2024 Data Sources

The data sources for facts and figures featured in the 2024 Goalkeepers Report are listed here by section. Brief methodological notes are included for unpublished analyses.
Read the 2024 Goalkeepers Report

The Race to Nourish a Warming World

Institute for Health Metrics and Evaluation. (2024). Financing global health 2023: The future of health financing in the post-pandemic era.
https://www.healthdata.org/research-analysis/library/financing-global-health-2023-future-health-financing-post-pandemic-era

Stalling health financing puts progress at risk

Institute for Health Metrics and Evaluation. (2024). Financing global health 2023: The future of health financing in the post-pandemic era.
https://www.healthdata.org/research-analysis/library/financing-global-health-2023-future-health-financing-post-pandemic-era

Global Burden of Disease Collaborative Network. (2024). Global Burden of Disease 2021: Findings from the GBD 2021 Study.
https://www.healthdata.org/research-analysis/library/global-burden-disease-2021-findings-gbd-2021-study

United Nations Inter-agency Group for Child Mortality Estimation. (2024). Levels & trends in child mortality: Report 2023.
https://childmortality.org/wp-content/uploads/2024/03/UNIGME-2023-Child-Mortality-Report.pdf

Hoogeveen, J., Mistiaen, J. A. & Wu, H. (2024). Accelerating Poverty Reduction in Sub-Saharan Africa Requires Stability. World Bank.
https://blogs.worldbank.org/en/africacan/accelerating-poverty-reduction-sub-saharan-africa-requires-stability

ONE Campaign. (2024). Official Development Assistance (ODA). https://data.one.org/topics/official-development-assistance/

United Nations Children’s Fund. (2024). Child food poverty: Nutrition deprivation in early childhood. https://www.unicef.org/media/157661/file/Child-food-poverty-2024.pdf

In 2024, UNICEF released its first report on child food poverty. Its analysis found that currently, over 440 million children around the world experience food poverty. UNICEF defines child food poverty as children’s inability to access and consume a nutritious and diverse diet in early childhood.

Also, the World Health Organization (WHO) released global estimates of specific forms of malnutrition. In 2022, WHO estimates that 148.1 million children under age 5 were too short for their age (stunting), 45 million were too thin for their height (wasting), and 37 million were too heavy for their height (overweight).

Institute for Health Metrics and Evaluation. (2024, August). [Bespoke modeling. Full methodology is detailed below].

Nations can’t grow if their people can’t

1,000 Days. (n.d.). From cradle to career: The lifelong impact of early nutrition on minds and futures. https://thousanddays.org/updates/from-cradle-to-career-the-lifelong-impact-of-early-nutrition-on-minds-and-futures/

Horton, S., Shekar, M., McDonald, C., Mahal, A., & Brooks, J. K. (2010). Scaling up nutrition: What will it cost? World Bank. https://openknowledge.worldbank.org/server/api/core/bitstreams/7cf62331-2e10-523e-acb8-17d71e8ce779/content

Hoddinott, J., Maluccio, J., Behrman, J. R., Martorell, R., Melgar, P.,
Quisumbing, A. R., Ramirez-Zea, M., Stein, A. D., & Yount, K. M. (2011). The consequences of early childhood growth failure over the life course (Discussion Paper 01073). International Food Policy Research Institute. https://www.almendron.com/tribuna/wp-content/uploads/2019/07/the-consequences-of-early-childhood-growth-failure-over-the-life-course.pdf

World Bank. (2023). The World Bank and nutrition. https://www.worldbank.org/en/topic/nutrition/overview

United Nations Children’s Fund, WHO, & World Bank Group (2023). Levels and trends in child malnutrition: UNICEF/WHO/World Bank Group joint child malnutrition estimates: Key findings of the 2023 edition. https://www.who.int/publications/i/item/9789240073791

Impact of Increased Milk Production

Headey, D., & de Vries, A. (2024). Can dairy development reduce stunting at scale? Projections for India, Ethiopia, Kenya, Tanzania and Nigeria for 2020-2050 [Unpublished manuscript]. International Food Policy Research Institute.

