2023 Data Sources

The data sources for facts and figures featured in the 2023 Goalkeepers Report are listed here by section. Brief methodological notes are included for unpublished analyses.
Read the 2023 Goalkeepers Report


Pande, R., et al. (2015). Continuing with “…a heavy heart” - consequences of maternal death in rural Kenya. Reproductive Health, 12(Suppl 1), S2. Accessed May 2023. https://doi.org/10.1186/1742-4755-12-S1-S2

World Health Organization (WHO). (2023). Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group, and UNDESA/Population Division. Geneva: World Health Organization. Accessed May 2023. https://apps.who.int/iris/rest/bitstreams/1492307/retrieve

UN Inter-agency Group for Child Mortality Estimation (UN IGME). (2022). Levels and trends in child mortality: estimates developed by the United Nations Inter-agency Group for child mortality estimation. Accessed May 2023. https://childmortality.org/wp-content/uploads/2023/01/UN-IGME-Child-Mortality-Report-2022.pdf

UN Inter-agency Group for Child Mortality Estimation (UN IGME). (2022). Never Forgotten: The situation of stillbirth around the globe. Accessed May 2023. https://childmortality.org/wp-content/uploads/2023/03/UN-IGME-Stillbirth-Report-2022.pdf

Two SDG targets that are off track
Institute for Health Metrics and Evaluation (IHME). (2023). Maternal mortality ratio and neonatal mortality ratio [Data set]. IHME.
Note: Neonatal refers to the first 28 days (about 4 weeks) of life.

Fleszar, L., et al. (2023). Trends in state-level maternal mortality by racial and ethnic group in the United States. JAMA, 330(1), 52–61. https://doi.org/10.1001/jama.2023.9043

Opportunity to save millions of lives
Bespoke modeling by the foundation in collaboration with Burnet Institute. August 2023. Full methodology is detailed below.

New tools and practices to accelerate progress and boost survival rates for mothers and babies.

The package of breakthroughs modeled included maternal azithromycin (pregnancy), maternal azithromycin (intrapartum), infant azithromycin, multiple micronutrient supplements (MMS), maternal intravenous (IV) iron, AI-enabled ultrasound, antenatal corticosteroids (ACS), B. infantis probiotic, and postpartum hemorrhage treatment bundles.

In addition to the tools mentioned in this report, new practices to accelerate progress and boost survival rates for mothers and babies are also being used. For example, earlier this year, the World Health Organization released a global position paper on kangaroo mother care (KMC), an intervention that enables a mother to take a central role in her own and her newborn’s care.

Researchers believe many of these innovations could also be used to fight the epidemic of maternal mortality globally, including in the United Kingdom and the United States, where death rates for Black mothers have doubled since 1999.

In wealthy countries, pregnant women could benefit from increased use of IV iron, maternal intrapartum azithromycin, and the postpartum hemorrhage treatment bundle described in Melinda’s essay.

Delivering hope

Petersen, E., et al. (2019). Racial/ethnic disparities in pregnancy-related deaths - United States, 2007–2016. Morbidity and Mortality Weekly Report, 68(35), 762–765. https://dx.doi.org/10.15585/mmwr.mm6835a3

A big impact for mothers
Bespoke modeling by the foundation in collaboration with Burnet Institute. August 2023. Full methodology is detailed below.

Treating postpartum hemorrhage

World Health Organization (WHO). (2023). Postpartum haemorrhage. Accessed June 2023. https://www.who.int/teams/sexual-and-reproductive-health-and-research-(srh)/areas-of-work/maternal-and-perinatal-health/postpartum-haemorrhage

World Health Organization (WHO). (2023, May 9). Lifesaving solution dramatically reduces severe bleeding after childbirth. Accessed June 2023. https://www.who.int/news/item/09-05-2023-lifesaving-solution-dramatically-reduces-severe-bleeding-after-childbirth

Preventing PPH in the first place

World Health Organization (WHO). (2021). WHO global anemia estimates, 2021 edition. Accessed June 2023. https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children

