remains below full coverage. A significant proportion of pregnant women do not attend ANC (20% in 2015) and, of those who do, 30% do not receive a single dose of IPTp. The proportion of women receiving IPTp varied across the continent, with 24 countries reporting that more than 50% of pregnant women received one or more doses, and 17 countries reporting more than 50% received two or more doses. Only three countries reported that more than 50% of pregnant women received three or more doses of IPTp.

reports and United Nations population estimates 100%

Figure 3.7 Proportion of pregnant women receiving IPTp, by dose, sub-Saharan Africa, 2010-2015. Source: National malaria control programme 95% uncertainty interval

80% Proportion of pregnant women

60% Receiving at least 1 dose of IPTp

40% Receiving at least 2 doses of IPTp

20%

Receiving at least 3 doses of IPTp

0

2010

2011

2012

2013

2014

2015

IPTp, intermittent preventive treatment in pregnancy

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Box 4.1 Indicators related to diagnostic testing and treatment Care seeking >> Proportion of children under 5 with fever in the previous 2 weeks for whom advice or treatment was sought

Diagnostic testing heel stick

>> Proportion of children under 5 with fever in the previous 2 weeks who had a finger or >> Proportion of patients with suspected malaria attending public health facilities who received a parasitological test

Treatment

>> Proportion of patients with confirmed malaria who received first-line antimalarial >> Proportion of treatments with ACTs (or other appropriate treatment according to national policy) among febrile children <5 treatment according to national policy

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4. Diagnostic testing and treatment Prompt diagnosis and treatment of malaria can cure a patient, preventing the development of severe malaria and subsequent death. It also reduces the length of time that patients carry malaria parasites in their blood, which in turn reduces the risk of onward transmission. Diagnostic testing WHO recommends that every suspected malaria case be confirmed by microscopy or an RDT before treatment (20). Accurate diagnosis improves the management of febrile illnesses and ensures that antimalarial medicines are only used when necessary. Only in areas where parasite-based diagnostic testing is not possible should malaria treatment be initiated solely on clinical suspicion. Treatment Prompt and appropriate treatment of uncomplicated malaria is critical in preventing progression to severe disease and death. WHO recommends ACTs for the treatment of uncomplicated Plasmodium falciparum malaria. ACTs have been estimated to reduce malaria mortality in children aged 1–23 months by 99% (range: 94–100%), and in children aged 24–59 months by 97% (range: 86–99%) (21). Indicators The ability of health systems to diagnose and treat cases is influenced by the extent to which patients with suspected malaria seek treatment, and by the proportion of patients who receive a diagnostic test and appropriate treatment after seeking health care. This section of the report discusses indicators covering care seeking, diagnostic testing and treatment, as listed in Box 4.1. It also considers the parasite’s evolutionary responses to interventions; namely, the potential for selection of parasites that can evade diagnostic tests and the evolution of drug resistance.

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Diagnostic testing and treatment

4.1 Children aged under 5 years with fever for whom advice or treatment was sought from a trained provider Evidence on the extent to which patients with suspected malaria seek treatment is derived mainly from household surveys that measure the proportion of children with fever for whom advice or treatment is sought. A disadvantage of this indicator is that it considers fever rather than confirmed malaria. Nonetheless, malaria should be suspected in febrile children who live in malaria endemic areas, and such children should be taken to a trained provider to obtain a diagnostic test and treatment, if appropriate. Although the indicator’s measurement is largely confined to sub-Saharan Africa and children aged under 5 years, sub-Saharan Africa accounts for more than 90% of global malaria cases, with most cases occurring in children aged under 5 years. Among 23 nationally representative surveys completed in sub-Saharan Africa between 2013 and 2015 (representing 61% of the population at risk), a higher proportion of febrile children sought care in the public sector (median: 42%, interquartile range [IQR]: 31–50%) than in the private sector (median: 20%, IQR: 12–28%), as shown in Figure 4.1. Most visits to the private sector were to the informal sector (median: 11%, IQR: 7–21%), which comprises pharmacies, kiosks and traditional healers, rather than to the formal private sector (median: 5%, IQR: 7–21%), which comprises private hospitals and clinics. Overall, a median of 54% (IQR: 41–59%) of febrile children were taken to a trained provider (i.e. to public sector health facilities, formal private sector facilities or community health workers). A large proportion of febrile children are not brought for care (median: 36%, IQR: 26–42%); possible reasons for this are poor access to health-care providers or a lack of awareness among caregivers about necessary care for febrile children.

Figure 4.1 Proportion of febrile children seeking care, by health sector, sub-Saharan Africa, 2013–2015. Sources: Nationally representative household survey data from demographic and health surveys, and malaria indicator surveys 100% 80% 60% 40% 20% 0

Proportion of children <5 years with fever in previous 2 weeks

Public sector

Formal private sector

Informal private sector

Community health worker

No treatment sought

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4.2 Suspected malaria cases receiving a parasitological test Since 2010, WHO has recommended that all persons with suspected malaria should undergo malaria diagnostic testing, by either microscopy or RDT. Household surveys can provide information on diagnostic testing among febrile children aged under 5 years across all sources of care. Among 22 nationally representative surveys completed in sub-Saharan Africa between 2013 and 2015 that asked questions on diagnostic testing, the proportion of febrile children who received a finger or a heel stick, indicating that a malaria diagnostic test was performed, was greater in the public sector (median: 51%, IQR: 35–60%) than in both the formal private sector (median: 40%, IQR: 28–57%) and the informal private sector (median: 9%, IQR: 4–12%), as shown in Figure 4.2. Although the proportion of children seeking care from a community health worker was low, about a third received a diagnostic test (median: 31%; IQR: 11–46%). Combining the proportions of febrile children aged under 5 years who sought care with the proportion who received a parasitological test among those who sought care, a median of 31% of febrile children received a parasitological test among the 22 nationally representative household surveys analysed between 2013 and 2015 (IQR: 16–37%).

survey data from demographic and health surveys, and malaria indicator surveys 100% Proportion of febrile children that sought care at treatment outlet

Figure 4.2 Proportion of febrile children receiving a blood test, by health sector, sub-Saharan Africa, 2013–2015. Proportions shown are among those that sought care. Sources: Nationally representative household

80% 60% 40% 20% 0

Public sector

Formal private sector

Informal private sector

Community health worker

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Diagnostic testing and treatment

4.3 Suspected malaria cases attending public health facilities and receiving a parasitological test Data reported by NMCPs indicate that the proportion of suspected malaria cases receiving a parasitological test among patients presenting for care in the public sector has increased in most WHO regions since 2010 (Figure 4.3). The largest increase has been in the WHO African Region, where diagnostic testing increased from 40% of suspected malaria cases in 2010 to 76% in 2015, mainly owing to an increase in the use of RDTs, which accounted for 74% of diagnostic testing among suspected cases in 2015. The reported testing rate may overestimate the true extent of diagnostic testing in the public sector, because, among other factors, the rate relies on accurate reporting of suspected malaria cases, and reporting completeness may be higher in countries with stronger surveillance systems and higher testing rates. A trend of increased testing in the public sector is also evident in the results of household surveys, where the proportion of febrile children who received a malaria diagnostic test in the public sector rose from a median of 29% in 2010 (IQR: 19–46%) to a median of 51% in 2015 (IQR: 35–60%) (Figure 4.4). However, the two sources of information are not directly comparable because the numbers reported by NMCPs relate to all age groups, and because household surveys are undertaken in only a limited number of countries each year.

Figure 4.3 Proportion of suspected malaria cases attending public health facilities who receive a diagnostic test, by WHO region, 2010– 2015. Source: National malaria control programme reports 100% Proportion of suspected malaria cases

Figure 4.4 Proportion of febrile children attending public sector health facilities who receive a blood test, sub-Saharan Africa, 2010–2015. Sources: Nationally representative household survey data from demographic and health surveys, and malaria indicator surveys 100% 80% 60% 40% 20% 0

80% 60% 40% 20% 0

AMR SEAR WPR EMR AFR 2010 2011 2012 2013 2014 2015

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; SEAR, WHO South-East Asia Region; WPR, WHO Western Pacific Region

Proportion of children <5 years with fever in previous 2 weeks

2010–2012

2011–2013

2012–2014

2013–2015

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4.4 Malaria cases receiving first-line antimalarial treatment according to national policy In recent years, more nationally representative household surveys have administered an RDT to children included in the survey. Thus, it is now possible to examine the treatment received by children with both a fever in the previous 2 weeks and a positive RDT at the time of survey (Figure 4.5). The median proportion of children aged under 5 years with evidence of recent or current P. falciparum infection and a history of fever, and who received any antimalarial drug was 30% among 11 household surveys conducted in sub-Saharan Africa in 2013–2015 (IQR: 20–51%). The median proportion receiving an ACT was 14% (IQR: 5–45%). The low values can be attributed to two factors: many febrile children are not taken for care to a qualified provider (Section 4.2) and, in cases where children are taken for care, a significant proportion of antimalarial treatments dispensed are not ACTs (Section 4.6). The apparent proportions and trends indicated are uncertain because the interquartile ranges of the medians are wide, indicating considerable variation among countries. Moreover, the number of household surveys is comparatively small, covering an average of 37% of the population at risk in sub-Saharan African in any one 3-year period. Further investments are needed to better track malaria treatment at health facilities (through routine reporting systems and surveys) and at community level, to gain a greater understanding of the extent of barriers to accessing malaria treatment.

Figure 4.5 Proportion of febrile children with a positive RDT at time of survey who received antimalarial medicines, sub-Saharan Africa, 2010–2015. Sources: Nationally representative household survey data from demographic and health surveys, and malaria indicator surveys Any antimalarial ACT

100% Proportion of children with fever in previous 2 weeks and positive RDT at time of survey

80%

60%

40%

20%

0

2010–2021

2011–2013

2012–2014

2013–2015

ACT, artemisinin-based combination therapy; RDT, rapid diagnostic test

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Diagnostic testing and treatment

4.5 ACT treatments among all malaria treatments Based on nationally representative household surveys, the proportion of antimalarial treatments that are ACTs (for children with both a fever in the previous 2 weeks and a positive RDT at the time of survey) increased from a median of 29% in 2010–2012 (IQR: 17–55%) to 80% in 2013–2015 (IQR: 29–95%) (Figure 4.6). However, the ranges associated with the medians are wide, indicating large variation between countries, and the number of household surveys covering any one 3-year period is comparatively small. Antimalarial treatments are more likely to be ACTs if children seek treatment at public health facilities or via community health workers than if they seek treatment in the private sector (Figure 4.7).

4.6 Parasite resistance As the coverage of malaria programmes increases, malaria parasites respond to the selection pressure applied and parasite evolution can potentially compromise the effectiveness of current tools to diagnose and treat malaria. Diagnostic testing Some malaria parasites lack the HRP2 protein, the most common target antigen used in RDTs for detection of P. falciparum. Hence, the parasites can evade detection by diagnostic tests and subsequent treatment with an ACT. This not only prevents a patient from receiving appropriate treatment, but also enables the parasite to survive, reproduce and increase in prevalence. In 2014–2015, HRP2 or 3 deletions were reported in studies from the China– Myanmar border, Ghana and South America (Bolivia, Brazil, Colombia and Suriname). Other studies have reported HRP2 or 3 gene deletions in Democratic

representative household survey data from demographic and health surveys, and malaria indicator surveys 100% Proportion of antimalarial treatments

Figure 4.6 Proportion of antimalarial treatments that are ACTs received by febrile children that are RDT positive at the time of survey, subSaharan Africa, 2005–2015. Sources: Nationally

Figure 4.7 Proportion of antimalarial treatments that are ACTs received by febrile children, by health sector, sub-Saharan Africa, 2013–2015. Sources: Nationally representative household survey data from demographic and health surveys, and malaria indicator surveys Proportion of antimalarial treatments

100% 80% 60% 40% 20% 0 Public sector Formal private sector Informal private sector Community health worker

80% 60% 40% 20% 0

2010–2012

2011–2013

2012–2014

2013–2015

ACT, artemisinin-based combination therapy; RDT, rapid diagnostic test

ACT, artemisinin-based combination therapy

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Republic of the Congo, Eritrea, India, Mozambique, Uganda, the United Republic of Tanzania, western Indonesia and western Kenya. Populations of P. falciparum lacking one or both of the HRP2 or 3 genes are now present outside South America in both high and low transmission areas, and with varying prevalence across narrow geographical ranges. In South America, deletions were observed in parasite samples collected before HRP2-based RDTs were introduced; deletions have spread due to human migration. To ensure detection of non-HRP2-expressing parasites, only RDTs that specifically target Pf-pLDH (i.e. pan-pLDH-only tests) should be used. Currently, only a few non-HRP2-based RDTs meet WHO’s recommended procurement criteria. Treatment Plasmodium falciparum resistance to artemisinin has been detected in five countries in the Greater Mekong subregion. Artemisinin resistance is defined as delayed clearance of the parasites; it represents a partial resistance. Most patients who have delayed parasite clearance after treatment with an ACT are still able to clear their infections, except where the parasites are also resistant to the ACT partner drug. Resistance to ACT partner drugs can pose a challenge to the treatment of malaria in some areas. In Cambodia, high failure rates after treatment with an ACT have been detected for four different ACTs (Figure 4.8). Resistance to dihydroartemisininpiperaquine, first detected in Cambodia in 2008, has spread eastwards and was detected in Viet Nam in 2015. Selection of an appropriate antimalarial medicine is based on the efficacy of the medicine against the malaria parasite. Monitoring the therapeutic efficacy of antimalarial Figure 4.8 Distribution of malarial multidrug resistance medicine is therefore a fundamental 2016. Source: WHO database component of treatment strategies. WHO recommends that all malaria endemic countries conduct therapeutic Yunnan Province, efficacy studies at least every 2 years China to inform national treatment policy (22). Studies of molecular markers of drug resistance can provide important additional information for detecting and Myanmar Lao People’s Democratic Republic tracking antimalarial drug resistance. WHO collects information on therapeutic efficacy and molecular markers in a Thailand global database. Viet Nam Cambodia

1 ACT 2 ACTs 4 ACTs

ACT, artemisinin-based combination therapy

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5. Malaria surveillance systems

Effective surveillance of malaria cases and deaths is essential for identifying which areas or population groups are most affected by malaria, and for targeting resources to communities most in need. Such surveillance also alerts ministries of health to epidemics, enabling control measures to be intensified when necessary. The transformation of surveillance into a core intervention constitutes the third pillar of the GTS, and recommendations for establishing effective surveillance systems have been published by WHO (23,24). Surveillance systems do not detect all malaria cases for several reasons. First, not all malaria patients seek care or, if they do, they may not seek care at health facilities that are covered by a country’s surveillance system (Section 5.1). Second, not all patients seeking care receive a diagnostic test (Section 5.2). Finally, recording and reporting within the surveillance system is not always complete. This section of the report summarizes indicators covering surveillance of malaria cases, listed in Box 5.1.

Box 5.1 Indicators related to malaria surveillance systems >> Proportion of expected health facility reports received at the national level >> Proportion of malaria cases detected by surveillance systems

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Malaria surveillance systems

5.1 Health facility reports received at national level The completeness of health facility reporting is a good indicator of a surveillance system’s performance, because achieving a high reporting rate requires health facilities to adhere to several processes. These processes include the enumeration of a complete list of reporting units, compliance with reporting requirements and monitoring of that compliance. A high reporting rate is also critical to the eventual interpretation of indicators. Health facility reporting rates become less relevant as countries progress towards elimination and begin to report individual cases. Nonetheless, to ensure that coverage of surveillance systems is complete, the number of health facilities testing for malaria should continue to be tracked. In 2015, among the countries that could report on this indicator, most (40 of 47) reported health facility reporting rates of over 80% (Figure 5.1). However, this indicator could not be calculated for about half of the countries in which malaria was endemic in 2015, either because the number of health facilities that were expected to report was not specified (two countries) or because the number of reports submitted was not stated (17 countries), or both (24 countries). A total of 23 countries received reports from private health facilities, but these comprised a minority of all reports received in those countries (median: 2.1%, IQR: 0.6–13%).

Figure 5.1 Health facility reporting rates by WHO region, 2015. Source: National malaria control programme reports 100% 80–99% 60–79% <60% Unable to calculate

100%

80% Proportion of countries

60%

40%

20%

0

AFR

AMR

EMR

SEAR

WPR

World

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; SEAR, WHO SouthEast Asia Region; WPR, WHO Western Pacific Region

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5.2 Malaria cases detected by surveillance systems It is estimated that, in 2015, malaria surveillance systems detected 19% of cases that occur globally (UI: 16–21%) (Figure 5.2). The bottlenecks in case detection vary by WHO region. In the WHO African Region, the WHO Eastern Mediterranean Region, the WHO South-East Asia Region and the WHO Western Pacific Region, a large proportion of patients seek treatment in the private sector, and these cases are not captured by existing surveillance systems. Also, in the WHO African Region, the WHO Eastern Mediterranean Region and the WHO Western Pacific Region, a relatively low proportion of patients attending public health facilities also receive a diagnostic test. The regional patterns are sometimes dominated by individual countries with the highest number of cases; for instance, a large proportion of patients in India seek treatment in the private sector. Case detection rates have increased by 10% since 2010, with most of this improvement being due to increased diagnostic testing in sub-Saharan Africa.

survey data and national malaria control programme reports Seeking treatment

Figure 5.2 Bottlenecks in case detection 2015, by WHO region. Sources: Nationally representative household Seeking treatment at facility covered by surveillance system Receiving diagnostic test Case reported

100%

80% Proportion of all malaria cases

60%

40%

20%

0

AFR

AMR

EMR

SEAR

WPR

World

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; SEAR, WHO SouthEast Asia Region; WPR, WHO Western Pacific Region

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Box 6.1 Indicators related to impact >> Parasite prevalence: proportion of population with evidence of infection with malaria >> >> >> >> parasites Malaria case incidence: number and rate per 1000 persons per year Malaria mortality rate: number and rate per 100 000 persons per year Number of countries that have newly eliminated malaria since 2015 Number of countries that were malaria free in 2015 in which malaria has been re-established

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6. Impact The GTS set ambitious yet achievable targets for 2030; namely, to reduce malaria incidence and mortality rates globally by at least 90% by 2030, with a milestone of at least a 40% reduction by 2020 (2). The GTS also set a target to eliminate malaria from at least 35 countries by 2030 (with a milestone of elimination in at least 10 countries by 2020), and simultaneously to prevent the re-establishment of malaria in all countries that were malaria free in 2015. To assess progress towards the targets and milestones of the GTS, this section of the report reviews the total number of malaria cases and deaths estimated to have occurred in 2015, and reviews progress according to the indicators listed in Box 6.1. It also considers the gains in life expectancy that have occurred owing to a reduction in malaria mortality rates, and the economic value of such gains. The prevalence of infections with malarial parasites in people of all ages, including children, can provide information on the level of malaria transmission in a country. Parasite prevalence is most relevant for sub-Saharan Africa, where it is measured through nationally representative household surveys. Such surveys can be brought together in a geospatial model to facilitate the mapping of parasite prevalence and the analysis of trends over time (see Annex 1). This form of analysis is restricted to sub-Saharan Africa. Malaria case incidence and mortality rates are relevant in all settings. Surveillance systems do not capture all malaria cases and deaths that occur; hence, it is necessary to use estimates of the number of cases or deaths in a country to make inferences about global trends in malaria case incidence and mortality rates (as described in Annex 1). The methods for producing estimates either adjust the number of reported cases to account for the estimated proportion of cases that are not captured by a surveillance system, or model the relationship between parasite prevalence and case incidence or mortality. The latter method is used for countries in sub-Saharan Africa for which surveillance data are lacking. The estimates aim to fill gaps in reported data; however, because they rely on relationships between variables that are uncertain, and draw on data that may be imprecisely measured, the estimates have a considerable degree of uncertainty.

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6

Impact

6.1 Estimated number of malaria cases by WHO region, 2000–2015 In 2015, an estimated 212 million cases of malaria occurred worldwide (UI: 148–304 million), a fall of 22% since 2000 and of 14% since 2010 (Table 6.1). Most of the cases in 2015 were in the WHO African Region (90%), followed by the WHO South-East Asia Region (7%) and the WHO Eastern Mediterranean Region (2%) (Table 6.2, Figure 6.1). About 4% of estimated cases globally are caused by P. vivax, but outside the African continent this proportion increases to 41% (Table 6.2). Most cases of malaria caused by P. vivax occur in the WHO South-East Asia Region (58%), followed by the WHO Eastern Mediterranean Region (16%) and the WHO African Region (12%). About 76% of estimated malaria cases in 2015 occurred in just 13 countries (Figure 6.2). Four countries (Ethiopia, India, Indonesia and Pakistan) accounted for 78% of P. vivax cases.

Table 6.1 Estimated malaria cases, 2000–2015. Estimated cases are shown with 95% upper and lower uncertainty intervals. Source: WHO estimates 2000 Number of cases (000’s) 2005 2010 2011 2012 2013 2014 2015 % change 2010–2015

Lower Estimated total Upper Lower Estimated P. vivax Upper % cases P. vivax

202 000 271 000 314 000 18 000 28 900 37 400 8%

202 000 266 000 313 000 18 700 25 700 32 300 10%

192 000 245 000 287 000 13 700 17 500 22 100 7%

183 000 235 000 276 000 13 100 16 600 21 000 7%

171 000 224 000 272 000 11 200 14 200 17 400 6%

158 000 217 000 271 000 9 200 11 300 14 300 5%

152 000 212 000 306 000 8 000 9 100 12 200 4%

148 000 212 000 304 000 6 600 8 500 10 800 4% -51% -14%

lower uncertainty intervals. Source: WHO estimates

Table 6.2. Estimated malaria cases by WHO region, 2015.

Estimated cases are shown with 95% upper and

Number of cases (000’s) AFR AMR EMR EUR SEAR WPR World Outside sub-Saharan Africa

Lower Estimated total Upper Lower Estimated P. vivax Upper % cases P. vivax

 131 000  191 000  258 000   300  1 000  2 100 1%

  500   800  1 200   400   500   800 69%

 2 400  3 800  7 500  1 100  1 400  1 700 35%

0 0 0 0 0 0

 13 300  14 400  35 200  3 400  4 900  6 800 34%

 1 000  1 200  2 200   500   700   900 58%

 148 000  212 000  304 000  6 600  8 500  10 800 4%

 16 300  18 100  40 300  5 800  7 400  9 300 41%

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; SEAR, WHO SouthEast Asia Region; WPR, WHO Western Pacific Region

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WORLD MALARIA REPORT 2016

to the estimated number of cases in each region. Source: WHO estimates

Figure 6.1 Estimated malaria cases (millions) by WHO region, 2015. The area of the circles is proportional P. falciparum P. vivax

AFR 191

SEAR 14

EMR 3.8

WPR 1.2

AMR 0.8

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; SEAR, WHO SouthEast Asia Region; WPR, WHO Western Pacific Region

Figure 6.2 Estimated country share of (a) total malaria cases and (b) P. vivax malaria cases, 2015. Source: WHO estimates

Others, 24%

Nigeria, 29%

Niger, 2% United Republic of Tanzania, 2% Cameroon, 3% Kenya, 3% Burkina Faso, 3% Ghana, 3% Mali, 4%

(a)

Democratic Republic of the Congo, 9%

India, 6% Uganda, 4% Côte d’Ivoire, 4% Mozambique, 4%

Others, 22%

India, 49%

Indonesia, 7%

(b)

Pakistan, 10%

Ethiopia, 12%

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Impact

6.2 Estimated number of malaria deaths by WHO region, 2000–2015 In 2015, it was estimated that 429 000 deaths from malaria occurred globally (UI: 235 000–639 000), a decrease of 50% since 2000 and of 22% since 2010 (Table 6.3). Most deaths in 2015 were estimated to have occurred in the WHO African Region (92%), followed by the WHO South-East Asia Region (6%) and the WHO Eastern Mediterranean Region (2%) (Table 6.4, Figure 6.3). Almost all deaths (99%) resulted from P. falciparum malaria. Plasmodium vivax is estimated to have been responsible for 3100 deaths in 2015 (range: 1800–4900), with most (86%) occurring outside Africa. In 2015, 303 000 malaria deaths (range: 165 000–450 000) were estimated to have occurred in children aged under 5 years, equivalent to 70% of the global total (Table 6.4). The number of malaria deaths in children aged under 5 years is estimated to have decreased by 60% since 2000 and by 29% since 2010. Nevertheless, malaria remains a major killer of children, and is estimated to take the life of a child every 2 minutes.

Table 6.3 Estimated malaria deaths 2000–2015. uncertainty intervals. Source: WHO estimates 2000

Estimated deaths are shown with 95% upper and lower % change 2010–2015

Number of deaths 2005 2010 2011 2012 2013 2014 2015

Lower Estimated deaths Upper Lower Estimated P. vivax deaths Upper Lower Upper % deaths P. vivax % deaths <5 years

655 000 525 000 370 000 334 000 303 000 287 000 248 000 235 000 864 000 741 000 554 000 511 000 474 000 452 000 435 000 429 000 1 087 000 955 000 740 000 687 000 635 000 610 000 656 000 639 000 4 600 11 100 15 700 4 600 9 700 14 300 3 300 6 400 10 700 3 300 6 100 9 500 2 800 5 200 8 200 2 400 4 100 6 300 2 200 3 300 5 200 1 800 3 100 4 900 -29% -52% -22%

571 000 437 000 286 000 253 000 224 000 210 000 180 000 165 000 947 000 794 000 573 000 520 000 470 000 446 000 476 000 450 000 1.3% 87% 1.3% 83% 1.2% 77% 1.2% 76% 1.1% 74% 0.9% 73% 0.8% 73% 0.7% 70%

Estimated deaths <5 years 753 000 616 000 428 000 387 000 351 000 330 000 315 000 303 000

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Table 6.4 Estimated malaria deaths by WHO region, 2015. Estimated deaths are shown with 95% upper and lower uncertainty intervals. Source: WHO estimates Number of deaths AFR AMR EMR EUR SEAR WPR World Outside sub-Saharan Africa

Lower Estimated total deaths Upper Lower Estimated P. vivax deaths Upper Lower Upper % deaths P. vivax % deaths <5 years

230 000 394 000 549 000 70 380 1 000 171 000 408 000 0,1% 74%

90 490 1 100 60 110 190 20 130 280 22% 26%

900 7 300 14 600 250 510 830 300 2 400 4 700 7% 32%

0 0 0 0 0 0 0 0 0

4 100 26 200 67 100 700 1 800 3 400 1 100 7 100 18 300 7% 27%

300 1 500 6 800 120 260 420 100 500 17% 34%

235 000 429 000 639 000 1 800 3 100 4 900 165 000 303 000 0,7% 70%

6 000 30 000 77 000 1 500 2 700 4 300 2 000 8 000 21 000 9% 27%

Estimated deaths <5 years 292 000

2 300 450 000

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; EUR, WHO European Region; SEAR, WHO South-East Asia Region; WPR, WHO Western Pacific Region

proportional to the estimated number of cases in each region. Source: WHO estimates

Figure 6.3 Estimated malaria deaths (thousands) by WHO region, 2015.

The area of the circles is P. falciparum P. vivax

AFR 394

SEAR 26

EMR 7.3

WPR 1.5

AMR 0.5

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; SEAR, WHO SouthEast Asia Region; WPR, WHO Western Pacific Region

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Impact

In 2015, it is estimated that 13 countries accounted for 75% of malaria deaths ( Figure 6.4 ). The global burden of mortality is dominated by countries in sub-Saharan Africa, with Democratic Republic of the Congo and Nigeria together accounting for more than 36% of the global total of estimated malaria deaths. Four countries accounted for 81% of estimated deaths due to P. vivax malaria (Ethiopia, India, Indonesia and Pakistan).

Figure 6.4 Estimated country share of (a) total malaria deaths and (b) P. vivax malaria deaths, 2015. Source: WHO estimates

Others, 25%

Nigeria, 26%

Niger, 2% Kenya, 3% Uganda, 3% Ghana, 3%

(a)

Democratic Republic of the Congo, 10%

Côte d’Ivoire, 3% India, 6% Angola, 3% Burkina Faso, 3% Mali, 5% Mozambique, 4% United Republic of Tanzania, 4% Others, 19% India, 51%

Indonesia, 7% (b)

Pakistan, 11%

Ethiopia, 12%

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6.3 Parasite prevalence The proportion of the population at risk in sub-Saharan Africa who are infected with malaria parasites is estimated to have declined from 22% in 2005 (UI: 20–23%) to 17% in 2010 (UI: 16–18%), and to 13% in 2015 (UI: 11–15%) (Figure 6.5). The number of people infected in sub-Saharan Africa is also estimated to have decreased, from 146 million in 2005 (UI: 135–156 million) to 131 million in 2010 (UI: 126–136 million), and to 114 million in 2015 (UI: 99–130 million). Infection rates are higher in children aged 2–10, but the majority of infected people are in other age groups. In 2015, it is estimated that 7 of the 43 countries in sub-Saharan Africa with malaria transmission had more than 25% of their population infected with malaria parasites (Burkina Faso, Cameroon, Equatorial Guinea, Guinea, Mali, Sierra Leone and Togo); this number has decreased from 12 countries in 2010. Outside Africa, surveys of parasite prevalence conducted in Papua New Guinea showed a fall in the proportion of children infected, from 12.4% in 2009 to 1.8% in 2014 (25).

Figure 6.5 Estimated (a) parasite prevalence and (b) number of people infected, sub-Saharan Africa, 2005–2015. Source: Malaria Atlas Project (http://www.map.ox.ac.uk/) (1) (a) 40% Proportion of population infected 95% confidence interval

30%

Aged 2–10 years

20%

All ages

10%

0 (b) 160 Number of people infected (millions)

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

120

Other ages

80

40 Aged 2–10 years 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

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Impact

6.4 Malaria case incidence rate The incidence rate of malaria, which takes into account population growth, is estimated to have decreased by 41% globally between 2000 and 2015, and by 21% between 2010 and 2015 (Figure 6.6). Reductions in incidence rates need to be accelerated if the GTS milestone of a 40% reduction by 2020 is to be achieved (2). Decreases in incidence rates are estimated to have been greatest in the WHO European Region (100%) and the WHO South-East Asia Region (54%). Of 91 countries and territories with malaria transmission in 2015, 40 are estimated to have achieved a reduction in incidence rates of 40% or more between 2010 and 2015, and can be considered on track to achieve the GTS milestone of a further reduction of 40% by 2020 (Figure 6.7). Another 20 countries achieved reductions of 20–40%. Most of the 40 countries with reductions of more than 40% had fewer than 1 million cases in 2010; countries with more than 1 million cases had smaller reductions. These data suggest that the GTS milestone of a 40% reduction in case incidence by 2020 will be achieved only if reductions in case incidence are accelerated in countries with high case numbers. Incidence rates changed by less than or equal to ±20% in 18 countries, and increased by more than 20% in 13 countries between 2010 and 2015 (Figure 6.7). The proportion of countries with fewer than 10 000 cases that reported increased incidence rates (21%) was higher than the proportion of countries with 10 000 to 1 million cases (15%) and of countries with more than 1 million cases (9%). These figures may be related to the greater variability in case incidence in low-transmission settings. In addition, countries with fewer cases that previously had high levels of malaria transmission may be more prone to resurgences if the coverage of their malaria control programme is reduced.

cases were recorded in the WHO European Region in 2015. Source: WHO estimates Europe South-East Asia Americas Western Pacific African Eastern Mediterranean World 11% 21% 21% 31% 30% 54% 100%

Figure 6.6 Reduction in malaria case incidence rate by WHO region, 2010–2015. No indigenous

Figure 6.7 Country-level changes in malaria case incidence rate 2010–2015, by number of cases in 2010. Source: WHO estimates Estimated number of cases in 2010: 10 000 to 1 000 000 < 10 000

50 40 Number of countries

> 1 000 000

30 20 10 0 Decrease >40% Decrease 20–40% Change <±20% Increase >20%

Change in malaria incidence 2010–2015

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6.5 Malaria mortality rate Malaria mortality rates are estimated to have declined by 62% globally between 2000 and 2015, and by 29% between 2010 and 2015 (Figure 6.8). The rate of decline between 2010 and 2015 has been fastest in the WHO Western Pacific Region (58%) and the WHO South-East Asia Region (46%). In children aged under 5 years, malaria mortality rates are estimated to have fallen by 69% globally between 2000 and 2015 and by 35% globally between 2010 and 2015. They fell by 38% in the WHO African Region between 2010 and 2015. Of 91 countries and territories with malaria transmission in 2015, 39 are estimated to have achieved a reduction of 40% or more in mortality rates between 2010 and 2015, 14 had reductions of 20–40% and 8 experienced increases in mortality rates of >20%. A further 10 countries reported no deaths in 2010 and in 2015 (the remaining 20 countries experienced changes <±20%). Reductions in mortality rates were generally faster in countries with a smaller initial number of malaria deaths (Figure 6.9). For the GTS milestone of a 40% reduction in mortality rates to be achieved by 2020, rates of reduction will need to increase in those countries that have higher numbers of deaths.