Impacts of Food Fortification in Nigeria and Ethiopia

Bill & Melinda Gates Foundation & Institute for Health Metrics and Evaluation Simulation Science Team. (2024, August). [Bespoke modeling].

Impact of MMS in Low- and Middle-income countries

Bill & Melinda Gates Foundation & Burnet Institute. (2024, August). [Bespoke modeling]. Full methodology is detailed below.

In May 2024, a collaborative of private philanthropies (the Bill & Melinda Gates Foundation, Children’s Investment Fund Foundation, Eleanor Crook Foundation, and Kirk Humanitarian released a global investment roadmap designed to catalyze and prioritize action and investment in multiple micronutrient supplements (MMS). The plan presents an opportunity to reach at least 260 million women in 45 high-burden countries with MMS by the end of 2030, an ambition that would save more than 600,000 lives, improve birth outcomes for more than 5 million babies, and prevent anemia in over 15 million pregnant women. Read the roadmap, Healthier Pregnancies and Brighter Futures for Mothers and Babies.

A Recipe for Progress 4 Solutions to Nourish Our Planet

Ensuring more productive cows and safer milk

Headey, D., & de Vries, A. (2024). Can dairy development reduce stunting at scale? Projections for India, Ethiopia, Kenya, Tanzania and Nigeria for 2020-2050 [Unpublished manuscript]. International Food Policy Research Institute.

Fortifying the global pantry against micronutrient deficiencies

United Nations Children’s Fund. (2023, March). Iodine. https://data.unicef.org/topic/nutrition/iodine/

Bill & Melinda Gates Foundation & Institute for Health Metrics and Evaluation Simulation Science Team. (2024, August). [Bespoke modeling]. 

National Population Commission. (2019). Nigeria: Demographic and Health Survey 2018. Federal Republic of Nigeria. https://dhsprogram.com/pubs/pdf/FR359/FR359.pdf

Exploring the feasibility of fortifying bouillon is only one piece of the comprehensive nutrition program led by the Government of Nigeria. See additional notes from Mrs. Bako-Aiyegbusi,mni:

In my country, the nutrition-specific interventions target immediate causes of malnutrition such as food intake or childcare practices, whereas nutrition-sensitive interventions focus on underlying factors such as resource availability and accessibility. Some of the nutrition specific programmes being implemented in Nigeria include addressing treatment of severe acute malnutrition, disease management (e.g., oral rehydration salts for diarrhea), maternal infant and young child nutrition (early initiation of breastfeeding, exclusive breastfeeding, minimum dietary diversity, minimum acceptable diet, responsive feeding), access to health services, hygiene, and sanitation.

Aside [from] the above, other supplementation programmes (such as vitamin A supplementation, deworming twice a year, IFAS and MMS for pregnant women and women of reproductive age), fortification and biofortification are ongoing in the country. The mandatory staple food vehicles for large-scale guided food fortification include salt fortified with iodine, vegetable oil, sugar, wheat, and maize flour fortified with vitamin A. Likewise, Nigeria is introducing large-scale guided voluntary fortification of rice in line with laid-down rules and standards.

Expanding access to better prenatal vitamins

Expanding access to better prenatal vitamins Bill & Melinda Gates Foundation & Burnet Institute. (2024, August). [Bespoke modeling]. Full methodology is detailed below.

Financing progress through the Child Nutrition Fund

Global Fund. (2024). About the Global Fund. https://www.theglobalfund.org/en/about-the-global-fund/

Methodology for Goalkeepers 2024 Bespoke Modeling

Measuring the impact of climate change on child malnutrition

The Institute for Health Metrics and Evaluation (IHME) modeled the impacts of climate change on malnutrition, including child stunting and wasting, the details of which are described below.

