The Institute for Health Metrics and Evaluation (IHME) calculated the annualized rate of change for each health indicator tracked by them in this report for three time periods: from 2015 to 2021, from 2021 to 2030 as predicted by the reference scenario forecast, and the rate of change that would be required to meet the SDG target between 2021 and 2030. We compared these rates of change to evaluate the extent that our past and expected progress compared to what would be required to meet the SDG targets. For most of the indicators that we track with IHME, the pace of change needs to increase at least fivefold to meet the target by 2030.
A dramatic shift toward progress in the HIV/AIDS epidemic
UNAIDS. (2022). Trends of AIDS-related deaths, 2000–2022 [Data set]. AIDSinfo. Retrieved August 2022. https://aidsinfo.unaids.org/
Global Fund. (2021). Trends in AIDS-related deaths [Figure]. In Results Report 2021 (p. 25). Retrieved August 2022. https://www.theglobalfund.org/en/results/#download
Gender equality depends on women having power, not just “empowerment”
UN Women. (2022, June 14). Are we on track to achieve gender equality by 2030? https://data.unwomen.org/features/are-we-track-achieve-gender-equality-2030
The years to gender equality estimate is based on data from the Equal Measures 2030 (EM2030) SDG Gender Index. An independent audit of the 2022 SDG Gender Index was carried out by the European Commission’s Competence Centre on Composite Indicators and Scoreboards (JRC-COIN). Note: In the data, the estimated year to reach gender equality assumes that: (i) the 2021 global measured rate of progress in 2021 will be maintained until 2030; and (ii) a generation is approximately 28 years.
Hawke, A. & Equal Measures 2030. (2022). ‘Back to normal’ is not enough: The 2022 SDG Gender Index. EM30. https://www.equalmeasures2030.org/wp-content/uploads/2022/03/SDG-index_report_FINAL_EN.pdf
European Commission, Joint Research Centre, Caperna, G., Kovacic, M., & Papadimitriou, E. (2022). JRC statistical audit of the Equal Measures 2030 SDG Gender Index 2022. Luxembourg: Publications Office of the European Union. https://doi.org/10.2760/993717
The economic side effects of COVID-19
International Labour Organization (ILO). (2022, February). The gender gap in employment: What’s holding women back? https://www.ilo.org/infostories/en-GB/Stories/Employment/barriers-women#intro
International Labour Organization (ILO). (2022, May). ILO Monitor on the world of work. (9th ed.). https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_845642.pdf
International Labour Organization (ILO). (2022). Unemployment rate by sex and age — ILO modelled estimates [Data set]. ILOSTAT. Accessed July 2022. https://ilostat.ilo.org/data/
World Bank Group. (2022). Women, Business and the Law 2022. https://doi.org/10.1596/978-1-4648-1817-2. License: Creative Commons Attribution CC BY 3.0 IGO.
The difference between having money—and being able to spend it
Gentilini, U. (2022, July 13). Ten lessons from the largest scale up of cash transfers in history. World Bank Blogs: Let’s Talk Development. https://blogs.worldbank.org/developmenttalk/ten-lessons-largest-scale-cash-transfers-history
Alfers, L., Braham, C., Chen, M., Grapsa, E., Harvey, J., Ismail, G., Ogando, A. C., Reed, S. O., Roever, S., Rogan, M., Sinha, S., Skinner, C., & Valdivia, M. (2022). COVID-19 and informal work in 11 cities: Recovery pathways amidst continued crisis (WIEGO Working Paper No. 43). Women in Informal Employment: Global and Organizing (WIEGO). https://www.wiego.org/publications/covid-19-and-informal-work-11-cities-recovery-pathways-amidst-continued-crisis
Riley, E. (2020). Resisting social pressure in the household using mobile money: experimental evidence on microenterprise investment in Uganda (CSAE Working Paper Series No. WPS/2022-04). (S. Quinn, Ed.). Center for the Study of African Economies (CSAE), University of Oxford. 2022-04(04). https://ora.ox.ac.uk/objects/uuid:b7ed6a67-88a9-4714-a419-b4c43decc7e8/download_file?file_format=&safe_filename=Riley_2022_Resisting_social_pressure.pdf&type_of_work=Working+paper
Aker, J. C., Boumnijel, R., McClelland, A., & Tierney, N. (2016). Payment mechanisms and antipoverty programs: Evidence from a mobile money cash transfer experiment in Niger. Economic Development and Cultural Change, 65(1), 1–37. https://doi.org/10.1086/687578
Demirgüç-Kunt, A., Klapper, L., Singer, D., & Ansar, S. (2022). The Global Findex database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-1897-4. License: CC BY 3.0 IGO.
The difference between a job being available—and being able to take it
International Labour Organization (ILO). (2018). Care work and care jobs for the future of decent work. https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_633135.pdf
A smart investment in women, families, and societies
Fraym. (2022). Caregiving return on investment: Kenya summary. https://fraym.io/wp-content/uploads/2022/05/Child_Caregiving_Return_on_Investment-Study-Kenya_Summary_Report.pdf
Fraym. (2022). South Africa caregiving return on investment: Complete report. https://fraym.io/wp-content/uploads/2022/05/Estimating-the-Return-on-Investment-of-Child-Caregiving-Programs_Study-of-South-Africa_April-2022.pdfFraym. (2022). Caregiving return on investment: Nigeria summary. https://fraym.io/wp-content/uploads/2022/05/Fraym_Caregiving-ROI_-Nigeria-Report.pdf
A bright spot of progress—and opportunity
World Bank Group. (2022). Gender gap in financial account ownership in LMICs, 2017–2022 [Data set]. Global Findex Database. Retrieved July 2022. https://databank.worldbank.org/source/global-financial-inclusion
Field, E., Pande, R., Rigol, N., Schaner, S., & Moore, C. T. (2021). On her own account: How strengthening women’s financial control impacts labor supply and gender norms. American Economic Review, 111(7), 2342–2375. https://doi.org/10.1257/aer.20200705
We need to change how we think about world hunger
Food and Agriculture Organization of the United Nations (FAO). (2022, June 10). The importance of Ukraine and the Russian Federation for global agricultural markets and the risks associated with the war in Ukraine. Accessed June 2022. https://www.fao.org/3/cb9013en/cb9013en.pdf
Baffes, B. & Temaj, K. (2022, May 25). Food prices continued their two-year-long upward trajectory. World Bank Blogs: Data Blog. https://blogs.worldbank.org/opendata/food-prices-continued-their-two-year-long-upward-trajectory
Food aid to low-income countries is at record levels—and rising
Organisation for Economic Co-operation and Development (OECD). (2022). OECD Data: Food aid [Graph]. Accessed July 2022. https://data.oecd.org/oda/food-aid.htm
The size of your crop often depends on where you live
This internal analysis was developed from Food and Agriculture Organization FAOSTAT data. Note: The average area-weighted crop yield (AAWY) is calculated by (i) summing the total production for the main staple crops, (ii) summing the total area planted to those crops, and then dividing (i) by (ii), using FAOSTAT data. AAWY must be calculated separately for seed-propagated and vegetatively propagated crops, due to the great difference in water content of the two crop types. Considering national trends in AAWY, rather than individual commodities, provides insights into overall national conditions for intensification. AAWY is expected to be less affected by the weather variability that often impacts individual crops at sensitive stages while having little effect on crops at other stages. It is also less subject to the effect of variations in crop prices that arise from policies focusing on one value chain, or from global commodity price swings driven by events outside the region. AAWY also allows crop yield trends to be compared across countries with different crop mixes. It is a national index of the extent to which governments are successful in facilitating access to production inputs, output markets, and production information.
