Assessing Climate-Related Health Risks With Gridded Population Data: A Systematic Review

Climate change threatens human health globally, with impacts varying across populations and regions. Gridded population datasets - spatially explicit population estimates distributed across regular grids - have become essential tools for assessing climate-related health risks and informing adaptation strategies. This systematic review, funded by the Wellcome Trust (grant 308679/Z/23/Z), examines how these datasets are employed in climate and health research, identifying thematic priorities, geographic patterns, data characteristics, and policy implications. Following PRISMA 2020 guidelines, we reviewed literature published since 2015 that utilized WorldPop or Gridded Population of the World (GPW) datasets to assess climate-health risks. Using 48 search strings combining gridded population datasets with 23 WHO-defined climate-health terms, we screened 582 records via Web of Science and Google Scholar, ultimately including 228 peer-reviewed publications. The analysis reveals substantial research growth, particularly after 2021, with studies concentrated on temperature-related health impacts (n=70), flooding and water management (n=64), and disease, health and wellbeing (n=75). Geographic focus centers heavily on Asia (43%), while Africa remains significantly understudied. Despite recognized age and sex vulnerabilities, demographic disaggregation is seldom employed. Critical research gaps include cold temperature effects, groundwater temperature changes, and urban health dynamics in underrepresented regions. A consistent policy message emerges across reviewed literature: policymakers must identify exposed and high-risk populations to plan adaptation and mitigation strategies for climate impacts along coastlines, in heat-prone areas, and in high-density urban centers, implementing necessary adaptations to prevent deaths, illness, and productivity losses. While gridded population data enables spatially explicit vulnerability assessment, enhanced demographic disaggregation, better communication of uncertainties, and clearer policy translation are needed. Other priorities include addressing geographic imbalances in the geographies of both publication and study, encouraging a focus on understudied research themes, and developing frameworks that effectively link spatial data to targeted interventions protecting vulnerable populations.