IHME analyzed approximately 1 million geolocated child observations from 126 Demographic and Health Surveys, covering 54 countries, to quantify the relationship between climate variables (e.g., mean annual temperature, days more than 30 degrees Celsius), household income, and the prevalence of childhood stunting (height-for-age) and wasting (weight-for-height). The resulting statistical models were used to predict future stunting and wasting prevalence using climate variable forecasts from the Coupled Model Intercomparison Project 6 (CMIP6) ensemble projections. We considered the CMIP6 SSP2-4.5 scenario as the reference or most likely scenario. A second-stage model was used to forecast the residual trends in stunting and wasting prevalence not captured through base income and temperature model. We included the Socio-demographic Index (SDI) as a predictor in the second-stage model. Both the first- and second-stage models were to obtain final forecasts of stunting prevalence over time. In addition to the reference forecast, we compared the reference scenario to a scenario where 2024 climate variables were held constant into the future.

References:

  • Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023). ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service Climate Data Store. https://doi.org/10.24381/cds.adbb2d47
  • Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., & Rama, B. (Eds.). (2022). Climate change 2022: Impacts, adaptation, and vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://doi.org/10.1017/9781009325844

Measuring impact of impact of multiple micronutrient supplementation (MMS) in low- and middle-income countries

Methods: Bespoke modeling was conducted by ⁠the foundation in collaboration with Burnet Institute. We aimed to estimate the potential impact of multiple micronutrient supplementation (MMS) on maternal, neonatal, and infant burden in low- and middle-income countries (LMICs) from 2023 to 2040. To achieve this, we designed a dynamic compartmental modeling framework reflective of target populations, conditions, and interventional windows across the pregnancy, postpartum, newborn, and infancy periods. Within this framework, we built a series of deterministic transition models in which compartments were assigned rates of pregnancy, live birth, condition-specific incidence, and mortality to define population characteristics and outcomes. Receipt of MMS was assumed to affect the transition rates between compartments. Estimated impact on averted burden was measured by overall and condition-specific cases, deaths, and disability-adjusted life years (DALYs). Importantly, we counted stillbirths as deaths and calculated DALYs for stillbirths accordingly.

In addition to a baseline scenario where MMS was not introduced and condition burden forecasts were dependent only on secular trends, we ran counterfactual scenarios of increasing coverage of MMS among pregnant women attending at least one antenatal care visit during pregnancy. Our baseline forecasts of condition burden from 2023 to 2040 depended on forecasts of key drivers, including live births, antenatal care utilization, in-facility delivery, and prevalence of cesarean section operations. We used live birth forecasts produced by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington for the 2023 Goalkeepers Report and conducted forecasts of other drivers as a function of IHME forecasts of the Socio-demographic Index (SDI). Cause-specific condition incidence and burden forecasts were calibrated at a regional level to IHME Global Burden of Disease (GBD) 2019 estimates for the year 2019 and then projected to 2040 based on live birth forecasts to forecast secular trends. Counterfactual scenarios were compared against this baseline to quantify the condition burden averted by MMS. To estimate the change in maternal mortality ratio (MMR), neonatal mortality rate (NMR), and infant mortality rate (IMR), we aggregated the deaths averted by causes specific to each target population from the counterfactual scenario where MMS was introduced. To ensure consistency with Goalkeepers 2023 reference estimates of MMR, NMR, and IMR, we found the percentage of deaths averted in our models and applied that value to the Goalkeepers 2023 mortality estimates to quantify impact.

Data: We used published literature, available primary data sets, and IHME GBD 2019 estimates to assign values to the demographic, epidemiological, and health system parameters in our models. Models used region-specific data inputs where possible for three regional groupings: South Asia, sub-Saharan Africa, and other LMICs comprising countries in Latin America, North Africa/Middle East, and East/Southeast Asia/Oceania. We based MMS effect size assumptions on published literature and available primary data.

Acknowledgments

This report was developed in consultation with Bill & Melinda Gates Foundation’s partners and collaborators, including: 1,000 Days, Deepa Joshi, Development Initiatives, Equal Measures 2030, Exemplars in Global Health, Helen Keller International, Livestock Enhancement and Advancement Programme, MoreMilk, Our World in Data, The Institute for Health Metrics and Evaluation, The International Food Policy Research Institute, The International Livestock Research Institute, The University of Chicago, and University of Colorado School of Medicine. The team is grateful for their support.

