Article(id=1321169881387614849, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1321169839390053177, articleNumber=null, orderNo=null, doi=10.11847/zgggws1144529, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1711296000000, receivedDateStr=2024-03-25, revisedDate=1732464000000, revisedDateStr=2024-11-25, acceptedDate=1743523200000, acceptedDateStr=2025-04-02, onlineDate=1792921045872, onlineDateStr=2026-10-25, pubDate=1749484800000, pubDateStr=2025-06-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792921045872, onlineIssueDateStr=2026-10-25, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792921045872, creator=13701087609, updateTime=1792921045872, updator=13701087609, issue=Issue{id=1321169839390053177, tenantId=1146029695717560320, journalId=1319373219989389464, year='2025', volume='41', issue='6', pageStart='641', pageEnd='768', issueExtLink='null', onlineDate='null', pubDate='1749484800000', pubDateStr='2025-06-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792921035859, creator='13701087609', updateTime=1792921035859, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=728, endPage=734, ext={EN=ArticleExt(id=1321169883115668098, articleId=1321169881387614849, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Association of ambient sulfur dioxide with stroke incidence among residents of Shanghai: a time-series analysis of incidence registration and monitoring data from 2017 to 2021, columnId=1320427686514770171, journalTitle=Chinese Journal of Public Health, columnName=Survey and Report, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the correlation between atmospheric sulfur dioxide (SO2) and the incidence of stroke in residents of Shanghai city, and to provide a reference for the development of stroke prevention and control measures.

Methods

Data on 263 872 new stroke cases reported to the Shanghai Stroke and Acute Myocardial Infarction Registry Information System from 2017 through 2021 were collected. Daily mean concentrations of atmospheric pollutants-including SO2, nitrogen dioxide (NO2), ozone (O3), particulate matter with an aerodynamic diameter < 2.5 μm (PM2.5), and particulate matter with an aerodynamic diameter < 10 μm (PM10), as well as meteorological factors such as daily average temperature and relative humidity, were obtained from the national air pollution monitoring stations of the Ministry of Ecology and Environment during the same period. A generalized additive model with a quasi-Poisson distribution link function was used to analyze the time-series relationship between atmospheric SO2 concentration and the daily number of stroke cases.

Results

Throughout the study period conducted in Shanghai, the average daily incidence of registered stroke cases was 145 ± 28, while the mean daily concentration of SO2 was measured at 8.00 ± 3.71 µg/m3. The analysis of the single-day lag model demonstrated that a 10 µg/m³ increase in the mean SO2 concentration was significantly associated with percentage changes (%) in daily stroke incidence across different populations and lag days, as follows: 1. General population: Decreases of 0.21%, 0.28%, 0.29%, and 0.21% were observed at lag days 7, 8, 26, and 27, respectively, while increases of 0.16%, 0.34%, 0.33%, 0.42%, 0.32%, and 0.20% were noted at lag days 19, 20, 21, 22, 23, and 24, respectively. 2. Males: A 0.37% decrease was observed at lag day 26, whereas increases of 0.47%, 0.36%, 0.61%, and 0.33% were recorded at lag days 20, 21, 22, and 23, respectively. 3. Females: A 0.40% increase was observed at lag day 0. 4. Residents aged 60 years and above: Decreases of 0.33% and 0.36% were observed at lag days 8 and 26, respectively, while increases of 0.35%, 0.32%, 0.41%, and 0.32% were noted at lag days 20, 21, 22, and 23, respectively. All reported associations were statistically significant (P < 0.05). Multi-day lag model analysis yielded results consistent with those from the single-day lag analysis: 1. General population: Increases of 16.84%, 18.40%, and 17.95% were observed at lag days 0 - 24, 0 - 25, and 0 - 26, respectively. 2. Males: Increases of 16.73%, 21.09%, 22.39%, and 21.80% were observed at lag days 0 - 23, 0 - 24, 0 - 25 d, and 0 - 26, respectively. 3. Females: An increase of 4.10% was noted at lag days 0-1.4. Residents aged < 60 years: Decreases of 8.60%, 9.89%, 9.36%, 10.67%, 10.98%, and 11.38% were observed at lag days 0 - 4, 0 - 5, 0 - 6, 0 - 7, 0 - 8, and 0 - 9, respectively. 5. Residents aged 60 years and above: Decreases of 15.91%, 19.18%, 20.22%, and 18.99% were observed at lag days 0 - 23, 0 - 24, 0 - 25, and 0 - 26, respectively (all P < 0.05).

Conclusions

The elevated atmospheric SO2 concentration exhibited a significant correlation with a higher daily incidence of stroke on single-lag days 19 - 24 and multi-lag days 0 - 24 to 0 - 26 among Shanghai residents, particularly affecting male individuals and those aged 60 years or older.

, authors=Yuzhuo WANG, Xuena LA, Mengyin WU, Yingquan WANG, Danni LIU, Qundi YANG, Yang ZHENG, Yan SHI, authorsList=Yuzhuo WANG, Xuena LA, Mengyin WU, Yingquan WANG, Danni LIU, Qundi YANG, Yang ZHENG, Yan SHI, authorCompany=null, correspAuthors=Yang ZHENG, Yan SHI, authorNote=null, correspAuthorsNote=
ZHENG Yang, E-mail:
SHI Yan, E-mail:
, copyrightStatement=Copyright © 2025 Chinese Journal of Public Health. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321169883916780167, articleId=1321169881387614849, tenantId=1146029695717560320, journalId=1319373219989389464, language=CN, title=上海市大气二氧化硫暴露与居民脑卒中发病关系时间序列分析, columnId=1320427686770622717, journalTitle=中国公共卫生, columnName=调查报告与分析, runingTitle=null, highlight=null, articleAbstract=
目的

了解大气二氧化硫(SO2)暴露与上海市居民脑卒中发病的关系,为制定脑卒中的防控措施提供参考依据。

方法

收集上海市脑卒中及急性心肌梗死登记报告信息系统中2017年1月1日—2021年12月31日上报的263 872例脑卒中新发病例相关数据以及同期国家生态环境部的大气污染国控监测站中上海市SO2、二氧化氮(NO2)、臭氧(O3)、细颗粒物(PM2.5)和可吸入颗粒物(PM10)等大气污染物日均浓度及日均温度、日均相对湿度等气象因素监测数据,通过以类泊松分布为链接函数的广义相加模型时间序列研究分析大气SO2暴露与脑卒中每日发病数的关系。

结果

上海市2017—2021年共报告263 872例脑卒中新发病例,日均发病数为(145±28)例,SO2日均浓度为(8.00±3.71)µg/m3。单日滞后模型分析结果显示,SO2平均浓度每升高10 µg/m3,总体居民脑卒中每日发病数在Lag7 d、Lag8 d、Lag26 d和Lag27 d分别减少0.21%、0.28%、0.29%和0.21%,在Lag19 d、Lag20 d、Lag21 d、Lag22 d、Lag23 d和Lag24 d分别增加0.16%、0.34%、0.33%、0.42%、0.32%和0.20%;男性居民脑卒中每日发病数在Lag26 d减少0.37%,在Lag20 d、Lag21 d、Lag22 d和Lag23 d分别增加0.47%、0.36%、0.61%和0.33%;女性居民脑卒中每日发病数在Lag0 d增加0.40%;≥60岁居民脑卒中每日发病数在Lag8 d和Lag26 d分别减少0.33%和0.36%,在Lag20 d、Lag21 d、Lag22 d和Lag23 d分别增加0.35%、0.32%、0.41%和0.32%(均P<0.05)。多日滞后模型分析结果显示,SO2平均浓度每升高10 µg/m3,总体居民脑卒中每日发病数在Lag024 d、Lag025 d和Lag026 d分别增加16.84%、18.40%和17.95%;男性居民每日发病数在Lag023 d、Lag024 d、Lag025 d和Lag026 d分别增加16.73%、21.09%、22.39%和21.80%;女性居民每日发病数在Lag01 d增加4.10%;<60岁居民脑卒中每日发病数在Lag04 d、Lag05 d、Lag06 d、Lag07 d、Lag08 d和Lag09 d分别减少8.60%、9.89%、9.36%、10.67%、10.98%和11.38%;≥60岁居民脑卒中每日发病数在Lag023 d、Lag024 d、Lag025 d和Lag026 d分别增加15.91%、19.18%、20.22%和18.99%(均P<0.05)。

