Article(id=1322590080720548653, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202407061, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1720195200000, receivedDateStr=2024-07-06, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793259647778, onlineDateStr=2026-10-29, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793259647778, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793259647778, creator=13701087609, updateTime=1793259647778, updator=13701087609, issue=Issue{id=1322589954702688372, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='1', pageStart='1', pageEnd='192', issueExtLink='null', onlineDate='null', pubDate='1736438400000', pubDateStr='2025-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793259617734, creator='13701087609', updateTime=1793260023378, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322591656147276425, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322591656147276426, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=33, endPage=39, ext={EN=ArticleExt(id=1322590080930263854, articleId=1322590080720548653, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Trends in coronary heart disease mortality in China from 1992 to 2021: an age-period-cohort model analysis, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the trends in coronary heart disease (CHD) mortality in China from 1992 to 2021, providing a basis for adjusting and optimizing CHD prevention and treatment policies.

Methods

This study employed the Join point regression model to calculate the annual percentage change in mortality trends, exploring variations in CHD mortality over consecutive time periods. Additionally, an age-period-cohort (APC) model was utilized to compute effect coefficients, assessing the individual impacts of age, period, and birth cohort on CHD mortality risk.

Results

From 1992 to 2021, the overall mortality rates for CHD in both genders showed an upward trend, with average annual percentage changes of 0.584%, 0.979%, and 0.097%, respectively. The APC model estimates revealed that the mortality risk for the age group of 90 to 94 was 614.97 times higher for males and 519.08 times higher for females compared to the age group of 15 to 19. In terms of period effects, the CHD mortality risk for males and females from 2017 to 2021 was 2.58 times and 1.78 times higher, respectively, than that from 1992 to 1996. Regarding cohort effects, individuals born between 1902 and 1906 had a CHD mortality risk 25.07 times and 24.41 times higher for males and females, respectively, compared to those born between 2002 and 2006.

Conclusion

The overall CHD mortality rate in China is on the rise, with a focus on male and elderly populations. Age, period, and birth cohort each have independent effects on CHD mortality risk. Therefore, a multifaceted approach is necessary to advance preventive measures, control risk factors, and reduce mortality risks among residents.

, authors=Xia XIA, Sheng-bi WU, Li DIAO, authorsList=Xia XIA, Sheng-bi WU, Li DIAO, authorCompany=null, correspAuthors=Li DIAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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=1322590082008199991, articleId=1322590080720548653, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=1992—2021年中国冠心病死亡趋势及年龄-时期-队列模型分析, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

分析1992—2021年我国冠心病死亡趋势,为调整及优化冠心病防治政策提供依据。

方法

本研究通过joinpoint回归模型计算年度趋势变化百分比,探究在连续时间段中冠心病死亡趋势的变化,同时应用年龄-时期-队列模型(age-period-cohort,APC)计算效应系数,以此评估冠心病死亡风险在年龄、时期与出生队列方面的单个影响效应。

结果

1992—2021年全国、男性、女性冠心病死亡率总体均呈现上升趋势,平均年度变化百分比分别为0.584%、0.979%、0.097%。APC模型估计显示,年龄效应方面,男性、女性90~94岁年龄组的冠心病死亡风险分别是15~19岁年龄组的614.97和519.08倍;时期效应方面,男性、女性2017—2021年冠心病死亡风险分别是1992—1996年的2.58和1.78倍;队列效应方面,出生于1902—1906年的男性、女性分别是出生于2002—2006年的25.07和24.41倍。

结论

我国冠心病死亡率总体呈现上升趋势,男性与老年居民是重点关注人群。年龄、时期与出生队列对冠心病死亡风险均有独立影响效应。因此,应多措并举,前移关口,竭力控制冠心病危险因素,降低居民死亡风险。

, authors=夏霞, 吴生碧, 刁丽, authorsList=夏霞, 吴生碧, 刁丽, authorCompany=null, correspAuthors=刁丽, authorNote=

夏霞(1989—),女,本科,中级职称,研究方向:公共卫生

, correspAuthorsNote=
刁丽,E-mail:
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夏霞(1989—),女,本科,中级职称,研究方向:公共卫生

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夏霞(1989—),女,本科,中级职称,研究方向:公共卫生

