IHME Data

This dataset contains predicted 2017 smoking prevalence levels under unrealized tobacco control policy scenarios: 1) If WHO-attributed country achievement scores for select components of its MPOWER policy package (smoke-free (P), health warnings (W), and advertising (E)), and cigarette’s affordability (RIP) remained at the level they were at in 2008; 2) If the price of a cigarette pack was I$7.73 or higher; 3) If all countries had implemented each of the P, W, and E policies at the highest level; and 4) If countries had implemented both higher cigarette prices and P, W, and E policies at the highest level. Results were produced by sex and age group globally and for 155 countries. The dataset also includes data used to produce the counterfactual analysis, including GBD 2017 smoking prevalence estimates, different tobacco control policy indicators, cigarette prices and affordability, and more.

Annual estimates were produced for oral rehydration therapy coverage for children under 5 years of age who had diarrhea at the second administrative-level unit in Senegal, Mali, and Sierra Leone between 2000–2018. These estimates were produced using a geo-positioned dataset created from 23 household surveys. Survey sources used include the Demographic and Health Survey (DHS) and UNICEF Multiple Indicator Cluster Survey (MICS) series, and other country‐specific surveys.

This dataset includes the following:

• CSV files of aggregated oral rehydration therapy coverage estimates at the second administrative level. Estimates are provided for three measures: Any oral rehydration solutions, Recommended home fluids only, and No oral rehydration therapy
• Code files used to generate the estimates

Estimates were produced for first-dose coverage of measles-containing vaccine (MCV1) at the 5x5 km-level in 101 low- and middle-income countries (LMICs) between 2000-2019. These estimates were produced using data on vaccination coverage and geographical locations from 354 household-based surveys.

This dataset includes the following:

• GeoTIFF raster files for pixel-level estimates of MCV1 coverage
• CSV files of aggregated MCV1 coverage estimates for each country at the first and second administrative unit divisions
• Code files used to generate the estimates

Get Data Files (CSV data and documentation are also available in the "Files" tab above.) 

The COVID-19 Health Services Disruption Survey 2020 is a series of surveys developed to assess the level of disruption to a range of health services resulting from the COVID-19 global pandemic and subsequent government mandates and changes in behavior to mitigate the spread of the disease.

This survey was conducted in 76 countries using the smartphone-based Premise data collection platform. Respondents were individual members of the general population in 20 African countries where malaria is endemic. Data were collected from 14,615 respondents. The survey focused on the level of disruption to malaria prevention activities and malaria testing and treatment.

The survey was developed specifically to assess the change in levels of service delivery prior to, and immediately following, the onset of the COVID-19 global pandemic. Data generated from this survey is not intended to be used as an overall estimate of the level of health service delivery.

The COVID-19 Health Services Disruption Survey 2020 is a series of surveys developed to assess the level of disruption to a range of health services resulting from the COVID-19 global pandemic and subsequent government mandates and changes in behavior to mitigate the spread of the disease.

This survey was conducted in 76 countries using the smartphone-based Premise data collection platform. Respondents were individual members of the general population who were pregnant or had given birth within the past 6 months at the time of the survey. Data were collected from 2,129 respondents. The survey focused on the level of disruption to the provision of antenatal care and delivery services for pregnant women.

The survey was developed specifically to assess the change in levels of service delivery prior to, and immediately following, the onset of the COVID-19 global pandemic. Data generated from this survey is not intended to be used as an overall estimate of the level of health service delivery.

The COVID-19 Health Services Disruption Survey 2020 is a series of surveys developed to assess the level of disruption to a range of health services resulting from the COVID-19 pandemic and subsequent government mandates and changes in behavior to mitigate the spread of the disease.

This survey was conducted in 76 countries using the smartphone-based Premise data collection platform. Respondents were individual members of the general population. Data were collected from 52,492 respondents. The survey focused on the level of disruption to the provision of general health services, including visits to medical providers and access to medication.

The survey was developed specifically to assess the change in levels of service delivery prior to, and immediately following, the onset of the COVID-19 global pandemic. Data generated from this survey is not intended to be used as an overall estimate of the level of health service delivery.

The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations.

Annual estimates for fertility, population, migration, and all-cause mortality are available from the GBD Results Tool. Estimates are available by age and sex for 1950-2019. Select tables published in The Lancet in October 2020 in "Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019" are also available for download via the “Files” tab above.

For additional GBD results and resources, visit the GBD 2019 Data Resources page.

The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations.

Annual deaths, YLLs, YLDs, and DALYs attributable to 87 risk factors as well as estimates for summary exposure values (SEVs) by risk are available from the GBD Results Tool. Estimates are available by age and sex for 1990-2019. Select tables published in The Lancet in October 2020 in "Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019" are also available for download via the “Files” tab above.

For additional GBD results and resources, visit the GBD 2019 Data Resources page.

The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations.