结论

脑卒中每日发病数在大气SO2暴露Lag19 d~Lag24 d和Lag024 d~Lag026 d时明显增加,男性和≥60岁居民对SO2暴露的滞后效应更加易感。

, authors=王玉琢, 喇雪娜, 吴梦吟, 王英全, 刘丹妮, 杨群娣, 郑杨*, *, 施燕*, *, authorsList=王玉琢, 喇雪娜, 吴梦吟, 王英全, 刘丹妮, 杨群娣, 郑杨, 施燕, authorCompany=null, correspAuthors=郑杨, 施燕, authorNote=

王玉琢(1994—),主管医师,硕士,研究方向:慢性病预防与控制

, correspAuthorsNote=
郑杨,E-mail:
施燕,E-mail:
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Environ Health, 2020, 19(1): 1., articleTitle=null, refAbstract=null), Reference(id=1321169892867424993, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=23, rfOrder=22, authorNames=null, journalName=null, refType=null, unstructuredReference=Sun S, Stewart J D, Eliot M N, et al. Short-term exposure to air pollution and incidence of stroke in the Women's Health Initiative[J]. Environ Int, 2019, 132: 105065., articleTitle=null, refAbstract=null), Reference(id=1321169892947116770, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=24, rfOrder=23, authorNames=null, journalName=null, refType=null, unstructuredReference=Dong H, Yu Y, Yao S, et al. Acute effects of air pollution on ischaemic stroke onset and deaths: a time-series study in Changzhou, China[J]. BMJ Open, 2018, 8(7): e20425., articleTitle=null, refAbstract=null), Reference(id=1321169893014225635, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=25, rfOrder=24, authorNames=null, journalName=null, refType=null, unstructuredReference=吕晨光. 济南大气污染物与呼吸及心脑血管疾病患者入院风险的时间序列研究[D]. 济南: 山东大学, 2018., articleTitle=null, refAbstract=null), Reference(id=1321169893085528804, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=26, rfOrder=25, authorNames=null, journalName=null, refType=null, unstructuredReference=孟紫强, 李君灵. 二氧化硫生物学研究进展: 从毒理学到生理学[J]. 生理学报, 2011, 63(6): 8., articleTitle=null, refAbstract=null), Reference(id=1321169893161026277, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=27, rfOrder=26, authorNames=null, journalName=null, refType=null, unstructuredReference=Cai H, Wang X. Effect of sulfur dioxide on vascular biology[J]. Histol Histopathol, 2021, 36(5): 505-514., articleTitle=null, refAbstract=null), Reference(id=1321169893228135142, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=28, rfOrder=27, authorNames=null, journalName=null, refType=null, unstructuredReference=上海市生态环境局. 2021上海市生态环境状况公报[EB/OL]. [2024-11-19]. https://sthj.sh.gov.cn/hbzhywpt1143/hbzhywpt1144/20220603/878ab2328926470fa8af4c19770bd081.html., articleTitle=null, refAbstract=null), Reference(id=1321169893295244007, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=29, rfOrder=28, authorNames=null, journalName=null, refType=null, unstructuredReference=上海市人民政府办公厅. 上海市人民政府办公厅关于印发《上海市清洁空气行动计划(2023—2025年)》的通知(沪府办发〔2023〕13号)[EB/OL]. [2024-02-26]. https://en.shanghai.gov.cn/nw12344/20230807/495e682c56924cfb96c34b88022a071e.html., articleTitle=null, refAbstract=null), Reference(id=1321169893353964264, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=30, rfOrder=29, authorNames=null, journalName=null, refType=null, unstructuredReference=Vieira D O S P, Briones-Valdivieso C, Motairek I, et al. The cardiovascular disease burden attributable to particulate matter pollution in South America: analysis of the 1990-2019 global burden of disease[J]. Public Health, 2023, 224: 169-177., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1321169884176827016, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, xref=null, ext=[AuthorCompanyExt(id=1321169884185215625, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, companyId=1321169884176827016, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China), AuthorCompanyExt(id=1321169884193604234, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, companyId=1321169884176827016, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=上海市疾病预防控制中心,上海 200336)])], figs=[ArticleFig(id=1321169889050608323, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, language=EN, label=Tab.1, caption=

Monitoring data-based mean, minimum, 25th percentile, median, 75th percentile, and maximum values of SO2, NO2, O3, PM2.5, PM10, daily average temperature, and daily average relative humidity in Shanghai city from 2017 to 2021

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类 $ \overline{x}\pm s $MinP25MP75Max
大气污染物SO2日均浓度/(µg/m3)8.00±3.711.505.396.909.4032.1
NO2/(µg/m3)39.62±18.164.3726.7036.3548.80138.40
O3/(µg/m3)98.76±44.206.4067.4592.20122.63285.30
PM2.5/(µg/m3)34.02±22.253.0018.0027.6043.90178.40
PM10/(µg/m3)48.22±27.750.0029.9041.4060.40320.68
气象因素日均温度/℃17.55±8.41−4.7010.3918.2224.6333.71
日均相对湿度/%73.89±13.2731.8965.4474.8983.3699.04
), ArticleFig(id=1321169889134494404, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, language=CN, label=表1, caption=

上海市2017—2021年大气污染物和气象因素基本特征

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类 $ \overline{x}\pm s $MinP25MP75Max
大气污染物SO2日均浓度/(µg/m3)8.00±3.711.505.396.909.4032.1
NO2/(µg/m3)39.62±18.164.3726.7036.3548.80138.40
O3/(µg/m3)98.76±44.206.4067.4592.20122.63285.30
PM2.5/(µg/m3)34.02±22.253.0018.0027.6043.90178.40
PM10/(µg/m3)48.22±27.750.0029.9041.4060.40320.68
气象因素日均温度/℃17.55±8.41−4.7010.3918.2224.6333.71
日均相对湿度/%73.89±13.2731.8965.4474.8983.3699.04
), ArticleFig(id=1321169889209991877, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, language=EN, label=Tab.2, caption=

Correlation coefficients among the values of SO2, NO2, O3, PM2.5, PM10, daily average temperature, and daily average relative humidity in Shanghai city: Spearman rank correlation analysis of the monitoring data from 2017 to 2021

, figureFileSmall=null, figureFileBig=null, tableContent=
项目SO2NO2O3PM2.5PM10日均温度日均相对湿度
SO21.00
NO20.52 a1.00
O30.20 a−0.08 a1.00
PM2.50.55 a0.68 a0.21 a1.00
PM100.66 a0.60 a0.31 a0.79 a1.00
日均温度−0.23 a−0.44 a0.52 a−0.30 a−0.23 a1.00
日均相对湿度−0.45 a−0.22 a−0.21 a−0.21 a−0.49 a0.25 a1.00
), ArticleFig(id=1321169889289683654, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, language=CN, label=表2, caption=