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Functional Consequences, and potential therapeutics for cellular senescence, refAbstract=null), Reference(id=1322590093655782277, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, doi=null, pmid=null, pmcid=null, year=2024, volume=35, issue=3, pageStart=1, pageEnd=5, url=null, language=null, rfNumber=[26], rfOrder=39, authorNames=赵明日, 宇传华, journalName=公共卫生与预防医学, refType=null, unstructuredReference=赵明日,宇传华. 基于年龄-时期-队列模型分析1990-2019年中国归因于高盐饮食的缺血性心脏病疾病负担趋势[J]. 公共卫生与预防医学,2024,35(3):1-5., articleTitle=基于年龄-时期-队列模型分析1990-2019年中国归因于高盐饮食的缺血性心脏病疾病负担趋势, refAbstract=null), Reference(id=1322590093974549382, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, doi=null, pmid=null, pmcid=null, year=2024, volume=35, issue=3, pageStart=1, pageEnd=5, url=null, language=null, rfNumber=[26], rfOrder=40, authorNames=Zhao MR, Yu CH, journalName=Journal of Public Health and Preventive Medicine, refType=null, unstructuredReference=Zhao MR, Yu CH. Trends in ischemic heart disease burden attributable to high-salt diets in China from 1990 to 2019 based on age-period-cohort model analysis [J]. Journal of Public Health and Preventive Medicine, 2024, 35(3): 1-5.(In Chinese), articleTitle=Trends in ischemic heart disease burden attributable to high-salt diets in China from 1990 to 2019 based on age-period-cohort model analysis, refAbstract=null), Reference(id=1322590094050046855, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, doi=null, pmid=null, pmcid=null, year=2016, volume=15, issue=Suppl 1, pageStart=32, pageEnd=null, url=null, language=null, rfNumber=[27], rfOrder=41, authorNames=Li XH, Song JC, Lin T, journalName=Environmental Health: a Global Access Science Source, refType=null, unstructuredReference=Li XH, Song JC, Lin T, et al. Urbanization and health in China, thinking at the National, local and individual levels[J]. Environmental Health: a Global Access Science Source, 2016, 15 Suppl 1(Suppl 1): 32., articleTitle=Urbanization and health in China, thinking at the National, local and individual levels, refAbstract=null), Reference(id=1322590094138127240, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, doi=null, pmid=null, pmcid=null, year=2015, volume=203, issue=null, pageStart=116, pageEnd=121, url=null, language=null, rfNumber=[28], rfOrder=42, authorNames=Dai JP, Chen RJ, Meng X, journalName=Environmental Pollution, refType=null, unstructuredReference=Dai JP, Chen RJ, Meng X, et al. Ambient air pollution, temperature and out-of-hospital coronary deaths in Shanghai, China[J]. Environmental Pollution, 2015, 203: 116-121., articleTitle=Ambient air pollution, temperature and out-of-hospital coronary deaths in Shanghai, China, refAbstract=null), Reference(id=1322590094398174089, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, doi=null, pmid=null, pmcid=null, year=2023, volume=50, issue=20, pageStart=3663, pageEnd=3668, url=null, language=null, rfNumber=[29], rfOrder=43, authorNames=犹忆, 李德俊, 刘川, journalName=现代预防医学, refType=null, unstructuredReference=犹忆,李德俊,刘川,等. 1990-2019年中国食管癌死亡趋势及年龄-时期-队列模型分析[J]. 现代预防医学,2023,50(20):3663-3668., articleTitle=1990-2019年中国食管癌死亡趋势及年龄-时期-队列模型分析, refAbstract=null), Reference(id=1322590094477865866, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, doi=null, pmid=null, pmcid=null, year=2023, volume=50, issue=20, pageStart=3663, pageEnd=3668, url=null, language=null, rfNumber=[29], rfOrder=44, authorNames=You Y, Li DJ, Liu C, journalName=Modern Preventive Medicine, refType=null, unstructuredReference=You Y, Li DJ, Liu C, et al. Trend and age-period-cohort model analysis of esophageal cancer mortality in China,1990-2019[J]. Modern Preventive Medicine, 2023, 50(20): 3663-3668.(In Chinese), articleTitle=Trend and age-period-cohort model analysis of esophageal cancer mortality in China,1990-2019, refAbstract=null)], funds=[Fund(id=1322590086290584413, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, awardId=zdxk201606, language=CN, fundingSource=重庆市区域医学重点学科建设项目(zdxk201606), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322590082280829752, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, xref=null, ext=[AuthorCompanyExt(id=1322590082293412665, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, companyId=1322590082280829752, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=People’s Hospital of Dazu District, Chongqing 402360, China), AuthorCompanyExt(id=1322590082305995578, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, companyId=1322590082280829752, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=重庆市大足区人民医院,重庆 402360)])], figs=[ArticleFig(id=1322590085413974867, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, language=EN, label=Figure 1, caption=Trends in mortality of CHD in China and between men and women from 1992 to 2021, figureFileSmall=13mu33trb6hOuhYCjlzSCg==, figureFileBig=Pxduq7uyx35U1bd8OnZ5Zw==, tableContent=null), ArticleFig(id=1322590085485278036, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, language=CN, label=图1, caption=1992—2021年中国冠心病死亡率在全国及男女间的变化趋势, figureFileSmall=13mu33trb6hOuhYCjlzSCg==, figureFileBig=Pxduq7uyx35U1bd8OnZ5Zw==, tableContent=null), ArticleFig(id=1322590085665633109, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, language=EN, label=Figure 2, caption=Trends in CHD mortality by age and gender in China from 1992 to 2021, figureFileSmall=N7pVbL9jbGA2a7ljD8Nn5Q==, figureFileBig=m6kIBBMTtTZayv+RNgoS/w==, tableContent=null), ArticleFig(id=1322590085720159062, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, language=CN, label=图2, caption=1992—2021年中国不同性别年龄冠心病的死亡趋势变化

