How air pollution altered the association of meteorological exposures and the incidence of dengue fever
Meteorological exposures are well-documented factors underlying the dengue pandemics, and air pollution was reported to have the potential to change the behaviors and health conditions of mosquitos. However, it remains unclear whether air pollution could modify the association of meteorological expo...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2022-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/aca59f |
| _version_ | 1797747238314704896 |
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| author | Xu Ju Wangjian Zhang Wumitijiang Yimaer Jianyun Lu Jianpeng Xiao Yanji Qu Gonghua Wu Wenjing Wu Yuqin Zhang Shirui Chen Xiao Lin Ying Wang Xinran Wang Jie Jiang Ziqiang Lin Xiaowei Ma Zhicheng Du Yuantao Hao |
| author_facet | Xu Ju Wangjian Zhang Wumitijiang Yimaer Jianyun Lu Jianpeng Xiao Yanji Qu Gonghua Wu Wenjing Wu Yuqin Zhang Shirui Chen Xiao Lin Ying Wang Xinran Wang Jie Jiang Ziqiang Lin Xiaowei Ma Zhicheng Du Yuantao Hao |
| author_sort | Xu Ju |
| collection | DOAJ |
| description | Meteorological exposures are well-documented factors underlying the dengue pandemics, and air pollution was reported to have the potential to change the behaviors and health conditions of mosquitos. However, it remains unclear whether air pollution could modify the association of meteorological exposures and the incidence of dengue fever. We matched the dengue surveillance data with the meteorological and air pollution data collected from monitoring sites from 2015 through 2019 in Guangzhou area. We developed generalized additive models with Poisson distribution to regress the daily counts of dengue against four meteorological exposures, while controlling for pollution and normalized difference vegetation index to evaluate the risk ratio (RR) of dengue for each unit increase in different exposures. The interaction terms of meteorological exposures and air pollution were then included to assess the modification effect of different pollution on the associations. Daily dengue cases were nonlinearly associated with one-week cumulative temperature and precipitation, while not associated with humidity and wind speed. RRs were 1.07 (1.04, 1.11) and 0.95 (0.88, 1.03) for temperature below and above 27.1 °C, 0.97 (0.96, 0.98) and 1.05 (1.01, 1.08) for precipitation below and above 20.3 mm, respectively. For the modification effect, the RRs of low-temperature, wind speed on higher SO _2 days and low-precipitation on both higher PM _2.5 and SO _2 days were greater compared to the low-pollution days with P _interaction being 0.037, 0.030, 0.022 and 0.018. But the RRs of both high-temperature on higher SO _2 days and high-precipitation on higher PM _2.5 d were smaller with P _interaction being 0.001 and 0.043. Air pollution could alter the meteorology-dengue associations. The impact of low-temperature, low-precipitation and wind speed on dengue occurrence tended to increase on days with high SO _2 levels while the impact of high-temperature decreased. The impact of low-precipitation increased on high-PM _2.5 d while the impact of high-precipitation decreased. |
| first_indexed | 2024-03-12T15:48:55Z |
| format | Article |
| id | doaj.art-6c934a0c900b46178ab8c85b6ab47f0b |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:48:55Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-6c934a0c900b46178ab8c85b6ab47f0b2023-08-09T15:19:05ZengIOP PublishingEnvironmental Research Letters1748-93262022-01-01171212404110.1088/1748-9326/aca59fHow air pollution altered the association of meteorological exposures and the incidence of dengue feverXu Ju0Wangjian Zhang1Wumitijiang Yimaer2Jianyun Lu3Jianpeng Xiao4Yanji Qu5Gonghua Wu6Wenjing Wu7Yuqin Zhang8Shirui Chen9Xiao Lin10Ying Wang11Xinran Wang12Jie Jiang13Ziqiang Lin14Xiaowei Ma15Zhicheng Du16https://orcid.org/0000-0002-1155-8443Yuantao Hao17https://orcid.org/0000-0003-4146-9262Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaDepartment of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaDepartment of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaGuangzhou Baiyun Center for Disease Control and Prevention , Guangzhou, People’s Republic of ChinaGuangdong Provincial Institute of Public Health, Guangdong Center for Disease Control and Prevention , Guangzhou, People’s Republic of ChinaDepartment of Epidemiology, Guangdong Cardiovascular Institute, Guangdong General Hospital , Guangzhou, People’s Republic of ChinaDepartment of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaDepartment of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaDepartment of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaDepartment of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaDepartment of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaDepartment of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaDepartment of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaPeking University, Center for Public Health and Epidemic Preparedness & Response , Peking, People’s Republic of ChinaDepartment of Preventive Medicine, School of Basic Medicine and Public Health, Jinan University , Guangzhou, People’s Republic of ChinaExecutive Office for Managing Action in Disease Control and Prevention, Guangzhou Center for Disease Control and Prevention , Guangzhou, People’s Republic of ChinaDepartment of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University , Guangzhou, People’s Republic of ChinaPeking University, Center for Public Health and Epidemic Preparedness & Response , Peking, People’s Republic of ChinaMeteorological exposures are well-documented factors underlying the dengue pandemics, and air pollution was reported to have the potential to change the behaviors and health conditions of mosquitos. However, it remains unclear whether air pollution could modify the association of meteorological exposures and the incidence of dengue fever. We matched the dengue surveillance data with the meteorological and air pollution data collected from monitoring sites from 2015 through 2019 in Guangzhou area. We developed generalized additive models with Poisson distribution to regress the daily counts of dengue against four meteorological exposures, while controlling for pollution and normalized difference vegetation index to evaluate the risk ratio (RR) of dengue for each unit increase in different exposures. The interaction terms of meteorological exposures and air pollution were then included to assess the modification effect of different pollution on the associations. Daily dengue cases were nonlinearly associated with one-week cumulative temperature and precipitation, while not associated with humidity and wind speed. RRs were 1.07 (1.04, 1.11) and 0.95 (0.88, 1.03) for temperature below and above 27.1 °C, 0.97 (0.96, 0.98) and 1.05 (1.01, 1.08) for precipitation below and above 20.3 mm, respectively. For the modification effect, the RRs of low-temperature, wind speed on higher SO _2 days and low-precipitation on both higher PM _2.5 and SO _2 days were greater compared to the low-pollution days with P _interaction being 0.037, 0.030, 0.022 and 0.018. But the RRs of both high-temperature on higher SO _2 days and high-precipitation on higher PM _2.5 d were smaller with P _interaction being 0.001 and 0.043. Air pollution could alter the meteorology-dengue associations. The impact of low-temperature, low-precipitation and wind speed on dengue occurrence tended to increase on days with high SO _2 levels while the impact of high-temperature decreased. The impact of low-precipitation increased on high-PM _2.5 d while the impact of high-precipitation decreased.https://doi.org/10.1088/1748-9326/aca59fdengue fevermeteorologyair pollutiongeneralized additive modeleffect modification |
| spellingShingle | Xu Ju Wangjian Zhang Wumitijiang Yimaer Jianyun Lu Jianpeng Xiao Yanji Qu Gonghua Wu Wenjing Wu Yuqin Zhang Shirui Chen Xiao Lin Ying Wang Xinran Wang Jie Jiang Ziqiang Lin Xiaowei Ma Zhicheng Du Yuantao Hao How air pollution altered the association of meteorological exposures and the incidence of dengue fever Environmental Research Letters dengue fever meteorology air pollution generalized additive model effect modification |
| title | How air pollution altered the association of meteorological exposures and the incidence of dengue fever |
| title_full | How air pollution altered the association of meteorological exposures and the incidence of dengue fever |
| title_fullStr | How air pollution altered the association of meteorological exposures and the incidence of dengue fever |
| title_full_unstemmed | How air pollution altered the association of meteorological exposures and the incidence of dengue fever |
| title_short | How air pollution altered the association of meteorological exposures and the incidence of dengue fever |
| title_sort | how air pollution altered the association of meteorological exposures and the incidence of dengue fever |
| topic | dengue fever meteorology air pollution generalized additive model effect modification |
| url | https://doi.org/10.1088/1748-9326/aca59f |
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