Heat-mortality relationships show substantial heterogeneity across populations; however, extant literature sheds little light on the factors, especially social determinants of health, responsible for this differential heterogeneity. Our objective for this analysis was to assess the effect modification by social vulnerability metrics (SVMs) on the association between heat and cardiovascular mortality.
Methods
A two-stage time-series analysis with distributed lag non-linear models was used to model the exposure-response (E-R) relationships between extreme heat and cardiovascular mortality in 2,431 counties in the United States for years, 2001-2012. The risk estimates were pooled in a meta-regression framework to examine how SVMs modified the E-R relationships. A counterfactual health burden analysis was applied to quantify the extent of the effect modification by estimating fractions of cardiovascular deaths attributed to heat (AF) at various percentiles of each SVM. The SVMs used in this assessment ranged from commonly used sociodemographic variables to those providing insights into housing / transportation characteristics and minority status of a community. A community-specific Heat-Related Social Vulnerability Rating (HRSVR) was created using various aforementioned SVM was also incorporated in this analysis.
Results
The HRSVR and several SVMs, including socioeconomic status (e.g., % with no high school diploma), housing (e.g., % of households with no vehicles), and minority status (e.g., % of population with limited English speaking abilities) variables were significantly associated with an increase in heat-attributed cardiovascular mortality. Across all climate regions, the increase in AF of cardiovascular deaths for an interquartile increase in HRSVR levels was 0.25% (95%CI: 0.19%–0.30%).
Conclusions
Health burden is likely to increase with increasing social vulnerability levels and the extent of effect modification varies with SVMs. The results from this assessment could potentially be used to identify at-risk areas based on their levels of social vulnerability and design community-specific heat intervention plans to help prepare for extreme heat emergencies.
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