IHME Data
Download datasets created by IHME for our research projects and publications. You can learn more about our research and publications on our website
Data made available for download on IHME Websites can be used, shared, modified or built upon by non-commercial users in accordance with the IHME FREE-OF-CHARGE NON-COMMERCIAL USER AGREEMENT. For more information (and inquiries about commercial use), visit IHME Terms and Conditions.
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 annual estimates for age-specific fertility rate (ASFR), total fertility fate (TFR), total fertility under 25 years (TFU25), net reproductive rate (NRR), live births, and crude birth rate for 1950-2019. Data sources used to produce the ASFR estimates came from 8078 location-years of vital registration data, and 439 complete birth histories and 628 summary birth histories from 938 surveys, 349 censuses, and 238 other sources.
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.
This dataset represents estimates of the ongoing COVID-19 pandemic across the 50 U.S. States and DC through 28th February 2021. Projections for total and daily deaths, daily infections, and testing are included with hospital resource use statistics. In total five scenarios are presented: a 'plausible reference scenario,' which assumes social distancing mandates are re-imposed for 6 weeks when a threshold daily death rate of 8 per million is reached; a 'mandates easing' scenario, where mandates are not re-imposed; a 'universal mask-use' scenario, where mask utilization reaches 95% usage in public in every location; a less comprehensive mask scenario of 85% public use of masks (‘plausible reference + 85% mask-use’ scenario); and a scenario of universal mask wearing in the absence of any additional NPI (‘mandate easing + universal mask use’). These projections are produced with a model that incorporates data on observed COVID-19 deaths, hospitalizations, and cases, as well as multiple covariates.
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.
This dataset contains life tables with estimates for life expectancy and probability of death by location, single calendar year, age group, and sex for 1950-2019. The life tables contain both estimates produced including deaths from natural disasters, wars, etc., as well as estimates produced without these types of deaths. Locations covered include both GBD locations and special regions such as World Bank Income Levels. Data used to produce these tables came from vital registration (VR) systems, sample registration systems, household surveys, censuses, disease surveillance, and demographic surveillance systems (DSS).
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.
These tables contain International Classification of Diseases (ICD) codes, for both ICD-9 and ICD-10, mapped to GBD 2019 causes of death and nonfatal causes.
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.
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 dataset includes the following:
- Relative risks used by age and sex for each outcome for all risk factors except for ambient air pollution, alcohol, smoking, and temperature
- Relative risks used by age and sex for each outcome for the particulate matter integrated exposure response curve
- Relative risks used by age and sex for each outcome for alcohol use globally
- Relative risks used by age and sex for each outcome for smoking globally
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.
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.
Covariates, which are independent variables with a positive or negative relationship to GBD diseases and conditions, are used to inform the estimation process in models in all components and stages of the GBD study. Types of covariates used include socioeconomic, demographic, health system access, climate, and food consumption. This dataset contains data for 771 covariates for 1980-2019 used in the GBD 2019 study.
For additional GBD results and resources, visit the GBD 2019 Data Resources page.
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 child growth failure (CGF) among children younger than 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 geo-positioned data from 460 household surveys, including the Demographic and Health Survey (DHS), Multiple Indicator Cluster Survey (MICS), and other country‐specific 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 for 105 LMICs
- CSV files of aggregated estimates for 195 countries at the national level, 105 LMICs plus GBD subnational locations at the admin 1 level, and 105 LMICs at the admin 2 level
- Code files used to generate the estimates
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
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 2000-2019. These estimates were produced using geo-positioned data from 466 household surveys. Countries and subnational units outside of these 94 LMICs were supplemented with GBD results.
This dataset includes the following:
- GeoTIFF raster files for pixel-level estimates for 94 LMICs
- CSV files of aggregated estimates for 195 countries at the national level, 94 LMICs plus GBD subnational locations at the admin 1 level, and 94 LMICs at the admin 2 level
- Code files used to generate the estimates
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
Estimates from the Global Burden of Disease Study 2019 (GBD 2019) were used to create an index which estimates global progress towards universal health coverage (UHC) and specifically UHC effective coverage in 204 countries and territories in 1990, 2010, and 2019. The UHC effective coverage index is comprised of 23 indicators drawn across a range of health service areas and is meant to represent healthcare needs over the life course. This dataset contains estimates for the UHC effective coverage index, each UHC effective coverage indicator, and indicator-specific weights by location-year. Code used to produce the estimates is also available for download.
