A better count of heat-related deaths needed to help the vulnerable

Insufficient gender segregated data is minimizing the health risks to women in a warming world.

Growing evidence points to the higher risk of deaths caused by heatwave among women than men in the Global North. A recent study¹ using 42 indicators shows significant disparities in climate-health impacts across the European population. The findings reveal that impacts are not uniform as vulnerable groups bear a disproportionate burden. It also points to the urgent need for targeted policies and interventions to address the specific needs of these populations at more risk, ensuring that climate resilience efforts are inclusive and equitable.

Studies in the Global North have stronger evidence in support of women being at higher risk of weather-related deaths. The conclusions from research in the Global South are less clear. From among 16 studies in the region, seven conclude that women are at high risk, six say the opposite and three declare no significant difference between men and women in terms of temperature-related mortality.

Without precise evidence, national and regional climate action cannot be effective, particularly in reaching vulnerable sub-groups.

A recent experiment used 30 years of data from the Global Burden of Disease's annual extreme-temperature-related death for India, segmented by gender, to investigate the health challenges from heat. It also looked at the country's daily temperature data recorded by the national weather monitoring and forecasting agency, the India Meteorological Department (IMD), between 1990 and 2019.

The research could not conclude the comparative effects of heat on women's and men's health because of the lack of precise information on location and timelines. However, the study did find women more vulnerable to extreme temperature conditions (specifically heat) than men, supporting the need for attention to gender inclusivity in heat action plans.

Meteorological parameters, including temperature at a much finer scale² than what IMD makes publicly available, and gender-specific death records with the cause of death and detailed location and time, are key for robust and precise statistical inference. For example, the country-wide death data in the IMD dataset used in the study did not reflect regional differences in how temperatures affected mortality. Even summary statistics of a state, province, or city may help link mortality patterns to regional temperature fluctuations.

India should prioritize creating such databases. Information on different groups, such as ethnicities, minorities, and vulnerable populations, will help decipher how heat affects health and death rates. These indices are factored into Eurostat datasets.

The absence of relevant metrics skews information on health risks and co-benefits of physical infrastructure during heat waves. One such indicator is the number of sick days for outdoor workers who work in intense heat islands.

Data on heat exposures of individuals should be captured to develop customized place-based interventions hot and dry climate zones should have cooling solutions that focus on increasing humidity to acceptable human comfort levels, while warm and wet zones need dehumidification technologies.

Studies on gender and temperature change risk in India are observational and use broad ecological data. Because of their limited scope, these studies have reporting and aggregation biases. They don't consider socio-economic conditions, age, education, occupation, neighbourhood, housing types, and access to adaptation interventions. Therefore, conclusions often change over time for the same study cohort. One study³ did not find a long-term association between male or female mortality and factors like heat or cold. Another research⁴ on the same cohort looking at people aged 15 years or older, concluded that women had a higher risk of heat-related mortality than men.

Standardizing methodology⁵ to report climate-related death and illness would help collect meaningful long-term data. It would ease quantifying current impacts, projecting future risks, and developing responses by helping define a reference period for health data and integrating evolving health risks.