2025 Data Sources

References to reduction in global health funding used in this report refer to cuts to overseas development assistance (ODA) announced by donor governments in 2025 (Australia, Austria, Belgium, Canada, China, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Republic of Korea, Spain, Sweden, Switzerland, United Kingdom, United States). The Institute for Health Metrics and Evaluation (IHME) assumes that changes in ODA proportionally affect development assistance for health (DAH).

Institute for Health Metrics and Evaluation, (October 2025). [Bespoke modeling. Full methodology is detailed below].

Other estimates also show a reduction in under-five deaths every year from 2000 to 2024, including data published by the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME).

This calculation, meant solely for illustrative purposes, assumes that a student population of 200,000 and an average classroom size of 40 students roughly equals 5,000 classrooms.

Institute for Health Metrics and Evaluation, (October 2025). [Bespoke modeling. Full methodology is detailed below].

The chart shows the projected number of under-5 deaths from 2024 through 2045. The estimate for 2025 reflects the current best assumptions with the known and announced funding cuts as of October 17, 2025. Estimates for 2026 through 2045 show 20% and 30% reductions in global development assistance for health (DAH), relative to the 2024 levels.

12 million: An additional 12.5 million child deaths could occur by 2045 if DAH is reduced by 20% from 2024 levels. This assumes average government response to funding gaps.

16 million: An additional 16.3 million child deaths could occur by 2045 if DAH is reduced by 30% from 2024 levels. This assumes average government response to funding gaps.

13 million: Up to 13.2 million children’s lives could be saved by 2045 if DAH is restored to 2024 funding levels and new innovations against malaria, lower respiratory illness, diarrheal diseases, and maternal and neonatal disorders are scaled up.

United Nations Development Programme, 2023, A world of debt: A growing burden to global prosperity, https://unctad.org/publication/world-debt-2023.

The Lancet Child Survival Series, (2003), www.iycn.org/resource/the-lancet-series-on-child-survival.

World Health Organization, World Health Report 2008: Primary Health Care—Now More Than Ever, Primary Health Care Performance Initiative, 2018, www.paho.org/sites/default/files/PHC_The_World_Health_Report-2008.pdf.

United Nations Inter-agency Group for Child Mortality Estimation, (2023), Levels and Trends in Child Mortality: Report 2023, https://data.unicef.org/resources/levels-and-trends-in-child-mortality-2023/.

World Health Organization, (2023), Immunization coverage fact sheet, www.who.int/data/gho/data/themes/topics/immunization-coverage.

Ozawa, S., Clark, S., Portnoy, A., Grewal, S., Brenzel, L., & Walker, D. G. (2016), “Return on investment from childhood immunization in low- and middle-income countries, 2011–20.” Health Affairs, www.healthaffairs.org/doi/10.1377/hlthaff.2015.1086.

World Health Organization, (2023), Measles cases by country – Senegal, www.who.int/data/gho/data/indicators/indicator-details/GHO/measles---number-of-reported-cases.

World Health Organization, (2018), High burden to high impact: A targeted malaria response, www.who.int/publications/i/item/WHO-CDS-GMP-2018.25.

Winters, W., et al, (2024), “Cost and cost effectiveness of geospatial planning and delivery tools added to standard health campaigns in Luapula Province, Zambia,” Oxford Open Digital Health, www.academic.oup.com/oodh/article/2/Supplement_2/ii66/7911916.

World Health Organization, (2023), Pneumonia in children fact sheet, www.who.int/news-room/fact-sheets/detail/pneumonia.

Our World in Data, Causes of death in children under five, World (2021), www.ourworldindata.org/grapher/causes-of-death-in-children-under-5.

World Health Organization, (2025), Pneumococcal conjugate vaccine reduced-dosing schedule: A systematic review and meta-analysis, www.who.int/publications/m/item/report_who_sage_pcv_2025 who.int.

Gates Foundation, Integrated Portfolio Management, (2025), Budgetary impact of WHO’s SAGE recommendation on reduced (1+1) PCV dosing, [Unpublished, internal document].

World Health Organization, (2023), World malaria report 2023, www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2023.

