Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease – a retrospective study Generic Research Protocol
Regional Office for South-East Asia
SEA-CD-205 Distribution: General
Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease – a retrospective study Generic Research Protocol
Regional Office for South-East Asia
© World Health Organization 2010
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Acknowledgements This protocol has been prepared for WHO-SEARO by the National Institute of Cholera and Enteric Diseases (NICED), Kolkata and the National Institute of Malaria Research, Delhi, India. The document was jointly prepared by Dr G.B. Nair, Director, NICED, and Dr A.K. Deb, Epidemiologist (cholera part) and by Prof A P Dash, Director, NIMR and Dr R C Dhiman, Scientist F, NIMR, Delhi (vector-borne diseases). The efforts of the following experts in reviewing the protocol on and for providing valuable inputs are also acknowledged: Ø Ø Ø Ø Prof Rais Akhtar, National Fellow, CSRD, Jawahar Lal Nehru University, New Delhi Prof R K Seth, Department of Zoology, University of Delhi Dr C Sharma, Scientist EII, National Physical Laboratory, Delhi Dr Anita Chaudhary, Senior Scientist, Indian Agriculture Research Institute, Delhi Dr Krishna Kumar, Scientist E, Indian Institute of Tropical Meteorology, Pune Dr Ramesh Kumar, Oceanography, Goa Scientist F, National Institute of
Ø Ø Ø Ø Ø
Dr K Narayanan, Indian Institute of Technology, Mumbai Dr R S Yadav, Former Scientist F, National Institute of Malaria Research Field Unit, Nadiad, Gujarat Dr G S Sonal, Joint Director, National Vector Borne Disease Control Programme, Delhi Prof Joyshree Roy, Jadhavpur University, Kolkata.
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Contents Page Acronyms ...............................................................................................................v 1. 2. 3. 4. 5. 6. Introduction .................................................................................................. 1 Objectives..................................................................................................... 2 Significance................................................................................................... 2 Scope of the document................................................................................. 3 Duration of study: ......................................................................................... 3 Generic Protocols.......................................................................................... 3 6.1 Cholera................................................................................................. 3 6.2 Vector-borne disease .......................................................................... 12 7. 8. Budget........................................................................................................ 21 References.................................................................................................. 23
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Acronyms DALY SST GLS OLS GAM EIR ARIMA ADB ENSO IPCC Disability Adjusted Life Year Sea Surface Temperature Generalized least Square Ordinary least Square Generalized Additive Models Entomological Inoculation Rate Autoregressive Integrated Moving Average Asian Development Bank El Nino Southern Oscillation Inter-governmental Panel on Climate Change
NVBDCP National Vector Borne Disease Control Programme RH SPSS SEARO Relative Humidity Statistical Package for Sociological Studies Regional Office for South-East Asia
UNFCCC United Nations Framework Convention on Climate Change VBD WHO Vector Borne Disease World Health Organization
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1.
Introduction Human activities are known to alter the global climate. There is new evidence showing that most of the warming observed over the last 50 years is attributable to human activities (IPCC, 2001). The United Nations Framework Convention on Climate Change (UNFCCC) defines climate change as a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods1. The threat posed by climate change to human health is unequivocal. That is why there is greater interest in knowing the early impact of climate change on all aspects of biodiversity, food production, depletion of fresh water supplies and human health etc. The fourth assessment report of IPCC2 also projects a rise in temperature up to 40C and sea level rise up to 0.59m by the year 2100. Based on a range of models, it is likely that future tropical cyclones (typhoons and hurricanes) will become more intense, with larger peak wind speeds and more heavy precipitation associated with ongoing increases in tropical sea surface temperatures. WHO has also produced several documents on the issue of climate change and health3,4,5,6 . It has been highlighted that infectious diseases like diarrhoea, cholera, severe and acute respiratory syndrome and vectorborne diseases as well as respiratory diseases such as asthma, bronchitis and chronic obstructive pulmonary diseases are likely to be affected the most. In order to have uniformity, exchange of information and international collaboration, it is prudent to use common generic protocols to assess the impact of climate change on communicable diseases covering diarrhoea and vector-borne diseases (VBDs) and to identify the methods/actions for preparedness to meet the challenges. The present protocol is an initial effort in this direction and embodies a multi-country generic protocol for determining the relationship between climatic factors and disease incidence (diarrhoeal and vector-borne diseases) keeping in view the needs of the South- East Asia Region. Page 1
Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
Countries planning to undertake impact assessments should first identify climate sensitive diseases. As there is variable infrastructure, resources and need, the generic protocol may be divided into three phases: (1) (2) (3) Retrospective study to find out the relationship between climatic factors and communicable diseases like cholera and VBDs. Prospective study to find early evidence of climate change and future scenario in view of projected climate change. Assessing the national/local preparedness to respond to the impact of climate change on health, specifically diarrhoeal and vector-borne diseases.
