The Impact of Temperature on Mortality in Tianjin, China: A Case-Crossover Design with a Distributed Lag Nonlinear Model

Abstract

BACKGROUND: Although interest in assessing the impacts of temperature on mortality has increased, few studies have used a case-crossover design to examine nonlinear and distributed lag effects of temperature on mortality. Additionally, little evidence is available on the temperature-mortality relationship in China or on what temperature measure is the best predictor of mortality. OBJECTIVES: Our objectives were to use a distributed lag nonlinear model (DLNM) as a part of case-crossover design to examine the nonlinear and distributed lag effects of temperature on mortality in Tianjin, China and to explore which temperature measure is the best predictor of mortality. METHODS: We applied the DLNM to a case-crossover design to assess the nonlinear and delayed effects of temperatures (maximum, mean, and minimum) on deaths (nonaccidental, cardiopulmonary, cardiovascular, and respiratory). RESULTS: A U-shaped relationship was found consistently between temperature and mortality. Cold effects (i.e., significantly increased mortality associated with low temperatures) were delayed by 3 days and persisted for 10 days. Hot effects (i.e., significantly increased mortality associated with high temperatures) were acute and lasted for 3 days and were followed by mortality displacement for nonaccidental, cardiopulmonary, and cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature. CONCLUSIONS: In Tianjin, extreme cold and hot temperatures increased the risk of mortality. The effects of cold last longer than the effects of heat. Combining the DLNM and the case-crossover design allows the case-crossover design to flexibly estimate the nonlinear and delayed effects of temperature (or air pollution) while controlling for season.