Abstract

BackgroundFew studies have explored the added effect of heat waves, especially in China. Moreover, no prior studies have assessed whether the choice of heat wave definitions affected this added effect. This study compared the associations between heat waves defined by different heat wave definitions (HWs) and cause-specific mortality in warm season in Nanjing, China. MethodsA distributed lag model was applied to evaluate the differences in daily mortality during heat-wave days (defined using 15 HWs) compared with non-heat-wave days in Nanjing, during 2007 to 2013. For different HWs, model fits were examined by the Akaike Information Criterion for quasi-Poisson and effects were compared by stratified analysis and bootstrapping. In addition, we explored the effect modifications by individual characteristics under different HWs. ResultsDifferent HWs resulted in considerable differences in associations between heat waves and mortality. Heat waves defined as ≥4 consecutive days with daily average temperature >98th percentile had the best model fit and were associated with an increase of 24.6% (95% CI: 15.6%, 34.3%) total mortality, 46.9% (95% CI: 33.0%, 62.3%) cardiovascular mortality, 32.0% (95% CI: 8.5%, 60.5%) respiratory mortality, 51.3% (95% CI: 23.4%, 85.6%) stroke mortality, 63.4% (95% CI: 41.5%, 88.8%) ischemic heart disease mortality, and 47.6% (95% CI: 14.5%, 90.3%) chronic obstructive pulmonary disease mortality at lag day 2. Under different HWs, added effects of heat waves on mortality were higher for females versus males, the elderly versus young residents, and people with low education versus those with high education. Results were less sensitive to the inclusion of air pollutants. ConclusionsHeat wave definition plays a critical role in the relationship between heat waves and mortality. Selecting an appropriate definition of heat waves is therefore important to design local heat warning systems and to reduce the burden of disease during heat waves.

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