Results were published in The Lancet in September 2020 in “Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019”.
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
Annual estimates were produced for access to drinking water and sanitation Facilities at the 5x5 km-level for 90 low- and middle-income countries (LMICs) for 2000-2017. These estimates were produced using a geo-positioned dataset created from 634 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:
- GeoTIFF raster files for pixel-level estimates of drinking water and sanitation facility coverage percent (percent of people with the given type of access) and number (number of people with the given type of access)
- CSV files of aggregated estimates for each country at 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
Annual estimates were produced for oral rehydration therapy (ORT) coverage for children under 5 years of age who had diarrhea at the 5x5 km-level for 94 low- and middle-income countries (LMICs) between 2000-2017. These estimates were produced using a geo-positioned dataset created from 385 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:
- GeoTIFF raster files for pixel-level estimates of oral rehydration therapy percent (percent of children with diarrhea who received treatment) and number (number of children with diarrhea who received treatment)
- CSV files of aggregated oral rehydration therapy coverage percent and number for each country at zero, first and second administrative divisions
- Code files 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 adult male circumcision (MC) prevalence and the number of circumcised and uncircumcised males ages 15-49 at the 5x5 km-level for 38 countries in sub-Saharan Africa between 2000 and 2017. These estimates were produced using a geo-positioned dataset created from 109 household surveys. Survey sources used include the Demographic and Health Survey (DHS), AIDS Indicator Survey (AIS), Multiple Indicator Cluster Survey (MICS), Core Welfare Indicators Questionnaire Survey (CWIQ), Population-based HIV Impact Assessment Survey (PHIA), and other country‐specific surveys.
This dataset includes the following:
- GeoTIFF raster files for pixel-level estimates of male circumcision (MC) prevalence and the number of circumcised and uncircumcised males ages 15-49
- CSV files of aggregated circumcision estimates for each country at zero, first and second administrative divisions
- Code files used to generate the estimates
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
Research by the Global Burden of Disease Health Financing Collaborator Network estimated tuberculosis spending for 134 low- and middle-income countries for 2000-2017. The estimates cover tuberculosis spending disaggregated by source into three domestic financing source categories (government, out-of-pocket, and prepaid private) and development assistance for health (DAH). Spending is also disaggregated by function, including care and treatment, prevention, and other spending. Domestic tuberculosis spending by source and function was estimated based on data from sources including the WHO Global Tuberculosis database, the Global Fund, WHO National Health Accounts and sub-accounts, WHO Global Health Expenditure database (GHED), National Tuberculosis Reports, and Ministry of Health Reports. Development assistance for tuberculosis data were drawn from IHME's 2019 Development Assistance for Health Database. Estimates are reported in constant 2019 United States dollars.
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 estimated HIV/AIDS spending for 134 low- and middle-income countries for 2000-2017. The estimates cover HIV/AIDS spending disaggregated by source into three domestic financing source categories (government, out-of-pocket, and prepaid private) and development assistance for health (DAH). Spending is also disaggregated by function, including care and treatment, prevention, and other spending. Domestic HIV/AIDS spending by source and function was estimated based on data from sources including National AIDS Spending Assessments (NASA), the Global Fund, WHO National Health Accounts and sub-accounts, UNAIDS Global AIDS Response Progress Reports (GARPR), the GARPR database, UNAIDS health financing dashboard, and the AIDS data hub. Development assistance for HIV/AIDS data were drawn from IHME's 2019 Development Assistance for Health Database. 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.
Research by the Global Burden of Disease Health Financing Collaborator Network produced projected health spending estimates for 2018-2050 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). Retrospective health spending estimates for 1995-2017 and key covariates (including GDP per capita, total government spending, total fertility rate, and fraction of the population older than 65 years) were used to forecast GDP and health spending through 2050. Estimates are reported in constant 2019 US dollars, constant 2019 purchasing-power parity-adjusted (PPP) dollars, and as a percent of gross domestic product.