World Health Organization, (2021), World malaria report 2021, www.who.int/publications/i/item/9789240040496.

CDC, (2024), Life cycle of Anopheles mosquitoes, www.cdc.gov/mosquitoes/about/life-cycle-of-anopheles-mosquitoes.html.

The Global Fund, (2024, April 17), “New Nets Prevent 13 Million Malaria Cases in Sub-Saharan Africa,” Geneva: The Global Fund, www.theglobalfund.org/en/news/2024/2024-04-17-new-nets-prevent-13-million-malaria-cases-sub-saharan-africa.

The Global Fund, (2023), “Accelerating the introduction of new nets through Global Fund grants,” https://resources.theglobalfund.org/en/updates/2023-10-11-accelerating-the-introduction-of-new-nets-through-global-fund-grants/.

SC Johnson, (2023), “SC Johnson and partners pilot new spatial repellent to protect communities from mosquitoes in malaria-endemic regions,” www.scjohnson.com/en/news-stories/press-releases/major-milestone-breakthrough-mosquito-repellent-tool.

GSK plc, (2024), “First single-dose medicine for P. vivax malaria prequalified by WHO and included in WHO Guidelines,” www.gsk.com/en-gb/media/press-releases/first-single-dose-medicine-for-p-vivax-malaria-prequalified-by-who.

Institute for Health Metrics and Evaluation, (August 2025). [Bespoke modeling. Full methodology is detailed below].

New analyses (unpublished from the Gates Foundation) on a broad portfolio of new innovations against malaria show that if the tools are used effectively and deployed at scale, 5.7 million children’s lives can be saved by 2045. The projection assumes an innovation trajectory approximating median level of impact. The tools included in the modeling are innovations in vector control (dual active ingredient insecticide nets, novel indoor residual spraying, attractive targeted sugar baits, gene drive); drugs (single-encounter radical cure, novel dual drug combination, long-acting injectable drug, monoclonal antibody, endectocide); vaccines (next-generation malaria vaccine); and diagnostics (novel rapid diagnostic test, non-invasive diagnostic tool). 

Gilead, (June 2024), “Gilead’s Twice-Yearly Lenacapavir Demonstrated 100% Efficacy and Superiority to Daily Truvada® for HIV Prevention,” www.gilead.com/news/news-details/2024/gileads-twice-yearly-lenacapavir-demonstrated-100-efficacy-and-superiority-to-daily-truvada-for-hiv-prevention.

Merck, (2025), “Merck to Initiate Phase 3 Trials for Investigational Once-Monthly HIV Prevention Pill,” www.merck.com/news/merck-to-initiate-phase-3-trials-for-investigational-once-monthly-hiv-prevention-pill/.

Institute for Disease Modeling, (2025), Geographic HIV risk-prioritization for delivery of long-acting PrEP, [Unpublished internal document], Gates Foundation.

Wu, L., Kaftan, D., Wittenauer, R., Arrouzet, C., Patel, N., Saravis, A.L., Pfau, B., Mudimu, E., Bershteyn, A., and Sharma, M. (2024), “Health and budget impact, and price threshold for cost-effectiveness of lenacapavir for PrEP in Eastern and Southern Africa: a modeling analysis,” medRxiv, https://www.medrxiv.org/content/10.1101/2024.08.20.24312137v1.

Lynch, S., Cohen, R.M., Kavanagh, M., Sharma, A., Raphael, Y., Pillay, Y., and Bekker, L. (2025), “Lessons for long-acting lenacapavir: catalysing equitable PrEP access in low-income and middle-income countries,” The Lancet HIV, https://pubmed.ncbi.nlm.nih.gov/40659026/.

UNICEF, (2025), Levels and trends in child mortality 2024, https://data.unicef.org/topic/child-survival/under-five-mortality/.

Gavi, (March 2025), “Gavi welcomes first-ever prequalification of a maternal RSV vaccine,” www.gavi.org/news/media-room/gavi-welcomes-first-ever-prequalification-maternal-rsv-vaccine.

Madhi, S. A., Anderson, A. S., Absalon, J., Radley, D., et al. (2023), “Potential for maternally administered vaccine for infant group B streptococcus,” The New England Journal of Medicine, www.nejm.org/doi/10.1056/NEJMoa2116045.