This reference life table, or theoretical minimum risk life table (TMRLT), is used in GBD to calculate years of life lost (YLLs) due to premature mortality. It was constructed based on the lowest observed age-specific mortality rates by location and sex across all estimation years from all locations with populations over 5 million in 2016. YLLs are computed by multiplying the number of estimated deaths by the reference life table’s life expectancy at age of death. The table includes estimates for life expectancy at age x for ages 0 to 95+ at five-year intervals.

For additional GBD results and resources, visit the GBD 2019 Data Resources page.

The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations.

This dataset provides migration estimates by location, sex, age, and single calendar year for 1950-2019. Data sources used to produce these estimates came from 1,250 censuses and 747 population registry location-years. This dataset provides population estimates for 1950-2019 by the following: location; single calendar year; single year of age; 5-year age group and select custom age aggregates; and sex. Data sources used to produce these estimates came from 1,250 censuses and 747 population registry location-years.

For additional GBD results and resources, visit the GBD 2019 Data Resources page.

The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations.

This dataset provides migration estimates by location, sex, age, and single calendar year for 1950-2018. Data sources used to produce these estimates came from 1,250 censuses and 747 population registry location-years.

For additional GBD results and resources, visit the GBD 2019 Data Resources page.

The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations.

This dataset provides annual estimates for under-5 mortality (5q0, or ages 0-4) and adult mortality (45q15, or ages 15-59), as expressed by probability of death, by sex for 1950-2019. For under-5 mortality estimation, 7417 sources were used. These included 28,016 location-years of vital registration data, 481 surveys with complete birth histories, and 1081 sources on summary birth histories. For adult mortality, 7355 sources were used. These included 7000 location-years of vital registration and 322 location-years of sample vital registration, 66 sources of household deaths, 102 censuses, and 133 surveys.

For additional GBD results and resources, visit the GBD 2019 Data Resources page.

The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations.

Developed by GBD researchers and used to help produce these estimates, the Socio-demographic Index (SDI) is a composite indicator of development status strongly correlated with health outcomes. It is the geometric mean of 0 to 1 indices of total fertility rate under the age of 25 (TFU25), mean education for those ages 15 and older (EDU15+), and lag distributed income (LDI) per capita. As a composite, a location with an SDI of 0 would have a theoretical minimum level of development relevant to health, while a location with an SDI of 1 would have a theoretical maximum level.

This dataset provides tables with SDI values for all estimated GBD 2019 locations for 1950–2019, as well as reference SDI quintile values.

The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations.

Disability weights, which represent the magnitude of health loss associated with specific health outcomes, are used to calculate years lived with disability (YLD) for these outcomes in a given population. The weights are measured on a scale from 0 to 1, where 0 equals a state of full health and 1 equals death. This table provides disability weights for the 440 health states (including combined health states) used to estimate nonfatal health outcomes for the GBD 2019 study.

For additional GBD results and resources, visit the GBD 2019 Data Resources page.

The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 204 countries and territories and selected subnational locations.

This set of files contain the following for GBD 2019: the cause hierarchy; the risk, impairment, etiology, and injury n-code (REI) hierarchy; and locations hierarchies. The GBD Locations Hierarchy file contains only GBD locations, including subnational locations for which results were released at the time of the study's publication. (Locations will be added as additional subnational results are released.) The All Locations Hierarchies file also includes hierarchies for other regions for which estimates were produced, such as WHO and World Bank regions. These files allow users to filter for sets of values by level or parent category, including cause or risk group, GBD super region or region, or custom region.

This dataset is the result of a study to quantify health-care spending attributable to modifiable risk factors in the United States of America for 2016. Data from two existing studies were used to produce the estimates. The first dataset is the Institute for Health Metrics and Evaluation’s Disease Expenditure Study 2016, from which estimates of US health-care spending by condition, age, and sex were extracted. These results were merged with population attributable fraction estimates for 84 modifiable risk factors from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017. Estimates were produced for spending by 14 aggregate conditions attributable to 19 risk factors. The estimates are by sex and 5 age groups and reported in 2016 US dollars.

Annual estimates were produced for the prevalence and incidence of malaria and malaria mortality across all ages for all countries between 2000 and 2019. These estimates were produced using geo-positioned data from household surveys and routine surveillance data. Survey sources include the Demographic and Health Survey (DHS), Malaria Indicator Survey (MIS) and other country-specific surveys.

This dataset includes the following:

• GeoTIFF raster files for pixel-level estimates of malaria prevalence, incidence, and mortality
• CSV files of aggregated malaria prevalence, Incidence, and mortality for each country at zero, first and second administrative divisions

Get Data Files

Annual estimates were produced for overweight prevalence for children under 5 years of age at the 5x5 km-level for 105 low- and middle-income countries (LMICs) between 2000 and 2019. These estimates were produced using a geo-positioned dataset created from 420 household surveys. Countries and subnational units outside of these 105 LMICs were supplemented with GBD results.

