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Article(id=1241342456118178785, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241342451043061769, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202308526, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1694361600000, receivedDateStr=2023-09-11, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773888704566, onlineDateStr=2026-03-19, pubDate=1708790400000, pubDateStr=2024-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773888704566, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773888704566, creator=13701087609, updateTime=1773888704566, updator=13701087609, issue=Issue{id=1241342451043061769, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='4', pageStart='577', pageEnd='768', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773888703356, creator=13701087609, updateTime=1773893026321, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241360582939562716, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241342451043061769, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241360582939562717, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241342451043061769, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=728, endPage=735, ext={EN=ArticleExt(id=1241342456613106678, articleId=1241342456118178785, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analyses on the trends and projections of disease burden of self-harm in China, 1990-2019, columnId=1228016573156360233, journalTitle=Modern Preventive Medicine, columnName=Disease Control and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze and forecast the epidemic trends and disease burden of self-harm in China from 1990 to 2019, and to provide data for evaluating and formulating self-harm related prevention and treatment measures.

Methods

We used age-period-cohort modeling to estimate age, period, and cohort effects on self-harm incidence rates and mortality rates in China between 1990 and 2019. We projected the rates of self-harm in China from 2020 to 2030 using Bayesian age-period-cohort analysis.

Results

In 2019, the age-standardized incidence rate of self-harm in China was 51.24/100 000 and the age-standardized mortality rate was 6.8/100 000. From 1990 to 2019, the disease burden of self-harm showed a decreasing trend in both male and female (EAPC<0). The results of APC analysis showed that in the age effect, the RR value of incidence peaked in 20-24 years old and over 70 years old, and the RR value of mortality increased rapidly in people over 70 years old.The period effect results showed that the overall morbidity and mortality risk of Chinese residents showed a downward trend from 1990 to 2019.Birth cohort results showed that the risk of morbidity and mortality for both male and female increased slowly and then decreased gradually with the change of birth year, and the population born between 1950 and 1954 had a higher risk of morbidity and mortality. By 2030, the ASIR of male self-harm is expected to decrease to 28.38/100 000 and that of female is 57.02/100 000. The ASMR of male decreased to 7.53/100 000 and that of female decreased to 3.90/100 000.

Conclusion

Although the burden of self-harm disease among Chinese residents shows an overall downward trend, it is still one of the diseases with a high burden of disease. Young people aged 20-24 and elderly people over 70 years old are targeted for life assistance and psychological counseling, and attention is paid to the physical and mental health of women who have attempted suicide, so as to reduce the burden of disease caused by self-harm.

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目的

分析1990—2019年中国居民自残发病率和死亡率变化趋势并进行预测研究，为相关部门制定自残行为防治措施提供依据。

方法

收集 2019 年全球疾病负担（GBD）数据库中中国居民自残发病和死亡相关数据，使用年龄-时期-队列（APC）模型分析 1990—2019 年中国居民自残发病和死亡情况，采用贝叶斯年龄-时期-队列（BAPC）方法对2020—2030年中国居民自残的发病率和死亡率进行预测。

结果

2019年中国居民自残年龄标准化发病率（ASIR）为51.24/10万，年龄标准化死亡率（ASMR）为6.8/10万。1990—2019年，男女性自残发病率和死亡率年估计百分比变化（EAPC）均<0，均呈现下降趋势。APC分析结果显示，年龄效应中20～24岁和70岁以上呈现发病风险高峰，70岁以上人群死亡风险急速上升。时期效应结果显示，1990—2019年中国居民发病和死亡风险整体呈现下降趋势。出生队列结果显示男女性死亡和发病风险均随着出生年份的变化先缓慢上升再逐渐下降，1950—1954年出生的人群具有较高的发病和死亡风险。到2030年，男性自残ASIR预计降低为28.38/10万，女性ASIR为57.02/10万；男性ASMR降低至7.53/10万，女性ASMR降低至3.90/10万。

