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Temperature variation between neighboring days and mortality: a distributed lag non-linear analysis

Abstract

Objectives

To investigate whether a sudden temperature change between neighboring days has significant impact on mortality.

Methods

A Poisson generalized linear regression model combined with a distributed lag non-linear models was used to estimate the association of temperature change between neighboring days with mortality in a subtropical Chinese city during 2008–2012. Temperature change was calculated as the current day’s temperature minus the previous day’s temperature.

Results

A significant effect of temperature change between neighboring days on mortality was observed. Temperature increase was significantly associated with elevated mortality from non-accidental and cardiovascular diseases, while temperature decrease had a protective effect on non-accidental mortality and cardiovascular mortality. Males and people aged 65 years or older appeared to be more vulnerable to the impact of temperature change.

Conclusions

Temperature increase between neighboring days has a significant adverse impact on mortality. Further health mitigation strategies as a response to climate change should take into account temperature variation between neighboring days.

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Acknowledgments

This work was funded by Anhui Natural Science Fund (No. 1408085MH159). We thank Jian Song, Yangyang Zhou, and Liangliang Guo for their valuable comments.

Conflict of interest

The authors report no conflict of interest.

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Corresponding author

Correspondence to Hong Su.

Additional information

J. Cheng and R. Zhu are Co-first author.

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Cheng, J., Zhu, R., Xu, Z. et al. Temperature variation between neighboring days and mortality: a distributed lag non-linear analysis. Int J Public Health 59, 923–931 (2014). https://doi.org/10.1007/s00038-014-0611-5

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Keywords

  • Climate change
  • Temperature variation
  • Mortality
  • Time series