Food and Agriculture Organization of the United Nations (FAO). (2022.) Crop and livestock products [Data set]. FAOSTAT. Accessed April 8, 2022. https://www.fao.org/faostat/en/#data/QCL. License: CC BY-NC-SA 3.0 IGO.
Current domestic production isn’t enough to feed Africa
Food and Agriculture Organization of the United Nations (FAO). (2022.) Food balances [Data set]. FAOSTAT. Accessed July 27, 2022. https://www.fao.org/faostat/en/#data/FBS. License: CC BY-NC-SA 3.0 IGO.
Ekpa, O., Palacios-Rojas, N., Kruseman, G., Fogliano, V., & Linnemann, A. R. (2019). Sub-Saharan African maize-based foods - Processing practices, challenges and opportunities. Food Reviews International, 35(7), 609–639. https://doi.org/10.1080/87559129.2019.1588290
Jeschke, M. (2021, September 27). Heat stress effects on corn. Pioneer. https://www.pioneer.com/us/agronomy/heat-stress-corn.html
Waqas, M. A., Wang, X., Zafar, S. A., Noor, M. A., Hussain, H. A., Nawaz, M. A., & Farooq, M. (2021). Thermal stresses in maize: Effects and management strategies. Plants, 10(2), 293. https://doi.org/10.3390/plants10020293
Lobell, D., Bänziger, M., Magorokosho, C., & Bindiganavile, SV. (2011). Nonlinear heat effects on African maize as evidenced by historical yield trials. Nature Climate Change. 1(1), 42–45. https://doi.org/10.1038/nclimate1043
Sub-Saharan Africa’s most important crops are at risk
Agriculture Adaptation Atlas. Hazard Index: Heat stress maize [Data set]. Accessed July 27, 2022. adaptationatlas.cgiar.org
Graham, C. (2020). Quantifying future heat stress in crops in sub-Saharan Africa [Unpublished].
Food and Agriculture Organization (FAO), International Fund for Agricultural Development (IFAD), UNICEF, World Food Programme (WFP), & World Health Organization (WHO). (2022). The state of food security and nutrition in the world 2022: Repurposing food and agricultural policies to make healthy diets more affordable. FAO. https://doi.org/10.4060/cc0639en
University of Washington Evans School Policy Analysis and Research Group (EPAR) analysis based on the Nigeria General Household Panel Survey and part of the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) of the World Bank. The figure in the report that shows “Percent of rural agricultural households using various coping mechanisms to respond to the experience of climate and agricultural production shocks in 2010 and 2018” compiles data across four surveys.
How can farmers fight climate change? Magic seeds
African Agriculture Technology Foundation. (2021). Impact Evaluation of the WEMA Project in East African countries of Kenya, Tanzania, and Uganda [Unpublished].
Dhillon, B. & Gill, R. (2022, May 30). Short-duration varieties are turning the tide. The Tribune India. Accessed July 2022. https://www.tribuneindia.com/news/features/short-duration-varieties-are-turning-the-tide-399427
A missed opportunity to solve hunger over the long term
Ceres2030 and International Food Policy Research Institute (IFPRI) analysis of data from the Organisation for Economic Co-operation and Development Development Assistance Committee Creditor Reporting System.
Organisation for Economic Co-operation and Development (OECD) Development Assistance Committee Creditor Reporting System data. (2022). OECD.Stat. Accessed July 2022. https://stats.oecd.org/
AI for Ag
International Maize and Wheat Improvement Center (CIMMYT). (2019, November 4). Scientists develop an early warning system that delivers wheat rust predictions directly to farmers’ phones [Press release]. Accessed July 2022. https://www.cimmyt.org/news/scientists-develop-an-early-warning-system-that-delivers-wheat-rust-predictions-directly-to-farmers-phones/
Allen-Sader, C., Thurston, W., Meyer, M., Nure, E., Bacha, N., Alemayehu, Y., Stutt, R. O. J. H, Safka, D., Craig, A. P., Derso, E., Burgin, L. E., Millington, S. C., Hort, M. C., Hodson, D. P., & Gilligan, C. A. (2019). An early warning system to predict and mitigate wheat rust diseases in Ethiopia. Environmental Research Letters, 14(11), 115004. https://doi.org/10.1088/1748-9326/ab4034
Explore the Data
IHME General Methodology
IHME produced estimates and forecasts for 13 of the SDG indicators included in the Goalkeepers Report. To estimate the effects of the COVID-19 pandemic on the SDG indicators and their accompanying projections to 2030, IHME implemented an approach as shown in Figure 1 for capturing both the short-term effects of the COVID-19 pandemic as well as the long-term effects that reflect its impact on economic production, educational attainment, and development, and the consequences of these on the SDG indicators.
COVID-19 projections of infections, deaths, and mobility: These projections are based on a hybrid Susceptible-Exposed-Infectious-Recovered or SEIR model, in which the model analyzes data on cases, hospitalizations, deaths, and seroprevalence studies to estimate past infections. The SEIR model then fits a statistical model of transmission to the past infections using a range of drivers, including mobility (predicted by mandates and the underlying trend), mask use, and the pneumonia death rate seasonal pattern. It also takes into account variants of concern (e.g., delta, omicron) as well as the scale-up of COVID-19 vaccines by type, including their specific effect by variant.
For the purposes of this report, projections were extended to December 31, 2023.
Effect of the COVID-19 pandemic and the Ukraine-Russia conflict on health indicators as mediated by income, education, and development: To capture the impact of COVID-19 and the Ukraine-Russia conflict, IHME incorporated the current and projected effects of both on GDP per capita to 2030.
Historic data on GDP per capita from 1950 to 2021 was extracted from five leading sources of these estimates. Real GDP growth rates extending to 2027 using short-term projections from the World Bank (2022), International Monetary Fund (2022), and United Nations (UN Department of Economic and Social Affairs, 2022). These three sources reflect both the COVID-19 pandemic and the Ukraine-Russia conflict on GDP. A single series of GDP per capita for 1950 through 2027 was then generated using Gaussian processes, incorporating data from all GDP data sources and propagating uncertainty through the estimates. For 2025 to 2027, a weighted average of forecasts generated by the Gaussian process regression and forecasts generated for these years by IHME’s long-term forecasting methods were used. For estimating annual economic growth beyond 2027 and developing alternative scenarios, IHME used its existing IHME GDP forecasting ensemble framework.
In addition to modeling the impacts of the COVID-19 pandemic on GDP, IHME estimated the disruptions in schooling on educational attainment. IHME used daily school closure data from the Oxford COVID-19 Government Response Tracker, government-mandated primary documents, and local and international news sources. Closure data is split into primary and secondary education; IHME used UNESCO data to map primary and secondary closure data to specific ages in each country.