Institute for Health Metrics and Evaluation, (August 2025), [Bespoke modeling. Full methodology is detailed below].

To estimate the impact of recent aid cuts, there are two primary areas of work that IHME undertook: (i) modeling and estimating the impact of funding cuts across all relevant sources on projections of development assistance for health (DAH) and total health spending; and (ii) estimating the associated impact that reductions in health spending will have on the Goalkeepers 2025 SDG indicators.

To comprehensively measure foreign aid cuts made both domestically and internationally, IHME collected and standardized estimates of global health spending from diverse data sources, including commitments and disbursements from development project records, annual budgets, financial statements, and revenue reports. Estimates of DAH and total health expenditure (THE) were adjusted to account for donor announcements and budget cuts. Long-term projections through 2045 for DAH were generated using donor targets, historical trends, and GDP-based forecasting, while future THE was predicted through ensemble models assessing key indicators such as government expenditure and out-of-pocket costs. IHME’s estimates and forecasts of foreign aid are based on publicly available data as of October 17, 2025. Detailed methodologies are available in the online Methods Annex of the Financing Global Health 2025 report: www.healthdata.org/research-analysis/library/financing-global-health-2025-cuts-aid-and-future-outlook.

The following funding scenarios were produced:

To quantify the impact of reductions in development aid on the SDG indicators, IHME analyzed the relationship between each indicator and historical health spending, using a nonparametric stochastic frontier analysis approach where the “frontier” represents the best possible outcome (e.g., the lowest number of new tuberculosis cases) for a given level of health spending. The model assumes that this "efficiency” persists into the future to calculate the marginal cost of decreased health coverage, lives lost, or additional cases because of funding reductions. For each SDG indicator in this report, forecasts were produced based on the 20% reduction to DAH scenario, while both the 20% and 30% reduction to DAH scenario were produced for under-five child mortality.

IHME also analyzed the lives saved should a suite of new innovations be effectively delivered through the future health scenarios framework. The innovations included a portfolio of products and effect sizes on cause-specific mortality using simulations from the Gates Foundation. IHME used these effect sizes to estimate both cause-specific and total under-5 lives saved through innovation activities.

References:

• GBD 2021 Forecasting Collaborators. (2024), “Burden of disease scenarios for 204 countries and territories, 2022–2050: A forecasting analysis for the Global Burden of Disease Study 2021,” The Lancet, doi: 10.1016/S0140-6736(24)00685-8.

Our primary data partner, IHME, produced estimates and forecasts for 13 of the Sustainable Development Goal (SDG) indicators included in the 2025 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.

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 (HAZ), further disaggregation of under-5 age groups, and a standardized age-sex splitting approach.

Forecasts of stunting prevalence were driven by climate scenarios of days above 30 degrees Celsius, consumption per capita, the socio-demographic index (SDI), and location random effects. 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.

To estimate the impact of reduced DAH, we used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased total health spending including development assistance from the World Food Programme—varying by country and year—would increase stunting and adjusted our forecasts per the 20% reduction to DAH scenario for each country through 2030 accordingly.

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 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 2024 Goalkeepers Report are primarily driven by changes in the all-cause mortality envelope and the inclusion of additional input data sources. Methodological improvements in estimation of all-cause mortality allowed for the addition of more granular age-specific sources of input data, which generally resulted in increased estimates of all-cause mortality among women of reproductive age. Increases in all-cause mortality are most notable in sub-Saharan Africa, resulting in higher estimates of MMR. Conversely, all-cause mortality estimates among women of reproductive age were notably lower in Afghanistan compared to previous reports, resulting in lower estimates of MMR.

Data added since the last report cover additional location-years of the pandemic. COVID-19 impacts on maternal mortality were seen in the data for many locations and reflected in resulting model estimates, including countries in the Caribbean and Latin America, southern Latin America, high-income North America, Central Asia, Central Europe, and Southeast Asia.

To estimate the impact of reduced DAH, we used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased spending—varying by country and year—would increase MMR and adjusted our forecasts per the 20% reduction to DAH scenario for each country through 2030 accordingly.