This version of the Development Assistance for Health (DAH) Database includes estimates for 1990-2019, which are based on project databases, financial statements, annual reports, IRS 990s, and correspondence with agencies. The DAH Database enables comprehensive analysis of trends in international disbursements of grants and loans for health projects in low- and middle-income countries from key agencies. The data are disaggregated by source of funds, channel of funding, country and geographic region, health focus areas, and program areas. New in 2019: The current estimates of DAH incorporated improvements in methodology such as leveraging additional project-level descriptions from the Creditor Reporting System for the allocation of disbursements channeled through non-governmental organizations (NGOs) and ongoing refinement of the project’s keyword search list.
To better understand the data and how to use it, please refer to the IHME DAH Database 2019 User Guide.
Annual estimates were produced for overweight and wasting prevalence for children under 5 years of age at the 5x5 km-level for 105 low- and middle-income countries (LMICs) between 2000-2017. These estimates were produced using a geo-positioned dataset created from 420 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:
- GeoTIFF raster files for pixel-level estimates of under-5 overweight and wasting prevalence
- CSV files of aggregated overweight and wasting prevalence for each country at zero, first, and second administrative divisions
- Code files used to generate the estimates
IHME researchers produced this dataset as part of an analysis measuring and forecasting progress by countries towards education-related Sustainable Development Goal (SDG) targets. Annual estimates were created for the average years of schooling and single-year distribution of educational attainment by sex for adults ages 25-29 for 1970 to 2018. Projections were also generated for these indicators to 2030. Estimates were created for the 195 countries and territories examined in the Global Burden of Disease 2017 study. The estimates were produced using a compiled database of 3,180 nationally representative surveys and censuses describing the distribution of years of schooling by age and sex.
IHME has produced forecasts which show hospital bed use, need for intensive care beds, and ventilator use due to COVID-19 based on projected deaths for countries in the European Economic Area (EEA). Forecasts at the subnational level are included for three of these: Germany, Italy, and Spain. These projections are produced by models based on observed death rates from COVID-19, and include uncertainty intervals. They incorporate information about social distancing and other protective measures and are being updated daily with new data. These forecasts were developed in order to provide hospitals, policy makers, and the public with crucial information about how expected need aligns with existing resources, so that cities and states can best prepare.
IHME has produced forecasts which show hospital bed use, need for intensive care beds, and ventilator use due to COVID-19 based on projected deaths for all 50 U.S. states. These projections are produced by models based on observed death rates from COVID-19, and include uncertainty intervals. They incorporate information about social distancing and other protective measures and are being updated daily with new data. These forecasts were developed in order to provide hospitals, policy makers, and the public with crucial information about how expected need aligns with existing resources, so that cities and states can best prepare.
The HeartRescue Global Project, a multi-country, multi-year effort aims to improve access and quality for acute cardiovascular disease (CVD), including ST-elevation myocardial infarction (STEMI) and sudden cardiac arrest (SCA) in selected locations in China, India, and Brazil. This dataset is the product of a HeartRescue program impact evaluation. It includes results of a baseline health facility and emergency medical service (EMS) survey conducted in Beijing and Shanghai, China. Data were collected from three secondary hospitals and one tertiary hospital. Data were collected about facility capacity, equipment availability, pharmaceutical and supply stocks, staffing, and services provided. The data were collected through computer-assisted personal interviews (CAPI).
The HeartRescue Global Project, a multi-country, multi-year effort aims to improve access and quality for acute cardiovascular disease (CVD), including ST-elevation myocardial infarction (STEMI) and sudden cardiac arrest (SCA) in selected locations in China, India, and Brazil. This dataset is the product of a HeartRescue program impact evaluation. It includes results of a baseline household survey conducted in Beijing and Shanghai, China. Data were collected from 1,500 individuals ages 18 or older in each city, for a total of 3,000 respondents. Information was collected from respondents through computer-assisted personal interviews (CAPI). Data were collected about demographics, health history and status, health behaviors, health care use, and knowledge, attitudes and practices regarding CVD, risk factors, and CVD care.