The present generic protocol has been prepared to undertake study on the first phase. A retrospective study can be undertaken by those countries who have a robust disease surveillance system and infrastructure for generating meteorological data at various centres. Alternately such countries should opt for the second phase i.e. prospective study.
2.
Objectives (1) (2) To find out the relationship/correlation between climatic factors and diarrhoeal and vector- borne diseases. To identify and test a range of non-climatic indicators as potential co-variables in dynamics of diarrhoeal diseases.
3.
Significance Ø The outcome of the relationship will help in understanding the role of temperature, rainfall and RH etc. in disease transmission dynamics. Climatic determinants of transmission will be available which will serve as the baseline for assessing the impact of climate change. Identification of environmental indicators suitable for early warning will be known.
Ø Ø
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Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
4.
Scope of the document The outcome of the protocol would be useful for public health specialists, entomologists and policy makers to devise intervention measures, development of preparedness plan and allocation of funds for development of tools for early warning of diseases.
5.
Duration of study: One year.
6. 6.1
Generic Protocols Cholera Acute diarrhoeal disease is one of the most important health related impacts linked to short-term and long-term changes in the climate. The frequency and intensity of extreme climate events such as drought, flood and cyclone have a direct impact on the prevalence of diarrhoeal diseases. Studies from developing countries show that there are strong seasonal variations of diarrhoeal diseases in the case of hydrological extremes such as water shortages and flooding. Water shortages cause diarrhoea due to perpetuation of unhygienic and poor sanitary conditions and flooding contaminates drinking water supplies. Morbidity due to diarrhoeal diseases is also very high. Globally, 1.3 billion episodes of diarrhoea occur annually, mostly in children, with an average of 2-3 episodes per child per year7. The global diarrhoeal disease burden was estimated at 6,24,51,000 DALYs (Disability Adjusted Life Years) lost in 20018. The majority of the DALYs are from developing countries where children (5 years of age) suffer from as many as 12 episodes of diarrhoea each year. In fact in certain areas with poor environmental sanitation, children are ill with diarrhoea for 10% – 20% of their first three years of life. In developing countries, up to a third of paediatric admissions in hospitals are due to acute diarrhoea.
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Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
Association between a change in the climate and prevalence of diarrhoeal diseases with special reference to cholera, show that environmental and climatic factors which cause seasonal patterns of infection are the key factors for the te mporal variation of the disease 9-11. In this context several researchers have established a link between heavy rainfall and flooding—whether resulting from El Niño-associated events or from other meteorological impacts—and subsequent outbreaks of infectious diseases12. These climatic conditions are intermingled with other environmental and climatic conditions which also play a profound role in variation in the incidence of diarrhoeal disease. Extreme meteorological events can easily disrupt water purification and storm water and sewage systems, as well as contaminate uncovered wells and surface water, leading to an increased risk of illness. These risks are even higher when a population lives in a low-lying area, where the land’s hydrology causes draining tributaries to meet. Conversely, heavy rains and coastal events can also flush micro-organisms into watersheds, affecting those up-coast as well. No sustainable development, such as that which contributes to deforestation and soil erosion, influences water contamination by destroying the land's natural ability to absorb runoff, resulting in water-contaminating mudslides. Global scenario Considering the global scenario, climate variability and non-cholera diarrhoea has also a longitudinal relationship. Studies from Bangladesh show that the number of non-cholera diarrhoea cases increase with heavy rainfall, higher temperature particularly in lower socio- economic settings and poor sanitation13. The influence of climate variation on diarrhoeal diseases is evident in the Pacific Islands also. Evidence shows that more the temperature and lower the potential water availability, higher is the diarrhoea and rural water sources get contaminated both in drought (stagnation) and flooding14. Relation between climate extremes and cholera outbreak pattern Toxigenic Vibrio cholerae, the pathogen that causes cholera, is autochthonous to aquatic environs and is intricately associated with the microflora and fauna of aquatic environs. Cholera has the ability to spread as a worldwide pandemic and currently the seventh pandemic of cholera Page 4
Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
which started in 1961 is ongoing. Cholera infections vary greatly in frequency, severity, and duration, and the occurrence of cholera in different parts of the world is dynamic. In some south Asian countries it is endemic, in some parts like Africa and South America sporadic outbreaks occur, which is also a common feature in the endemic areas. Past studies have shown how cholera varies with seasons15. Monthly outbreaks by regions