Figure 6.8 Reduction in malaria mortality rate, by WHO region, 2010–2015. No deaths from indigenous malaria were recorded in the WHO European Region from 2010 to 2015. Source: WHO estimates Western Pacific 58%

Figure 6.9 Country-level changes in malaria mortality rate 2010–2015, by number of deaths in 2010. Source: WHO estimates Estimated number of deaths in 2010: >500 50–100 <50

50 South-East Asia 46% Number of countries

40 30 20 10 0 Decrease >40% Decrease 20–40% Change <±20% Increase >20% Change in malaria mortality rate 2010–2015

Americas

37%

African

31%

Eastern Mediterranean

6%

World

29%

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Impact

6.6 Malaria elimination and prevention of re-establishment A target of the GTS is, by 2030, to eliminate malaria from 35 countries in which malaria was transmitted in 2015, and a milestone is to eliminate malaria in at least 10 countries by 2020 (2). A further target of the strategy is to prevent re-establishment of malaria in all countries that are malaria free. A country must report zero indigenous cases of malaria for 3 consecutive years before it is considered to have eliminated the disease. Between 2000 and 2015, 17 countries attained zero indigenous cases for 3 years or more (Figure 6.10), and 10 of these countries attained zero indigenous cases for 3 years within the period 2011–2015. Malaria has not re-established in any of these countries. Countries that have attained zero indigenous cases for 3 years or more, and that have sufficiently robust surveillance systems in place to demonstrate this achievement, are eligible to request WHO to initiate procedures for certification that they are malaria free. The process of certification is optional. Between 2000 and 2015, six of the 17 countries that attained zero indigenous cases for 3 years or more were certified as free of malaria by WHO (Figure 6.10).

Figure 6.10 Countries attaining zero indigenous malaria cases since 2000.

Countries are shown by the year that they attained 3 consecutive years of zero indigenous cases. Countries that have been certified as free of malaria are shown in green, with the year of certification in brackets. Source: Country reports

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Egypt

United Arab Emirates (2007)

Oman

Morocco (2010) Armenia (2011) Turkmenistan (2010) Iraq Georgia Argentina Paraguay Azerbaijan

Syrian Arab Republic

Turkey Kyrgyzstan (2016) Costa Rica Uzbekistan Sri Lanka (2016)

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In progressing to malaria elimination, the 17 countries reported a median of 184 indigenous cases 5 years before attaining zero cases (IQR: 78–728), and a median of 1748 cases 10 years before attaining zero cases (IQR: 423–5731) (Figure 6.11). However, three countries (Cabo Verde, El Salvador and Saudi Arabia) did not reach zero cases by 2015, despite having fewer than 500 indigenous cases in 2000–2005. In 2015, 10 countries and territories reported fewer than 150 indigenous cases,1 and a further 9 countries reported between 150 and 1000 indigenous cases (Figure 6.12). Thus, there appears to be a good prospect of attaining the GTS milestone of eliminating malaria from 10 countries by 2020. In April 2016, WHO published an assessment of the likelihood of countries achieving malaria elimination by 2020. The assessment was based not only on the number of cases but also on the declared malaria objectives of affected countries and on the informed opinions of WHO experts in the field (26).

1. Excludes Tajikistan, which reported zero indigenous cases in 2015 but has not yet attained 3 years of zero indigenous cases.

line. Interquartile range is shaded in light blue. Source: Country reports 100 000

Figure 6.11 Indigenous malaria cases in the years before attaining zero indigenous cases for the 17 countries that eliminated malaria, 2000–2015. Median number of cases is shown as a blue

Figure 6.12 Number of indigenous malaria cases for countries endemic for malaria in 2015, by WHO region. Source: WHO estimates AFR <150 10 AMR EMR SEAR WPR

10 000 Number of malaria cases

Estimated cases in 2015

150–1000

9

1000–10 000

11

1000

10 000– 1 000 000

29

100 >1 000 000 32 0 5 10 15 20 25 Number of countries 30 35

10

1

15 14 13 12 11 10 9

8

7

6

5

4

3

2

1

Number of years before attaining zero cases

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; SEAR, WHO SouthEast Asia Region; WPR, WHO Western Pacific Region

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Impact

6.7 Malaria cases and deaths averted since 2000 and change in life expectancy It is estimated that a cumulative 1.3 billion fewer malaria cases and 6.8 million fewer malaria deaths occurred globally between 2001 and 2015 than would have occurred had incidence and mortality rates remained unchanged since 2000. The highest proportion of cases and deaths were averted in the WHO African Region (94%). Of the estimated 6.8 million fewer malaria deaths between 2001 and 2015, about 6.6 million (97%) were for children aged under 5 years. Not all of the cases and deaths averted can be attributed to malaria control efforts. Some progress is probably related to increased urbanization and overall economic development, which has led to improved housing and nutrition. However, it has previously been estimated that 70% of the cases averted between 2001 and 2015 were due to malaria interventions (1). In the WHO African Region, reduced malaria mortality rates, particularly among children aged under 5 years, have led to a rise in life expectancy at birth of 1.2 years, accounting for 12% of the total increase in life expectancy of 9.4 years from 50.6 years in 2000 to 60 years in 2015. Across all malaria endemic countries, the contribution of malaria mortality reduction was 0.26 years or 5% of the total increase in life expectancy between 2000 and 2015, from 66.4 years to 71.4 years (Table 6.5, Figure 6.13).

6.8 Economic value of reduced malaria mortality risk, estimated by full income approach The “full income approach” attempts to assign a value to gains in life expectancy by considering the importance that individuals and society place on reductions

Figure 6.13. Gains in life expectancy in malaria endemic countries, 2000–2015. Source: WHO estimates Life expectancy at birth in 2000 Gain in life expectancy due to malaria mortality reduction Gain in life expectancy due to reduction in deaths from other causes

80

Life expectancy (years)

70

60

50

40

AFR

AMR

EMR

EUR

SEAR

WPR

World

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; EUR, WHO European Region; SEAR, WHO South-East Asia Region; WPR, WHO Western Pacific Region

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in mortality (i.e. increased longevity). In monetary terms the method places a value of US$ 1810 billion on the life-expectancy gains observed in sub-Saharan Africa between 2000 and 2015, and US$ 2040 billion globally (Table 6.6). This is equivalent to 44% of the gross domestic product (GDP) of the affected countries in the WHO Africa Region in 2015, and 3.6% in affected countries globally. The economic value of longer life is expressed here as a percentage of GDP in order to provide a convenient and well-known comparison, but is not meant to suggest that the value of longevity is itself a component of domestic output (i.e. GDP), or that the value of these gains should enter directly into the national income accounts (27). Nonetheless, the comparison suggests that the value of the gains in life expectancy due to reduction in malaria mortality are substantial, and that the total investments called for in the GTS in order to achieve the 2030 target of a reduction in the malaria mortality rate of at least 90% would be repaid many times over.

Table 6.5. Gains in life expectancy in malaria endemic countries, 2000–2015. Source: WHO estimates Life expectancy at birth 2000 2015 Gain in life expectancy due to reductions in mortality from Malaria Other causes % gain due to malaria

AFR AMR EMR EUR SEAR WPR World

50.6 73.7 65.4 72.3 63.5 72.5 66.4

60.0 76.9 68.8 76.8 69.0 76.6 71.4

1.159 0.003 0.045 0.000 0.034 0.018 0.255

8.2 3.2 3.4 4.5 5.4 4.0 4.8

12.3% 0.1% 1.3% 0.0% 0.6% 0.4% 5.0%

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; EUR, WHO European Region; SEAR, WHO South-East Asia Region; WPR, WHO Western Pacific Region

Table 6.6. Economic value of reduced malaria mortality risk, estimated by full income approach, 2000–2015. Source: WHO estimates Value of malaria mortality risk reduction 2000–2015 (US$ 2015, PPP, billions) Estimate Lower Upper Value of malaria mortality risk reduction as % of GDP Estimate Lower Upper

AFR AMR EMR EUR SEAR WPR World

1 830 15 52 93 23 2 012

1 330 13 41 66 19 1 510

2 520 17 63 127 27 2 710

44.4% 0.1% 1.3% 0.0% 1.0% 0.1% 3.6%

32.6% 0.1% 1.1% 0.0% 0.8% 0.1% 2.8%

60.9% 0.1% 1.5% 0.0% 1.3% 0.1% 4.8%

AFR, WHO African Region; AMR, WHO Region of the Americas; EMR, WHO Eastern Mediterranean Region; EUR, WHO European Region; GDP, gross domestic product; PPP, purchasing power parity; SEAR, WHO South-East Asia Region; WPR, WHO Western Pacific Region

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Conclusions The World Malaria Report 2016 is the first such report to be released during the era of the GTS 2016–2030 (2). Because the latest data included in the report are mostly from 2015, direct reporting on the progress of the GTS is not possible. However, the World Malaria Report 2016 provides a baseline against which progress since 2015 can be assessed in the future. Also, by looking at trends in indicators since 2010, the report can give an indication of where programmes are on track to meet the GTS 2020 milestones and where progress needs to be accelerated. Although malaria funding increased considerably between 2000 and 2010, it has remained relatively stable since 2010. It totalled US$ 2.9 billion in 2015, representing only 45% of the GTS funding milestone for 2020. Governments of malaria endemic countries provided 31% of total funding in 2015, and the Global Fund accounted for about half of international financing. Pledges to the Global Fund for financing for 2017–2019 have increased by 8% compared to 2014–2016 pledges. Total funding must increase substantially if the GTS 2020 milestone of US$ 6.4 billion is to be achieved. The coverage of malaria interventions rose between 2010 and 2015. More than half of the population of sub-Saharan Africa (57%) now benefits from vector-control interventions (IRS or ITNs), and an increased proportion of pregnant women receive three doses of IPTp (31% in 2015). More than half of suspected malaria cases attending public health facilities in the WHO African Region receive a diagnostic test, and the proportion of malaria cases treated with effective antimalarial drugs is increasing. Nevertheless, significant gaps in programme coverage remain. Access to vector control has been greatly extended through mass-distribution campaigns; however, increasing the coverage of chemoprevention, diagnostic testing and treatment requires these interventions to be delivered through health systems that are frequently under-resourced and poorly accessible to those most at risk of malaria. Moreover, the potential for strengthening health systems in malaria endemic countries is often constrained by low national incomes and per capita domestic spending on health and malaria control. The limited ability to strengthen systems in order to deliver interventions remains a significant challenge for ensuring universal access to malaria prevention, diagnosis and treatment, as called for in Pillar 1 of the GTS (2). Pillar 2 of the GTS calls for countries to accelerate efforts towards malaria elimination and attainment of malaria free status (2). Ten countries eliminated 52 WORLD MALARIA REPORT 2016

malaria between 2010 and 2015, and malaria has not been re-established in any malaria free country since 2000. In 2015, 10 countries had fewer than 150 indigenous cases, and another nine had between 150 and 1000 cases. Thus, there appear to be good prospects of attaining the GTS milestone of eliminating malaria from at least 10 countries by 2020 and preventing re-establishment of malaria in all countries that are malaria free. Malaria surveillance systems detected a higher proportion of malaria cases globally in 2015 (20% of cases) than in 2010 (10%). Most of this improvement resulted from increased diagnostic testing in sub-Saharan Africa. However, a large proportion of people with malaria either do not seek treatment or seek treatment in the private sector, where they are less likely to receive a diagnostic test or to be reported in a malaria surveillance system. Although patients may seek care at public health facilities, diagnostic testing is not yet universal, nor is reporting complete. Addressing the bottlenecks in case detection, diagnosis and reporting is critical in order to transform malaria surveillance into a core intervention, as envisaged in Pillar 3 of the GTS. Malaria case incidence rates are estimated to have decreased by 21% globally between 2010 and 2015, and malaria mortality rates by 29%. If the GTS milestone of a 40% reduction in case incidence and mortality rates by 2020 is to be achieved globally, reductions in case incidence and mortality rates must be accelerated in countries with high numbers of cases and deaths. However, these countries are currently furthest from the per capita spending milestone for 2020 in the GTS (2). Target 3.3 of the SDGs – End the epidemics of AIDS, TB, malaria and NTDs by 2030 – is interpreted by WHO as the attainment of the GTS targets. The analysis summarized above indicates that the world is not on track to meet Target 3.3. for malaria. In addition to SDG Target 3.3, reaching the GTS targets will also contribute to other health-related goals of SDG 3, which are to ensure healthy lives and promote well-being for all at all ages. It will also contribute to other SDGs, particularly Goal 1 (end poverty in all its forms everywhere), Goal 4 (ensure inclusive and equitable quality education and promote lifelong learning opportunities for all), Goal 5 (achieve gender equality and empower all women and girls), Goal 8 (promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all) and Goal 10 (reduce inequality within and among countries). Although it will be challenging to reach the 2020 milestones of the GTS, recent experience in combatting malaria has shown that much progress is possible, and that such progress can greatly improve the health and well-being of populations. Reduced malaria mortality rates have led to an increase of 1.2 years in life expectancy at birth in the WHO African Region. This increase represents 12% of the total increase in life expectancy seen in sub-Saharan Africa, from 50.6 years in 2000 to 60 years in 2015, a highly significant contribution. Although placing a monetary value on malaria mortality reductions or increased life expectancy is difficult, current methodologies suggest that the change observed can be valued at US$ 1810 billion (UI: US$ 1330–2480 billion), which is equivalent to 44% of the GDP of the affected countries in 2015. Thus, the benefits of pursuing the goals and milestones of the GTS are considerable, and make it worth overcoming the challenges presented.

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12. Cairns M, Roca-Feltrer A, Garske T, Wilson AL, Diallo D, Milligan PJ et al. Estimating the potential public health impact of seasonal malaria chemoprevention in African children. Nat Commun. 2012;3:881. 13. WHO. Tables of malaria vaccine projects globally: «The Rainbow Tables» (last update 5 October 2016) [website]. World Health Organization; 2016 (http://www. who.int/vaccine_research/links/Rainbow/en/index.html, accessed 15 November 2016). 14. Committee for Medicinal Products for Human Use (CHMP), European Medicines Agency. Assessment Report. Mosquirix (TM). (2015) 15. WHO. Malaria vaccine: WHO position paper-January 2016. Wkly. Epidemiol. Rec. 91, 33–51; 2016 (http://www.who.int/wer/2016/wer9104.pdf, accessed 23 November 2016). 16. Bhatt S, Gething P. Insecticide-treated nets (ITNs) in Africa 2000–2016: coverage, system efficiency and future needs for achieving international targets. Malar J. 2014;13(Suppl 1):029. 17. WHO. WHO recommendations for achieving universal coverage with long-lasting insecticidal nets in malaria control (revised March 2014). 2013 (http://www.who. int/malaria/publications/atoz/who_recommendations_universal_coverage_llins. pdf?ua=1, accessed 15 November 2016). 18. Theiss-Nyland K, Lynch M, Lines J. Assessing the availability of LLINs for continuous distribution through routine antenatal care and the Expanded Programme on Immunizations in sub-Saharan Africa. Malar J. 2016;15(1):255. 19. WHO. Implications of insecticide resistance for malaria vector control. World Health Organization; 2016 (http://www.who.int/malaria/news/2016/iir-malaria-vectorcontrol-evaluation-nov2016.pdf, accessed 30 November 2016). 20. WHO. Guidelines for the treatment of malaria, third edition. World Health Organization; 2015 (http://www.who.int/malaria/publications/atoz/9789241549127/ en, accessed 15 November 2016). 21. Thwing J, Eisele TP, Steketee RW. Protective efficacy of malaria case management and intermittent preventive treatment for preventing malaria mortality in children: a systematic review for the Lives Saved Tool. BMC Public Health. 2011;11:S14 (http:// www.ncbi.nlm.nih.gov/pubmed/21501431, accessed 19 November 2014). 22. WHO. Methods for surveillance of antimalarial drug efficacy. World Health Organization; 2009 (http://www.who.int/malaria/publications/ atoz/9789241597531/en, accessed 15 November 2016). 23. WHO. Disease surveillance for malaria control: operational manual. World Health Organization; 2012 (http://www.who.int/malaria/areas/surveillance/ operationalmanuals/en, accessed 15 November 2016). 24. WHO. Disease surveillance for malaria elimination: operational manual. World Health Organization; 2012 (http://www.who.int/malaria/areas/surveillance/ operationalmanuals/en, accessed 15 November 2016). 25. Department of Health. Papua New Guinea Malaria Programme review 2014. Port Moresby: Government of Papua New Guinea; 2014. 26. WHO. Eliminating malaria. World Health Organization; 2016 (http://www.who.int/ malaria/publications/atoz/eliminating-malaria/en, accessed 15 November 2016). 27. The World Bank, Institute for Health Metrics and Evaluation (IHME). The cost of air pollution: strengthening the economic case for action. Washington (DC): The World Bank; 2016 (https://openknowledge.worldbank.org/bitstream/ handle/10986/25013/108141.pdf?sequence=4, accessed 15 November 2015).

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Annexes Annex 1 - Data sources and methods Annex 2 - Regional profiles >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> A - West Africa B - Central Africa C - East and Southern Africa D - Region of the Americas E - Eastern Mediterranean Region F - South-East Asia Region G - Western Pacific Region A - Funding per capita for malaria control and elimination (in US$) B - Proportion of population at risk sleeping under an ITN C - Estimated malaria case incidence rate (cases per 1000 population at risk) D - Estimated malaria mortality rate (deaths per 100 000 population at risk) E - Estimated change in malaria incidence and mortality rates, 2010–2015 A - Policy adoption, 2015 B - Antimalarial drug policy, 2015 C - Funding for malaria control, 2013–2015 D - Commodities distribution, 2013–2015 E - Household survey results, 2013–2015 F - Estimated malaria cases and deaths, 2000–2015 G - Population at risk and reported malaria cases by place of care, 2015 H - Reported malaria cases by method of confirmation, 2000–2015 I - Reported malaria cases by species, 2000–2015 J - Reported malaria deaths, 2000–2015

Annex 3 - Country trends in selected indicators

Annex 4 - Data tables

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Figure 1.1 Countries endemic for malaria in 2000 and 2016

Data on the number of indigenous cases (an indicator of whether countries are endemic for malaria) were as reported to WHO by national malaria control programmes (NMCPs). Countries with 3 consecutive years of zero indigenous cases are considered to have eliminated malaria.

Table 1.1 Global targets for 2030 and milestones for 2020 and 2025 Targets and milestones are as described in the Global Technical Strategy for Malaria 2016–2030 (GTS) (1) and Action and investment to defeat malaria 2016–2030 (AIM) (2).

Table 1.2 Indicators reviewed in World Malaria Report 2016 Indicators are as described in Monitoring and evaluation of the Global Technical Strategy for Malaria 2016–2030 and Action and investment to defeat malaria 2016–2030 (3).

secondary or teaching hospitals. Costs of outpatient visits and inpatient bed-stays were estimated from the perspective of the public health-care provider, using WHO-CHOICE estimates.1 The estimates were updated for 2005–2015 by rerunning the regression model using the relevant gross domestic product (GDP) per capita in each year. When no GDP data were available for a given year, outpatient department and inpatient unit costs were imputed using the values from the most recent year with available unit-cost data, and were adjusted with the GDP deflator. When no unit-cost data were available for the full period, a unit cost was imputed from the median unit cost in that year in countries within the same World Bank income group. Uncertainty around case and cost parameters was estimated through probabilistic uncertainty analysis; that is, by assigning a uniform distribution informed by lower and upper estimates for each parameter. The figure shows the mean total costs of service delivery for patient care from 1000 estimations. International financing data were obtained from several sources. The Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) provided disbursed amounts by year and country for 2005–2015. Data on funding from the government of the United States of America (USA) were sourced from the US Foreign Aid Dashboard, with the technical assistance of the Kaiser Family Foundation. Funding data were available for the US Agency for International Development (USAID), the US Centers for Disease Control (CDC) and the US Department of Defense. Country-level data were available for USAID for 2006–2015. Financing data for other international funders included annual disbursement flows for 2005– 2014, obtained from the Organisation for Economic Co-operation and Development (OECD) creditor reporting system (CRS) database on aid activity. For each year and each funder, the country-level and regional-level project-type interventions and other technical assistance were extracted. The 2014 value for international annual contributions was used as the 2015 value, except for contributions from the United Kingdom of Great Britain and Northern Ireland; for this value, a linear increase was assumed based on trends from 2012 1. http://www.who.int/choice/en/

Figure 2.1 Investments in malaria control activities by funding source, 2005–2015

Contributions from governments of endemic countries are estimated as the sum of NMCP expenditures reported by NMCPs for the World Malaria Report of the relevant year plus the estimated costs of delivery of patient-care services at government health facilities. If data on NMCP expenditures were missing for 2015, data from previous years were used after conversion to the equivalent 2015 US$ value. The number of malaria cases attending outpatient services at government facilities was derived from WHO estimates of malaria cases (see methods notes for Table 6.1) multiplied by the proportion of estimated cases seeking care at government facilities. Between 1% and 3% of uncomplicated cases were assumed to have moved to the severe stage of disease, and 50–80% of these severe cases were assumed to have been admitted to secondary or tertiary level hospitals. Outpatients were assumed to have been treated at health centres (with or without beds) or at primary level hospitals (e.g. district hospitals). Inpatients were assumed to have been admitted to primary,

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to 2014. To measure funding in real terms (i.e. correct for inflation), all values were converted to 2015 US$ values, using the GDP implicit price deflators published by the World Bank. Estimates of total spent on malaria control and elimination exclude household spending on malaria prevention and treatment.

Figure 2.2 Annual flow of funding for malaria control and elimination, 2014–2015

See methods notes for Figure 2.1 for sources of information on funding from governments of malaria endemic countries and on international flows to endemic countries. Contributions from individual countries to the Global Fund are shown when their 2014 and 2015 annual average core contributions to the fund accounted for 3% or more of the total amount of contributions received by the fund in 2014 and 2015. Contributions from funding sources to multilateral channels were estimated by calculating the proportion of the total contributions received by a multilateral in 2014 (2014 and 2015 in the case of the Global Fund) that was contributed by a funding source, then multiplying that figure by the multilateral’s estimated investment in malaria in 2015. These data were sourced from the Global Fund and, for other funders, from the OECD.Stat website2 using the CRS and the Development Assistance Committee (DAC) members’ total use of the multilateral system. Contributions from non-DAC countries and other sources were not available and were therefore not included in this figure. All funding flows were converted to 2015 equivalents in US$ (millions).

society strengthening, stigma-reduction efforts, and management and administration. For Figure 2.3, expenditures on health-system strengthening and supportive environment were combined. For expenditures of the US President’s Malaria Initiative (PMI), all operational plans that included planned obligations for 2013–2015 were reviewed and categorized as health-system strengthening, prevention or treatment. PMI health-system-strengthening categories included communications, capacitybuilding, surveillance, M&E, and research and strategic information. Prevention expenditures included those for long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS) and chemoprevention, which encompass, for example, expenditures on commodities, human resources, distribution and transport. Treatment expenditures included any resources used for malaria case management. Costs for in-country mission staffing were excluded from the analysis (representing 12% of total average spending). Government expenditures included data reported by NMCPs for the relevant World Malaria Report, in similar categories to those used by the Global Fund. We included data from 36 countries that had data for the expenditure categories for at least 2 years between 2013 and 2015.

Figure 2.3 Malaria financing, 2013–2015, by type of expenditure

Data on funding for malaria-related research and development for 2010–2014 were collected directly from the G-Finder Public Search tool.3 All data were converted to 2015 equivalents in US$.

Figure 2.4 Funding for malaria-related research and development, 2010–2014

The Global Fund provided expenditure data by category for 2013–2015. Expenditure categories were health-system strengthening, supportive environment, prevention and treatment. Expenditures related to health-system strengthening included communication and advocacy, human resources and technical assistance, training, monitoring and evaluation (M&E), procurement and supply management, and planning. Expenditures related to supportive environment included spending on policy development, civil2. http://stats.oecd.org/

Figure 2.5 Source of funding for malaria-related research and development, 2014 See methods notes for Figure 2.4.

Figure 2.6 Malaria financing per person at risk, 2013–2015, by estimated number of malaria cases, 2015

See methods notes for Figure 2.1 for sources of information on malaria financing. The total population of each country was taken from the 2015 revision of the World population prospects (4) and the proportion at 3. https://gfinder.policycures.org/PublicSearchTool

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risk of malaria was derived from NMCP reports. Funding milestones for 2020 were derived from the costing of the GTS (1).

RDTs) divided by the total number of tests undertaken, as reported by countries in the WHO African Region.

Figure 2.7 Number of ITNs delivered by manufacturers and distributed by NMCPs, 2009–2016 Data on the number of insecticide-treated mosquito nets (ITNs) delivered by manufacturers to countries were provided to WHO by Milliner Global Associates. Data from NMCP reports were used for the number of ITNs distributed within countries.

Figure 2.8 Number of RDTs sold by manufacturers and distributed by NMCPs, 2010–2015

The numbers of rapid diagnostic tests (RDTs) distributed by WHO region are the annual totals reported as having been distributed by NMCPs. Numbers of RDT sales were reported by 41 manufacturers that participated in RDT product testing by WHO, the Foundation for Innovative New Diagnostics, the CDC and the Special Programme for Research and Training in Tropical Diseases. The number of RDTs reported by manufacturers represents total sales to the public and private sectors worldwide.

Estimates of ITN coverage were derived from a model developed by the Malaria Atlas Project,4 using a two-stage process. First, we defined a mechanism for estimating net crop (i.e. the total number of ITNs in households in a country at a given point in time), taking into account inputs to the system (e.g. deliveries of ITNs to a country) and outputs (e.g. loss of ITNs from households). We then used empirical modelling to translate estimated net crops into resulting levels of coverage (e.g. access within households, use in all ages and use among children aged under 5 years). The model incorporates data from three sources: ■■

Figure 3.1 Proportion of population at risk with access to an ITN and sleeping under an ITN, and proportion of households with at least one ITN and enough ITNs for all occupants, sub-Saharan Africa, 2005–2015

the number of ITNs delivered by manufacturers to countries, as provided to WHO by Milliner Global Associates; the number of ITNs distributed within countries, as reported to WHO by NMCPs; and data from nationally representative household surveys from 39 countries in sub-Saharan Africa, from 2001 to 2015.

Figure 2.9 Number of ACT treatment courses delivered by manufacturers and distributed by NMCPs, 2010–2015

■■

■■

Data on artemisinin-based combination therapy (ACT) sales were provided by eight manufacturers eligible for procurement by WHO or the United Nations Children’s Fund (UNICEF). ACT sales were categorized as being to either the public sector or the private sector. Data on ACTs distributed within countries through the public sector were taken from NMCP reports to WHO.

Countries and populations at risk

Figure 2.10 Ratio of ACT treatment courses distributed to diagnostic tests performed (RDTs or microscopy), WHO African Region 2010–2015

The ratio was calculated using the number of ACTs distributed, the number of microscopic examinations of blood slides, and the number of RDTs performed in the WHO African Region, as reported by NMCPs to WHO. The test positivity rate was calculated as the total number of positive tests (i.e. slide examinations or

The main analysis covered 40 of the 47 malaria endemic countries or areas of sub-Saharan Africa. The islands of Mayotte (for which no ITN delivery or distribution data were available) and Cabo Verde (which does not distribute ITNs) were excluded, as were the low-transmission countries of Namibia, Sao Tome and Principe, South Africa and Swaziland, for which ITNs comprise a small proportion of vector control. Analyses were limited to populations categorized by NMCPs as being at risk.

Estimating national net crops through time

As described by Flaxman et al. (5), national ITN systems were represented using a discrete-time stock-and-flow 4. http://www.map.ox.ac.uk/

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model. Nets delivered to a country by manufacturers were modelled as first entering a “country stock” compartment (i.e. stored in-country but not yet distributed to households). Nets were then available from this stock for distribution to households by the NMCP or other distribution channels. To accommodate uncertainty in net distribution, the number of nets distributed in a given year was specified as a range, with all available country stock (i.e. the maximum number of nets that could be delivered) as the upper end of the range and the NMCP-reported value (i.e. the assumed minimum distribution) as the lower end. New nets reaching households joined older nets remaining from earlier time steps to constitute the total household net crop, with the duration of net retention by households governed by a loss function. Rather than fitting the loss function to a small external dataset, as was done by Flaxman et al. (5), the loss function was fitted directly to the distribution and net crop data within the stockand-flow model itself. Loss functions were fitted on a country-by-country basis, were allowed to vary through time, and were defined separately for conventional ITNs (cITNs) and LLINs. The fitted loss functions were compared to existing assumptions about rates of net loss from households. The stock-and-flow model was fitted using Bayesian inference and Markov chain Monte Carlo methods, which provided time-series estimates of national household net crop for cITNs and LLINs in each country, and an evaluation of underdistribution, all with posterior credible intervals.

net ownership pattern (i.e. the proportion of households with zero nets, one net, two nets and so on). In this way, the size of the net crop was linked to distribution patterns among households while accounting for household size in order to generate ownership distributions for each stratum of household size. The bivariate histogram of net crop to distribution of nets among households by household size made it possible to calculate the proportion of households with at least one ITN. Also, because the number of both ITNs and people in each household was available, it was possible to directly calculate the two additional indicators: the proportion of households with at least one ITN for every two people, and the proportion of the population with access to an ITN within their household. For the final ITN indicator – the proportion of the population who slept under an ITN the previous night – the relationship between ITN use and access was defined using 62 surveys in which both these indicators were available (ITN useall = 0.8133*ITN accessall ages + 0.0026, R² = 0.773). This ages relationship was applied to the Malaria Atlas Project’s country–year estimates of household access in order to obtain ITN use among all ages. The same method was used to obtain the country–year estimates of ITN use in children aged under 5 years (ITN usechildren under five = 0.9327x + 0.0282, R² = 0.754).

Figure 3.2 Proportion of ITNs distributed through different delivery channels in sub-Saharan Africa, 2013–2015 Data on the number of ITNs distributed within countries were as reported to WHO by 39 countries where ITNs are the primary method of vector control.

Estimating indicators of national ITN access and use from the net crop

Rates of ITN access within households depend not only on the total number of ITNs in a country (i.e. the net crop), but also on how those nets are distributed among households. One factor that is known to strongly influence the relationship between net crop and net distribution patterns among households is the size of households, which varies among countries, particularly across sub-Saharan Africa. Many recent national surveys report the number of ITNs observed in each household surveyed. Hence, it is possible to not only estimate net crop, but also to generate a histogram that summarizes the household

Figure 3.3 Proportion of the population at risk protected by IRS by WHO region, 2010–2015

The number of persons protected by IRS was reported to WHO by NMCPs. The total population of each country was taken from the 2015 revision of the World population prospects (4) and the proportion at risk of malaria was derived from NMCP reports.

Figure 3.4 Insecticide class used for indoor residual spraying, 2010–2015 Data on the type of insecticide used for IRS were reported to WHO by NMCPs. Insecticides were WORLD MALARIA REPORT 2016

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classified into pyrethroids or other classes (carbamates, organochlorines or organophosphates). If data were not reported for a particular year, data from the most recent year were used. For the period 2010–2015 this method of imputation was used for an average of 19 countries each year.

Figure 3.7 Proportion of pregnant women receiving IPTp, by dose, sub-Saharan Africa, 2010–2015

The proportion of the population at risk sleeping under an ITN was derived as described for Figure 3.1, and the proportion benefiting from IRS was derived as for Figure 3.4. In combining these proportions, the extent to which populations benefit from one or both of these interventions must be estimated. Analysis of household survey data indicates that about half of the people in IRS-sprayed households are also protected by ITNs, but the extent of overlap between intervention coverage can vary from 0% to 100% (if the proportions sum to <1). To reflect this uncertainty, we assumed the combined coverage to have a rectangular distribution with the range of maximum (0%, ITNcoverage + IRScoverage –100%) to minimum (ITN coverage, IRScoverage). Palisade’s @Risk software (version 6.0)5 was used to sample from the distributions for each country, and a continental estimate of vector-control coverage was obtained by summing the combined ITN and IRS coverage of all countries.