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 a newly developed statistical model for age-specific mortality rates that incorporates both parametric and non-parametric methods. Age specific mortality rates for ages 0-6 days, 7-27 days, 1-5 months, 6-11 months, 1-2 years, and 2-4 years were jointly estimated in the model, then converted to U5MR. Projections were based on a combination of key drivers, including Global Burden of Disease (GBD) risk factors, selected interventions (e.g., vaccines), and SDI. Changes in U5MR estimates in this report came from new and additional input mortality data incorporated since the previous Goalkeepers report, as well as the new statistical model.

To estimate the impact of reduced DAH, IHME used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased spending—varying by country and year—would increase under-five mortality and adjusted the forecasts per the 20% reduction to DAH scenario for each country through 2030 accordingly.

References:

• Schumacher, A.E., et al. (2024), “Global age-sex-specific all-cause mortality and life expectancy estimates for 204 countries and territories and 660 subnational locations, 1950–2023: A demographic analysis for the Global Burden of Disease Study 2023,” The Lancet. Unpublished manuscript.

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 a newly developed statistical model for age-specific mortality rates that incorporates both parametric and non-parametric methods. Age specific mortality rates for ages 0-6 days and 7-27 days were jointly estimated in the model, then converted to neonatal mortality rate. 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 discussed for the under-5 mortality rate estimates.

To estimate the impact of reduced development assistance for health, we used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased spending—varying by country and year—would increase neonatal mortality and adjusted our forecasts per the 20% reduction to DAH scenario for each country through 2030 accordingly.

References:

• Schumacher, A.E., et al. (2024), "Global age-sex-specific all-cause mortality and life expectancy estimates for 204 countries and territories and 660 subnational locations, 1950–2023: A demographic analysis for the Global Burden of Disease Study 2023," The Lancet, Unpublished manuscript.

HIV

IHME estimates the HIV rate as new HIV infections per 1,000 population. Changes in incidence in this year’s report were due to updates made during GBD 2023 estimation, which reflect substantial data updates from the following sources. Population-based HIV Impact Assessment (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.

To estimate the impact of reduced DAH, we used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased spending—varying by country and year—would result in decreased anti-retroviral therapy (ART) coverage. ART coverage modifies rates of mortality and incidence in our model, so both epidemiological metrics are affected by changes in ART. We took the estimated declines in ART coverage and simulated their impact in our forecasting framework.

Tuberculosis

IHME estimates new and relapse tuberculosis (TB) cases diagnosed within a given calendar year (incidence) using data from prevalence surveys, case notifications, remission estimates, excess mortality estimates, and cause-specific mortality estimates as inputs to a statistical model that enforces internal consistency among the estimates. At the global level, TB incidence estimates for this report show a slightly steeper downward trend than those from last year. This trend is influenced by the updated TB mortality time trend, which incorporates an updated mortality envelope for the GBD 2025 cycle, updated covariates informed by recent data, and newly available cause of death data.

To estimate the impact of reduced DAH, we used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased spending—varying by country and year—would increase TB and adjusted our forecasts per the 20% reduction to DAH scenario for each country through 2030 accordingly.

Malaria

IHME estimates the malaria rate as the number of new cases per 1,000 population. Estimates of malaria include new data points since last year, including three new national surveys from Ghana, Liberia, and Mozambique. On top of routine data updates, for the first time in several years, updates were also made to the reporting completeness data. These improvements have impacted results across several countries including Côte d’Ivoire, Ghana, Liberia, Mozambique, Senegal, and Guyana. Large spikes in the estimates for Myanmar, Ethiopia, Pakistan, and Zimbabwe can be attributed to known outbreaks.

Projections to 2030 used the malaria forecasting models developed for climate scenario models. In brief, these models had five components, all fit at the second administrative level. Model 1: modeling plasmodium falciparum prevalence in 2-10 year olds (pfpr2-10). Model 2: modeling the all-age prevalence-case rate given pfpr2-10. Model 3: modeling the age- and sex-specific case rate given all-age case rate. Model 4: modeling the all-age prevalence-fatality rate given pfpr2-10. Model 5: modeling the age- and sex-specific fatality rate given the all-age fatality rate. Each model used Admin 2-level data from 2000 to 2022 on both the malaria outcomes but also the weighted fraction of the year where temperature is suitable for transmission (weighted by level of suitability), yearly flood days per capita, GDP per capita, and DAH spending on malaria.