This temporal variation is believed to be due to environmental and climatic factors. In the bimodal seasonal cycle of cholera, cases and also in case of transmission of cholera there is a marked decrease during the early summer and monsoons, probably resulting from a reduction in cholera’s environmental concentration along with a decrease in salinity affecting its survival12. Cholera cases increase again and peak with a lag after this season, as floods presumably concentrate the population on the decreased land area available and break down of sanitary conditions, promoting secondary transmission through the more direct faeco-oral route. Outbreak pattern of cholera varies with season, mainly with heavy rainfall leading to water logging and lack of potable water; the socio economic, environmental and the climatic factors are all intermingled. Besides rainfall, two remote drivers of inter annual climate variability, the El Niño–Southern Oscillation (ENSO),sea surface temperatures (SSTs) and chlorophyll a in the Bay of Bengal, are proposed to influence cholera in Bangladesh12 . Effect of El Nino in Latin America show more hospital admissions which are directly related to excess increase in ambient temperature. In Peru, studies show 50C rise in temp cause 200% increase in Page 5
Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
diarrhea admission in 1997-1998 and with 10C rise in temperature causes 8% increase in the risk of getting severe diarrhea in children15.In China, there are evidences of 10C rise in maximum temperature causes 11% rise in bacillary dysentery and 10C rise in minimum temperature cause 12% rise in bacillary dysentery16. Even in England reports suggest that with each degree Celsius rise in temperature causes 5% rise in reported campylobacteriosis17. There are evidences for an increased role of inter annual climate variability on the temporal dynamics of cholera based on the time-series analyses of the relationship between El Niño Southern Oscillation (ENSO) and cholera prevalence 18,19 Recent analysis between cholera time series and ENSO with specific time intervals it has been found that this climate phenomenon accounts for over 70% of disease variance 20. Evidence shows that there are some existence of refractory periods during which climate-driven increases in transmission do not result in large outbreaks. Once the interplay of climate forcing and disease dynamics is taken into account, clear evidence emerges for a role of climate variability in the transmission of cholera. Meta analysis of 32 years of cholera data from WHO was attempted to see association between cholera cases and different environmental factors like rainfall, sea surface temperature, humidity and altitude 21 which show a great variability in cholera incidences. This is associated with upper troposphere humidity, cloud cover and level of solar radiation which are absorbed at the top of the atmosphere. In a retrospective case review of cholera-like diarrhoea during 1990-1991, it was hypothesized that El Niño-influenced ocean warming and its associated hyper growth of plankton contributed to the dispersal of Vibrio cholerae organisms—responsible for cholera—along the Pacific coast of Peru15. Several tools and models were used in the past like satellite imageries to show association of cholera with sea surface height and sea surface temperature 22-24. Katia-Koelle et al (2005) also established the association between seasonal drivers and cholera25. Additional factors like North Atlantic oscillation, sunlight, temperature, pollution, level of rainfall, and some human socio economic and demographic factors are responsible for outbreaks22. Inverse correlation between environmental phage concentration (post flood and post monsoon period) and epidemics has been also postulated recently23. Hence, simultaneous collection of such non-climatic data is strongly recommended to allow for adjustments of the estimated effects of climate changes on diarrhoeal diseases. Page 6
Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
Study site: One district (approx 0.3 to 0.4 million population) from a rural and one from an urban area may be selected. However, the size of district may vary depending on the size of the country. While selecting suitable site(s) and population to assess the effects of climate change on diarrhoea, we need to consider the availability of diarrhoea, climatic (and if possible, non-climatic) data for such sites over the specified time period. The population should be stable as far as possible with little inward / outward migration. Requirement of data Diarrhoeal data Monthly diarrhoeal data over a period of 10-15 years from the selected districts. In case of urban areas, even data on cases of cholera may be obtained. Case definition: There should be Uniform criteria to define “cases” and/or “episodes” of diarrhoea. Without such definitions being uniformly implemented, the resulting data would be impossible to compare across sites and over time. Thus, wherever possible, these criteria should be noted, so that some kind of adjustments can be considered during the analytic stage, if necessary. Climatic data Monthly data on minimum-maximum temperature, daily rainfall and relative humidity will be required for the same period for which disease incidence data have been collected. Additional data: Additionally, if remote sensing facilities were available during the period under consideration (or if such data are procured from appropriate sources), one can also use measurements on variables such as chlorophyll-a, sea surface temperature (SST), sea surface height and ENSO. Non-climatic data Many non-climatic factors can influence the occurrence of diarrhoeal diseases. The factors that can relatively easily be obtained include changes in population size and age structure, population mobility (e.g. net migration Page 7
Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
rate), economic status (e.g. per capita income or persons living below the poverty line), immunization practices, sources of drinking water (or proportion of population with access to safe drinking water), proportion of population with adequate sanitation facilities etc. However, these data will only be obtained at most on a yearly (not daily or monthly) basis and this should be sufficient. Data source A: Disease Data Types of data At various sub-national levels – Monthly no. of cases Monthly no. of deaths Monthly admissions Monthly deaths Monthly severe cases admitted * Area of residence * Age groups / Gender * Duration of diarrhoea Estimated / Actual – Monthly admissions Monthly deaths Monthly severe cases admitted Area of residence Age groups / Gender Duration of diarrhoea (Observed) Sources of data Government reports
