Data gaps towards health development goals, 47 low- and middle-income countries

Bull World Health Organ 2022;100:40–49 | doi: http://dx.doi.org/10.2471/BLT.21.286254 Research 40 Introduction The United Nations (UN) Transforming our world: the 2030 agenda for sustainable development is a global plan shared by Member States for a far healthier, more prosperous world.1,2 Fifty-seven of the 232 UN sustainable development goal (SDG) indicators were subsequently identified as health-related in- dicators by the World Health Organization (WHO) in 2019 with two more added afterwards.3,4 In alignment with the SDGs, WHO Member States approved the Thirteenth general programme of work 2019−2023 in 2018 and set the triple billion targets to accelerate delivering impact in countries.5 The results framework of the general programme of work identified 46 health outcome indicators to track countries’ progress towards the triple billion targets,6 including 39 SDG indicators and seven non-SDG indicators.7 The seven non-SDG indicators were approved in World Health Assembly (WHA) resolu- tions including two that relate to health emergencies. These 46 indicators assist WHO Member States to stay focused and accelerate their progress towards achieving the SDGs and the triple billion targets. The recommendations of the thirteenth general pro- gramme of work would require gathering data in many dimen- sions, yet little is known about the efforts needed by countries to measure these 46 indicators. Researchers have estimated that 12 data systems would be required in each country to moni- tor 57 health-related SDG indicators.3 A similar number of systems would likely be needed to measure the 46 indicators. Representative household surveys, civil registration and vital statistics, and other administrative health data systems are the primary and preferred data source for monitoring health indicators.8 Regular population-based health surveys are a key component of well-functioning data systems for surveying populations and health risks.9 Household surveys can provide data for an estimated 29 of the 57 health-related indicators.3 Additionally, valid measurement of some indicators, such as out-of-pocket health expenditure, are mostly achieved through surveys. Many countries, however, especially low- and middle-income countries, lack the technical capacity and financial resources to perform these surveys, and have relied on development partners to implement surveys.10 The WHO SCORE (survey, count, optimize, review, en- able) global report in 2020 revealed that most low-income a Data, Analytics and Delivery for Impact Division, World Health Organization, Avenue Appia 20, 1211 Geneva 27, Switzerland. b Regional Office for the Americas, World Health Organization, Washington, DC, United States of America (USA). c Regional Office for the Eastern Mediterranean, World Health Organization, Cairo, Egypt. d Regional Office for Africa, World Health Organization, Brazzaville, Congo. e Regional Office for Europe, World Health Organization, Copenhagen, Denmark. f Regional Office for South-East Asia, World Health Organization, New Delhi, India. g Regional Office for the Western Pacific, World Health Organization, Manila, Philippines. h RTI International, Research Triangle Park, USA. Correspondence to Luhua Zhao (email: lzhao@ who .int). (Submitted: 15 June 2021 – Revised version received: 11 October 2021 – Accepted: 22 October 2021 – Published online: 4 November 2021 ) Data gaps towards health development goals, 47 low- and middle-income countries Luhua Zhao,a Bochen Cao,a Elaine Borghi,a Somnath Chatterji,a Sebastian Garcia-Saiso,b Arash Rashidian,c Henry Victor Doctor,c Marcelo D'Agostino,b Humphrey C Karamagi,d David Novillo-Ortiz,e Mark Landry,f Ahmad Reza Hosseinpoor,a Abdisalan Noor,a Leanne Riley,a Adrienne Cox,b Jun Gao,g Steve Litaveczh & Samira Asmaa Objective To assess the availability and gaps in data for measuring progress towards health-related sustainable development goals and other targets in selected low- and middle-income countries. Methods We used 14 international population surveys to evaluate the health data systems in the 47 least developed countries over the years 2015–2020. We reviewed the survey instruments to determine whether they contained tools that could be used to measure 46 health- related indicators defined by the World Health Organization. We recorded the number of countries with data available on the indicators from these surveys. Findings Twenty-seven indicators were measurable by the surveys we identified. The two health emergency indicators were not measurable by current surveys. The percentage of countries that used surveys to collect data over 2015–2020 were lowest for tuberculosis (2/47; 4.3%), hepatitis B (3/47; 6.4%), human immunodeficiency virus (11/47; 23.4%), child development status and child abuse (both 13/47; 27.7%), compared with safe drinking water (37/47; 78.7%) and births attended by skilled health personnel (36/47; 76.6%). Nineteen countries collected data on 21 or more indicators over 2015–2020 while nine collected data on no indicators; over 2018–2020 these numbers reduced to six and 20, respectively. Conclusion Examining selected international surveys provided a quick summary of health data available in the 47 least developed countries. We found major gaps in health data due to long survey cycles and lack of appropriate survey instruments. Novel indicators and survey instruments would be needed to track the fast-changing situation of health emergencies. 