Our reference forecast was based on forecasted estimates of each covariate (using RCP 4.5 for climate-based covariates) and the reference estimates of DAH malaria spending.

References:

• World Health Organization, (2022), Third round of the global pulse survey on continuity of essential health services during the COVID-19 pandemic (November – December 2021), www.who.int/publications/i/item/WHO-2019-nCoV-EHS_continuity-survey-2022.1.

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. Projections to 2030 used an ensemble model, driven both by trends in the past as well as projections of SDI.

To estimate the impact of reduced DAH, we used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased spending—varying by country and year—would increase NTD prevalence and adjusted our forecasts per the 20% reduction to DAH scenario for each country through 2030 accordingly.

Family planning

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 or female sterilization, male or female condoms, diaphragms, cervical caps, sponges, spermicidal agents, oral hormonal pills, patches, rings, implants, injections, intrauterine devices (IUDs), and emergency contraceptives. Need is defined as it is defined by the Demographic and Health Surveys. Projections to 2030 used an ensemble model, based both on past trends as well as using SDI as a key driver, which incorporates projections of income per capita and education and the effects of the COVID-19 pandemic.

To estimate the impact of reduced DAH, we used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased spending—varying by country and year—would decrease met need for modern contraceptive prevalence and adjusted our forecasts per the 20% reduction to DAH scenario for each country through 2030 accordingly.

References:

• Performance Monitoring for Action, (2020), Performance Monitoring for Action (PMA) Survey, www.pmadata.org/data.
• Bradley, Sarah E.K., Trevor N. Croft, Joy D. Fishel, and Charles F. Westoff, (2012), Revising Unmet Need for Family Planning, DHS Analytical Studies, 25, ICF International, www.dhsprogram.com/pubs/pdf/AS25/AS25[12June2012].pdf.

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 or 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 TB, as well as coverage of ART among those with HIV/AIDS. Indicators of treatment of non-communicable 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 non-communicable diseases also include scaled 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. 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 from 2025 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 total health spending per capita estimates, were substituted into the previously fit frontier to obtain forecasted UHC for all countries for 2025–2030.

To estimate the impact of reduced DAH, we used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased spending—varying by country and year—would decrease universal health care coverage and adjusted our forecasts per the 20% reduction to DAH scenario for each country through 2030 accordingly.

Smoking

IHME measures the age-standardized prevalence of any current use of smoked tobacco among those aged 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 using trends in country-reported coverage. To do this, IHME extended the modelling framework used to capture other acute temporal disruptions (i.e., drops) in coverage due to stockouts or other similar events. In that framework, IHME first modelled the magnitude of disruptions for vaccine-country-years with reported stockout events reported via the 2025 Joint Reporting Form or other identified disruption events. To do this, IHME first estimated counterfactual country-reported coverage using models that excluded vaccine-country-years with identified disruptions, then compared these counterfactual estimates to the values reported by countries for those years. These disruption magnitudes were then included as a covariate in vaccine coverage modelling. To account for disruptions due to the COVID-19 pandemic, IHME considered all vaccine-country-years for 2020–2023 as candidates for disruptions. For vaccine-country-years in the pandemic period without available country-reported data, we imputed disruption magnitudes based on vaccine-year-specific distributions from locations with data.

These estimates also reflect new methods to better account for rapid scale-ups in coverage in MCV2 and PCV3 in years following country-specific introductions using hierarchical spline models. For each vaccine, the model first estimated global scale-up patterns, then used these global patterns as priors for country-specific scale-up models. This approach better captures country-specific scale-up where data were available and borrows strength from global scale-up patterns to inform estimates where data are missing.

To estimate the impact of reduced DAH, IHME used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased spending—varying by country and year—would decrease vaccine coverage and adjusted our forecasts per the 20% reduction to DAH scenario for each country through 2030 accordingly.

