Global health data

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Namibia. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Namibia. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Point locations of health facilities in Mozambique including hospitals, health centres, clinics, dispensaries and health posts. Each feature records the facility name, type and ownership. Use this layer to map healthcare coverage, identify service gaps, plan facility distribution, and support health sector analysis.

Abstract and Limitations of Use: The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03 consists of annual concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter (PM2.5). This data set combines AOD retrievals from multiple satellite algorithms including the NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementation of Atmospheric Correction Collection 6 (MAIAC C6), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Deep Blue Version 4. The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements from the World Health Organization (WHO) database to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. These estimates are primarily intended to aid in large-scale studies. Gridded data sets are provided at a resolution of 0.01 degrees to allow users to agglomerate data as best meets their particular needs. Data sets are gridded at the finest resolution of the information sources that were incorporated, but do not fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution. The data are distributed as GeoTIFF files and are in WGS84 projection. Because this is a historical dataset, this item does not follow a routine update frequency.   Intended Use: To provide an annual global surface of concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter of 2.5 micrometers or smaller (PM2.5) for large-scale health and environmental research. Legend:ColorLegend LabelDescription: 80 - 139Annual concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter (PM2.5) 40 - 80 20 - 40 10 - 20 5 - 10 0 - 5Citation: Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03. Hammer, M. S., A. van Donkelaar, C. Li, A. Lyapustin, A. M. Sayer, N. C. Hsu, R. C. Levy, M. J. Garay, O. V. Kalashnikova, R. A. Kahn, M. Brauer, J. S. Apte, D. K. Henze, L. Zhang, Q. Zhang, B. Ford, J. R. Pierce, and R. V. Martin. (2022). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/fx80-4n39 For inquiries about this service, please contact ciesin.info@ciesin.columbia.edu. Publication References:Documentation for the Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03. Hammer, M. S., A. van Donkelaar, C. Li, A. Lyapustin, A. M. Sayer, N. C. Hsu, R. C. Levy, M. J. Garay, O. V. Kalashnikova, R. A. Kahn, M. Brauer, J. S. Apte, D. K. Henze, L. Zhang, Q. Zhang, B. Ford, J. R. Pierce, and R. V. Martin. (2022). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/thr9-xe64

The Harmonized Food Insecurity Dataset (HFID) is an open-source resource consolidating four key data sources: the Integrated Food Security Phase Classification (IPC)/Cadre Harmonisé (CH) phases, the Famine Early Warning Systems Network (FEWS NET) IPC-compatible phases, and the World Food Program’s (WFP) Food Consumption Score (FCS) and reduced Coping Strategy Index (rCSI). Updated monthly and using a common reference system for administrative units, the HFID provides comprehensive spatial and temporal coverage to the extent permitted by the available data. It serves as a vital tool for food insecurity experts and humanitarian agencies, providing a unified resource for analyzing food insecurity conditions and highlighting global data disparities and gaps. The scientific community can also leverage the HFID to develop data-driven predictive models, enhancing the capacity to forecast and prevent future food crises. This dataset is the first static version created and openly shared to accompany the publication of the paper "A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling". It includes data from 2007 until 2024. A dynamic dataset, updated monthly with all of the above indicators will be shared on HDX as medium-term objective.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['healthcare'] IS NOT NULL OR tags['amenity'] IN ('doctors', 'dentist', 'clinic', 'hospital', 'pharmacy') Features may have these attributes: - name - name:en - amenity - building - healthcare - healthcare:speciality - operator:type - capacity:persons - addr:full - addr:city - source - name:no This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['healthcare'] IS NOT NULL OR tags['amenity'] IN ('doctors', 'dentist', 'clinic', 'hospital', 'pharmacy') Features may have these attributes: - name - name:en - amenity - building - healthcare - healthcare:speciality - operator:type - capacity:persons - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['healthcare'] IS NOT NULL OR tags['amenity'] IN ('doctors', 'dentist', 'clinic', 'hospital', 'pharmacy') Features may have these attributes: - name - name:en - amenity - building - healthcare - healthcare:speciality - operator:type - capacity:persons - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: healthcare IS NOT NULL OR amenity IN ('doctors','dentist','clinic','hospital','pharmacy') Features may have these attributes: - healthcare:speciality - building - capacity:persons - addr:full - addr:city - healthcare - operator:type - amenity - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: healthcare IS NOT NULL OR amenity IN ('doctors','dentist','clinic','hospital','pharmacy') Features may have these attributes: - healthcare:speciality - building - capacity:persons - addr:full - addr:city - healthcare - operator:type - amenity - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: healthcare IS NOT NULL OR amenity IN ('doctors','dentist','clinic','hospital','pharmacy') Features may have these attributes: - healthcare:speciality - building - capacity:persons - addr:full - addr:city - healthcare - operator:type - amenity - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Indicator values by sub-national level from The Demographic and Health Surveys (DHS) Program, covering 82 countries. Includes child nutrition, child mortality, fertility, family planning, vaccination, HIV, education and other indicators, with the boundaries of each survey region. Data source : The DHS Program Spatial Data Repository **Data publication:** 2026-10-19 **Supplemental Information:** All data is from The Demographic and Health Surveys (DHS) Program, which conducts nationally representative population-based surveys. These surveys are representative at the sub-national level (generally, 1st administrative level). However, in some cases the sampling frame combines two or more 1st administrative areas or includes major cities as separate regions. The DHS region boundaries are created on a survey-specific basis to correspond to the sampled areas of the survey, therefore the boundaries may not correspond to the Administrative1 or other official administrative boundaries of a country. Geographic coverage : Subnational (DHS Survey regions) - 82 countries. Update frequency : Single ingestion **Contact points:** Metadata Contact: [DHS program](mailto:spatialdata@dhsprogram.com) **Data lineage:** Downloaded from the DHS Program Spatial Data Repository and published as provided by the source, without modifications. Known limitations : Each country is covered by a single survey round. Region boundaries may not correspond to official administrative boundaries. Not all indicators are available for all surveys. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:**