Figure 3.5 Proportion of the population at risk protected by IRS or sleeping under an ITN in sub-Saharan Africa, 2010–2015

The total number of pregnant women eligible for intermittent preventive treatment in pregnancy (IPTp) was calculated by adding total live births calculated from the United Nations (UN) population data and spontaneous pregnancy loss (specifically, miscarriages and stillbirths) after the first trimester. Spontaneous pregnancy loss has previously been calculated by Dellicour et al. (6). Country-specific estimates of IPTp coverage were calculated as the ratio of pregnant women receiving IPTp at antenatal care (ANC) clinics to the estimated number of IPTp-eligible pregnant women in a given year. ANC attendance rates were derived in the same way, using the number of initial ANC visits reported through routine information systems. Local linear interpolation was used to compute missing values. Annual aggregate estimates exclude countries for which a report or interpolation was not available for the specific year. Among 34 countries with IPTp policy, IPTp1 dose coverage could be calculated for 34 countries, IPTp2 for 33 countries, and IPTp3 for 20 countries. Aggregate estimates of IPTp1 and IPTp2 coverage for 20 countries with IPTp3 estimates were similar to estimates of IPT1 and IPTp2 coverage using data from all countries.

Figure 3.6 Insecticide resistance and monitoring status for malaria endemic countries (2015), by insecticide class and WHO region, 2010–2015 Insecticide resistance monitoring results were collected from NMCP reports to WHO, the African Network for Vector Resistance, the Malaria Atlas Project, PMI and the published literature. In these studies, confirmed resistance was defined as mosquito mortality <90% in bioassay tests with standard insecticide doses. Where multiple insecticide classes or types, mosquito species or time points were tested, the highest resistance status was considered.

Figure 4.1 Proportion of febrile children seeking care, by health sector, sub-Saharan Africa, 2013– 2015

Estimates were derived from 23 nationally representative household surveys (demographic health surveys and malaria indicator surveys) conducted between 2013 and 2015. The surveys asked caregivers whether their child had had a fever in the 2 weeks preceding the survey, whether care was sought for the fever and, if so, where care was sought.

Figure 4.2 Proportion of febrile children receiving a blood test, by health sector, sub-Saharan Africa, 2013–2015

5. https://www.palisade.com/risk/

Estimates were derived from 22 nationally representative household surveys (demographic health surveys and malaria indicator surveys) conducted between 2013 and

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2015. The surveys asked caregivers whether their child had had a fever in the 2 weeks preceding the survey; whether care was sought for the fever and, if so, where care was sought; they also asked whether the child had received a finger or heel stick as part of the care (indicating that a malaria diagnostic test was performed).

and interquartile ranges were calculated from available surveys in 3 year moving averages.

Figure 4.3 Proportion of suspected malaria cases attending public health facilities who receive a diagnostic test, by WHO region, 2010–2015

Figure 4.5 Proportion of febrile children with a positive RDT at time of survey who received antimalarial medicines, sub-Saharan Africa, 2010–2015

The proportion of suspected malaria cases receiving a malaria diagnostic test in public facilities was calculated from NMCP reports to WHO. The number of malaria diagnostic tests performed comprised the number of RDTs and the number of microscopic slide examinations. Few countries reported the number of suspected malaria cases as an independent value. For countries reporting the total number of malaria cases as the sum of presumed malaria cases (i.e. cases classified as malaria without undergoing malaria parasitological testing) and confirmed malaria cases, the number of suspected cases was calculated by adding the number of negative diagnostic tests to the number of presumed and confirmed cases. Using this method, for countries that reported only confirmed malaria cases as the total number of malaria cases, the number of suspected cases is equal to the number of cases tested. This value is not informative in determining the proportion of suspected cases tested; therefore, countries were excluded from the regional calculation for the years in which they reported only confirmed cases as total malaria cases.

Data from nationally representative household surveys were used to examine the treatment received by children who had had both a fever in the previous 2 weeks and a positive RDT at the time of survey. Estimates were derived from 29 nationally representative household surveys (demographic health surveys and malaria indicator surveys). The surveys must have undertaken diagnostic testing with a histidine rich protein 2 (HRP2) RDT at the time of the survey; also, they must have asked caregivers whether their child had had a fever in the 2 weeks preceding the survey, where care was sought, and what treatment was received for the fever, particularly whether the child received an ACT or other antimalarial medicine.

Figure 4.6 Proportion of antimalarial treatments that are ACTs received by febrile children that are RDT positive at the time of survey, sub-Saharan Africa, 2010–2015 See methods notes for Figure 4.5.

Figure 4.7 Proportion of antimalarial treatments that are ACTs received by febrile children, by health sector, sub-Saharan Africa, 2013–2015 See methods notes for Figure 4.5.

Figure 4.4 Proportion of febrile children attending public health facilities who receive a blood test, sub-Saharan Africa, 2010–2015 Estimates were derived from 41 nationally representative household surveys (demographic health surveys and malaria indicator surveys) conducted between 2010 and 2015. The surveys asked caregivers whether their child had had a fever in the 2 weeks preceding the survey; whether care was sought for the fever and, if so, where care was sought; and whether the child had received a finger or heel stick as part of the care (indicating that a malaria diagnostic test was performed). Median values

Figure 4.8 Distribution of multidrug resistance, 2016

Information was derived from WHO’s database on antimalarial treatment efficacy.6

Figure 5.1 Health facility reporting rates by WHO region, 2015

Using data provided by NMCPs, reporting rates of health facilities were calculated as follows: (the number of health facility reports received in 2015) ÷ (number of

6. http://www.who.int/malaria/areas/drug_resistance/drug_efficacy_database/en/

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health facilities providing treatment for uncomplicated malaria × reporting frequency).

and 80%. Countries that were approaching elimination were assigned a value of more than 80%. The number of malaria cases was estimated by one of two methods. The first method was used for countries outside Africa and for low-transmission countries in Africa. Estimates were made by adjusting the number of reported malaria cases for completeness of reporting, the likelihood that cases were parasite positive, and the extent of health-service use. The procedure, which is described in the World Malaria Report 2008 (7,8), combines data reported by NMCPs (reported cases, reporting completeness and likelihood that cases are parasite positive) with data obtained from nationally representative household surveys on health-service use. The number of malaria cases caused by Plasmodium vivax in each country was estimated by multiplying the country’s reported proportion of P. vivax cases by the total number of estimated cases for the country. The second method was used for high-transmission countries in Africa in which the quality of surveillance data did not permit a robust estimate from the number of reported cases. Estimates of the number of malaria cases were derived from information on parasite prevalence obtained from household surveys. First, data on parasite prevalence from 27 573 georeferenced population clusters between 1995 and 2014 were assembled within a spatiotemporal Bayesian geostatistical model, along with environmental and sociodemographic covariates, and data on both the use of ITNs and access to ACTs. The geospatial model enabled predictions of P. falciparum prevalence in children aged 2–10 years, at a resolution of 5 × 5 km2, throughout all malaria endemic African countries for each year from 2000 to 2015. Second, an ensemble model was developed to predict malaria incidence as a function of parasite prevalence. The model was then applied to the estimated parasite prevalence in order to obtain estimates of the malaria case incidence at 5 × 5 km2 resolution for each year from 2000 to 2015. Data for each 5 × 5 km2 area were then aggregated within country and regional boundaries to obtain both national and regional estimates of malaria cases (9).

Figure 5.2 Bottlenecks in case detection 2015, by WHO region

Table 6.1 Estimated malaria cases, 2000–2015

The procedure for estimating the proportion of cases detected by surveillance systems follows the method by which WHO estimates the number of malaria cases in a country using data reported by NMCPs (7,8). The procedure considers four proportions: the proportion of cases that seek treatment, the proportion of cases that seek treatment in health facilities covered by a country’s malaria surveillance system, the proportion of cases in these facilities that receive a diagnostic test and the proportion of cases in these facilities that are reported through the system. The proportion of malaria cases seeking treatment was estimated using the latest nationally representative household survey for a country. If no household survey was available, the proportion was derived by sampling at random from results for other countries and areas in the region that had a household survey: Bolivia (Plurinational State of), Botswana, Cabo Verde, French Guiana, Guatemala, South Sudan, Suriname, Thailand and Venezuela (Bolivarian Republic of). For 13 countries approaching malaria elimination (Algeria, Belize, Bhutan, China, Democratic People’s Republic of Korea, Ecuador, El Salvador, Iran [Islamic Republic of], Malaysia, Mexico, Panama, Republic of Korea and Saudi Arabia), it was assumed that 99% of cases sought treatment. The proportion of cases seeking treatment at a facility covered by a country’s surveillance system was derived in a similar way; the types of facility covered by a country’s surveillance system were provided through NMCP reports. Reporting rates of health facilities were calculated according to the methods notes for Figure 5.1. The reporting rates were assigned to three ranges (<50%, 50–80% and >80%) to reflect uncertainty about the number of cases represented in facility reports. The rates were assigned a triangular distribution in the outer ranges and a uniform distribution in mid-range, with expected values in the low, mid and high ranges of 33%, 65% and 87%, respectively. If the reporting completeness was not available for 2015, the value from the most recent year reported was used. If this value was missing for all years, it was assumed to lie between 50%

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Table 6.2 Estimated malaria cases by WHO region, 2015 See methods notes for Table 6.1.

Figure 6.1 Estimated malaria cases (millions) by WHO region, 2015 See methods notes for Table 6.1.

Figure 6.3 Estimated country share of (a) total malaria cases and (b) P. vivax malaria cases, 2015 See methods notes for Table 6.1.

Table 6.3 Estimated malaria deaths, 2000–2015

Numbers of malaria deaths were estimated by two main categories of method.

Category 1 methods

Category 1 methods were used for countries outside Africa and for low-transmission countries in Africa. Method 1(a). For countries in which vital registration is estimated to capture more than 50% of all deaths, and a high proportion of malaria cases are confirmed by parasite testing, reported malaria deaths are adjusted for completeness of death reporting. Method 1b. For countries considered in the elimination programme phase as described in the World Malaria Report 2015 (10), reported malaria deaths are adjusted for completeness of case reporting. Method 1c. For other countries for which a Category 1 method was used, a case fatality rate of 0.256% was applied to the estimated number of P. falciparum cases, which represents the average of case fatality rates reported in the literature (11-13) and rates from unpublished data from Indonesia, 2004–2009 (Dr Ric Price, Menzies School of Health Research, personal communication). A case fatality rate of 0.0375% was applied to the estimated number of P. vivax cases, representing the midpoint of the range of case fatality rates reported in a study by Douglas et al. (14).

a verbal autopsy multicause model developed by the Maternal and Child Health Epidemiology Estimation Group to estimate causes of death in children aged 1–59 months (15 ). Mortality estimates were derived for seven causes of post-neonatal death (pneumonia, diarrhoea, malaria, meningitis, injuries, pertussis and other disorders), four causes arising in the neonatal period (prematurity, birth asphyxia and trauma, sepsis, and other conditions of the neonate), and other causes (e.g. malnutrition). Deaths due to measles, unknown causes and HIV/AIDS were estimated separately. The resulting cause-specific estimates were adjusted, country by country, to fit the estimated mortality envelope of 1–59 months (excluding HIV/AIDS and measles deaths) for corresponding years. Estimated prevalence of malaria parasites (see methods notes for Table 6.1) was used as a covariate within the model. The malaria mortality rate in children aged under 5 years that was estimated with this method was then used to infer malaria-specific mortality in those aged over 5 years, using the relationship between levels of malaria mortality in a series of age groups and the intensity of malaria transmission (16).

Table 6.4 Estimated malaria deaths by WHO region, 2015 See methods notes for Table 6.3.

Figure 6.3 Estimated malaria deaths (thousands) by WHO region, 2015 See methods notes for Table 6.3.

Figure 6.4 Estimated country share of (a) total malaria deaths and (b) P. vivax malaria deaths, 2015 See methods notes for Table 6.3.

Figure 6.5 Estimated (a) parasite prevalence and (b) number of people infected, sub-Saharan Africa, 2005–2015 See methods notes for Table 6.1.

Category 2 method

Figure 6.6 Reduction in malaria case incidence rate by WHO region, 2010–2015

A Category 2 method was used for countries in Africa with a high proportion of deaths due to malaria. In this method, child malaria deaths were estimated using

See the methods notes for Table 6.1 for the estimation of the number of malaria cases. Incidence rates were derived by dividing estimated malaria cases by the population at

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Annex 1 – Data sources and methods

risk of malaria within each country. The total population of each country was taken from the 2015 revision of the World population prospects (4), and the proportion at risk of malaria was derived from NMCP reports.

Figure 6.7 Country-level changes in malaria case incidence rate, 2010–2015, by number of cases in 2010 See methods notes for Figure 6.6 for estimates of case incidence. See methods notes for Table 6.1 for estimates of number of cases.

Figure 6.8 Reduction in malaria mortality rate by WHO region, 2010–2015

the peak number of cases were excluded. Thus, if a country had experienced zero cases and malaria returned, cases were only included from the year in which they peaked. This inclusion criterion generates a slope that is steeper than if cases from all years were included (because some increases are excluded). In some earlier years where data on indigenous case were not available, the total number of reported cases was used (i.e. for country years with larger numbers of cases, in which the proportion of imported cases is expected to be low).

See methods notes for Table 6.3 for estimation of number of deaths. Malaria death rates were derived by dividing annual malaria deaths by the midyear population at risk of malaria within each country. The total population of each country was taken from the 2015 revision of the World population prospects (4), and the proportion at risk of malaria was derived from NMCP reports. Where death rates were quoted for children aged under 5 years, the number of deaths estimated in children aged under 5 years was divided by the estimated number of children aged under 5 years at risk of malaria.

Figure 6.12 Number of indigenous malaria cases for countries endemic for malaria in 2015, by WHO region See methods notes for Table 6.1 for the estimation of number of cases. For 18 countries (Algeria, Belize, Bhutan, Cabo Verde, China, Democratic People’s Republic of Korea, Dominican Republic, Ecuador, El Salvador, Iran [Islamic Republic of], Malaysia, Mexico, Panama, Republic of Korea, Saudi Arabia, Suriname, Swaziland and Tajikistan), estimates were based on indigenous cases only; these values were very close to the reported numbers of cases. For other countries in which the numbers of locally transmitted and imported cases were not individually available, estimates included imported cases; however, imported cases were expected to comprise only a small proportion of the large total number of cases in these countries.

Figure 6.9 Country-level changes in malaria mortality rate 2010–2015, by number of deaths in 2010

See methods notes for Figure 6.8 for estimates of mortality rates. See methods notes for Table 6.3 for estimates of number of deaths.

Figure 6.13 and Table 6.5 Gains in life expectancy in malaria endemic countries, 2000–2015 The relative contribution of the decline in malaria mortality risk to total life expectancy gain between 2000 and 2015 was estimated using WHO annual life tables for 2000–2015 for countries with malaria transmission in 2000, and WHO estimates of malaria age-specific death rates (17). A cause-decomposition of life expectancy gain approach was followed, with the analysis conducted at WHO regional level (18).

Figure 6.10 Countries attaining zero indigenous malaria cases since 2000 Countries are shown by the year in which they attained zero indigenous cases for 3 consecutive years, according to reports submitted by NMCPs.

Figure 6.11 Indigenous malaria cases in the years before attaining zero indigenous cases, for the 17 countries that eliminated malaria, 2000–2015

For the 17 countries that attained zero indigenous cases for 3 consecutive years between 2000 and 2015, the number of NMCP-reported indigenous cases was tabulated according to the number of years preceding the attainment of zero cases. Data from years before

Table 6.6 Economic value of reduced malaria mortality risk, estimated by full income approach, 2000–2015

Malaria mortality risk reductions between 2000 and 2015 were valued using a full income approach. The

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analysis, which covered 106 countries with malaria transmission in 2000, was conducted from the current perspective by estimating how much individuals would need to be compensated in 2015 to accept malaria mortality risks at their year 2000 levels. Changes in malaria mortality risk were valued as the payment that individuals would need to receive to accept an increase in mortality risk (19). This approach, referred to as value of a statistical life (VSL), is a common method for valuing mortality risks in public policy studies in high-income settings. It involves asking individuals about their willingness to accept (WTA) compensation for an increase in mortality risk, in “stated-preference” surveys ( 20 ). These surveys have placed a value of US$ 380 (range: US$ 189–569) on a 1 in 10 000 increase in mortality risk for a given year for individuals aged 50 years with an average life expectancy of 33 years, living in OECD countries that had an average GDP per capita of US$ 37 787 (in 2015 purchasing power parity [PPP] adjusted US$) (20,21). For this reference VSL to be applied to other settings, it is necessary to take into account differences in life expectancy and the GDP per capita using the following formula: ec50 GDPpc ε VSLc=VSLr× ⎯ × ⎯⎯ 33 GDPr

decreases, individuals require a smaller percentage of their income to accept an increase in mortality risk, because of competing basic needs in lower income populations, although this can vary across individual and community characteristics (21,22). Changes in malaria mortality risks were valued as the sum of WTA of all individuals assumed to experience these changes; 2015 life tables were used, and the calculations were as described in Jamison et al. (19). VSL conversions used the OECD consumer price index data. 7 Calculations were conducted in 2015 US$, at PPP with GDP data sourced from the World Bank. 8 Probabilistic uncertainty analysis through 1000 Monte Carlo simulations was used to determine the mean and 95% uncertainty range for the value of change in mortality risk across malaria endemic countries in 2000–2015. The reference VSL was assigned a uniform distribution (range: US$ 189–569), as were elasticity values (range: 1–1.4).

(

)

Where: VSLc = VSL in country c; ec50 = life expectancy at age 50 in country c; 33 = average remaining life expectancy, in years, at age 50 in OECD reference countries; VSLr = VSL in OECD reference countries; GDPc = 2015 GDP per capita in country c; GDP r = average GDP per capita in group of OECD reference countries, converted to 2015 equivalent; and ε = income elasticity of the VSLc to changes in GDP. The income elasticity ε – that is, the responsiveness of the VSL to a change in income – was assumed to range between 1 and 1.4 ( 20-22 ). An ε equal to 1 reflects situations where individuals require the same proportional change in income as compensation for an increase in mortality risk, irrespective of income level. An ε greater than 1 reflects situations where, as income

7. https://data.oecd.org/price/inflation-cpi.htm#indicator-chart (accessed 1 November 2016) 8. http://databank.worldbank.org/data/home.aspx (accessed 1 November 2016)

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Annex 1 – Data sources and methods

References 1. WHO. Global Technical Strategy for Malaria 2016– 2030. Geneva: World Health Organization (WHO); 2015 (http://www.who.int/malaria/areas/global_ technical_strategy/en, accessed 16 November 2016). 2. Roll Back Malaria Partnership. Action and investment to defeat malaria 2016–2030. For a Malaria free World. Geneva: World Health Organization (WHO); 2015 (http://www.rollbackmalaria.org/files/files/ aim/RBM_AIM_Report_A4_EN-Sept2015.pdf, accessed 16 November 2016). 3. Malaria Policy Advisory Committee. Monitoring and evaluation of the Global Technical Strategy for Malaria 2016–2030 and Action and investment to defeat malaria 2016–2030 . Background document for Session 8, Malaria Policy Advisory Committee Meeting, 14–16 September 2016, Geneva, Switzerland: World Health Organization; 2016 (http:// www.who.int/malaria/mpac/mpac-sept2016-SMErecommendations-session8.pdf?ua=1l, accessed 17 November 2016). 4. UN. Revision of world population prospects [website]. United Nations; 2015 (http://esa.un.org/unpd/wpp, accessed 1 August 2015). 5. Flaxman AD, Fullman N, Otten MW, Menon M, Cibulskis RE, Ng M et al. Rapid scaling up of insecticide-treated bed net coverage in Africa and its relationship with development assistance for health: a systematic synthesis of supply, distribution, and household survey data. PLoS Med. 2010;7(8):e1000328. 6. Dellicour S, Tatem AJ, Guerra CA, Snow RW, ter Kuile FO. Quantifying the number of pregnancies at risk of malaria in 2007: a demographic study. PLoS Med. 2010;7(1):e1000221. 7. Cibulskis RE, Aregawi M, Williams R, Otten M, Dye C. Worldwide incidence of malaria in 2009: estimates, time trends, and a critique of methods. PLoS Med. 2011;8(12):e1001142. 8. WHO. World Malaria Report. Geneva: World Health Organization; 2008 (http://www.who.int/malaria/ publications/atoz/9789241563697/en, accessed 15 October 2013). 9. Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015;526(7572):207–211. 10. WHO. World Malaria Report. Geneva: World Health Organization; 2015 (http://www.who.int/malaria/ publications/world-malaria-report-2015/report/ en/, accessed 15 October 2013). 11. Alles HK, Mendis KN, Carter R. Malaria mortality rates in South Asia and in Africa: implications for malaria control. Parasitol Today. 1998;14(9):369–375. 12. Luxemburger C, Ricci F, Nosten F, Raimond D, Bathet S, White NJ. The epidemiology of severe malaria in an area of low transmission in Thailand. Trans R Soc Trop Med Hyg. 1997;91(3):256–262. 13. Meek SR. Epidemiology of malaria in displaced Khmers on the Thai-Kampuchean border. Southeast Asian J Trop Med Public Health. 1988;19(2):243–252. 14. Douglas NM, Pontororing GJ, Lampah DA, Yeo TW, Kenangalem E, Poespoprodjo JR et al. Mortality attributable to Plasmodium vivax malaria: a clinical audit from Papua, Indonesia. BMC Med. 2014;12(1):217. 15. Liu L, Oza S, Hogan D, Perin J, Rudan I, Lawn JE et al. Global, regional, and national causes of child

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mortality in 2000–13, with projections to inform post-2015 priorities: an updated systematic analysis. Lancet. 2015;385(9966):430–440. 16. Ross A, Maire N, Molineaux L, Smith T. An epidemiologic model of severe morbidity and mortality caused by Plasmodium falciparum. Am J Trop Med Hyg. 2006;75(2 Suppl):63–73. 17. WHO. WHO methods and data sources for life tables 1990–2015. Department of Information, Evidence and Research, Global Health Estimates Technical Paper WHO/HIS/IER/GHE/2016.8, Geneva: World Health Organization (WHO); 2016. 18. Beltran-Sanchez H, Preston S, Canudas-Romo V. An integrated approach to cause-of-death analysis: cause-deleted life tables and decompositions of life expectancy. Demography Res. 2008;19:1323–1350. 19. Jamison DT, Summers LH, Alleyne G, Arrow KJ, Berkley S, Binagwaho A et al. Global health 2035: a world converging within a generation. Lancet. 2013;382(9908):1898-1955 20. OECD. Mortality risk valuation in environment, health and transport policies. OECD Publishing. 2012 (http:// dx.doi.org/10.1787/9789264130807-en, accessed 30 November 2016). 21. Hammitt J, Robinson L. The income elasticity of the value per statistical life: transferring estimates between high and low income populations. J BenefitCost Anal. 2011;2(1). 22. Narain U, Sall C. Methodology for valuing the health impacts of air pollution: discussion of challenges and proposed solutions. Washington, World Bank Group 2016.

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Annex 2 – A. Regional profile: West Africa A. Parasite prevalence, 2015

355 million people at risk for malaria in 2015 297 million at high risk Funding for malaria increased from US$ 233 million to US$ 262 million between 2010 and 2015 Estimated malaria case incidence decreased by 15% between 2010 and 2015 Estimated malaria mortality rate reduced by 29% between 2010 and 2015 Zero countries eliminated malaria since 2010

>85 0 Not applicable

B. Share of malaria cases, 2015 Others, 5% Togo, 2% Benin, 3% Guinea, 4% Niger, 5% Bukina Faso, 6% Ghana, 6% Mali, 7%

Nigeria, 55%

Côte d’Ivoire, 7%

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C. Malaria funding by source, 2010–2015 Domestic 1000 USAID Global Fund UK World Bank Others

D. Malaria funding per person at risk, average 2013–2015 Domestic Cabo Verde Liberia Gambia Ghana Benin Senegal Sierra Leone International

800

US$ (million)

600

Mali Guinea-Bissau Nigeria

400

Burkina Faso Guinea Côte d’Ivoire

200

Togo Niger Mauritania Algeria 2010 2011 2012 2013 2014 2015 0 4 8 US$ 12 16 20

0

Global Fund, Global Fund to Fight AIDS, Tuberculosis and Malaria; UK, United Kingdom of Great Britain and Northern Ireland; USAID, United States Agency for International Development

E. Proportion of population sleeping under an ITN or protected with IRS, 2015 ITN Cabo Verde Guinea-Bissau Togo Côte d’Ivoire Sierra Leone Benin Senegal Ghana Gambia Burkina Faso Mali Guinea Liberia Nigeria Niger Mauritania Algeria 0% 20% 40% 60% 80% 100% IRS

F. Change in reported malaria incidence and mortality rates, 2010–2015 Incidence 2020 milestone: -40%

Mortality

Liberia* Côte d’Ivoire* Benin* Guinea* Ghana* Burkina Faso* Guinea-Bissau* Senegal* Niger* Sierra Leone* Togo* Nigeria* Gambia* Mauritania* Cabo Verde* Mali* Algeria* -100% -50% f Reduction * Change in admission rate (■)

0%

50% Increase p

100%

IRS, indoor residual spraying; ITN, insecticide-treated mosquito net

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Annex 2 – B. Regional profile: Central Africa A. Parasite prevalence, 2015

174 million people at risk for malaria in 2015 161 million at high risk Funding for malaria increased from US$ 65 million to US$ 116 million between 2010 and 2015

>85

Estimated malaria case incidence decreased by 33% between 2010 and 2015 Estimated malaria mortality rate reduced by 42% between 2010 and 2015 Zero countries eliminated malaria since 2010

0 Not applicable

B. Share of malaria cases, 2015 Others, 4% Burundi, 4% Central African Republic, 4% Chad, 6% Democratic Republic of the Congo, 57%

Angola, 9%

Cameroon, 16%

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C. Malaria funding by source, 2010–2015 Domestic 500 USAID Global Fund UK World Bank Others

D. Malaria funding per person at risk, average 2013–2015 Domestic Sao Tome and Principe Equatorial Guinea International

400

Angola Gabon

US$ (million)

300

Democratic Republic of the Congo Burundi

200

Central African Republic Chad

100

Cameroon Congo 2010 2011 2012 2013 2014 2015 0 4 8 US$ 12 16 20

0

Global Fund, Global Fund to Fight AIDS, Tuberculosis and Malaria; UK, United Kingdom of Great Britain and Northern Ireland; USAID, United States Agency for International Development

E. Proportion of population sleeping under an ITN or protected with IRS, 2015 ITN Sao Tome and Principe* Burundi Chad Democratic Republic of the Congo Central African Republic Cameroon Angola Congo Equatorial Guinea Gabon 0% 20% 40% 60% 80% 100% IRS

F. Change in reported malaria incidence and mortality rates, 2010–2015 Incidence 2020 milestone: -40%

Mortality

Central African* Republic* Gabon* Burundi* Democratic Republic* of the Congo* Chad* Angola* Cameroon* Sao Tome* and Principe* Congo* Equatorial Guinea* -100% * Change in admission rate (■)

-50% f Reduction

0%

50% Increase p

100%

IRS, indoor residual spraying; ITN, insecticide-treated mosquito net * Administrative ITN coverage

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Annex 2 – C. Regional profile: East and Southern Africa A. Parasite prevalence, 2015

319 million people at risk for malaria in 2015 232 million at high risk Funding for malaria decreased from US$ 156 million to US$ 150 million between 2010 and 2015

>85

Estimated malaria case incidence decreased by 22% between 2010 and 2015 Estimated malaria mortality rate reduced by 22% between 2010 and 2015 Zero countries eliminated malaria since 2010

0 Not applicable

B. Share of malaria cases, 2015 Others, 7% Madagascar, 5% Ethiopia, 6% Zambia, 6% Malawi, 7% Mozambique, 18%

Uganda, 18%

Rwanda, 8%

United Republic of Tanzania, 11%

Kenya, 14%

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C. Malaria funding by source, 2010–2015 Domestic 1000 USAID Global Fund UK World Bank Others

D. Malaria funding per person at risk, average 2013–2015 Domestic Swaziland Zambia Namibia Rwanda South Sudan Malawi Zimbabwe International

800

US$ (million)

600

Mozambique United Republic of Tanzania South Africa Comoros Uganda Kenya

400

200

Eritrea Madagascar Botswana Ethiopia 2010 2011 2012 2013 2014 2015 0 4 8 US$ 12 16 20

0

Global Fund, Global Fund to Fight AIDS, Tuberculosis and Malaria; UK, United Kingdom of Great Britain and Northern Ireland; USAID, United States Agency for International Development

E. Proportion of population sleeping under an ITN or protected with IRS, 2015 ITN Swaziland* Botswana* Zimbabwe Madagascar Rwanda Mozambique Uganda Zambia Kenya Ethiopia South Sudan Comoros Malawi South Africa Namibia* United Republic of Tanzania (Zanzibar) United Republic of Tanzania (Mainland) Eritrea 0% 20% 40% 60% 80% 100% IRS

F. Change in reported malaria incidence and mortality rates, 2010–2015 Incidence 2020 milestone: -40%

Mortality

South Sudan* Namibia* Rwanda* Kenya* Madagascar* Malawi* United Republic of* Tanzania (Mainland)* Uganda* Zimbabwe* Mozambique* Swaziland* Ethiopia* Eritrea* Zambia* Botswana* United Republic of* Tanzania (Zanzibar)* South Africa* Comoros* -100% * Change in admission rate (■)

-50% f Reduction

0%

50% Increase p

100%

IRS, indoor residual spraying; ITN, insecticide-treated mosquito net * Administrative ITN coverage

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Annex 2 – D. Regional profile: Region of the Americas A. Confirmed malaria cases per 1000  population, 2015

132 million people at risk for malaria in 2015 21 million at high risk Funding for malaria increased from US$ 170 million to US$ 201 million between 2010 and 2015 Estimated malaria case incidence decreased by 31% between 2010 and 2015 Estimated malaria mortality rate reduced by 37% between 2010 and 2015 Three countries achieved zero indigenous cases for 3 years since 2010

Confirmed cases per 1000 population Insu cient data 0 0–0.1 0.1–1.0 1.0–10 10–50 50–100 > 100

B. Share of malaria cases, 2015 Guyana, 3% Others, 5%

Haiti, 9%

Colombia, 10%

Venezuela (Bolivarian Republic of), 30%

Peru, 19%

Brazil, 24%

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C. Malaria funding by source, 2010–2015 Domestic 300 USAID Global Fund UK World Bank Others

D. Malaria funding per person at risk, average 2013–2015 Domestic Panama Suriname Mexico Peru El Salvador Colombia Brazil Guyana Venezuela (Bolivarian Republic of) Belize Nicaragua Dominican Republic Bolivia (Plurinational State of) Honduras Haiti Guatemala Ecuador French Guiana 0 4 8 US$ 12 16 20 International

200 US$ (million)

100

0

2010

2011

2012

2013

2014

2015

Global Fund, Global Fund to Fight AIDS, Tuberculosis and Malaria; UK, United Kingdom of Great Britain and Northern Ireland; USAID, United States Agency for International Development

E. Proportion of cases due to P. falciparum and P. vivax, 2013–2015 P. falciparum Haiti Dominican Republic Colombia French Guiana Guyana Suriname Ecuador Venezuela (Bolivarian Republic of) Honduras Peru Nicaragua Brazil Bolivia (Plurinational State of) Guatemala Panama Belize El Salvador Mexico 0% 20% 40% 60% 80% 100% P. vivax Other

F. Change in reported malaria incidence and mortality rates, 2010–2015 Incidence 2020 milestone: -40%

Mortality

Nicaragua Venezuela (Bolivarian Republic of) Peru Panama Guatemala Haiti Bolivia (Plurinational State of) Guyana Brazil Mexico Colombia Ecuador French Guiana Dominican Republic Honduras El Salvador Belize Suriname -100% -50% f Reduction * Changes in case incidence (■)

0%

50% Increase p

100%

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Annex 2 – E. Regional profile: Eastern Mediterranean Region A. Confirmed malaria cases per 1000 population/parasite prevalence (PP), 2015

291 million people at risk for malaria in 2015 111 million at high risk Funding for malaria decreased from US$ 55 million to US$ 45 million between 2010 and 2015 Estimated malaria case incidence decreased by 11% between 2010 and 2015 Estimated malaria mortality rate reduced by 6% between 2010 and 2015 One country achieved zero indigenous cases for 3 years since 2010