上海市2017—2021年大气污染物和气象因素Spearman秩相关分析(r)

, figureFileSmall=null, figureFileBig=null, tableContent=
项目SO2NO2O3PM2.5PM10日均温度日均相对湿度
SO21.00
NO20.52 a1.00
O30.20 a−0.08 a1.00
PM2.50.55 a0.68 a0.21 a1.00
PM100.66 a0.60 a0.31 a0.79 a1.00
日均温度−0.23 a−0.44 a0.52 a−0.30 a−0.23 a1.00
日均相对湿度−0.45 a−0.22 a−0.21 a−0.21 a−0.49 a0.25 a1.00
), ArticleFig(id=1321169889386152647, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, language=EN, label=Tab.3, caption=

Single-day lag-specific percentage change in stroke incidence associated with a 10 µg/m3 increase in ambient SO2 concentration among all residents, males, females, individuals younger than 60 years, and those 60 years or older in Shanghai city, based on incidence registration and atmospheric pollutants monitoring data from 2017 to 2021

, figureFileSmall=null, figureFileBig=null, tableContent=
滞后天数总体男性女性<60岁≥60岁
%95%CI%95%CI%95%CI%95%CI%95%CI
Lag0 d0.17−0.01~0.35−0.02−0.37~0.340.400.02~0.78−0.29−0.84~0.270.24−0.08~0.56
Lag1 d−0.09−0.26~0.08−0.33−0.67~0.000.21−0.15~0.58−0.46−0.98~0.07−0.03−0.34~0.27
Lag2 d0.04−0.12~0.21−0.10−0.42~0.220.22−0.12~0.57−0.35−0.85~0.160.10−0.19~0.40
Lag3 d0.10−0.06~0.270.13−0.18~0.450.06−0.28~0.40−0.32−0.82~0.180.17−0.12~0.46
Lag4 d0.15−0.01~0.310.21−0.10~0.530.07−0.27~0.41−0.28−0.78~0.220.22−0.07~0.51
Lag5 d0.05−0.12~0.210.20−0.11~0.51−0.15−0.49~0.190.01−0.48~0.510.05−0.24~0.34
Lag6 d−0.04−0.20~0.120.17−0.15~0.48−0.31−0.65~0.03−0.22−0.71~0.27−0.01−0.30~0.28
Lag7 d−0.21−0.37~ –0.05−0.13−0.44~0.19−0.31−0.65~0.03−0.10−0.59~0.40−0.23−0.51~0.06
Lag8 d−0.28−0.44~ –0.12−0.30−0.61~0.01−0.25−0.59~0.080.02−0.47~0.51−0.33−0.61~ –0.04
Lag9 d0.01−0.15~0.17−0.02−0.33~0.290.04−0.29~0.380.13−0.35~0.62−0.01−0.30~0.27
Lag10 d0.11−0.05~0.270.10−0.21~0.410.12−0.21~0.46−0.01−0.50~0.480.13−0.16~0.42
Lag11 d−0.06−0.22~0.100.00−0.30~0.30−0.14−0.46~0.19−0.15−0.63~0.33−0.05−0.31~0.22
Lag12 d−0.02−0.18~0.140.04−0.28~0.35−0.10−0.44~0.250.24−0.25~0.74−0.06−0.35~0.23
Lag13 d0.07−0.10~0.230.00−0.32~0.320.15−0.20~0.490.32−0.18~0.820.02−0.27~0.32
Lag14 d0.04−0.12~0.200.03−0.29~0.350.05−0.29~0.390.15−0.34~0.650.02−0.27~0.31
Lag15 d0.10−0.06~0.270.12−0.20~0.440.08−0.26~0.420.27−0.23~0.770.08−0.21~0.37
Lag16 d−0.02−0.18~0.150.04−0.28~0.35−0.09−0.43~0.26−0.24−0.74~0.260.02−0.27~0.31
Lag17 d−0.11−0.28~0.05−0.12−0.44~0.20−0.11−0.45~0.24−0.44−0.93~0.06−0.06−0.35~0.23
Lag18 d0.08−0.08~0.240.03−0.28~0.350.13−0.21~0.480.04−0.46~0.540.08−0.21~0.38
Lag19 d0.160.00~0.330.21−0.11~0.530.10−0.24~0.450.25−0.25~0.760.15−0.15~0.44
Lag20 d0.340.18~0.510.470.15~0.790.18−0.16~0.520.31−0.19~0.810.350.06~0.64
Lag21 d0.330.17~0.490.360.04~0.670.30−0.04~0.640.39−0.10~0.890.320.03~0.61
Lag22 d0.420.26~0.580.610.29~0.920.18−0.16~0.520.47−0.03~0.960.410.13~0.70
Lag23 d0.320.16~0.480.330.02~0.640.30−0.04~0.640.29−0.20~0.790.320.03~0.61
Lag24 d0.200.03~0.360.16−0.15~0.470.24−0.10~0.580.45−0.04~0.940.15−0.13~0.44
Lag25 d−0.14−0.30~0.02−0.18−0.49~0.14−0.09−0.43~0.260.29−0.20~0.79−0.21−0.50~0.08
Lag26 d−0.29−0.45~ –0.12−0.37−0.69~ –0.06−0.18−0.52~0.160.16−0.33~0.66−0.36−0.65~ –0.07
Lag27 d−0.21−0.37~ –0.04−0.22−0.54~0.09−0.18−0.52~0.16−0.17−0.67~0.32−0.21−0.50~0.08
Lag28 d−0.16−0.32~0.00−0.13−0.45~0.19−0.20−0.54~0.15−0.47−0.96~0.03−0.11−0.40~0.18
Lag29 d−0.09−0.25~0.08−0.19−0.51~0.130.05−0.30~0.39−0.45−0.94~0.05−0.03−0.32~0.26
Lag30 d0.00−0.16~0.17−0.22−0.54~0.090.30−0.04~0.64−0.21−0.70~0.290.04−0.25~0.33
), ArticleFig(id=1321169889474233032, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, language=CN, label=表3, caption=

大气SO2每升高10 µg/m3上海市居民脑卒中发病数的百分比改变(单日滞后模型)