Preventing infections

Tita, A., et al. for the A-PLUS Trial Group. (2023). Azithromycin to prevent sepsis or death in women planning a vaginal birth. The New England Journal of Medicine, 388, 1161-1170. https://dx.doi.org/10.1056/NEJMoa2212111

Chen, L., et al. (2021). The global burden and trends of maternal sepsis and other maternal infections in 204 countries and territories from 1990 to 2019. BMC Infectious Diseases, 21, Article 1074. https://doi.org/10.1186/s12879-021-06779-0

Gunja, M., Gumas, E., Williams, R. (2022, December 1). The U.S. maternal mortality crisis continues to worsen: an international comparison. The Commonwealth Fund. Accessed July 2023. https://www.commonwealthfund.org/blog/2022/us-maternal-mortality-crisis-continues-worsen-international-comparison

The baby knowledge boom

More precise understanding of why children die
Child Health and Mortality Prevention Surveillance (CHAMPS). (2023). CHAMPS data as of July 2023 [Data set]. CHAMPS. Summarized data, links to access full data set, and R packages for analysis are available at https://champshealth.org/data/

United Nations. (2010). Millennium Development Goals report. United Nations. https://www.un.org/millenniumgoals/pdf/MDG%20Report%202010%20En%20r15%20-low%20res%2020100615%20-.pdf

Bespoke modeling by the foundation in collaboration with Burnet Institute. August 2023. Full methodology is detailed below.

Our foundation estimates that
ACS could save the lives of 144,000 infants in sub-Saharan Africa and South Asia by 2030 and nearly 400,000 by 2040.
To save even more lives, doctors can couple ACS with the use of lung surfactant, which is a mixture of fat and proteins made in the lungs. Paired together, these tools could ensure that nearly every premature baby survives their first, most dangerous days of life.

Gut check

Delivering healthy babies and saving millions of lives
Bespoke modeling by the foundation in collaboration with Burnet Institute. August 2023. Full methodology is detailed below.

Methodology for Goalkeepers 2023 bespoke modeling: impact of novel maternal, neonatal, and infant interventions in low- and middle-income countries


Bespoke modeling was conducted by ⁠the foundation in collaboration with Burnet Institute. We aimed to estimate the potential impact of novel interventions 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 intervention 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. We constructed 14 distinct, interconnected modules for maternal, newborn, and infant condition pathways to account for the intergenerational linkages between maternal, fetal, and newborn/infant risk factors and conditions. Interventions were assumed to affect the transition rates between compartments across the intergenerational framework. 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 neonatal deaths and calculated DALYs for stillbirths accordingly.

In addition to a baseline scenario where no interventions were introduced and condition burden forecasts were dependent only on secular trends, we ran more than 8,000 counterfactual scenarios of various intervention combinations and delivery assumptions. We selected interventions for inclusion based on potential to yield large, unrealized impact as determined by (i) available data showing significant effect on maternal, neonatal, and infant condition burden; and (ii) status as a novel intervention not currently launched or scaled in most LMIC countries. 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 Caesarean 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 generate forecasted secular trends. Counterfactual scenarios were compared against this baseline to quantify the condition burden averted by each intervention. 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 all novel interventions were 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.

Products modeled were AI-enabled ultrasound; multiple micronutrient supplements (MMS); maternal IV iron; maternal azithromycin (pregnancy); maternal azithromycin (intrapartum); postpartum hemorrhage (PPH) management bundle; antenatal corticosteroids; B. infantis probiotic; and infant azithromycin.


We utilized 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. All 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 product effect size assumptions on published literature and available primary data. Coverage parameter values were constrained by intervention delivery channel access (e.g., antenatal care coverage, in-facility delivery coverage) where applicable and based on assumed product launch within the next three years followed by a three-year period of scale-up to 60 percent held constant through 2040.