Short-term disruptions to education were applied to age-specific cohorts and converted to all-age period-based measures of educational attainment. IHME reflected revised GDP and education projections described above in its projections of the Socio-demographic Index (SDI), which incorporates income, fertility, and education. Using the existing framework, IHME also estimated the downstream consequences of reduced GDP on health spending and development assistance of health. Key drivers were identified for each indicator and were in turn used for the projections to 2030. This approach allows for the forecasted COVID-19 and Ukraine conflict impacts on GDP and education to be reflected in the projections to 2030.
Reiner, R. C. Jr., Collins, J. K., Forecasting Team C-19, & Murray, C. J. L. (2022, June 20.) Forecasting the trajectory of the COVID-19 pandemic under plausible variant and intervention scenarios: A global modelling study. SSRN. https://doi.org/10.2139/ssrn.4126660
Barber, R. M., Sorensen, R. J. D., Pigott, D. M., Bisignano, C., Carter, A., Amlag, J. O., Collins, J. K., Abbafati, C., Adolph, C., Allorant, A., Aravkin, A. Y., Bang-Jensen, B. L., Castro, E., Chakrabarti, S., Cogen, R. M., Combs, E., Comfort, H., Cooperrider, K., Dai, X., . . . Murray, J. L. (2022). Estimating global, regional, and national daily and cumulative infections with SARS-CoV-2 through Nov 14, 2021: A statistical analysis. The Lancet. 399(10344), 2351–2380. https://doi.org/10.1016/S0140-6736(22)00484-6
Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The next generation of the Penn World Table. American Economic Review, 105(10), 3150–3182. http://dx.doi.org/10.1257/aer.20130954
International Monetary Fund. (2022). World economic outlook, April 2022: War sets back the global recovery. https://www.imf.org/en/Publications/WEO/Issues/2022/04/19/world-economic-outlook-april-2022
World Bank. (2022). Global economic prospects. https://doi.org/ 10.1596/978-1-4648-1843-1. License: Creative Commons Attribution CC BY 3.0 IGO.
United Nations Department of Economic and Social Affairs. (2022). World economic situation and prospects: May 2022 briefing, no. 160. https://www.un.org/development/desa/dpad/publication/world-economic-situation-and-prospects-may-2022-briefing-no-160/
Bolt, J., & van Zanden, J. L. (2020). Maddison style estimates of the evolution of the world economy. A new 2020 update (Maddison-Project Working Paper No. WP-15). https://www.rug.nl/ggdc/historicaldevelopment/maddison/publications/wp15.pdf
World Bank. (2022). World Development Indicators [Database]. Accessed July 2022. https://databank.worldbank.org/source/world-development-indicators
Micah, A. E., Cogswell, I. E., Cunningham, B., Ezoe, S., Harle, A. C., Maddison, E. R., McCracken, D., Nomura, S., Simpson, K. E., Stutzman, H. N., Tsakalos, G., Wallace, L. E., Zhao, Y., Zende, R. R., Abbafati, C., Abdelmasseh, M., Abedi, A., Abegaz, K. H., Abhilash, E. S., . . . Dieleman, J. L. (2021). Tracking development assistance for health and for COVID-19: A review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990–2050. The Lancet, 398(10308), 1317–1343. https://doi.org/10.1016/S0140-6736(21)01258-7
Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Pethereick, A., Phillips, T., Webster, S., Cameron-Blake, E., Hallas, L., Majumdar, S., & Tatlow, H. (2021). A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature Human Behavior, 5(4), 529–538. https://doi.org/10.1038/s41562-021-01079-8
Desvars-Larrive, A., Dervic, E., Haug, N., Niederkrotenthaler, T., Chen, J., Di Natale, A., Lasser, J., Gliga, D. S., Roux, A., Sorger, J., Chakraborty, A., Ten, A., Dervic, A., Pacheco, A., Jurczak, A., Cserjan, D., Lederhilger, D., Bulska, D., Berishaj, D., . . . Thurner, S. (2020). A structured open data set of government interventions in response to COVID-19. Scientific Data, 7(285). https://doi.org/10.1038/s41597-020-00609-9
Adolph, C., Amano, K., Bang-Jensen, B. L., Fullman, N., & Wilkerson, J. (2021). Pandemic politics: Timing state-level social distancing responses to COVID-19. Journal of Health Politics, Policy and Law; 46:(2), 211–233. https://doi.org/10.1215/03616878-8802162
International Business Machines Research (IBM). (2021). Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC) [Data set]. Accessed June 6, 2022. https://ibm.github.io/wntrac/
KFF. (2022). State COVID-19 data and policy actions [Data set]. Accessed July 27, 2022. https://www.kff.org/coronavirus-covid-19/issue-brief/state-covid-19-data-and-policy-actions/
UNESCO. (2016.) ISCED mappings. Accessed July 27, 2022. http://uis.unesco.org/en/isced-mappings
Effect of the COVID-19 pandemic on health service delivery in 2020–2022: Health services have also been disrupted due to the implementation of social distancing mandates and lockdowns. IHME relied on multiple data sources to estimate this acute effect of the pandemic on health indicators:
- Smartphone-based surveys
- The University of Maryland Social Data Science Center Global COVID-19 Trends and Impact Survey (UMD Global CTIS), in partnership with Facebook (UMD Global CTIS)
- United States Census Household Pulse surveys
- Performance Monitoring for Action (PMA) surveys that incorporate a telephone-based follow-up of respondents from surveys prior to the pandemic (Kenya, Burkina Faso, Nigeria, Democratic Republic of the Congo)
- Administrative data
Smartphone-based surveys were conducted during 2020 and 2021 for the following target populations:
- General population: Main indicators include health care use and missed medication doses. Respondents to the 2020 survey were invited to respond to a new survey in 2021.
- General population in malaria endemic countries: Main indicators include receipt of insecticide-treated bed nets (ITN) and artemisinin-based combination therapy (ACT) use among respondents reporting a positive malaria test. Respondents to the 2020 survey were invited to respond to a new survey in 2021.
- Women of reproductive age: Main indicators are use of contraceptives and interruption in access to contraceptives. Respondents to the 2020 survey were invited to respond to a new survey in 2021.
- Pregnant women or women who have given birth in the last six months: Main indicators include number of antenatal care visits, in-facility delivery, and presence of a skilled birth attendant. A new sample of eligible participants was recruited in 2021.
- Caregivers of children under the age of 2 years:The main indicator is vaccine coverage. A new sample of eligible participants was recruited in 2021.
- Caregivers of school-age children: This survey was conducted only in 2021.
Probability weights were estimated for the data according to the age, sex, and educational attainment profiles by country. Survey data were used to estimate the change in the key indicators or underlying drivers among three time periods: the pre-pandemic period (December 2019–February 2020), during the early months of the pandemic (March 2020–June 2020), and later in the pandemic (February 2021–May 2021). Additional information and raw data from these surveys are available for download at https://ghdx.healthdata.org/series/covid-19-health-services-disruption-survey.
In addition to the survey data collection noted above, IHME also analyzed survey data from: (i) the University of Maryland Social Data Science Center Global COVID-19 Trends and Impact Survey (UMD Global CTIS), in partnership with Facebook (UMD Global CTIS), which asks respondents about missed health service use (services, products, treatment, and medicine) over the past 30 days; and (ii) United States Census Household Pulse surveys.