Explore the Data

IHME methodology

Our primary data partner, IHME, produced estimates and forecasts for 13 of the Sustainable Development Goal (SDG) indicators included in the 2024 Goalkeepers Report. IHME worked together with many partners and used novel methods to generate a set of contemporary estimates, some as part of the Global Burden of Disease project. The indicator estimates presented may differ from other sources, particularly at the subnational level, due to differences in statistical models, data inputs, and assumptions used between modeling groups. The section below provides detail on how each indicator is estimated.

Indicators estimated by IHME

IHME produced estimates and forecasts for 13 of the SDG indicators included in the Goalkeepers Report. The section below provides details on how each indicator is estimated.

Stunting

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 0–59 months. Estimates leveraged several methods and data processing improvements, including ensemble model predictions for severity-specific stunting prevalence and mean height-for-age z-scores, further disaggregation of under-5 age groups. This led to improved estimates broadly, with the most notable changes in the youngest age groups (under 6 months) and in a number of countries, including Democratic People’s Republic of Korea, Ecuador, Japan, Libya, Mauritius, Puerto Rico, Togo, and Tonga. Forecasts of stunting prevalence were produced using the methods described above in the section on climate change and child malnutrition. Briefly, forecasts of stunting prevalence were driven by climate scenarios of days above 30 degrees Celsius, income, the Socio-demographic Index (SDI) and temporal trends. The better and worse scenarios were produced by taking the 85th and 15th percentile rates of change observed across location-years in the past and applying those rates of change to all locations in the future.

Maternal mortality ratio

The maternal mortality ratio (MMR) is defined as the number of maternal deaths among women ages 15–49 during a given time period per 100,000 live births during the same time period. It depicts the risk of maternal death relative to the number of live births to approximate the risk of death in pregnancy. Projections to 2030 were modeled using an ensemble approach to forecast MMR, using SDI as a key driver.

Differences in MMR estimates from the 2023 Goalkeepers Report are primarily driven by the addition of new input data. These include new location-years of sibling history data from household surveys, including surveys from several countries in sub-Saharan Africa. Data added since the last report cover additional pandemic years, primarily in locations with vital registration systems. Several location-years of Demographic and Health Surveys (DHS) input data were also reprocessed with a correction to noise reduction methods, which generally resulted in decreases in input cause fractions across the time series. Estimates of all-cause mortality were also updated with new data, which impact maternal death counts and ultimately, MMR.

Data added since the last report cover additional pandemic years, primarily in locations with vital registration systems. These were sufficient country-years of data for 2020 and onward to capture pandemic-year trends, and no additional corrections to account for the COVID-19 pandemic were performed. This is in contrast to the 2023 Goalkeepers Report, where we modeled COVID-impact-free MMR through 2021 and separately modeled the excess in indirect maternal deaths during the pandemic years using data from 30 countries with pandemic-year vital registration already available.

Under-5 mortality rate

The under-5 mortality rate (U5MR) is the probability of death between birth and age 5. It is expressed as the number of deaths per 1,000 live births. Estimates used all available data from vital registration, sample registration, surveys, and censuses, which were modeled via spatiotemporal Gaussian process regression. Projections were based on a combination of key drivers, including Global Burden of Disease (GBD) risk factors, selected interventions (e.g., vaccines), and SDI. Most of the changes in U5MR estimates in the current Goalkeepers Report results came from new and additional input mortality data we have incorporated since the previous Goalkeepers Report. Methodological changes included using vital registration and survey data directly for 2020 and 2021 and not adding in separately modeled estimates of excess mortality observed during the COVID-19 pandemic. This was due to increased availability of data during the pandemic period that showed no robust evidence of a strong or consistent raising or lowering of child mortality.

References:

  • GBD 2021 Demographics Collaborators. (2024). Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: A comprehensive demographic analysis for the Global Burden of Disease Study 2021. The Lancet, 403(10440), 1989–2056. https://doi.org/10.1016/S0140-6736(24)00476-8

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. Estimates used all available data from vital registration, sample registration, surveys, and censuses, which were modeled via spatiotemporal Gaussian process regression as the conditional probability of dying in the neonatal period given death in the under-5 period, then converted into neonatal mortality rates. 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 and the methodological changes to the under-5 mortality rate estimates.

