Temperature change between neighboring days and mortality in United States: A nationwide study

Temperature change between neighboring days (TCN), an indicator to reflect sudden temperature variation, has been identified as an independent risk factor for human health by small-scale studies. However, the adverse impact of TCN on mortality and effect modification are insufficiently studied, and a larger multi-cities analysis at national level is needed to provide an insightful knowledge. Using daily mortality and meteorological data from 106 communities of United States during 1987 to 2000, we employed a quasi-Poisson regression with distributed lag non-linear model to quantitatively estimate the effect of TCN on mortality for each community and a multivariate meta-analysis to pool the community-specific estimates. At national level, a monotonic increasing curve of TCN–mortality association was observed, which indicated that negative TCN (temperature decrease from the previous day) was associated with reduced mortality and positive TCN (temperature increase) elevated the risk of mortality. The relative risk for lag 0–21 days was 0.63 (95% confidence interval: 0.59–0.68) for extremely negative TCN (1st percentile) and 1.46 (1.39–1.54) for extremely positive TCN (99th percentile) on non-accidental mortality. We also found prominent effects of extreme TCNs on mortality for cardiovascular, respiratory, pneumonia, and COPD diseases. People ≥ 75 years and those with respiratory disease, especially pneumonia-deaths, were identified as a particularly vulnerable population to TCN. The TCN–mortality association was modified by season and region. A positive TCN was associated with an elevated risk of mortality in United States, with different effect patterns by region and season. Identification of the effect modifiers presented a significantly stronger influence on older adults and those with respiratory disease.