Hospital records
Surveillance data – Hospital / Community
B. Climate Data: Indian Meteorology Department Optional data: remote-sensing data – (chlorophyll A), sea surface height and sea surface temperature from climate prediction Centre, USA. C: Non-climate data: from census data of the country. Ø Demographic/household surveys - govt./non-govt.(like national family health survey, sample registration system)
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Ø Ø
Ministry reports Reports of international agencies, e.g. World Bank, ADB, UN agencies.
Data processing: Usually obtained as daily values (e.g. daily maximum and minimum temperatures); monthly averages will be calculated from these daily values and used as climatic predictors. Reported cases of diarrhoea will be analysed as age-specific cases of diarrhoea, e.g. cases reported among under-5 children. Severity of diarrhoea: proportion of severe cases – which can be obtained for the hospital based data (total no. of episodes requiring IV fluid in a month)/(total no. of cases in that month). Proportion of cholera: will be measured for non-bloody diarrhoea only (total no. of cholera positive cases)/(total no. of diarrhoeal cases). Case fatality from diarrhoea: (total no. of deaths due to diarrhoea)/(total no. of episodes) * 100. Data management: Initially, the collected data are to be manually checked for completeness and inconsistencies (such as checking each variable for impossible or unusual values), and will be subsequently entered into a computer using available software like MS-Excel. After entry, the entire distribution of each variable can be checked g raphically (such as using box plots) to identify unusual values. If there are many missing values for one or more variables, they need to be handled appropriately to avoid bias in the results. Two general approaches can be taken – (i) use only complete set data, which may, however, result in substantial loss in information and hence bias the results, (ii) impute missing values with values generated by appropriate technique (e.g. regression) using missing data patterns. After the initial exercise is done, one will also need to transform daily values of some variables into monthly (or weekly / fortnightly) averages before analysis. Data analysis: The predictors and endpoints will then be analyzed to describe their patterns individually. Since these data are collected repeatedly over a long time, they represent a set of time series data. The goals of time series analysis are description, explanation, prediction or control . The descriptive analysis is used to understand the underlying patterns of a given time series, whereas to explain the dependence of a response (outcome) time series Y t on a number of predictor series X 1t, …,
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Xpt we typically use regression analysis taking into account the lack of independence among the time series observations. Descriptive analysis A time series (i.e., each data value in the series) may be composed of three main components (sometimes with an additional “cyclical” component) – (a) The “trend” component (b) The “seasonal” component, and (c) The “irregular” component The seasonal component can also be compared quantitatively. First, the trend component is obtained by some smoothing technique, usually using moving averages. Then the trend can be examined in two ways – (a) by looking at the moving average line at different points or (b) by fitting a regression line (linear, quadratic or other such as a non-linear regression line, as seems appropriate from the data). When the trend values (obtained through moving averages) are subtracted from respective data values, one gets the seasonal and irregular components together for each value. If we again compute moving averages for these values, we would get the seasonal component for the series. To control irregular components (random fluctuations) in the data, one can employ exponential smoothing. Thus, checking the time series plots with “trend” for the predictors and outcomes and comparing them would indicate the patterns of change in each variable with time and if any relation exists among them. Sometimes an appropriate time lag is also applied in the analysis. Explanatory analysis When the objective of analysis is explanation – i.e., how the explanatory variables (climatic and non-climatic) influence an outcome (diarrhoea or cholera) over time, we can model the dependence of the outcome Yt on one or more predictor time series Xt. This is done using regression analysis. In standard regression model the responses are assumed to be independent of one another, whereas with time series data, neighbouring values of Y tend to be correlated. This “autocorrelation” must be taken into account to make valid inferences. Specifically, in such situations we would like to use models based on generalized least squares (GLS) rather than using ordinary least squares (OLS) methods. Several approaches may be taken, based on the characteristics of obtained data – one common approach is using the ARIMA model that incorporates trends and temporal autocorrelation into a Page 10
Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
single model, especially when the time series data are non-stationary (i.e., display a clear trend). To summarize, the analytical process involved in quantifying climate– disease links can be separated into the following steps: (1) Fitting trend lines and sine–cosine waves (or similar) to remove long-term trends and potentially non-climatic seasonal variation from outcome and predictor variables. Testing, by parametric or non-parametric means, for correlations between climate variability and variability in the outcome variable. Using cross-validation techniques to test the robustness of the model.