References:

  • GBD 2021 Demographics Collaborators. (2024). Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: A comprehensive demographic analysis for the Global Burden of Disease Study 2021. The Lancet, 403(10440), 1989–2056. https://doi.org/10.1016/S0140-6736(24)00476-8

HIV

IHME estimates the HIV rate as new HIV infections per 1,000 population. Changes in Goalkeepers 2024 incidence were due to updates made during GBD23 estimation, which reflect substantial data updates from the following sources. PHIA: Five countries published their first ever reports for 2020–2023, and seven countries provided new microdata. Household Surveys: 13 countries provided new surveys. Case reports: 54 countries were updated, with recent years providing 546 additional country-years. UNAIDS: 145 countries provided refreshed time series in their Spectrum country files.

Tuberculosis

IHME estimates new and relapse tuberculosis 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. Although TB estimates for Goalkeepers 2024 are similar to those for Goalkeepers 2023 at the global level, they do differ slightly for select locations due to new input data used in estimates of risk exposures related to TB and used as covariates in the modeling process.

We also evaluated the impact of the COVID-19 pandemic on mortality and TB diagnoses in two recent publications. Due to data availability and varying results from these analyses, we did not implement a COVID-specific adjustment for GBD tuberculosis estimates but will continue to explore further options as more data become available.

Projections to 2030 were modeled using an ensemble approach to forecast the incidence of TB, using SDI as a key driver to capture the effects of the COVID-19 pandemic on income per capita and education.

References:

  • GBD 2021 Tuberculosis Collaborators. (2024). Global, regional, and national age-specific progress towards the 2020 milestones of the WHO End TB Strategy: A systematic analysis for the Global Burden of Disease Study 2021. The Lancet Infectious Diseases, 24(7), 698–725. https://doi.org/10.1016/S1473-3099(24)00007-0
  • Ledesma, J. R., Basting, A., Chu, H. T., Ma, J., Zhang, M., Vongpradith, A., Novotney, A., Dalos, J., Zheng, P., Murray, C. J. L., & Kyu, H. H. (2023). Global-, regional-, and national-level impacts of the COVID-19 pandemic on tuberculosis diagnoses, 2020–2021. Microorganisms, 11(9), 2191. https://doi.org/10.3390/microorganisms11092191

Malaria

IHME estimates the malaria rate as the number of new cases per 1,000 population. To estimate malaria incidence in 2020 and 2021, we took into account reports regarding pandemic-related disruptions to treatment-seeking. were used to apply an adjustment to estimates of effective treatment with an antimalarial drug (AM), which were used as a covariate when modeling malaria prevalence and, subsequently, clinical incidence of P. falciparum infections in sub-Saharan Africa. Projections to 2030 were derived using an ensemble model. First, coverages of AM and insecticide treated bednets (ITNs) were forecast as a function of the Socio-demographic Index (SDI), which was itself predicted by projections of income per capita and education. For countries that had available data on both intervention coverages, malaria incidence was forecast through 2030 using an ensemble approach, which incorporated past trends and forecasts of AM and ITN coverage to produce the projections. For countries where there were no available data on AM and/or ITN coverages, an ensemble approach was used based upon past trends in incidence as well as projections of SDI, which incorporated the effects of the COVID-19 pandemic through income per capita and education.

Due to reporting lags, there were still relatively few data sets available to inform pandemic-related impacts on malaria incidence. The WHO PULSE surveys, which were used to adjust 2020 and 2021 incidence results, were only applied to 33 countries in Africa, and a comparable data set amenable with which to apply this methodology to other regions was lacking. Furthermore, although the PULSE surveys currently allowed us to make preliminary estimates of malaria pandemic-related impacts, the surveys were potentially biased because they were based only on individual assessments by public health officials of how the pandemic impacted care-seeking.