This dataset includes the following:

• GeoTIFF raster files for pixel-level estimates of under-5 overweight prevalence for 105 LMICs
• CSV files of aggregated for 195 countries at the national level, 105 LMICs plus GBD subnational locations at the first-level administrative divisions, and 105 LMICs at the second level administrative divisions
• Code files used to generate the estimates

Get Data Files

Estimates were produced for lymphatic filariasis (LF) all-age prevalence at the 5x5 km-level in endemic countries across Africa, Asia, and Hispaniola, annually between 2000 and 2018. Bayesian time series estimates were produced for 17 small area geographies in South America, the Indian Ocean, and Oceania. These estimates were produced using data on LF and geographical locations from endemicity mapping surveys, sentinel surveillance surveys, transmission assessment surveys (TAS), and other sources.

This dataset includes the following:

• GeoTIFF raster files for pixel-level estimates of LF prevalence rate, counts, and posterior probability that prevalence was lower than 2% in 2018
• CSV files of aggregated estimates of LF prevalence rate, count and posterior probability of prevalence below 2% (2018) for each country at the zero, first, and second administrative divisions
• Code files used to generate the estimates

This dataset includes predictions for the environmental suitability of Rift Valley Fever (RVF) transmission at the monthly level, as well as calculations of spillover potential, which combines suitability predictions with human and livestock population data. It also includes occurrence data extracted from a literature review combined with that downloaded in October 2018 from the Food and Agriculture Organization of the United Nations’ (FAO) EMPRES-i database of RVF occurrences in mammals.

The dataset includes the following:

• GeoTIFF raster files for pixel-level mean environmental suitability predictions for each of the 12 calendar months and average months of suitability per year for 1995-2016
• CSV files of each administrative level 2 units’ average spillover quintile for each of the 12 calendar months and average months per year in the top quintile of spillover values
• Extracted occurrence data
• Code files and custom polygons used to generate the estimates

IHME researchers forecasted population from 2018 to 2100 for 195 countries and territories. They produced these using estimates from the Global Burden of Disease Study (GBD) 2017 and and forecasts for fertility, migration, and mortality rates. This dataset includes the following: past estimates for population and deaths; forecasts for population, deaths, life expectancy, live births, total fertility rate (TFR), and migration; and annual life tables for 2018-2100. The projections for population, deaths, life expectancy, live births, total fertility rate (TFR) each include a reference scenario as well as four alternative scenarios that reflect faster or slower trajectories for two key drivers of fertility rates: education of females and access to modern reproductive health services, measured using contraceptive met need.

Click here to access the life tables.

Annual estimates were produced for the prevalence and incidence of diarrhea and diarrhea-related mortality among children younger than 5 years of age at the 5x5 km-level for 94 low- and middle-income countries (LMICs) between 2000-2017. These estimates were produced using geo-positioned data from 466 household surveys. Survey sources include the Demographic and Health Survey (DHS) and UNICEF Multiple Indicator Cluster Survey (MICS) series, and other country‐specific surveys.

This dataset includes the following:

• GeoTIFF raster files for pixel-level estimates of under-5 diarrhea prevalence, incidence, and diarrhea-related mortality
• CSV files of aggregated under-5 diarrhea prevalence, incidence, and diarrhea-related mortality for each country at zero, first and second administrative divisions
• Code files used to generate the estimates

Get Data Files

Research by the by the Global Burden of Disease Health Financing Collaborator Network estimated estimated malaria spending for 106 countries for 2000-2017. The estimates cover malaria spending disaggregated by source into three domestic financing source categories (government, out-of-pocket, and prepaid private) and development assistance for health (DAH). Domestic malaria spending estimates were produced from a diverse set of data, including the World Malaria Report, WHO National Health Accounts and sub-accounts, the Global Fund Price Quality Reporting, WHO Global Price Reporting Mechanism, Management Sciences for Health reference prices, the Malaria Atlas Project, and more. Development assistance for malaria data were drawn from IHME's 2019 Development Assistance for Health Database. This database is also informed by a diverse set of sources, including program reports, budget data, national estimates, and NHAs. Estimates are reported in constant 2019 United States dollars.

Research by the Global Burden of Disease Health Financing Collaborator Network produced retrospective health spending estimates for 1995-2017 for 195 countries and territories. The estimates cover total health spending, health spending disaggregated by source into three domestic financing source categories (government, out-of-pocket, and prepaid private), and development assistance for health (DAH). Domestic health spending source data came primarily from the WHO’s Global Health Expenditure Database (GHED). DAH data came from a diverse set of sources, including program reports, budget data, national estimates, and National Health Accounts (NHAs). The resulting estimates were used to help produce prospective health spending estimates for 2018-2050. Estimates are reported in constant 2019 United States dollars, constant 2019 purchasing-power parity-adjusted (PPP) dollars, and as a percent of gross domestic product.