Global

The Climate and Health Risk Index (CHRI) shows how climate hazards, national and subnational population vulnerability, and health system readiness combine to create health risks in each country.

It helps governments quickly identify where potential climate impacts on health are greatest and what is driving those risks. In doing so, CHRI supports governments and development partners to 

  • prioritize where to invest limited resources,
  • advocate for climate in health action, and
  • plan more detailed assessments or targeted programs. 

This enables country teams and multilateral organizations to design more effective and resilient investments at the intersection of public health and climate adaptation. 

CHRI brings together 69 indicators across three risk components: 

  • Exposure: how much people are exposed to climate hazards
  • Sensitivity: factors that make people more affected by climate impacts
  • Adaptive capacity: how well people and health systems can cope 

Using mostly publicly available data, CHRI provides a comparable score for 185 countries and territories, covering 99% of the world’s population. 

By 2025, about 718 million people (around 9% of the global population) are projected to face high climate-related health risks (CHRI ≥75). Most of this population lives in Sub-Saharan Africa and South Asia, which together account for 99% of those exposed. 

Additional insights: 

  • 13 countries are expected to have at least 80% of their population at moderately high risk (CHRI ≥60).
  • 20  countries will have half or more of their population affected by climate-related health risks. 

CHRI scores are strongly linked to WHO’s Universal Health Coverage (UHC) index: countries with limited access to health services tend to face higher climate-related health risks, meaning climate impacts often hit hardest where health systems are already strained. 

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01 - Climate and Health Risk Index

This view compares the overall Climate and Health Risk Index (CHRI) with a selected risk component (Exposure, Sensitivity, or Adaptive Capacity). Use the menu on the left to choose the component to compare with CHRI, and the toggle above the map to switch between CHRI and the selected component. 

For risk to exist, hazard, exposure and vulnerability must occur simultaneously.   

  • Hazard: occurrences of climate change stressors (mostly temperature and precipitation anomalies) and shocks (extreme events involving hydrological and meteorological systems, such as floods and cyclones).
  • Exposure: presence of people; livelihoods; species or ecosystems; environmental functions, services and resources; infrastructure; or economic, social or cultural assets in places and settings that could be adversely affected by hazards. Here, population exposure refers to percent of people affected by different hazards, so hazard and exposure have already been combined.
  • Sensitivity: factors that make populations more affected when hazards occur.
  • Adaptive capacity: the ability of people and health systems to cope with climate impacts by absorbing, deflecting, or reducing their effects.
  • Vulnerability a combination of increased sensitivity and/or reduced adaptive capacity. 

Look for where high CHRI values overlap with high exposure or sensitivity, or with low adaptive capacity. These areas tend to face the highest climate-related health risks. 

This scatter plot shows how each risk component (Exposure, Sensitivity, and Adaptive Capacity) relates to the overall CHRI score. Each color represents one component of risk. 

Note: The scatter plot remains the same when switching component layers because it is designed to show all components together for comparison. But you can click on the legend to select or deselect individual layers shown in the scatterplot. 

You can also click on the “show data table” switch below to see the findings for each country organized by geographic region. When you hover over a country in the table, it will be highlighted in the scatter plot.

Countries (points) clustered toward the upper part of the figure indicate higher overall risk. These countries typically exhibit high exposure and sensitivity (points positioned toward the upper right) combined with lower adaptive capacity (points toward the upper left).

02 - Population Exposure

Exposure is the presence of people or assets in areas that may be affected. In this analysis, population exposure is defined as the spatial overlap between climate or pollution hazards and population.

This view allows you to compare the overall population exposure index with a selected exposure subcomponent, which can be chosen from the menu on the left. Use the toggle above the map to switch between total population exposure and the selected subcomponent. This enables comparison of spatial patterns across exposure types and helps illustrate how each subcomponent contributes to overall exposure.

For each subcomponent (e.g. percentage or fraction of the population exposed to high temperatures, humidity, droughts, floods, or cyclones), use the slider to switch between the Historical and Future periods and explore how population exposure is projected to change over time. The values shown represent historical conditions (period centered around 2000, on the left) and projected conditions (period centered around 2022 or 2035, on the right) under the SSP3-7.0 scenario. Overall exposure was calculated by averaging historical and projected percentages, with greater weight given to time periods that had larger populations.  