注:A表示女性;B表示男性。

, figureFileSmall=N7pVbL9jbGA2a7ljD8Nn5Q==, figureFileBig=m6kIBBMTtTZayv+RNgoS/w==, tableContent=null), ArticleFig(id=1322590085778879319, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, language=EN, label=Figure 3, caption=Relative risk of coronary heart disease mortality in China based on APC model effects, figureFileSmall=lFyRmfhEdlyLKCqKqaNR5Q==, figureFileBig=MrAhk5YyziY+ePZwV7XtIQ==, tableContent=null), ArticleFig(id=1322590085862765400, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, language=CN, label=图3, caption=基于APC模型效应的中国冠心病致死风险相对危险度

注:A1~A16分别表示15~19岁、……、90~94岁;P1~P6分别表示1992—1996年、……、2017—2021年;C1~C21分别表示1902—1906年、……、2002—2006年。

, figureFileSmall=lFyRmfhEdlyLKCqKqaNR5Q==, figureFileBig=MrAhk5YyziY+ePZwV7XtIQ==, tableContent=null), ArticleFig(id=1322590085913097049, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, language=EN, label=Table 1, caption=

Trends in mortality from CHD in China from 1992 to 2021

, figureFileSmall=null, figureFileBig=null, tableContent=
年份(年)死亡率(1/10万)APC(%,95%CI)值AAPC(%,95%CI)值t值P值
1992年2021年
全国93.24110.910.584(0.268~0.901)3.63<0.001
1992—19990.530(0.141~0.920) *
1999—20045.948(5.185~6.717) *
2004—2007-1.975(-4.127~0.224)
2007—20111.931(0.675~3.202) *
2011—2021-1.721(-1.998~-1.444) *
女性81.9886.100.097(-0.196~0.392)0.650.516
1992—1998-0.605(-1.057~-0.152) *
1998—20045.155(4.619~5.693) *
2004—2007-2.435(-4.418~-0.410) *
2007—20111.146(-0.066~2.372)
2011—2021-2.064(-2.321~-1.807) *
男性111.14148.400.979(0.582~1.377)4.85<0.001
1992—19990.948(0.450~1.447) *
1999—20046.321(5.374~7.277) *
2004—2007-1.004(-3.539~1.598)
2007—20112.252(0.590~3.941) *
2011—2021-1.478(-1.882~-1.073) *
), ArticleFig(id=1322590085992788826, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, language=CN, label=表1, caption=

1992—2021年中国冠心病死亡率变化趋势

, figureFileSmall=null, figureFileBig=null, tableContent=
年份(年)死亡率(1/10万)APC(%,95%CI)值AAPC(%,95%CI)值t值P值
1992年2021年
全国93.24110.910.584(0.268~0.901)3.63<0.001
1992—19990.530(0.141~0.920) *
1999—20045.948(5.185~6.717) *
2004—2007-1.975(-4.127~0.224)
2007—20111.931(0.675~3.202) *
2011—2021-1.721(-1.998~-1.444) *
女性81.9886.100.097(-0.196~0.392)0.650.516
1992—1998-0.605(-1.057~-0.152) *
1998—20045.155(4.619~5.693) *
2004—2007-2.435(-4.418~-0.410) *
2007—20111.146(-0.066~2.372)
2011—2021-2.064(-2.321~-1.807) *
男性111.14148.400.979(0.582~1.377)4.85<0.001
1992—19990.948(0.450~1.447) *
1999—20046.321(5.374~7.277) *
2004—2007-1.004(-3.539~1.598)
2007—20112.252(0.590~3.941) *
2011—2021-1.478(-1.882~-1.073) *
), ArticleFig(id=1322590086072480603, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, language=EN, label=Table 2, caption=