(2)
(3)
Data presentation and interpretation : Analysed data will be presented in the form of graphs and regression figures. Limitations: Retrospective data would have limitations in getting patient-specific information. Thus, information on duration of diarrhoea (obtained through date of onset and date of outcome) or frequency of stool are unlikely to be available. At most, such data will be limited to daily / monthly information on total number of cases, number of admissions, number of cases with severe dehydration (requiring IV fluids), and number of deaths due to diarrhoea. Rarely, these information may be available for cholera-specific diarrhoea. Due to the nature of retrospective data, there will be a few special concerns during analysis of such data. The two most important concerns are completeness and quality of collected data. There may be times for which information is not available; There are problems when there is partial reporting; data quality may not be the same throughout the coverage period; no change in disease detection over time (including intensity of disease detection efforts); use of similar data collection instruments (disease and/or climate data) over time. These consistency requirements are to ensure comparability of data collected over time and space.
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Timelin e: Months à 1 Recruitment & training of staff Data collection Data entry and verification Data editing Data analysis Report writing, presentation 2 3 4 5 6 7 8 9 10 11 12
6.2
Vector-borne disease Background In the South-East Asia Region, malaria, dengue, chikungunya, Japanese encephalitis, filariasis, and leishmaniasis are the major VBDs of which malaria and dengue are of major public health importance (Table 1). In 2006, about 2.5 million cases of malaria were reported in South-East Asia. India alone constituted 54.9 % of the VBD in the Region followed by Indonesia and Myanmar (Figure 1). Table 1: Burden of vector-borne diseases in ******** of South-East Asia Region (2006)
SEAR countries Bangladesh Bhutan DPR Korea India Indonesia Maldives Myanmar
Malaria 48389 1868 9353 1765371 347197 0 200679
Dengue 2198 116 0 11251 106425 2768 11383
Chikungunya 1 0 0 0 1380000 15207 (2001 to 2007) 10831 0
Japanese encephalit is NA 0 NA 2842 0 0 0
Filariasis* (In mill.) 70 465 150 NA 17
Leishmaniasi s 7495 0 0 33613 0 0 0
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Nepal Sri-Lanka Thailand Timor-Leste
5349 591 30294 33524
18 11906 42456 163
0 0 0 0 1
176 NA NA 0
22 10 0.017 0.92
1138 0 0 0
Source: WHO SEARO and NVBDCP, India; suspected cases; *Population at risk in million (in 2004)
Figure 1: Proportion of malaria reported by countries in the SEA Region in 2006 (Source: WHO-SEARO) Bangladesh 1.59% Timor-Leste 1.25% Bhutan 0.06% DPR Korea 0.31%
Thailand 1.00% Sri Lanka 0.02% Nepal 0.18% Myanmar 6.60%
Indonesia 34.11%
India 54.90%
The work carried out on establishing the relationship between climatic factors and various VBDs has been reviewed by IPCC2. Highlights of the work undertaken are given below: Malaria: The role of climatic factors has been studied extensively in the epidemiology of malaria due to its global public health importance. Based on the minimum temperature needed for completion of progeny (development of malaria parasite in mosquito) in Anopheles vectors, Detinova (1962)26 who followed the method of Organov-Rayevsky, expressed the relationship between temperature and duration of sporogony of P vivax as a mathematical expression. At 160C it will take 55 days for completion of sporogony of P vivax while at 280C, the process can be completed in seven days and at 180C it will take 29 days27 (WHO, 1975). The duration of sporogony in Anopheles mosquitoes decreases with an increase in temperature from 200 to 250C (Table 2). Page 13
Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
Table 2 Average duration of sporogony of human Plasmodia Parasite species No. of days required for sporogony at different temperatures 200 C P falciparum P vivax P malariae P ovale (Adapted from WHO, 1975) 22-23 16-17 30-35 250 C 12-14 9-10 23-24 15-16