Explore the data

IHME methodology

Our primary data partner, IHME, produced estimates and forecasts for 13 of the SDG indicators included in the 2023 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 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 used several methods improvements, including ensemble model predictions for severity-specific stunting prevalence and mean height-for-age z-scores (HAZ) and further disaggregation of <5 age groups. This led to improved estimates in a number of countries, including South Africa, Democratic Republic of the Congo, India, and Pakistan. In addition, new data has improved estimates in a number of countries as well, including Pakistan.

To project stunting prevalence to 2030, we first projected overall risk-weighted prevalence of HAZ using the summary exposure value (SEV) with an ensemble modelling approach. We used a cascading random spline model to estimate age-specific stunting prevalence from the SEV. To optimize model configuration, we trained models on historical stunting estimates from 1990 to 2014 and used each model version to predict prevalence from SEVs for 2015 to 2021. We then used the best model to fit the full set of SEV and prevalence estimates from 1990 to 2021, and input corresponding SEV forecasts and SDI projections, to generate stunting prevalence projections through 2030.

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 and essentially captures the risk of death in pregnancy. Projections to 2030 were modeled using an ensemble approach to forecast MMR, using SDI as a key driver.

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). There was, however, a significant relationship between the COVID-19 pandemic and indirect maternal mortality. This relationship with indirect maternal mortality was modeled using COVID-19 mortality rate as a covariate. Our estimates of excess indirect maternal mortality related to COVID-19 were corrected to remove incidental COVID-19 deaths in pregnant or postpartum women that were not due to pregnancy. We employed the same general method and pandemic-year cause of death this year as in the 2022 Goalkeepers Report, but we incorporated more years of prepandemic data to estimate secular trends.

Under-5 Mortality Rate

The under-5 mortality rate is 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 Global Burden of Disease (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 [RSV], measles, pertussis) observed during the pandemic, driven primarily by social distancing and mask use. We also incorporate increases in malaria deaths due to service disruption, as well as child deaths due directly and indirectly to COVID-19. 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 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., et al. (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., et al. (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 antiretroviral therapy (ART), prevention of maternal-to-child transmission (PMTCT) coverage, and transmission rate as inputs into a modified version of Avenir Health’s Spectrum software (Mahy et al, 2017). Adult ART is forecasted using location-specific rates of change, capping forecasted coverage using CD4 count-specific coverage caps developed for allocation of ART in GBD estimation. 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, et al. (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., et al. (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., et al. (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–e651. https://doi.org/10.1016/S2352-3018(21)00152-1


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. GBD estimates in the 2022 Goalkeepers Report incorporated methodological improvements to better capture the quality of case notification data. We refined this approach for the current Goalkeepers Report. This refinement is mainly reflected in the time trends of countries in North Africa and the Middle East. Additionally, refinements were made to the model to better follow the data, which resulted in changes in the time trend in some countries, including Malawi and Botswana.

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 prepandemic time periods and modeling assumptions used in the current studies, we did 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, we take into account updated reports regarding pandemic-related disruptions to malaria interventions and effective treatment with an antimalarial drug (which includes insecticide-treated bed nets (ITN), indoor residual spraying, antimalarial treatment, and drug effectiveness). These reports were used to apply an adjustment to estimates of effective antimalarial treatment coverage, which were then utilized to produce estimates of malaria prevalence and subsequently incidence. Projections to 2030 were derived using an ensemble model. First, coverage of artemisinin-based combination therapy (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 pulse surveys, which were used to adjust 2020 and 2021 incidence results, were applied to only 33 countries in Africa at this time 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 impacted care-seeking.

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  

Neglected Tropical Diseases

IHME measures the sum of the prevalence of 15 neglected tropical diseases (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. In the 2022 Goalkeepers Report, IHME applied an adjustment to the dengue estimates to account for COVID-19 disruptions from Chen et al (2022). 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 dengue this year, or similar to last year, an effect on other NTDs. 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). While modeling studies can characterize potential disruptions under different scenarios, reliable data to quantify the true magnitude of pandemic effects on NTD epidemiology is 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.