Notes:

  • A scatter plot below provides an additional perspective, showing how each exposure component relates to the overall exposure index.
  • Using the on/off switch below the scatter plot, users can also view the original high-resolution hazard and population data used in the exposure calculations. Hazard maps apply threshold values (e.g., for extreme heat, precipitation, or pollution), which are combined with population data to estimate the percentage of people exposed for each country.
  • Together, these views help users quickly identify which exposure types contribute most to total population exposure. 

 

  • Countries with higher exposure show a greater overlap between hazards and population. By comparing overall exposure with specific exposure types, users can visually identify which hazards are most relevant across different regions of the globe.
  • Switching between the Historical and Future views allows you to see where exposure is projected to increase in the near future. Note that only the individual exposure components have separate historical and future values; a more detailed technical explanation is provided below.

Download details here. 

In brief, historical and future data were compiled for each type of hazard. Any area where hazard levels rise above a set “danger” threshold for a sufficient duration was marked as having the hazard present. These maps were then combined with population data to estimate the percentage of people exposed in each time period. Overall exposure was calculated by averaging these percentages, with greater weight given to time periods that had larger populations.  

Heat Stress

  • Percent of population exposed to maximum temperatures > 42⁰C (centered on 2000 and 2035) - Heat stress - 1975-2024, 2010-2059 - Source: CCKP
  • Percent of population exposed to Heat Index > 39⁰C (centered on 2000 and 2035) - Heat stress - 1975-2024, 2010-2059 - Source: CCKP
  • Percent of population exposed to wet bulb temperatures > 27⁰C (centered on 2000 and 2035) - Heat stress - 1975-2024, 2010-2059 - Source: CCKP
  • Percent of population exposed to night temperatures > 29⁰C (centered on 2000 and 2035) - Heat stress - 1975-2024, 2010-2059 - Source: CCKP

Extreme precipitation

  • Percent of population exposed to 5 consecutive days of precipitation > 20mm (centered on 2000 and 2035) - Extreme precipitation - 1975-2024, 2010-2059 - Source: CCKP

Air pollution

  • Percent of population exposed to PM2.5 >10 ug/m3 (centered on 2000 and 2035) - Air pollution - 2000, 2035 - Source: CCKP

Drought

  • Fraction of population exposed to drought (centered on 2000 and 2022) - Drought - 1984-2022 - Source: FAO Agriculture Stress Index (FAO ASI), processed by EPVGE WBG Scorecard   

Flooding

  • Fraction of population exposed to flood (centered on 2000 and 2022) - Flood - 1975-2050 - Source:  Fathom 2.0 and Deltares, processed by EPVGE WBG Scorecard

Tropical Cyclones

  • Fraction of population exposed to tropical cyclones (centered on 2000 and 2022) - Tropical cyclone - 1979-2050 - Source: STORM (Bloemendaal et al. 2020), processed by EPVGE WBG Scorecard

This scatter plot compares the overall exposure index (y-axis) with the percentage of the population exposed to the selected hazard (x-axis) for both the historical and future periods. 

Each point represents a country and is shown twice to reflect both historical and future values.

The plot helps illustrate the relationship between each exposure component and a country’s overall exposure score, and how this relationship may change over time. 

  • The scatter plot shows how strongly the selected exposure component is associated with overall exposure.
  • A more scattered pattern suggests a weaker relationship, meaning the exposure type selected does not strongly explain variations in the overall exposure index (dots very spread out).
  • A clearer positive correlation suggests that countries with higher exposure to the selected hazard tend to have higher overall exposure scores (this type of hazard is an important driver of overall exposure when dots slope upward).
  • Historical and future points (lighter vs. darker colors) allow you to see whether the relationship becomes stronger, weaker, or shifts over time. For most components, the relationship remains similar in the near-future.
  • You can click on the legend to select or deselect the historical or future series to focus on a single timeframe. 

Here we show the hazard maps used— together with population data—to estimate the percentage of people exposed to each hazard type. The historical period is shown on the left, and the future period is on the right. Note that we do not show data for flood or drought.

  • Countries where high hazard values overlap with high percentages of people face the greatest climate-related risks.
  • Comparing the Historical (left) and Future (right) views allows you to see where exposure is projected to increase, decrease, or remain similar. While temperature-related hazards are projected to increase everywhere, changes in other hazards are more region-specific.