For the analysis of health visit disruption, each of the sources asked had different questions, recall periods, and study populations. In the primary data collection survey, IHME constructed an indicator of the average number of monthly health visits among those who had a need to see a health provider for any condition excluding preventive or routine care relying on the following questions and a three- to four-month recall period:
- During February 2021–May 2021, did you have a need to see a health provider?
- What health condition(s) required you to see a health provider during February 2021-May 2021?
(There were many choices here, and respondents could choose one or more. IHME excluded respondents with a need to see a health provider for only “preventive or routine care.”)
- Were you able to see a health provider during February 2021–May 2021?
- How many times did you see a health care provider?
In the UMD Global CTIS survey, IHME constructed an indicator of the proportion of respondents who were able to receive needed health services with a recall period of 30 days based on the following questions:
- In the last 30 days, was there any time when you needed any of the following health services or products but could not get it?
- Emergency transportation services or emergency rescue
- Medical care with overnight stay in any type of facility
- Medical or dental care or treatment without an overnight stay
- Preventive health services (including immunization/vaccination, family planning, prenatal/postnatal care, routine check-up services)
- Mask, medical gloves, or other protective equipment
- Eyeglasses, hearing aid, crutches, adhesive bandages/plasters, thermometer, or any other health product
IHME included the first three options, including all non-routine and preventive health visits. In the United States Household Pulse surveys, they constructed an indicator of the proportion of people who were able to receive all needed non-COVID-19 medical care with a recall period of four weeks from the following question:
- At any time in the last 4 weeks, did you need medical care for something other than coronavirus, but DID NOT GET IT because of the coronavirus pandemic?
In the PMA survey data sets, IHME constructed an indicator of the proportion of respondents who indicated they were using contraception in two time periods, a baseline period (December 2019–February 2020) from the phase 1 household and female surveys (HQFQ) and a follow-up period (a COVID-19 follow-up period (June 2020–July 2020) from the same respondents. Evaluation of this data set did not suggest a significant disruption, and thus this data set did not get used in the construction of the health service disruptions inputs.
IHME also analyzed available administrative data for the pandemic period, including administrative vaccine coverage data for three-dose diphtheria-tetanus-pertussis (DTP3) and measles-containing-vaccine first-dose (MCV1) reported to WHO, antiretroviral therapy (ART) doses reported to UNAIDS, inpatient utilization data for selected countries, and mortality data by cause for selected countries.
IHME related the level of health service interruption (e.g., relative ratio of health visits) to measures of cell phone mobility developed as part of the COVID-19 projections. These mobility estimates are based on data from Facebook, Google, Descartes Labs, and SafeGraph; this data has been shown to be highly related to the imposition of social distancing mandates by governments. To estimate the relationship between health service interruption and mobility, IHME used a two-step random-spline meta-regression. The first step captures the nonlinear relationship between the level of interruption and mobility changes, and it allows for variation in this relationship by country. The second step analyzes the residuals over time to capture temporal changes in the relationship by country. This function is then combined with the projections of mobility to December 31, 2021, to estimate changes in indicators or drivers for 2020 and 2021. These are then used to adjust the long-term projections as described earlier in this section.
Finally, IHME reviewed the published literature about estimated disruptions to specific indicators (e.g., neglected tropical diseases). Those adjustments are detailed within the indicator-specific methods below.
Limitations: Given reporting lags, there was limited data, particularly from administrative data sources, for both economic and specific health indicators. Although IHME collected important survey data, the representativeness of the sample is limited by the mode of collection (smartphone), and the precision of several indicator estimates is limited by small sub-sample sizes.
International Telecommunications Union (ITU). (2021). Measuring digital development: Facts and figures 2021. Geneva, Switzerland: ITU. Accessed July 27, 2022. https://www.itu.int:443/en/ITU-D/Statistics/Pages/facts/default.aspx
University of Maryland Social Data Science Center & Facebook. (2022). The University of Maryland Social Data Science Center Global COVID-19 Trends and Impact Survey [Data set]. Accessed June 2022. https://covidmap.umd.edu/
U. S. Census Bureau. (2022). Household Pulse Survey: Measuring household experiences during the coronavirus pandemic [Data set]. Accessed July 27, 2022. https://www.census.gov/householdpulsedata
Performance Monitoring for Action. (2020). Performance Monitoring for Action Household Survey. https://www.pmadata.org/data
Reiner, R.C. Jr., Collins, J. K., Forecasting Team C-19, & Murray, C. J. L. (2022, June 20). Forecasting the trajectory of the COVID-19 pandemic under plausible variant and intervention scenarios: A global modelling study. SSRN. https://doi.org/10.2139/ssrn.4126660
Zheng, P., Barber, R., Sorensen, R. J. D., Murray, C. J. L., & Aravkin, A. Y. (2021). Trimmed constrained mixed effects models: Formulations and algorithms. Journal of Computational and Graphical Statistics, 30(3), 544–556. https://doi.org/10.1080/10618600.2020.1868303
Indicators Estimated by IHME
IHME measures stunting prevalence as height-for-age more than two standard deviations below the reference median on the height-age growth curve, based on WHO 2006 growth standards for children of age 0–59 months. Projections to 2030 were modeled using an ensemble approach to forecast the prevalence of stunting, using SDI as a key driver in order to capture the effects of the COVID-19 pandemic on income per capita and education.
Estimates in Global Burden of Disease (GBD) 2020 leveraged several methodological advances including ensemble model predictions for severity-specific stunting prevalence and mean height-for-age z-scores (HAZ), further disaggregation of <5 age groups, and an improved distribution fitting model that focuses on HAZ scores of < -2 (i.e., under the range for stunting) rather than across the full range of HAZ scores. This led to improved estimates in a number of countries, notably including South Africa, the Democratic Republic of the Congo, India, and Pakistan. In addition, new data has improved estimates in a number of countries as well, including Pakistan.
Maternal Mortality Ratio
The maternal mortality ratio (MMR) is defined as the number of maternal deaths among women ages 15–49 years during a given time period per 100,000 live births. It depicts the risk of maternal death relative to the number of live births and essentially captures the risk of death in a single pregnancy or a single live birth. Projections to 2030 were modeled using an ensemble approach to forecast MMR, using SDI as a key driver in order to capture the effects of the COVID-19 pandemic on income per capita and education.
Our analysis of direct and indirect maternal mortality in selected countries showed no significant relationship between direct mortality and indicators of the COVID-19 pandemic (i.e., COVID-19 infection incidence rate, COVID-19 mortality rate, changes in mobility). However, there was a significant effect of the COVID-19 pandemic on indirect maternal mortality. This effect on indirect maternal mortality was modeled using COVID-19 mortality rate as a covariate. This year, our estimates of excess indirect maternal mortality also include a correction for the proportion of deaths that are considered incidental, or unrelated to pregnancy status. Currently available data does not suggest a consistent relationship between the pandemic and indicators of maternal care (antenatal care, skilled birth attendance), and IHME has not incorporated an effect of the pandemic on these indicators.