41Bull World Health Organ 2022;100:40–49| doi: http://dx.doi.org/10.2471/BLT.21.286254 Research Health data gaps for SDGsLuhua Zhao et al. countries relied heavily on external funding for surveys, with only 3% of the surveys fully funded by a government.9 International surveys provided essential health data and this was sometimes the only data available in these countries to monitor some important health trends. Reviewing the survey methods and procedures in low- and middle-income countries would offer valuable insight into whether and to what extent key health data, measurable by the 46 indi- cators, were available in these countries. Our literature search showed that not much work has been done in this area. The UN Intersecretariat Working Group on Household Surveys conducted a re- view to map the SDG global indicators to household surveys.11 The report found that 77 of the SDG indicators could be sourced from household surveys; how- ever, the availability of the data was not assessed. We therefore aimed to assess the availability and gaps in the relevant data and to explore new methods for improving health data in low- and middle-income countries. Our study focused on reviewing international surveys that have been implemented in these countries. We reviewed each survey to determine if it could measure any of the 46 health outcome indicators defined by the WHO. We also reviewed the implementation of the surveys between the years 2015 and 2020 in 47 low- and middle-income countries that are designated by the UN as the least developed countries.12 These countries lack adequate local capacity, and depend heavily on surveys sponsored by inter- national partners to provide essential health data. We therefore found it fea- sible to assess their health data systems through examining the international surveys used. The results provide evi- dence on countries’ progress towards the health-related SDGs. We also propose exploring new methods to reduce the data gaps through surveys, particularly for timely and rapid data collection in health emergency settings. Methods We identified potential international population surveys based on a literature review and consultation with experts across WHO technical programmes. We included surveys in our study if they covered health topics; were coordinated or facilitated by international agencies or partners; had been implemented in multiple least developed countries since 2000 using nationally representative samples; and had a core questionnaire or instrument and a standard protocol. We included surveys concerning hu- man immunodeficiency virus (HIV), tuberculosis, malaria and hepatitis B only if rapid diagnosis tests or labora- tory testing were used in the surveys in addition to data collected through oral interviews. For each type of survey, we con- ducted an extensive online review to identify standard and country-specific survey instruments. We examined the instruments to determine whether they contained tools (such as survey questions, health examinations or collection of biomarkers) that could potentially be used to measure any of the 46 health outcome indicators. When the country-specific survey instrument was not available, we used the standard international questionnaire for assess- ment. We categorized each indicator into one of three groups according to its measurability: (i) measurable (if the surveys we studied contained suitable questions, health examinations or bio- markers); (ii) potentially measurable (if the surveys we studied did not contain suitable questions, health examinations or biomarkers but such tools could be formulated and added); and (iii) not measurable (if suitable questions, health examinations or biomarkers could not be formulated for any surveys). If surveys containing tools to measure an indicator were conducted in a country between 2015 and 2020, we assumed that data were available for this indicator in this country for this period. We judged that an indicator was not measurable by surveys if its calculation involved important information unavailable from surveys. For SDG 3.4 (incidence of HIV, tuberculosis, malaria and hepatitis B infections), for example, we judged that surveys needed to include rapid diagnostic tests or laboratory testing to measure the indicators. For all 47 least developed coun- tries12 we assessed the availability of data for all indicators that were deemed measurable by the surveys we studied. We did this by mapping the indicators each survey can measure. We first as- sessed data availability and the number of indicators measured by the surveys for the 47 countries for the period of 2015–2020. We conducted a second analysis using the period 2018–2020 to evaluate the impact of survey fre- quency on availability of health data in these countries. For each indicator, we recorded the number of countries that could measure it and then calculated the percentage out of the 47 countries. Results We identified 14 different surveys that could measure at least one of the 46 health outcome indicators from 2015 to 2020 (Table 1). The surveys included comprehensive multi-topic surveys such as demographic and health surveys (DHS), labour force surveys, living stan- dards measurement surveys and mul- tiple indicator cluster surveys (MICS), as well as single-topic surveys such as household income and expenditure survey and reproductive health surveys. More details of these surveys are pro- vided in the authors’ data repository.13 DHS were able to measure the greatest number of indicators (22 indica- tors), followed by MICS (14 indicators), living standards measurement surveys (11 indicators), reproductive health surveys (eight indicators), household income and expenditure surveys (five indicators) and labour force surveys (five indicators). There were major over- laps in survey contents among them. Nine indicators measured by DHS could also be measured by MICS and living standards measurement surveys. Out of 14 indicators measurable by MICS, 11 indicators could also be measured by DHS. Similarly, 10 of 11 indicators that are measured by living standards measurement surveys are also measured by DHS. We found that 27 indicators were measurable by the selected surveys. The percentages of countries that used the surveys to measure each indicator between 2015 and 2020 are listed in Table 1. The percentages were lowest for tuberculosis incidence (SDG 3.3.2, two countries, 4.3%), hepatitis B incidence (SDG 3.3.4, three countries, 6.4%), new HIV infection (SDG 3.3.1, 11 countries, 23.4%), child development status and child abuse (SDG 4.2.1 and SDG 16.2.1, both 13 countries, 27.7%). Except for the indicators on households with large health expenditures (SDG 3.8.2, 16 countries, 34.0%) and malaria inci- dence (SDG 3.3.3, 18 countries, 38.3%), the remaining indicators all achieved coverage of 50% or more. The highest 42 Bull World Health Organ 2022;100:40–49| doi: http://dx.doi.org/10.2471/BLT.21.286254 Research Health data gaps for SDGs Luhua Zhao et al. Ta bl e 1. Su ita bi lit y o f s ur ve ys a nd av ai la bi lit y o f d at a fo r m ea su rin g 46 W HO h ea lth o ut co m e in di ca to rs in 4 7 lo w - a nd m id dl e- in co m e co un tr ie s, 20 15 –2 02 0 He al th in di ca to ra Su ita bi lit y f or m ea su re m en t b y av ai la bl e su rv ey sb Su rv ey u se d to m ea su re in di ca to r No . ( % ) o f c ou nt rie s w ith d at a av ai la bl e on in di ca to r ( n = 47 )c Ye ar s 20 15 –2 02 0 Ye ar s 20 18 –2 02 0 SD G 1. 