Confirmed cases per 1000 population Insu cient data 0 0–0.1 0.1–1.0 1.0–10 10–50 50–100 > 100

PP

>85 0 Not applicable

B. Share of malaria cases, 2015 Yemen, 8%

Afghanistan, 11%

Sudan, 36%

Somalia, 18%

Pakistan, 27%

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C. Malaria funding by source, 2010–2015 Domestic 200 USAID Global Fund UK World Bank Others

D. Malaria funding per person at risk, average 2013–2015 Domestic Saudi Arabia Iran (Islamic Republic of) International

150

Djibouti Sudan

US$ (million)

100

Somalia Afghanistan

50 Yemen Pakistan 2010 2011 2012 2013 2014 2015 0 4 8 US$ 12 16 20

0

Global Fund, Global Fund to Fight AIDS, Tuberculosis and Malaria; UK, United Kingdom of Great Britain and Northern Ireland; USAID, United States Agency for International Development

E. Proportion of cases due to P. falciparum and P. vivax, 2013–2015 P. falciparum Saudi Arabia Djibouti Somalia Yemen Sudan Pakistan Iran (Islamic Republic of) Afghanistan 0% 20% 40% 60% 80% 100% P. vivax Other

F. Change in reported malaria incidence and mortality rates, 2010–2015 Incidence 2020 milestone: -40%

Mortality

Djibouti Saudi Arabia Afghanistan Somalia Pakistan Sudan Yemen Iran (Islamic Republic of) -100% -50% f Reduction * Changes in case incidence (■)

0%

50% Increase p

100%

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Annex 2 – F. Regional profile: South-East Asia Region A. Confirmed malaria cases per 1000 population, 2015

1.4 billion people at risk for malaria in 2015 237 million at high risk Funding for malaria decreased from US$ 170 million to US$ 92 million between 2010 and 2015 Estimated malaria case incidence decreased by 54% between 2010 and 2015 Estimated malaria mortality rate reduced by 46% between 2010 and 2015 One country achieved zero indigenous cases for 3 years since 2010

Confirmed cases per 1000 population Insu cient data 0 0–0.1 0.1–1.0 1.0–10 10–50 50–100 > 100 Not applicable

B. Share of malaria cases, 2015 Myanmar, 2% Others, 0%

Indonesia, 9%

India, 89%

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C. Malaria funding by source, 2010–2015 Domestic 350 300 250 USAID Global Fund UK World Bank Others

D. Malaria funding per person at risk, average 2013–2015 Domestic Timor-Leste Bhutan Myanmar Bangladesh International

US$ (million)

200 150 100 50 0

Thailand Democratic People’s Republic of Korea Indonesia Nepal India 2010 2011 2012 2013 2014 2015 0 4 8 US$ 12 16 20

Global Fund, Global Fund to Fight AIDS, Tuberculosis and Malaria; UK, United Kingdom of Great Britain and Northern Ireland; USAID, United States Agency for International Development

E. Proportion of cases due to P. falciparum and P. vivax, 2013–2015 P. falciparum Bangladesh Myanmar India Timor-Leste Indonesia Thailand Bhutan Nepal Democratic People’s Republic of Korea 0% 20% 40% 60% 80% 100% P. vivax Other

F. Change in reported malaria incidence and mortality rates, 2010–2015 Incidence 2020 milestone: -40%

Mortality

India Democratic People’s Republic of Korea Indonesia Myanmar Nepal Thailand Bangladesh Bhutan Timor-Leste -100% -50% f Reduction * Changes in case incidence (■)

0%

50% Increase p

100%

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Annex 2 – G. Regional profile: Western Pacific Region A. Confirmed malaria cases per 1000 population, 2015 Confirmed cases per 1000 population Insu cient data 0 0–0.1 0.1–1.0 1.0–10

740 million people at risk for malaria in 2015 32 million at high risk Funding for malaria increased from US$ 29 million to US$ 50 million between 2010 and 2015 Estimated malaria case incidence decreased by 30% between 2010 and 2015 Estimated malaria mortality rate reduced by 58% between 2010 and 2015 Zero countries eliminated malaria since 2010

10–50 50–100 > 100 Not applicable

B. Share of malaria cases, 2015 Solomon Islands, 3% Others, 3%

Lao People’s Democratic Republic, 7% Cambodia, 10%

Papua New Guinea, 77%

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C. Malaria funding by source, 2010–2015 Domestic 200 USAID Global Fund UK World Bank Others

D. Malaria funding per person at risk, average 2013–2015 Domestic Malaysia Vanuatu Solomon Islands International

150 Papua New Guinea US$ (million)

Cambodia 100 Lao People’s Democratic Republic Philippines 50 Viet Nam Republic of Korea 0 China 2010 2011 2012 2013 2014 2015 0 4 8 US$ 12 16 20

Global Fund, Global Fund to Fight AIDS, Tuberculosis and Malaria; UK, United Kingdom of Great Britain and Northern Ireland; USAID, United States Agency for International Development

E. Proportion of cases due to P. falciparum and P. vivax, 2013–2015 P. falciparum Philippines Papua New Guinea Cambodia Viet Nam Solomon Islands Lao People’s Democratic Republic China Vanuatu Malaysia Republic of Korea 0% 20% 40% 60% 80% 100% P. vivax Other

F. Change in reported malaria incidence and mortality rates, 2010–2015 Incidence 2020 milestone: -40%

Mortality

Lao People’s Democratic Republic Cambodia Solomon Islands Viet Nam Republic of Korea Malaysia Philippines Papua New Guinea* Vanuatu China -100% * Change in admission rate (■)

-50% f Reduction

0%

50% Increase p

100%

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Annex 3 –  A.  Funding per capita for malaria control and elimination (in US$)

Algeria >20 15 10 5 0

Angola

Benin

Botswana

Burkina Faso

Burundi

Cabo Verde >20 15 10 5 0

Cameroon

Central African Republic

Chad

Comoros

Congo

Côte d’Ivoire >20 15 10 5 0

Democratic Republic of the Congo

Equatorial Guinea

Eritrea

Ethiopia

Gabon

Gambia >20 15 10 5 0

Ghana

Guinea

Guinea-Bissau

Kenya

Liberia

Madagascar >20 15 10 5 0

Malawi

Mali

Mauritania

Mayotte

Mozambique

Namibia >20 15 10 5 0

Niger

Nigeria

Rwanda

Sao Tome and Principe

Senegal

Sierra Leone >20 15 10 5 0

South Africa

South Sudan

Swaziland

Togo

Uganda

United Republic of Tanzania >20 15 10 5 0 2005 2010 2015 2005

Zambia

Zimbabwe Domestic Total African Region

2010

2015 2005

2010

2015

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Belize >20 15 10 5 0

Bolivia (Plurinational State of)

Brazil

Colombia

Dominican Republic

Ecuador

El Salvador >20 15 10 5 0

French Guiana

Guatemala

Guyana

Haiti

Honduras

Mexico >20 15 10 5 0

Nicaragua

Panama

Peru

Suriname

Venezuela (Bolivarian Republic of)

Afghanistan >20 15 10 5 0

Djibouti

Iran (Islamic Republic of)

Pakistan

Saudi Arabia

Somalia

Sudan >20 15 10 5 0

Yemen

Tajikistan

Bangladesh

Bhutan

Democratic People’s Republic of Korea

India >20 15 10 5 0

Indonesia

Myanmar

Nepal

Thailand

Timor-Leste

Cambodia >20 15 10 5 0

China

Lao People’s Democratic Republic

Malaysia

Papua New Guinea

Philippines

Republic of Korea >20 15 10 5 0 2005 2010 2015 2005

Solomon Islands

Vanuatu

Viet Nam

2010

2015 2005

2010

2015

2005

2010

2015

Domestic Total Region of the Americas Eastern Mediterranean Region European Region South-East Asia Region Western Pacific Region

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Annex 3 – B. Proportion of population at risk sleeping under an ITN

Angola 100% 75% 50% 25% 0

Benin

Burkina Faso

Burundi

Cameroon

Central African Republic

Chad 100% 75% 50% 25% 0

Comoros

Congo

Côte d’Ivoire

Democratic Republic of the Congo

Equatorial Guinea

Eritrea 100% 75% 50% 25% 0

Ethiopia

Gabon

Gambia

Ghana

Guinea

Guinea-Bissau 100% 75% 50% 25% 0

Kenya

Liberia

Madagascar

Malawi

Mali

Mauritania 100% 75% 50% 25% 0

Mozambique

Niger

Nigeria

Rwanda

Senegal

Sierra Leone 100% 75% 50% 25% 0 2000

South Sudan

Togo

Uganda

United Republic of Tanzania

Zambia

2005

2010

2015 2000

2005

2010

2015 2000

2005

2010

2015 2000

2005

2010

2015 2000

2005

2010

2015

Zimbabwe 100% 75% 50% 25% 0 2000 2005 2010 2015

Modelled data 95% confidence interval African Region No model estimates are available for Algeria, Botswana, Cabo Verde, Mayotte, Namibia, Sao Tome and Principe, South Africa and Swaziland, because ITNs are not the primary method of vector control in these countries

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Annex 3 –  C.  Estimated malaria case incidence rate (cases per 1000 population at risk) Angola Benin

Algeria >750 500 250 <1

Botswana

Burkina Faso

Burundi

Cabo Verde >750 500 250 <1

Cameroon

Central African Republic

Chad

Comoros

Congo

Côte d’Ivoire >750 500 250 <1

Democratic Republic of the Congo

Equatorial Guinea

Eritrea

Ethiopia

Gabon

Gambia >750 500 250 <1

Ghana

Guinea

Guinea-Bissau

Kenya

Liberia

Madagascar >750 500 250 <1

Malawi

Mali

Mauritania

Mayotte

Mozambique

Namibia >750 500 250 <1

Niger

Nigeria

Rwanda

Sao Tome and Principe

Senegal

Sierra Leone >750 500 250 <1

South Africa

South Sudan

Swaziland

Togo

Uganda

United Republic of Tanzania >750 500 250 <1

Zambia

Zimbabwe

Point estimate 95% confidence interval African Region 2000 2005 2010 2015 2000 2005 2010 2015 2000 2005 2010 2015

88

WORLD MALARIA REPORT 2016

Belize >500

Bolivia (Plurinational State of)

Brazil

Colombia

Dominican Republic

Ecuador

250

<1

El Salvador >500

French Guiana

Guatemala

Guyana

Haiti

Honduras

250

<1

Mexico >500

Nicaragua

Panama

Peru

Suriname

Venezuela (Bolivarian Republic of)

250

<1

Afghanistan >500

Djibouti

Iran (Islamic Republic of)

Pakistan

Saudi Arabia

Somalia

250

<1

Sudan >500

Yemen

Tajikistan

Bangladesh

Bhutan

Democratic People’s Republic of Korea

250

<1

India >500

Indonesia

Myanmar

Nepal

Thailand

Timor-Leste

250

<1

Cambodia >500

China

Lao People’s Democratic Republic

Malaysia

Papua New Guinea

Philippines

250

<1

Republic of Korea >500

Solomon Islands

Vanuatu

Viet Nam Point estimate 95% confidence interval Region of the Americas Eastern Mediterranean Region European Region South-East Asia Region Western Pacific Region

250

<1

2000

2005

2010

2015 2000

2005

2010

2015 2000

2005

2010

2015 2000

2005

2010

2015

WORLD MALARIA REPORT 2016

89

Annex 3 –  D.  Estimated malaria mortality rate

(deaths per 100 000 population at risk) Angola Benin Botswana Burkina Faso Burundi

Algeria 400 300 200 100 0

Cabo Verde 400 300 200 100 0

Cameroon

Centra African Republic

Chad

Comoros

Congo

Côte d’Ivoire 400 300 200 100 0

Democratic Republic of the Congo

Equatorial Guinea

Eritrea

Ethiopia

Gabon

Gambia 400 300 200 100 0

Ghana

Guinea

Guinea-Bissau

Kenya

Liberia

Madagascar 400 300 200 100 0

Malawi

Mali

Mauritania

Mayotte

Mozambique

Namibia 400 300 200 100 0

Niger

Nigeria

Rwanda

Sao Tome and Principe

Senegal

Sierra Leone 400 300 200 100 0

South Africa

South Sudan

Swaziland

Togo

Uganda

United Republic of Tanzania 400 300 200 100 0 2000 2005 2010 2015 2000

Zambia

Zimbabwe

Point estimate 95% confidence interval African Region 2005 2010 2015 2000 2005 2010 2015

90

WORLD MALARIA REPORT 2016

Belize >100 75 50 25 0

Bolivia (Plurinational State of)

Brazil

Colombia

Dominican Republic

Ecuador

El Salvador >100 75 50 25 0

French Guiana

Guatemala

Guyana

Haiti

Honduras

Mexico >100 75 50 25 0

Nicaragua

Panama

Peru

Suriname

Venezuela (Bolivarian Republic of)

Afghanistan >100 75 50 25 0

Djibouti

Iran (Islamic Republic of)

Pakistan

Saudi Arabia

Somalia

Sudan >100 75 50 25 0

Yemen

Tajikistan

Bangladesh

Bhutan

Democratic People’s Republic of Korea

India >100 75 50 25 0

Indonesia

Myanmar

Nepal

Thailand

Timor-Leste

Cambodia >100 75 50 25 0

China

Lao People’s Democratic Republic

Malaysia

Papua New Guinea

Philippines

Republic of Korea >100 75 50 25 0 2000 2005 2010 2015 2000

Solomon Islands

Vanuatu

Viet Nam Point estimate 95% confidence interval Region of the Americas Eastern Mediterranean Region European Region South-East Asia Region Western Pacific Region

2005

2010

2015 2000

2005

2010

2015 2000

2005

2010

2015

WORLD MALARIA REPORT 2016

91

Annex 3 –  E.  Estimated change in malaria incidence and mortality rates, 2010–2015 Decrease WHO region & subregion African, West African, Central African, West African, South-East African, West African, Central African, Central African, West African, Central African, Central African, South-East African, Central African, West African, Central African, Central African, South-East African, South-East African, Central African, West African, West African, West African, West African, South-East African, West African, South-East African, South-East African, West African, West African African, South-East African, South-East African, West African, West African, South-East African, Central African, West African, West African, South-East African, South-East African, South-East African, West African, South-East African, South-East African, South-East African, South-East

Country Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Cabo Verde Central African Republic Chad Comoros Congo Côte d'Ivoire Democratic Republic of the Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mayotte Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Sierra Leone South Africa South Sudan Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe

>40%

20–40%

Change <±20%

Increase >20%

Zero indigenous deaths in 2015

● ● ●● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ●● ●

●

● ●

●●

●● ●● ●● ● ●● ●● ● ● ●● ● ●

● ●●

●● ● ● ● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● Change in estimated mortality rate

● ●

● ● ● ●

●

●

● ●

●● ● ●●

● 92 WORLD MALARIA REPORT 2016

Change in estimated incidence rate

Decrease WHO region & subregion Americas

Country Belize Bolivia (Plurinational State of) Brazil Colombia Dominican Republic Ecuador El Salvador French Guiana Guatemala Guyana Haiti Honduras Mexico Nicaragua Panama Peru Suriname Venezuela (Bolivarian Republic of)

>40%

20–40%

Change <±20%

Increase >20%

Zero indigenous deaths in 2015

● ●● ●● ● ●● ● ● ●● ●● ●● ●● ●● ● ●●

●

● ● ● ●●

●● ● ●● ●● ●● ●●

Eastern Mediterranean

Afghanistan Djibouti Iran (Islamic Republic of) Pakistan Saudi Arabia Somalia Sudan Yemen

●● ●

● ● ●● ●● ●

European South-East Asia

Tajikistan Bangladesh Bhutan Democratic People's Republic of Korea India Indonesia Myanmar Nepal Thailand Timor-Leste

Western Pacific

Cambodia China Lao People's Democratic Republic Malaysia Papua New Guinea Philippines Republic of Korea Solomon Islands Vanuatu Viet Nam

●● ● ●● ●● ● ● ●● ●● ● ●● ●● ● ● ● ●● ● ●● ●● ●● ●●

●

● ●

●

● ● ● ● ● ●

WORLD MALARIA REPORT 2016

93

Annex 4 – A. Policy adoption, 2015 WHO region Country/area Insecticide-treated mosquito nets ITNs/ LLINs are distributed free of charge ITNs/ LLINs are distributed to all age groups Indoor residual spraying DDT is used for IRS Chemoprevention IPTp used to prevent malaria during pregnancy Seasonal malaria chemo­ prevention (SMC or IPTc) is used

ITNs/ LLINs IRS is distributed recommended through mass by malaria campaigns control to all age programme groups

AFRICAN Algeria Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Republic Chad Comoros Congo Côte d'Ivoire Democratic Republic of the Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mayotte Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Sierra Leone South Africa South Sudan2 Swaziland Togo Uganda United Republic of Tanzania Mainland Zanzibar Zambia Zimbabwe AMERICAS Belize Bolivia (Plurinational State of) Brazil Colombia Dominican Republic

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • -

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • -

• • • • • • •

• • • • • • • • • • • • • • • • • •

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

-

• • • • • • -

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

-

-

• • • • •

• • • • •

• • • • •

NA NA NA NA NA

NA NA NA NA NA

94

WORLD MALARIA REPORT 2016

Testing Patients of all ages should get diagnostic test Malaria diagnosis is free of charge in the public sector RDTs used at community level G6PD test is recommended before treatment with primaquine ACT for treatment of P. f.

Treatment Pre-referral Single dose of treatment with primaquine quinine or is used as artemether IM gametocidal or artesunate medicine for suppositories P. falciparum1 Primaquine is used for radical treatment of P. vivax cases Directly observed treatment with primaquine is undertaken

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • -

NA

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

-

-

-

-

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • • -

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • • • • • • • • • • • • • • • -

-

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

-

NA

• • •

NA

WORLD MALARIA REPORT 2016

95

Annex 4 – A. Policy adoption, 2015 WHO region Country/area Insecticide-treated mosquito nets ITNs/ LLINs are distributed free of charge ITNs/ LLINs are distributed to all age groups Indoor residual spraying DDT is used for IRS Chemoprevention IPTp used to prevent malaria during pregnancy Seasonal malaria chemo­ prevention (SMC or IPTc) is used

ITNs/ LLINs IRS is distributed recommended through mass by malaria campaigns control to all age programme groups

AMERICAS Ecuador El Salvador French Guiana Guatemala Guyana Haiti Honduras Mexico Nicaragua Panama Peru Suriname Venezuela (Bolivarian Republic of) EASTERN MEDITERRANEAN Afghanistan Djibouti Iran (Islamic Republic of) Pakistan Saudi Arabia Somalia Sudan Yemen EUROPEAN Tajikistan SOUTH-EAST ASIA Bangladesh Bhutan Democratic People's Republic of Korea India Indonesia Myanmar Nepal Thailand Timor-Leste WESTERN PACIFIC Cambodia China Lao People's Democratic Republic Malaysia Papua New Guinea Philippines Republic of Korea Solomon Islands Vanuatu Viet Nam

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • • • • • • • • • • • • • • -

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • -

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

NA NA NA NA NA NA NA NA NA NA NA NA NA

NA NA NA NA NA NA NA NA NA NA NA NA NA

NA

•

NA

•

• • •

NA NA NA

• •

NA NA NA

• •

NA

NA

-

NA

NA

• • • • • • • • • • • • -

NA NA NA NA NA NA NA NA NA

NA NA NA NA NA NA NA NA NA

• • • • •

• • • • •

-

-

NA NA NA NA NA NA NA NA NA NA

NA NA NA NA NA NA NA NA NA NA

ACT, artemisinin-based combination therapy; DDT, dichloro-diphenyl-trichloroethane; G6PD, glucose-6-phosphate dehydrogenase; IM, intramuscular; IPTc, intermittent preventive treatment in children; IPTp, intermittent preventive treatment in pregnancy; IRS, indoor residual spraying; ITN, insecticide-treated mosquito net; LLIN, long-lasting insecticidal net; NA, not applicable; NMCP, national malaria control programme; RDT, rapid diagnostic test; SMC, seasonal malaria chemoprevention

96

WORLD MALARIA REPORT 2016

Testing Patients of all ages should get diagnostic test Malaria diagnosis is free of charge in the public sector RDTs used at community level G6PD test is recommended before treatment with primaquine ACT for treatment of P. f.

Treatment Pre-referral Single dose of treatment with primaquine quinine or is used as artemether IM gametocidal or artesunate medicine for suppositories P. falciparum1 Primaquine is used for radical treatment of P. vivax cases Directly observed treatment with primaquine is undertaken

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • • • • • • • • • • -

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• NA NA NA

•

NA NA NA NA NA

• • • • • • • • • • • • • •

• • • • • • • • • • • • • • • -

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • •

-

• • • •

-

-

-

• • -

• • -

NA

• • • • • • • • • -

• • • • • • • • • • • • NA

• • • -

• • • • •

• • • • •

-

• • • • •

-

• • •

-

(•) = Actually implemented. (•) = Not implemented. (-) = Question not answered or not applicable. 1  Single dose of primaquine (0.75 mg base/kg) for countries in the WHO Region of the Americas 2  In May 2013 South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R21-en.pdf)

WORLD MALARIA REPORT 2016

97

Annex 4 – B. Antimalarial drug policy, 2015 WHO region Country/area P. falciparum Uncomplicated unconfirmed AL AL AL AL; AS+AQ AS+AQ AL AS+AQ AL AL; AS+AQ AL AS+AQ AS+AQ AS+AQ AS+AQ AS+AQ AL AS+AQ AL AS+AQ AS+AQ AL AL AS+AQ AS+AQ AL AS+AQ AS+AQ AL AL AL AL; AS+AQ AL AS+AQ AS+AQ AS+AQ AS+AQ AL; AS+AQ AL AL; AS+AQ AL AS+AQ AL AL -

P. vivax Severe Prevention during pregnancy AS; QN AS; QN QN AS; QN AS; QN QN AS, AM;QN AS, AM; QN AS,QN QN QN QN AS, QN AS QN AS; AM; QN AS; AM; QN QN AS; AM; QN AS AS;QN AS; AM; QN AS; AM; QN QN AS; QN QN QN QN; AS; QN+AS; AS+D; QN+D AS, QN QN AS; QN AS; AM; QN AS; QN QN AS; QN AS; AM; QN QN AM; AS; QN AS AS; AM; QN AS, QN AS, AM; QN AS, AM; QN AS; QN AS; AM; QN QN AL; QN AM+CL; AS+CL; QN+CL AS+AL QN+CL QN

Uncomplicated confirmed AL AL AL AL; AS+AQ AS+AQ AL AS+AQ AL AL; AS+AQ AL AS+AQ AS+AQ AS+AQ AS+AQ AS+AQ AL AS+AQ AL AL; AS+AQ AS+AQ AL AL AS+AQ AS+AQ AL AL; AS+AQ AL; AS+AQ AL AL AL AL AL; AS+AQ AL AS+AQ AL; AS+AQ AL; AS+AQ AL; QN+CL; QN+D AS+AQ AL AL; AS+AQ AL AL; AS+AQ AL AS+AQ AL AL CQ+PQ(1d) AL+PQ AL+PQ(1d); AS+MQ+PQ(1d) AL CQ+PQ(1d) AL+PQ

Treatment

AFRICAN Algeria Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Republic Chad Comoros Congo Côte d'Ivoire Democratic Republic of the Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mayotte Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Sierra Leone South Africa South Sudan1 Swaziland Togo Uganda United Republic of Tanzania Mainland Zanzibar Zambia Zimbabwe AMERICAS Belize Bolivia (Plurinational State of) Brazil Colombia Dominican Republic Ecuador QN QN+D+PQ QN+CL AS+D QN+CL CQ+PQ(14d) CQ+PQ(7d) CQ+PQ(7d) CQ+PQ(14d) CQ+PQ(14d) CQ (3d)+PQ(7d) QN QN QN QN QN QN QN QN QN QN AL AL QN QN QN QN AL QN QN QN QN QN QN QN AS+AQ AL QN QN QN QN QN AL QN AS; QN AL QN QN QN QN QN QN QN CQ AS+AQ+PQ CQ CQ+PQ AL AL+PQ; CQ+PQ AS+AQ+PQ -

98

WORLD MALARIA REPORT 2016

WHO region Country/area

P. falciparum Uncomplicated unconfirmed CQ AL CQ AL AS+SP; AL AS+SP CQ CQ -

P. vivax Severe Prevention during pregnancy QN AS; AL QN AM QN QN AL QN QN AS+MQ AS AM; QN QN AS AS AS AS; AM; QN AS; AM; QN AS AM; QN AM; QN AM; QN AM; AS; QN AM; AS; QN AM; AS; QN AS; QN QN+D AM; AS; QN

Uncomplicated confirmed CQ+PQ(1d) AL AL+PQ(1d) CQ+PQ(1d) CQ+PQ(1d) CQ+PQ CQ+PQ(1d) AL+PQ(1d) AS+MQ AL+PQ(1d) AS+MQ+PQ AS+SP+PQ AL+PQ AS+SP; AS+SP+PQ AS+SP+PQ AS+SP+PQ AS+PQ AS+SP; AL AS+SP AL AL AS+SP+PQ DHA-PP+PQ AL; AM; AS+MQ; DHA-PPQ; PQ AL+PQ DHA-PPQ AL AS+MQ; DHAPPQ+PQ ART+NQ; ART-PPQ; AS+AQ; DHA-PPQ AL AS+MQ AL AL+PQ AL AL DHA-PPQ

Treatment

Americas El Salvador French Guiana Guatemala Guyana Haiti Honduras Mexico Nicaragua Panama Peru Suriname Venezuela (Bolivarian Republic of) Eastern Mediterranean Afghanistan Djibouti Iran (Islamic Republic of) Pakistan Saudi Arabia Somalia Sudan Yemen South-East Asia Bangladesh Bhutan Democratic People's Republic of Korea India Indonesia Myanmar Nepal Thailand Timor-Leste Western Pacific Cambodia China Lao People's Democratic Republic Western Pacific Malaysia Papua New Guinea Philippines Republic of Korea Western Pacific Solomon Islands Vanuatu Viet Nam AL DHA-PPQ QN QN QN+CL; QN+D AL; AS AS AS; QN AL+PQ(14d) AL+PQ(14d) CQ+PQ(14d) AL CQ QN+T QN+T DHA-PPQ AM; AS QN+CL; QN+D; QN+T QN+T; QN+D; QN+CL CQ+PQ(14d) AL+PQ CQ+PQ(14d) CQ+PQ(14d) QN+T QN+D AM; AS; QN AM; AS; PYR AS+AL DHA-PPQ CQ+PQ(8d) CQ+PQ(14d) QN+D; QN+T QN QN+D; QN+T QN+D+PQ AS+D; AS+T AS; QN QN+D QN+D CQ+PQ(14d) CQ+PQ(14d) CQ+PQ(14d) CQ+PQ(14d) DHA-PP+PQ(14d) CQ+PQ(14d) CQ+PQ(14d) CQ+PQ(14d) CQ+PQ(14d) AS; AM; QN QN AS;QN AS;QN AS; AM; QN AS; AM; QN QN; AM QN; AM CQ+PQ(8w) CQ+PQ(14d) CQ+PQ(14d & 8w) CQ+PQ(14d) CQ+PQ(14d) AL+PQ(14d) AL+PQ(14d) CQ+PQ(14d) AL AQ+PG CQ+PQ QN+T MQ; SP SP AL+QN AS+MQ; AS+SP AS+M AS+MQ CQ+PQ(14d) CQ+PQ CQ+PQ(14d) CQ+PQ(14d) CQ+PQ(14d) CQ+PQ(14d) CQ+PQ CQ+PQ(7d) CQ+PQ(7d); CQ+PQ(14d) CQ+PQ CQ+PQ(14d) CQ+PQ(14d)

AL=Artemether-lumefantrine AS=Artesunate D=Doxycycline PG=Proguanil QN=Quinine AT= Atovaquone DHA=Dihydroartemisinin PPQ=Piperaquine SP=Sulphadoxine-pyrimethamine AM=Artemether CL=Clindamycline MQ=Mefloquine PQ=Primaquine T=Tetracycline AQ=Amodiaquine CQ=Chloroquine NQ=Naphroquine PYR=Pyronaridine ART=Artemisinin 1  In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R21-en.pdf) world malaria report 2016

99

Annex 4 – C. Funding for malaria control, 2013–2015 WHO region Country/area Year Global Fund¹ Contributions reported by donors PMI/ USAID² The World Bank³ UK4

AFRICAN Algeria 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015

Angola

25 215 799 -249 158 27 645 452 13 105 187

28 548 000 29 000 000 16 653 000 16 500 000 0

Benin

Botswana

Burkina Faso

9 399 940 5 963 608 22 752 851 4 774 243 892 644

9 421 000 9 500 000 9 229 000 9 500 000

4 254 781

281 893

Burundi

Cabo Verde

Cameroon

10 878 702 8 613 320 12 276 042 1 991 913 34 674 177 12 587 947 3 541 013 1 107 319 735 866

Central African Republic

Chad

Comoros

Congo

Côte d'Ivoire

45 346 542 27 496 568 58 206 877 78 117 103 41 869 000 50 000 000 11 238 171 13 731 500

Democratic Republic of the Congo

Equatorial Guinea

-138 121 14 460 101 6 797 703 113 143 096 9 890 472 -118 -154 828 43 773 000 45 000 000

Eritrea

Ethiopia

Gabon

100

WORLD MALARIA REPORT 2016

Contributions reported by countries Government Global Fund The World Bank PMI/ USAID Other bilaterals WHO UNICEF Other contributions6

0 1 705 134 1 335 355 64 047 348 27 851 717 47 356 258 980 000 1 082 000 1 947 775 2 142 552 1 605 618 58 920 267 3 126 963 576 253 1 134 923 2 001 113 464 515 397 920 253 251 1 520 070 5 246 883 43 709 021 12 122 087 160 000 530 000 530 000 7 493 400 9 122 400 1 184 508 137 147 94 797 114 685 1 651 000 1 675 000 446 000 54 723 090 53 942 249 913 958 253 7 812 690 8 104 841 7 014 345 2 582 747 0 0 19 705 028 226 596 123 200 27 677 576

5

0 5

12 000 27 200 000 27 000 000 28 000 000 3 555 239

0 0

5

19 286 339 5 378 690 2 675 645 40 580 540 0 0 280 899 40 645 351 2 433 376 42 735 771 19 481 377 6 027 330 4 523 416 555 169 64 285 325 273 15 293 706 147 856 497 54 918 697 5 342 710 2 852 385 0 0 0 0 697 173 284 328

0 0 0 8 552 723 8 571 017 8 579 441 9 260 000 9 229 345 9 500 000

0 0 0 0 70 804 9 454 2 602 730 0

5 5 5 5

5 415 537 1 123 490 0 0

37 800 19 048 11 800 65 000 79 050 32 595 130 448 19 638 19 142 904 218 460 000 221 000 20 500 100 000

0 0 0 521 760 136 540 305 704 453 631 475 936 47 445 292

0 0 0 942 955 379 610 2 533 200 1 277 376 1 324 385

118 341 14 718 2 000 000 5 596 000

5 415 537 669 000

5 5 5 5

30 125 205 6 141 762 499 000 1 074 877 224 643 0 0 74 853 096 33 611 939 14 414 815 784 86 281 277 102 540 781 107 594 221 0

239 735 0 0 0 0 0 13 119 140 0 2 952 042 0 0 0 0 0 0 0 9 839 355 9 839 355 0 37 001 000 34 000 000 34 000 000 0 0 0 0 0 244 000 0 0 24 838 023 23 018 218

54 574 20 000 40 000 104 000 30 000 45 000 45 000 68 000 36 338 6 245 966 0 0 2 100 000 2 933 630

2 667 358 216 491 5 576 51 630 6 221 10 000 18 000 24 975 817 29 250 235 15 070 138 1 790 452 7 196 262 808 130

673 440 0 58 500 0 0 3 827 0 244 000 22 954 890 35 020 370 0 0 4 490 030

5

5

15 871 769 4 906 745 6 216 618 85 723 876 93 201 479 18 448 416 0 0 0

0 0

0 0 29 370 000 3 800 000 0 0 0

0

58 832 46 081 111 677

0 0

0 0 15 000 000 13 114 670

0 0 0

0 0 0

11 276 34 855 47 147

0 0 0

272 289

WORLD MALARIA REPORT 2016

101

Annex 4 – C. Funding for malaria control, 2013–2015 WHO region Country/area Year Global Fund¹ Contributions reported by donors PMI/ USAID² The World Bank³ UK4

