2024 Data Sources

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.

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.

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

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

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.

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

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:

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

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

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:

• Performance Monitoring for Action. (2020). Data. https://www.pmadata.org/data

• Bradley, Sarah E. K., Croft, T. N., Fishel, J. D., & Westoff, C. F. (2012). Revising unmet need for family planning [DHS Analytical Studies No. 25]. ICF International. https://dhsprogram.com/pubs/pdf/AS25/AS25[12June2012].pdf

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:

• Bill & Melinda Gates Foundation. (2022). 2022 Goalkeepers report: The future of progress.
  https://www.gatesfoundation.org/goalkeepers/report/2022-report/

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.

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:

• World Health Organization. (2023). The big catch-up: An essential immunization recovery plan for 2023 and beyond.
  https://www.who.int/publications/i/item/9789240075511.

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:

• World Health Organization & UNICEF Joint Monitoring Programme. (2021). SDG indicator metadata.
  https://washdata.org/sites/default/files/2022-01/jmp-2021-metadata-sdg-621a.pdf

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/

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:

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

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:

• To download 2024 index data and the latest index report and for more information about index methodology, see: https://equalmeasures2030.org/2024-sdg-gender-index

• To access interactive index data visualizations, see: https://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 https://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.
https://equalmeasures2030.org/2024-sdg-gender-index

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