5. 1: N um be r o f d ea th s, m iss in g pe rs on s a nd d ire ct ly a ffe ct ed p er so ns a tt rib ut ed to di sa st er s p er 1 00 0 00 p op ul at io n N ot m ea su ra bl e N A N A N A SD G 1. a. 2: P ro po rt io n of to ta l g ov er nm en t s pe nd in g on e ss en tia l s er vi ce s ( ed uc at io n, h ea lth an d so ci al p ro te ct io n) N ot m ea su ra bl e N A N A N A SD G 2. 2. 1: P re va le nc e of st un tin g (h ei gh t f or a ge < − 2 st an da rd d ev ia tio ns fr om th e m ed ia n of th e W H O c hi ld g ro w th st an da rd s) a m on g ch ild re n un de r 5 y ea rs o f a ge M ea su ra bl e D H S; li vi ng st an da rd s m ea su re m en t s ur ve y; M IC S 35 (7 4. 5) 23 (4 8. 9) SD G 2. 2. 2: P re va le nc e of m al nu tri tio n (w ei gh t f or h ei gh t > + 2 or < − 2 st an da rd d ev ia tio ns fro m th e m ed ia n of th e W H O c hi ld g ro w th st an da rd s) a m on g ch ild re n un de r 5 y ea rs o f a ge , by ty pe (w as tin g) M ea su ra bl e D H S; li vi ng st an da rd s m ea su re m en t s ur ve y; M IC S 35 (7 4. 5) 23 (4 8. 9) SD G 2. 2. 2: P re va le nc e of m al nu tri tio n (w ei gh t f or h ei gh t > + 2 or < − 2 st an da rd d ev ia tio ns fro m th e m ed ia n of th e W H O c hi ld g ro w th st an da rd s) a m on g ch ild re n un de r 5 y ea rs o f a ge , by ty pe (o ve rw ei gh t) M ea su ra bl e D H S; li vi ng st an da rd s m ea su re m en t s ur ve y; M IC S 35 (7 4. 5) 23 (4 8. 9) SD G 3. 1. 1: M at er na l m or ta lit y ra tio M ea su ra bl e D H S 25 (5 3. 2) 12 (2 5. 5) SD G 3. 1. 2: P ro po rt io n of b irt hs a tte nd ed b y sk ill ed h ea lth p er so nn el M ea su ra bl e AI D S in di ca to r s ur ve ys ; D H S; h ou se ho ld in co m e an d ex pe nd itu re su rv ey ; l ab ou r f or ce su rv ey ; l iv in g st an da rd s m ea su re m en t s ur ve y; M IC S; re pr od uc tiv e he al th su rv ey 36 (7 6. 6) 24 (5 1. 1) SD G 3. 2. 1: U nd er -fi ve m or ta lit y ra te M ea su ra bl e D H S; li vi ng st an da rd s m ea su re m en t s ur ve y; M IC S; re pr od uc tiv e he al th su rv ey 35 (7 4. 5) 23 (4 8. 9) SD G 3. 2. 2: N eo na ta l m or ta lit y ra te M ea su ra bl e D H S; li vi ng st an da rd s m ea su re m en t s ur ve y; M IC S; re pr od uc tiv e he al th su rv ey 35 (7 4. 5) 23 (4 8. 9) SD G 3. 3. 1: N um be r o f n ew H IV in fe ct io ns p er 1 00 0 un in fe ct ed p op ul at io n, b y se x, a ge a nd ke y po pu la tio ns M ea su ra bl e D H S; A ID S in di ca to r s ur ve y 11 (2 3. 4) 4 (8 .5 ) SD G 3. 3. 2: Tu be rc ul os is in ci de nc e pe r 1 00 0 00 p op ul at io n M ea su ra bl e Tu be rc ul os is pr ev al en ce su rv ey 2 (4 .3 ) 0 (0 .0 ) SD G 3. 3. 3: M al ar ia in ci de nc e pe r 1 00 0 po pu la tio n M ea su ra bl e D H S; m al ar ia in di ca to r s ur ve y 18 (3 8. 3) 8 (1 7. 0) SD G 3. 3. 4 H ep at iti s B in ci de nc e pe r 1 00 0 00 p op ul at io n M ea su ra bl e D H S 3 (6 .4 ) 3 (6 .4 ) SD G 3. 3. 5 N um be r o f p eo pl e re qu iri ng in te rv en tio ns a ga in st n eg le ct ed tr op ic al d ise as es Po te nt ia lly m ea su ra bl e N A N A N A SD G 3. 4. 1: M or ta lit y ra te a tt rib ut ed to c ar di ov as cu la r d ise as e, c an ce r, di ab et es o r c hr on ic re sp ira to ry d ise as e Po te nt ia lly m ea su ra bl e N A N A N A SD G 3. 4. 2: S ui ci de m or ta lit y ra te M ea su ra bl e D H S; w or ld m en ta l h ea lth su rv ey 25 (5 3. 2) 12 (2 5. 5) SD G 3. 5. 1: C ov er ag e of tr ea tm en t i nt er ve nt io ns (p ha rm ac ol og ic al , p sy ch os oc ia l a nd re ha bi lit at io n an d af te rc ar e se rv ic es ) f or su bs ta nc e us e di so rd er s Po te nt ia lly m ea su ra bl e N A N A N A SD G 3. 5. 2: H ar m fu l u se o f a lc oh ol , d efi ne d ac co rd in g to th e na tio na l c on te xt a s a lc oh ol p er ca pi ta c on su m pt io n (a ge d 15 y ea rs a nd o ld er ) w ith in a c al en da r y ea r i n lit re s o f p ur e al co ho l M ea su ra bl e M IC S; S TE Pw ise a pp ro ac h to su rv ei lla nc e; w or ld m en ta l h ea lth su rv ey 24 (5 1. 1) 13 (2 7. 7) (c on tin ue s. . . ) 43Bull World Health Organ 2022;100:40–49| doi: http://dx.doi.org/10.2471/BLT.21.286254 Research Health data gaps for SDGsLuhua Zhao et al. He al th in di ca to ra Su ita bi lit y f or m ea su re m en t b y av ai la bl e su rv ey sb Su rv ey u se d to m ea su re in di ca to r No . ( % ) o f c ou nt rie s w ith d at a av ai la bl e on in di ca to r ( n = 47 )c Ye ar s 20 15 –2 02 0 Ye ar s 20 18 –2 02 0 SD G 3. 6. 1: D ea th ra te d ue to ro ad tr affi c in ju rie s M ea su ra bl e D H S 25 (5 3. 2) 12 (2 5. 5) SD G 3. 7. 1: P ro po rt io n of w om en o f r ep ro du ct iv e ag e (a ge d 15 –4 9 ye ar s) w ho h av e th ei r ne ed fo r f am ily p la nn in g sa tis fie d w ith m od er n m et ho ds M ea su ra bl e D H S; M IC S; p er fo rm an ce m on ito rin g fo r a ct io n; re pr od uc tiv e he al th su rv ey 35 (7 4. 5) 25 (5 3. 2) SD G 3. 8. 1: C ov er ag e of e ss en tia l h ea lth se rv ic es (d efi ne d as th e av er ag e co ve ra ge o f e ss en tia l se rv ic es b as ed o n tra ce r i nt er ve nt io ns th at in cl ud e re pr od uc tiv e, m at er na l, n ew bo rn a nd ch ild h ea lth , i nf ec tio us d ise as es , n on co m m un ic ab le d ise as es a nd se rv ic e ca pa ci ty a nd ac ce ss , a m on g th e ge ne ra l a nd th e m os t d isa dv an ta ge d po pu la tio n) N ot m ea su ra bl e N A N A N A SD G 3. 