, figureFileSmall=null, figureFileBig=null, tableContent=
滞后天数总体男性女性<60岁≥60岁
%95%CI%95%CI%95%CI%95%CI%95%CI
Lag0 d0.17−0.01~0.35−0.02−0.37~0.340.400.02~0.78−0.29−0.84~0.270.24−0.08~0.56
Lag1 d−0.09−0.26~0.08−0.33−0.67~0.000.21−0.15~0.58−0.46−0.98~0.07−0.03−0.34~0.27
Lag2 d0.04−0.12~0.21−0.10−0.42~0.220.22−0.12~0.57−0.35−0.85~0.160.10−0.19~0.40
Lag3 d0.10−0.06~0.270.13−0.18~0.450.06−0.28~0.40−0.32−0.82~0.180.17−0.12~0.46
Lag4 d0.15−0.01~0.310.21−0.10~0.530.07−0.27~0.41−0.28−0.78~0.220.22−0.07~0.51
Lag5 d0.05−0.12~0.210.20−0.11~0.51−0.15−0.49~0.190.01−0.48~0.510.05−0.24~0.34
Lag6 d−0.04−0.20~0.120.17−0.15~0.48−0.31−0.65~0.03−0.22−0.71~0.27−0.01−0.30~0.28
Lag7 d−0.21−0.37~ –0.05−0.13−0.44~0.19−0.31−0.65~0.03−0.10−0.59~0.40−0.23−0.51~0.06
Lag8 d−0.28−0.44~ –0.12−0.30−0.61~0.01−0.25−0.59~0.080.02−0.47~0.51−0.33−0.61~ –0.04
Lag9 d0.01−0.15~0.17−0.02−0.33~0.290.04−0.29~0.380.13−0.35~0.62−0.01−0.30~0.27
Lag10 d0.11−0.05~0.270.10−0.21~0.410.12−0.21~0.46−0.01−0.50~0.480.13−0.16~0.42
Lag11 d−0.06−0.22~0.100.00−0.30~0.30−0.14−0.46~0.19−0.15−0.63~0.33−0.05−0.31~0.22
Lag12 d−0.02−0.18~0.140.04−0.28~0.35−0.10−0.44~0.250.24−0.25~0.74−0.06−0.35~0.23
Lag13 d0.07−0.10~0.230.00−0.32~0.320.15−0.20~0.490.32−0.18~0.820.02−0.27~0.32
Lag14 d0.04−0.12~0.200.03−0.29~0.350.05−0.29~0.390.15−0.34~0.650.02−0.27~0.31
Lag15 d0.10−0.06~0.270.12−0.20~0.440.08−0.26~0.420.27−0.23~0.770.08−0.21~0.37
Lag16 d−0.02−0.18~0.150.04−0.28~0.35−0.09−0.43~0.26−0.24−0.74~0.260.02−0.27~0.31
Lag17 d−0.11−0.28~0.05−0.12−0.44~0.20−0.11−0.45~0.24−0.44−0.93~0.06−0.06−0.35~0.23
Lag18 d0.08−0.08~0.240.03−0.28~0.350.13−0.21~0.480.04−0.46~0.540.08−0.21~0.38
Lag19 d0.160.00~0.330.21−0.11~0.530.10−0.24~0.450.25−0.25~0.760.15−0.15~0.44
Lag20 d0.340.18~0.510.470.15~0.790.18−0.16~0.520.31−0.19~0.810.350.06~0.64
Lag21 d0.330.17~0.490.360.04~0.670.30−0.04~0.640.39−0.10~0.890.320.03~0.61
Lag22 d0.420.26~0.580.610.29~0.920.18−0.16~0.520.47−0.03~0.960.410.13~0.70
Lag23 d0.320.16~0.480.330.02~0.640.30−0.04~0.640.29−0.20~0.790.320.03~0.61
Lag24 d0.200.03~0.360.16−0.15~0.470.24−0.10~0.580.45−0.04~0.940.15−0.13~0.44
Lag25 d−0.14−0.30~0.02−0.18−0.49~0.14−0.09−0.43~0.260.29−0.20~0.79−0.21−0.50~0.08
Lag26 d−0.29−0.45~ –0.12−0.37−0.69~ –0.06−0.18−0.52~0.160.16−0.33~0.66−0.36−0.65~ –0.07
Lag27 d−0.21−0.37~ –0.04−0.22−0.54~0.09−0.18−0.52~0.16−0.17−0.67~0.32−0.21−0.50~0.08
Lag28 d−0.16−0.32~0.00−0.13−0.45~0.19−0.20−0.54~0.15−0.47−0.96~0.03−0.11−0.40~0.18
Lag29 d−0.09−0.25~0.08−0.19−0.51~0.130.05−0.30~0.39−0.45−0.94~0.05−0.03−0.32~0.26
Lag30 d0.00−0.16~0.17−0.22−0.54~0.090.30−0.04~0.64−0.21−0.70~0.290.04−0.25~0.33
), ArticleFig(id=1321169889549730505, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, language=EN, label=Tab.4, caption=

Multi-day lag-specific percentage change in stroke incidence associated with a 10 µg/m3 increase in ambient SO2 concentration among all residents, males, females, individuals younger than 60 years, and those 60 years or older in Shanghai city, based on incidence registration and atmospheric pollutants monitoring data from 2017 to 2021

, figureFileSmall=null, figureFileBig=null, tableContent=
滞后天数总体男性女性<60岁≥60岁
%95%CI%95%CI%95%CI%95%CI%95%CI
Lag01 d1.69−1.43~4.90−0.17−3.65~3.444.100.22~8.12−2.82−8.09~2.742.42−0.83~5.78
Lag02 d0.25−3.29~3.93−2.72−6.63~1.364.19−0.28~8.86−5.34−11.27~0.991.17−2.53~5.01
Lag03 d0.44−3.45~4.48−2.79−7.06~1.684.73−0.20~9.91−6.66−13.06~0.211.61−2.47~5.85
Lag04 d0.64−3.60~5.06−1.81−6.50~3.123.87−1.45~9.48−8.60−15.39~ –1.272.18−2.28~6.84
Lag05 d0.99−3.62~5.82−0.90−6.03~4.513.47−2.28~9.56−9.89−17.14~ –2.022.82−2.04~7.93
Lag06 d1.28−3.68~6.490.35−5.21~6.232.49−3.65~9.01−9.36−17.19~ –0.793.07−2.16~8.57
Lag07 d0.80−4.47~6.360.93−5.05~7.290.62−5.82~7.50−10.67−18.92~ –1.572.74−2.83~8.63
Lag08 d−0.05−5.63~5.860.63−5.74~7.42−0.93−7.69~6.32−10.98−19.76~ –1.241.79−4.10~8.04
Lag09 d−1.29−7.16~4.95−0.76−7.45~6.41−1.99−9.12~5.71−11.38−20.68~ –0.990.40−5.79~7.00
Lag010 d−0.76−7.05~5.95−0.68−7.81~7.01−0.89−8.59~7.45−10.74−20.73~0.520.91−5.73~8.01
Lag011 d−0.41−7.11~6.79−0.95−8.51~7.250.27−8.01~9.30−11.35−21.93~0.681.43−5.65~9.04
Lag012 d1.54−5.33~8.911.23−6.68~9.811.92−6.67~11.29−9.09−20.45~3.913.32−3.95~11.13
Lag013 d2.64−4.75~10.602.58−5.95~11.872.68−6.52~12.79−6.15−18.59~8.214.08−3.71~12.51
Lag014 d2.78−5.11~11.342.81−6.30~12.802.71−7.11~13.56−5.53−18.83~9.944.15−4.17~13.20
Lag015 d2.50−5.89~11.632.84−6.86~13.542.01−8.36~13.57−5.73−19.81~10.823.85−4.98~13.51
Lag016 d2.52−6.33~12.212.88−7.36~14.262.02−8.91~14.27−5.77−20.63~11.873.89−5.45~14.15
Lag017 d2.26−7.03~12.492.71−8.04~14.731.64−9.82~14.56−8.10−23.31~10.123.99−5.85~14.85
Lag018 d1.52−8.14~12.201.33−9.75~13.781.75−10.27~15.38−12.75−27.82~5.453.94−6.35~15.36
Lag019 d1.62−8.48~12.830.96−10.57~13.982.49−10.11~16.87−12.92−28.55~6.124.09−6.67~16.09
Lag020 d1.90−8.66~13.681.89−10.22~15.631.92−11.17~16.94−11.86−28.30~8.344.22−7.02~16.81
Lag021 d4.42−6.86~17.065.58−7.51~20.522.93−10.87~18.86−9.84−27.34~11.876.81−5.19~20.34
Lag022 d8.59−3.61~22.3310.24−3.97~26.566.49−8.40~23.79−6.24−25.16~17.4611.09−1.91~25.81
Lag023 d13.15−0.11~28.1716.731.02~34.878.63−7.21~27.17−3.05−23.40~22.7015.911.76~32.02
Lag024 d16.842.58~33.0821.094.10~40.8411.48−5.45~31.442.80−19.59~31.4319.184.02~36.55
Lag025 d18.403.38~35.5922.394.57~43.2513.36−4.54~34.637.27−17.04~38.6920.224.33~38.52
Lag026 d17.952.39~35.8821.803.35~43.5513.11−5.45~35.3211.51−14.70~45.7818.992.64~37.95
Lag027 d15.48−0.16~33.5617.31−0.98~38.9813.10−6.04~36.1412.51−14.86~48.6815.95−0.42~35.02
Lag028 d14.30−1.73~32.9416.53−2.29~38.9711.46−8.09~35.1613.64−14.93~51.8014.40−2.33~33.99
Lag029 d14.17−2.45~33.6316.01−3.43~39.3511.93−8.40~36.7710.18−18.53~49.0214.82−2.60~35.36
Lag030 d13.67−3.49~33.8815.29−4.74~39.5411.58−9.42~37.472.92−24.85~40.9515.45−2.72~37.01
), ArticleFig(id=1321169889642005194, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169881387614849, language=CN, label=表4, caption=