AFRICAN Gambia 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 9 288 845 4 134 951 67 802 357 14 840 935 4 603 535 9 144 353 7 320 497 2 340 811 33 311 280 49 541 177 5 882 949 10 405 293 22 647 300 499 317 9 084 196 7 129 260 13 845 815 10 803 020 34 256 000 35 000 000 12 370 000 12 000 000 26 026 000 26 000 000 24 075 000 22 000 000 25 007 000 25 000 000 264 584 22 345 400 28 547 000 28 000 000 12 371 000 12 500 000 1 903 200 2 982 020

145 948

Ghana

Guinea

Guinea-Bissau

Kenya

Liberia

Madagascar

Malawi

Mali

Mauritania

Mayotte

Mozambique

12 626 612 34 642 279 3 608 532 556 809 9 305 823 24 009 643 45 365 287 144 939 061 22 881 569 15 427 182 3 699 517 3 306 066 3 662 132 21 674 466

29 023 000 29 000 000

2 031 197

7 739 210

Namibia

Niger

Nigeria

73 272 000 75 000 000 18 003 000 17 500 000 0 0 24 124 000 24 000 000

27 963 280

30 852 400

Rwanda

9 455

Sao Tome and Principe

Senegal

102

WORLD MALARIA REPORT 2016

Contributions reported by countries Government Global Fund The World Bank PMI/ USAID Other bilaterals WHO UNICEF Other contributions6

726 578 799 091 793 818 8 736 726 8 855 177 9 832 327 3 015 335 956 833 48 178 445 0 100 000 1 372 093 1 178 804 1 520 205 284 306 11 341 797 15 286 23 658 25 400 4 266 640 1 871 915 1 756 941 5 670 552 1 130 593 2 328 000 173 720 65 800 000 4 186 129 5 146 910 14 811 934 2 996 923 4 051 428 2 668 014 2 859 000 8 999 547 5 541 401 0 531 541 10 724 11 084 47 033 13 986 24 800 2 069 404

4 919 685 5 934 320 2 887 213 67 804 357 64 952 156 39 759 327 15 603 972 28 859 411 701 363 2 952 761 29 089 771 48 916 476 64 945 727 14 026 642 10 399 555 29 994 536 2 524 013 23 199 442 880 267 8 023 075 22 777 197 18 180 392 26 392 018 21 201 959

0 0 0 0

0 0 27 000 000 4 730 000 28 000 000 10 000 000 12 052 476 12 500 000 0 0 32 400 000 32 400 000 32 400 000 12 000 000 12 000 000 27 000 000 25 920 000 26 000 000 23 000 000 19 118 000 12 234 171 25 500 000 25 500 000 25 500 000

0 0 38 817 825 000 520 000

16 000 132 833 47 050 32 514 60 000 105 114 21 886 73 734 16 869

26 229 150 000 3 062 0 7 519 0 36 639 10 419 218 811 7 231 0

100 000 120 814 2 406 568 6 429 0 16 581

3 979 774 0 23 457 627 25 635 413

5

0 0 1 127 907

0 23 457 627

5

0 0 0 600 000 0

832 402 604 058 44 890

0 369 500 0 213 615 299 000 3 369 341 298 946 150 000 150 000 92 000 95 000 120 000 11 767 46 000 67 000

100 000 340 647 0 737 588 254 170 70 000

0 0 0 56 422

5

0 0 0

0

3 092 000 1 437 552 574 693 42 583 42 000 67 000

1 082 008 0 5 326 854

2 497 243 37 646 902 4 357 070 882 630 2 910 095 2 796 269 19 000 000 2 494 013 9 324 003 100 362 906 137 920 815 126 250 194 0 10 893 838 1 002 778 1 715 622 1 668 679 4 675 836 15 023 299 2 427 578

11 000 000 3 500 000 0 0 0 0 0 0 7 040 569 52 220 588

29 000 000 29 023 096 29 000 000 0 0 0 72 000 60 462 012 73 771 000 75 000 000 0 18 000 000 0 0 0 24 500 000 25 302 960 23 666 000

100 000 0 0 0 200 000 100 000 100 000 100 000 27 000 70 248 86 567 934 980 861 615 964 784 0 32 512 125 209 60 006 12 490 12 491

2 668 555 268 993 1 688 356 0 4 000 000 1 249 000 18 500 3 000 000 1 000 000

139 501 0 0 136 929 44 000 0

0 0 36 736 654 20 157 565 12 322 449 0 1 050 830 1 020 102 1 000 000

4 809 717 0 0 0 1 293 200 000 9 780 0 2 000 1 600 1 600

0 0 0 0

1 000 000

25 705

WORLD MALARIA REPORT 2016

103

Annex 4 – C. Funding for malaria control, 2013–2015 WHO region Country/area Year Global Fund¹ Contributions reported by donors PMI/ USAID² The World Bank³ UK4

AFRICAN Sierra Leone 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 6 214 513 13 788 079 0 6 097 560

South Africa

South Sudan7

8 716 372 14 253 512 1 336 085 1 654 211 20 510 821 7 413 283 19 511 505 14 223 217 56 328 793 28 943 792 52 221 547 28 943 792 4 107 246

6 947 000 6 000 000

8 955 920

Swaziland

Togo

Uganda

33 782 000 34 000 000 46 056 000 46 000 000

680 702

7 354 400

United Republic of Tanzania8

Mainland

Zanzibar

29 335 147

Zambia

24 028 000 24 000 000 15 035 000 15 000 000

4 903 770

19 235 700

Zimbabwe AMERICAS

9 985 457 10 695 816

0

Argentina

0

Belize

Bolivia (Plurinational State of)

2 112 710 1 318 174 -228 780

Brazil

Colombia

6 737 839 2 894 197 1 149 536 514 691

Dominican Republic

104

WORLD MALARIA REPORT 2016

Contributions reported by countries Government Global Fund The World Bank PMI/ USAID Other bilaterals WHO UNICEF Other contributions6

26 898 3 074 190 741 13 511 860 17 096 911 0 0 556 245 678 718 11 847 354 5 139 088 8 035 963 8 035 963 937 500 6 022 000 30 523 723 15 152 407 082 185 325 15 462 950 22 640 090 706 200 520 000 780 000 1 082 700 1 082 700 1 082 700 261 500 270 000 297 500 787 966 718 391 531 609 73 291 509 72 248 286 60 803 769 23 100 498 11 493 708 13 059 553 1 966 812 1 883 503 2 663 837

13 216 219 13 525 631 5 353 621

1 952 807 0 0

0 0

5

0 46 437 577

0

0 6 900 000

6 156 320 0 152 277 68 180 41 140 0

64 000 50 000 101 207

7 874 921 17 912 100 847

112 855 2 200 067

40 000 2 934 000

0 1 000 000

0 4 108 159

1 715 525 1 203 444 1 714 840 4 897 544 20 146 401 24 195 015 74 643 525 142 485 233 147 632 422 28 982 597 140 356 602 145 506 422 28 982 597 2 128 631 2 126 000 19 361 732 24 362 218 10 614 665 7 460 006 7 626 664 33 425 777 0 0 0 0 10 121 189 879 365 193 1 631 520 1 170 000 0 0 0 4 832 745 3 257 687 0 1 158 508 852 947 72 511

0

0

132 445

20 250 0

0

0 0

17 304

0 33 781 000 33 000 000 33 000 000 40 602 700 1 975 000 1 060 714 37 117 700 450 000 1 060 714 3 485 000 1 525 000 24 000 000 24 000 000 24 000 000 13 000 000 12 000 000 12 000 000 0 0 0 14 223 6 761 12 747 0 0 0 18 700 47 495 129 288 142 406 96 194 73 391 0 0 0

0

1 779

222 460

0

5 5

3 418 520 0 0 0 0 0 0 0 0 0 0

39 623 353 32 222 500 0 50 000 77 966 100 0 0 77 966 100 50 000 3 500 000

850 850 0 500 500 0 350 350 204 466 170 500 90 060 39 649

1 359 595 5 676 820 41 153 0 0 0 0 0 41 153 0 27 318 20 000 1 006 000 42 500

4 896 045 4 899 062 2 528 703 0 480 412 2 487 550 0 480 412 41 153

0 6 000 000 6 500 000

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 38 991 0 0 0 0 0 0 21 930 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0

0 0 0 0 0 0 0 0 0 0 23 382 106 598 213 094

WORLD MALARIA REPORT 2016

105

Annex 4 – C. Funding for malaria control, 2013–2015 WHO region Country/area Year Global Fund¹ Contributions reported by donors PMI/ USAID² The World Bank³ UK4

AMERICAS Ecuador 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 1 110 598 1 002 244 0

El Salvador

French Guiana

Guatemala

-2 089 393 4 388 420 379 266

Guyana

Haiti

3 902 655 4 531 760 954 631 967 393 0

Honduras

Mexico

Nicaragua

2 431 682 1 010 094 0

Panama

0

Peru

Suriname

549 463 158 751 0

Venezuela (Bolivarian Republic of) EASTERN MEDITERRANEAN Afghanistan

Djibouti

Iran (Islamic Republic of)

Pakistan

2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015

17 626 010 8 403 364

3 154 876

52 000

3 180 088 2 665 232 5 849 945 9 003 535

106

WORLD MALARIA REPORT 2016

Contributions reported by countries Government Global Fund The World Bank PMI/ USAID Other bilaterals WHO UNICEF Other contributions6

1 852 740 2 444 718 2 854 844 0 0 1 385 919 542 663 2 610 850 883 314 800 439 1 023 795 2 433 241 971 742 543 312 25 256 768 23 827 054 46 662 926 980 326 2 596 547 2 886 581 7 220 410 7 469 311 7 964 427 429 285 152 805 1 650 498 1 049 230 800 000 1 000 000 19 600 139

5

5 5 5 5

735 047 983 835 0 0 0 0 0 0 3 498 024 3 278 171 8 232 108 809 474 451 597 337 939 1 248 119 1 161 379 1 415 674 1 106 404 792 634 0 0 0 2 075 252 1 214 811 1 013 568 0 100 000 10 000 0 0 0 550 000 479 600 975 757 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

19 719 98 057 0 0 13 376 0 0 105 373 92 461 56 824 297 569 115 708 288 169 102 864 62 156 99 330 113 187 118 071 0 0 0 37 630 51 323 59 175 32 136 77 562 49 079 56 703 91 037 98 598 157 887 30 198 47 762 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 470 000 6 000 0 0 0 0 0 0 0 0 0 0 0 0 400 000 400 541 400 541

0 141 000 56 948 54 340 11 563 0 0 0 0 0 71 370 140 486 47 500 169 000 24 413 0 0 18 457 0 0 0 4 814 21 868 28 098 0 0 11 000 0 0 100 000 77 264 41 437

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 820 000 250 064 0 6 046 0 0 0 0 0 0 0 0 0 0 400 000 0 0

5 5 5 5 5 5 5

5 5

5 5 5

5 5 5 5

5 5 5 5 5 5

5 000 000 6 300 000 2 500 000 -

16 651 753 9 083 870 4 571 460

109 068 113 341 89 167 121 616

200 563

9 200

0 2 979 260 2 418 943 8 057 177 10 718 906 5 910 215

60 500 34 000 5 000 154 000 89 000

WORLD MALARIA REPORT 2016

107

Annex 4 – C. Funding for malaria control, 2013–2015 WHO region Country/area Year Global Fund¹ Contributions reported by donors PMI/ USAID² The World Bank³ UK4

EASTERN MEDITERRANEAN Saudi Arabia 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 0

Somalia

2 266 628 9 672 384 35 680 104 16 053 353 5 973 123 2 017 535 0 0

Sudan

Yemen EUROPEAN Tajikistan SOUTH-EAST ASIA Bangladesh

2013 2014 2015

1 308 106 1 032 277

Bhutan

Democratic People's Republic of Korea

India

Indonesia

Myanmar

Nepal

Thailand

Timor-Leste WESTERN PACIFIC Cambodia

2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015

16 404 817 4 395 406 405 271 239 889 2 706 329 6 704 605 7 174 057 4 481 942 31 045 276 11 488 128 15 032 712 18 254 744 4 922 108 1 813 110 11 325 529 16 524 453 2 604 409 1 527 841 0 0 6 566 000 8 000 000 5 377 070

297 389

11 283 400

2013 2014 2015

12 111 758 17 983 122

3 997 000 4 500 000

108

WORLD MALARIA REPORT 2016

Contributions reported by countries Government Global Fund The World Bank PMI/ USAID Other bilaterals WHO UNICEF Other contributions6

29 440 000 30 000 000 30 000 000 64 515 67 740 79 488 26 724 830 27 316 109 21 536 529 2 293 553 8 480 0

5

0 0 15 062 018 9 604 810 7 365 620 34 938 594 35 883 294 16 251 350 6 256 730 2 110 776 14 326 025

0 0 0 0

0 0 0 0

0 0 0

0

0

0 258 495

0 0 138 400 85 000 121 800 475 893 446 160 471 552 200 000 465 713 390 259

0 0 140 000 0

0 0 0 0

0 1 986 444 1 674 350

633 740 773 000 -

1 714 393 1 057 879

35 000 75 000

0

4 134 615 5 586 290 935 897 180 328 179 104 1 895 000 1 957 000 2 042 000 51 336 600 43 802 468 48 419 018 15 288 402 16 108 194 10 940 000 1 028 807 5 272 824 1 910 485 2 315 400 5 893 255 7 546 409 7 934 078 2 981 432 791 375

8 033 087 8 912 484 9 507 849 390 420 487 909 2 706 329 1 571 206 6 817 631 4 811 540 16 129 032 5 244 575 34 580 791 15 913 410 10 966 688 14 863 117 42 620 577 31 629 898 3 110 685 5 199 862 9 937 671 20 175 612 13 830 845 4 372 545 3 482 955 2 610 355

399 189 0 0 0 65 000 10 000 5 552 25 000 98 000 30 200 0 0 166 639 0 0 0

5 5 5

0 0 0 0 4 299 233 0 0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

5

0

0 0 0 0 5 400 000 6 565 881 6 500 000

0 0 0 0 451 400 2 800 000

5

0 0

278 311 345 667 685 341

0 0

400 000 277 282 277 282 142 500 25 000 25 000 46 500 46 500 45 000 139 166 0 0 65 012 27 280

0 3 525 000 3 490 400 1 691 397 1 000 000 0

0 0 0 5 561 917 0

0 0

70 833 0 0 120 000 0

0

0

0

0

3 484 029 714 343 692 698

13 240 888 2 917 174 4 042 964

0 0 0

3 996 624 4 500 000 4 500 000

0 0 0

431 792 334 029 406 393

0 0 0

WORLD MALARIA REPORT 2016

109

Annex 4 – C. Funding for malaria control, 2013–2015 WHO region Country/area Year Global Fund¹ Contributions reported by donors PMI/ USAID² The World Bank³ UK4

WESTERN PACIFIC China 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 1 856 499 -1 738 247 3 256 001 2 322 590 695 423

Lao People's Democratic Republic

0

Malaysia

Papua New Guinea

22 970 152 10 970 461 4 806 916 6 932 455 0

Philippines

Republic of Korea

Solomon Islands

0

Vanuatu

Viet Nam

4 249 171 3 777 902

-2 733

PMI, United States President’s Malaria Initiative; UK, Funding from the United Kingdom of Great Britain and Northern Ireland government; UNICEF, United Nations Children’s Fund; USAID, United States Agency for International Development 1  Source: The Global Fund 2  Source: www.foreignassistance.gov 3  Source: OECD Database 4  Source: OECD Database 5  Budget not expenditure 6  Other contributions as reported by countries: NGOs, foundations, etc. 7  South Sudan became an independent State on 9 July 2011 and a Member State of WHO on 27 September 2011. South Sudan and Sudan have distinct epidemiological profiles comprising high-transmission and low-transmission areas, respectively. For this reason data up to June 2011 from the high-transmission areas of Sudan (10 southern states which correspond to contemporary South Sudan) and low-transmission areas (15 northern states which correspond to contemporary Sudan) are reported separately. 8  Where national totals for the United Republic of Tanzania are unavailable, refer to the sum of Mainland and Zanzibar. * Negative disbursements reflect recovery of funds on behalf of the financing organization.

110

WORLD MALARIA REPORT 2016

Contributions reported by countries Government Global Fund The World Bank PMI/ USAID Other bilaterals WHO UNICEF Other contributions6

16 812 725 20 843 118 17 620 404 1 122 915 247 375 211 874 39 845 997 57 535 038 64 881 663 388 000 377 000 1 637 421 5 235 686 5 861 758 6 165 334 519 102 556 200 538 495 270 180 260 505 281 324 812 377 812 377 166 359 4 523 810 2 666 667 2 666 666

0 0 4 038 937 2 475 938 6 458 501 0 25 311 547 695 052 19 431 536 8 612 874 7 395 343 6 087 433 0 0 0 1 305 840 1 362 022 2 232 220 1 162 890 1 310 500 687 267 5 254 143 15 263 816 5 528 000

0

0

0

0 0 20 000 113 000 198 357 0 0

0

0 0 0 43 620 0 0 0

0 0 0

120 132 0 216 986

0 0 600 000

0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0

0 315 326 0 0 0 0 0 852 472 654 985 464 914 287 615 287 615 175 894 410 000 640 700 560 000

0 0 0 0 0

0 22 220 0 0 0 0 0 674 896 0 0 0 0 0 0 0 200 000

5 5

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 1 987 523 1 820 735 1 017 390 1 692 091 1 064 592 424 136 0 0 0

0 0 0 0 0 0 0 0 0 0

WORLD MALARIA REPORT 2016

111

Annex 4 – D. Commodities distribution, 2013–2015 WHO region Country/area AFRICAN Algeria Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Republic Chad Comoros Congo Côte d'Ivoire Democratic Republic of the Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 0 0 1 182 519 2 978 937 2 138 331 584 285 6 203 924 0 50 000 9 959 820 239 559 481 107 731 981 5 752 583 726 767 0 0 0 2 751 112 150 000 555 334 1 170 566 230 043 6 321 676 1 218 640 377 252 13 576 16 969 14 005 180 595 447 1 821 267 12 627 282 3 663 080 7 947 747 13 918 109 15 419 488 8 397 10 010 86 597 0 2 054 194 11 709 780 13 388 552 17 233 074 21 666 10 000 10 730 138 149 1 046 510 93 375 1 926 300 5 190 887 8 423 676 5 268 245 73 145 357 706 17 407 419 353 58 370 694 729 789 883 802 597 176 887 205 831 143 268 0 0 0 0 0 298 475 25 780 308 586 0 0 0 31 150 22 475 20 275 0 0 185 252 194 566 77 643 129 000 165 944 275 857 320 881 328 915 23 150 388 16 709 249 0 800 290 350 442 438 234 2 936 037 2 154 924 0 900 000 2 500 000 1 332 948 1 486 667 1 600 1 135 5 728 612 6 224 055 8 290 188 2 857 991 3 089 202 5 075 437 920 382 1 573 992 25 000 303 582 759 245 994 779 1 144 686 1 057 033 23 565 5 375 14 813 39 375 19 746 0 3 891 695 5 600 100 9 746 694 13 962 862 13 574 891 17 630 9 801 393 780 54 516 645 18 300 000 7 416 167 13 148 960 907 880 603 900 875 850 3 840 000 9 309 200 3 778 325 2 436 825 2 870 250 2 412 597 603 266 747 2 814 900 3 185 160 1 101 154 1 177 261 3 953 1 386 5 797 938 7 494 498 7 824 634 3 836 437 4 772 805 4 798 379 4 824 46 26 1 048 811 1 270 172 826 434 420 000 522 270 1 043 674 814 449 1 038 000 1 326 091 60 868 4 750 577 0 0 1 304 959 2 358 567 3 296 991 14 941 450 19 008 927 9 871 484 40 911 14 577 182 911 216 195 255 602 12 800 000 7 321 471 7 036 620 984 423 468 767 319 182 351 677 8 330 784 14 267 045 2 715 640 370 771 1 312 802 1 645 493 0 92 2 814 900 3 185 160 1 101 154 1 177 261 3 953 1 386 5 797 938 7 494 498 7 824 634 3 836 437 4 263 178 4 798 376 3 144 41 26 497 022 1 270 172 826 434 420 000 522 270 1 043 674 814 449 1 038 000 1 326 091 60 868 4 750 550 0 0 1 304 959 2 358 567 3 296 991 7 112 841 19 008 927 9 871 484 40 911 182 911 216 195 255 602 9 164 641 5 321 471 6 049 320 984 423 468 767 319 182 351 677 8 330 784 14 267 045 2 715 640 1 402 400 644 829 -

Year

No. of ITN + LLIN sold or delivered

No. of people protected by IRS

No. of RDTs distributed

First-line treatment courses delivered (including ACT)

ACT treatment courses delivered

112

WORLD MALARIA REPORT 2016

WHO region Country/area AFRICAN Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mayotte Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Sierra Leone South Africa South Sudan1 Swaziland Togo Uganda

Year

No. of ITN + LLIN sold or delivered

No. of people protected by IRS

No. of RDTs distributed

First-line treatment courses delivered (including ACT) 171 540 8 300 000 10 839 611 11 052 564 1 332 055 100 535 2 172 536 1 648 093 2 040 289 7 601 460 8 735 160 6 240 060 3 080 130 2 211 118 3 761 319 56 015 176 192 13 477 650 15 976 059 13 653 685 90 377 79 215 6 556 070 5 731 036 3 698 674 32 568 349 22 145 889 1 204 913 1 917 021 4 392 006 8 752 1 456 1 704 976 840 703 712 958 492 2 201 370 1 391 273 1 687 031 8 272 14 036 0 3 125 448 356 588 491 964 927 1 134 604 1 508 016 24 375 450 21 698 700 30 166 620

ACT treatment courses delivered

2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015

116 268 1 109 568 1 641 982 5 450 064 11 637 493 95 775 236 996 6 458 693 105 442 11 249 042 636 318 1 423 507 1 100 000 636 465 3 790 403 6 080 030 105 000 178 922 240 000 39 400 5 252 3 315 727 6 112 245 5 126 340 104 249 163 526 409 400 2 048 430 6 253 448 8 559 372 23 328 225 27 628 073 5 249 761 1 373 582 2 066 915 14 596 11 385 113 221 3 902 145 3 785 595 556 135 441 859 3 846 204 395 061 0 0 0 3 144 818 0 5 399 3 808 468 575 4 042 425 8 600 13 219 306 10 615 631 1 442 500

0 0 0 0 0 1 579 521 1 307 384 1 327 326 826 386 836 568 494 163 381 450 9 647 202 5 597 770 3 659 845 598 901 467 930 386 759 0 0 0 132 211 316 255 1 562 411 1 243 704 153 514 124 692 143 571 690 090 708 999 514 833 0 0 2 318 129 5 650 177 1 178 719 332 968 0 3 971 0 0 2 581 839 3 219 122 3 895 232

917 200 5 000 000 5 500 000 4 319 000 610 225 58 248 1 640 095 2 839 325 4 962 600 8 197 250 8 462 325 4 101 525 2 563 993 4 381 050 225 680 269 941 360 000 10 547 052 17 374 342 17 219 225 185 025 30 120 2 561 900 4 197 381 3 039 594 13 200 766 10 679 235 604 565 444 729 2 015 100 30 909 58 005 72 407 1 453 000 1 193 075 2 570 500 2 522 058 2 057 306 2 494 935 242 123 499 086 16 007 764 670 21 575 58 700 989 436 1 633 891 1 633 891 19 048 750 17 157 725 27 110 800

171 540 7 000 000 10 614 717 10 321 221 443 900 96 787 2 172 536 1 648 093 2 040 289 7 601 460 8 735 160 6 240 060 3 080 130 2 211 118 3 761 319 56 015 176 192 109 000 13 477 650 15 976 059 13 653 685 87 520 6 556 070 5 731 036 3 698 674 32 568 349 22 145 889 1 204 913 1 917 021 4 392 006 8 752 1 456 1 704 976 840 703 712 958 492 2 201 370 1 391 273 1 687 031 5 444 14 036 0 3 125 448 307 558 396 802 904 1 208 529 1 208 529 24 375 450 21 698 700 30 166 620

WORLD MALARIA REPORT 2016

113

Annex 4 – D. Commodities distribution, 2013–2015 WHO region Country/area AFRICAN United Republic of Tanzania Mainland Zanzibar Zambia Zimbabwe AMERICAS Belize Bolivia (Plurinational State of) Brazil Colombia Dominican Republic Ecuador El Salvador French Guiana Guatemala Guyana Haiti Honduras Mexico Nicaragua Panama Peru 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2 324 2 452 4 152 20 965 23 580 17 514 147 736 229 947 146 196 169 500 25 100 54 139 6 733 105 906 20 337 120 532 10 000 0 0 2 920 2 990 282 788 49 905 600 049 27 921 152 996 24 201 0 0 66 920 25 118 36 149 4 500 7 500 15 000 17 100 83 279 0 0 0 0 4 600 45 000 64 687 21 413 21 413 36 796 30 280 16 573 11 138 324 477 287 150 276 278 154 000 519 333 252 500 49 510 6 066 100 090 94 321 15 076 6 424 37 500 16 932 37 450 1 700 41 000 25 592 146 0 0 121 121 116 490 125 975 49 401 47 775 214 032 127 601 56 675 59 282 17 055 11 422 11 581 43 617 69 155 142 253 0 0 0 15 000 100 050 46 950 101 700 43 600 2 960 0 71 000 54 425 50 220 0 0 0 139 525 50 459 108 900 0 0 0 0 8 000 4 275 9 750 0 19 029 15 620 12 527 0 0 0 26 19 13 7 342 7 401 6 907 452 990 334 740 290 580 68 879 86 228 108 469 579 496 661 378 686 10 865 8 9 0 31 479 12 354 9 984 109 625 2 030 300 37 248 54 466 2 974 4 592 3 133 1 162 1 142 2 307 705 874 562 42 670 65 252 66 609 0 0 0 959 325 6 907 122 290 59 690 94 380 48 285 32 489 55 469 4 7 3 161 227 0 0 0 0 13 655 12 354 3 219 0 2 8 8 4 6 6 0 0 0 0 0 6 504 10 416 13 618 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2 547 391 619 189 21 141 998 2 489 536 510 000 20 794 000 57 855 109 189 347 998 3 362 588 6 368 026 2 010 000 1 743 542 84 087 3 793 027 2 224 900 14 684 925 3 537 097 2 000 000 14 386 280 255 930 224 900 298 645 1 063 460 5 538 574 5 930 141 3 106 659 3 460 871 3 548 246 21 785 950 24 126 300 17 031 950 21 491 950 24 126 300 16 416 675 294 000 615 275 9 221 210 7 500 000 11 310 350 1 671 832 2 446 996 1 981 613 20 382 485 19 937 820 10 164 660 20 377 410 19 937 820 10 160 910 5 075 3 750 15 926 301 13 000 845 14 365 969 815 260 960 455 847 333 20 382 485 19 937 820 10 164 660 20 377 410 19 937 820 10 160 910 5 075 3 750 15 926 301 13 000 845 14 365 969 815 260 960 455 847 333

Year

No. of ITN + LLIN sold or delivered

No. of people protected by IRS

No. of RDTs distributed

First-line treatment courses delivered (including ACT)

ACT treatment courses delivered

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WORLD MALARIA REPORT 2016

WHO region Country/area AMERICAS Suriname Venezuela (Bolivarian Republic of) EASTERN MEDITERRANEAN Afghanistan Djibouti Iran (Islamic Republic of) Pakistan Saudi Arabia Somalia Sudan Yemen EUROPEAN Tajikistan SOUTH-EAST ASIA Bangladesh Bhutan Democratic People's Republic of Korea India Indonesia Myanmar Nepal Thailand Timor-Leste

Year

No. of ITN + LLIN sold or delivered

No. of people protected by IRS

No. of RDTs distributed

First-line treatment courses delivered (including ACT) 800 401 120 979 136 389 11 135 21 625 8 920 6 230 8 830 37 971 2 150 000 907 200 890 500 974 1 155 1 444 292 000 155 450 386 200 2 630 400 3 823 175 2 551 310 303 847 215 486 153 682 1 0 42 390 75 479 40 742 518 118 416 15 673 11 212 29 272 147 000 211 500 2 123 760 300 008 212 346 406 614 371 663 281 103 243 515 38 113 24 500 3 350 15 069 19 314 8 125 1 042 347 80

ACT treatment courses delivered

2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015

4 892 3 000 0 467 2 666 1 041 359 622 4 325 552 58 830 25 700 25 000 0 169 084 70 360 91 845 2 238 300 1 519 947 1 822 015 750 000 1 450 000 125 000 525 000 413 000 291 085 5 803 319 4 432 714 2 729 334 1 405 837 375 899 847 946 100 000 50 000 612 000 728 773 2 380 759 93 726 10 609 26 000 0 0 864 750 0 0 7 241 418 913 135 6 416 947 56 337 1 508 557 904 613 3 398 941 1 395 865 1 064 518 304 437 670 000 528 850 251 500 253 037 99 572 24 607

0 0 4 369 755 4 189 850 2 739 290 0 0 0 36 630 281 203 289 249 217 773 1 161 825 1 103 480 1 685 264 1 736 400 752 851 131 661 90 060 61 362 15 645 3 902 712 3 942 110 2 460 816 2 204 429 2 188 436 798 707 437 436 387 010 0 0 32 824 144 669 70 926 2 651 612 2 617 120 1 146 750 45 854 424 45 150 612 41 849 017 253 815 103 285 53 497 48 626 129 545 345 000 372 000 235 000 106 374 362 469 348 713 51 627 110 707 93 019

24 425 17 625 0 188 370 355 160 98 065 20 800 40 761 114 450 1 170 000 857 690 770 074 809 520 617 640 424 140 1 800 000 2 200 000 4 344 150 233 311 412 350 334 525 186 700 259 171 16 875 0 0 253 320 16 200 000 15 562 000 21 182 000 1 047 504 879 650 300 000 1 497 545 3 048 440 1 309 300 65 500 60 000 56 000 160 000 258 823 15 400 121 991 86 592 90 818

300 144 27 659 32 005 35 509 11 135 21 625 200 8 920 3 400 8 830 2 042 590 840 162 880 80 000 974 1 155 1 444 292 000 155 450 386 200 2 077 204 3 823 175 2 551 310 303 847 215 486 153 682 1 0 42 390 58 770 35 708 518 118 416 0 0 0 147 000 211 500 2 123 760 300 008 212 165 406 614 371 663 281 103 243 515 325 195 300 15 069 19 314 8 125 513 105 56

WORLD MALARIA REPORT 2016

115

Annex 4 – D. Commodities distribution, 2013–2015 WHO region Country/area WESTERN PACIFIC Cambodia China Lao People's Democratic Republic Malaysia Papua New Guinea Philippines Republic of Korea Solomon Islands Vanuatu Viet Nam 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 2013 2014 2015 5 418 70 411 1 517 074 0 19 899 29 611 439 677 276 655 152 791 317 943 622 673 285 946 1 625 831 1 613 140 991 440 715 125 996 180 932 736 0 5 250 5 250 371 124 47 258 10 721 94 232 42 916 38 211 0 526 366 658 450 0 0 447 639 504 936 1 697 188 13 113 4 691 682 288 615 384 489 030 0 1 108 220 1 175 136 847 845 98 971 128 673 175 683 3 033 0 1 310 820 616 670 620 093 1 085 325 538 500 483 600 821 000 160 000 312 075 324 225 1 032 600 963 900 1 000 000 70 550 201 775 79 300 4 900 1 677 47 450 107 425 35 000 50 000 53 400 412 530 434 160 459 332 117 547 118 483 128 004 4 127 43 150 67 555 58 470 50 092 86 456 3 850 3 923 2 311 915 330 802 080 728 310 24 771 30 095 16 989 443 638 699 146 439 147 430 242 456 24 000 24 000 20 256 218 389 194 397 97 570 117 547 114 159 122 013 3 919 9 350 20 710 58 470 50 092 86 456 2 873 3 182 1 616 915 330 802 080 728 310 24 771 30 095 16 989 146 439 147 430 242 456 24 000 24 000 20 256 141 570 106 100 45 000

Year

No. of ITN + LLIN sold or delivered

No. of people protected by IRS

No. of RDTs distributed

First-line treatment courses delivered (including ACT)

ACT treatment courses delivered

ACT, artemisinin-based combination therapy; IRS, indoor residual spraying; ITN, insecticide-treated mosquito net; LLIN, long-lasting insecticidal net; RDT, rapid diagnostic test 1  In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R21-en.pdf)

