Dataset Information

Temperature changes between neighboring days and mortality in summer: a distributed lag non-linear time series analysis.

ABSTRACT:

Background

Many studies have shown that high temperatures or heat waves were associated with mortality and morbidity. However, few studies have examined whether temperature changes between neighboring days have any significant impact on human health.

Method

A distributed lag non-linear model was employed to investigate the effect of temperature changes on mortality in summer during 2006-2010 in two subtropical Chinese cities. The temperature change was defined as the difference of the current day's and the previous day's mean temperature.

Results

We found non-linear effects of temperature changes between neighboring days in summer on mortality in both cities. Temperature increase was associated with increased mortality from non-accidental diseases and cardiovascular diseases, while temperature decrease had a protective effect on non-accidental mortality and cardiovascular mortality in both cities. Significant association between temperature changes and respiratory mortality was only found in Guangzhou.

Conclusion

This study suggests that temperature changes between neighboring days might be an alternative temperature indicator for studying temperature-mortality relationship.

SUBMITTER: Lin H

PROVIDER: S-EPMC3691212 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Temperature changes between neighboring days and mortality in summer: a distributed lag non-linear time series analysis.

Lin Hualiang H Zhang Yonghui Y Xu Yanjun Y Xu Xiaojun X Liu Tao T Luo Yuan Y Xiao Jianpeng J Wu Wei W Ma Wenjun W

PloS one 20130624 6

<h4>Background</h4>Many studies have shown that high temperatures or heat waves were associated with mortality and morbidity. However, few studies have examined whether temperature changes between neighboring days have any significant impact on human health.<h4>Method</h4>A distributed lag non-linear model was employed to investigate the effect of temperature changes on mortality in summer during 2006-2010 in two subtropical Chinese cities. The temperature change was defined as the difference of ...[more]

PMID: 23826095

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Project description:Less evidence concerning the association between ambient temperature and mortality is available in developing countries/regions, especially inland areas of China, and few previous studies have compared the predictive ability of different temperature indictors (minimum, mean, and maximum temperature) on mortality. We assessed the effects of temperature on daily mortality from 2003 to 2010 in Jiang'an District of Wuhan, the largest city in central China. Quasi-Poisson generalized linear models combined with both non-threshold and double-threshold distributed lag non-linear models (DLNM) were used to examine the associations between different temperature indictors and cause-specific mortality. We found a U-shaped relationship between temperature and mortality in Wuhan. Double-threshold DLNM with mean temperature performed best in predicting temperature-mortality relationship. Cold effect was delayed, whereas hot effect was acute, both of which lasted for several days. For cold effects over lag 0-21 days, a 1 °C decrease in mean temperature below the cold thresholds was associated with a 2.39% (95% CI: 1.71, 3.08) increase in non-accidental mortality, 3.65% (95% CI: 2.62, 4.69) increase in cardiovascular mortality, 3.87% (95% CI: 1.57, 6.22) increase in respiratory mortality, 3.13% (95% CI: 1.88, 4.38) increase in stroke mortality, and 21.57% (95% CI: 12.59, 31.26) increase in ischemic heart disease (IHD) mortality. For hot effects over lag 0-7 days, a 1 °C increase in mean temperature above the hot thresholds was associated with a 25.18% (95% CI: 18.74, 31.96) increase in non-accidental mortality, 34.10% (95% CI: 25.63, 43.16) increase in cardiovascular mortality, 24.27% (95% CI: 7.55, 43.59) increase in respiratory mortality, 59.1% (95% CI: 41.81, 78.5) increase in stroke mortality, and 17.00% (95% CI: 7.91, 26.87) increase in IHD mortality. This study suggested that both low and high temperature were associated with increased mortality in Wuhan, and that mean temperature had better predictive ability than minimum and maximum temperature in the association between temperature and mortality.

Dataset Information

Temperature changes between neighboring days and mortality in summer: a distributed lag non-linear time series analysis.

Background

Method

Results

Conclusion

Publications

Temperature changes between neighboring days and mortality in summer: a distributed lag non-linear time series analysis.

Similar Datasets

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