大气SO2每升高10 µg/m3上海市居民脑卒中发病数的百分比改变(多日滞后模型)

, figureFileSmall=null, figureFileBig=null, tableContent=
滞后天数总体男性女性<60岁≥60岁
%95%CI%95%CI%95%CI%95%CI%95%CI
Lag01 d1.69−1.43~4.90−0.17−3.65~3.444.100.22~8.12−2.82−8.09~2.742.42−0.83~5.78
Lag02 d0.25−3.29~3.93−2.72−6.63~1.364.19−0.28~8.86−5.34−11.27~0.991.17−2.53~5.01
Lag03 d0.44−3.45~4.48−2.79−7.06~1.684.73−0.20~9.91−6.66−13.06~0.211.61−2.47~5.85
Lag04 d0.64−3.60~5.06−1.81−6.50~3.123.87−1.45~9.48−8.60−15.39~ –1.272.18−2.28~6.84
Lag05 d0.99−3.62~5.82−0.90−6.03~4.513.47−2.28~9.56−9.89−17.14~ –2.022.82−2.04~7.93
Lag06 d1.28−3.68~6.490.35−5.21~6.232.49−3.65~9.01−9.36−17.19~ –0.793.07−2.16~8.57
Lag07 d0.80−4.47~6.360.93−5.05~7.290.62−5.82~7.50−10.67−18.92~ –1.572.74−2.83~8.63
Lag08 d−0.05−5.63~5.860.63−5.74~7.42−0.93−7.69~6.32−10.98−19.76~ –1.241.79−4.10~8.04
Lag09 d−1.29−7.16~4.95−0.76−7.45~6.41−1.99−9.12~5.71−11.38−20.68~ –0.990.40−5.79~7.00
Lag010 d−0.76−7.05~5.95−0.68−7.81~7.01−0.89−8.59~7.45−10.74−20.73~0.520.91−5.73~8.01
Lag011 d−0.41−7.11~6.79−0.95−8.51~7.250.27−8.01~9.30−11.35−21.93~0.681.43−5.65~9.04
Lag012 d1.54−5.33~8.911.23−6.68~9.811.92−6.67~11.29−9.09−20.45~3.913.32−3.95~11.13
Lag013 d2.64−4.75~10.602.58−5.95~11.872.68−6.52~12.79−6.15−18.59~8.214.08−3.71~12.51
Lag014 d2.78−5.11~11.342.81−6.30~12.802.71−7.11~13.56−5.53−18.83~9.944.15−4.17~13.20
Lag015 d2.50−5.89~11.632.84−6.86~13.542.01−8.36~13.57−5.73−19.81~10.823.85−4.98~13.51
Lag016 d2.52−6.33~12.212.88−7.36~14.262.02−8.91~14.27−5.77−20.63~11.873.89−5.45~14.15
Lag017 d2.26−7.03~12.492.71−8.04~14.731.64−9.82~14.56−8.10−23.31~10.123.99−5.85~14.85
Lag018 d1.52−8.14~12.201.33−9.75~13.781.75−10.27~15.38−12.75−27.82~5.453.94−6.35~15.36
Lag019 d1.62−8.48~12.830.96−10.57~13.982.49−10.11~16.87−12.92−28.55~6.124.09−6.67~16.09
Lag020 d1.90−8.66~13.681.89−10.22~15.631.92−11.17~16.94−11.86−28.30~8.344.22−7.02~16.81
Lag021 d4.42−6.86~17.065.58−7.51~20.522.93−10.87~18.86−9.84−27.34~11.876.81−5.19~20.34
Lag022 d8.59−3.61~22.3310.24−3.97~26.566.49−8.40~23.79−6.24−25.16~17.4611.09−1.91~25.81
Lag023 d13.15−0.11~28.1716.731.02~34.878.63−7.21~27.17−3.05−23.40~22.7015.911.76~32.02
Lag024 d16.842.58~33.0821.094.10~40.8411.48−5.45~31.442.80−19.59~31.4319.184.02~36.55
Lag025 d18.403.38~35.5922.394.57~43.2513.36−4.54~34.637.27−17.04~38.6920.224.33~38.52
Lag026 d17.952.39~35.8821.803.35~43.5513.11−5.45~35.3211.51−14.70~45.7818.992.64~37.95
Lag027 d15.48−0.16~33.5617.31−0.98~38.9813.10−6.04~36.1412.51−14.86~48.6815.95−0.42~35.02
Lag028 d14.30−1.73~32.9416.53−2.29~38.9711.46−8.09~35.1613.64−14.93~51.8014.40−2.33~33.99
Lag029 d14.17−2.45~33.6316.01−3.43~39.3511.93−8.40~36.7710.18−18.53~49.0214.82−2.60~35.36
Lag030 d13.67−3.49~33.8815.29−4.74~39.5411.58−9.42~37.472.92−24.85~40.9515.45−2.72~37.01
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上海市大气二氧化硫暴露与居民脑卒中发病关系时间序列分析
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王玉琢 , 喇雪娜 , 吴梦吟 , 王英全 , 刘丹妮 , 杨群娣 , 郑杨 *, * , 施燕 *, *
中国公共卫生 | 调查报告与分析 2025,41(6): 728-734
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中国公共卫生 |调查报告与分析 2025 , 41 (6) : 728 -734
上海市大气二氧化硫暴露与居民脑卒中发病关系时间序列分析
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王玉琢, 喇雪娜, 吴梦吟, 王英全, 刘丹妮, 杨群娣, 郑杨*, * , 施燕*, *
作者信息
  • 上海市疾病预防控制中心,上海 200336
通讯作者:
郑杨,E-mail:
施燕,E-mail:
作者简介:

王玉琢(1994—),主管医师,硕士,研究方向:慢性病预防与控制

Association of ambient sulfur dioxide with stroke incidence among residents of Shanghai: a time-series analysis of incidence registration and monitoring data from 2017 to 2021
Yuzhuo WANG, Xuena LA, Mengyin WU, Yingquan WANG, Danni LIU, Qundi YANG, Yang ZHENG , Yan SHI
Affiliations
  • Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
  • Corresponding Author:
    ZHENG Yang, E-mail:
    SHI Yan, E-mail:
出版时间: 2025-06-10 doi: 10.11847/zgggws1144529
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目的

