Objective: To examine temperature in relation to stroke mortality in a multicity time series study in China.
Methods: We obtained data on daily temperature and mortality from 8 large cities in China. We used quasi-Poisson generalized additive models and distributed lag nonlinear models to estimate the accumulative effects of temperature on stroke mortality across multiple days, adjusting for long-term and seasonal trends, day of the week, air pollution, and relative humidity. We applied the Bayesian hierarchical model to pool city-specific effect estimates.
Results: Both cold and hot temperatures were associated with increased risk of stroke mortality. The potential effect of cold temperature might last more than 2 weeks. The pooled relative risks of extreme cold (first percentile of temperature) and cold (10th percentile of temperature) temperatures over lags 0-14 days were 1.39 (95% posterior intervals [PI] 1.18-1.64) and 1.11 (95% PI 1.06-1.17), compared with the 25th percentile of temperature. In contrast, the effect of hot temperature was more immediate. The relative risks of stroke mortality over lags 0-3 days were 1.06 (95% PI 1.02-1.10) for extreme hot temperature (99th percentile of temperature) and 1.14 (95% PI 1.05-1.24) for hot temperature (90th percentile of temperature), compared with the 75th percentile of temperature.
Conclusions: This study showed that both cold and hot temperatures were associated with increased risk of stroke mortality in China. Our findings may have important implications for stroke prevention in China.