From 320 to 390C temperature, there is high mortality in mosquitoes28 and at 400 C, their survival becomes zero29. The interplay between temperature and mosquitoes has recently been reviewed by Dhiman et l30. Temperature affects the developmental period in the life cycle of mosquitoes; blood feeding rate, gonotrophic cycle (physiological process consisting of digestion of blood meal and development of ovaries) and longevity31. Reduction in duration of the gonotrophic cycle and the sporogony are related with increased rate of transmission32,26,33,34. Two entomological indices i.e. vectorial capacity and entomological inoculation rates (EIR) are directly affected by the density of vectors in relation to the number of humans in a given local situation, daily survival rate and feeding rate of vector mosquitoes and the duration of sporogonic cycle and these are sensitive to changes in environmental temperature35,-37 being reduced with increase in temperature. Rainfall helps in creation of mosquito breeding habitats and/or flushing off the immature stages of mosquitoes. Excess rainfall can increase the breeding sites of mosquitoes and dry conditions can either eliminate or create several new breeding habitats in large water bodies such as lakes and rivers. The amount, intensity and duration of rainfall affect the population of mosquitoes. Rainfall also helps increase relative humidity (RH) and modifies temperature, which affects the longevity of mosquitoes, and thus transmission of disease. If RH is below 60%, the life of mosquitoes is shortened which in turn reduces disease transmission. RH between 60%80% is considered to be optimum for effective transmission of malaria38. Van der Hoek39 studied the role of rainfall in Sri Lanka and the significance of temperature was studied in malaria outbreaks in African highlands by Pascual et al40 .
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A systematic review of studies on the El Nino-Southern Oscillation (ENSO) and malaria epidemics is well documented41. Based on the knowledge of the relationship between climate and malaria, indicators for early warning of malaria have been identified in different parts of the world42-46. In India, seasonal forecasts of malaria based on rainfall as one of the important parameters have been provided long ago42,47. The role of rainfall and ENSO in malaria outbreaks has also been studied48-53, The relationship between rainfall and malaria has been studied in Madhya Pradesh and Assam54,55 where no correlation was found. The statistical approach is empirical and has been used to establish the climate-disease relationship using retrospective data of disease vector/incidence and climatic parameters. Various studies have been reviewed1,2,6 . The epidemiology of VBDs is complex and involves many factors. The distribution of any vector species in abundance does not necessarily imply the presence of the disease transmitted by it. Developmental activities like construction of dams, increased irrigation, agricultural practices, migration, health infrastructure, types of intervention measures used, education, behaviour and economic conditions influence transmission of VBDs. Therefore, in addition to causative and transmitting agents, demographic and social factors also affect disease transmission. Dengue: Climatic conditions play a very important role in the transmission of dengue as well. The role of temperature, rainfall and RH in the biology of Aedes mosquitoes and the epidemiology of dengue has very well been established56,57 . The minimum temperature required for survival of dengue viruses in Aedes mosquitoes is 11.90 C1 and the viruses cannot multiply in the vector at lower than 180 C temperature 58. Temperatures beyond 420C are inimical for the survival of immature stages of Aedes mosquitoes59. Several studies have reported an association between the spatial60 and temporal6162 patterns of dengue and climate. Climate-based (tempe rature, rainfall, cloud cover) density maps of the main dengue vector Stegomyia (previously called Aedes ) aegypti are a good match with the observed disease distribution63.