了解大气二氧化硫(SO2)暴露与上海市居民脑卒中发病的关系,为制定脑卒中的防控措施提供参考依据。

方法

收集上海市脑卒中及急性心肌梗死登记报告信息系统中2017年1月1日—2021年12月31日上报的263 872例脑卒中新发病例相关数据以及同期国家生态环境部的大气污染国控监测站中上海市SO2、二氧化氮(NO2)、臭氧(O3)、细颗粒物(PM2.5)和可吸入颗粒物(PM10)等大气污染物日均浓度及日均温度、日均相对湿度等气象因素监测数据,通过以类泊松分布为链接函数的广义相加模型时间序列研究分析大气SO2暴露与脑卒中每日发病数的关系。

结果

上海市2017—2021年共报告263 872例脑卒中新发病例,日均发病数为(145±28)例,SO2日均浓度为(8.00±3.71)µg/m3。单日滞后模型分析结果显示,SO2平均浓度每升高10 µg/m3,总体居民脑卒中每日发病数在Lag7 d、Lag8 d、Lag26 d和Lag27 d分别减少0.21%、0.28%、0.29%和0.21%,在Lag19 d、Lag20 d、Lag21 d、Lag22 d、Lag23 d和Lag24 d分别增加0.16%、0.34%、0.33%、0.42%、0.32%和0.20%;男性居民脑卒中每日发病数在Lag26 d减少0.37%,在Lag20 d、Lag21 d、Lag22 d和Lag23 d分别增加0.47%、0.36%、0.61%和0.33%;女性居民脑卒中每日发病数在Lag0 d增加0.40%;≥60岁居民脑卒中每日发病数在Lag8 d和Lag26 d分别减少0.33%和0.36%,在Lag20 d、Lag21 d、Lag22 d和Lag23 d分别增加0.35%、0.32%、0.41%和0.32%(均P<0.05)。多日滞后模型分析结果显示,SO2平均浓度每升高10 µg/m3,总体居民脑卒中每日发病数在Lag024 d、Lag025 d和Lag026 d分别增加16.84%、18.40%和17.95%;男性居民每日发病数在Lag023 d、Lag024 d、Lag025 d和Lag026 d分别增加16.73%、21.09%、22.39%和21.80%;女性居民每日发病数在Lag01 d增加4.10%;<60岁居民脑卒中每日发病数在Lag04 d、Lag05 d、Lag06 d、Lag07 d、Lag08 d和Lag09 d分别减少8.60%、9.89%、9.36%、10.67%、10.98%和11.38%;≥60岁居民脑卒中每日发病数在Lag023 d、Lag024 d、Lag025 d和Lag026 d分别增加15.91%、19.18%、20.22%和18.99%(均P<0.05)。

结论

脑卒中每日发病数在大气SO2暴露Lag19 d~Lag24 d和Lag024 d~Lag026 d时明显增加,男性和≥60岁居民对SO2暴露的滞后效应更加易感。

脑卒中  /  发病  /  二氧化硫(SO2)  /  暴露  /  大气  /  关系  /  时间序列分析  /  上海市
Objective

To investigate the correlation between atmospheric sulfur dioxide (SO2) and the incidence of stroke in residents of Shanghai city, and to provide a reference for the development of stroke prevention and control measures.

Methods

Data on 263 872 new stroke cases reported to the Shanghai Stroke and Acute Myocardial Infarction Registry Information System from 2017 through 2021 were collected. Daily mean concentrations of atmospheric pollutants-including SO2, nitrogen dioxide (NO2), ozone (O3), particulate matter with an aerodynamic diameter < 2.5 μm (PM2.5), and particulate matter with an aerodynamic diameter < 10 μm (PM10), as well as meteorological factors such as daily average temperature and relative humidity, were obtained from the national air pollution monitoring stations of the Ministry of Ecology and Environment during the same period. A generalized additive model with a quasi-Poisson distribution link function was used to analyze the time-series relationship between atmospheric SO2 concentration and the daily number of stroke cases.

Results

Throughout the study period conducted in Shanghai, the average daily incidence of registered stroke cases was 145 ± 28, while the mean daily concentration of SO2 was measured at 8.00 ± 3.71 µg/m3. The analysis of the single-day lag model demonstrated that a 10 µg/m³ increase in the mean SO2 concentration was significantly associated with percentage changes (%) in daily stroke incidence across different populations and lag days, as follows: 1. General population: Decreases of 0.21%, 0.28%, 0.29%, and 0.21% were observed at lag days 7, 8, 26, and 27, respectively, while increases of 0.16%, 0.34%, 0.33%, 0.42%, 0.32%, and 0.20% were noted at lag days 19, 20, 21, 22, 23, and 24, respectively. 2. Males: A 0.37% decrease was observed at lag day 26, whereas increases of 0.47%, 0.36%, 0.61%, and 0.33% were recorded at lag days 20, 21, 22, and 23, respectively. 3. Females: A 0.40% increase was observed at lag day 0. 4. Residents aged 60 years and above: Decreases of 0.33% and 0.36% were observed at lag days 8 and 26, respectively, while increases of 0.35%, 0.32%, 0.41%, and 0.32% were noted at lag days 20, 21, 22, and 23, respectively. All reported associations were statistically significant (P < 0.05). Multi-day lag model analysis yielded results consistent with those from the single-day lag analysis: 1. General population: Increases of 16.84%, 18.40%, and 17.95% were observed at lag days 0 - 24, 0 - 25, and 0 - 26, respectively. 2. Males: Increases of 16.73%, 21.09%, 22.39%, and 21.80% were observed at lag days 0 - 23, 0 - 24, 0 - 25 d, and 0 - 26, respectively. 3. Females: An increase of 4.10% was noted at lag days 0-1.4. Residents aged < 60 years: Decreases of 8.60%, 9.89%, 9.36%, 10.67%, 10.98%, and 11.38% were observed at lag days 0 - 4, 0 - 5, 0 - 6, 0 - 7, 0 - 8, and 0 - 9, respectively. 5. Residents aged 60 years and above: Decreases of 15.91%, 19.18%, 20.22%, and 18.99% were observed at lag days 0 - 23, 0 - 24, 0 - 25, and 0 - 26, respectively (all P < 0.05).

Conclusions

The elevated atmospheric SO2 concentration exhibited a significant correlation with a higher daily incidence of stroke on single-lag days 19 - 24 and multi-lag days 0 - 24 to 0 - 26 among Shanghai residents, particularly affecting male individuals and those aged 60 years or older.