The HeartRescue Global Project, a multi-country, multi-year effort aims to improve access and quality for acute cardiovascular disease (CVD), including ST-elevation myocardial infarction (STEMI) and sudden cardiac arrest (SCA) in selected locations in China, India, and Brazil. This dataset is the product of a HeartRescue program impact evaluation. It includes results of a baseline household survey conducted in Bangalore, India. Data were collected from 2,400 households. One eligible adult per household was randomly selected from the household roster. Information was collected from respondents through computer-assisted personal interviews (CAPI). Data were collected about demographics, health history and status, health behaviors, health care use, and knowledge, attitudes and practices regarding CVD, risk factors, and CVD care.
The HeartRescue Global Project, a multi-country, multi-year effort aims to improve access and quality for acute cardiovascular disease (CVD), including ST-elevation myocardial infarction (STEMI) and sudden cardiac arrest (SCA) in selected locations in China, India, and Brazil. This dataset is the product of a HeartRescue program impact evaluation. It includes results of a baseline health facility emergency medical service (EMS) survey conducted in Bangalore, India. Data were collected from 8 EMS units. Data were collected about facility capacity, equipment availability, pharmaceutical and supply stocks, staffing, and services provided. The data were collected through computer-assisted personal interviews (CAPI).
The HeartRescue Global Project, a multi-country, multi-year effort aims to improve access and quality for acute cardiovascular disease (CVD), including ST-elevation myocardial infarction (STEMI) and sudden cardiac arrest (SCA) in selected locations in China, India, and Brazil. This dataset is the product of a HeartRescue program impact evaluation. It includes results of a baseline health facility and emergency medical services (EMS) survey conducted in Vitória da Conquista, Brazil. Data were collected from three private hospitals, one public hospital, and one EMS unit. Data were collected about facility capacity, equipment availability, pharmaceutical and supply stocks, staffing, and services provided. The data were collected through computer-assisted personal interviews (CAPI).
The HeartRescue Global Project, a multi-country, multi-year effort aims to improve access and quality for acute cardiovascular disease (CVD), including ST-elevation myocardial infarction (STEMI) and sudden cardiac arrest (SCA) in selected locations in China, India, and Brazil. This dataset is the product of a HeartRescue program impact evaluation. It includes results of a baseline household survey conducted in Vitória da Conquista, Brazil. An adult age 30 years or older was interviewed from each eligible household. Topics covered in the interview included demographic and household characteristics; healthcare use and access; health knowledge, attitudes, and practices related to CVD health; CVD risk factors; and participants' health histories. In total, data were collected from 1,054 households.
The Disease Expenditure Project (DEX) at IHME produced estimates for US spending on health care according to 3 types of payers (public insurance [including Medicare, Medicaid, and other government programs], private insurance, or out-of-pocket payments) and by health condition, age group, sex, and type of care for 1996 through 2016. Types of care include ambulatory care, inpatient care, nursing care facility stay, emergency department care, dental care, prescribed pharmaceutical care, and government administration and net cost of insurance programs. Government budgets, insurance claims, facility records, household surveys, and official US records from 1996 through 2016 were used to produce the results. Spending estimates were produced for 154 conditons, which were aggregated into 14 health categories. This dataset contains estimates for the aggregate health categories.
These data are the product of a collaboration between the Institute for Health Metrics and Evaluation (IHME) and the Universidad Autónoma de Yucatán (UADY): a cross-sectional study exploring the delays faced during the search for care by caregivers of children under the age of 5 who died in the State of Yucatán, Mexico, during 2015–2016. Two datasets resulting from the project are available for download. The first contains results of a household census in which interviews were conducted with caregivers of the deceased children. The interview consisted of two parts, a standardized verbal autopsy using neonatal and child modules of the Population Health Metrics Research Consortium (PHMRC) Shortened Questionnaire and a section with questions about health care-seeking behavior during the final illness and household characteristics. The second dataset includes the review of medical records for children who died in medical units of the Secretary of Health of Yucatán.