Analysis of CHD mortality in China based on APC model

, figureFileSmall=null, figureFileBig=null, tableContent=
组别女性男性
效应系数(95%CI)P值效应系数(95%CI)P值
年龄(岁)
15~19-2.720(-3.467~-1.973)<0.001-2.897(-3.672~-2.123)<0.001
20~24-2.222(-2.733~-1.711)<0.001-2.425(-2.967~-1.883)<0.001
25~29-2.135(-2.590~-1.679)<0.001-2.128(-2.579~-1.677)<0.001
30~34-1.848(-2.222~-1.474)<0.001-1.643(-1.998~-1.289)<0.001
35~39-1.493(-1.797~-1.188)<0.001-1.181(-1.470~-0.892)<0.001
40~44-1.097(-1.346~-0.849)<0.001-0.744(-0.984~-0.503)<0.001
45~49-0.904(-1.119~-0.690)<0.001-0.591(-0.803~-0.378)<0.001
50~54-0.509(-0.682~-0.336)<0.001-0.288(-0.465~-0.111)0.001
55~59-0.149(-0.288~-0.010)0.035-0.015(-0.160~0.130)0.841
60~640.279(0.170~0.388)<0.0010.259(0.142~0.375)<0.001
65~690.737(0.651~0.823)<0.0010.610(0.517~0.702)<0.001
70~741.283(1.208~1.358)<0.0011.050(0.973~1.128)<0.001
75~791.800(1.719~1.881)<0.0011.487(1.409~1.564)<0.001
80~842.458(2.359~2.556)<0.0012.067(1.976~2.158)<0.001
85~892.988(2.864~3.112)<0.0012.916(2.803~3.029)<0.001
90~943.532(3.380~3.685)<0.0013.524(3.384~3.664)<0.001
时期(年)
1992—1996-0.396(-0.481~-0.310)<0.001-0.606(-0.691~-0.521)<0.001
1997—2001-0.158(-0.211~-0.106)<0.001-0.276(-0.328~-0.224)<0.001
2002—20060.042(0.019~0.066)<0.0010.017(-0.008~0.042)0.186
2007—20110.191(0.168~0.214)<0.0010.234(0.209~0.259)<0.001
2012—20160.137(0.085~0.189)<0.0010.291(0.239~0.342)<0.001
2017—20210.183(0.099~0.267)<0.0010.341(0.258~0.423)<0.001
出生队列(年)
1902—19061.238(1.039~1.437)<0.0011.470(1.273~1.666)<0.001
1907—19111.235(1.064~1.406)<0.0011.273(1.104~1.441)<0.001
1912—19161.167(1.020~1.314)<0.0011.248(1.104~1.392)<0.001
1917—19211.101(0.974~1.228)<0.0011.102(0.978~1.226)<0.001
1922—19261.037(0.924~1.151)<0.0010.942(0.832~1.052)<0.001
1927—19310.955(0.847~1.064)<0.0010.805(0.701~0.909)<0.001
1932—19360.867(0.753~0.982)<0.0010.716(0.606~0.826)<0.001
1937—19410.769(0.639~0.900)<0.0010.582(0.456~0.708)<0.001
1942—19460.605(0.452~0.758)<0.0010.387(0.238~0.535)<0.001
1947—19510.459(0.279~0.639)<0.0010.239(0.064~0.414)0.008
1952—19560.254(0.043~0.465)0.0180.047(-0.158~0.251)0.656
1957—19610.034(-0.211~0.279)0.784-0.128(-0.364~0.108)0.287
1962—1966-0.181(-0.463~0.101)0.209-0.310(-0.578~-0.041)0.024
1967—1971-0.423(-0.746~-0.100)0.010-0.490(-0.794~-0.187)0.002
1972—1976-0.682(-1.051~-0.314)<0.001-0.705(-1.049~-0.361)<0.001
1977—1981-0.880(-1.294~-0.466)<0.001-0.797(-1.176~-0.418)<0.001
1982—1986-1.090(-1.592~-0.588)<0.001-0.910(-1.356~-0.463)<0.001
1987—1991-1.308(-1.939~-0.676)<0.001-1.049(-1.604~-0.494)<0.001
1992—1996-1.526(-2.354~-0.698)<0.001-1.257(-2.009~-0.504)0.001
1997—2001-1.675(-2.800~-0.551)0.004-1.411(-2.469~-0.353)0.009
2002—2006-1.957(-4.244~0.329)0.093-1.752(-3.936~0.431)0.116
模型拟合检验
偏差(Deviance)0.761.65
最小信息量准则(AIC)7.128.28
贝叶斯信息准则(BIC)-213.02-163.46
), ArticleFig(id=1322590086164755292, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590080720548653, language=CN, label=表2, caption=