116

WORLD MALARIA REPORT 2016

WHO region Country/area a a hemo- positive globin microsmeacopy sureblood ment smear <8g/dL for who who whom received had a advice an ACT finger or among or heel treatthose stick ment who was received sought any antimalarial

Source

% of HH % of HH that with have enough at least ITNs for one ITN individuals who slept in the house the previous night

% of % of % of the popula- existing population with ITNs tion who access in HH slept to an ITN used the under in their previous an ITN household night the previous night

% of children <5 years who slept under an ITN the previous night

% of pregnant women who slept under an ITN the previous night

% of HH sprayed by IRS within last 12 months

% of HH with = 1 ITN for 2 pers. and/or sprayed by IRS within last 12 months

% of women who received at least 3 doses of IPT during ANC visits during their last pregnancy

% of children aged 6-59 months with

% children <5 years with fever in last 2 weeks

AFRICAN 24 47 60 79 32 49 14 38 93 61 77 65 39 66 71 34 58 63 27 57 66 39 39 50 41 33 67 34 41 64 80 60 41 66 75 60 34 55 60 37 22 36 35 13 12 18 23 4 4 16 49 74 73 43 38 51 52 40 75 41 37 70 91 63 78 38 65 90 58 73 30 52 86 52 61 9 6 5 17 2 1 12 13 10 5 5 5 31 28 48 85 54 61 30 20 37 71 31 36 13 39 53 78 47 58 30 37 42 40 26 23 35 41 42 63 48 32 48 79 41 50 2 34 44 59 50 35 43 12 51 19 45 77 36 46 32 43 6 40 10 23 18 13 3 7 22 3 8 24 28 50 24 47 85 49 59 6 24 47 85 49 59 27 40 47 21 22 1 41 8 8 8 12 9 4 7 21 3 10 2 10 5 8 17 9 5 23 46 83 47 55 6 47 71 85 66 76 1 47 46 23 1 8 33 53 36 27 1 1 0 38 19 64 43 59 66 80 73 73 80 67 49 58 66 78 68 59 59 51 61 83 77 28 69 10 19 18 31 78 85 92 43 41 93 17 29 46 18 38 93 99 18 10 14 77 48 87 90 31 48 13 19 19 37 34 35 39 42 13 49 12 14 22 11 13 30 36 11 18 40 24 36 49

Burkina Faso

MIS 2014

Burundi

DHS 2013

Chad

DHIS 2015

Democratic Republic DHS 2013 of the Congo DHS 2014

Gambia

DHS 2013

Ghana

DHS 2014

Kenya

DHS 2014

DHIS 2015

Liberia

DHS 2013

Madagascar

DHS 2013

Malawi

MIS 2014

Mali

DHS 2013

DHIS 2015

Namibia

DHS 2013

Annex 4 – E. Household surveys results, 2013–2015

Nigeria

DHS 2013

DHIS 2015

Rwanda

DHS 2013

DHIS 2015

DHS 2013

Senegal

DHS 2014

DHIS 2015

Sierra Leone

DHS 2013

Togo

DHS 2014

Uganda

MIS 2015

WORLD MALARIA REPORT 2016 -

Zambia

DHS 2014

WESTERN PACIFIC 3 89 63 14

Cambodia

DHS 2014

ACT, artemisinin-based combination therapy; ANC, antenatal care; DHS, demographic and health survey; HH, households; IPT, intermittent preventive treatment; IRS, indoor residual spraying; ITN, insecticide-treated mosquito net; MIS, malaria indicator survey

117

Annex 4 – F. Estimated malaria cases and deaths, 2000–2015 WHO region Country/area AFRICAN Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Cabo Verde Central African Republic Chad Comoros Congo Côte d’Ivoire Democratic Republic of the Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mozambique Namibia Niger cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths <50 <10 4 800 000 22 000 2 700 000 7 400 27 000 70 7 200 000 39 000 2 800 000 10 000 6 300 000 20 000 490 <10 1 600 000 6 400 1 700 000 6 200 110 000 280 1 100 000 2 800 8 700 000 33 000 24 000 000 100 000 190 000 680 70 000 140 21 000 000 47 000 440 000 460 410 000 740 9 200 000 19 000 4 200 000 15 000 570 000 1 600 7 200 000 14 000 1 400 000 6 700 1 700 000 4 400 4 800 000 16 000 5 000 000 27 000 250 000 920 9 400 000 40 000 84 000 210 3 700 000 14 000 <10 <10 5 400 000 22 000 3 400 000 8 600 2 300 <10 7 400 000 32 000 2 200 000 6 800 8 000 000 21 000 220 <10 1 900 000 7 400 2 200 000 7 400 110 000 280 1 200 000 2 400 9 600 000 32 000 29 000 000 110 000 250 000 790 28 000 <100 4 800 000 9 300 230 000 310 410 000 570 8 300 000 16 000 4 100 000 12 000 190 000 730 5 200 000 12 000 1 500 000 4 100 1 300 000 3 300 4 100 000 9 700 6 100 000 23 000 310 000 1 000 9 300 000 25 000 70 000 180 4 600 000 13 000

2000 Lower Point Upper Lower

2005 Point Upper

3 300 000 17 000 1 700 000 5 600 12 000 1 5 500 000 36 000 1 900 000 7 300 4 600 000 16 000 210 1 100 000 5 100 810 000 4 400 65 000 9 750 000 2 100 6 500 000 27 000 17 000 000 87 000 120 000 540 21 000 3 1 100 000 450 290 000 330 310 000 520 6 800 000 15 000 3 200 000 12 000 350 000 1 200 5 500 000 8 700 950 000 5 400 69 000 9 3 300 000 12 000 3 900 000 21 000 31 000 510 7 400 000 31 000 47 000 6 1 900 000 11 000

6 400 000 28 000 3 900 000 9 500 77 000 240 9 000 000 55 000 4 000 000 12 000 8 200 000 26 000 1 400 2 300 000 8 200 2 800 000 9 000 190 000 620 1 500 000 3 600 11 000 000 40 000 31 000 000 140 000 270 000 870 170 000 590 34 000 000 74 000 630 000 590 540 000 990 12 000 000 25 000 5 200 000 20 000 790 000 2 000 9 300 000 16 000 2 100 000 8 700 5 600 000 18 000 6 400 000 20 000 6 200 000 34 000 730 000 1 200 12 000 000 51 000 150 000 520 5 700 000 20 000

4 100 000 16 000 2 400 000 6 600 1 000 5 700 000 25 000 1 500 000 3 600 5 900 000 15 000 97 1 200 000 5 700 870 000 3 800 66 000 9 840 000 1 100 6 800 000 25 000 20 000 000 88 000 180 000 570 18 000 1 200 000 280 140 000 78 310 000 160 6 500 000 8 400 2 800 000 8 800 96 000 240 3 700 000 3 500 980 000 2 800 22 000 5 3 100 000 4 800 4 800 000 18 000 44 000 280 7 600 000 17 000 45 000 5 2 400 000 9 100

6 700 000 28 000 4 400 000 11 000 5 600 9 100 000 49 000 3 000 000 7 600 10 000 000 27 000 590 2 700 000 9 400 3 900 000 11 000 190 000 650 1 700 000 3 100 13 000 000 39 000 38 000 000 150 000 310 000 1 000 41 000 12 000 000 29 000 340 000 430 530 000 820 10 000 000 20 000 5 700 000 16 000 290 000 1 000 6 800 000 13 000 2 000 000 5 300 3 500 000 12 000 5 100 000 13 000 7 400 000 29 000 890 000 1 400 11 000 000 32 000 110 000 400 7 000 000 19 000

118

WORLD MALARIA REPORT 2016

2010 Lower Point Upper Lower

2015 Point Upper

Method used

1 700 000 8 800 2 300 000 5 100 1 700 <10 7 300 000 22 000 1 100 000 2 000 4 200 000 6 500 66 980 000 3 700 850 000 3 400 96 000 12 530 000 390 6 900 000 17 000 21 000 000 60 000 80 000 180 59 000 11 480 000 230 100 000 69 310 000 120 7 600 000 7 300 3 400 000 8 000 95 000 170 2 500 000 2 100 1 100 000 1 400 380 000 49 5 100 000 4 700 4 200 000 12 000 32 000 260 7 700 000 11 000 2 200 3 400 000 9 700

<10 <10 2 400 000 14 000 3 200 000 6 800 3 400 9 400 000 29 000 1 900 000 5 300 5 700 000 11 000 140 <10 1 600 000 5 000 1 900 000 7 400 140 000 350 880 000 1 700 9 000 000 22 000 28 000 000 82 000 150 000 350 93 000 180 4 400 000 8 100 230 000 320 410 000 570 9 600 000 16 000 4 500 000 11 000 170 000 670 3 300 000 11 000 1 300 000 2 400 650 000 1 700 6 200 000 10 000 5 300 000 16 000 240 000 1 100 9 300 000 18 000 2 900 <10 6 000 000 14 000

3 300 000 20 000 4 200 000 8 900 7 500 11 000 000 45 000 2 800 000 5 700 7 300 000 15 000 300 2 500 000 6 400 3 500 000 11 000 210 000 720 1 400 000 2 300 11 000 000 28 000 35 000 000 110 000 220 000 460 140 000 380 10 000 000 25 000 420 000 460 550 000 870 12 000 000 20 000 5 900 000 14 000 250 000 970 4 200 000 11 000 1 700 000 3 100 980 000 3 500 7 300 000 13 000 6 300 000 20 000 700 000 1 500 11 000 000 24 000 3 800 8 600 000 20 000

1 800 000 9 200 2 300 000 4 200 370 4 500 000 10 000 890 000 1 500 3 500 000 4 900 770 000 2 500 720 000 3 200 2 000 490 000 260 5 900 000 9 800 14 000 000 26 000 75 000 160 38 000 7 820 000 240 140 000 100 320 000 110 4 800 000 4 600 3 600 000 6 700 55 000 150 3 800 000 2 500 670 000 970 1 500 000 180 2 400 000 1 800 6 100 000 16 000 50 000 250 6 300 000 8 100 17 000 2 800 000 6 600

0 0 3 100 000 14 000 3 200 000 6 000 710 <10 7 000 000 15 000 1 400 000 5 200 5 300 000 9 200 <50 <10 1 400 000 3 600 1 900 000 7 500 2 900 <10 800 000 1 600 7 900 000 14 000 19 000 000 42 000 180 000 340 65 000 130 2 800 000 4 900 400 000 390 420 000 630 7 300 000 13 000 4 600 000 9 900 160 000 680 6 500 000 12 000 1 100 000 2 000 2 400 000 6 000 3 300 000 7 200 7 500 000 21 000 260 000 1 200 8 300 000 15 000 22 000 <100 5 200 000 10 000

4 700 000 21 000 4 100 000 8 000 1 500 10 000 000 29 000 2 000 000 5 600 7 700 000 13 000 2 300 000 4 600 3 400 000 11 000 4 500 1 200 000 2 400 10 000 000 17 000 24 000 000 65 000 310 000 450 100 000 290 5 500 000 13 000 710 000 530 520 000 960 10 000 000 17 000 5 700 000 12 000 330 000 1 000 11 000 000 12 000 1 600 000 2 600 4 000 000 13 000 4 200 000 10 000 9 100 000 25 000 560 000 1 600 11 000 000 20 000 27 000 8 400 000 16 000

1 1b 2 2 2 2 1 1c 2 2 2 2 2 2 1 1a 2 2 2 2 1 1c 2 2 2 2 2 2 2 2 1 1c 1 1c 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 1c 2 2 2 2 1 2 2 2 1 2 2 2

WORLD MALARIA REPORT 2016

119

Annex 4 – F. Estimated malaria cases and deaths, 2000–2015 WHO region Country/area AFRICAN Nigeria Rwanda Sao Tome and Principe Senegal Sierra Leone South Africa South Sudan1 Swaziland Togo Uganda United Republic of Tanzania Zambia Zimbabwe AMERICAS Belize Bolivia (Plurinational State of) Brazil Colombia Dominican Republic Ecuador El Salvador French Guiana Guatemala Guyana Haiti Honduras Mexico Nicaragua Panama Peru cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths 1 600 33 000 950 000 370 200 000 1 300 110 000 770 4 200 56 000 35 000 7 72 000 9 56 000 7 500 38 000 1 100 99 000 1 700 0 49 000 <50 1 200 000 370 320 000 <50 1 600 <10 110 000 0 820 0 7 400 <50 98 000 <50 52 000 78 130 000 330 81 000 <50 8 100 0 49 000 <50 1 200 <10 140 000 <50 1 900 110 000 1 600 000 370 470 000 2 300 130 000 920 24 000 340 000 83 000 160 210 000 740 110 000 9 100 62 000 1 300 180 000 1 600 21 000 710 000 180 140 000 4 200 17 000 68 3 700 43 000 59 000 12 78 000 10 26 000 3 000 10 000 3 900 130 000 1 800 0 30 000 <50 820 000 180 190 000 <50 5 300 <50 19 000 0 73 0 6 000 <10 68 000 <50 89 000 120 140 000 370 37 000 <50 3 200 0 13 000 <10 4 300 <10 160 000 <10 2 000 62 000 930 000 180 240 000 6 500 21 000 82 16 000 190 000 140 000 240 220 000 780 52 000 3 600 17 000 4 600 200 000 cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths 41 000 000 160 000 950 000 3 400 40 000 110 1 100 000 4 600 1 200 000 10 000 23 000 1 200 000 5 600 630 1 900 000 5 500 9 300 000 39 000 8 400 000 22 000 3 000 000 11 000 78 000 23 54 000 000 200 000 3 400 000 5 200 47 000 110 2 300 000 6 500 2 000 000 12 000 39 000 530 2 000 000 6 100 1 900 <10 2 500 000 6 900 12 000 000 49 000 12 000 000 30 000 4 000 000 14 000 960 000 2 500 66 000 000 260 000 8 700 000 7 200 55 000 110 3 800 000 8 400 2 800 000 17 000 65 000 2 900 000 9 600 3 900 3 500 000 8 800 16 000 000 63 000 15 000 000 38 000 5 200 000 18 000 2 700 000 9 100 46 000 000 140 000 550 000 1 000 24 000 640 000 1 400 1 400 000 9 000 13 000 1 200 000 2 500 710 <10 2 100 000 5 200 10 000 000 24 000 7 400 000 7 800 2 200 000 3 400 85 000 25 59 000 000 190 000 1 600 000 3 600 30 000 <100 1 300 000 4 600 2 400 000 12 000 17 000 <100 1 800 000 4 000 970 2 800 000 6 900 13 000 000 35 000 9 700 000 20 000 2 900 000 7 900 990 000 2 500 74 000 000 240 000 3 500 000 5 200 39 000 2 200 000 6 400 3 400 000 16 000 21 000 2 600 000 7 400 1 300 3 500 000 8 900 17 000 000 45 000 12 000 000 26 000 3 700 000 10 000 3 000 000 9 200

2000 Lower Point Upper Lower

2005 Point Upper

120

WORLD MALARIA REPORT 2016

2010 Lower Point Upper Lower

2015 Point Upper

Method used

47 000 000 94 000 730 000 530 3 600 1 100 000 800 2 000 000 8 300 14 000 970 000 1 800 370 2 200 000 4 500 12 000 000 12 000 5 300 000 3 800 1 700 000 1 700 450 000 58 160 15 000 380 000 98 140 000 3 800 1 900 <50 2 200 7 800 38 000 6 87 000 11 16 000 1 200 1 100 440 50 000

59 000 000 130 000 1 100 000 3 000 4 900 <100 1 800 000 4 100 2 800 000 11 000 17 000 <100 1 800 000 3 200 530 <10 2 900 000 6 300 14 000 000 20 000 6 900 000 16 000 2 200 000 6 300 970 000 2 500 180 0 20 000 <50 440 000 98 180 000 <50 4 700 <50 2 100 0 <50 0 3 400 <10 12 000 <10 52 000 93 150 000 390 21 000 <50 1 300 0 1 400 <10 490 <10 63 000 <10

71 000 000 170 000 1 500 000 4 600 6 700 2 700 000 6 000 3 700 000 15 000 22 000 2 800 000 7 100 790 3 800 000 7 900 17 000 000 25 000 8 700 000 22 000 2 600 000 8 800 1 800 000 6 000 190 36 000 490 000 98 240 000 5 800 2 300 <50 9 200 32 000 76 000 180 250 000 850 28 000 1 500 1 700 530 78 000

42 000 000 78 000 2 800 000 320 2 600 950 000 640 1 200 000 4 000 9 000 970 000 1 400 190 2 000 000 2 700 4 500 000 4 300 3 900 000 3 100 2 200 000 1 900 610 000 69

61 000 000 110 000 3 500 000 3 000 3 400 <100 1 400 000 4 400 2 000 000 5 800 12 000 160 1 900 000 2 800 260 <10 2 500 000 4 200 8 500 000 12 000 5 300 000 17 000 2 800 000 7 100 960 000 2 400 <50 0

82 000 000 150 000 4 600 000 4 600 4 500 2 100 000 6 500 2 800 000 8 900 15 000 3 200 000 7 400 380 3 000 000 5 300 13 000 000 17 000 6 900 000 24 000 3 600 000 9 900 1 500 000 5 200

2 2 1 1c 1 1a 1 2 2 2 1 1a 2 2 1 1c 2 2 2 2 2 2 2 2 1 1c 1 1a

7 300 160 000 58 000 700 630 <10 470 7 500 14 000 42 000 5 5 400 530 3 500 590 120 000

9 900 <10 180 000 <50 79 000 <50 870 <10 680 0 <10 0 730 <10 11 000 <10 20 000 <50 69 000 180 7 200 <10 560 0 4 600 <10 660 0 150 000 <10

20 000 210 000 100 000 1 100 760 <10 1 500 25 000 28 000 100 000 370 9 600 630 5 800 710 180 000

1 1c 1 1a 1 1c 1 1c 1 1b 1 1b 1 1c 1 1c 1 1c 1 1c 1 1c 1 1b 1 1c 1 1a 1 1a

WORLD MALARIA REPORT 2016

121

Annex 4 – F. Estimated malaria cases and deaths, 2000–2015 WHO region Country/area AMERICAS Suriname Venezuela (Bolivarian Republic of) EASTERN MEDITERRANEAN Afghanistan Djibouti Iran (Islamic Republic of) Pakistan Saudi Arabia Somalia Sudan Yemen EUROPE Tajikistan SOUTH-EAST ASIA Bangladesh Bhutan Democratic People’s Republic of Korea India Indonesia Myanmar Nepal Thailand Timor-Leste WESTERN PACIFIC Cambodia China Lao People’s Democratic Republic Malaysia Papua New Guinea Philippines cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths 950 000 130 23 000 180 000 21 12 000 1 000 000 150 79 000 13 1 500 000 3 600 29 000 <50 260 000 630 13 000 <50 1 400 000 3 100 110 000 230 2 300 000 7 300 36 000 360 000 1 300 15 000 1 900 000 5 700 160 000 460 270 000 47 21 000 34 000 4 5 300 1 000 000 160 96 000 16 390 000 710 23 000 <50 50 000 120 5 600 <50 1 400 000 2 800 140 000 300 530 000 1 400 25 000 71 000 250 6 300 1 800 000 5 300 210 000 590 cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths 71 000 13 6 000 110 000 210 6 500 <50 150 000 0 24 000 000 36 000 4 000 000 4 600 1 400 000 3 100 110 000 60 220 000 810 250 000 470 150 000 430 7 300 76 000 11 1 900 120 000 250 2 000 <10 7 400 0 29 000 000 41 000 5 100 000 7 200 1 500 000 3 100 82 000 62 120 000 210 270 000 530 170 000 520 2 200 cases deaths 19 000 21 000 0 23 000 2 400 2 500 0 2 800 cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths 580 000 170 2 000 12 000 1 900 000 410 4 800 330 000 50 1 600 000 210 290 000 44 1 100 000 540 10 000 <50 13 000 <10 3 900 000 4 000 5 200 0 610 000 1 800 2 400 000 6 300 730 000 1 800 1 800 000 1 100 28 000 15 000 14 000 000 15 000 5 800 1 100 000 3 900 3 500 000 12 000 1 900 000 6 400 380 000 86 2 200 15 000 1 900 000 400 210 740 000 98 1 600 000 190 260 000 35 580 000 280 7 900 <50 16 000 <10 3 900 000 4 400 220 0 1 100 000 2 800 2 200 000 5 500 600 000 1 500 890 000 540 14 000 18 000 13 000 000 16 000 250 1 400 000 5 300 2 900 000 11 000 2 100 000 5 400 cases deaths cases deaths 12 000 40 000 11 18 000 <50 78 000 60 41 000 230 000 180 9 800 49 000 12 13 000 <10 78 000 52 28 000 210 000 140

2000 Lower Point Upper Lower

2005 Point Upper

40 000 18 000 000 3 100 2 200 000 600 970 000 150 71 000 20 45 000 800 130 000 22

300 000 31 000 000 64 000 6 600 000 9 900 2 100 000 6 800 160 000 100 1 000 000 820 500 000 1 300

6 800 19 000 000 3 500 3 600 000 660 1 000 000 160 50 000 16 33 000 210 190 000 30

8 200 36 000 000 63 000 7 300 000 14 000 2 100 000 6 300 130 000 110 500 000 210 370 000 1 000

122

WORLD MALARIA REPORT 2016

2010 Lower Point Upper Lower

2015 Point Upper

Method used

1 800 52 000 11 250 000 58 690 1 900 1 100 000 250 190 000 26 880 000 110 320 000 42 110

2 500 <10 78 000 72 340 000 200 1 600 <10 2 000 <10 1 500 000 1 700 <50 0 280 000 740 1 200 000 3 000 510 000 1 300 120 0 84 000 200 480 <10 16 000 0 21 000 000 33 000 5 900 000 8 900 1 600 000 3 000 38 000 <50 120 000 100 110 000 220 180 000 320 5 900 <10 69 000 170 6 400 <50 1 200 000 2 600 53 000 110

4 500 210 000 210 480 000 340 3 100 2 300 2 100 000 3 200 390 000 1 400 1 600 000 5 700 810 000 2 800 140

110 150 000 27 300 000 66 1 100 170 730 000 170 84 310 000 52 970 000 130 200 000 24

150 0 230 000 220 390 000 190 5 600 <50 180 <10 1 000 000 740 91 0 700 000 2 100 1 400 000 3 500 310 000 770 0 0

270 490 000 500 510 000 330 18 000 200 1 500 000 1 300 100 1 300 000 4 800 1 900 000 6 800 460 000 1 600

1 1a 1 1c 1 1c 1 1c 1 1b 1 1c 1 1b 1 1c 1 1c 1 1c 1 1b

69 000 8 440

100 000 360 530

7 100

8 400 <50 <50 0 7 700 0 13 000 000 24 000 1 300 000 1 900 240 000 490 24 000 <50 52 000 <50 120 <10 120 000 120 <50 0 88 000 <50 2 000 <10 900 000 1 200 13 000 <50

10 000

1 1c 1 1b 1 1b 1 1c 1 1c 1 1c 1 1c 1 1a 1 1c 1 1c 1 1b 1 1c 1 1b 1 1c 1 1c

15 000 16 000 000 2 800 4 600 000 830 1 100 000 180 25 000 36 000 100 90 000 14 140 000 22 5 200 48 000 6 5 900 890 000 130 35 000 5

18 000 31 000 000 63 000 7 700 000 17 000 2 200 000 6 100 58 000 370 000 100 150 000 420 220 000 560 6 300 97 000 350 7 100 1 600 000 5 100 75 000 240

7 200 9 900 000 1 500 990 000 160 170 000 27 17 000 16 000 97

8 600 18 000 000 47 000 1 600 000 3 600 340 000 980 35 000 150 000 160

95 000 17 68 000 1 900 650 000 140 9 200

150 000 200 110 000 2 300 1 200 000 2 300 17 000

WORLD MALARIA REPORT 2016

123

Annex 4 – F. Estimated malaria cases and deaths, 2000–2015 WHO region Country/area WESTERN PACIFIC Republic of Korea Solomon Islands Vanuatu Viet Nam REGIONAL SUMMARY African Americas Eastern Mediterranean European South-East Asia Western Pacific Total cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths cases deaths 146 716 840 588 411 1 717 470 397 4 718 800 884 19 000 21 533 000 4 705 2 585 200 363 177 290 310 594 760 225 899 390 787 840 2 345 820 838 8 768 200 14 440 21 000 0 30 246 500 45 250 3 739 500 8 360 271 020 410 856 728 308 872 300 1 064 830 3 605 520 1 450 22 348 800 38 400 23 000 41 817 300 83 350 5 287 100 16 190 381 954 020 1 204 220 154 924 807 458 152 1 309 268 214 4 897 410 809 2 400 23 957 700 4 587 1 658 600 260 186 750 185 464 022 216 738 490 667 360 1 677 673 722 8 404 120 14 480 2 500 0 36 201 400 52 352 2 295 000 4 429 265 319 183 739 343 290 668 490 903 200 2 342 782 1 340 20 322 250 38 240 2 800 46 580 400 85 140 2 984 900 8 410 362 901 622 1 036 330 cases deaths cases deaths cases deaths cases deaths 4 200 160 000 25 17 000 160 000 24 4 500 0 190 000 370 23 000 <50 210 000 430 5 100 230 000 650 31 000 250 000 780 1 300 180 000 28 19 000 32 000 5 1 400 0 220 000 420 26 000 <50 39 000 79 1 600 260 000 730 34 000 47 000 140

2000 Lower Point Upper Lower

2005 Point Upper

1  South Sudan became an independent State on 9 July 2011 and a Member State of WHO on 27 September 2011. South Sudan and Sudan have distinct epidemiological profiles comprising high-transmission and low-transmission areas respectively. For this reason, data up to June 2011 from the high-transmission areas of Sudan (10 southern states, which correspond to contemporary South Sudan) and low-transmission areas (15 northern states which correspond to contemporary Sudan) are reported separately. Cases: (1) Estimated from reported confirmed cases, (2) Estimated from parasite prevalence surveys Deaths: (1a) Reported deaths adjusted for completeness of death reporting, (1b) Reported deaths adjusted for case reporting completeness (1c) Estimated by applying case fatality rate to estimated cases, (2) Modelled from verbal autopsy data

124

WORLD MALARIA REPORT 2016

2010 Lower Point Upper Lower

2015 Point Upper

Method used

1 300 58 000 10 14 000 21 000 3 156 963 936 313 679 798 400 126 2 742 590 486 110 21 935 440 3 932 1 218 400 176 183 658 876 318 399

1 400 <10 70 000 130 18 000 <50 25 000 50 209 461 870 498 340 1 032 070 653 3 833 600 6 940 120 0 28 868 480 45 420 1 628 700 3 380 244 824 840 554 733

1 600 83 000 230 25 000 29 000 88 268 111 090 683 660 1 465 720 1 338 5 385 400 13 440 140 41 596 530 86 980 2 144 000 6 568 318 702 880 791 986

1 300 32 000 6 610 11 000

1 400 0 39 000 51 820 <10 13 000 <50 191 386 270 391 330 764 350 400 3 805 871 7 300 0 0 14 632 220 26 390 1 177 220 1 371 211 765 931 426 791

1 600 45 000 88 1 100 14 000

1 1b 1 1c 1 1c 1 1c

129 499 160 216 456 570 730 32 2 511 354 442 11 107 397 1 687 869 010 163 144 557 651 218 780

265 182 880 560 830 1 173 370 870 5 688 300 14 830 20 143 760 51 580 1 541 000 2 588 293 729 310 630 698

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Annex 4 – G.  Population at risk and reported malaria cases by place of care, 2015 WHO region Country/area UN population AFRICAN Algeria Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Republic Chad Comoros Congo Côte d'Ivoire Democratic Republic of the Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mayotte Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Sierra Leone South Africa South Sudan1 Swaziland Togo Uganda United Republic of Tanzania Mainland Zanzibar Zambia Zimbabwe AMERICAS Belize Bolivia (Plurinational State of) Brazil Colombia Dominican Republic Ecuador El Salvador French Guiana Guatemala 359 287 10 724 705 207 847 528 48 228 704 10 528 391 16 144 363 6 126 583 268 606 16 342 897 4 865 489 42 193 048 10 182 444 5 072 515 268 606 12 539 759 267 944 4 780 493 4 875 710 97 337 229 658 4 069 177 23 917 251 369 22 000 39 666 519 25 021 974 10 879 829 2 262 485 18 105 570 11 178 921 520 502 23 344 179 4 900 274 14 037 472 788 474 4 620 330 22 701 556 77 266 814 845 060 5 227 791 99 390 750 1 725 292 1 990 924 27 409 893 12 608 590 1 844 325 46 050 302 4 503 438 24 235 390 17 215 232 17 599 694 4 067 564 233 993 27 977 863 2 458 830 19 899 120 182 201 962 11 609 666 190 344 15 129 273 6 453 184 54 490 406 12 339 812 1 286 970 7 304 578 39 032 383 53 470 420 51 957 514 1 512 906 16 211 767 15 602 751 25 021 974 10 879 829 1 499 989 18 105 570 11 178 921 23 344 179 4 900 274 13 883 825 788 474 4 620 330 22 701 556 77 266 814 845 060 5 227 791 67 585 709 1 725 292 1 990 924 27 409 893 12 608 590 1 844 325 46 050 302 4 503 438 24 235 390 17 215 232 17 599 694 4 067 564 27 977 863 1 951 686 18 705 173 182 201 962 11 609 666 190 344 15 129 273 6 453 184 5 449 041 12 339 812 360 352 7 304 578 39 032 383 53 470 420 51 957 514 1 512 906 16 211 767 12 286 025 25 021 974 10 879 829 95 305 18 105 570 11 178 921 16 574 367 4 900 274 9 454 923 375 159 4 620 330 22 701 556 74 948 810 845 060 3 711 732 27 034 284 1 725 292 1 990 924 27 409 893 12 608 590 1 844 325 32 324 967 4 503 438 21 271 015 17 215 232 15 839 725 2 847 295 27 977 863 1 135 022 10 546 534 139 161 989 11 609 666 190 344 14 524 102 6 453 184 2 179 616 12 339 812 0 7 304 578 39 032 383 52 884 689 51 957 514 927 175 16 211 767 4 464 890 308 626 -

Population At risk (low + high) At risk (high) Number of people living in active foci

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WORLD MALARIA REPORT 2016

Public sector P C P

Private sector C P

Community level C

0 6 839 963 2 009 959 1 298 9 783 385 8 414 481 3 117 3 312 273 1 218 246 1 641 285 101 330 300 592 5 216 344 16 452 476 68 058 111 950 5 987 580 285 489 891 511 13 368 757 1 251 096 15 915 943 2 306 116 1 536 344 8 518 905 4 410 839 219 184 14 241 392 207 612 4 497 920 17 388 046 6 093 114 84 348 1 421 221 2 337 297 35 982 651 1 756 701 22 095 860 20 797 048 20 451 119 345 929 8 116 962 1 384 893

747 3 254 270 1 495 375 340 8 286 453 5 243 410 28 2 321 933 953 535 1 490 556 1 517 264 574 3 606 725 11 627 473 15 142 24 310 2 174 707 217 287 249 437 10 186 510 891 175 7 676 980 1 781 092 752 176 4 933 416 3 317 001 181 562 7 718 782 12 050 3 817 634 14 732 621 2 505 794 2 058 502 084 1 569 606 8 976 651 1 113 928 13 421 804 7 746 258 7 741 816 4 442 5 094 123 391 651

0 15 848 0 3 966 2 145 778 39 254 208 556 16 084 494 445 3 338 0 161 371 83 613 83 613 -

6 584 73 800 913 1 337 177 23 898 460 109 2 416 968 551 10 541 300 275 085 659 921 658 721 1 200 -

94 030 0 29 162 0 0 0 0 154 619 40 118 0 43 521 418 475 1 165 029 67 678 84 172 0 259 93 231 467 748 110 0 0

256 392 269 004 30 497 94 078 911 332 8 664 5 053 0 80 196 82 141 10 625 193 138 197 354 158 897 504 032 120 108 188 772 74 580 0 602 394 088 90 728