8. 2: P ro po rt io n of p op ul at io n w ith la rg e ho us eh ol d ex pe nd itu re s o n he al th a s a sh ar e of to ta l h ou se ho ld e xp en di tu re o r i nc om e M ea su ra bl e H ou se ho ld in co m e an d ex pe nd itu re su rv ey ; l ab ou r fo rc e su rv ey ; l iv in g st an da rd s m ea su re m en t s ur ve y; re pr od uc tiv e he al th su rv ey ; w or ld m en ta l h ea lth su rv ey ; w or ld h ea lth su rv ey 16 (3 4. 0) 3 (6 .4 ) SD G 3. 9. 1: M or ta lit y ra te a tt rib ut ed to h ou se ho ld a nd a m bi en t a ir po llu tio n N ot m ea su ra bl e N A N A N A SD G 3. 9. 2: M or ta lit y ra te a tt rib ut ed to u ns af e w at er , u ns af e sa ni ta tio n an d la ck o f h yg ie ne (e xp os ur e to u ns af e W at er , S an ita tio n an d H yg ie ne fo r A ll (W AS H ) s er vi ce s) Po te nt ia lly m ea su ra bl e N A N A N A SD G 3. 9. 3: M or ta lit y ra te a tt rib ut ed to u ni nt en tio na l p oi so ni ng Po te nt ia lly m ea su ra bl e N A N A N A SD G 7. 1. 2: P ro po rt io n of p op ul at io n w ith p rim ar y re lia nc e on c le an fu el s a nd te ch no lo gy M ea su ra bl e AI D S in di ca to r s ur ve ys ; D H S; h ou se ho ld in co m e an d ex pe nd itu re su rv ey ; l ab ou r f or ce su rv ey ; l iv in g st an da rd s m ea su re m en t s ur ve y; tu be rc ul os is pr ev al en ce su rv ey ; r ep ro du ct iv e he al th su rv ey 27 (5 7. 4) 13 (2 7. 7) SD G 11 .6 .2 : A nn ua l m ea n le ve l o f fi ne p ar tic ul at e m at te r ( su ch a s P M 2. 5 a nd P M 10 ) i n ci tie s (p op ul at io n w ei gh te d) N ot m ea su ra bl e N A N A N A SD G 3. a. 1: A ge -s ta nd ar di ze d pr ev al en ce o f c ur re nt to ba cc o us e am on g pe rs on s a ge d 15 y ea rs a nd o ld er M ea su ra bl e D H S; g lo ba l a du lt to ba cc o su rv ey ; l iv in g st an da rd s m ea su re m en t s ur ve y; M IC S; S TE Pw ise a pp ro ac h to su rv ei lla nc e 36 (7 6. 6) 24 (5 1. 1) SD G 3. b. 1: P ro po rt io n of th e ta rg et p op ul at io n co ve re d by a ll va cc in es in cl ud ed in th ei r na tio na l p ro gr am m e M ea su ra bl e D H S 25 (5 3. 2) 12 (2 5. 5) SD G 3. b. 3: P ro po rt io n of h ea lth fa ci lit ie s t ha t h av e a co re se t o f r el ev an t e ss en tia l m ed ic in es av ai la bl e an d aff or da bl e on a su st ai na bl e ba sis N ot m ea su ra bl e N A N A N A SD G 3. c.1 : H ea lth w or ke r d en sit y an d di st rib ut io n N ot m ea su ra bl e N A N A N A SD G 3. d. 1: In te rn at io na l H ea lth R eg ul at io ns c ap ac ity a nd h ea lth e m er ge nc y pr ep ar ed ne ss N ot m ea su ra bl e N A N A N A SD G 4. 2. 1: P ro po rt io n of c hi ld re n un de r 5 y ea rs o f a ge w ho a re d ev el op m en ta lly o n tra ck in he al th , l ea rn in g an d ps yc ho so ci al w el l-b ei ng , b y se x M ea su ra bl e M IC S (2 01 8 an d af te r) 13 (2 7. 7) 13 (2 7. 7) SD G 5. 2. 1: P ro po rt io n of e ve r- pa rt ne re d w om en a nd g irl s a ge d 15 y ea rs a nd o ld er su bj ec te d to p hy sic al , s ex ua l o r p sy ch ol og ic al v io le nc e by a c ur re nt o r f or m er in tim at e pa rt ne r i n th e pr ev io us 1 2 m on th s, by fo rm o f v io le nc e an d by a ge M ea su ra bl e D H S; M IC S 34 (7 2. 3) 23 (4 8. 9) (. . . co nt in ue d) (c on tin ue s. . . ) 44 Bull World Health Organ 2022;100:40–49| doi: http://dx.doi.org/10.2471/BLT.21.286254 Research Health data gaps for SDGs Luhua Zhao et al. He al th in di ca to ra Su ita bi lit y f or m ea su re m en t b y av ai la bl e su rv ey sb Su rv ey u se d to m ea su re in di ca to r No . ( % ) o f c ou nt rie s w ith d at a av ai la bl e on in di ca to r ( n = 47 )c Ye ar s 20 15 –2 02 0 Ye ar s 20 18 –2 02 0 SD G 5. 6. 1: P ro po rt io n of w om en a ge d 15 –4 9 ye ar s w ho m ak e th ei r o w n in fo rm ed d ec isi on s re ga rd in g se xu al re la tio ns , c on tra ce pt iv e us e an d re pr od uc tiv e he al th c ar e M ea su ra bl e D H S; p er fo rm an ce m on ito rin g fo r a ct io n 27 (5 7. 4) 15 (3 1. 9) SD G 6. 1. 1: P ro po rt io n of p op ul at io n us in g sa fe ly m an ag ed d rin ki ng -w at er se rv ic es M ea su ra bl e AI D S in di ca to r s ur ve y; D H S; h ou se ho ld in co m e an d ex pe nd itu re su rv ey ; l ab ou r f or ce su rv ey ; l iv in g st an da rd s m ea su re m en t s ur ve y; M IC S; m al ar ia in di ca to r s ur ve y; tu be rc ul os is pr ev al en ce su rv ey ; re pr od uc tiv e he al th su rv ey 37 (7 8. 7) 26 (5 5. 3) SD G 6. 2. 1: P ro po rt io n of p op ul at io n us in g sa fe ly m an ag ed sa ni ta tio n se rv ic es , i nc lu di ng a ha nd -w as hi ng fa ci lit y w ith so ap a nd w at er M ea su ra bl e D H S; h ou se ho ld in co m e an d ex pe nd itu re su rv ey ; la bo ur fo rc e su rv ey ; l iv in g st an da rd s m ea su re m en t su rv ey ; M IC S; m al ar ia in di ca to r s ur ve y; tu be rc ul os is pr ev al en ce su rv ey ; r ep ro du ct iv e he al th su rv ey 37 (7 8. 7) 26 (5 5. 3) SD G 16 .2 .1 : P ro po rt io n of c hi ld re n ag ed 1 –1 7 ye ar s w ho e xp er ie nc ed a ny p hy sic al pu ni sh m en t a nd /o r p sy ch ol og ic al a gg re ss io n by c ar eg iv er s i n th e pa st m on th M ea su ra bl e M IC S (2 01 8 an d af te r) 13 (2 7. 7) 13 (2 7. 7) H ea lth e m er ge nc y in di ca to r: Va cc in e co ve ra ge o f a t- ris k gr ou ps fo r e pi de m ic o r p an de m ic pr on e di se as es N ot m ea su ra bl e N A N A N A H ea lth e m er ge nc y in di ca to r: Pr op or tio n of v ul ne ra bl e pe op le in fr ag ile se tt in gs p ro vi de d w ith e ss en tia l h ea lth se rv ic es N ot m ea su ra bl e N A N A N A W H A 68 .3 : N um be r o f c as es o f p ol io m ye lit is ca us ed b y w ild p ol io vi ru s N ot m ea su ra bl e N A N A N A W H A 68 .7 : P at te rn s o f a nt ib io tic c on su m pt io n at n at io na l l ev el Po te nt ia lly m ea su ra bl e N A N A N A W H A 67 .2 5, W H A 68 .7 : P er ce nt ag e of b lo od st re am in fe ct io ns d ue to a nt im ic ro bi al re sis ta nt or ga ni sm s N ot m ea su ra bl e N A N A N A W H A 66 .1 0: A ge -s ta nd ar di ze d pr ev al en ce o f r ai se d bl oo d pr es su re a m on g pe rs on s a ge d 18 + y ea rs (d efi ne d as sy st ol ic b lo od p re ss ur e of ≥ 1 40 m m H g an d/ or d ia st ol ic b lo od p re ss ur e ≥ 9 0 m m H g) M ea su ra bl e D H S; S TE Pw ise a pp ro ac h to su rv ei lla nc e 27 (5 7. 