stroke  /  incidence  /  sulfur dioxide  /  exposure  /  ambient air  /  relationship  /  time-series analysis  /  Shanghai city
王玉琢, 喇雪娜, 吴梦吟, 王英全, 刘丹妮, 杨群娣, 郑杨, 施燕. 上海市大气二氧化硫暴露与居民脑卒中发病关系时间序列分析. 中国公共卫生, 2025 , 41 (6) : 728 -734 . DOI: 10.11847/zgggws1144529
Yuzhuo WANG, Xuena LA, Mengyin WU, Yingquan WANG, Danni LIU, Qundi YANG, Yang ZHENG, Yan SHI. Association of ambient sulfur dioxide with stroke incidence among residents of Shanghai: a time-series analysis of incidence registration and monitoring data from 2017 to 2021[J]. Chinese Journal of Public Health, 2025 , 41 (6) : 728 -734 . DOI: 10.11847/zgggws1144529
脑卒中是一种急性脑血管疾病,具有高发病率、高致死率和高致残率等特点,严重威胁中国居民的健康与生命[1–2]。国家心血管病中心制定的《空气污染与心血管疾病专家共识》指出,空气污染已成为一项重要的可纠正的心血管病危险因素[3]。大气污染物按成分可大致分为颗粒污染物和气态污染物,已有研究表明大气污染物各成分不仅对脑卒中发病具有整体协同效应,而且具有独立影响效应[4–6]。近年来,多项研究初步证实了短期和长期暴露于颗粒污染物如细颗粒物(particulate matter with an aerodynamic diameter <2.5 μm, PM2.5)等均可增加脑卒中的发病风险[7–10]。气态污染物二氧化硫(sulfur dioxide, SO2)作为主要的大气污染物之一,大部分来自煤和石油等含硫燃料的燃烧,其对人体健康亦有诸多不良影响[11]。在广东省广州市[12]和浙江省宁波市[13]等地的研究发现,短期暴露于环境SO2可能会显著增加脑卒中的就诊和住院风险,但关于SO2这类气态污染物对脑卒中发病风险影响的研究证据仍不充分,且相关研究中SO2的暴露窗口期多集中在Lag0 d~Lag14 d,对于更长滞后天数的效应尚有待探索。为了解大气SO2暴露与上海市居民脑卒中发病的关系,为制定脑卒中的防控措施提供参考依据,本研究收集上海市脑卒中及急性心肌梗死登记报告信息系统中2017年1月1日—2021年12月31日上报的263 872例脑卒中新发病例相关数据以及同期国家生态环境部的大气污染国控监测站中上海市SO2、二氧化氮(nitrogen dioxide,NO2)、臭氧(ozone, O3)、PM2.5和可吸入颗粒物(particulate matter with an aerodynamic diameter <10 μm, PM10)等大气污染物日均浓度及日均温度、日均相对湿度数据,通过以类泊松分布为链接函数的广义相加模型时间序列研究分析大气SO2暴露与脑卒中每日发病数的关系。结果报告如下。
收集上海市脑卒中及急性心肌梗死登记报告信息系统中上海市各级各类医疗机构2017年1月1日—2021年12月31日上报的263 872例脑卒中新发病例相关数据,内容包括患者性别、年龄、发病时间、疾病诊断等。按照《国际疾病与相关健康问题统计分类第十版(The International Statistical Classification of Diseases and Related Health Problems - 10th Revision, ICD-10)》[14]编码规则,将编码为蛛网膜下腔出血(I60)、脑实质内出血(I61)、脑梗死(I63)和卒中不分型(I64)的脑卒中患者作为研究对象,若脑卒中患者发病28 d后再次急性发作,按又一新发病例报告,患者在28 d内的重复发病记录则被删除[15]。同期大气SO2、NO2、O3、PM2.5和PM10等大气污染物日均浓度以及日均温度、日均相对湿度等气象因素监测数据均来源于国家生态环境部的大气污染国控监测站(https://air.cnemc.cn:18007/)。
采用Excel 2016建立数据库,应用R 4.3.1软件进行统计分析。本研究对2017年1月1日—2021年12月31日上海市常住居民每日脑卒中发病数,SO2、NO2、O3、PM2.5和PM10等大气污染物日均浓度以及日均温度、日均相对湿度等气象因素数据进行描述性分析。因不符合正态分布,对同期SO2、NO2、O3、PM2.5和PM10等大气污染物日均浓度、日均温度和日均相对湿度之间进行了Spearman相关性分析。本研究采用时间序列研究方法,由于脑卒中每日发病数是一个小概率事件,且经过R软件中的qcc包检验后显示数据存在过度离势,故采用以类泊松分布为链接函数的广义相加模型分析脑卒中发病风险与大气SO2暴露浓度之间的关系。除SO2暴露浓度外,模型校正了时间趋势、日均温度、日均相对湿度和工作日效应等因素对脑卒中发病数的影响,模型为:$ \mathrm{\mathrm{L\text{o}}g}[E\left(Y_t\right)]=\alpha+\beta X+\text{ns}\left(\mathrm{temp},df=3\right)+ ns(\mathrm{humid},df=3)+ns\left(\mathrm{time},df=8*5\right)+\mathrm{DOW} $。式中,E(Yt)代表第t天脑卒中发病数的期望值,α代表截距项,β代表回归系数,X代表第t天大气SO2、NO2、O3、PM2.5和PM10日均浓度,temp代表日均温度,humid代表日均相对湿度,time代表第t天日期,ns代表自然立方样条函数,df代表自然立方样条函数的自由度,DOW代表星期几效应。根据既往专家经验设定日均温度和日均相对湿度的ns函数自由度为3[16],时间的自由度通过最小化模型残差自相关来选择,当自由度为8时残差之和最小,故时间的ns函数自由度设定为8/年。由于污染物的影响可能存在滞后效应,本研究构建了0~30 d的单日滞后模型(Lag0 d~Lag30 d,Lag0 d代表当天的SO2浓度,Lag30 d代表30 d前的SO2浓度)和多日滞后模型(Lag01 d~Lag030 d,Lag01 d代表当天和前1 d SO2浓度的移动均值,Lag030 d代表当天和前30 d SO2浓度的移动均值)进行分析,并按性别(男性、女性)和年龄(<60岁、≥60岁)进行分层分析。广义相加模型报告的结果为超额危险度(excess risk, ER)及其95%CI,指SO2浓度每增加10 µg/m3所引起的脑卒中发病数改变百分比及其95%CI。此外,本研究在多日滞后模型Lag025的基础上,分别引入PM2.5、PM10、NO2和O3进行双污染物和多污染物模型拟合,通过调整各污染物效应进行敏感性分析以验证SO2暴露对脑卒中不良效应的稳定性。检验水准为双侧检验α=0.05。
上海市2017—2021年共报告263 872例脑卒中新发病例,日均发病数为(145±28)例。其中,男性患者150239例(56.94%),女性患者113633例(43.06%);年龄<60岁患者35189例(13.34%),≥60岁患者228683例(86.66%)。上海市2017—2021年大气污染物和气象因素基本特征见表1。
Spearman秩相关分析结果显示,SO2日均浓度与NO2、O3、PM2.5和PM10日均浓度均呈正相关,与日均温度和日均相对湿度均呈负相关(均P<0.001);NO2日均浓度与PM2.5和PM10日均浓度均呈正相关,与O3、日均温度和日均相对湿度均呈负相关(均P<0.001);O3日均浓度与PM2.5和PM10日均浓度及日均温度均呈正相关,与日均相对湿度呈负相关(均P<0.001);PM2.5日均浓度与PM10日均浓度呈正相关,与日均温度和日均相对湿度均呈负相关(均P<0.001);PM10日均浓度与日均温度和日均相对湿度均呈负相关(P<0.001);日均温度与日均相对湿度呈正相关(r=0.25,P<0.001)。
单日滞后模型结果显示,SO2平均浓度每升高10 µg/m3,总体居民脑卒中每日发病数在Lag7 d~Lag8 d和Lag26 d~Lag27 d均有所减少,在Lag19 d~Lag24 d均有所增加;男性居民脑卒中每日发病数在Lag26 d减少,在Lag20 d~Lag23 d均有所增加;女性居民脑卒中每日发病数在Lag0 d增加;≥60岁居民脑卒中每日发病数在Lag8 d和Lag26 d均有所减少,在Lag20 d~Lag23 d均有所增加(均P<0.05)。
