Cross sectional analysis of the association between extreme climate events and stroke incidence in middle aged and older Chinese adults

Extreme climate events are increasingly recognized as important environmental determinants of health. However, their associations with stroke incidence remain unclear, particularly among aging populations in developing countries. We conducted a cross-sectional study to examine the associations between four types of extreme climate events-extreme low temperature (LTD), extreme high temperature (HTD), extreme rainfall (ERD), and extreme drought (EDD)-and stroke among Chinese adults aged over 45 years. Climate data were obtained from the Climate Physical Risk Index (CPRI), developed using NOAA meteorological records, while health and demographic data were drawn from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). Multivariable logistic regression models were applied to estimate the odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for sociodemographic, behavioral, and clinical covariates. Subgroup analyses were conducted to explore potential effect modifications including sex, residence, education status, marital status, smoking status, drinking frequency, and regional category. Extreme climate events are increasingly recognized as important environmental determinants of health. However, their associations with stroke incidence remain unclear, particularly among aging populations in developing countries. We conducted a cross-sectional study to examine the associations between four types of extreme climate events-extreme low temperature (LTD), extreme high temperature (HTD), extreme rainfall (ERD), and extreme drought (EDD)-and stroke among Chinese adults aged over 45 years. Climate data were obtained from the Climate Physical Risk Index (CPRI), developed using NOAA meteorological records, while health and demographic data were drawn from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). Multivariable logistic regression models were applied to estimate the odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for sociodemographic, behavioral, and clinical covariates. Subgroup analyses were conducted to explore potential effect modifications including sex, residence, education status, marital status, smoking status, drinking frequency, and regional category. LTD was significantly associated with a lower incidence of stroke in all models (fully adjusted OR = 0.98, 95% CI: 0.95-1.00, p = 0.007). In contrast, EDD was associated with a higher incidence of stroke in the fully adjusted model (OR = 1.02, 95% CI: 1.00-1.04, p = 0.035). No significant associations were found for HTD or ERD in the overall analysis. Subgroup analyses revealed stronger associations of LTD and EDD with stroke among females, non-smokers, non-drinkers, and residents in specific regions. Our findings suggest a potentially protective role of extreme low temperatures and a modest adverse effect of extreme drought on stroke incidence among middle-aged and older adults in China. These associations vary across population subgroups and underscore the nEDD for climate-adaptive public health strategies tailored to vulnerable populations.