HealthRise is a collaborative multicountry initiative to implement and evaluate innovative community-based programs intended to improve heart disease and diabetes care in underserved communities. Conducted as part of HealthRise Brazil, this household survey was carried out in approximately 2,000 households in Padre Paraíso in the state of Minas Gerais and Poções in the state of Bahia. Data were collected regarding sociodemographic background, risk factors, medical history, and knowledge, attitudes, and practices related to NCDs. Anthropometric data, including height, weight, and abdominal circumference were also collected, in addition to blood pressure and random blood glucose (RBG) measurements. The data were collected through computer-assisted personal interviews (CAPI) with one adult age 30 years or older in each household.
HealthRise is a collaborative multicountry initiative to implement and evaluate innovative community-based programs intended to improve heart disease and diabetes care in underserved communities. Conducted as part of HealthRise India, this health facility survey was carried out in 48 facilities in the Shimla district in the state of Himachal Pradesh and the Udaipur district in the state of Rajasthan. Data were collected about facility capacity, equipment availability, pharmaceutical and supply stocks, staffing, and services provided. The data were collected through computer-assisted personal interviews (CAPI).
Annual estimates were produced for child growth failure (CGF), expressed as stunting, wasting, and underweight prevalence for children under 5 years of age, at the 5x5 km-level for 105 low- and middle-income countries (LMICs) between 2000-2017. These estimates were produced using a geo-positioned dataset created from 460 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:
- GeoTIFF raster files for pixel-level estimates of under-5 stunting, wasting, and underweight prevalence
- CSV files of aggregated stunting, wasting and underweight prevalence for each country at zero, first and second administrative divisions
- Code files used to generate the estimates
This dataset contains estimates produced for educational attainment for adults ages 15-49, and the 20–24 subgroup, by sex at the 5x5 km-level for 105 low- and middle-income countries for 2000-2017. It provides years of education and proportion of the population attaining key levels of education. These estimates were produced using individual records from 528 geo-referenced household sample survey and census sources.
The dataset includes the following:
- GeoTIFF raster files for pixel-level estimates of mean educational attainment, and proportion of the population achieving zero, less than primary, primary, and secondary schooling for adults ages 15-49 and 20-24, divided by sex
- CSV files of aggregated estimates for each country at the zero, first, and second administrative divisions
- Code files used to generate the estimates
HealthRise is a collaborative multicountry initiative to implement and evaluate innovative community-based programs intended to improve heart disease and diabetes care in underserved communities. Conducted as part of HealthRise South Africa, this health facility survey was carried out in 38 facilities in the Pixley ka Seme district in the province of Northern Cape and the uMgungundlovu district in the province of KwaZulu-Natal. The survey was adapted from questionnaires created and conducted for the HealthRise projects in South Africa and India in 2015. Data were collected about facility capacity, equipment availability, pharmaceutical and supply stocks, staffing, and services provided. Patient exit interviews were also conducted with eligible patients at health facilities. The data were collected through computer-assisted personal interviews (CAPI).
HealthRise is a collaborative multicountry initiative to implement and evaluate innovative community-based programs intended to improve heart disease and diabetes care in underserved communities. Conducted as part of HealthRise India, this health facility survey was carried out in 30 facilities in Shimla district in the state of Himachal Pradesh. The survey was adapted from a questionnaire created and conducted for the HealthRise project in India in 2015. Data were collected about facility capacity, equipment availability, pharmaceutical and supply stocks, staffing, and services provided. Patient exit interviews were also conducted with eligible patients at health facilities. The data were collected through computer-assisted personal interviews (CAPI).
Annual estimates were produced for mortality probability and death counts in three age groups – neonates (0-28 days old), infants (under-1 year old), and under-5 (0-5 years old) – at the 5x5 km-level in 99 low- and middle-income countries (LMICs) between 2000-2017. These estimates were produced using data on child mortality and geographical locations from censuses and several household survey series. 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:
- GeoTIFF raster files for pixel-level estimates of mortality probability and death counts in 3 age bins
- CSV files of aggregated mortality probability and death count estimates for each country at the zero, first, and second administrative divisions, by age group
- Code files used to generate the estimates
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