Under-5 Mortality Rate
IHME defines the under-5 mortality rate (U5MR) as the probability of death between birth and age 5. It is expressed as number of deaths per 1,000 live births. Projections were based on a combination of key drivers, including GBD risk factors, selected interventions (e.g., vaccines), and SDI. Additional short-term disruptions (2020–2021) from the COVID-19 pandemic incorporated the reductions seen in child deaths from infectious diseases (flu, respiratory syncytial virus, measles, pertussis) observed during the pandemic, driven primarily by social distancing and mask use, as well as increases in child deaths due directly to COVID-19. Most of the changes in U5MR estimates in the current Goalkeepers Report results came from new and additional input mortality data that IHME has incorporated since the GBD 2019 study, including estimates of excess mortality observed during the COVID-19 pandemic.
Wang, H., Paulson, K. R., Pease, S. A., Watson, S., Comfort, H., Zheng, P., Aravkin, A. Y., Bisignano, C., Barber, R. M., Alam, T., Fuller, J. E., May, E. A., Jones, D. P., Frisch, M. E., Abbafati, C., Adolph, C., Allorant, A., Amlag, J. O., Bang-Jensen, B. L., . . . Murray, C. J. L. (2022). Estimating excess mortality due to the COVID-19 pandemic: A systematic analysis of COVID-19-related mortality, 2020–21. The Lancet, 399(10334), 1513–1536. https://doi.org/10.1016/S0140-6736(21)02796-3
Neonatal Mortality Rate
IHME defines the neonatal mortality rate as the probability of death in the first 28 completed days of life. It is expressed as the number of deaths per 1,000 live births. Projections were based on a combination of key drivers, including GBD risk factors, selected interventions (e.g., vaccines), and SDI. Most of the changes in neonatal mortality estimates in this year’s Goalkeepers Report are the result of new data, including estimates of excess mortality observed during the COVID-19 pandemic.
Wang, H., Paulson, K. R., Pease, S. A., Watson, S., Comfort, H., Zheng, P., Aravkin, A. Y., Bisignano, C., Barber, R. M., Alam, T., Fuller, J. E., May, E. A., Jones, D. P., Frisch, M. E., Abbafati, C., Adolph, C., Allorant, A., Amlag, J. O., Bang-Jensen, B. L., . . . Murray, C. J. L. (2022). Estimating excess mortality due to the COVID-19 pandemic: A systematic analysis of COVID-19-related mortality, 2020–21. The Lancet, 399:(10334), 1513–1536. https://doi.org/10.1016/S0140-6736(21)02796-3
IHME estimates the HIV rate as new HIV infections per 1,000 population. Forecasts of HIV incidence were based on forecasted ART, prevention of maternal-to-child transmission (PMTCT) coverage, and incidence as inputs into a modified version of Avenir Health’s Spectrum software. Adult ART is forecasted using the expected spending on HIV curative care—which in turn was forecasted based on income per capita, including the effect of the COVID-19 pandemic—and ART prices. GBD estimates incorporated methodological changes to cause of death data for HIV as well as the adjustment of incidence estimates, to be consistent with vital registration data.
Mahy, M., Penazzato, M., Ciaranello, A., Mofenson, L., Yiannoustsos, C., Davies, M-A., &Stover, J. (2017). Improving estimates of children living with HIV from the Spectrum AIDS Impact Model. AIDS, 31(Suppl 1), S13 - S22. https://doi.org/10.1097/QAD.0000000000001306
Eaton, J. W., Brown, T., Puckett, R., Glaubius, R., Mutai, K., Bao, L., Salomon, J. A., Stover, J., Mahy, M., & Hallett, T. B. (2019). The Estimation and Projection Package Age-Sex Model and the r-hybrid model: New tools for estimating HIV incidence trends in sub-Saharan Africa. AIDS, 33(Suppl 3), S235–S44. https://doi.org/10.1097/QAD.0000000000002437
Jahagirdar, D., Walters, M. K., Novotney, A., Brewer, E. D., Frank, T. D., Carter, A., Biehl, M. H., Abbastabar, H., Abhilash, E. S., Abu-Gharbieh, E., Abu-Raddad, L. J., Adekanmbi, V., Adeyinka, D. A., Adnani, Q. E. S., Afzal, S., Aghababaei, S., Ahinkorah, B. O., Ahmad, S., Ahmadi, K., & Kyu, H. H. (2021). Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019. The Lancet HIV, 8(10), e633-651. https://doi.org/10.1016/S2352-3018(21)00152-1
IHME estimates new and relapse tuberculosis (TB) cases diagnosed within a given calendar year (incidence) using data from prevalence surveys, case notifications, and cause-specific mortality estimates as inputs to a statistical model that enforces internal consistency among the estimates. GBD estimates in this round incorporate methodological improvements in using case notification data.
IHME evaluated the literature on COVID-19 disruptions to TB incidence and identified three types of studies: studies reporting raw data on diagnosis and treatment in 2020, studies reporting on service disruption from new surveys, and studies reporting on models of TB impacts using notification data or theoretical COVID scenarios. Due to the lack of counterfactual data in pre-pandemic time periods and modeling assumptions used in the current studies, IHME could not estimate an additional disruption in TB incidence due to COVID-19. IHME will continue to evaluate and analyze as more data is released. In addition to historical trends, projections to 2030 were modeled using an ensemble approach to forecast the incidence of TB, using SDI as a key driver in order to capture the effects of the COVID-19 pandemic on income per capita and education
IHME estimates the malaria rate as the number of new cases per 1,000 population. To estimate malaria incidence in 2020 and 2021, IHME takes into account updated reports regarding pandemic-related disruptions to malaria interventions and effective treatment with an antimalarial drug (which includes ITN, indoor residual spraying, antimalarial treatment, and drug effectiveness). These reports were used to apply an adjustment to estimates of antimalarial treatment coverage, which were then used to produce estimates of malaria incidence. Projections to 2030 were derived using an ensemble model. First, coverage of ACT and ITNs is forecast as a function of the SDI, which is predicted in turn by projections of income per capita and education. For countries where there exists available data on intervention coverage, malaria incidence is forecasted through 2030 using an ensemble approach, incorporating past trends and forecasts of ACT and ITN coverage to produce the projections. For countries where there is no available data on ACT or ITN coverage, an ensemble approach is used based on past trends in incidence as well as projections of SDI, which incorporates the effects of the COVID-19 pandemic through income per capita and education.
Due to reporting lags, there is still relatively little data to inform pandemic-related impacts on malaria incidence. The WHO global pulse surveys, which were used to adjust 2020 and 2021 incidence results, were applied only to countries in sub-Saharan Africa due to a lack of comparable method for applying the adjustment to other regions arising from the difference in incidence estimation. Furthermore, although those pulse surveys currently allow us to begin trying to capture malaria pandemic-related impacts, the surveys were completed by national-level health officials and capture only their individual assessment of how the pandemic has affected care seeking.