The IPC Classification System distinguishes and links acute food insecurity, chronic food insecurity and acute malnutrition to support more strategic and better coordinated responses. The protocols used by the IPC are harmonized across the three individual scales (IPC Acute Food Insecurity, IPC Chronic Food Insecurity, and IPC Acute Malnutrition). This allows for the analysis of linkages between the three conditions and the possibility of detangling acute food insecurity, chronic food insecurity and acute malnutrition, in support of a more strategic response analysis. This dataset shows data for __Acute food insecurity (Second Projected - Rural)__ **Data creation:** 2020-09-22 **Contact points:** Resource Contact: [IPC](mailto:IPC-Tech-Support@fao.org) Metadata Contact: [Melanie Rabier](mailto:melanie.rabier@fao.org) **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:**

The IPC Classification System distinguishes and links acute food insecurity, chronic food insecurity and acute malnutrition to support more strategic and better coordinated responses. The protocols used by the IPC are harmonized across the three individual scales (IPC Acute Food Insecurity, IPC Chronic Food Insecurity, and IPC Acute Malnutrition). This allows for the analysis of linkages between the three conditions and the possibility of detangling acute food insecurity, chronic food insecurity and acute malnutrition, in support of a more strategic response analysis. This dataset shows data for __Chronic food insecurity (Current - Rural)__ **Data creation:** 2020-09-22 **Contact points:** Resource Contact: [IPC](mailto:IPC-Tech-Support@fao.org) Metadata Contact: [Melanie Rabier](mailto:melanie.rabier@fao.org) **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:**

The IPC Classification System distinguishes and links acute food insecurity, chronic food insecurity and acute malnutrition to support more strategic and better coordinated responses. The protocols used by the IPC are harmonized across the three individual scales (IPC Acute Food Insecurity, IPC Chronic Food Insecurity, and IPC Acute Malnutrition). This allows for the analysis of linkages between the three conditions and the possibility of detangling acute food insecurity, chronic food insecurity and acute malnutrition, in support of a more strategic response analysis. This dataset shows data for __Acute food insecurity (Current - Rural)__ **Data creation:** 2020-09-22 **Contact points:** Resource Contact: [IPC](mailto:IPC-Tech-Support@fao.org) Metadata Contact: [Melanie Rabier](mailto:melanie.rabier@fao.org) **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:**

The IPC Classification System distinguishes and links acute food insecurity, chronic food insecurity and acute malnutrition to support more strategic and better coordinated responses. The protocols used by the IPC are harmonized across the three individual scales (IPC Acute Food Insecurity, IPC Chronic Food Insecurity, and IPC Acute Malnutrition). This allows for the analysis of linkages between the three conditions and the possibility of detangling acute food insecurity, chronic food insecurity and acute malnutrition, in support of a more strategic response analysis. This dataset shows data for __Acute food insecurity (Projected - Rural)__ **Data creation:** 2020-09-22 **Contact points:** Resource Contact: [IPC](mailto:IPC-Tech-Support@fao.org) Metadata Contact: [Melanie Rabier](mailto:melanie.rabier@fao.org) **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:**

Community health workers include various types of community health aides, many with country-specific occupational titles such as community health officers, community health-education workers, family health workers, lady health visitors and health extension package workers.

UNICEF Data Warehouse dataflow WASH_HEALTHCARE_FACILITY: WASH Healthcare Facility, by country and year.

Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.

Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons.

Prevalence of underweight, male, is the percentage of boys under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards.

Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.

Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.

Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.

Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).

Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).