Back

Harms from Heat-Health Risks: Morbidity Evidence from India and Global Learnings for Policy Action

Dasgupta, P.; Dasgupta, R.; Ebi, K. L.; Sharma, G.; Chowdhuri, M.; Shankar, S.; Pujari, S.

2025-07-08 health policy
10.1101/2025.07.07.25330912 medRxiv
Show abstract

Increasing temperatures in India, along with a rise in the frequency, intensity and duration of heatwaves, pose health risks. Similar challenges from heat events are being faced across geographies, in both high and low-and-middle-income countries. This paper examines the evidence on heat-health risks for the Indian population using a national level dataset on over 500,000 individuals and 63 illnesses. It also synthesizes global evidence and scholarship on heat-health risks through a narrative review of the literature on morbidity outcomes. The results from the data analysis and the synthesized findings establish that significant morbidity is associated with heat stress experienced during the summer months, and that specific illnesses are aggravated by heat, especially for vulnerable sub-groups such as older adults, females and outdoor workers. The paper presents evidence on stressors and factors that influence health outcomes including pre-existing illnesses, socio-economic vulnerability and planned heat adaptation responses. It identifies the determinants of risk, specific knowledge gaps for further research and multiple options for risk management which can be considered particularly for low income contexts and LMICs, including India. The study provides an evidence base along with specific recommendations to inform policy on adoption of short- and long-term strategies for reducing HRIs and strengthening health system resilience. There is an urgent need for health sector actors to actively engage in expanding evidence on heat related health risks and in building resilience.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.