4) 13 (2 7. 7) W H A 66 .1 0: P er ce nt ag e of p eo pl e pr ot ec te d by e ffe ct iv e re gu la tio n on tr an s- fa ts N ot m ea su ra bl e N A N A N A W H A 66 .1 0: P re va le nc e of o be sit y M ea su ra bl e D H S; S TE Pw ise a pp ro ac h to su rv ei lla nc e 27 (5 7. 4) 13 (2 7. 7) AI D S: a cq ui re d im m un od efi ci en cy sy nd ro m e; D HS : d em og ra ph ic a nd h ea lth su rv ey ; M IC S: m ul tip le in di ca to r c lu st er su rv ey ; N A: n ot a pp lic ab le ; S D G: su st ai na bl e de ve lo pm en t g oa l; W HA : W or ld H ea lth A ss em bl y; W HO : W or ld H ea lth O rg an iza tio n. a Th e 46 h ea lth o ut co m e in di ca to rs w er e th os e id en tifi ed in W HO ’s th irt ee nt h ge ne ra l p ro gr am m e of w or k fo r m ea su rin g th e UN su st ai na bl e de ve lo pm en t g oa ls an d tri pl e bi lli on ta rg et s.6 b W e cl as sifi ed in di ca to rs a s: m ea su ra bl e, if th e su rv ey s w e st ud ie d co nt ai ne d su ita bl e qu es tio ns , h ea lth e xa m in at io ns o r b io m ar ke rs ; p ot en tia lly m ea su ra bl e, if th e su rv ey s w e st ud ie d di d no t c on ta in su ita bl e qu es tio ns , h ea lth e xa m in at io ns o r bi om ar ke rs b ut su ch to ol s c ou ld b e fo rm ul at ed a nd a dd ed ; o r n ot m ea su ra bl e, if su ita bl e qu es tio ns , h ea lth e xa m in at io ns o r b io m ar ke rs c ou ld n ot b e fo rm ul at ed fo r a ny su rv ey s. c Th e 47 lo w - a nd m id dl e- in co m e co un tri es a re : A fg ha ni st an , A ng ol a, B an gl ad es h, B en in , B hu ta n, B ur ki na Fa so , B ur un di , C am bo di a, C en tra l A fri ca n Re pu bl ic, C ha d, C om or os , D em oc ra tic R ep ub lic o f t he C on go , D jib ou ti, E rit re a, E th io pi a, G am bi a, Gu in ea , G ui ne a- Bi ss au , H ai ti, K iri ba ti, L ao P eo pl e' s D em oc ra tic R ep ub lic , L es ot ho , L ib er ia , M ad ag as ca r, M al aw i, M al i, M au rit an ia , M oz am bi qu e, M ya nm ar , N ep al , N ig er , R w an da , S ao To m e an d Pr in ci pe , S en eg al , S ie rra L eo ne , S ol om on Is la nd s, So m al ia , So ut h Su da n, S ud an , T im or -L es te , T og o, Tu va lu , U ga nd a, U ni te d Re pu bl ic o f T an za ni a, V an ua tu , Y em en , Z am bi a. (. . . co nt in ue d) 45Bull World Health Organ 2022;100:40–49| doi: http://dx.doi.org/10.2471/BLT.21.286254 Research Health data gaps for SDGsLuhua Zhao et al. coverage was for safe drinking-water and sanitation services (SDG 6.1.1 and SDG 6.2.1),which both reached 78.7% (37 countries), followed by SDG 3.1.2 (birth attended by skilled health per- sonnel) and SDG 3.a.1 (tobacco use) at 76.6% (36 countries). The percentage coverage was also high for SDG 2.2.1 (stunting), SDG 2.2.2 (overweight and wasting), SDG 3.2.1 (under-five mortal- ity rate), SDG 3.2.2 (neonatal mortality rate) and SDG 3.7.1 (women satisfied with modern family planning methods), measured in 35 countries (74.5%) each. About half of the countries had data to measure adult obesity (WHA 66.10), hypertension (WHA 66.10) and primary reliance on clean fuel (SDG 7.1.2), all measured in 27 countries (57.4%). When the observation period changed to 2018–2020, the percentages dropped for all 27 indicators, with most countries decreasing their measurement of the indicators by 20 or more percentage points. Thirteen indicators were not mea- surable by surveys. These indicators fell into six groups: (i) indicators that concern health facility and health work- ers, including SDG 3.b.3 (health facility operation) and SDG 3.c.1 (health work- force); (ii) indicators that involve gov- ernment policies, including WHA 66.10 (trans-fats regulation), SDG 3.d.1 (In- ternational Health Regulations com- pliance) and SDG 1.a.2 (government spending); (iii) indicators that involve sophisticated testing and diagnosis, including WHA 68.3 (poliomyelitis) and WHA 67.25 (antimicrobial resis- tance); (iv) indicators that are usually reported through civil registration and vital statistics and administrative data systems, including SDG 1.5.1 (death from disasters) and SDG 11.6.2 (fine particulate matter); (v) indicators that involve complex calculation algorithms, including SDG 3.9.1 (mortality rate attributable to household and ambi- ent environment); and (vi) composite indicators that involve multiple tracer variables, including SDG 3.8.1 (coverage of universal health care) and the two indicators that are related to readiness and response to health emergencies. The remaining six indicators were potentially measurable by surveys, but were not measured by the surveys we studied. These indicators were SDG 3.3.5 (intervention against neglected tropical diseases), SDG 3.5.1 (treatment inter- vention for substance abuse disorder), WHA 68.7 (antibiotic consumption level) and SDG 3.4.1, SDG 3.9.2 and SDG 3.9.3 (mortality indicators). Our results also provided informa- tion to assess how the 47 countries fared overall in tracking public health trends. Over the period 2015–2020, 19 of the countries collected data on 21 or more of the 46 health outcome indicators and 16 countries had data on 11 to 20 indica- tors; nine countries collected no data, of which five countries were from WHO African Region, three from Western Pacific Region and one from Eastern Mediterranean Region (Table 2). When we analysed the time period 2018–2020, the number of countries with data on 21 or more indicators dropped to six, and countries with no data increased to 20, across five WHO regions. Discussion As WHO Member States are mandated to track and report their progress at country level towards SDGs and triple billion targets,5 it is essential that they collect and use health data to identify the key priority areas for improvement. Well-functioning health information systems, particularly civil registration and vital statistics and other adminis- trative data systems, were lacking in the 47 least developed countries.12 WHO’s SCORE global report estimated that 