结论

中国居民自残疾病负担虽然整体呈现下降趋势，但仍是高疾病负担疾病之一，将20～24岁年轻人群和70岁以上老年人群作为重点对象进行生活帮扶和心理疏导，并着重关注自杀未遂女性身心健康，降低自残带来的疾病负担。

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刘燕，E-mail：；
王朝才，E-mail：
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=rDXlJsvB/rp2SmHqSkGgVg==, magXml=zcTT2X1WCodzR4lO5qwBsQ==, pdfUrl=null, pdf=MQbn1JS6QIOo2XYyOOdgiA==, pdfFileSize=1853234, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=1GBZ+kkeC3SUCC393vMoYg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=1mQAJLrbUxhC3h8A5S9JWw==, mapNumber=null, authorCompany=null, fund=null, authors=

蒙元洁（2001—），女，硕士在读，研究方向：卫生统计学

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蒙元洁（2001—），女，硕士在读，研究方向：卫生统计学

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蒙元洁（2001—），女，硕士在读，研究方向：卫生统计学

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Chinese Mental Health Journal, 2020, 34(10): 828-833., articleTitle=Joinpoint regression analysis of suicide mortality rate trends among urban and rural in China from 2002 to 2018, refAbstract=null)], funds=[Fund(id=1241359971456184336, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, awardId=qyjg202204, language=CN, fundingSource=青海大学医学部教育教学改革项目(qyjg202204), fundOrder=null, country=null), Fund(id=1241359971540070418, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, awardId=青人才字（2020）18号, language=CN, fundingSource=青海省“昆仑英才”行动计划(青人才字（2020）18号), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241359966762759049, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, xref=1., ext=[AuthorCompanyExt(id=1241359966775341962, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, companyId=1241359966762759049, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Public Health, Faculty of Medicine, Qinghai University, Xining, Qinghai 810000, China), AuthorCompanyExt(id=1241359966779536267, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, companyId=1241359966762759049, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.青海大学医学部公共卫生系，青海 西宁 810000)]), AuthorCompany(id=1241359966846645135, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, xref=2., ext=[AuthorCompanyExt(id=1241359966855033744, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, companyId=1241359966846645135, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.青海省疾病预防控制中心传染病预防控制所)])], figs=[ArticleFig(id=1241359970038510562, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=EN, label=Fig.1, caption=Trend of age-standardized incidence and age-standardized mortality of self-harm in China, 1990 to 2019, figureFileSmall=HxqXssmaC9IzOYshZBTQhw==, figureFileBig=t9L5of1DcTVtHObwjeT9Zw==, tableContent=null), ArticleFig(id=1241359970122396644, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=CN, label=图1, caption=中国居民1990-2019年自残年龄标准化发病率及年龄标准化死亡率变化趋势

注：A表示年龄标准化发病率；B表示年龄标准化死亡率。

, figureFileSmall=HxqXssmaC9IzOYshZBTQhw==, figureFileBig=t9L5of1DcTVtHObwjeT9Zw==, tableContent=null), ArticleFig(id=1241359970218865641, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=EN, label=Fig.2, caption=APC model analysis of incidence risk of self-harm in China, from 1990 to 2019, figureFileSmall=hCcQxapX9ye/MYHiAqJoYg==, figureFileBig=8WjsuzgRVb3+6ntXfY7ZGA==, tableContent=null), ArticleFig(id=1241359970315334637, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=CN, label=图2, caption=中国居民1990—2019年自残发病风险APC模型分析

注：A表示年龄效应；B表示时期效应；C表示队列效应。

, figureFileSmall=hCcQxapX9ye/MYHiAqJoYg==, figureFileBig=8WjsuzgRVb3+6ntXfY7ZGA==, tableContent=null), ArticleFig(id=1241359970390832111, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=EN, label=Fig.3, caption=APC model analysis of mortality risk of self-harm in China, from 1990 to 2019, figureFileSmall=xwvjS4MM6ai0bhAjHdjAuw==, figureFileBig=93bbkYjDuojrjxH4e5F87g==, tableContent=null), ArticleFig(id=1241359970457940979, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=CN, label=图3, caption=中国居民1990—2019年自残死亡风险APC模型分析