• World Health Organization, (July 2025), “Global childhood vaccination coverage holds steady, yet over 14 million infants remain unvaccinated,” news release, www.who.int/news/item/15-07-2025-global-childhood-vaccination-coverage-holds-steady-yet-over-14-million-infants-remain-unvaccinated-who-unicef.
• GBD 2023 Vaccine Coverage Collaborators, (2024), “Global, regional, and national trends in routine childhood vaccination coverage from 1980 to 2023 with forecasts to 2030: A systematic analysis for the Global Burden of Disease Study 2023,” The Lancet, www.thelancet.com/journals/lancet/article/PIIS0140-6736(25)01037-2/abstract.

Sanitation

IHME estimates the proportion of the population with access to safely managed sanitation. As defined by the Joint Monitoring Programme (JMP), safely managed facilities must meet three criteria: 1) not shared with multiple households, 2) an improved sanitation facility, and 3) wastewater is disposed of safely (World Health Organization 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 (World Health Organization, 2021). Safely managed treated wastewater must have received at least secondary treatment (World Health Organization 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 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.

For the 2025 Goalkeepers Report, IHME developed models to estimate three components of safely managed sanitation: 1) the proportion of treated wastewater that receives at least secondary treatment, 2) the proportion of sewer-connected facilities that are safely managed, and 3) the proportion of improved, non-sewer facilities that are safely managed.

Data for estimating the proportion of treated wastewater that receives at least secondary treatment were extracted from Eurostat, Aquastat, the Organisation for Economic Co-operation and Development (OECD), and national surveys. 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), OECD, and national surveys (Republic of Korea, Singapore, Andorra, Austria, and Ireland). Data for estimating the proportion of improved, non-sewer facilities that are safely managed were extracted from MICS, DHS, Eurostat, and national surveys (Canada, Norway, and the United States).

IHME 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.

To estimate the impact of reduced DAH, IHME used a nonparametric stochastic frontier approach (as detailed in the “Measuring funding cuts to foreign aid and their impact on SDGs” section above) to model how decreased government and overseas development for water, sanitation and hygiene (WASH)—varying by country and year—would decrease the proportion of population with access to safely managed sanitation. and adjusted our forecasts per the 20% reduction to DAH scenario for each country through 2030 accordingly.

References:

• World Health Organization & UNICEF Joint Monitoring Programme, (2021), WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) | UN_Water, www.washdata.org/sites/default/files/2022-01/jmp-2021-metadata-sdg-621a.pdf.

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

Poverty

World Bank, (2025), Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population), [Data set], https://data360.worldbank.org/en/indicator/WB_PIP_HEADCOUNT_IPL.

For methodology, see: World Bank, (2025), Poverty and inequality platform methodology handbook, https://datanalytics.worldbank.org/PIP-Methodology/.

This report uses the international poverty line and purchasing power parity (PPP) data that were in effect prior to the World Bank’s June 2025 update. The new poverty line and PPPs will be incorporated in the next edition of this report to ensure consistency and comparability over time.

Agriculture

Food and Agriculture Organization of the United Nations, (2025), Average annual income from agriculture, [Data set], http://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:

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], 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 percent 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:

• To download 2024 index data and the latest index report and for more information about index methodology, see: www.equalmeasures2030.org/2024-sdg-gender-index
• To access interactive index data visualizations, see: www.equalmeasures2030.org/2024-sdg-gender-index/explore-the-data/
• To view the technical audit conducted by the COIN center of the EU’s Joint Research Centre, see www.equalmeasures2030.org/2024-sdg-gender-index/about-the-index/

Equal Measures 2030, (2024), A gender equal future in crisis? Findings from the 2024 SDG Gender Index, www.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 percent of households versus the poorest 40 percent of households.

Klapper, L., Singer, D., Starita, L., & Norris, A. (2025), “The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy,” World Bank, www.hdl.handle.net/10986/43438.

World Bank, (2025), “Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+),” [Data set], Global Findex Database, https://genderdata.worldbank.org/en/indicator/fx-own-totl-zs.

For methodology, see:
World Bank, (2025), Survey methodology, In The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy, 267–294, www.worldbank.org/en/publication/globalfindex/methodology.