26 367 159 167 1 502 840 332 706 367 167 261 824 89 267 11 558 301 746

13 6 907 143 162 55 866 661 686 9 434 6 836

5 0 -

0 129 -

277 -

0 -

WORLD MALARIA REPORT 2016

127

Annex 4 – G.  Population at risk and reported malaria cases by place of care, 2015 WHO region Country/area UN population AMERICAS Guyana Haiti Honduras Mexico Nicaragua Panama Peru Suriname Venezuela (Bolivarian Republic of) EASTERN MEDITERRANEAN Afghanistan Djibouti Iran (Islamic Republic of) Pakistan Saudi Arabia Somalia Sudan Yemen EUROPEAN Tajikistan SOUTH-EAST ASIA Bangladesh Bhutan Democratic People's Republic of Korea India Indonesia Myanmar Nepal Thailand Timor-Leste WESTERN PACIFIC Cambodia China Lao People's Democratic Republic Malaysia Papua New Guinea Philippines Republic of Korea WESTERN PACIFIC Solomon Islands Vanuatu Viet Nam REGIONAL SUMMARY African Americas Eastern Mediterranean European South-East Asia Western Pacific Total 985 902 466 536 180 401 468 571 774 8 481 855 1 907 095 109 1 689 543 460 5 595 775 065 857 774 467 105 992 566 274 334 476 0 1 357 824 801 156 056 619 2 751 982 929 716 045 227 29 563 231 106 092 630 0 233 533 166 30 581 582 1 115 815 836 308 626 4 763 857 79 037 0 36 042 33 340 5 220 902 583 591 264 652 93 447 601 577 755 264 652 68 869 834 577 755 230 048 6 352 108 15 577 899 1 383 924 532 6 802 023 30 331 007 7 619 321 100 699 395 50 293 439 11 016 604 6 299 338 7 619 321 61 409 115 7 497 002 2 125 078 7 162 162 6 637 429 33 340 160 995 642 774 830 25 155 317 1 311 050 527 257 563 815 53 897 154 28 513 700 67 959 359 1 184 765 16 679 149 1 193 055 980 67 296 487 32 078 320 13 672 319 33 979 680 1 062 868 4 282 484 183 547 074 30 311 412 8 521 440 1 035 047 5 436 749 398 960 36 042 8 481 855 32 526 562 887 861 79 109 272 188 924 874 31 540 372 10 787 104 40 234 882 26 832 215 24 582 076 443 931 185 733 706 10 787 104 40 234 882 20 899 635 8 753 666 0 54 631 264 5 490 347 34 964 112 6 724 424 692 020 42 995 767 085 10 711 067 8 075 060 127 017 224 6 082 032 3 929 141 31 376 670 542 975 31 108 083 713 389 10 711 067 5 117 453 3 428 487 184 172 4 437 249 85 247 6 193 641 268 480 5 676 866 376 477 270 047 172 882 3 414 952 85 247 4 977 960 4 466 571 -

Population At risk (low + high) At risk (high) Number of people living in active foci

C = Confirmed P = Presumed 1  In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R21-en.pdf)

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WORLD MALARIA REPORT 2016

Public sector P C P

Private sector C P

Community level C

132 941 302 740 153 906 867 853 604 418 64 511 865 980 15 236 625 174

9 984 17 583 3 564 551 2 307 562 66 609 376 136 402

0 0 0 0 -

58 7 3 463 -

0 -

343 -

801 938 630 886 8 885 456 1 306 700 119 008 1 102 186 668 024

350 044 1 378 3 776 244 2 620 39 169 1 102 186 95 287

-

610 337 -

0 -

16 482 -

-

5

-

-

-

-

122 806 74 087 91 007 140 841 230 1 599 427 714 075 225 353 1 370 461 121 110

6 608 104 7 409 1 169 261 217 025 77 842 113 595 14 755 80

0 0 -

119 21 -

0 0 0 0

32 992 104 925 725 9 405 21

163 680 4 052 616 284 003 1 066 470 909 940 260 645 699

33 930 3 116 36 056 2 311 553 103 5 135 699

0 0 0 22 0

17 809 5 561 48 716 662

0 0 19 038 0 -

16 370 9 107 48 644 2 428 -

192 044 14 938 2 673 662

50 916 697 19 252

-

-

0 -

148 -

210 625 568 6 685 401 12 278 267 0 145 159 556 9 618 697 384 367 489

129 585 751 452 512 5 022 223 5 1 606 679 705 215 137 372 385

3 172 253 5 0 0 0 22 3 172 280

3 813 301 660 140 0 140 24 796 3 839 037

2 658 152 277 0 0 0 19 038 2 677 467

3 670 281 343 32 992 0 148 068 76 697 3 928 381

WORLD MALARIA REPORT 2016

129

Annex 4 – H.  Reported malaria cases by method of confirmation, 2000–2015 WHO region Country/area AFRICAN Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases 541 27 733 541 506 2 080 348 71 555 3 252 692 484 249 308 095 144 6 843 144 89 614 437 041 45 283 40 078 299 18 392 299 297 2 329 316 889 572 803 462 11 242 1 615 695 73 262 21 335 2 334 067 903 942 327 464 68 7 902 68 277 413 131 856 501 846 37 439 31 668 408 12 224 408 396 3 687 574 1 947 349 1 324 264 639 476 358 606 1 432 095 12 196 1 046 5 723 481 177 879 88 540 940 985 715 999 4 255 301 2 825 558 1 599 908 273 324 163 539 47 47 1 845 691 66 484 544 243 89 749 75 342 309 927 125 106 191 11 974 191 187 3 501 953 1 765 933 1 147 473 833 753 484 809 1 424 335 88 134 68 745 475 986 354 223 1 141 432 5 024 697 400 005 83 857 450 281 344 256 3 298 979 2 859 720 1 485 332 181 489 86 542 36 26 508 36 29 1 829 266 1 110 308 120 466 221 980 528 454 86 348 114 122 94 778 887 15 790 887 828 3 031 546 2 245 223 1 056 563 1 069 483 440 271 1 513 212 243 008 825 005 705 839 308 193 6 970 700 223 372 90 089 4 516 273 3 767 957 2 570 754 2 659 372 1 484 676 1 148 965 666 400 36 8 715 36 35 1 589 317 1 182 610 93 392 459 999 55 746 46 759 660 575 69 789 603 12 762 603 587 3 144 100 3 025 258 1 462 941 1 103 815 536 927 1 670 273 291 479 99 368 1 158 526 979 466 506 456 7 146 026 183 971 82 875 4 296 350 3 686 176 4 469 007 4 123 012 2 366 134 2 933 869 1 775 253 46 10 621 46 24 1 824 633 1 236 306 591 670 407 131 63 695 36 943 136 548 79 357 1 272 841 206 082 621 469 548 483 266 8 690 266 260 3 180 021 3 398 029 1 431 313 1 855 400 867 666 1 509 221 155 205 108 714 1 335 582 935 521 1 485 1 346 8 280 183 198 947 83 259 6 224 055 5 345 396 4 831 758 4 471 998 2 718 391 2 903 679 1 866 882 46 6 894 46 20 1 369 518 1 086 095 1 254 293 495 238 55 943 41 436 369 208 253 652 1 513 772 160 260 1 137 455 753 772 747 8 000 747 0 0 727 3 254 270 3 345 693 1 396 773 3 009 305 1 372 532 1 495 375 296 264 108 061 1 486 667 1 160 286 340 1 284 326 48 8 286 453 222 190 92 589 8 290 188 6 922 857 5 243 410 3 254 670 1 964 862 5 076 107 3 194 844 28 3 117 28 21 2 321 933 1 024 306 592 351 1 128 818 570 433 953 535 139 241 106 524 724 303 492 309 1 490 556 149 574 937 775 637 472 -

2000

2005

2010

2011

2012

2013

2014

2015

Algeria

Angola

Benin

Botswana

Burkina Faso

Burundi

Cabo Verde

Cameroon

Central African Republic

Chad

130

WORLD MALARIA REPORT 2016

WHO region Country/area AFRICAN Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases

2000

2005

2010

2011

2012

2013

2014

2015

Comoros

Congo

Côte d'Ivoire

Democratic Republic of the Congo

Equatorial Guinea

Eritrea

Ethiopia

Gabon

Gambia

Ghana

801 784 15 751 964 623 3 758 897 127 024 50 810 3 349 528 -

29 554 6 086 67 1 280 914 6 334 608 5 531 2 971 24 192 48 937 9 073 3 901 957 1 364 194 538 942 235 479 129 513 70 644 329 426 3 452 969 655 093 0 -

103 670 87 595 35 199 5 249 1 339 446 656 1 721 461 62 726 9 252 959 3 678 849 2 374 930 54 728 42 850 78 095 42 585 39 636 16 772 14 177 53 750 79 024 13 894 22 088 4 068 764 2 509 543 1 158 197 185 105 54 714 12 816 7 887 1 120 194 009 290 842 52 245 123 564 64 108 3 849 536 2 031 674 1 029 384 247 278 42 253 -

76 661 63 217 22 278 20 226 2 578 277 263 37 744 2 588 004 49 828 29 976 9 442 144 4 226 533 2 700 818 2 912 088 1 861 163 37 267 23 004 20 601 2 899 1 865 39 567 67 190 15 308 25 570 19 540 3 549 559 3 418 719 1 480 306 178 822 261 967 172 241 71 588 190 379 4 154 261 1 172 838 624 756 781 892 416 504 -

65 139 125 030 45 507 27 714 4 333 120 319 120 319 2 795 919 195 546 107 563 1 572 785 1 033 064 9 128 398 4 329 318 2 656 864 3 327 071 2 134 734 20 890 33 245 13 196 6 826 1 973 42 178 84 861 11 557 33 758 10 258 3 876 745 3 778 479 1 692 578 188 089 66 018 18 694 4 129 1 059 300 363 156 580 29 325 705 862 271 038 10 676 731 4 219 097 2 971 699 1 438 284 783 467 -

62 565 154 824 46 130 21 546 7 026 183 026 69 375 43 232 0 0 4 708 425 395 914 215 104 3 384 765 2 291 849 11 363 817 4 126 129 2 611 478 6 096 993 4 103 745 25 162 27 039 11 235 5 489 1 894 34 678 81 541 10 890 39 281 10 427 3 316 013 8 573 335 2 645 454 185 196 90 185 26 432 10 132 2 550 279 829 236 329 65 666 614 128 175 126 7 200 797 1 394 249 721 898 1 488 822 917 553 -

2 465 93 444 1 987 9 839 216 248 159 88 764 54 523 19 746 11 800 4 658 774 568 562 306 926 4 904 066 3 405 905 9 968 983 3 533 165 2 126 554 11 114 215 7 842 429 20 417 47 322 17 685 9 807 2 732 35 725 63 766 10 993 53 032 19 775 2 513 863 7 062 717 2 118 815 185 996 90 275 27 687 11 812 4 213 166 229 286 111 66 253 317 313 99 976 8 453 557 1 987 959 970 448 3 610 453 2 445 464 -

1 517 89 634 963 11 479 337 264 574 87 547 51 529 0 0 3 606 725 811 426 478 870 4 174 097 2 897 034 11 627 473 2 877 585 1 902 640 13 574 891 9 724 833 15 142 21 831 8 564 46 227 6 578 24 310 59 268 8 332 47 744 11 040 2 174 707 5 679 932 1 867 059 217 287 79 308 20 390 12 761 3 477 249 437 272 604 49 649 609 852 190 733 10 186 510 2 023 581 934 304 5 478 585 3 385 615 -

WORLD MALARIA REPORT 2016

131

Annex 4 – H.  Reported malaria cases by method of confirmation, 2000–2015 WHO region Country/area AFRICAN Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases 816 539 4 800 246 316 4 216 531 1 392 483 31 575 6 946 3 646 212 546 634 850 309 50 452 185 493 33 721 14 659 9 181 224 44 875 8 718 5 025 57 325 39 850 1 229 385 37 943 6 753 3 688 389 962 706 223 472 500 500 1 092 554 20 936 140 143 48 799 30 239 56 455 20 152 6 071 583 2 384 402 898 531 2 675 816 335 973 212 927 998 043 709 246 293 910 24 393 2 173 604 114 200 277 6 851 108 2 171 542 1 380 178 227 482 244 319 5 449 909 2 299 1 085 396 2 023 396 236 3 381 371 1 950 933 644 568 2 287 536 878 009 1 189 016 43 549 5 450 139 066 90 124 174 986 57 698 21 320 139 531 50 662 11 120 812 3 009 051 1 002 805 2 480 748 728 443 577 641 1 593 676 1 338 121 255 814 34 813 3 447 739 572 221 051 5 338 701 119 996 50 526 580 708 253 973 1 961 070 974 558 307 035 154 003 3 752 1 130 7 991 1 796 92 1 214 92 51 3 344 413 2 504 720 1 093 742 2 966 853 663 132 1 220 574 191 421 125 779 129 684 61 048 23 547 97 047 26 834 9 335 951 4 836 617 1 426 719 164 424 26 752 1 800 372 772 362 507 967 1 276 521 899 488 395 149 38 453 3 667 906 080 355 753 4 922 596 406 907 283 138 2 763 986 1 281 846 2 171 739 97 995 788 487 169 104 1 865 255 3 293 1 633 72 1 463 72 47 3 203 338 2 546 213 886 143 2 234 994 927 841 775 341 63 353 147 904 132 176 58 909 17 733 102 079 36 851 9 750 953 6 606 885 2 060 608 655 285 274 678 1 483 676 818 352 496 269 1 144 405 747 951 387 045 42 573 4 947 1 026 110 380 651 3 906 838 132 475 44 501 3 029 020 1 236 391 2 327 385 190 337 1 889 286 1 176 881 128 486 5 510 957 3 576 630 82 82 71 3 924 832 2 058 998 774 891 5 215 893 2 223 983 1 595 828 116 767 82 818 577 389 98 952 106 882 35 546 197 536 57 885 9 655 905 7 444 865 2 415 950 850 884 392 981 1 066 107 1 318 801 302 708 912 382 561 496 433 101 37 362 3 853 926 998 374 110 5 065 703 198 534 77 635 5 344 724 2 827 675 2 590 643 219 637 1 820 216 156 529 47 500 15 835 15 15 14 7 117 648 2 295 823 1 009 496 9 944 222 6 108 152 891 175 78 377 52 211 1 092 523 758 768 7 676 980 7 772 329 1 025 508 1 965 661 473 519 1 781 092 509 062 305 981 947 048 625 105 752 176 39 604 4 748 1 488 667 739 355 1 167 4 933 416 216 643 75 923 7 030 084 3 585 315 3 317 001 243 151 3 389 449 2 052 460 181 562 60 253 22 631 7 718 782 2 313 129 735 750 11 928 263 6 983 032 -

2000

2005

2010

2011

2012

2013

2014

2015

Guinea

Guinea-Bissau

Kenya

Liberia

Madagascar

Malawi

Mali

Mauritania

Mayotte

Mozambique

132

WORLD MALARIA REPORT 2016

WHO region Country/area AFRICAN Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases

2000

2005

2010

2011

2012

2013

2014

2015

Namibia

Niger

Nigeria

Rwanda

Sao Tome and Principe

Senegal

Sierra Leone

South Africa

South Sudan1

Swaziland

2 476 608 32 149 66 076 31 975 1 123 377 56 169 44 959 460 881 64 624 29 374 -

339 204 23 339 817 707 107 092 46 170 21 230 9 873 3 532 108 1 654 246 1 438 603 683 769 22 370 68 819 18 139 1 346 158 105 093 33 160 233 833 10 605 3 702 3 452 1 106 7 755 7 755 337 582 6 066 4 587 279 -

25 889 14 522 556 3 643 803 165 514 49 285 7 426 774 570 773 3 873 463 523 513 45 924 27 674 638 669 2 708 973 638 669 3 346 48 366 2 233 9 989 507 707 772 27 793 17 750 651 737 325 920 934 028 718 473 218 473 1 609 455 715 555 8 060 3 787 276 669 4 273 900 283 900 283 1 722 87 181 -

14 406 13 262 335 48 599 1 525 3 157 482 130 658 68 529 1 130 514 712 347 4 306 945 672 185 242 526 208 858 1 602 271 208 858 8 442 83 355 6 373 33 924 2 069 604 290 18 325 14 142 555 614 263 184 856 332 46 280 25 511 886 994 613 348 9 866 178 387 5 986 204 047 3 880 795 784 112 024 797 130 419 170

3 163 7 875 194 4 592 519 1 781 505 1 119 929 1 781 505 1 119 929 6 938 519 1 953 399 2 898 052 483 470 2 904 793 422 224 190 593 61 246 12 550 103 773 10 706 23 124 1 844 634 106 19 946 15 612 524 971 265 468 1 945 859 194 787 104 533 1 975 972 1 432 789 6 846 121 291 1 632 30 053 3 997 1 125 039 225 371 626 345 217 153

4 911 1 507 136 32 495 4 775 4 288 425 1 799 299 1 176 711 1 799 299 1 176 711 12 830 911 1 633 960 7 194 960 962 618 2 862 877 879 316 201 708 83 302 9 243 73 866 6 352 34 768 2 891 772 222 24 205 20 801 668 562 325 088 1 715 851 185 403 76 077 2 377 254 1 625 881 8 851 364 021 2 572 239 705 6 073 1 855 501 262 520 962 488 474 234

15 914 1 894 222 185 078 15 692 3 222 613 2 872 710 0 2 872 710 1 953 309 16 512 127 1 681 469 1 233 654 9 188 933 6 593 300 1 610 812 4 010 202 1 528 825 168 004 81 987 1 754 33 355 569 58 090 1 185 628 642 19 343 12 636 697 175 252 988 1 898 852 66 277 39 414 2 056 722 1 335 062 13 988 300 291 4 101 240 622 7 604 711 711 322

12 050 207 612 12 050 2 888 3 817 634 295 229 206 660 2 657 057 2 065 340 14 732 621 851 183 569 036 8 655 024 6 281 746 2 505 794 5 811 267 2 354 400 281 847 151 394 2 058 11 941 140 72 407 1 918 2 502 084 26 556 17 846 1 384 834 474 407 352 1 569 606 75 025 37 820 2 176 042 1 445 556 8 976 13 917 785 17 446 3 572 3 568 651 43 152 282

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Annex 4 – H.  Reported malaria cases by method of confirmation, 2000–2015 WHO region Country/area AFRICAN Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases 3 552 859 45 643 53 533 17 734 45 643 53 533 17 734 3 337 796 1 486 18 559 1 486 31 469 143 990 31 469 613 241 2 562 576 613 241 437 662 9 867 174 2 107 011 1 104 310 11 466 713 8 037 619 2 764 049 11 441 681 7 993 977 2 756 421 25 032 43 642 7 628 4 121 356 1 494 518 1 549 25 119 1 549 21 442 202 021 20 142 6 000 1 300 606 067 2 660 539 606 067 983 430 478 354 224 087 575 245 393 014 13 208 169 3 705 284 1 581 160 12 893 535 3 637 659 1 277 024 136 123 1 974 12 819 192 3 573 710 1 276 660 74 343 63 949 364 136 123 1 974 4 229 839 648 965 513 032 249 379 150 27 366 150 13 769 133 463 12 252 7 394 1 517 334 668 2 711 432 334 667 519 450 502 977 237 305 390 611 282 145 12 173 358 385 928 134 726 194 819 97 147 10 164 967 5 656 907 1 813 179 1 628 092 337 582 10 160 478 5 513 619 1 812 704 1 315 662 333 568 4 489 143 288 475 312 430 4 014 4 607 908 10 004 470 007 319 935 79 22 996 79 7 7 143 143 272 6 108 7 390 1 035 267 146 2 476 335 266 713 1 486 433 768 287 579 507 260 535 660 627 436 839 13 591 932 3 466 571 1 413 149 2 449 526 1 249 109 8 477 435 6 931 025 1 772 062 1 091 615 214 893 8 474 278 6 784 639 1 771 388 701 477 212 636 3 157 146 386 674 390 138 2 257 4 695 400 727 174 276 963 37 20 789 37 4 7 415 121 944 6 293 10 960 1 122 242 758 2 325 775 237 978 23 566 4 780 882 430 560 096 272 855 882 475 609 575 16 541 563 3 718 588 1 502 362 7 387 826 8 585 482 6 804 085 1 481 275 813 103 71 169 8 582 934 6 720 141 1 480 791 369 444 69 459 2 548 83 944 484 443 659 1 710 5 465 122 1 115 005 422 633 26 25 351 26 4 7 342 133 260 6 272 10 789 1 070 178 546 1 873 518 174 048 19 500 3 719 1 130 251 621 119 310 207 1 135 581 820 044 13 724 345 2 048 185 578 289 7 060 545 3 053 650 7 403 562 727 130 572 289 17 740 207 107 728 7 399 316 592 320 571 598 17 566 750 106 609 4 246 134 810 691 173 457 1 119 5 972 933 5 964 354 4 077 547 535 983 1 420 894 535 931 19 24 122 19 0 7 401 124 900 7 401 143 415 1 658 976 142 031 11 043 1 384 1 113 928 621 119 305 727 1 135 581 808 200 13 421 804 3 684 722 1 248 576 12 126 996 5 889 086 7 746 258 673 223 412 702 16 620 299 3 830 030 2 550 7 741 816 532 118 411 741 16 416 675 3 827 749 4 442 141 105 961 203 624 2 281 2 550 5 094 123 7 207 500 4 184 661 391 651 1 384 893 391 651 180 13 26 367 13 4 6 907 159 167 6 907 143 162 1 488 072 139 844 14 655 3 205 4 949

2000

2005

2010

2011

2012

2013

2014

2015

Togo

Uganda

United Republic of Tanzania

Mainland

Zanzibar

Zambia

Zimbabwe

AMERICAS

Belize

Bolivia (Plurinational State of)

Brazil

134

WORLD MALARIA REPORT 2016

WHO region Country/area AMERICAS Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases

2000

2005

2010

2011

2012

2013

2014

2015

Colombia

Dominican Republic

Ecuador

El Salvador

French Guiana

Guatemala

Guyana

Haiti

Honduras

Mexico

144 432 478 820 144 432 1 233 427 297 1 233 104 528 544 646 104 528 753 279 072 753 3 708 48 162 3 708 53 311 246 642 53 311 24 018 209 197 24 018 16 897 21 190 16 897 35 125 175 577 35 125 7 390 2 003 569 7 390 -

121 629 493 562 121 629 3 837 397 108 3 837 17 050 358 361 17 050 67 102 479 67 3 414 32 402 3 414 39 571 178 726 39 571 38 984 210 429 38 984 21 778 3 541 506 21 778 15 943 153 474 15 943 2 500 2 967 1 559 076 2 967 -

117 650 521 342 117 637 13 3 414 469 052 2 482 26 585 932 1 888 481 030 1 888 7 800 24 115 256 24 7 1 632 14 373 688 944 7 384 235 075 7 384 2 000 0 22 935 212 863 22 935 84 153 270 427 84 153 9 685 152 961 9 685 4 000 1 226 1 192 081 1 226 7

64 436 396 861 60 121 21 171 4 188 1 616 421 405 1 616 56 150 1 233 460 785 1 233 14 16 100 883 15 1 1 6 1 209 14 429 505 704 6 817 195 080 6 817 29 506 201 693 29 471 0 35 32 969 184 934 32 969 7 618 152 451 7 465 4 000 45 1 130 1 035 424 1 130 6

60 179 346 599 50 938 70 168 9 241 952 415 808 952 90 775 558 459 157 558 14 19 124 885 19 6 900 13 638 401 499 5 346 186 645 5 346 0 0 31 656 196 622 31 601 55 25 423 167 726 25 423 46 6 439 155 165 6 439 4 000 10 842 1 025 659 842 9

51 722 284 332 44 293 42 723 7 403 579 431 683 579 71 000 378 397 628 378 10 7 103 748 7 1 875 22 327 324 551 6 214 153 731 6 214 0 0 31 479 205 903 31 479 0 0 26 543 165 823 20 957 5 586 5 428 144 436 5 364 237 64 499 1 017 508 499 0 0 4

40 768 325 713 36 166 77 819 4 602 496 362 304 496 54 425 241 370 825 241 8 106 915 8 2 448 14 651 187 261 4 931 264 269 4 931 50 025 754 12 354 142 843 12 354 0 0 17 696 134 766 10 893 126 637 6 803 3 380 151 420 3 380 1 427 102 664 900 578 664 0 0 8

55 866 316 451 48 059 11 983 3 535 7 785 661 316 947 661 50 220 30 686 261 824 686 68 9 89 267 9 0 0 6 434 11 558 272 162 6 836 295 246 5 538 6 500 1 298 2 9 984 132 941 9 984 0 0 17 583 69 659 5 224 233 081 12 359 3 564 150 854 3 555 3 052 20 0 551 867 853 551 0 0 34

WORLD MALARIA REPORT 2016

135

Annex 4 – H.  Reported malaria cases by method of confirmation, 2000–2015 WHO region Country/area AMERICAS Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases 23 878 509 443 23 878 1 036 149 702 1 036 68 321 1 483 816 68 321 11 361 63 377 11 361 29 736 261 866 29 736 203 911 257 429 94 475 4 667 19 716 1 732 778 19 716 7 422 3 337 054 82 526 6 608 6 608 1 872 6 642 516 313 6 642 3 667 208 582 3 667 87 699 1 438 925 87 699 9 131 59 855 9 131 45 049 420 165 45 049 326 694 338 253 116 444 2 469 1 913 413 18 966 1 674 895 18 966 4 570 4 022 823 4 776 274 127 826 290 1 059 715 878 1 059 855 692 535 914 692 18 500 0 418 141 038 418 31 546 744 627 31 545 23 1 1 771 16 533 1 574 541 138 45 155 400 495 45 155 392 463 524 523 69 397 1 010 1 010 3 031 614 817 3 031 1 184 4 281 356 4 281 346 220 870 279 724 19 721 1 941 944 723 1 941 1 912 925 521 904 925 14 201 354 116 588 354 0 0 25 039 702 894 25 005 58 34 795 15 135 751 1 025 20 45 824 382 303 45 824 482 748 531 053 77 549 0 0 230 124 3 239 530 470 3 239 1 529 4 065 802 4 168 648 287 592 518 709 46 997 2 788 1 062 827 2 788 2 719 1 235 536 278 1 235 16 444 0 844 107 711 844 0 0 31 570 758 723 31 436 562 569 17 464 306 4 008 50 52 803 410 663 52 803 391 365 511 408 54 840 0 0 27 1 410 22 3 1 629 479 655 1 629 0 0 842 4 285 449 4 497 330 250 526 410 949 40 255 3 406 1 186 179 3 406 0 0 3 324 1 194 519 993 1 196 19 029 705 93 624 705 0 0 43 139 863 790 48 719 858 729 13 693 530 6 043 199 78 643 476 764 78 643 319 742 507 145 39 263 0 0 1 684 7 189 1 684 1 373 385 172 1 373 853 3 472 727 3 933 321 196 078 628 504 85 677 2 513 1 309 783 2 513 2 479 1 163 605 357 1 163 15 620 0 874 80 701 874 0 0 65 252 864 413 65 252 1 634 400 17 608 98 15 489 303 90 708 522 617 90 708 290 079 1 028 932 122 724 155 919 22 558 9 439 39 284 9 439 1 243 468 513 1 243 867 3 666 257 4 343 418 193 952 779 815 81 197 2 305 1 249 752 2 305 2 254 2 307 604 418 2 307 29 562 64 511 562 0 0 16 66 609 865 980 66 609 0 376 15 083 345 153 31 274 136 402 625 174 136 402 1 594 350 044 538 789 86 895 1 378 610 337 799 20 549 579 632 3 776 244 4 619 980 137 401 691 245 64 612 2 620 1 306 700 2 620 2 537

2000

2005

2010

2011

2012

2013

2014

2015

Nicaragua

Panama

Peru

Suriname

Venezuela (Bolivarian Republic of)

EASTERN MEDITERRANEAN

Afghanistan

Djibouti

Iran (Islamic Republic of)

Pakistan

Saudi Arabia

136

WORLD MALARIA REPORT 2016

WHO region Country/area EASTERN MEDITERRANEAN Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases

2000

2005

2010

2011

2012

2013

2014

2015

Somalia

Sudan

Yemen

10 364 4 332 827 368 557 1 394 495 1 394 495 233 785 233 785 19 064 437 838 360 300 55 599 5 935 76 445 5 935 204 428 90 582 2 031 790 2 031 790 256 993 1 752 763 245 612 -

28 404 47 882 12 516 2 515 693 628 417 200 560 472 970 44 150 216 197 216 197 2 309 290 418 220 025 48 121 1 825 60 152 1 825 11 507 11 315 1 816 569

24 553 20 593 5 629 200 105 18 924 1 465 496 625 365 1 653 300 95 192 198 963 645 463 78 269 97 289 28 428 112 173 523 112 1 91 227 308 326 20 519 152 936 35 354 487 54 709 436 13 520 25 147 13 520 1 599 986

41 167 26 351 1 627 35 236 1 724 1 214 004 506 806 2 222 380 142 147 645 093 60 207 108 110 30 203 78 173 367 78 13

35 712 37 273 6 817 964 698 526 931 2 000 700 165 678 685 406 68 849 150 218 41 059 33 209 239 33 15

9 135 67 464 7 407 989 946 592 383 1 800 000 149 451 723 691 63 484 157 457 39 294 14 213 916 14 7

26 174 64 480 11 001 1 207 771 579 038 788 281 489 468 97 089 643 994 51 768 141 519 34 939 7 200 241 7 5

39 169 100 792 20 953 1 102 186 586 827 95 287 529 932 38 254 111 787 30 728 5 5

EUROPEAN

Tajikistan

SOUTH-EAST ASIA Presumed and confirmed Microscopy examined Confirmed with microscopy Bangladesh RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy Bhutan RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Democratic Confirmed with microscopy People's Republic RDT examined of Korea Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined India Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases 51 773 29 518 3 864 10 216 6 608 270 253 253 887 74 755 78 719 69 093 20 232 4 016 1 866 3 249 1 612 119 849 35 675 19 171 46 482 53 713 31 541 5 885 1 998 6 967 4 996 129 207 82 45 48 104 44 481 42 512 31 632 33 586 26 149 194 82 45 48 84 47 938 20 0 23 29 70 16 760 23 537 15 673 11 212 7 409 26 513 39 238 71 453 38 201 29 272 16 760 21 850 14 407 10 535 7 010 0 0 0 61 348 0 0 0 12 205 0 0 0 1 310 656 1 067 824 881 730 1 102 205 1 169 261 108 969 109 033 790 113 109 094 124 066 331 121 141 970 660 1 310 656 1 067 824 881 730 1 102 205 1 169 261 10 500 384 13 125 480 14 782 104 14 562 000 19 699 260 422 447 417 819 1 833 256 252 027 217 025 962 090 1 429 139 1 447 980 1 300 835 1 224 504 422 447 417 819 343 527 252 027 217 025 250 709 471 586 260 181 249 461 342 946 -

86 790 375 104 120 792 108 679 429 1 816 569 1 599 986 - 10 600 000 315 394 465 764 1 178 457 1 335 445 315 394 465 764 255 734 -

Indonesia

WORLD MALARIA REPORT 2016

137

Annex 4 – H.  Reported malaria cases by method of confirmation, 2000–2015 WHO region Country/area SOUTH-EAST ASIA Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy RDT examined Confirmed with RDT Imported cases 581 560 381 610 120 083 48 686 100 063 7 981 78 561 4 403 739 78 561 15 212 15 212 203 164 122 555 51 320 18 167 11 122 279 903 256 273 40 106 874 894 1 832 802 12 705 1 751 883 225 535 79 839 36 596 516 041 437 387 165 737 178 056 188 930 5 050 29 782 2 524 788 29 782 130 679 97 781 43 093 67 036 88 991 26 914 58 791 22 522 100 106 3 814 715 21 936 2 632 30 359 156 954 13 615 573 788 1 425 997 5 569 1 788 318 267 132 92 957 46 342 581 871 12 125 693 124 275 374 103 285 729 878 317 523 96 383 102 977 3 115 17 887 779 32 480 1 695 980 22 969 81 997 9 511 119 072 109 806 40 250 85 643 7 887 49 356 90 175 14 277 103 035 35 079 7 855 7 115 784 4 990 23 047 150 512 4 524 127 790 16 276 6 650 1 619 074 6 650 831 1 379 787 198 742 75 985 20 820 17 971 19 106 301 031 18 560 567 452 312 689 91 752 795 618 373 542 71 752 95 011 1 910 25 353 1 504 24 897 1 354 215 14 478 96 670 10 419 36 064 82 175 19 739 127 272 57 423 86 526 13 792 130 186 43 631 4 498 9 189 270 3 367 17 904 213 578 6 226 7 743 11 609 5 306 1 600 439 5 306 1 142 1 151 343 184 466 70 603 27 391 13 457 9 617 327 060 9 552 480 586 265 135 75 220 1 158 831 405 366 70 272 152 780 1 659 22 472 433 32 569 1 130 757 32 569 6 148 64 318 5 211 117 599 45 553 80 212 10 124 108 974 30 352 2 678 6 918 657 2 603 2 399 46 819 223 934 13 232 145 425 32 970 4 725 1 566 872 4 725 924 878 371 156 495 67 202 228 857 82 993 8 154 332 063 7 133 315 509 138 473 25 215 1 162 083 226 058 38 113 100 336 1 197 32 989 777 41 362 1 830 090 33 302 1 042 56 192 1 025 121 991 24 130 54 716 4 598 94 600 16 711 4 121 5 554 960 4 086 4 007 41 385 202 422 10 036 133 337 28 095 3 850 1 576 012 3 850 865 1 125 808 139 972 70 658 468 380 209 336 7 720 317 360 5 826 1 523 688 152 195 93 842 11 952 797 071 140 243 122 874 127 130 1 469 48 444 37 921 1 756 528 37 921 342 30 515 342 86 592 0 26 278 48 591 5 288 92 525 19 864 2 921 4 403 633 2 921 2 864 48 071 133 916 8 018 160 626 40 053 3 923 1 443 958 3 923 766 644 688 83 257 68 114 475 654 213 068 4 903 286 222 3 618 28 598 1 285 77 842 52 076 6 569 661 999 71 273 345 113 595 63 946 1 112 49 649 725 517 14 755 1 358 953 14 135 10 888 0 9 890 80 30 275 80 90 835 0 33 930 49 357 7 423 114 323 26 507 3 116 4 052 588 3 088 3 055 36 056 110 084 4 167 173 919 31 889 0 2 311 1 066 470 2 311 435 553 103 112 864 64 719 541 760 233 068 5 135 224 843 4 988 35 789 134 18