注：A表示年龄效应；B表示时期效应；C表示队列效应。

, figureFileSmall=xwvjS4MM6ai0bhAjHdjAuw==, figureFileBig=93bbkYjDuojrjxH4e5F87g==, tableContent=null), ArticleFig(id=1241359970579575796, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=EN, label=Fig.4, caption=Projected incidence of self-harm in China, 2020-2030, figureFileSmall=k653IKz8QewNleSsS7qlsw==, figureFileBig=2ELo94hk254YBCC2wiGrRA==, tableContent=null), ArticleFig(id=1241359970655073270, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=CN, label=图4, caption=2020—2030年中国居民自残发病率预测

注：A表示男性；B表示女性。

, figureFileSmall=k653IKz8QewNleSsS7qlsw==, figureFileBig=2ELo94hk254YBCC2wiGrRA==, tableContent=null), ArticleFig(id=1241359970743153656, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=EN, label=Fig.5, caption=Projected mortality of self-harm in China, 2020-2030, figureFileSmall=M/rKC4dFB8ryGGuXO3BNQQ==, figureFileBig=WsXdjHNMj/ImGByni1jUuw==, tableContent=null), ArticleFig(id=1241359970827039740, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=CN, label=图5, caption=2020—2030年中国居民自残死亡率预测

注：A表示男性；B表示女性。

, figureFileSmall=M/rKC4dFB8ryGGuXO3BNQQ==, figureFileBig=WsXdjHNMj/ImGByni1jUuw==, tableContent=null), ArticleFig(id=1241359970915120127, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=EN, label=Table 1, caption=

