The heterogeneity of mortality burden of lower respiratory infection associated with ambient temperature and its drivers in China - Sujuan Chen; Jinlei Qi; Yulin Zhuo; Jianxiong Hu; Jiangmei Liu; Peng Yin; Tao Liu; Mengen Guo; Guanhao He; Ziqiang Lin; Fengrui Jing; Jinling You; et al. (Ecotoxicology and Environmental Safety)
Abstract：: Ambient temperature is linked to an increased risk of lower respiratory infections (LRI) yet the evidence on the population and spatial heterogeneity of LRI mortality burden attributed to non-optimal temperatures and its socioeconomic drivers remain limited. Based on 1249,452 LRI deaths and related meteorological data collected from 2820 counties/districts during 2013–2022 in China, we conducted a time-stratified case-crossover analysis using distributed lag nonlinear model (DLNM) to examine the spatial and population heterogeneity of the association between temperature and LRI mortality. Additionally, we explored the driving factors of the spatial heterogeneity using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) methods. We found that both extreme cold (RR = 1.89, 95 % CI: 1.76–2.04) and extreme heat (RR = 1.19, 95 % CI: 1.17–1.22) were associated with increased LRI mortality. The attributable fraction (AF) of LRI mortality due to non-optimal temperatures was 16.20 % (95 % eCI: 16.13 %–16.27 %), with a higher AF for low temperatures (14.97 %, 95 % eCI: 14.90 %–15.03 %) than for high temperatures (1.23 %, 95 % eCI: 1.20 %–1.27 %). Groups with higher temperature-related LRI mortality included individuals with influenza, and females. The main driving factors for spatial heterogeneity in extreme cold-related LRI mortality were GDP, air conditioners per 100 households (NAC), illiteracy rate (ILR), number of healthcare facilities (HF), and healthcare workers per 10,000 people (NMP), while for extreme heat, they were the proportion of elderly population (ELD), HF, ILR, NMP, and NAC. Our findings offer key insights for reducing temperature-related LRI mortality in vulnerable populations and regions in China.