References:

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. Based on an updated literature review and due to data gaps, lags in availability, and challenges in accounting for the likely disruptions to NTD surveillance during the pandemic, we did not estimate a COVID-19 effect on any NTD causes. 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 (Hollingsworth et al., 2021). Although modeling studies can characterize potential disruptions under different scenarios, reliable data to quantify the true magnitude of pandemic effects on NTD epidemiology are sparse. 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.

References:

  • 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

Family planning

Our analysis of Performance Monitoring for Action surveys and other pandemic era surveys and review of the literature did not demonstrate any consistent or significant reduction in contraception use due to the pandemic. As a result, we did not incorporate a separate pandemic effect into estimates of the met need indicator. Changes to the historical estimates can be attributed primarily to the addition of new data from 19 countries: Benin, Burkina Faso, Comoros, Côte d'Ivoire, Eswatini, Ethiopia, Gabon, Ghana, India, Kenya, Mozambique, Nepal, Niger, Philippines, Thailand, Trinidad and Tobago, Tunisia, Uganda, and the United Republic of Tanzania. We model met need via three underlying components of the indicator—any contraceptive use, proportion of use that is modern, and the proportion of non-use that is unmet need—separately for partnered and unpartnered women. This modeling approach aligns with data restrictions such as surveying only partnered (married or in-union) women and allows us to construct the full range of family planning indicators.

References:

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 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 scaled mortality-to-incidence (MI) ratios for lower respiratory infections, diarrhea, and tuberculosis, as well as coverage of antiretroviral therapy among those with HIV/AIDS. Indicators of treatment of noncommunicable diseases include scaled 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 scaled mortality-to-prevalence ratios for stroke, chronic kidney disease, epilepsy, asthma, chronic obstructive pulmonary disease, diabetes, and the risk-standardized death rate due to ischemic heart disease. The effective coverage indicators are weighted in the index according to the potential health gain that each country could achieve if it were to improve coverage of that indicator.

To produce forecasts of the UHC index for 2022–2030, 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 forecast to 2030 using a linear regression with exponential weights across time for each country level. These forecast inefficiencies, along with forecast total health spending per capita estimates, were substituted into the previously fit frontier to obtain forecast UHC for all countries for 2022–2030

Effects due to the pandemic were included in our final results for the years 2020 and 2021 with some exceptions. Antiretroviral therapy coverage scores and met demand for family planning were not adjusted due to limitations in data as described in previous 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, we applied 25% of the reduction in monthly missed health care visits (excluding routine services). Details of the estimation of missed health care visits is described in last year’s report. UHC was adjusted for countries with major conflicts, including Ukraine, Palestine, and Sudan using data from the Uppsala Conflict Data Program.

References:

Smoking

IHME measures the age-standardized prevalence of any current use of smoked tobacco among those ages 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, as well as 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. 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.

Vaccines

IHME’s measurement of immunization coverage reports on the coverage of the following vaccines separately: three-dose diphtheria-tetanus-pertussis (DTP3), measles second dose (MCV2), and three-dose pneumococcal conjugate vaccine (PCV3). IHME estimated the pandemic era (2020–2023) effects on vaccine coverage via administrative data coverage. To estimate disruptions in vaccine coverage during the COVID-19 pandemic, IHME used administrative vaccine coverage data collected through the 2024 Joint Reporting Form. First, we 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, we omitted all data points from 2020 to 2023 for all countries due to the COVID pandemic. Second, we 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, we 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, we used these estimated disruptions in administrative coverage to generate as covariates in our final ST-GPR coverage models, which were fit to survey data and bias-adjusted administrative data. If administrative data were missing for 2020–2023, we imputed disruptions using vaccine- and year-specific distributions of observed disruptions in countries with available administrative data, propagating uncertainty throughout this imputation process. Trends in 2023 country-reported data informed our decision to continue applying disruptions in this year. This approach allowed us to harness the magnitude of coverage disruptions implied by administrative data while still adjusting for bias in these data. To account for rapid expansions in coverage in MCV2 and PCV3 in years following country-specific introductions, models for these two vaccines included a first stage of hierarchical spline models, where country-specific expansions models were informed by global expansion patterns.