基于APC模型的中国冠心病死亡率分析

, figureFileSmall=null, figureFileBig=null, tableContent=
组别女性男性
效应系数(95%CI)P值效应系数(95%CI)P值
年龄(岁)
15~19-2.720(-3.467~-1.973)<0.001-2.897(-3.672~-2.123)<0.001
20~24-2.222(-2.733~-1.711)<0.001-2.425(-2.967~-1.883)<0.001
25~29-2.135(-2.590~-1.679)<0.001-2.128(-2.579~-1.677)<0.001
30~34-1.848(-2.222~-1.474)<0.001-1.643(-1.998~-1.289)<0.001
35~39-1.493(-1.797~-1.188)<0.001-1.181(-1.470~-0.892)<0.001
40~44-1.097(-1.346~-0.849)<0.001-0.744(-0.984~-0.503)<0.001
45~49-0.904(-1.119~-0.690)<0.001-0.591(-0.803~-0.378)<0.001
50~54-0.509(-0.682~-0.336)<0.001-0.288(-0.465~-0.111)0.001
55~59-0.149(-0.288~-0.010)0.035-0.015(-0.160~0.130)0.841
60~640.279(0.170~0.388)<0.0010.259(0.142~0.375)<0.001
65~690.737(0.651~0.823)<0.0010.610(0.517~0.702)<0.001
70~741.283(1.208~1.358)<0.0011.050(0.973~1.128)<0.001
75~791.800(1.719~1.881)<0.0011.487(1.409~1.564)<0.001
80~842.458(2.359~2.556)<0.0012.067(1.976~2.158)<0.001
85~892.988(2.864~3.112)<0.0012.916(2.803~3.029)<0.001
90~943.532(3.380~3.685)<0.0013.524(3.384~3.664)<0.001
时期(年)
1992—1996-0.396(-0.481~-0.310)<0.001-0.606(-0.691~-0.521)<0.001
1997—2001-0.158(-0.211~-0.106)<0.001-0.276(-0.328~-0.224)<0.001
2002—20060.042(0.019~0.066)<0.0010.017(-0.008~0.042)0.186
2007—20110.191(0.168~0.214)<0.0010.234(0.209~0.259)<0.001
2012—20160.137(0.085~0.189)<0.0010.291(0.239~0.342)<0.001
2017—20210.183(0.099~0.267)<0.0010.341(0.258~0.423)<0.001
出生队列(年)
1902—19061.238(1.039~1.437)<0.0011.470(1.273~1.666)<0.001
1907—19111.235(1.064~1.406)<0.0011.273(1.104~1.441)<0.001
1912—19161.167(1.020~1.314)<0.0011.248(1.104~1.392)<0.001
1917—19211.101(0.974~1.228)<0.0011.102(0.978~1.226)<0.001
1922—19261.037(0.924~1.151)<0.0010.942(0.832~1.052)<0.001
1927—19310.955(0.847~1.064)<0.0010.805(0.701~0.909)<0.001
1932—19360.867(0.753~0.982)<0.0010.716(0.606~0.826)<0.001
1937—19410.769(0.639~0.900)<0.0010.582(0.456~0.708)<0.001
1942—19460.605(0.452~0.758)<0.0010.387(0.238~0.535)<0.001
1947—19510.459(0.279~0.639)<0.0010.239(0.064~0.414)0.008
1952—19560.254(0.043~0.465)0.0180.047(-0.158~0.251)0.656
1957—19610.034(-0.211~0.279)0.784-0.128(-0.364~0.108)0.287
1962—1966-0.181(-0.463~0.101)0.209-0.310(-0.578~-0.041)0.024
1967—1971-0.423(-0.746~-0.100)0.010-0.490(-0.794~-0.187)0.002
1972—1976-0.682(-1.051~-0.314)<0.001-0.705(-1.049~-0.361)<0.001
1977—1981-0.880(-1.294~-0.466)<0.001-0.797(-1.176~-0.418)<0.001
1982—1986-1.090(-1.592~-0.588)<0.001-0.910(-1.356~-0.463)<0.001
1987—1991-1.308(-1.939~-0.676)<0.001-1.049(-1.604~-0.494)<0.001
1992—1996-1.526(-2.354~-0.698)<0.001-1.257(-2.009~-0.504)0.001
1997—2001-1.675(-2.800~-0.551)0.004-1.411(-2.469~-0.353)0.009
2002—2006-1.957(-4.244~0.329)0.093-1.752(-3.936~0.431)0.116
模型拟合检验
偏差(Deviance)0.761.65
最小信息量准则(AIC)7.128.28
贝叶斯信息准则(BIC)-213.02-163.46
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1992—2021年中国冠心病死亡趋势及年龄-时期-队列模型分析
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夏霞 , 吴生碧 , 刁丽
现代预防医学 | 流行病与统计方法 2025,52(1): 33-39
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现代预防医学 |流行病与统计方法 2025 , 52 (1) : 33 -39
1992—2021年中国冠心病死亡趋势及年龄-时期-队列模型分析
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夏霞, 吴生碧, 刁丽
作者信息
  • 重庆市大足区人民医院,重庆 402360
通讯作者:
作者简介:

夏霞(1989—),女,本科,中级职称,研究方向:公共卫生

Trends in coronary heart disease mortality in China from 1992 to 2021: an age-period-cohort model analysis
Xia XIA, Sheng-bi WU, Li DIAO
Affiliations
  • People’s Hospital of Dazu District, Chongqing 402360, China
出版时间: 2025-01-10 doi: 10.20043/j.cnki.MPM.202407061
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目的

分析1992—2021年我国冠心病死亡趋势,为调整及优化冠心病防治政策提供依据。

方法

本研究通过joinpoint回归模型计算年度趋势变化百分比,探究在连续时间段中冠心病死亡趋势的变化,同时应用年龄-时期-队列模型(age-period-cohort,APC)计算效应系数,以此评估冠心病死亡风险在年龄、时期与出生队列方面的单个影响效应。

结果

1992—2021年全国、男性、女性冠心病死亡率总体均呈现上升趋势,平均年度变化百分比分别为0.584%、0.979%、0.097%。APC模型估计显示,年龄效应方面,男性、女性90~94岁年龄组的冠心病死亡风险分别是15~19岁年龄组的614.97和519.08倍;时期效应方面,男性、女性2017—2021年冠心病死亡风险分别是1992—1996年的2.58和1.78倍;队列效应方面,出生于1902—1906年的男性、女性分别是出生于2002—2006年的25.07和24.41倍。

结论

我国冠心病死亡率总体呈现上升趋势,男性与老年居民是重点关注人群。年龄、时期与出生队列对冠心病死亡风险均有独立影响效应。因此,应多措并举,前移关口,竭力控制冠心病危险因素,降低居民死亡风险。

冠心病  /  死亡趋势  /  Joinpoint回归模型  /  APC模型
Objective

To analyze the trends in coronary heart disease (CHD) mortality in China from 1992 to 2021, providing a basis for adjusting and optimizing CHD prevention and treatment policies.