多日滞后模型结果显示,SO2平均浓度每升高10 µg/m3,总体居民脑卒中每日发病数在Lag024 d~Lag026 d均有所增加;男性居民每日发病数在Lag023 d~Lag026 d均有所增加;女性居民每日发病数在Lag01 d增加;<60岁居民脑卒中每日发病数在Lag04 d~Lag09 d均有所减少;≥60岁居民脑卒中每日发病数在Lag23 d~Lag26 d均有所增加(均P<0.05)。
本研究在多日滞后模型(Lag025 d)的基础上,分别引入相同滞后天数的NO2、O3、PM10和PM2.5等大气污染物构建了双污染物模型和多污染物模型进行敏感性分析。结果显示,SO2在Lag025 d时的多日滞后效应RR值为1.02(95%CI=1.00~1.03);在此基础上分别调整NO2、O3、PM10和PM2.5后的双污染物模型在Lag025 d时的多日滞后效应RR值分别为1.02(95%CI=1.00~1.04)、1.02(95%CI=1.00~1.03)、1.02(95%CI=1.01~1.04)和1.02(95%CI=1.01~1.04);将所有污染物全部纳入的多污染物模型在Lag025 d时的多日滞后效应RR值为1.02(95%CI=1.00~1.03)。双污染物模型和多污染物模型在Lag025 d时SO2对脑卒中发病风险的影响效应均无明显改变,模型结果较为稳定。
本研究结果显示,2017—2021年大气SO2暴露对上海市居民脑卒中发病的影响存在滞后效应,主要见于SO2浓度升高之后的3~4周。在滞后第25 d(Lag025 d)时SO2暴露的累积效应达到最大,SO2平均浓度每升高10 µg/m3,脑卒中发病数增加18.40%;在SO2单污染物模型的基础上,调整NO2、O3、PM10和PM2.5等其他大气污染物的影响后,SO2暴露对脑卒中发病的影响仍然显著,提示虽然各污染物间具有相关性,但SO2暴露对脑卒中发病的影响仍具有独立作用,与Wu Z等[17]和Tang E J等[18]结果一致。本研究发现,与既往研究[12]比较,上海市SO2暴露增加脑卒中发病风险滞后效应的发生时间较晚,而滞后效应却较强。Shen S等[12]关于SO2短期暴露与脑卒中住院风险的研究结果表明,在SO2暴露当天和滞后1 d对脑卒中住院风险影响的效应最大,SO2浓度每上升10 µg/m3,脑卒中住院人数分别增加1.27%和1.55%;吴梦吟[13]在宁波市鄞州区进行的空气污染与脑卒中关联的时间序列研究结果表明,SO2对脑卒中就诊人数的影响存在滞后效应,在滞后第14 d时效应最强;Chen H等[19]在重庆市进行的空气污染与脑卒中住院风险的时间序列研究表明,在滞后第4 d时,SO2暴露对缺血性卒中住院风险影响的效应最强,SO2浓度每升高10 µg/m3,脑卒中入院人数增加4.8%。由于以上研究在不同地域和不同人群中进行,因此滞后效应发生时间和效应大小的差异可能由SO2暴露水平、人群特征、样本量和研究设计不同所致。与这些研究相比,本研究对SO2暴露的观察期更长(0~30 d),可以更全面地评估SO2暴露对脑卒中发病的滞后效应,但因受限于时间序列研究的设计,未能控制可能的混杂因素,在今后的研究中还需对本研究结果进一步加以验证。但SO2暴露对于脑卒中发病风险的影响具有生物学合理性。研究表明,吸入SO2可改变心率变异性,提高氧化水平,增强凝血功能,促进血栓形成,增加脑卒中发生的风险[20];动物实验研究亦发现,SO2提高了内皮素–1、诱导型一氧化氮合酶、环氧合酶–2、细胞间黏附分子–1信使RNA和蛋白质表达的水平,这些病理生理变化均可能与脑卒中的发生发展具有相关性[21]。
识别脑卒中发病风险受SO2暴露影响较大的脆弱群体具有重要的公共卫生意义,因此本研究按性别和年龄分层进行了亚组分析,进一步分析SO2对不同亚组居民脑卒中发病风险的影响。性别亚组分析结果显示,在单日滞后模型(Lag20 d~Lag23 d)和多日滞后模型(Lag023 d~Lag026 d)中,男性居民每日发病数均有所增加;而除了暴露当天(Lag0 d)和滞后第1天(Lag01 d)女性居民每日发病数均有所增加外,SO2对女性脑卒中每日发病数的影响均无统计学意义(均P>0.05)。提示SO2暴露对男性居民脑卒中发病风险的影响较女性居民大,女性居民对SO2暴露相对不敏感。这一结果与既往研究[22]结果一致。Wang Z等[22]在广东省深圳市进行的空气污染短期暴露与缺血性脑卒中发病风险关联的研究表明,SO2对男性缺血性脑卒中发病风险的影响更强;一项在美国绝经后女性队列中进行的空气污染短期暴露与脑卒中发病风险关联的研究亦表明,在该女性队列中SO2短期暴露与脑卒中发病风险间无显著相关性[23]。年龄亚组分析结果显示,在单日滞后模型(Lag20 d~Lag23 d)和多日滞后模型(Lag023 d~Lag026 d)中,≥60岁居民每日发病数均有所增加;而在相同的滞后期,SO2对<60岁居民脑卒中每日发病数的影响却无统计学意义(均P<0.05),提示SO2暴露对老年人脑卒中发病风险的影响较大,与既往研究[24–25]结果一致,可能与老年人本身患有心血管疾病的可能性较大且更易出现脑卒中反复发作的情况有关。本研究结果显示,在SO2浓度升高后的部分滞后天数,脑卒中每日发病数减少,这可能是因为在某些天SO2暴露浓度相对较低,SO2在低浓度下可引起血管舒张,表现出对心血管系统的保护作用,而SO2在高浓度下产生了毒理学作用[26]。既往研究表明,SO2在体内可通过含硫氨基酸的新陈代谢产生,作为内源性气体信号分子调节多种离子通道,具有维持心血管系统稳态、调控内皮细胞功能和保护内皮细胞免受各种损伤等生物学效应,这种调节作用可能在某种程度上降低了脑卒中的发病风险[27]。但有关SO2对脑卒中发病调节作用的具体机制尚有待进一步研究。
综上所述,大气SO2暴露对上海市居民脑卒中发病的影响存在滞后效应和累积效应。虽然2017—2021年上海市大气SO2浓度总体上呈下降趋势[28],但SO2相关空气污染仍然是需要重视的环境问题,应持续推进落实SO2减排措施,如严格控制煤炭消费、推广使用脱硫工艺、优先使用电、天然气或其他清洁能源等[29]。既往研究已初步证实暴露于颗粒污染物会增加脑卒中的发病风险[30],而本研究则发现SO2暴露亦与脑卒中发病风险独立相关,因此建议脑卒中高危人群尤其是男性和老年人群在重污染天气时应尽量减少户外活动,在空气污染滞后期对脑卒中的相关症状加以重点关注,若发生脑卒中应及早就医。但本研究仍存在一定的局限性:首先,本研究参与者仅限于上海市常住居民,部分流动人口可能被排除在研究之外,但因流动人口以青壮年为主,并非脑卒中发病的主要人群,故影响可能较小;其次,本研究为观察性研究,SO2暴露与脑卒中发病之间的负向关联可能受到其他未考虑因素的干扰;再次,关于空气污染物暴露的评估仅限于环境空气暴露,不包括个人室内空气暴露及其他暴露途径,因此在今后的研究中可探索评估个体暴露的模型以及应用个体暴露测量技术进一步对本研究结果加以验证。

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2025年第41卷第6期
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doi: 10.11847/zgggws1144529
  • 接收时间:2024-03-25
  • 首发时间:2026-10-25
  • 出版时间:2025-06-10
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  • 收稿日期:2024-03-25
  • 修回日期:2024-11-25
  • 录用日期:2025-04-02
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    上海市疾病预防控制中心,上海 200336

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2种不同金属材料的力学参数
科
Family
属数
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genus
种数
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species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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