References:

Sanitation

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: it is not shared with multiple households, it is an improved sanitation facility, and its wastewater is disposed of safely (World Health Organization [WHO], 2021). 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 (WHO, 2021). Safely managed treated wastewater must have received at least secondary treatment (WHO, 2021). 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 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.

For the 2024 Goalkeepers Report, we developed models to estimate two components of safely managed sanitation: the proportion of sewer-connected facilities that are safely managed and the proportion of improved, non-sewer facilities that are safely managed. For both components we selected the final model from a collection of candidate models based on out-of-sample root-mean-squared error (RMSE) estimated by cross-validation. Candidate models varied in model type (MR-BRT Bayesian spline cascade models versus shape constrained additive models [SCAM]), and predictive covariates (SDI, lag distributed income per capita [LDI], and both linear and log transformations); and, for the Bayesian spline cascade models, we tested models that varied in the strength of the priors used in the spline cascade.

Data for estimating the proportion of sewer-connected facilities that are safely managed were extracted from Eurostat, Aquastat, Demographic and Health Surveys (DHS), UNICEF Multiple Indicator Cluster Surveys (MICS), the Organisation for Economic Co-operation and Development, and national surveys (Andorra, Austria, Ireland, Republic of Korea, and Singapore). The resulting 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.

Data for estimating the proportion of improved, non-sewer facilities that are safely managed were extracted from Eurostat, DHS, MICS, and national surveys (Canada, Norway, and the United States). Crosswalks were performed to estimate toilet type and wastewater treatment where data were unknown within survey microdata. The resulting 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.

We estimated the proportion of the total population with safely managed sanitation as the sum of the proportion of the population with safely managed sewer-connected facilities and the proportion of the population with safely managed improved non-sewer facilities.

Updates this year include updating input data, and a change in model type for the improved, non-sewer facilities that are safely managed estimates. Data updates included re-extracting from updated databases, incorporating new sources, and outlier-ing data that overlapped across databases. The improved, non-sewer facilities that are safely managed model changed from a SCAM model in 2023 to a MR-BRT Bayesian spline cascade model, based on the RMSE results of cross-validation.

References:

IHME indicator sources

Data source information for each indicator is below. A detailed reporting of data sourcing for GBD 2021 estimates can be found at https://ghdx.healthdata.org/gbd-2021/sources.

 

Indicator and Component Goalkeepers 2024 Total Sources
Child mortality 26,745
Child stunting 1,695
Family planning (met need) 1,197
Malaria 13,611
Maternal mortality 8,006
Neonatal mortality 26,745
HIV 5,115
NTD chagas 1,085
NTD visceral leishmaniasis 4,590
NTD cutaneous and mucocutaneous leishmaniasis 662
NTD African trypanosomiasis 2,970
NTD schistosomiasis 3,398
NTD cysticercosis 3,548
NTD cystic echinococcosis 3,397 
NTD lymphatic filariasis 487
NTD onchocerciasis 351
NTD trachoma 114
NTD dengue 3,568
NTD rabies 4,059
NTD ascariasis 3,550
NTD trichuriasis 205
NTD hookworm disease 208
NTD food-borne trematodiases 57
NTD leprosy 1,595
NTD guinea worm disease 450
Sanitation safely managed 1,244
Smoking prevalence 4,172
Tuberculosis 4,582
UHC maternal disorders 8,336
UHC met need 1,197
UHC live births 47,665
UHC neonatal mortality 20,634
UHC diphtheria 3,821
UHC pertussis 9,291
UHC tetanus 4,075
UHC DTP vaccination 10,165
UHC measles 12,351
UHC measles vaccination 3,024
UHC LRI 4,407 
UHC diarrhea 6,137
UHC HIV treatment 5,155
UHC TB 4,059
UHC lymphoid leukemia 7,624
UHC asthma 2,804
UHC diabetes 4,005
UHC IHD treatment 3,991 
UHC stroke 4,017
UHC chronic kidney disease
4,397
UHC chronic obstructive pulmonary disease 2,820
UHC cervical cancer 7,627 
UHC breast cancer 7,812
UHC uterine cancer 7,635
UHC colon and rectum cancer 7,800
UHC epilepsy 3,798
UHC appendicitis 3,871
UHC paralytic ileus and intestinal obstruction treatment 3,737
Vaccine coverage DTP3 9,772
Vaccine coverage MVC2 3,158
Vaccine coverage PCV3 2,013

