Association of extreme precipitation with hospitalizations for acute myocardial infarction in Beijing, China: A time-series study
BackgroundIn the context of global climate changes, increasing extreme weather events have aroused great public concern. Limited evidence has focused on the association between extreme precipitation and hospitalizations for acute myocardial infarction (AMI). Our study aimed to examine the effect of...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.1024816/full |
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| author | Yuxiong Chen Zhen'ge Chang Yakun Zhao Yanbo Liu Jia Fu Yijie Liu Xiaole Liu Dehui Kong Yitao Han Siqi Tang Zhongjie Fan |
| author_facet | Yuxiong Chen Zhen'ge Chang Yakun Zhao Yanbo Liu Jia Fu Yijie Liu Xiaole Liu Dehui Kong Yitao Han Siqi Tang Zhongjie Fan |
| author_sort | Yuxiong Chen |
| collection | DOAJ |
| description | BackgroundIn the context of global climate changes, increasing extreme weather events have aroused great public concern. Limited evidence has focused on the association between extreme precipitation and hospitalizations for acute myocardial infarction (AMI). Our study aimed to examine the effect of extreme precipitation on AMI hospitalizations.MethodsDaily AMI hospitalizations, weather variables and air pollution data in Beijing from 2013 to 2018 were obtained. We used a time-series analysis with a distributed lag model to evaluate the association of extreme precipitation (≥95th percentile of daily precipitation) with AMI hospitalizations. Subgroup analysis was conducted to identify the vulnerable subpopulations and further assessed the attributable burden.ResultsExtreme precipitation increased the risk of AMI hospitalizations with significant single-day effects from Lag 4 to Lag 11, and the maximum cumulative effects at Lag 0–14 (CRR = 1.177, 95% CI: 1.045, 1.326). Older people (≥65 years) and females were more vulnerable to extreme precipitation. The attributable fraction and numbers of extreme precipitation on AMI hospitalizations were 0.68% (95% CI: 0.20%, 1.12%) and 854 (95% CI: 244, 1,395), respectively.ConclusionExtreme precipitation is correlated with a higher risk of AMI hospitalizations. The elderly (≥65 years) and females are more susceptible to AMI triggered by extreme precipitation. |
| first_indexed | 2024-04-12T18:13:40Z |
| format | Article |
| id | doaj.art-0f6bc263f883471b9559d84ec5e28b37 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-12T18:13:40Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-0f6bc263f883471b9559d84ec5e28b372022-12-22T03:21:44ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-09-011010.3389/fpubh.2022.10248161024816Association of extreme precipitation with hospitalizations for acute myocardial infarction in Beijing, China: A time-series studyYuxiong ChenZhen'ge ChangYakun ZhaoYanbo LiuJia FuYijie LiuXiaole LiuDehui KongYitao HanSiqi TangZhongjie FanBackgroundIn the context of global climate changes, increasing extreme weather events have aroused great public concern. Limited evidence has focused on the association between extreme precipitation and hospitalizations for acute myocardial infarction (AMI). Our study aimed to examine the effect of extreme precipitation on AMI hospitalizations.MethodsDaily AMI hospitalizations, weather variables and air pollution data in Beijing from 2013 to 2018 were obtained. We used a time-series analysis with a distributed lag model to evaluate the association of extreme precipitation (≥95th percentile of daily precipitation) with AMI hospitalizations. Subgroup analysis was conducted to identify the vulnerable subpopulations and further assessed the attributable burden.ResultsExtreme precipitation increased the risk of AMI hospitalizations with significant single-day effects from Lag 4 to Lag 11, and the maximum cumulative effects at Lag 0–14 (CRR = 1.177, 95% CI: 1.045, 1.326). Older people (≥65 years) and females were more vulnerable to extreme precipitation. The attributable fraction and numbers of extreme precipitation on AMI hospitalizations were 0.68% (95% CI: 0.20%, 1.12%) and 854 (95% CI: 244, 1,395), respectively.ConclusionExtreme precipitation is correlated with a higher risk of AMI hospitalizations. The elderly (≥65 years) and females are more susceptible to AMI triggered by extreme precipitation.https://www.frontiersin.org/articles/10.3389/fpubh.2022.1024816/fullextreme precipitationacute myocardial infarctiondistributed lag modeltime-series analysishospitalizations |
| spellingShingle | Yuxiong Chen Zhen'ge Chang Yakun Zhao Yanbo Liu Jia Fu Yijie Liu Xiaole Liu Dehui Kong Yitao Han Siqi Tang Zhongjie Fan Association of extreme precipitation with hospitalizations for acute myocardial infarction in Beijing, China: A time-series study Frontiers in Public Health extreme precipitation acute myocardial infarction distributed lag model time-series analysis hospitalizations |
| title | Association of extreme precipitation with hospitalizations for acute myocardial infarction in Beijing, China: A time-series study |
| title_full | Association of extreme precipitation with hospitalizations for acute myocardial infarction in Beijing, China: A time-series study |
| title_fullStr | Association of extreme precipitation with hospitalizations for acute myocardial infarction in Beijing, China: A time-series study |
| title_full_unstemmed | Association of extreme precipitation with hospitalizations for acute myocardial infarction in Beijing, China: A time-series study |
| title_short | Association of extreme precipitation with hospitalizations for acute myocardial infarction in Beijing, China: A time-series study |
| title_sort | association of extreme precipitation with hospitalizations for acute myocardial infarction in beijing china a time series study |
| topic | extreme precipitation acute myocardial infarction distributed lag model time-series analysis hospitalizations |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.1024816/full |
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