Excess risk of heat-related hospitalization associated with temperature and PM2.5 among older adults

TL;DR

This study is the first to observe synergism between temperature and PM2.5 exposures associated with the risk of heat-related hospitalization, highlighting the importance of considering air pollution in effective public health and clinical interventions to prevent heat-related illness.

Abstract

Background: With rising temperatures and an aging population, understanding how to prevent heat-related illness among older adults will be increasingly crucial. Despite biological plausibility, no study to date has investigated whether fine particulate matter air pollution (PM2.5) contributes to the risk of hospitalization with a diagnosis code indicating heat-related illness, referred to as heat-related hospitalization. This study aims to fill this gap by investigating the independent and combined effects of temperature and PM2.5 on heat-related hospitalization risk. Methods: We identified Medicare fee-for-service beneficiaries in the contiguous United States who experienced a heat-related hospitalization between 2008 and 2016. Using a case-crossover design and Bayesian conditional logistic regression, we characterized the associations of temperature and PM2.5 with heat-related hospitalization. We then estimated the relative excess risk due to interaction to quantify the additive interaction of simultaneous exposure to heat and PM2.5. Results: We observed 112,969 heat-related hospitalizations. Fixing PM2.5 at the case day median, the odds ratio for increasing temperature from its case day median to the 95th percentile was 1.05 (95% CI: 1.03, 1.06). Fixing temperature at the case day median, the odds ratio for increasing PM2.5 from its median to the 95th percentile was 1.01 (95% CI: 0.99, 1.04). The relative excess risk due to interaction for simultaneous median-to-95th percentile increases in temperature and PM2.5 was 0.03 (95% CI: 0.01, 0.06). Conclusions: Our study is the first to observe synergism between temperature and PM2.5 associated with the risk of heat-related hospitalization. These findings highlight the importance of considering air pollution in effective public health and clinical interventions to prevent heat-related illness.

Figures (8)

• Figure 1: From left to right, aggregated county-level maps of 1) mean case day maximum temperature, 2) mean case day three-day PM$_{2.5}$ concentrations, and 3) number of heat-related hospitalizations per 1000 Medicare FFS beneficiaries at risk of hospitalization during the study period. Counties with 10 or fewer heat-related hospitalizations observed during the study period are suppressed for confidentiality and are displayed in white.
• Figure 2: Independent nonlinear effects of temperature and PM$_{2.5}$ on heat-related hospitalization. The left panel displays the odds ratio of heat-related hospitalization comparing the median case day temperature (29.6$\degree$C), shown with a vertical dashed line, to various alternative temperatures across the x axis, while holding PM$_{2.5}$ exposure fixed at the median (8.9 $\mu g/m^3$). The right panel displays the odds ratio of heat-related hospitalization comparing the median case day PM$_{2.5}$ exposure, shown with a vertical dashed line, to various alternative concentration levels across the x axis, while holding temperature exposure fixed at the median.
• Figure 3: Comparison of the main and sensitivity analyses. The left panel displays odds ratios with 95% Bayesian credible intervals associated with (1) increasing temperature from the median to the 95th percentile while holding PM$_{2.5}$ at the median, (2) increasing PM$_{2.5}$ from the median to the 95th percentile while holding temperature at the median, and (3) increasing both temperature and PM$_{2.5}$ from the median to the 95th percentile simultaneously. The right panel displays the relative excess risk due to interaction (RERI) with 95% Bayesian credible intervals. The same exposure contrasts were used across models. All numerical results are presented in Table S2 in the Supplementary Material.
• Figure 4: Synergistic effects of temperature and PM$_{2.5}$ across a range of exposure values. The tile color indicates the odds ratio of heat-related hospitalization for a given pair of temperature and PM$_{2.5}$ exposures versus the median temperature and PM$_{2.5}$ exposures (marked on the grid with an $\times$).
• Figure S1: Case day distributions of daily maximum temperature and three-day average PM$_{2.5}$ exposures. The dashed lines indicate the median and 95th percentiles of each exposure, which we use to estimate the RERI. Note that days on which PM$_{2.5}$ exceeded 18.4 $\mu g/m^3$ were excluded from the analysis. Prior to trimming, the maximum case day three-day average PM$_{2.5}$ exposure was 120.4 $\mu g/m^3$.