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., et al. (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

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. Projections to 2030 used an ensemble model, based both on past trends as well as utilizing 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 other pandemic era surveys do not show a consistent, significant reduction in contraception use due to the pandemic. As a result, we did not incorporate a pandemic effect on the family planning indicator. Changes to the historical estimates can be attributed to methodological updates and the addition of new data from eight countries: Pakistan, India, Vietnam, Madagascar, Nigeria, Fiji, Uzbekistan, and Cambodia. We model demand satisfied 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 modelling approach 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. In prior iterations we had constrained modern contraceptive prevalence to the sum of all modern methods, but this year we estimate modern contraceptive prevalence as a proportion of all use directly.

Performance Monitoring for Action. (2023). Available Datasets [Data set]. https://www.pmadata.org/data


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. 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.


IHME estimates the proportion of population with access to safely managed sanitation. As defined by the Joint Monitoring Programme (JMP), safely managed facilities must meet three criteria: 1) is not shared with multiple households, 2) is 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 2023 Goalkeepers Report, we developed models to estimate two components of safely managed sanitation: 1) the proportion of sewer-connected facilities that are safely managed and 2) the proportion of improved, nonsewer 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), and predictive covariates (SDI, lag distributed income per capita [LDI], and both linear and log transformations). 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, and the Organization for Economic Co-operation and Development (OECD). 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 Multiple Indicator Cluster Surveys (MICS), Demographic and Health Surveys (DHS), national surveys (in Canada and Norway), and Eurostat. Crosswalks were performed to estimate toilet type and wastewater treatment where data was unknown within the MICS and DHS microdata. The resulting estimates from this model were multiplied by the IHME estimates of the proportion of the population with improved, nonsewer-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 nonsewer facilities.

World Health Organization & UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). (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


IHME 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 (2020–2022) effects on vaccine coverage via administrative data coverage. To estimate disruptions in vaccine coverage during the COVID pandemic, IHME used administrative vaccine coverage data collected through the 2023 Joint Reporting Form. First, they 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 through 2022 for all countries due to the COVID pandemic. Second, they 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, they 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, they used these estimated disruptions in administrative coverage to generate as covariates in the final ST-GPR coverage models, which were fit to survey data and bias-adjusted administrative data. If administrative data was missing for 2020 through 2022, IHME 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 the use of the magnitude of coverage disruptions implied by administrative data, while still adjusting for bias in this data.

World Health Organization. (2023). The Big Catch-Up: An Essential Immunization Recovery Plan for 2023 and Beyond. Accessed August 8, 2023. https://www.who.int/publications/i/item/9789240075511

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 (DTP) 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 (ART) 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 (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 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 total health spending per capita estimates, were substituted into the previously fit frontier to obtain forecasted UHC for all countries for 2022–2030.

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 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 percent of the reduction in monthly missed health care visits (excluding routine services). Details of the estimation of missed health care visits are described in last year’s report.

Bill & Melinda Gates Foundation. (2022) 2022 Goalkeepers Report: The Future of Progress. https://www.gatesfoundation.org/goalkeepers/report/2022-report/

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 Total Sources 2022
Child mortality 20,634  
Child stunting 1,897  
Family planning (met need) 1,084  
Malaria incidence 12,882  
Maternal mortality 8,336  
Neonatal mortality 20,634  
HIV incidence 5,156   
NTD prevalence chagas 1,169  
NTD prevalence visceral leishmaniasis 4,609  
NTD prevalence cutaneous and mucocutaneous leishmaniasis 662  
NTD prevalence African trypanosomiasis 2,970   
NTD prevalence schistosomiasis 4,415  
NTD prevalence cysticercosis 3,578  
NTD prevalence cystic echinococcosis 3,748  
NTD prevalence lymphatic filariasis 565   
NTD prevalence onchocerciasis 351   
NTD prevalence trachoma 114   
NTD prevalence dengue 5,446  
NTD prevalence rabies 3,700  
NTD prevalence ascariasis 3,551  
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 446  
Sanitation safely managed 1,207  
Smoking prevalence 3,603  
TB incidence 8,527  
UHC maternal disorders 8,336  
UHC met need 1,084  
UHC live births 14,815   
UHC neonatal mortality 20,634  
UHC diphtheria 3,852  
UHC pertussis 9,292  
UHC tetanus 4,330  
UHC DTP vaccination 8,477   
UHC measles 12,353  
UHC measles vaccination 2,814   
UHC LRI 7,350  
UHC diarrhea 6,934  
UHC HIV treatment 5,156   
UHC TB 8,527  
UHC lymphoid leukemia 3,902  
UHC asthma 3,235  
UHC diabetes 5,191  
UHC IHD treatment 4,580  
UHC stroke 4,655  
UHC chronic kidney disease
UHC chronic obstructive pulmonary disease 3,021  
UHC cervical cancer 7,630  
UHC breast cancer 7,820  
UHC uterine cancer 7,638  
UHC colon and rectum cancer 7,805  
UHC epilepsy 4,289  
UHC appendicitis 4,201  
UHC paralytic ileus and intestinal obstruction treatment 4,067  
Vaccine coverage DTP3 10,165  
Vaccine coverage MVC2 3,024  
Vaccine coverage PCV3 1,861  

