Impact of temperature variation on mortality: An observational study from 12 counties across Hubei Province in China.

BACKGROUND: Compared with cold- and heat-related health impacts, the evidence was very limited in assessing the mortality effects of temperature variation (TV) accounting for both intra-day and inter-day changes in temperature.

OBJECTIVE: We used a newly proposed composite indicator of intra-day and inter-day TV and evaluated TV-mortality associations in Hubei, China at the provincial level.

METHODS: Daily mortality and meteorological data during 2009-2012 were obtained from 12 urban and rural counties across Hubei Province in China. TV was calculated using the standard deviation of the minimum and maximum temperatures during the exposure days. A quasi-Poisson generalized linear regression combined with distributed lag non-linear model was first applied to estimate county-specific relationship between mortality and TV, adjusting for long-term trend and seasonality, mean temperature, relative humidity, public holiday, and day of the week. A meta-analysis was then conducted to pool the county-specific estimates of TV-related mortality effects.

RESULTS: A significant positive association was observed between TV and cause-specific mortality (except for respiratory mortality and ischemic heart disease mortality). The effect estimates varied by exposure days, with the highest at 0-7days. Season-stratified analyses showed similar results, while stronger TV-mortality associations were found in warm season than in cold season. The elderly were more susceptible to TV-related mortality effects than younger groups. Some slight differences in effect estimates were also observed in subgroups stratified by gender, education attainment, place of death, and urban/rural areas.

CONCLUSION: Our study strengthened the evidence that temperature variation was an independent risk factor for non-accidental mortality. Some preventive and intervention strategies should be efficiently developed in response to global climate change, so as to minimize public health burden due to unstable weather patterns.