World Health Organization.(2020, August). Pulse survey on continuity of essential health services during the COVID-19 pandemic: Interim report, 27 August 2020. Accessed November 2021. https://www.who.int/publications/i/item/WHO-2019-nCoV-EHS_continuity-survey-2020.1
World Health Organization. (2021, April). Second round of the national pulse survey on continuity of essential health services during the COVID-19 pandemic: January-March 2021 (Interim report). Accessed November 2021. https://www.who.int/publications/i/item/WHO-2019-nCoV-EHS-continuity-survey-2021.1
Neglected Tropical Diseases
IHME measures the sum of the prevalence of 15 NTDs per 100,000 that are currently measured in the annual Global Burden of Disease study: human African trypanosomiasis, Chagas disease, cystic echinococcosis, cysticercosis, dengue, food-borne trematodiases, Guinea worm, soil-transmitted helminths (STH, comprising hookworm, trichuriasis, and ascariasis), leishmaniasis, leprosy, lymphatic filariasis, onchocerciasis, rabies, schistosomiasis, and trachoma. Since the 2020 Goalkeepers Report, changes in historical trends in this indicator reflect updates to the estimated prevalence of each NTD made for the GBD 2020 study. Specifically, changes in the summary NTD prevalence indicator between the 2020 Goalkeepers Report and these estimates largely reflect the addition of new data to STH models, especially in Latin America and South Asia.
In the 2021 Goalkeepers Report, IHME did not estimate a COVID-19 effect on this indicator, due to limited surveillance and control program data availability. Modeling studies and available data suggest that the COVID pandemic likely resulted in disruptions to NTD epidemiology, though these disruptions are likely to vary by disease and location and may be variably amenable to mitigation through increased control efforts. Although modeling studies can characterize potential disruptions under various scenarios, reliable data to quantify the true magnitude of pandemic effects on NTD epidemiology are sparse.
For this year’s report, IHME searched for published and gray literature quantifying the impact of the COVID-19 pandemic on NTD prevalence. Due to data gaps, lags in availability, and challenges in accounting for the likely disruptions to NTD surveillance during the pandemic, IHME found evidence to support adjustment for COVID-19 disruptions only for dengue. IHME adjusted dengue estimates in 2020 and 2021 using country-specific estimates of COVID disruptions from Chen et al. (2022), including updated estimates for 2021 graciously provided by the study authors via personal communication. For 2020, IHME adjusted only the proportion of cases occurring in April through December, reflecting the timing of the start of the pandemic; for 2021, IHME adjusted the full year. IHME excluded Brazil from the country-specific analysis due to data inconsistencies. For countries not estimated directly by this analysis, IHME applied regional or global disruption ratios. Projections to 2030 used an ensemble model, driven both by trends in the past as well as projections of SDI, which incorporated disruptions from the COVID-19 pandemic on income per capita and education.
Hollingsworth, T. D., Mwinzi, P., Vasconcelos, A., & de Vlas, S. J. (2021). Evaluating the potential impact of interruptions to neglected tropical disease programmes due to COVID-19. Transactions of The Royal Society of Tropical Medicine and Hygiene, 115(3), 201–204. https://doi.org/10.1093/trstmh/trab023
Chen, Y., Li, N., Lourenço, J., Wang, L., Cazelles, B., Dong, L., Li, B., Liu, Y., Jit, M., Bosse, N. I., Abbot, S., Velayudhan, R., Wilder-Smith, A., Tian, H., & Brady, O. J. (2022). Measuring the effects of COVID-19-related disruption on dengue transmission in southeast Asia and Latin America: A statistical modelling study. The Lancet Infectious Diseases, 22(5), 657–667. https://doi.org/10.1016/S1473-3099(22)00025-1
IHME estimates the proportion of women of reproductive age (15–49 years) who have their need for family planning satisfied with modern contraceptive methods. Modern contraceptive methods include the current use of male and female sterilization, male and female condoms, diaphragms, cervical caps, sponges, spermicidal agents, oral hormonal pills, patches, rings, implants, injections, intrauterine devices (IUDs), and emergency contraceptives. Projections to 2030 used an ensemble model, based both on past trends and using SDI as a key driver, which incorporates projections of income per capita and education and the effects of the COVID-19 pandemic.
Our analysis of PMA surveys and the smartphone-based follow-up survey referenced above does not show a consistent, significant reduction in contraception use due to the pandemic. As a result, IHME did not incorporate a short-term effect on the family planning indicator. Changes to the historical estimates can be attributed to methodological updates and the addition of new data sources, including the Generations and Gender Programme surveys. They switched from modeling the demand that is satisfied with modern methods directly for all women to modeling the three underlying components of the indicator separately for partnered and unpartnered women: any contraceptive use, proportion of use that is modern, and proportion of non-use that is unmet need. This modeling approach better aligns with data restrictions such as only surveying partnered (married or in-union) women and allows us to construct the full range of family planning indicators.
Universal Health Coverage
The universal health coverage (UHC) effective coverage index is a metric composed of 23 effective coverage indicators that cover population-age groups across the entire life course (maternal and newborn age groups, children under age 5, youths ages 5–19 years, adults ages 20–64, and adults ages 65 years old and older). These indicators fall within several health service domains: promotion, prevention, and treatment.
Health system promotion indicators include met need for family planning with modern contraception.
Health system prevention indicators include the proportion of children receiving the third dose of the diphtheria-tetanus-pertussis vaccine and children receiving the first dose of measles-containing vaccine. Antenatal care for mothers and antenatal care for newborns are considered indicators of health system prevention and treatment of diseases affecting maternal and child health.
Indicators of treatment of communicable diseases are the mortality-to-incidence (MI) ratios for lower respiratory infections, diarrhea, and tuberculosis, as well as coverage of ART among those with HIV/AIDS. Indicators of treatment of noncommunicable diseases include MI ratios for acute lymphoid leukemia, appendicitis, paralytic ileus and intestinal obstruction, cervical cancer, breast cancer, uterine cancer, and colorectal cancer. Indicators of treatment of noncommunicable diseases also include mortality-to-prevalence (MP) ratios for stroke, chronic kidney disease, epilepsy, asthma, chronic obstructive pulmonary disease, diabetes, and the risk-standardized death rate due to ischemic heart disease.
To produce forecasts of the UHC index from 2022 to 2030, a meta-stochastic frontier model for UHC was fit, using total health spending per capita projections as the independent variable. Country- and year-specific inefficiencies were then extracted from the model and forecasted to 2030 using a linear regression with exponential weights across time for each country level. These forecasted inefficiencies, along with forecasted estimates of total health spending per capita, were substituted into the previously fit frontier to obtain forecasted UHC for all countries for 2022–2030.
Short-term effects due to the pandemic were included in our final results with some exceptions. ART coverage scores and met demand for family planning were not adjusted, due to limitations in data as described in previous indicator sections. Adjustments for vaccine delivery are described in the Vaccines section. For other indicators (19 out of 23), in the absence of data to inform the correspondence between reductions in utilization and reductions in coverage, IHME applied 25% of the reduction in monthly missed medical visits (excluding routine services).
IHME measures the age-standardized prevalence of any current use of smoked tobacco among those age 15 and older. IHME collates information from available representative surveys that include questions about self-reported current use of tobacco and information on the type of tobacco product smoked (including cigarettes, cigars, pipes, hookahs, and local products). IHME converts all data to its standard definition of any current smoking within the last 30 days, so that meaningful comparisons can be made across locations and over time. Estimates this year are higher than last year to reflect the update in the indicator from daily smoking to any smoking within the last 30 days, to better align with the SDG definition. Projections to 2030 used SDI as a key driver, which incorporates projections of income per capita, education, and the effect of the COVID-19 pandemic.