Methods

This study employed the Join point regression model to calculate the annual percentage change in mortality trends, exploring variations in CHD mortality over consecutive time periods. Additionally, an age-period-cohort (APC) model was utilized to compute effect coefficients, assessing the individual impacts of age, period, and birth cohort on CHD mortality risk.

Results

From 1992 to 2021, the overall mortality rates for CHD in both genders showed an upward trend, with average annual percentage changes of 0.584%, 0.979%, and 0.097%, respectively. The APC model estimates revealed that the mortality risk for the age group of 90 to 94 was 614.97 times higher for males and 519.08 times higher for females compared to the age group of 15 to 19. In terms of period effects, the CHD mortality risk for males and females from 2017 to 2021 was 2.58 times and 1.78 times higher, respectively, than that from 1992 to 1996. Regarding cohort effects, individuals born between 1902 and 1906 had a CHD mortality risk 25.07 times and 24.41 times higher for males and females, respectively, compared to those born between 2002 and 2006.

Conclusion

The overall CHD mortality rate in China is on the rise, with a focus on male and elderly populations. Age, period, and birth cohort each have independent effects on CHD mortality risk. Therefore, a multifaceted approach is necessary to advance preventive measures, control risk factors, and reduce mortality risks among residents.

Coronary heart disease  /  Mortality trends  /  Join point regression model  /  APC model
夏霞, 吴生碧, 刁丽. 1992—2021年中国冠心病死亡趋势及年龄-时期-队列模型分析. 现代预防医学, 2025 , 52 (1) : 33 -39 . DOI: 10.20043/j.cnki.MPM.202407061
Xia XIA, Sheng-bi WU, Li DIAO. Trends in coronary heart disease mortality in China from 1992 to 2021: an age-period-cohort model analysis[J]. Modern Preventive Medicine, 2025 , 52 (1) : 33 -39 . DOI: 10.20043/j.cnki.MPM.202407061
冠心病(coronary heart disease),又称缺血性心脏病,是一种因冠状动脉发生粥样硬化而导致血管狭窄甚至阻塞的心血管疾病,通常表现为心肌缺血、缺氧,甚至心肌坏死[1]。冠心病是全球范围内导致死亡的主要因素之一,已经成为一项重要的公共卫生挑战。据2019年全球疾病负担(global burden of disease, GBD)数据显示[2],全世界冠心病死因顺位已由1990年的第四位升至2019年的第二位。近年来,伴随社会经济发展及居民生活方式改变,中国冠心病负担持续加重,现患病人数高达1 139万[3],也是居民的第二大死因[4]。因此,现阶段了解冠心病死亡趋势变化,把控关键环节尤为必要,有利于公共卫生政策制定。回溯既往研究,学者多以探讨冠心病死亡的影响因素、风险预测、临床特征、治疗方案等[5-8],鲜见利用前沿统计学模型分析长时序列的冠心病死亡率。本研究应用joinpoint回归模型与年龄-时期-队列模型对1992—2021年中国冠心病死亡趋势进行分析,估计居民死亡风险相关的年龄、时期与出生队列独立影响效应,旨在为调整及优化冠心病防治提供政策依据。
冠心病相关信息来源于全球健康数据交换数据库(global health data exchange, GHDx)。该数据库囊括了全球204个国家与地区的369种疾病和伤害,并提供了相关的发病率、死亡率及疾病负担数据[2]。依据《国际疾病分类第十版(International Classification of Diseases Version 10, ICD-10)》的统一标准,冠心病的编码范围被设定为I20~I25。基于GHDx数据库,本研究获取了1992—2021年中国冠心病总体与不同性别、年龄段的年龄标准化死亡率。