40% of annual deaths went unregistered globally, particularly in Africa.9 Many low- and middle-income countries, par- ticularly the least developed countries, relied on international surveys as the primary source for many essential health data. We were therefore able to assess the key health data gaps in the studied countries by examining international surveys. In conjunction with the 46 health outcome indicators identified by WHO, international surveys can pro- vide a relatively simple and quick way to assess gaps in essential health data in these countries. We found data gaps in the 47 coun- tries studied. None of the countries had data for all 27 indicators measurable by surveys between 2015 and 2020. The most monitored indicators were measured in about three quarters of countries. The least tracked indicators were those for infectious diseases, par- ticularly tuberculosis and hepatitis B, each of which were reported by 4.3% and 6.4% of the countries, respectively. This result is expected given that the diagno- sis of these health conditions requires laboratory work that was not provided by most surveys. Furthermore, less than one third of the countries had data to monitor child development status and child abuse, a sign that more investment is needed to track children’s welfare. The pandemic of coronavirus dis- ease 2019 (COVID-19) has highlighted the urgency of timely and accurate measurement of population mortality, as well as the need for rapid collection of key data for informing policies and actions. Household surveys are one of the major sources for mortality data in countries that do not have reliable civil registration and vital statistics systems. However, surveys often lack instruments to obtain mortality statistics. Five of the 10 mortality indicators identified by the WHO thirteenth general programme of work are either not measurable by surveys or potentially measurable but not measured by the surveys we stud- ied. Sibling survival history – a method commonly used in surveys to measure mortality – only provides all-cause and pregnancy-related mortality, and tends to produce sparse data among older adults. Researchers have tested additional questions for sibling survival history to determine HIV/AIDS mortal- ity,15 while others have explored the idea of a social network survival method to capture death data more efficiently.16 Further research is needed to develop more innovative methods to enhance the capacity of surveys for rapid measure- ment of population mortality. The COVID-19 pandemic also showed the need to rapidly assess the local situation during health emergen- cies. Health emergency readiness is usually measured through reports based on the International Health Regula- tions,17,18 which relies on government reporting prepared in advance and does not provide up-to-date information. Surveys can be used to obtain quick results. However, out of the 46 indica- tors studied, only two indicators were directly related to health emergencies and neither were measurable directly by surveys. Developing suitable indicators for population-based surveys is needed to track and monitor the rapidly chang- ing situation during health emergencies. Heavy reliance on a few surveys can have negative consequences. Com- prehensive population surveys such as DHS and MICS take significant time, manpower and financial invest- 46 Bull World Health Organ 2022;100:40–49| doi: http://dx.doi.org/10.2471/BLT.21.286254 Research Health data gaps for SDGs Luhua Zhao et al. ment to implement, and are thus usu- ally conducted every 5 years or more.11 Countries relying on these surveys may find themselves without data to monitor important health trends between survey cycles, which can be affected by unex- pected events such as pandemics. In the current study, the number of countries with no data for the studied indicators rose from nine to 20 when the time period studied was shortened from 2015–2020 to 2018–2020, showing the risk of dependence on a small number of surveys. Our analyses revealed that the difference was primarily due to the scheduling of DHS and MICS. Greater efforts are needed to find new ways of fill- ing the data gaps between these surveys. Moreover, most health surveys still rely on face-to-face interviews for data collection. As the COVID-19 pan- demic has shown, this method is often not feasible due to safety concerns and travel restrictions imposed by authori- ties. Many countries have responded by adopting mobile phone surveys and web-based surveys as alternatives,19 even though guidelines have been developed to restore the capacity to run in-person interviews. Adapting to the challenges posed by COVID-19 would help not only to collect data that is urgently needed for responding to the pandemic but also to prepare surveys for other emergencies and rapid responses. Mo- bile phone surveys are suitable for this purpose as they can collect data rapidly with lower costs. Such surveys can also be used to quickly fill the specific data gaps in combination with prior health data evaluations, such as the assessment introduced in our study and WHO SCORE technical package.9,20 Interna- tional regulations need to be developed to reduce unnecessary ethical review requirements for non-sensitive surveys and minimize restrictions on mobile network access. Such barriers can offset the speediness of mobile phone surveys. Data that are comparable across countries and consistent over time are important not only for national planning and evaluation of health systems but also for global assessment and progress tracking. Previous studies have indi- cated poor correlation of data on SDG indicators across various reports due to differences in indicator definitions, data sources, data processing and methods of synthesis.21,22 International surveys may adopt indicator definitions that are not comparable with those approved Table 2. Numbers of WHO health outcome indicators measured by international surveys in 47 low- and middle-income countries, 2015–2020 Country by WHO Region No. of indicators measured (maximum 46) Years 2015–2020 Years 2018–2020 African