2000

2005

2010

2011

2012

2013

2014

2015

Myanmar

Nepal

Thailand

Timor-Leste

WESTERN PACIFIC

Cambodia

China

Lao People's Democratic Republic

Malaysia

Papua New Guinea

Philippines

138

WORLD MALARIA REPORT 2016

WHO region Country/area Western pACIFIC Presumed and confirmed Microscopy examined Confirmed with microscopy Republic of Korea RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy Solomon Islands RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy Vanuatu RDT examined Confirmed with RDT Imported cases Presumed and confirmed Microscopy examined Confirmed with microscopy Viet Nam RDT examined Confirmed with RDT Imported cases

2000

2005

2010

2011

2012

2013

2014

2015

4 183 368 913 300 806 68 107 33 779 31 668 6 768 274 910 2 682 862 74 316 -

1 369 393 288 316 898 76 390 34 912 61 092 9 834 84 473 2 728 481 19 496 -

1 772 1 772 56 95 006 212 329 35 373 17 300 4 331 16 831 29 180 4 013 10 246 4 156 54 297 2 760 119 17 515 7 017 -

838 838 64 80 859 182 847 23 202 17 457 3 455 5 764 19 183 2 077 12 529 2 743 45 588 2 791 917 16 612 491 373 -

555 555 47 57 296 202 620 21 904 13 987 2 479 3 435 16 981 733 16 292 2 702 43 717 2 897 730 19 638 514 725 -

443 443 50 53 270 191 137 21 540 26 216 4 069 2 381 15 219 767 13 724 1 614 35 406 2 684 996 17 128 412 530 -

638 638 78 51 649 173 900 13 865 26 658 4 539 982 18 135 190 17 435 792 27 868 2 357 536 15 752 416 483 -

699 699 65 50 916 124 376 14 793 40 750 9 205 697 4 870 15 9 794 408 0 19 252 2 204 409 9 331 459 332 -

RDT, rapid diagnostic test Cases reported before 2000 can be presumed and confirmed cases, or only confirmed cases, depending on the country. 1  In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R21-en.pdf)

world malaria report 2016

139

Annex 4 – I. Reported malaria cases by species, 2000–2015 WHO region Country/area AFRICAN Algeria Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other 27 733 261 277 2 080 348 71 555 3 428 846 6 843 144 0 0 89 614 442 246 20 977 19 101 967 484 889 0 18 392 242 57 2 329 316 803 462 11 242 1 667 622 0 0 0 2 910 545 7 902 68 0 0 277 413 131 856 507 617 14 770 16 898 29 554 1 280 914 6 337 168 2 844 110 64 056 7 506 1 567 5 12 224 7 4 1 4 591 529 1 432 095 12 196 1 046 0 6 037 806 5 590 736 47 47 0 0 1 845 691 66 484 743 471 159 976 33 791 528 880 446 656 1 721 461 10 568 756 0 0 0 83 639 53 813 0 96 792 9 785 3 989 57 11 974 4 0 0 4 469 357 1 565 487 68 745 0 0 1 141 432 0 5 446 870 4 768 314 26 508 7 0 0 3 060 040 221 980 528 454 135 248 21 387 334 557 277 263 37 744 0 0 2 607 856 12 018 784 0 0 0 40 704 22 466 0 97 479 10 263 4 932 19 15 790 48 11 0 4 849 418 1 875 386 0 0 0 308 193 7 852 299 4 228 015 8 715 1 0 0 2 865 319 468 986 730 364 168 043 43 681 637 1 189 117 640 120 319 0 0 3 423 623 11 993 189 0 0 0 45 792 15 169 0 138 982 12 121 9 204 346 12 762 14 2 0 5 273 305 2 041 444 506 456 7 857 296 7 384 501 10 621 22 0 0 3 652 609 491 074 1 272 841 185 779 45 669 72 363 209 169 43 232 0 0 5 982 151 14 871 716 0 0 0 44 561 13 129 0 134 183 12 482 7 361 1 350 8 690 5 0 0 6 134 471 1 955 773 1 485 1 346 0 9 274 530 7 622 162 6 894 26 0 0 3 709 906 625 301 295 088 0 0 1 737 195 103 545 2 203 0 0 290 346 66 323 0 0 6 418 571 3 712 831 0 0 14 647 380 57 129 17 452 0 121 755 23 787 6 780 94 8 000 0 0 0 6 839 963 2 009 959 1 298 326 0 9 783 385 8 414 481 3 117 7 0 0 3 312 273 592 351 0 1 218 246 598 833 0 1 641 285 101 330 1 300 0 0 300 592 51 529 0 0 5 216 344 3 375 904 0 0 16 452 476 68 058 111 950 14 510 4 780 21

2000

2005

2010

2011

2012

2013

2014

2015

Angola

Benin

Botswana

Burkina Faso

Burundi

Cabo Verde

Cameroon

Central African Republic

Chad

Comoros

Congo

Côte d'Ivoire Democratic Republic of the Congo Equatorial Guinea

Eritrea

140

WORLD MALARIA REPORT 2016

WHO region Country/area AFRICAN Ethiopia Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other

2000

2005

2010

2011

2012

2013

2014

2015

Gabon

Gambia

Ghana

Guinea

Guinea-Bissau

Kenya

Liberia

Madagascar

Malawi

Mali

Mauritania

Mayotte

Mozambique

Namibia

Niger

127 024 50 810 0 3 349 528 816 539 4 800 0 246 316 4 216 531 1 417 112 3 646 212 546 634 -

4 727 209 374 335 158 658 5 949 294 348 70 644 0 329 426 3 452 969 850 309 50 452 0 204 555 9 181 224 66 043 44 875 0 1 260 575 3 688 389 962 706 223 472 500 339 204 889 986 74 129 0 1 878

5 420 110 732 776 390 252 0 233 770 2 157 720 2 015 492 062 64 108 0 5 056 851 926 447 0 102 937 1 092 554 20 936 0 195 006 7 557 454 898 531 0 3 087 659 212 927 0 0 719 967 6 851 108 3 324 238 250 073 2 023 138 3 19 6 097 263 878 009 0 39 855 556 0 0 10 616 033 601 455 0 17 123

5 487 972 814 547 665 813 178 822 261 967 190 379 0 5 067 731 593 518 0 31 238 1 276 057 5 450 0 300 233 13 127 058 1 002 805 0 2 887 105 577 641 0 805 701 5 734 906 2 628 593 162 820 1 214 38 2 0 7 059 112 663 132 0 74 407 335 0 0 3 637 778 757 449 0 21 370

5 962 646 946 595 745 983 238 483 862 442 271 038 0 12 578 946 3 755 166 0 0 1 220 574 191 421 0 237 398 12 883 521 1 453 471 0 2 441 800 1 407 455 0 980 262 6 528 505 2 171 739 172 374 1 463 21 2 2 6 170 561 927 841 0 10 844 194 0 0 5 915 671 817 072 0 25 270

9 243 894 1 687 163 958 291 256 531 26 432 0 0 889 494 175 126 0 8 444 417 1 629 198 0 0 775 341 63 353 0 0 238 580 14 677 837 2 335 286 0 2 202 213 1 244 220 0 0 1 071 310 5 787 441 2 849 453 135 985 82 9 0 8 200 849 2 998 874 0 34 002 136 0 0 5 533 601 1 426 696 0 5 102

7 457 765 1 250 110 868 705 256 183 26 117 0 603 424 99 976 0 10 636 057 3 415 912 0 0 1 595 828 660 207 0 309 939 15 142 723 2 808 931 0 2 433 086 864 204 0 0 977 228 7 703 651 2 905 310 0 2 590 643 188 194 15 1 0 0 12 240 045 7 117 648 0 186 972 15 914 0 0 7 014 724 3 828 486 0 39 066

5 987 580 1 188 627 678 432 285 489 891 511 240 382 0 13 368 757 4 319 919 0 0 1 251 096 810 979 0 15 915 943 1 499 027 0 2 306 116 931 086 0 0 1 536 344 8 518 905 3 585 315 0 4 410 839 219 184 14 241 392 7 718 782 0 207 612 12 050 0 0 4 497 920 2 267 867 0 0

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Annex 4 – I. Reported malaria cases by species, 2000–2015 WHO region Country/area AFRICAN Nigeria Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other 2 476 608 66 250 1 134 587 44 959 0 460 881 64 624 29 374 0 0 0 3 552 859 81 442 17 734 0 81 442 17 734 0 3 337 796 18 559 20 1 466 3 532 108 2 409 080 73 050 1 418 091 38 746 0 243 082 3 702 0 0 7 755 337 582 10 374 279 0 0 437 662 10 869 875 1 082 223 0 22 086 16 740 283 7 628 0 16 679 237 61 046 7 628 0 4 121 356 1 494 518 25 119 32 1 517 0 3 873 463 523 513 0 2 708 973 638 669 0 58 961 2 219 14 0 1 043 632 343 670 0 2 327 928 218 473 0 276 669 2 181 0 5 900 283 1 722 87 0 0 1 419 928 224 080 0 7 15 332 293 1 565 348 15 812 0 15 388 319 2 338 0 0 15 116 242 272 077 2 338 0 0 4 229 839 912 618 249 379 0 27 366 0 149 0 5 221 656 1 602 271 208 858 0 117 279 6 363 4 6 900 903 277 326 0 1 150 747 25 511 0 382 434 6 906 14 0 795 784 112 024 0 797 0 0 893 588 237 282 0 23 12 522 232 231 873 0 0 15 299 205 4 489 0 0 14 843 487 455 718 4 489 0 0 4 607 908 480 011 319 935 0 0 22 996 0 72 0 11 789 970 3 095 386 483 470 126 897 10 700 1 0 897 943 281 080 1 2 579 296 1 537 322 0 152 561 3 109 5 7 1 125 039 626 0 0 1 311 047 260 526 0 9 16 845 771 2 662 258 0 0 14 513 120 2 730 0 201 13 976 370 536 750 2 730 0 201 4 695 400 727 174 276 963 0 20 789 0 33 0 21 659 831 3 064 585 962 618 108 634 9 242 1 0 1 119 100 345 889 0 0 2 576 550 1 701 958 0 603 932 8 645 0 0 1 855 501 669 0 1 1 442 571 272 847 0 8 26 145 615 1 502 362 14 650 226 1 673 0 52 14 122 269 527 957 1 673 0 52 5 465 122 1 115 005 422 633 0 25 351 0 22 0 19 555 575 4 178 206 1 623 176 0 0 91 445 1 754 0 0 1 079 536 265 624 0 0 2 647 375 1 374 476 0 0 543 196 11 563 0 0 711 389 0 0 1 756 700 1 130 234 0 0 19 201 136 3 631 939 0 0 25 190 092 2 235 0 106 764 24 880 179 0 0 106 609 309 913 2 235 0 155 7 859 740 1 420 946 535 931 0 24 122 0 18 0 17 388 046 6 093 114 84 348 2 055 0 0 1 421 221 491 901 0 0 2 337 297 1 483 376 0 0 35 982 554 0 1 651 157 0 0 1 756 701 1 113 910 0 0 22 095 860 7 137 662 0 0 20 797 048 413 615 0 175 20 451 119 411 741 0 345 929 1 874 0 175 8 116 962 1 384 893 391 651 0 0 26 367 0 9 0

2000

2005

2010

2011

2012

2013

2014

2015

Rwanda

Sao Tome and Principe

Senegal

Sierra Leone

South Africa

South Sudan1

Swaziland

Togo

Uganda

United Republic of Tanzania

Mainland

Zanzibar

Zambia

Zimbabwe AMERICAS Belize

142

WORLD MALARIA REPORT 2016

WHO region Country/area AMERICAS Bolivia (Plurinational State of) Brazil Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other

2000

2005

2010

2011

2012

2013

2014

2015

Colombia

Dominican Republic

Ecuador

El Salvador

French Guiana

Guatemala

Guyana

Haiti

Honduras

Mexico

Nicaragua

Panama

Peru

Suriname

143 990 2 437 28 932 0 2 562 576 124 939 478 212 932 478 820 50 476 92 702 0 427 297 1 225 7 0 544 646 48 974 55 624 0 279 072 9 744 0 48 162 3 051 657 214 246 642 1 474 50 171 36 209 197 12 188 11 694 0 21 190 16 897 0 0 175 577 1 425 33 679 0 2 003 569 131 7 259 0 509 443 1 369 22 645 0 149 702 45 991 0 1 483 816 20 618 47 690 13 63 377 10 608 1 673 811

208 021 1 031 19 062 0 2 660 539 147 150 450 687 211 493 562 41 781 78 157 0 397 108 3 829 8 0 358 361 2 212 14 836 0 102 479 2 65 0 32 402 1 649 1 637 71 178 726 1 017 38 641 48 210 429 15 558 21 255 1 291 3 541 506 21 778 0 0 153 474 976 15 011 0 1 559 076 22 2 945 0 516 313 1 114 5 498 0 208 582 764 2 901 0 1 438 925 14 954 72 611 59 855 6 877 1 611 589

140 857 1 557 13 694 0 2 711 433 47 406 283 435 183 521 342 32 900 83 255 48 495 637 2 480 2 0 488 830 258 1 630 0 115 256 0 17 0 14 373 987 476 548 237 075 30 7 163 0 212 863 11 244 8 402 132 270 427 84 153 0 0 152 961 866 8 759 0 1 192 081 0 1 226 0 554 414 154 538 0 141 038 20 398 0 744 650 2 291 29 169 3 17 133 638 817 36

150 662 526 7 635 0 2 477 821 32 100 231 368 362 418 159 14 650 44 701 16 477 555 1 614 2 0 460 785 290 929 0 100 884 1 8 0 14 429 584 339 489 195 080 64 6 707 0 201 693 15 945 9 066 96 184 934 32 969 0 0 152 604 585 7 044 10 1 035 424 0 1 124 0 536 105 150 775 0 116 588 1 353 0 702 952 2 929 21 984 3 16 184 310 382 17

132 904 385 8 141 0 2 349 341 31 913 203 018 4 361 416 767 17 612 44 283 175 506 583 950 2 0 459 157 78 466 0 124 885 0 15 0 13 638 382 257 377 186 645 54 5 278 0 196 622 16 722 11 244 9 167 772 25 423 0 0 155 165 560 5 865 0 1 025 659 0 833 0 552 722 236 999 0 107 711 1 843 0 759 285 3 399 28 030 7 21 685 115 167 2

144 049 975 7 398 2 1 893 797 29 201 143 050 3 235 327 081 17 110 33 345 177 502 683 576 3 0 397 628 160 208 0 103 748 0 6 0 22 327 744 337 345 153 731 101 6 062 0 205 903 13 655 13 953 101 20 586 20 378 0 0 144 673 1 153 4 293 0 1 017 508 0 495 0 536 170 220 974 0 93 624 6 699 0 864 648 6 630 36 285 0 19 736 420 359 64

124 900 325 7 060 0 1 670 019 21 105 115 299 1 245 403 532 20 067 20 129 130 416 729 491 5 0 370 825 49 199 106 915 0 6 0 14 651 137 98 200 314 294 24 5 593 0 142 843 3 943 7 173 258 817 17 662 0 0 151 420 564 2 881 0 900 578 0 656 0 620 977 161 1 000 0 80 701 8 866 0 866 047 10 282 54 394 26 964 177 158 35

159 167 84 6 811 0 1 502 840 14 764 122 615 46 332 706 25 322 21 987 739 367 167 631 0 0 261 824 184 434 0 89 267 0 3 0 11 558 85 227 116 301 746 43 5 487 0 132 941 3 219 6 002 32 302 740 17 583 0 0 153 906 904 2 631 2 867 853 0 517 0 604 418 338 1 937 4 64 511 0 546 0 865 980 13 618 52 919 8 15 236 17 61 21

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Annex 4 – I. Reported malaria cases by species, 2000–2015 WHO region Country/area AMERICAS Venezuela (Bolivarian Republic of) Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other 261 866 5 491 24 829 1 366 865 5 115 89 240 2 546 0 233 785 831 18 233 0 742 539 39 475 16 124 76 445 2 738 3 197 241 204 428 86 790 375 1 047 218 984 572 2 048 3 178 212 89 289 156 323 420 165 5 725 38 985 38 548 503 5 917 110 527 0 3 969 413 0 0 2 219 16 747 0 8 671 271 42 056 85 748 0 1 63 770 12 516 0 0 629 380 42 627 1 442 27 216 197 81 2 228 0 462 322 37 679 10 442 60 152 853 871 101 11 507 0 6 728 104 120 792 805 077 1 011 492 4 680 2 113 265 127 594 147 543 400 495 10 629 32 710 60 847 589 6 142 63 255 0 1 010 0 0 166 1 656 0 8 601 835 73 857 143 136 0 29 0 0 220 698 5 629 0 0 835 018 77 271 966 2 173 523 0 111 0 496 616 52 012 3 824 0 54 760 140 261 0 25 147 0 13 520 0 119 279 429 830 779 765 622 3 585 2 205 293 220 077 221 176 2 547 382 303 9 724 34 651 6 936 252 5 581 71 968 0 354 152 1 502 0 8 418 570 73 925 205 879 0 69 0 0 99 403 804 940 59 689 478 33 173 367 0 65 0 390 102 49 084 2 579 0 44 494 87 92 0 26 513 0 16 760 0 119 470 044 662 748 645 652 2 256 2 092 187 200 662 187 989 2 261 410 663 10 978 39 478 23 847 933 1 231 53 609 0 1 412 20 0 0 44 711 0 8 902 947 95 095 228 215 2 901 82 0 0 70 459 891 394 109 504 398 4 209 239 0 18 0 309 179 9 428 396 36 42 512 33 47 0 40 925 0 21 850 0 122 159 270 524 370 534 129 9 325 2 051 425 199 977 187 583 981 476 764 22 777 50 938 4 882 787 624 1 877 43 369 0 939 0 0 72 426 0 7 752 797 46 067 283 661 10 506 34 0 0 85 174 927 821 102 369 408 0 213 916 0 7 0 93 926 3 597 262 2 31 632 14 9 72 719 0 14 407 0 127 891 198 462 079 417 884 1 767 1 833 256 170 848 150 985 1 342 522 617 21 074 62 850 6 769 743 183 3 000 58 362 39 276 21 351 8 514 341 33 391 232 332 8 870 51 0 6 79 653 1 207 771 725 169 67 261 239 0 200 241 0 2 0 125 201 8 981 489 727 28 716 17 31 38 878 0 10 535 0 138 628 331 720 795 379 659 1 575 907 124 051 107 260 625 174 24 018 100 880 11 491 801 938 4 004 82 891 84 632 4 8 885 456 30 075 163 872 7 178 83 0 0 119 008 1 102 186 668 024 68 655 300 0 0 0 122 806 5 279 477 748 74 087 14 20 0 91 007 0 6 817 0 140 841 230 774 627 390 440 0 1 599 427 103 315 94 267 8

2000

2005

2010

2011

2012

2013

2014

2015

EASTERN MEDITERRANEAN Afghanistan

Djibouti

Iran (Islamic Republic of)

Pakistan

Saudi Arabia

Somalia

Sudan

Yemen EUROPEAN Tajikistan SOUTH-EAST ASIA

Suspected No Pf No Pv No Other Suspected No Pf Bhutan No Pv No Other Suspected Democratic No Pf People's Republic No Pv of Korea No Other Suspected No Pf India No Pv No Other Suspected No Pf Indonesia No Pv No Other Bangladesh

144

WORLD MALARIA REPORT 2016

WHO region Country/area SOUTH-EAST ASIA Myanmar Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other Suspected No Pf No Pv No Other

2000

2005

2010

2011

2012

2013

2014

2015

Nepal

Thailand

Timor-Leste WESTERN PACIFIC

843 087 95 499 21 802 252 140 768 560 7 056 4 403 739 43 717 37 975 47 15 212 281 444 46 150 4 505 665 496 070 38 271 1 689 146 2 694 991 6 000 5 953 287 1 897 579 63 591 14 721 729 36 596 25 912 0 4 183 601 612 46 703 21 322 82 58 679 3 226 2 972 10 2 883 456 57 605 15 935 772

787 691 124 644 37 014 638 361 936 1 181 5 691 2 524 788 14 670 14 921 59 185 367 43 093 15 523 266 165 382 17 482 9 004 428 3 892 885 3 588 18 187 161 173 698 13 106 473 36 1 994 216 2 222 2 729 212 1 962 493 62 926 22 833 2 632 593 996 20 033 6 482 213 1 369 633 796 54 001 22 515 126 86 170 3 817 4 453 64 2 793 458 14 231 5 102 163

1 277 568 70 941 29 944 346 213 353 550 2 349 0 1 777 977 9 401 13 401 20 266 384 28 350 11 432 0 193 210 8 213 4 794 0 7 118 649 1 269 3 675 20 280 549 4 393 122 1 1 619 074 1 344 3 387 943 1 505 393 56 735 13 171 1 990 301 577 11 824 2 885 175 1 772 27 1 691 0 284 931 22 892 12 281 200 48 088 1 545 2 265 10 2 803 918 12 763 4 466 0

1 210 465 59 604 28 966 162 188 702 0 908 0 1 450 885 5 710 8 608 13 225 772 14 261 3 758 0 216 712 7 054 5 155 0 9 190 401 1 370 1 907 50 221 390 5 770 442 14 1 600 439 634 1 750 1 660 1 279 140 59 153 9 654 632 327 125 6 877 2 380 127 838 20 754 0 254 506 14 454 8 665 0 32 656 770 1 224 2 3 312 266 10 101 5 602 0

1 423 966 314 676 135 388 27 917 243 432 108 1 480 0 1 130 757 11 553 17 506 3 172 182 854 1 962 2 288 0 194 263 14 896 19 575 4 971 6 918 732 16 179 60 369 976 37 692 7 634 769 1 566 872 651 915 2 187 1 113 528 58 747 7 108 609 333 084 4 774 2 189 57 555 36 473 0 249 520 14 748 9 339 232 33 273 1 257 1 680 470 3 436 534 11 448 7 220 0

1 364 792 222 770 98 860 11 548 169 464 273 1 659 22 1 838 150 14 449 15 573 3 084 178 200 373 512 0 152 137 7 092 11 267 2 418 5 554 995 8 71 0 339 013 24 538 12 537 955 1 576 012 422 385 2 136 1 454 166 119 469 7 579 1 279 320 089 4 968 1 357 16 443 0 383 0 245 014 13 194 11 628 446 28 943 1 039 1 342 0 3 115 804 9 532 6 901 0

890 913 104 863 41 866 5 087 296 979 195 1 154 1 756 528 13 743 20 513 117 107 118 139 0 142 242 8 332 10 356 5 582 4 403 633 6 50 1 294 542 23 928 22 625 1 341 1 443 958 177 241 2 706 922 417 120 641 78 846 77 759 314 820 3 760 834 196 638 0 557 0 233 803 9 835 7 845 593 35 570 279 703 0 2 786 135 8 245 7 220 0

714 075 49 311 26 316 1 689 225 353 103 504 40 1 370 461 3 291 4 655 57 121 110 33 24 0 163 680 17 830 13 146 2 498 4 052 616 1 26 0 284 003 14 430 20 804 735 1 066 470 110 84 22 909 940 118 452 62 228 114 320 260 645 4 145 694 66 699 0 627 0 192 044 10 478 12 150 1 141 14 938 150 273 0 2 673 662 4 327 4 756 0

Suspected No Pf No Pv No Other Suspected No Pf China No Pv No Other Suspected Lao People's No Pf Democratic No Pv Republic No Other Suspected No Pf Malaysia No Pv No Other Suspected No Pf Papua New Guinea No Pv No Other Suspected No Pf Philippines No Pv No Other Suspected No Pf Republic of Korea No Pv No Other Suspected No Pf Solomon Islands No Pv No Other Suspected No Pf Vanuatu No Pv No Other Suspected No Pf Viet Nam No Pv No Other Cambodia

Pf, Plasmodium falciparum ; Pv, Plasmodium vivax 1  In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R21-en.pdf)

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Annex 4 – J. Reported malaria deaths, 2000–2015 WHO region Country/area AFRICAN Algeria Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Republic Chad Comoros Congo Côte d'Ivoire Democratic Republic of the Congo Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mayotte Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Sierra Leone South Africa South Sudan1 Swaziland Togo Uganda United Republic of Tanzania Mainland Zanzibar Zambia Zimbabwe AMERICAS Belize Bolivia (Plurinational State of) Brazil Colombia Dominican Republic Ecuador El Salvador French Guiana Guatemala Guyana Haiti Honduras Mexico Nicaragua 0 11 245 124 6 66 0 0 0 29 16 0 0 4 0 0 123 87 16 22 0 2 4 33 29 1 0 6 0 0 76 42 15 0 0 1 0 24 8 3 0 1 0 0 70 23 10 0 0 2 0 36 5 2 0 1 0 0 60 24 8 0 0 2 0 35 6 1 0 2 0 0 40 10 5 0 0 3 1 14 10 1 0 0 0 1 36 17 4 0 0 0 1 11 9 2 0 0 0 0 37 18 3 0 0 0 1 12 15 0 0 1 2 9 510 0 0 0 691 0 0 439 712 0 0 0 3 856 0 0 0 2 016 0 6 108 626 0 48 767 0 591 0 748 0 0 0 0 1 244 0 0 254 1 275 0 424 0 0 0 0 379 0 379 0 0 0 13 768 322 11 5 224 776 2 836 668 558 92 0 0 15 322 0 49 1 086 353 426 2 037 490 565 44 328 41 699 5 070 1 285 0 0 0 1 325 2 060 6 494 2 581 85 1 587 50 63 0 17 1 024 0 18 322 18 075 247 7 737 1 916 1 8 114 964 8 9 024 2 677 1 4 536 526 886 53 0 1 023 23 476 30 27 1 581 182 151 3 859 735 296 26 017 1 422 427 8 206 3 006 211 0 3 354 63 3 929 4 238 670 14 553 8 188 83 1 053 8 1 507 8 431 15 867 15 819 48 4 834 255 0 6 909 1 753 8 7 001 2 233 1 3 808 858 1 220 19 892 1 389 23 748 52 12 936 74 440 3 259 743 472 713 0 398 6 674 2 128 77 0 3 086 36 2 802 3 353 380 19 472 3 573 54 406 1 1 314 5 958 11 806 11 799 7 4 540 451 0 5 736 2 261 3 7 963 2 263 0 3 209 1 442 1 359 17 623 1 534 21 601 77 30 1 621 134 289 2 855 979 370 785 1 725 552 5 516 1 894 106 0 2 818 4 2 825 7 734 459 7 649 3 611 72 1 321 3 1 197 6 585 7 820 7 812 8 3 705 351 0 7 300 2 288 7 6 294 3 411 0 4 349 1 026 1 881 15 2 870 3 261 30 918 66 6 358 273 262 2 506 108 418 360 1 191 641 3 723 1 680 25 0 2 941 21 2 209 7 878 409 11 815 4 326 105 1 311 4 1 361 7 277 8 528 8 526 2 3 548 352 0 5 714 1 869 22 5 632 2 974 2 4 398 635 1 720 0 271 4 069 25 502 0 15 213 159 170 2 200 1 067 357 472 2 288 551 4 490 2 309 19 0 3 245 61 2 691 6 082 496 0 500 2 848 174 0 4 1 205 5 921 5 373 5 368 5 3 257 406 1 7 832 1 416 5 5 379 3 799 0 3 440 1 763 1 572 1 435 2 604 39 054 28 12 662 309 167 2 137 846 0 15 061 1 379 841 3 799 1 544 39 0 2 467 45 2 778 0 516 0 526 1 107 110 0 5 1 205 6 100 6 313 6 311 2 2 389 200

2000

2005

2010

2011

2012

2013

2014

2015

146

WORLD MALARIA REPORT 2016

WHO region Country/area AMERICAS Panama Peru Suriname Venezuela (Bolivarian Republic of) EASTERN MEDITERRANEAN Afghanistan Djibouti Iran (Islamic Republic of) Pakistan Saudi Arabia Somalia Sudan Yemen EUROPEAN Tajikistan SOUTH-EAST ASIA Bangladesh Bhutan Democratic People's Republic of Korea India Indonesia SOUTH-EAST ASIA Myanmar Nepal Thailand Timor-Leste WESTERN PACIFIC Cambodia China Lao People's Democratic Republic Malaysia Papua New Guinea Philippines Republic of Korea Solomon Islands Vanuatu Viet Nam REGIONAL SUMMARY African Americas Eastern Mediterranean European South-East Asia Western Pacific Total

2000

2005

2010

2011

2012

2013

2014

2015

1 20 24 24 0 0 4 0 0 0 2 162 0 0 484 15 0 892 833 2 556 0 625 0 608 31 350 35 617 536 0 38 3 142 77 642 570 2 166 0 5 405 2 360 88 143

1 4 1 17 0 0 1 52 0 15 1 789 0 0 501 5 0 963 88 1 707 10 161 71 296 48 77 33 725 145 0 38 5 18 137 269 346 1 857 0 3 506 1 385 144 363

1 0 1 18 22 0 0 0 0 6 1 023 92 0 37 2 0 1 018 432 788 6 80 58 151 19 24 13 616 30 1 34 1 21 150 486 190 1 143 0 2 421 910 155 150

0 1 1 16 40 0 0 4 0 5 612 75 0 36 1 0 754 388 581 2 43 16 94 33 17 12 523 12 2 19 1 14 104 068 167 736 0 1 821 727 107 519

1 7 0 10 36 0 0 260 0 10 618 72 0 11 1 0 519 252 403 0 37 3 45 0 44 12 381 16 0 18 0 8 104 105 156 996 0 1 226 524 107 007

0 4 1 6 24 17 0 244 0 23 685 55 0 15 0 0 440 385 236 0 47 3 12 0 28 10 307 12 0 18 0 6 116 333 95 1 048 0 1 126 393 118 995

0 5 0 5 64 28 0 56 0 14 823 23 0 45 0 0 562 217 92 0 38 1 18 0 4 4 203 10 0 23 0 6 99 381 91 1 008 0 955 268 101 703

0 3 0 8 49 0 1 34 0 27 868 12 0 9 0 0 384 157 37 0 33 0 10 20 2 8 163 20 0 13 0 3 117 886 98 991 0 620 239 119 834

Deaths reported before 2000 can be presumed and confirmed or only confirmed deaths depending on the country. 1  In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R21-en.pdf)

WORLD MALARIA REPORT 2016

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Notes

148

WORLD MALARIA REPORT 2016

m a l a r i a

a t l a s

p r o j e c t

The mark “CDC” is owned by the US Dept. of Health and Human Services and is used with permission. Use of this logo is not an endorsement by HHS or CDC of any particular product, service, or enterprise.

For further information please contact: Global Malaria Programme World Health Organization 20, avenue Appia CH-1211 Geneva 27 Web: www.who.int/malaria Email: infogmp@who.int

ISBN 978 92 4 151171 1