Joinpoint回归模型通过蒙特卡洛置换检验法确定有统计学意义的转折点,然后以若干个连续区间展现疾病的死亡趋势变化,该模型现已广泛用于流行病学分析[9]。本研究利用其方法学优势,探析1992—2021年中国冠心病死亡率的长期演变趋势,并进一步计算年度趋势变化百分比(annual percent change,APC)以及平均年度趋势变化百分比(average annual percent change,AAPC)。解释APC与AAPC含义,APC<0、APC>0分别表示在该研究时段内冠心病死亡率呈下降、上升趋势;类似地,AAPC<0、AAPC>0分别释义在整个研究时段内冠心病死亡率呈下降、上升趋势;AAPC= APC则表明在整个研究时段内冠心病死亡率呈单调递增或递减趋势[10]。
年龄-时期-队列模型(age-period-cohort,APC)基于泊松分布,通过调整年龄、时期与出生队列之间的交互作用以估计它们对冠心病死亡风险的单个影响效应[11]。由于模型需要将年龄、时期与出生队列的间隔保持一致,根据现有相关研究[12],本研究将年龄以连续5岁为间隔划分15~19岁、20~24岁、……、90~94岁共计16组,时期以连续5年为间隔划分为6组,最后再以研究时期减去年龄的方式,得到21个不同的出生队列。这种分组方法在另一方面也导致了年龄、时期与出生队列之间呈现完全线性关联,存在模型识别问题[13]。为此,本研究利用Fu[14]与Yang[15]等提出的内生因子估算法来解决模型共线性问题,从而获得无偏且可估计的唯一确定解。整个模型的基本表达式形如:
式中,D表示冠心病死亡率,Xa、α分别释义年龄及其效应,Xp、β分别表示时期及其效应,Xc、λ分别释义出生队列及其效应,φ为常数项,ε为残差。
采用Excel 2016软件进行数据初步处理,汇总并创建冠心病死亡数据库。Joinpoint回归分析模型运用学者普遍使用的Joinpoint Regression Program 4.9.1.0软件建立,以确定最佳转折点个数及位置。APC模型利用Stata 14.0软件进行深入分析,从而估计出冠心病死亡风险在年龄、时期与出生队列方面的独立影响系数及死亡相对危险度(relative risk, RR)。RR=EXP(效应系数)[16],RR值越大提示冠心病死亡风险越高。检验水准α=0.05。
1992—2021年我国冠心病死亡率总体呈现上升趋势,由1992年的93.24/10万升至2021年的110.91/10万。Joinpoint回归模型显示1992—2004年冠心病死亡率呈现上升,2004—2007年下降,2007—2011年又上升,2011—2021年呈现再下降趋势,年均增速为0.584%,趋势具有统计学意义(AAPC=0.584%,t=3.63,P<0.001)。
男女性冠心病死亡率分别从1992年的111.14/10万、81.98/10万波动升至2021年的148.40/10万、86.10/10万。男性死亡率分别在1999、2004、2007和2011年出现转折点,年平均增长速度高于全国整体水平,趋势具有统计学意义(AAPC= 0.979%,t=4.85,P<0.001)。观之女性,其死亡率转折年份与男性略有差异,依次位于1998、2004、2007和2011年,年平均增长速度低于全国整体水平,趋势不具有统计学意义(AAPC=0.097%,t=0.65,P=0.516)。见图1、表1。
1992—2021年,无论男、女性冠心病患者,其死亡率均随年龄的增长而上升。在15~69岁年龄组,男、女性的死亡趋势变化均相对平缓。然而,一旦超过70岁,其死亡趋势的增加则均尤为显著。见图2。
控制时期与出生队列效应后,无论男、女性冠心病患者,其死亡风险均随年龄的增长而上升。在15~24岁、60~94岁这两个年龄组,女性冠心病死亡风险超过了男性;相反地,在25~59岁这个年龄组,男性死亡风险则更高。男性、女性在90~94岁年龄组的冠心病死亡风险均达最高(男性、女性RR值分别为33.932、34.196),而在15~19岁年龄组的死亡风险均呈最低(男性、女性RR值分别为0.055、0.066),前者死亡风险分别是后者的614.97和519.08倍。见表2、图3。
控制年龄与出生队列效应后,无论男性、女性冠心病患者,其死亡风险均随时期的推移而波动上升。在1992—2006年,女性冠心病死亡风险超过了男性;然而,在2007—2021年,男性死亡风险则更高。在2017—2021年,男性、女性冠心病死亡风险分别较1992—1996年增加了2.58和1.78倍。见表2、图3。
控制年龄与时期效应后,无论男性、女性冠心病患者,其死亡风险均随出生队列的推移而下降。在1902—1921年、1977—2006年这两个出生队列,男性冠心病死亡风险超过了女性;然而,在1922—1976年出生的女性,其死亡风险则更高。男性、女性冠心病死亡风险最高的出生队列均居于1902—1906年,而死亡风险最低的出生队列均位于2002—2006年,前者死亡风险分别是后者的25.07和24.41倍。见表2、图3。
1992—2021年我国冠心病死亡率总体呈现上升趋势,这与买力曼·巴哈尼等[17]的研究结果一致,提示冠心病的防控形势变得愈发严峻,需要引起全社会足够重视。有研究认为经济发展水平与冠心病的发病及死亡呈正相关。这可解释为经济发展水平的提升,可以促进居民转变饮食习惯和生活方式,但更多居于不健康状态,如高盐高脂饮食、吸烟酗酒、低运动量等,各类危险因素聚集,极易诱发冠心病。Joinpoint回归模型显示,冠心病死亡趋势有统计学意义的转折点分别位于2004、2007和2011年,通过转折点将研究时段分为四个连续区间,表现为1992—2004年冠心病死亡率呈现上升,2004—2007年下降,2007—2011年又上升,2011—2021年再次呈现下降。究其原因,一方面我国于2011年以来相继发布冠心病介入诊疗技术管理规范、冠心病分级诊疗服务技术方案,以及实施健康中国心脑血管疾病防治行动,使得医疗机构防治能力及质量显著改善,居民健康素养水平不断提升,因而冠心病死亡率上升趋势得到有效控制。另一方面,近年来全国多家医疗机构积极建立“胸痛中心”,尤其是经皮冠状动脉介入治疗和冠状动脉旁路移植术的实施和发展,也使得冠心病死亡率开始呈现下降趋势[18]。