03 - Sensitivity

Sensitivity reflects demographic, socioeconomic, population health, and availability of basic service (among others) that influence how strongly people may be affected when exposed to climate hazards.  

 

This view compares the overall sensitivity index with the selected sensitivity component, chosen from the Sub Layers menu. 

Use the toggle above the map to switch between total sensitivity and the selected subcomponent, enabling comparison of spatial patterns across sensitivity types and helping illustrate how each contributes to overall sensitivity.

The menu on the left includes a variety of indicators. Some are available only at the country level, while others also have high-resolution map versions. Both types appear in the dropdown. 

This comparison helps users identify which factors contribute most to a country's sensitivity to climate impacts. A scatter plot below further illustrates how the selected component relates to the overall sensitivity index. 

  • Areas where the right-hand indicator is high and the overall sensitivity (left map) is also high suggest that this factor contributes strongly to sensitivity.
  • Switching between indicators helps illustrate which characteristics drive sensitivity in different regions.
  • Differences between country-level and high-resolution maps can show how vulnerabilities vary within national borders (where available). 

Built Environment 

  • Ratio of built-up area to non-built-up area (built-mean) - 2020 - Source: Poverty Mapping - GRDI BUILT Component - Global Gridded Relative Deprivation Index (GRDI) 

Vector Exposure (Mosquito Occurrence) 

  • Fraction of population exposed to Aedes aegypti based on probability of occurrence (aegypti) - 2015 - Source: Kraemer, M., Sinka, M., Duda, K. et al. The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci Data 2, 150035 (2015). https://doi.org/10.1038/sdata.2015.35
  • Fraction of population exposed to Aedes albopictus based on probability of occurrence (albopictus) - 2015  - Source: Kraemer et al. 2015

Demographics 

  • Child Dependency Ratio calculated by pop 0-14/pop 15-64 (cdr) - 2010  - Source: GPWv4.11
  • Percent of population aged 65+ in 2010 (a065plus-pct) - 2010  - Source: GPWv4.11

Pre-existing Health & Nutrition Status 

  • Infant mortality rate (per 1000) (imr) - 2010 - Source: Poverty Mapping - Global Subnational Infant Mortality Rates GSIMR v2.01
  • Non-communicable disease DALY estimates per 100000 (ncd-daly) - 2021 -  Source: Global Burden of Disease (GBD)
  • Nutritional disease DALY estimates per 100000 (nutri-daly) - 2021 -  Source: Global Burden of Disease (GBD)

Socioeconomic Status 

  • Share of population below poverty line ($6.85) (dep-poor685) - 2021 - Source: EPVGE WBG Scorecard
  • Share of population with no primary education (dep-educ-com) - 2021 - Source: EPVGE WBG Scorecard
  • Share of population in a household with no access to electricity (dep-infra-elec) - 2021 - Source: EPVGE WBG Scorecard
  • Share of population in a household with no access to improved water (dep-infra-impw) - 2021 -  Source: EPVGE WBG Scorecard

This scatter plot compares the final sensitivity index (y-axis) with the selected subcomponent on the x-axis.  

The purpose is to assess: 

  • how strongly the indicator is associated with overall sensitivity
  • how widely the data points (countries) are dispersed 
  • Each dot represents a country.
  • If the dots rise upward from left to right, the selected factor is strongly associated with higher sensitivity.
  • A more scattered pattern means the factor is less strongly linked to sensitivity.  

04 - Adaptive capacity

Adaptive capacity reflects the ability of people, communities, and health systems to prepare for, cope with, and recover from climate impacts. 
It includes factors such as access to services, transportation, financial inclusion, emergency preparedness, and institutional capacity. 

This view compares the overall adaptive capacity index with the selected adaptive capacity component, chosen from the menu on the left. 

Use the toggle above the map to switch between total adaptive capacity and the selected subcomponent, enabling comparison of spatial patterns across adaptation types and helping illustrate how each contributes to overall adaptive capacity.

Some indicators are shown only at the country level, while others also have high-resolution map versions. Both appear in the menu. 

This comparison helps users identify which factors contribute most to a country’s ability to prepare for, cope with, and recover from climate impacts. 

A scatter or violin plot below further illustrates how the selected indicator relates to the overall adaptive capacity index. 

  • Areas where low adaptive capacity overlaps with poor performance on the selected indicator represent priority vulnerabilities.
  • Switching indicators helps reveal which systems (health, infrastructure, finance, preparedness) are most constraining adaptive capacity.
  • High-resolution maps (where available) help visualize geographic variation within countries. 