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In a study in Delhi, the seasonality in the populations of Aedes mosquitoes has been found to be associated with rainfall and RH64. The role of rainfall in dengue outbreak in Delhi has also been established65 . The main reasons for dengue epidemics have been urbanization, lack of surveillance and vector control and population movement 66,67. The role of climatic factors in dengue outbreaks has been studied in Senegal,Thailand, India and in Caribbean countries68-71. Leishmaniasis: Leishmaniasis is caused by Leishmania parasite and transmitted by phlebotomine sand flies. Leishmaniasis is a climate-sensitive disease basically due to preferred breeding of sand fly vectors in alluvial soil with high sub-soil water table, distributed in temperatures ranging from 70 to 370C, and RH above 70% in India72. Thomson et al73 have mapped the distribution of Phlebotomus orientalis and found that mean annual maximum temperature and soil type were the determining factors for distribution. The life cycle of sand flies is also influenced by RH and temperature resulting in fluctuations in density 74. In North-eastern Colombia, it was found that during El Niño, cases of leishmaniasis increased, whereas during La Niña phases, leishmaniasis cases decreased75. Leptospirosis: There have been reports of flood-associated outbreaks of leptospirosis from a wide range of countries in Central and South America and South Asia1,2 . Sehgal76 has reviewed the distribution of leptospirosis with reference to India. Risk factors for prevalence of leptospirosis in peri-urban populations in low-income countries include flooding of open sewers and streets during the rainy season77. Plague: There is good evidence that diseases transmitted by rodents sometimes increase during heavy rainfall and flooding because of altered patterns of human–pathogen–rodent contact. Recent investigations of plague foci in North America and Asia with respect to the relationships between climatic variables, human disease cases78 and in animal reservoirs79,80 have suggested that temporal variations in plague risk can be estimated by monitoring key climatic variables. There appears to be a correlation between rainfall patterns and rodent populations81. However, more work is required to establish the relationship. Studies on the relationship between climatic variables and Chagas disease, tick-borne diseases, trypanosomiasis and schistosomiasis etc. have been reviewed1,2,6 .
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Viz Urban, rural, epidemic prone, tribal/forested with population of about 0.3 to 0.4 million each. Depending on the size of the country, even one district may also be adequate for analysis. Study design : Correlation coefficient between monthly climatic variables (temperature, rainfall and RH) and disease incidence of the last 15 to 20 years, with month-to-month, one-month and two-month lag (and so on ), will be determined in respect of selected districts from each paradigm of disease using univariate, bivariate and multiple regression analysis. Similarly the relationship between monthly ENSO (sea surface temperature derived from www.cpc.noaa.gov.) and disease incidence should also be established to find out the significance of El Nino (dry conditions) or La Nina (wet conditions i.e. rainy) in disease incidence/outbreaks. Data requirement Epidemiological data : Monthly retrospective epidemiological data of a particular disease for around 15 -20 years should be procured from the office of the chief medical officer/district malaria officer of the district. Annual data are of little use for determining climate-disease relationship as the effect of seasonality is lost in climate-sensitive diseases. Climatic data: Retrospective data covering of 15-20 years on temperature (minimum and maximum), daily rainfall and relative humidity (recorded at 0830 and 1730 hrs) are procured from meteorological departments. If such data are not available in a country, data from MODIS satellite (http://www.edcdaac.usgs.gov/dataproducts.asp) or from NASA (http:// precip.gstc.nasa.gov.) may be retrieved. Data Management: Data collected from different sources should be subjected to the following: Ø Ø Ø Quality assessment and quality control. Verification of primary and computed data by second check to overcome human error in encoding. Verification of statistical analysis by second person.
Study sites: One district from each paradigm of vector borne diseases
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Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
Data processing: Epidemiological data on the monthly number of cases are entered in excel sheet. Data on temperature (0 C) are available in the format of daily minimum and maximum. In order to get the monthly average temperature, first the daily average of the minimum and maximum temperature is computed, thereafter the monthly average is derived by dividing the sum of daily averaged temperatures of total days of month by the number of days of that month. Similarly, the data on RH (%) is available in the format of 08:30 and 17:30 hrs on a daily basis. By computing RH twice in a day, the daily average RH is derived. Total RH of the month is divided by the number of days of that particular month to get monthly average RH. Data on rainfall are available in the form of one-time daily data. In order to get monthly rainfall, the sum of rainfall of all the days of the month is taken as monthly rainfall. Verification of computed data is done by another person to verify human error in encoding of data. Depending on the pattern of missing data, optimal statistical methods ranging from imputation to generalized additive models (GAMs) may be used to adjust for missing data. Average monthly temperature, RH and rainfall data are also entered in excel sheet. Data analysis : To find out the relationship between climate variables and disease prevalence, a two-fold analysis will be employed. In all these analyses, climate measures will serve as the exposure or independent variable, with disease prevalence and incidence as dependent variable. Spearman’s correlation analysis to examine the relationship between monthly climatic variables and incidence of malaria. If the value of r is >0.5 it is considered as positive correlation. A correlation coefficient that ranges between 0.7-1.0, and a coefficient of determination of 1 or near 1, would mean a strong correlation between the two variables.
Keeping in view the developmental period of vector species and incubation period of disease, the correlation coefficient should be determined with a time lag of one to two months between exposure (climatic factors) and dependent variable (incidence). This will be performed in MS Excel or using the software SPSS. In the event that time lags are discovered, computations for the multivariate analysis will be adjusted accordingly.