Region Angola 21 0 Benin 21 20 Burkina Faso 5 5 Burundi 20 0 Central African Republic 14 14 Chad 22 14 Comoros 0 0 Democratic Republic of the Congo 15 15 Djibouti 0 0 Eritrea 0 0 Ethiopia 22 19 Gambia 24 23 Guinea 20 20 Guinea-Bissau 14 14 Lesotho 14 14 Liberia 22 20 Madagascar 24 14 Malawi 25 14 Mali 22 21 Mauritania 22 21 Mozambique 14 6 Niger 22 0 Rwanda 22 21 Sao Tome and Principe 14 14 Senegal 23 21 Sierra Leone 22 20 Somalia 0 0 South Sudan 0 0 Sudan 4 0 Uganda 22 5 United Republic of Tanzania 21 1 Zambia 21 20 Americas Region Haiti 20 0 Eastern Mediterranean Region Afghanistan 19 0 Yemen 0 0 South-East Asia Region Bangladesh 24 22 Bhutan 5 0 Myanmar 20 0 Nepal 23 16 Timor-Leste 19 0 Western Pacific Region Cambodia 0 0 Kiribati 16 14 Lao People's Democratic Republic 12 0 Solomon Islands 0 0 Togo 13 0 Tuvalu 14 14 Vanuatu 0 0 WHO: World Health Organization. Notes: The 46 health outcome indicators were those identified in WHO’s thirteenth general programme of work for measuring the UN sustainable development goals and triple billion targets.6 Detailed information at country level is available online from the authors’ data repository.14 47Bull World Health Organ 2022;100:40–49| doi: http://dx.doi.org/10.2471/BLT.21.286254 Research Health data gaps for SDGsLuhua Zhao et al. by WHO. For example, DHS conduct HIV testing, but only for anonymous respondents between 15 and 49 years old,23 whereas measuring progress on SDG 3.3.1 requires HIV indicators for broader populations disaggregated by sex, age group and other categories. This inconsistency would likely represent a challenge for tracking progress towards the SDG and triple billion targets in countries that rely on these data. Despite the efforts among international partners to coordinate the monitoring of health trends, greater collaboration is needed to increase data comparability and close data gaps. This study has a few limitations. First, we did not include important data sources such as civil registration and vital statistics and administrative data sources. However, the impact on our findings should be minimal as most of the indicators examined rely on surveys as the primary data source, and civil registration and vital statistics systems are mostly too poorly developed in the 47 least developed countries to play a key role. Second, when details were not available online for some countries, we used core questionnaires from the same survey series for assessment. This approach could cause potential dis- crepancies for countries with tailored questionnaires. Last, low data quality can render some data unusable. How- ever, as data quality varies across surveys and can be influenced by many factors that are difficult to measure, this issue, although important, was not included in the discussion. Examining international surveys provided a quick summary of essential health data in the 47 least developed countries. Current population surveys, the bulk of data sources, are not frequent enough to provide timely monitoring for health-related indicators in these coun- tries. Population surveys also lacked suitable instruments to collect data for informing actions during health emer- gencies. We propose exploring novel indicators and survey instruments to monitor and track health emergencies, along with rapid data collection meth- ods such as mobile phone surveys. ■ Funding: This study was supported by WHO internal funding. Competing interests: None declared. 摘要 47 个中低收入国家卫生发展目标的数据差距 目的 评估在特定中低收入国家可用于衡量与卫生相关 的可持续发展目标和其他目标进展的数据可得性及空 缺。 方法 我们使用 14 项国际人口调查评估了 47 个最不发 达国家 2015 年至 2020 年间的卫生数据系统。我们审 阅了其调查问卷和其他调查方式以评估这些人口调查 是否可用于衡量世界卫生组织制定的 46 个与卫生相 关的指标。我们记录了通过上述调查来获取卫生数据 以衡量这些指标的国家数量。 结论 二十七项指标可通过我们选定的调查衡量。两 项与突发卫生事件相关的指标不能被当前调查所衡 量。对比安全饮用水 (37/47 ；78.7%) 和由专业医护人 员接生的出生率 (36/47 ；76.6%)，2015 年至 2020 年间 使用调查收集以下方面数据的国家比例最低 ：结核病 (2/47 ；4.3%)、乙型肝炎 (3/47 ；6.4%)、人类免疫缺陷 病毒 (11/47 ；23.4%)、儿童发展状况和虐待儿童 ( 均为 13/47 ；27.7%)。2015 年至 2020 年间十九个国家收集了 21 项或更多指标的数据，而九个国家未收集任何指标 的数据，上述数值在 2018 年至 2020 年间分别减至六 和 20。 结论 通过审阅特定国际人口调查可提供对 47 个最不 发达国家可用卫生数据的快捷评估。造成这些国家卫 生数据空缺的主要原因是较长的调查周期以及缺乏适 当的调查手段。需要新的指标和调查工具以追踪快速 变化的突发卫生事件。 صخلم لخدلا ةطسوتمو لخدلا ةضفخنم ةلود 47 ،ةيحصلا ةيمنتلا فادهأ قيقحتل يعسلا هجاوت يتلا تانايبلا تاوجف في ،ابه ةدوجولما تارغثلاو ،تانايبلا رفاوت ىدم مييقت ضرغلا ةلصلا تاذ ةمادتسلما ةيمنتلا فادهأ قيقتح وحن مدقتلا سايق ليبس ضفخنم لخد تاذ ةراتمخ لود في ىرخلأا فادهلأاو ،ةحصلاب .طسوتم لخدو ةمظنأ مييقتل ناكسلل اًيلود اًحسم 14 انمدختسا ةقيرطلا ماوعلأا للاخ ومنلا ثيح نم ةلود 47 لقأ في ةيحصلا تانايبلا ام ديدحتل حسلما تاودأ ةعجارمب انمقو .2020 لىإ 2015 نم اًشرؤم 46 سايقل اهمادختسا نكمي تاودأ لىع يوتتح تناك اذإ دقل .اهديدحتب ةيلماعلا ةحصلا ةمظنم تماق ،ةحصلا تاشرؤم نم هذه نم تاشرؤلما نع ةرفوتم تانايب ايهدل يتلا لودلا ددع انلجس .حوسلما ةطساوب سايقلل لاباق اشرؤم نوشرعو ةعبس ناك جئاتنلا ينلباق ةيحصلا ئراوطلا اشرؤم نكي لم .اهانددح يتلا حوسلما لودلل ةيوئلما ةبسنلا تناك .ةيلالحا تاحوسلما ةطساوب سايقلل 2015 نم ةترفلا للاخ تانايبلا عملج حوسلما تمدختسا يتلا ،(4.3% ؛47/2) لسلا ضرلم ةبسنلاب ىندلأا يه 2020 لىإ ةعانلما صقن سويرفو ،(6.4% ؛47/3) ب يدبكلا باهتللااو ةلماعم ةءاسإو لفطلا ومن ةلاحو ،(23.4% ؛47/11) ةيشربلا ةنملآا بشرلا هايمب ةنراقم ،(27.7% ؛47/13 اهملاك) لافطلأا ينيئاصخأ ةطساوب ةدلاوـلا تايلمعو ،(78.7% ؛47/37) نع تانايب ةلود 19 تعجم .(76.6% ؛47/36) ةرهم ينيحص تعجم مانيب ،2020 لىإ 2015 نم ةترفلا للاخ رثكأ وأ اًشرؤم 21 2018 نم ةترفلا للاخو ؛تاشرؤم دوجو مدع نع تانايب لود 9 .لياوتلا لىع 20و 6 لىإ ماقرلأا هذه تضفخنا 2020 لىإ ميدقت لىإ ةراتخلما ةيلودلا تاحوسلما صحف ىدأ جاتنتسلاا نم لقلأا ةلود 47 ـلا في ةرفوتلما ةيحصلا تانايبلل عيسر صخلم ببسب ةيحصلا تانايبلا في ةعساو تاوجف اندجوو .ومنلا ثيح نوكتس .ةبسانلما حسلما تاودأ صقنو ،ةليوطلا حسلما تارود عيسر فقولما عبتتل ةديدج حسم تاودأو تاشرؤم لىإ ةجاح كانه .