Association between short-term exposure to air pollutants and daily stroke incidence among residents in Qingdao city: a time series analysis of disease surveillance, environmental, and meteorological monitoring data
ObjectiveTo investigate the association between exposure to ambient air pollutants and stroke incidence among residents of Qingdao city, Shandong province, and to provide evidence for stroke prevention and control. MethodsData on new cases of stroke reported in Qingdao municipality from 2014 to 2020...
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Editorial Office of Chinese Journal of Public Health
2024-10-01
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| Series: | Zhongguo gonggong weisheng |
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| Online Access: | https://www.zgggws.com/article/doi/10.11847/zgggws1143237 |
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| author | Nan GE Lu PAN Xin ZHANG Dandan LI Yin WANG Jingya YIN Hui ZHOU Haoyan YU Xiuqin ZHANG Chunsheng XU Yuan FANG Yan MA Bingling WANG Haiping DUAN |
| author_facet | Nan GE Lu PAN Xin ZHANG Dandan LI Yin WANG Jingya YIN Hui ZHOU Haoyan YU Xiuqin ZHANG Chunsheng XU Yuan FANG Yan MA Bingling WANG Haiping DUAN |
| author_sort | Nan GE |
| collection | DOAJ |
| description | ObjectiveTo investigate the association between exposure to ambient air pollutants and stroke incidence among residents of Qingdao city, Shandong province, and to provide evidence for stroke prevention and control. MethodsData on new cases of stroke reported in Qingdao municipality from 2014 to 2020 were collected from the National Chronic Disease Surveillance Network Reporting System, together with air pollution monitoring data from the Qingdao Ecological Environment Monitoring Center and meteorological monitoring data from the Qingdao Meteorological Observatory for the same period. The distributed Lag nonlinear model (DLNM) was used to analyze the associations between daily mean concentrations of particulate matter with an aerodynamic diameter of less than 2.5 mum (PM2.5), particulate matter with an aerodynamic diameter of less than 10 mum (PM10), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2) with daily stroke incidence, while controlling for the effects of potential confounders such as long-term trend and day of the week in Qingdao city. The effects of air pollutants on stroke incidence were analyzed using a single-pollutant model. ResultsA total of 51 120 new cases of stroke were reported in the city during the period, with an average daily incidence of 19.99 ± 15.53. The exposure-response relationship analysis showed that the risk of stroke incidence increased when ambient PM2.5 concentration was higher than 121.90 μg/m3, CO concentration was higher than 1.56 μg/m3, but O3 concentration was lower than 64.00 μg/m3, while the risk of stroke incidence showed an up-and-down fluctuating trend when O3 concentration was higher than 64.00 μg/m3 (all P<0.05). The single-pollutant model analysis showed that the sensitive Lag periods for increased risk of stroke incidence were from Lag day 2 to Lag day 5 for ambient PM2.5 and CO exposure, from Lag day 3 to Lag day 6 for O3 exposure, from Lag day 3 to Lag day 4 for PM10 exposure, and from Lag day 6 for SO2 exposure. The relative risk (RR) values for daily stroke incidence at Lag day 3 were 1.018 (95% confidence interval [95%CI]: 1.005 – 1.031) and 2.027 (95%CI: 1.232 – 3.334) for each 32.01 μg/m3 increase in PM2.5 concentration and for each 52.21 μg/m3 increase in PM10 concentration, respectively. The RR values for daily stroke incidence at Lag day 4 were 1.155 (95%CI: 1.080 – 1.234) and 1.033 (95%CI: 1.016 – 1.050) for each 53.00 μg/m3 increase in O3 concentration and each 0.39 mg/m3 increase in CO concentration, respectively. The RR value for daily stroke incidence at Lag day 6 was 1.431 (95%CI: 1.049 – 1.951) for each 14.93 μg/m3 increase in SO2 concentration. Sensitivity analysis results showed that changing the degrees of freedom of daily mean temperature, barometric pressure, and relative humidity to 4, 5, and 6 had little effect on the effect of ambient PM2.5, O3, CO, PM10, SO2, and NO2 exposure on the risk of daily stroke incidence, indicating that the established models produced relatively stable results. ConclusionExposure to ambient PM2.5, PM10, O3, CO, and SO2 has a delayed effect on stroke incidence among Qingdao residents. |
| format | Article |
| id | doaj-art-8436aef42bbe40fe9236479aeff90939 |
| institution | Kabale University |
| issn | 1001-0580 |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | Editorial Office of Chinese Journal of Public Health |
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| series | Zhongguo gonggong weisheng |
| spelling | doaj-art-8436aef42bbe40fe9236479aeff909392025-01-23T05:11:54ZzhoEditorial Office of Chinese Journal of Public HealthZhongguo gonggong weisheng1001-05802024-10-0140101161116810.11847/zgggws11432371143237Association between short-term exposure to air pollutants and daily stroke incidence among residents in Qingdao city: a time series analysis of disease surveillance, environmental, and meteorological monitoring dataNan GE0Lu PAN1Xin ZHANG2Dandan LI3Yin WANG4Jingya YIN5Hui ZHOU6Haoyan YU7Xiuqin ZHANG8Chunsheng XU9Yuan FANG10Yan MA11Bingling WANG12Haiping DUAN13Department of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaDepartment of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaSchool of Public Health and Management, Binzhou Medical University, Yantai 264003, ChinaDepartment of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaDepartment of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaDepartment of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaDepartment of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaDepartment of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaDepartment of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaDepartment of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaQingdao Ecological Environment Monitoring Center, Qingdao 266003, ChinaQingdao Meteorological Observatory, Qingdao 266003, ChinaDepartment of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaDepartment of Environmental Health, Qingdao Center for Disease Control and Prevention, Qingdao Institute of Preventive Medicine, Qingdao 266033, ChinaObjectiveTo investigate the association between exposure to ambient air pollutants and stroke incidence among residents of Qingdao city, Shandong province, and to provide evidence for stroke prevention and control. MethodsData on new cases of stroke reported in Qingdao municipality from 2014 to 2020 were collected from the National Chronic Disease Surveillance Network Reporting System, together with air pollution monitoring data from the Qingdao Ecological Environment Monitoring Center and meteorological monitoring data from the Qingdao Meteorological Observatory for the same period. The distributed Lag nonlinear model (DLNM) was used to analyze the associations between daily mean concentrations of particulate matter with an aerodynamic diameter of less than 2.5 mum (PM2.5), particulate matter with an aerodynamic diameter of less than 10 mum (PM10), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2) with daily stroke incidence, while controlling for the effects of potential confounders such as long-term trend and day of the week in Qingdao city. The effects of air pollutants on stroke incidence were analyzed using a single-pollutant model. ResultsA total of 51 120 new cases of stroke were reported in the city during the period, with an average daily incidence of 19.99 ± 15.53. The exposure-response relationship analysis showed that the risk of stroke incidence increased when ambient PM2.5 concentration was higher than 121.90 μg/m3, CO concentration was higher than 1.56 μg/m3, but O3 concentration was lower than 64.00 μg/m3, while the risk of stroke incidence showed an up-and-down fluctuating trend when O3 concentration was higher than 64.00 μg/m3 (all P<0.05). The single-pollutant model analysis showed that the sensitive Lag periods for increased risk of stroke incidence were from Lag day 2 to Lag day 5 for ambient PM2.5 and CO exposure, from Lag day 3 to Lag day 6 for O3 exposure, from Lag day 3 to Lag day 4 for PM10 exposure, and from Lag day 6 for SO2 exposure. The relative risk (RR) values for daily stroke incidence at Lag day 3 were 1.018 (95% confidence interval [95%CI]: 1.005 – 1.031) and 2.027 (95%CI: 1.232 – 3.334) for each 32.01 μg/m3 increase in PM2.5 concentration and for each 52.21 μg/m3 increase in PM10 concentration, respectively. The RR values for daily stroke incidence at Lag day 4 were 1.155 (95%CI: 1.080 – 1.234) and 1.033 (95%CI: 1.016 – 1.050) for each 53.00 μg/m3 increase in O3 concentration and each 0.39 mg/m3 increase in CO concentration, respectively. The RR value for daily stroke incidence at Lag day 6 was 1.431 (95%CI: 1.049 – 1.951) for each 14.93 μg/m3 increase in SO2 concentration. Sensitivity analysis results showed that changing the degrees of freedom of daily mean temperature, barometric pressure, and relative humidity to 4, 5, and 6 had little effect on the effect of ambient PM2.5, O3, CO, PM10, SO2, and NO2 exposure on the risk of daily stroke incidence, indicating that the established models produced relatively stable results. ConclusionExposure to ambient PM2.5, PM10, O3, CO, and SO2 has a delayed effect on stroke incidence among Qingdao residents.https://www.zgggws.com/article/doi/10.11847/zgggws1143237strokeincidenceexposure to air pollutantsrelationshiptime series analysis |
| spellingShingle | Nan GE Lu PAN Xin ZHANG Dandan LI Yin WANG Jingya YIN Hui ZHOU Haoyan YU Xiuqin ZHANG Chunsheng XU Yuan FANG Yan MA Bingling WANG Haiping DUAN Association between short-term exposure to air pollutants and daily stroke incidence among residents in Qingdao city: a time series analysis of disease surveillance, environmental, and meteorological monitoring data Zhongguo gonggong weisheng stroke incidence exposure to air pollutants relationship time series analysis |
| title | Association between short-term exposure to air pollutants and daily stroke incidence among residents in Qingdao city: a time series analysis of disease surveillance, environmental, and meteorological monitoring data |
| title_full | Association between short-term exposure to air pollutants and daily stroke incidence among residents in Qingdao city: a time series analysis of disease surveillance, environmental, and meteorological monitoring data |
| title_fullStr | Association between short-term exposure to air pollutants and daily stroke incidence among residents in Qingdao city: a time series analysis of disease surveillance, environmental, and meteorological monitoring data |
| title_full_unstemmed | Association between short-term exposure to air pollutants and daily stroke incidence among residents in Qingdao city: a time series analysis of disease surveillance, environmental, and meteorological monitoring data |
| title_short | Association between short-term exposure to air pollutants and daily stroke incidence among residents in Qingdao city: a time series analysis of disease surveillance, environmental, and meteorological monitoring data |
| title_sort | association between short term exposure to air pollutants and daily stroke incidence among residents in qingdao city a time series analysis of disease surveillance environmental and meteorological monitoring data |
| topic | stroke incidence exposure to air pollutants relationship time series analysis |
| url | https://www.zgggws.com/article/doi/10.11847/zgggws1143237 |
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