World Health Organization. (2021). WHO global report on trends in prevalence of tobacco use 2000-2025. (4th ed.).https://www.who.int/publications/i/item/9789240039322. Licence: CC BY-NC-SA 3.0 IGO.
IHME’s measurement of immunization coverage reports on the coverage of the following vaccines separately: DTP3, measles second dose (MCV2), and three-dose pneumococcal conjugate vaccine (PCV3). IHME estimated the short-term (2020–2021) effects via administrative data on vaccine doses. In the 2021 Goalkeepers Report, IHME used a two-step random-spline meta-regression model to estimate coverage disruptions, fit to monthly administrative data and using mobility disruptions as a predictor. In this year’s report, IHME estimated coverage disruptions due to the COVID pandemic directly within our modeling framework, in the same way that stockouts and other disruptions are accounted for in pre-pandemic years. This change was made for several reasons. First, full-year administrative data for both 2020 and 2021 are now available through WHO and UNICEF’s Joint Reporting Process, representing a more comprehensive annual data set than was available for last year’s report. Second, the availability of timely monthly coverage data has decreased throughout the pandemic. Third, though mobility disruptions were a strong predictor of coverage disruptions early in the pandemic, the reasons for ongoing vaccination service disruptions have become increasingly complex over time, including persistent supply disruptions, workforce shortages, and decreased care seeking. In this year’s report, therefore, IHME has adapted our modeling strategy to leverage the increasing amount of annual data and decrease the model’s reliance on mobility as a predictor of coverage disruptions.
To estimate disruptions in vaccine coverage, IHME used administrative vaccine coverage data collected through the 2022 Joint Reporting Form. First, IHME assembled a “shock-free” time series of administrative vaccine coverage data, omitting country-year-vaccine data points for which countries reported stockouts or for which other known service delivery disruptions made sudden decreases in vaccine coverage plausible. In this step, they omitted all data points from 2020 and 2021 for all countries due to the COVID pandemic. Second, IHME then fit spatiotemporal Gaussian process regression (ST-GPR) models to this “shock-free” administrative time series, producing estimates of expected administrative coverage in the absence of disruptions. Third, IHME compared the reported administrative coverage to these expectations, to estimate the magnitude of disruption implied by the administrative data for each country, vaccine, and year. Last, IHME used these estimated disruptions in administrative coverage to generate covariates in our final ST-GPR coverage models, which were fit to survey data and bias-adjusted administrative data. If administrative data was missing in 2020 or 2021, they imputed disruptions using vaccine- and year-specific distributions of observed disruptions in countries with available administrative data, propagating uncertainty throughout this imputation process. This approach allowed IHME to leverage the magnitude of coverage disruptions implied by administrative data, while still adjusting for bias in this data.
Causey, K., Fullman, N., Sorensen, R. J. D., Galles, N. C., Zheng, P., Aravkin, A., Danovaro-Holliday, M. C., Martínez-Piedra, R., Sohda, S. V., Velandia-Gonzáles, M. P., Gacic-Dobo, M., Castro, E., He, J., Schipp, M., Deen, A., Hay, S. I., Lim, S. S., & Mosser, J. F. (2021). Estimating global and regional disruptions to routine childhood vaccine coverage during the COVID-19 pandemic in 2020: A modelling study. The Lancet, 398(10299), 522–534. https://doi.org/10.1016/S0140-6736(21)01337-4
World Health Organization. (2022, February). Third round of the global pulse survey on continuity of essential health services during the COVID-19 pandemic (Interim report, November–December 2021). Accessed July 27, 2022. https://www.who.int/publications-detail-redirect/WHO-2019-nCoV-EHS_continuity-survey-2022.1
IHME estimates the proportion of population with access to safely managed sanitation. As defined by the Joint Monitoring Programme (JMP), a safely managed facility must meet three criteria: (i) is not shared with multiple households, (ii) is an improved sanitation facility, and (iii) its wastewater is disposed of safely. Safe wastewater disposal can consist of being treated and disposed of in situ, stored temporarily and treated off-site, or transported through a sewer and treated. Safely managed treated wastewater must have received at least secondary treatment. IHME measured households with piped sanitation (with a sewer connection or septic tank); households with improved sanitation but without a sewer connection (pit latrine, ventilated improved latrine, pit latrine with slab, composting toilet); households without improved sanitation (flush toilet that is not piped to sewer or septic tank, pit latrine without a slab or open pit, bucket, hanging toilet or hanging latrine, no facilities); and wastewater treatment type for sewer-connected households, as defined by the JMP for Water Supply and Sanitation. Two new models were developed for the 2021 Goalkeepers Report, those being the proportion of sewer-connected facilities that are safely managed and the proportion of improved, non-sewer facilities that are safely managed.
IHME used a meta-regression, Bayesian, regularized, trimmed (MR-BRT) spline cascade model, with SDI as a predictor, cascading on super-region and country to estimate the proportion of sewer-connected facilities that are safely managed. Using cross-validation, they selected this model from a collection of candidate models based on out-of-sample root-mean-square deviation (RMSE). The estimates from this model were multiplied by the existing IHME estimates of the proportion of the population with sewer-connected facilities to estimate the proportion of the population with safely managed sewer-connected facilities.
IHME used a shape constrained additive model, with lag-distributed income per capita (LDI) as a predictor and random effects on super-region and country to estimate the proportion of improved, non-sewer facilities that are safely managed. Using cross-validation, they selected this model from a collection of candidate models based on out-of-sample RMSE. The estimates from this model were multiplied by the IHME estimates of the proportion of the population with improved, non-sewer-connected facilities to estimate the proportion of the population with safely managed improved non-sewer facilities.
To estimate the proportion of the total population with safely managed sanitation, the proportion of the population with safely managed sewer-connected facilities were added to the proportion of the population with safely managed improved non-sewer facilities. IHME propagated uncertainty through all components of the modeling chain using posterior simulation in which all calculations were performed on 1,000 draws from the posterior distribution of each model. Projections to 2030 were modeled using an ensemble approach to forecast the summary exposure value of unsafe sanitation, using SDI as a key driver in order to capture the effects of the COVID-19 pandemic and projections of income per capita and education.
World Health Organization & UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. (2021). Proportion of population using safely managed sanitation services [SDG indicator 6.2.1a metadata]. JMP. Accessed December 12, 2021. https://washdata.org/sites/default/files/2022-01/jmp-2021-metadata-sdg-621a.pdf
Zheng, P., Barber, R., Sorensen, R. J. D., Murray, C. J. L., & Aravkin, A. Y. (2021). Trimmed constrained mixed effects models: Formulations and algorithms. Journal of Computational and Graphical Statistics, 30(3), 544-556. https://doi.org/10.1080/10618600.2020.1868303
IHME Indicator Sources
Data source information for each indicator is listed below and will be available online at https://ghdx.healthdata.org/ following publication of GBD 2021.