ةيحصلا ئراوطلل يرغتلا 48 Bull World Health Organ 2022;100:40–49| doi: http://dx.doi.org/10.2471/BLT.21.286254 Research Health data gaps for SDGs Luhua Zhao et al. Résumé Lacunes dans les données relatives aux objectifs de développement liés à la santé dans 47 pays à faible et moyen revenu Objectif Évaluer les données disponibles et les lacunes dans la mesure des progrès effectués pour atteindre les objectifs de développement durable liés à la santé et d'autres objectifs dans une série de pays à faible et moyen revenu. Méthodes Nous nous sommes basés sur 14 enquêtes internationales menées au sein de la population afin d'analyser les systèmes de données sur la santé dans 47 pays figurant parmi les moins développés entre 2015 et 2020. Nous avons également passé en revue le matériel d'enquête pour déterminer s'il contenait des outils pouvant servir à mesurer 46 indicateurs sanitaires, tels que définis par l'Organisation mondiale de la Santé. Enfin, nous avons recensé le nombre de pays possédant des données sur les indicateurs issus de ces enquêtes. Résultats Nous avons identifié des enquêtes dans lesquelles 27 indicateurs étaient mesurables. Les deux indicateurs relatifs aux urgences sanitaires étaient impossibles à évaluer à l'aide des enquêtes existantes. Les pourcentages les plus bas de pays recourant à des enquêtes pour récolter des données entre 2015 et 2020 s'appliquaient à la tuberculose (2/47; 4,3%), à l'hépatite B (3/47; 6,4%), au virus de l'immunodéficience humaine (11/47; 23,4%), au stade de développement des enfants et à leur maltraitance (tous deux 13/47; 27,7%). À l'autre extrémité se trouvaient l'accès à l'eau potable (37/47; 78,7%) et les accouchements assistés par du personnel qualifié (36/47; 76,6%). Dix-neuf pays ont recueilli des données sur minimum 21 indicateurs durant la période comprise entre 2015 et 2020, tandis que neuf n'en ont collecté sur aucun d'entre eux; entre 2018 et 2020, ces chiffres sont passés à 6 et 20 respectivement. Conclusion L'examen d'une sélection d'enquêtes internationales nous a fourni un bref aperçu des données disponibles dans les 47 pays les moins développés. Nous avons décelé des lacunes considérables dans les données liées à la santé, en raison des longs cycles d'enquête et du manque de matériel adéquat. De nouveaux outils et indicateurs seraient nécessaires pour suivre l'évolution rapide de la situation en cas d'urgence sanitaire. Резюме Пробелы в данных по достижению целей в области развития здравоохранения в 47 странах с низким и средним уровнем доходов Цель Оценить наличие необходимых данных и пробелы в них для измерения прогресса в достижении связанных со здоровьем целей в области устойчивого развития и других задач в отдельных странах с низким и средним уровнем доходов. Методы Авторы применили 14 международных обследований населения для оценки систем данных о состоянии здоровья населения в 47 наименее развитых странах за 2015–2020 годы. Авторы изучили средства обследований, чтобы определить, содержат ли они инструменты, которые можно использовать для измерения 46 показателей, связанных с состоянием здоровья, определенных Всемирной организацией здравоохранения. Авторы зарегистрировали количество стран, по которым доступны данные по показателям этих обследований. Результаты Двадцать семь показателей можно было измерить с помощью выбранных обследований. Два показателя чрезвычайных ситуаций в области здравоохранения не поддавались количественной оценке с помощью текущих обследований. Процент стран, которые использовали обследования для сбора данных за 2015–2020 годы, был самым низким по туберкулезу (2/47; 4,3%), гепатиту B (3/47; 6,4%), вирусу иммунодефицита человека (11/47; 23,4%), статусу развития ребенка и жестокого обращения с детьми (13/47; 27,7%) по сравнению с безопасной питьевой водой (37/47; 78,7%) и родами, проводимыми квалифицированным медицинским персоналом (36/47; 76,6%). Девятнадцать стран собрали данные по 21 или более показателям за 2015–2020 годы, в то время как девять стран не собрали данные ни по каким показателям; за 2018–2020 годы их количество сократилось до шести и 20 соответственно. Вывод Изучение отдельных международных обследований позволило получить краткую характеристику данных о состоянии здоровья в 47 наименее развитых странах. Авторы обнаружили серьезные пробелы в данных о состоянии здоровья из-за длительных циклов обследований и отсутствия соответствующих инструментов обследований. Для отслеживания быстро меняющейся ситуации чрезвычайных ситуаций в области здравоохранения потребуются новые показатели и инструменты обследования. Resumen Carencias de datos hacia los objetivos de desarrollo sanitario en 47 países de ingresos bajos y medios Objetivo Evaluar la disponibilidad y las carencias de datos para medir el progreso hacia los objetivos de desarrollo sostenible relacionados con la salud y otras metas en países seleccionados de ingresos bajos y medios. Métodos Se utilizaron 14 encuestas internacionales de población para evaluar los sistemas de datos sanitarios en 47 países menos desarrollados durante los años 2015 a 2020. Se revisaron los instrumentos de las encuestas para determinar si incluían herramientas que se pudieran aplicar para medir 46 indicadores de salud que establece la Organización Mundial de la Salud. Se registró el número de países con datos disponibles sobre los indicadores de estas encuestas. Resultados Las encuestas que se seleccionaron permitieron medir 27 indicadores, pero no fue posible medir los dos indicadores de emergencias sanitarias mediante las encuestas actuales. El porcentaje de países que aplicaron encuestas para recopilar datos entre 2015 y 2020 fue el más bajo para la tuberculosis (2/47; 4,3 %), la hepatitis B (3/47; 6,4 %), el virus de la inmunodeficiencia humana (11/47; 23,4 %), el estado de desarrollo infantil y el maltrato infantil (ambos 13/47; 27,7 %), en comparación con el agua potable (37/47; 78,7 %) y los partos a cargo de personal sanitario cualificado (36/47; 76,6 %). Diecinueve países recopilaron datos sobre 21 o más indicadores entre 2015 y 2020, mientras que 9 no recopilaron datos sobre ningún indicador; entre 2018 y 2020 estas cifras disminuyeron a 6 y 20, respectivamente. Conclusión El análisis de encuestas internacionales seleccionadas proporcionó un breve resumen de los datos sanitarios disponibles en los 49Bull World Health Organ 2022;100:40–49| doi: http://dx.doi.org/10.2471/BLT.21.286254 Research Health data gaps for SDGsLuhua Zhao et al. 47 países menos desarrollados. 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