Household-level Adaptive Capacity 

  • Access to financial services - Share of population in a household with no account (bank or mobile money account): financial inclusion index (dep-fin) - 2021 - Source: EPVGE WBG Scorecard
  • Access to health facilities - Average motorized travel time to healthcare facilities (hcaccess) - 2021 - Source: EPVGE WBG Scorecard
  • Access to protective schemes -Share of population in a household with no access to social protection (not contributor nor beneficiary) (dep-sp) - 2021 - Source: EPVGE WBG Scorecard
  • Access to transport - Share of population in a household that is rural and more than 2km from an all season road (RAI) (dep-rai) - 2021 -  Source: EPVGE WBG Scorecard  

Health System-level Adaptive Capacity 

  • Higher and more responsive financing
    • Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%) (heaexp10) - 2022 - Source: World Bank HEFPI (Health Equity and Financial Protection)
    • Mean share of household consumption or income used on out-of-pocket health spending (sh-hexp-1) - 2022 - Source: World Bank HEFPI (Health Equity and Financial Protection)
    • Evidence of allocated national funds to improve capacity to address epidemic threats (GHS 5.5.1a) (var551a) - 2021 -  Source: Global Health Security Index (GHS)
    • Public healthcare spending levels per capita (GHS 6.5.3) (var653) - 2021 - Source: Global Health Security Index (GHS)
  • Better data collection and management
    • Evidence of ongoing event-based surveillance and analysis (GHS 2.3.1a) (var231a) - 2021 - Source: Global Health Security Index (GHS)
    • Electronic national and sub-national reporting surveillance system (GHS 2.3.2a) (var232a) - 2021 - Source: Global Health Security Index (GHS)
    • Collection of ongoing/real-time lab data by electronic surveillance system (GHS 2.3.2b) (var232b) - 2021 - Source: Global Health Security Index (GHS)
    • National support to conduct contact tracing in the event of a public health emergency (GHS 2.5.1a) (var251a) - 2021 - Source: Global
  • Improved health infrastructure
    • Lab capacity for detecting priority diseases (GHS 2.1.1) (var211) - 2021 - Source: Global Health Security Index (GHS)
    • Doctors per 100,000 people (GHS 4.1.1a) (var411a) - 2021 - Source: Global Health Security Index (GHS)
    • Nurses and midwives per 100,000 people (GHS 4.1.1b) (var411b) - 2021 - Source: Global Health Security Index (GHS)
    • Hospital beds per 100,000 people (GHS 4.1.2a) (var412a) - 2021 - Source: Global Health Security Index (GHS)
  • Improved planning for climate hazards
    • National public health emergency preparedness and response plan (GHS 3.1.1) (var311) - 2021 - Source: Global Health Security Index (GHS)
    • Activating response plans (GHS 3.2.1) (var321) - 2021 - Source: Global Health Security Index (GHS)
    • Integration of health into disaster risk reduction (GHS 5.1.2) (var512) - 2021 - Source: Global Health Security Index (GHS)
    • Completion and publication of a JEE assessment and gap analysis (GHS 5.4.1) (var541) - 2021 - Source: Global Health Security Index (GHS)
    • Communication with healthcare workers (GHS 4.5.1) (var451) - 2021 - Source: Global Health Security Index (GHS)
    • Health National Adaptation Plan (hnap) - 2024 - Source: UNFCCC
  • Improved service delivery capacity - UHC Service Coverage Index (SDG 3.8.1) (uhccov) - 2023  - Source: WHO SDG Indicators 

This scatter or violin plot compares each country’s adaptive capacity score (y-axis) with the selected indicator (x-axis), illustrating how strongly the indicator is associated with overall adaptive capacity.

For discrete indicators, violin plots are shown for each x-value to display the distribution of scores across countries sharing that value.

  • Each dot represents a country. Hover over a dot to see its adaptive capacity score and the value of the selected indicator.
  • If the dots rise upward from left to right, it means countries performing better on the indicator tend to have higher adaptive capacity. Example: countries with shorter travel times to healthcare facilities often show higher/better adaptive capacity.
  • If the dots are widely scattered, it means the indicator has a weaker or inconsistent relationship with adaptive capacity, countries could vary a lot in how this factor affects them.