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Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
Autoregressive Integrated Moving Average (ARIMA) generalised least square (GLS) regression analysis will be performed to control for autocorrelation. After adjusting for effect of auto correlation, multiple regression analysis will be conducted between all climate variables and disease incidence to assess the independent effect of each climatic variable. Data presentation and interpretation : The data obtained on the climatic factors and the prevalence of a particular disease will be presented in the form of a graph of the monthly averages for a climate variable plotted along a timeline (Fig 3). From this time series plot, general trends between climate and disease variables over time can be derived. A rough correlation between the patterns of disease and climate variable can also be visualized as the two plots are superimposed upon each other. Figure 3. Time series meteorological data vis-a-vis malaria Incidence in Bikaner (1986-2001)
250 200 150 100 50 0
Source: Dhiman et al 2003
Following the time series plot, an XY scatter plot and linear regression between significant climate variables and disease measures will also be produced. In this presentation, the significant climate variables and disease measures will be plotted along an XY axis. A line that minimizes the square of the distances between the line and all points will then provide the equation from where the correlation coefficient (R) and coefficient of determination (R2) can be derived. The correlation coefficient indicates the Page 19
De ce No mbe ve r m b Oc er Se tobe pte r m be r Au gu st Jul y Jun e M ay Ap r M il arc h Fe Jan b ,98 De ce No mbe ve r mb Oc er tob er
Jan ,86
Years
Rainfall, Temp, RH
4000 3000 2000 1000 0
Pv &Pf
Pv Pf Temp Rainfall RH
Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
strength of association between the climate and disease variables while the coefficient of determination (R2) indicates how well the regression line fits the data points, as illustrated in Fig 4. Figure 4: linear regression of temperature, RH and malaria
A cross-correlation graph will be generated which would indicate the correlation between time series of the significant climate variable and disease incidence. Limitations of the study: The study will have the following limitations basically due to the type of data used. Ø Ø Ø Ø Main risk factors: Variation in Met. data in different countries, missing data Co- variables: Variable surveillance, missing data Disease incidence data are not a true representation of the disease burden as surveillance may not capture all the cases. Correlation of disease incidence and climatic factors is affected by soil, terrain features, intervention measures and socioeconomic conditions.
Timeline MONTHS ? TIME SERIES INVESTIGATION 1 2 3 4 5 6 7 8 9 10 11 12
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Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease Recruitment & Training of staff Exposure data Climate data • Temperature • Rainfall • Relative Humidity Outcome data • Incidence Analysis Data entry and verification Data editing Data analysis Verification of analysis Report Data write-up, presentation and paper preparation
7.
Budget
a. cholera Budget items No. / Quantity Cost per unit/month (approx. USD) 444 222 333 122 500 100 1500 650 250 50 Total cost in 3m (approx. US$) 1333 667 1000 367 500 100 1500 650 250 50 Page 21
Personnel Research fellow Data entry personnel Statistician / Programmer Clerk / Attendant Stationeries Consumables – General Computing facilities Computers with UPS Printer – Laser Storage media for back-up Software – Antivirus
1 1 1 1
1 1 1
Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
Budget items
No. / Quantity
Purchase of meteorological data Purchase of other data Training, meeting & training materials, Transportation Communications Miscellaneous TOTAL
Cost per Total cost in unit/month 3m (approx. USD) (approx. US$) 1700 1700 1700 250 1700 250 1200 375 375 12017
1
400 125 125
b. Vector-borne diseases (for four districts in a country) item Personnel: Project fellow-one Data entry Operator-one Statistician-one Contingency Non-recurring: 1. One computer with printer and UPS 2. SPSS software-One Recurring 1. Cost of Meteorological data 2. Stationery 3. Travelling allowance 4. Communication charges 5. Training /meetings 6. Establishment charge 7. Report writing ( honorarium & production charge) Miscellaneous TOTAL: Cost ( US $) 3900 2250 2250
2000 4000 1000 500 2000 500 2000 1000 1000 1200 23600
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Assessing the relationship between climatic factors and diarrhoeal and vector-borne disease
8.
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Human activities are known to alter the global climate. Climate change can impact health significantly; in particular more frequent floods and other events can increase diarrhoeal diseases and vector-borne diseases among others. WHO-SEARO has developed generic research protocols to assess the relationship between climate change and human health, with particular reference to diarrhoeal diseases and vector-borne diseases like malaria and dengue. This document describes the methodology that can be used to explore such associations, both retrospectively and prospectively. This methodology can be used in various countries to obtain results that can form the basis for inter-country comparisons.
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