研究发现截至2021年,男性、女性冠心病死亡率分别为148.40/10万、86.10/10万,提示男性冠心病死亡率显著高于女性。分析原因,一是较之女性,男性可能涉及更多导致冠心病死亡的危险因素,如吸烟、饮酒、不合理膳食、高压力和低运动量等[19]。二是女性有雌激素的保护作用,有助于提升血液中高密度脂蛋白的浓度,同时降低甘油三酯与低密度脂蛋白的水平,从而在一定程度上减少患病风险[20-21]。此外,也有研究表明女性更为注重身体健康,倾向于积极寻求健康护理[22]。因此,建议加强冠心病防治健康宣教,尤其是男性居民,倡导积极健康的生活方式,如戒烟戒酒,合理膳食,增加体育锻炼等,逐渐形成良好的生活习惯,遏制冠心病危险因素,控制疾病的发生及发展[23]。
年龄效应显示,在控制时期与出生队列效应后,男女性冠心病死亡风险均随年龄的增长而上升。相较于15~19岁年龄组,男性、女性在90~94岁年龄组的冠心病死亡风险分别增加614.97和519.08倍,这与李兴辉等[18]研究甘肃省河西走廊地区结果一致。有学者指出人体衰老伴随生理储备功能下降,与心脏病死亡率呈正相关[24]。老年居民生理机能退化,加之累积更多的冠心病危险因素,故而死亡风险更高[25]。现如今,随着人口结构逐步迈入深度老龄化,老年人口占比进一步增加,引发的社会负担问题不容忽视。因此,老年居民应该成为冠心病防治重点关注对象。建议加大疾病早期筛查与早诊早治力度,做优老年居民个体化冠心病风险综合评估,做到早发现、早诊断及早治疗,同时也积极向老年居民宣传冠心病防治健康知识,提升个人健康管理意识[23]。
时期效应显示,在控制年龄与出生队列效应后,男性、女性冠心病死亡风险均随时期的推移而波动上升。2017—2021年男性、女性冠心病死亡风险分别是1992—1996年的2.58和1.78倍。分析其原因,一方面可能由于改革开放以来,我国经济体系迅速转型,居民生活水平逐步提升,进而出现高油高盐、饮食失衡、久坐不动等不良行为生活方式,增加了冠心病死亡风险[26]。另一方面,城镇化、工业化进程的加快,引发了日益严重的空气污染问题[27]。Dai J等通过分析2006—2011年上海市空气污染与冠心病病例数据,发现PM2.5、PM10浓度每增加10 μg/m3,冠心病死亡率分别增加0.68%、0.49%[28]。空气污染是冠心病发病与死亡的重要环境风险因素。遂建议加强对居民不良行为的干预力度,提倡科学运动,多渠道传播健康知识,将冠心病健康促进融入现行卫生政策。此外,政府部门应积极改善空气环境,落实大气污染防治行动,从而降低居民环境暴露风险。
队列效应显示,在控制年龄与时期效应后,男女性冠心病死亡风险均随出生队列的推移而下降。1902—1906年出生的男性、女性冠心病死亡风险分别是2002—2006年的25.07和24.41倍。对于出生年代越晚,冠心病死亡风险越低的原因可能有如下两点:一是出生年代较早的居民,生活条件较差,医疗资源亦较为匮乏,故接触冠心病危险因素的机会更大,且不能及时获得所需的医疗服务,导致死亡风险增加[17]。二是源于后期心脑血管疾病防治政策的不断完善以及医疗技术的快速发展,为出生年代较晚、暴露危险因素较少的居民创造了更好的遏制疾病风险的机会[29]。因此,现今新时期为进一步提升居民健康水平,应深入开展冠心病防治工作,动员全社会参与,统筹区域医疗资源,加强防治政策引导,推进“互联网+医疗健康”服务,并不断丰富冠心病相关诊疗指南及技术操作规范,持续解决防治的关键技术问题。
综上所述,本研究利用joinpoint回归模型探讨冠心病在连续时间段中的死亡趋势变化,并运用APC模型估计了年龄、时期与出生队列对其死亡风险的单个影响效应。主要研究结果表明1992—2021年我国冠心病死亡率总体呈现上升趋势,男性与老年居民的死亡风险更高。其次,年龄、时期与出生队列对冠心病死亡风险均有独立影响效应。因此,未来应将男性与老年居民纳入重点监测对象,加强冠心病防治健康教育,普及健康知识,倡导健康生活方式,不断提升居民健康素养水平。同时,前移关口,加强冠心病早期筛查与早诊早治,控制危险因素暴露,依靠创新技术科学防治。
  • 重庆市区域医学重点学科建设项目(zdxk201606)
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2025年第52卷第1期
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doi: 10.20043/j.cnki.MPM.202407061
  • 接收时间:2024-07-06
  • 首发时间:2026-10-29
  • 出版时间:2025-01-10
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  • 收稿日期:2024-07-06
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重庆市区域医学重点学科建设项目(zdxk201606)
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    重庆市大足区人民医院,重庆 402360

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2种不同金属材料的力学参数
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属数
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genus
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species
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Percentage of
total species (%)
属
Genus
种数
Number of
species
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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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