Indicators estimated from other sources

Poverty

World Bank. Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population) [Data set]. Retrieved July 2023 from
https://data.worldbank.org/indicator/SI.POV.DDAY

For methodology, see:
World Bank. (2024). Poverty and inequality platform methodology handbook. https://datanalytics.worldbank.org/PIP-Methodology/

Agriculture

Food and Agriculture Organization of the United Nations. (2024). Average annual income from agriculture, PPP (constant 2011 international USD) [Data set]. Retrieved June 2024 from https://dataexplorer.fao.org

Small food producers’ income growth for selected countries with at least two entries in the data set are included. For all countries without data for 2014 and 2019, the earliest and most recent years were used to calculate income growth. Small food producers’ income growth is calculated per country using years listed below:

 

Location Year range
Burkina Faso 2014–2019
Côte d’Ivoire 2008–2019
Ethiopia 2014–2019
Ghana 2013–2017
India 2005–2012
Malawi 2011–2020
Mali 2014–2019
Mongolia 2014–2019
Niger 2011–2019
Nigeria 2013–2019
Senegal 2011–2021
Sierra Leone
2011–2018 
Tanzania 2009–2019  
Uganda 2010–2020

Education

World Bank, UNESCO Institutes for Statistics, UNICEF, USAID, Bill & Melinda Gates Foundation, & Foreign, Commonwealth, and Development Office. (2022). The state of global learning poverty: 2022 Update [Conference edition]. https://www.unicef.org/media/122921/file/StateofLearningPoverty2022.pdf

Source for Learning Poverty 2022 simulations:
Azevedo, J. P., Demombynes, G., & Wong, Y. N. (2023). Why has the pandemic not sparked more concern for learning losses in Latin America? The perils of an invisible crisis. Education for Global Development. https://blogs.worldbank.org/en/education/why-hasnt-pandemic-sparked-more-concern-learning-losses-latin-america-perils-invisible

Gender equality

The Equal Measures 2030 (EM2030) SDG Gender Index is the most comprehensive global tool to measure progress toward gender equality aligned to the Sustainable Development Goals (SDGs). The index tracks 56 key gender indicators that provide the “big picture” across and within 14 of the 17 SDGs.

It is the only index that adds a gender lens to each of the goals, including the many SDGs that lack such a lens in the official framework. Going beyond SDG 5 (the single goal dedicated to gender equality) is important in capturing the broader trends that influence progress on gender equality and highlighting how issues such as hunger, poverty, and climate change affect girls and women.

The 2024 index covers 139 countries, which represent 96% of the world’s women and girls. The index tracks scores for three reference years: 2015, 2019, and 2022 and forecasts a scenario for 2030 based on current trends.

This is the third edition of the SDG Gender Index—it was previously released in 2019 and 2022. It is one of the few global gender indices to be formally audited by the Competence Centre on Composite Indicators and Scoreboards (JRC-COIN) at the European Union’s Joint Research Centre.

The index was developed by a coalition of national, regional, and global leaders from feminist networks, civil society, and international development.

Resources:

Equal Measures 2030. (2024). A gender equal future in crisis? Findings from the 2024 SDG Gender Index.
https://equalmeasures2030.org/2024-sdg-gender-index

Inclusive financial systems

The “Income” comparison refers to what the World Bank calculates as account ownership of the richest 60% of households versus the poorest 40% of households.

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. World Bank. https://openknowledge.worldbank.org/handle/10986/37578

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. Retrieved June 2023 from https://data.worldbank.org/indicator/FX.OWN.TOTL.ZS

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). https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf