Dataset Information

Excess mortality in Wuhan city and other parts of China during the three months of the covid-19 outbreak: findings from nationwide mortality registries.

ABSTRACT:

Objective

To assess excess all cause and cause specific mortality during the three months (1 January to 31 March 2020) of the coronavirus disease 2019 (covid-19) outbreak in Wuhan city and other parts of China.

Design

Nationwide mortality registries.

Setting

605 urban districts and rural counties in China's nationally representative Disease Surveillance Point (DSP) system.

Participants

More than 300 million people of all ages.

Main outcome measures

Observed overall and weekly mortality rates from all cause and cause specific diseases for three months (1 January to 31 March 2020) of the covid-19 outbreak compared with the predicted (or mean rates for 2015-19) in different areas to yield rate ratio.

Results

The DSP system recorded 580?819 deaths from January to March 2020. In Wuhan DSP districts (n=3), the observed total mortality rate was 56% (rate ratio 1.56, 95% confidence interval 1.33 to 1.87) higher than the predicted rate (1147 v 735 per 100?000), chiefly as a result of an eightfold increase in deaths from pneumonia (n=1682; 275 v 33 per 100?000; 8.32, 5.19 to 17.02), mainly covid-19 related, but a more modest increase in deaths from certain other diseases, including cardiovascular disease (n=2347; 408 v 316 per 100?000; 1.29, 1.05 to 1.65) and diabetes (n=262; 46 v 25 per 100?000; 1.83, 1.08 to 4.37). In Wuhan city (n=13 districts), 5954 additional (4573 pneumonia) deaths occurred in 2020 compared with 2019, with excess risks greater in central than in suburban districts (50% v 15%). In other parts of Hubei province (n=19 DSP areas), the observed mortality rates from pneumonia and chronic respiratory diseases were non-significantly 28% and 23% lower than the predicted rates, despite excess deaths from covid-19 related pneumonia. Outside Hubei (n=583 DSP areas), the observed total mortality rate was non-significantly lower than the predicted rate (675 v 715 per 100?000), with significantly lower death rates from pneumonia (0.53, 0.46 to 0.63), chronic respiratory diseases (0.82, 0.71 to 0.96), and road traffic incidents (0.77, 0.68 to 0.88).

Conclusions

Except in Wuhan, no increase in overall mortality was found during the three months of the covid-19 outbreak in other parts of China. The lower death rates from certain non-covid-19 related diseases might be attributable to the associated behaviour changes during lockdown.

SUBMITTER: Liu J

PROVIDER: S-EPMC7900645 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Excess mortality in Wuhan city and other parts of China during the three months of the covid-19 outbreak: findings from nationwide mortality registries.

Liu Jiangmei J Zhang Lan L Yan Yaqiong Y Zhou Yuchang Y Yin Peng P Qi Jinlei J Wang Lijun L Pan Jingju J You Jinling J Yang Jing J Zhao Zhenping Z Wang Wei W Liu Yunning Y Lin Lin L Wu Jing J Li Xinhua X Chen Zhengming Z Zhou Maigeng M

BMJ (Clinical research ed.) 20210224

<h4>Objective</h4>To assess excess all cause and cause specific mortality during the three months (1 January to 31 March 2020) of the coronavirus disease 2019 (covid-19) outbreak in Wuhan city and other parts of China.<h4>Design</h4>Nationwide mortality registries.<h4>Setting</h4>605 urban districts and rural counties in China's nationally representative Disease Surveillance Point (DSP) system.<h4>Participants</h4>More than 300 million people of all ages.<h4>Main outcome measures</h4>Observed ov ...[more]

PMID: 33627311

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Project description:BackgroundA significant increase in mortality was observed during cold winters in many temperate regions. However, there is a lack of evidence from tropical and subtropical regions, and the influence of ambient temperatures on seasonal variation of mortality was not well documented.MethodsThis study included 213,737 registered deaths from January 2003 to December 2011 in Guangzhou, a subtropical city in Southern China. Excess winter mortality was calculated by the excess percentage of monthly mortality in winters over that of non-winter months. A generalized linear model with a quasi-Poisson distribution was applied to analyze the association between monthly mean temperature and mortality, after controlling for other meteorological measures and air pollution.ResultsThe mortality rate in the winter was 26% higher than the average rate in other seasons. On average, there were 1,848 excess winter deaths annually, with around half (52%) from cardiovascular diseases and a quarter (24%) from respiratory diseases. Excess winter mortality was higher in the elderly, females and those with low education level than the young, males and those with high education level, respectively. A much larger winter increase was observed in out-of-hospital mortality compared to in-hospital mortality (45% vs. 17%). We found a significant negative correlation of annual excess winter mortality with average winter temperature (rs=-0.738, P=0.037), but not with air pollution levels. A 1 °C decrease in monthly mean temperature was associated with an increase of 1.38% (95% CI:0.34%-2.40%) and 0.88% (95% CI:0.11%-1.64%) in monthly mortality at lags of 0-1 month, respectively.ConclusionSimilar to temperate regions, a subtropical city Guangzhou showed a clear seasonal pattern in mortality, with a sharper spike in winter. Our results highlight the role of cold temperature on the winter mortality even in warm climate. Precautionary measures should be strengthened to mitigate cold-related mortality for people living in warm climate.

Dataset Information

Excess mortality in Wuhan city and other parts of China during the three months of the covid-19 outbreak: findings from nationwide mortality registries.

Objective

Design

Setting

Participants

Main outcome measures

Results

Conclusions

Publications

Excess mortality in Wuhan city and other parts of China during the three months of the covid-19 outbreak: findings from nationwide mortality registries.

Similar Datasets

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