Indicators estimated from other sources


World Bank. Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population). [Data set]. Poverty and Inequality Platform: World Development Indicators. Accessed July 2023. https://data.worldbank.org/indicator/SI.POV.DDAY. License: CC BY-4.0.

For methodology, see:
World Bank. (2023). 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. (2023). 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 2023. https://www.fao.org/in-action/rural-livelihoods-dataset-rulis/data-application/data/en

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]. https://www.fao.org/3/cb4762en/cb4762en.pdf. License: CC BY-NC-SA 3.0 IGO.

Small food producers’ income growth for selected countries with at least two entries for small food producers’ income from 2005 to 2022. 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 and using years listed below: 

Location Year range
Burkina Faso 2014–2019  
Cambodia 2019–2020  
Ethiopia 2014–2019  
India 2005–2012  
Malawi 2011–2020  
Mali 2014–2019  
Niger 2011–2014   
Nigeria 2016–2019  
Paraguay 2015–2020  
Senegal 2018–2021   
Tanzania 2009–2015   
Uganda 2010–2019  

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.  https://www.fao.org/3/ca2813en/CA2813EN.pdf. License: CC BY-NC-SA 3.0 IGO.


World Bank, UNESCO Institutes for Statistics (UIS), UNICEF, the Foreign, Commonwealth & Development Office (FCDO), USAID, et al. (2022). The State of Global Learning Poverty: 2022 Update. https://www.unicef.org/media/122921/file/StateofLearningPoverty2022.pdf

Source for Learning Poverty 2022 simulations:
Azevedo, J., Demombynes, G. & Wong, Y.N. (2023, April 20). Why has the pandemic not sparked more concern for learning losses in Latin America? World Bank Blog. https://blogs.worldbank.org/education/why-hasnt-pandemic-sparked-more-concern-learning-losses-latin-america-perils-invisible

Gender Equality

The chart is based on data from the United Nations Global SDG Database, the Government of India’s National Statistical Office, and the International Labour Organization. 

The data is the most recent available for 93 countries and territories (2001–2022). The age group is 15 or older where available (18 or older in Ghana). In a number of cases, data is for those ages 10 or older (n=13) or 12 or older (n=3). The data for Malaysia, Ireland, and Cambodia refers to individuals ages 15–64. In the case of Thailand (2015) and India (2019), it is for those ages 6 or older, and in the United Republic of Tanzania (2014) for those ages 5 or older. Data for Bulgaria, Denmark, Latvia, the Netherlands, Slovenia, and Spain correspond to time spent on unpaid care among those ages 20 to 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.

The regional average ratios are the averages of the ratios of the component countries, and the global average ratio is the average of the ratios of all countries included.

For further information on the country-level data, see:

Data for India and Madagascar are not currently available in the SDG data portal, so they come from:

Financial Services for the Poor

The “Income” comparison refers to what the World Bank calculates as account ownership of the richest 60 percent of households and poorest 40 percent of households, respectively.

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://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 2023. 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.