|Indicator and Component||Goalkeepers 2022 Total Sources|
|NTD prevalence chagas||1,156|
|NTD prevalence visceral leishmaniasis||4,425|
|NTD prevalence cutaneous and mucocutaneous leishmaniasis||662|
|NTD prevalence African trypanosomiasis||2,970|
|NTD prevalence schistosomiasis||4,229|
|NTD prevalence cysticerocosis||3,388|
|NTD prevalence cystic echinococcosis||3,559|
|NTD prevalence lymphatic filariasis||565|
|NTD prevalence onchocerciasis||351|
|NTD prevalence trachoma||114|
|NTD prevalence dengue||5,268|
|NTD prevalence rabies||3,565|
|NTD prevalence ascariasis||3,368|
|NTD prevalence trichuriasis||205|
|NTD prevalence hookworm disease||208|
|NTD prevalence food-borne trematodiases||57|
|NTD prevalence leprosy||1,595|
|NTD prevalence guinea worm disease||436|
|Sanitation safely managed||1,148|
|UHC maternal disorders||8,305|
|UHC met need||1,076|
|UHC live births||14,815|
|UHC neonatal mortality||22,099|
|UHC DTP vaccination||8,477|
|UHC measles vaccination||2,814|
|UHC HIV treatment||5,156|
|UHC lymphoid leukemia||3,763|
|UHC IHD treatment||3,469|
|UHC cervical cancer||7,513|
|UHC breast cancer||7,698|
|UHC uterine cancer||7,521|
|UHC colon and rectum cancer||3,884|
|UHC paralytic ileus and intestinal obstruction treatment||3,877|
|Vaccine coverage DTP3||8,477|
|Vaccine coverage MVC2||2,814|
|Vaccine coverage pcv3||1,537|
Indicators Estimated from Other Sources
Poverty data is based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies comes primarily from the LIS (formerly Luxembourg Income Study) database.
For 2019–2022 estimates, extreme poverty is measured as the headcount ratio of people living on less than US$1.90 per day. 2018 is the last year with official global poverty estimates. Baseline and pessimistic projections utilize growth forecasts based on April 2022 Macro Poverty Outlook data sets from the Poverty and Inequality Platform database. The baseline scenario distributes the impacts of the COVID-19 pandemic, rising inflation, and the conflict in Ukraine equally to all households. The pessimistic scenario includes the disproportionate impact of rising food prices on the bottom 40% compared to the top 60% over the baseline scenario. Official poverty estimates are available for East Asia and Pacific, Europe and Central Asia, Latin America and Caribbean, sub-Saharan Africa, and the rest of the world for up to 2019, and for Middle East and North Africa up to 2018. Official South Asia estimates are only available up to 2014. Regions are categorized using the Poverty and Inequality Platform definition.
Luxembourg Income Study Database (LIS). https://www.lisdatacenter.org/
World Bank. Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) [Data set]. Poverty and Inequality Platform: World Development Indicators. Accessed June 2022. https://data.worldbank.org/indicator/SI.POV.DDAY. License: CC BY-4.0.
Lakner, C., Mahler, D. G., Negre, M., & Prydz, E. B. (2022). How much does reducing inequality matter for global poverty? Journal of Economic Equality. https://doi.org/10.1007/s10888-021-09510-w
World Bank. Macro Poverty Outlook [Data set]. Poverty and Inequality Platform: World Development Indicators. Accessed July 2022. https://www.worldbank.org/en/publication/macro-poverty-outlook. Headcount ratio provided by the World Bank upon request.
For methodology, see:
World Bank. (2022). Poverty and Inequality Platform Methodology Handbook. https://worldbank.github.io/PIP-Methodology/
The FAO computation on national survey data (RuLIS Project) and official estimates were computed with the support of the 50x2030 Initiative.
50x2030. (2022). A partnership for data-smart agriculture. https://www.50x2030.org/
Food and Agriculture Organization of the United Nations (FAO). Average annual income from agriculture, PPP (constant 2011 international USD) [Data set]. RuLIS - Rural Livelihoods Information System. FAO. Accessed June 2022. www.fao.org/in-action/rural-livelihoods-dataset-rulis/
The most recent year available was used for selected countries, ranging from 2005 through 2020.
Food and Agriculture Organization of the United Nations (FAO). (2021). Use of AGRISurvey data for computing SDG’s and national indicators: Experience in three countries [Country brief]. www.fao.org/3/cb4762en/cb4762en.pdf. License: CC BY-NC-SA 3.0 IGO.
For methodology, see:
Food and Agriculture Organization of the United Nations (FAO). (2018). Rural Livelihoods Information System (RuLIS): Technical notes on concepts and definitions used for the indicators derived from household surveys [Report]. FAO. www.fao.org/3/ca2813en/CA2813EN.pdf. Licence: CC BY-NC-SA 3.0 IGO
UNESCO Institute for Statistics (UIS). Sustainable Development Goal 4. UIS. Data accessed June 2022. http://sdg4-data.uis.unesco.org/
Source for Learning Poverty 2019 data:
World Bank & UNESCO Institute for Statistics. (2019). Historical data and sub-components [Data set]. Learning Poverty Database. https://datacatalog.worldbank.org/search/dataset/0038947
Source for Learning Poverty 2022 simulations:
2022 simulation results taken from Azevedo, J. P., Demombynes, G., & Wong, Y. N. 2022. Why has the pandemic not sparked more concern for learning losses in Latin America? World Bank Blogs: Education for Global Development (forthcoming).
The chart is based on data from the United Nations Global Sustainable Development Goals Database, the Government of India’s National Sample Survey Office, and the International Labour Organization.
The data is the most recent available for 92 countries and territories (2001–2019). The age group is 15 and older where available (18 and older in Ghana). In a number of cases, data are for those ages 10 and older (n=13) or 12 and older (n=3). The data for Malaysia, Ireland, and Cambodia refers to individuals ages 15 through 64. In the case of Thailand (2015) and India (2019), data covers those ages 6 and older, and in the United Republic of Tanzania (2014) those ages 5 and older. Data for Bulgaria, Denmark, Latvia, the Netherlands, Slovenia, and Spain corresponds to time spent on unpaid care among those ages 20 through 74 only. Differences across countries should be interpreted with caution, given heterogeneity across surveys and countries in definitions, methodology, and sample coverage. Time-diary data often excludes supervisory responsibilities, leading to underestimation of the time constraints of care.
For further information on the country-level data excluding India and Madagascar, see:
United Nations Statistics Division. (2022, May). Global SDG Indicators Data Platform. https://unstats.un.org/sdgs/dataportal
Data for India and Madagascar is available from:
Ministry of Statistics and Programme Implementation. (2019). Time Use Survey Report. Government of India. http://220.127.116.11/download-reports
Addati, L., Cattaneo, U., Esquivel, V., & Valarino, I. (2018). Care Work and Care Jobs for the Future of Decent Work. Geneva: International Labour Organization. https://www.ilo.org/global/publications/books/WCMS_633135/lang--en/index.htm
Financial Services for the Poor
The “Income” comparison refers to what the World Bank calculates as account ownership of the richest 60% of households and poorest 40% of households, respectively.
Demirgüç-Kunt, A., Klapper, L., Singer, D., and S. Ansar. (2022). The Global Findex database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19. Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/37578 License: CC BY 3.0 IGO.
World Bank. (2022). Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+) [Data set]. Global Findex Database. Accessed June 2022. https://data.worldbank.org/indicator/FX.OWN.TOTL.ZS License: CC BY-4.0.
For methodology, see:
World Bank. (2022). Survey Methodology. In The Global Findex database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19 (pp. 181–197). Washington, DC: World Bank. https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf License: CC BY-4.0.