APC model analysis of incidence risk of self-harm in China, from 1990 to 2019

, figureFileSmall=null, figureFileBig=null, tableContent=
效应全人群男性女性
RR (95% CI)PRR (95% CI)PRR (95% CI)P
年龄效应（岁）
10～140.676(0.664～0.687)<0.0010.777(0.724～0.833)<0.0010.665(0.653～0.677)<0.001
15～191.575(1.552～1.598)<0.0011.650(1.552～1.755)<0.0011.626(1.602～1.651)<0.001
20～241.779(1.757～1.802)<0.0011.672(1.585～1.763)<0.0011.922(1.897～1.947)<0.001
25～291.508(1.492～1.524)<0.0011.342(1.282～1.403)<0.0011.666(1.647～1.684)<0.001
30～341.275(1.263～1.286)<0.0011.210(1.166～1.256)<0.0011.377(1.364～1.389)<0.001
35～391.095(1.087～1.103)<0.0011.200(1.164～1.236)<0.0011.123(1.114～1.132)<0.001
40～441.013(1.007～1.019)<0.0011.277(1.247～1.308)<0.0010.980(0.974～0.987)<0.001
45～490.897(0.893～0.902)<0.0011.201(1.178～1.225)<0.0010.844(0.839～0.849)<0.001
50～540.612(0.609～0.616)<0.0010.767(0.752～0.782)<0.0010.596(0.592～0.599)<0.001
55～590.547(0.543～0.550)<0.0010.687(0.672～0.703)<0.0010.529(0.525～0.533)<0.001
60～640.551(0.547～0.555)<0.0010.658(0.640～0.677)<0.0010.545(0.540～0.550)<0.001
65～690.605(0.599～0.610)<0.0010.673(0.650～0.697)<0.0010.612(0.606～0.618)<0.001
70～740.651(0.644～0.658)<0.0010.680(0.652～0.711)<0.0010.670(0.662～0.678)<0.001
75～790.869(0.858～0.880)<0.0010.791(0.751～0.832)<0.0010.920(0.907～0.932)<0.001
80～841.356(1.337～1.376)<0.0011.205(1.135～1.279)<0.0011.397(1.375～1.418)<0.001
85～891.523(1.496～1.550)<0.0011.220(1.136～1.309)<0.0011.497(1.470～1.526)<0.001
90～941.569(1.529～1.610)<0.0011.064(0.957～1.183)0.2521.405(1.368～1.442)<0.001
≥951.351(1.276～1.431)<0.0010.795(0.595～1.062)0.1201.187(1.120～1.259)<0.001
时期效应 （年份）
1990—19941.383(1.376～1.391)<0.0011.260(1.233～1.288)<0.0011.453(1.445～1.461)<0.001
1995—19991.294(1.290～1.299)<0.0011.160(1.145～1.176)<0.0011.367(1.362～1.372)<0.001
2000—20041.072(1.070～1.075)<0.0011.025(1.020～1.031)<0.0011.100(1.097～1.102)<0.001
2005—20090.897(0.895～0.899)<0.0010.927(0.922～0.932)<0.0010.885(0.883～0.887)<0.001
2010—20140.797(0.794～0.800)<0.0010.863(0.851～0.875)<0.0010.765(0.762～0.768)<0.001
2015—20190.729(0.725～0.733)<0.0010.834(0.815～0.852)<0.0010.676(0.672～0.680)<0.001
队列效应（年份）
1895—18990.717(0.585～0.879)0.0010.929(0.380～2.276)0.8730.640(0.519～0.789)<0.001
1900—19040.807(0.752～0.867)<0.0010.806(0.604～1.076)0.1430.742(0.690～0.799)<0.001
1905—19090.876(0.842～0.911)<0.0010.784(0.675～0.911)0.0020.822(0.789～0.856)<0.001
1910—19140.926(0.898～0.956)<0.0010.836(0.738～0.946)0.0040.885(0.857～0.914)<0.001
1915—19191.022(0.995～1.050)<0.0010.982(0.878～1.098)0.7490.977(0.950～1.005)0.113
1920—19241.093(1.067～1.120)<0.0011.061(0.959～1.175)0.2511.068(1.041～1.095)<0.001
1925—19291.102(1.078～1.127)<0.0011.046(0.954～1.147)0.3381.115(1.089～1.141)<0.001
1930—19341.095(1.074～1.118)<0.0011.033(0.950～1.123)0.4471.139(1.116～1.163)<0.001
1935—19391.105(1.086～1.126)<0.0011.025(0.951～1.104)0.5171.174(1.152～1.196)<0.001
1940—19441.134(1.116～1.152)<0.0011.027(0.961～1.097)0.4321.223(1.202～1.244)<0.001
1945—19491.183(1.166～1.200)<0.0011.042(0.985～1.103)0.1551.285(1.267～1.304)<0.001
1950—19541.196(1.182～1.210)<0.0011.035(0.987～1.086)0.1581.305(1.288～1.321)<0.001
1955—19591.177(1.166～1.189)<0.0011.016(0.977～1.057)0.4261.281(1.267～1.294)<0.001
1960—19641.144(1.135～1.153)<0.0011.001(0.971～1.033)0.9351.234(1.224～1.244)<0.001
1965—19691.125(1.119～1.132)<0.0010.990(0.968～1.012)0.3711.201(1.194～1.208)<0.001
1970—19741.057(1.053～1.061)<0.0010.990(0.976～1.003)0.1351.109(1.104～1.113)<0.001
1975—19790.968(0.966～0.971)<0.0010.995(0.989～1.002)0.1680.980(0.976～0.983)<0.001
1980—19840.890(0.887～0.893)<0.0011.011(1.003～1.018)0.0070.867(0.864～0.870)<0.001
1985—19890.868(0.864～0.872)<0.0011.051(1.035～1.067)<0.0010.823(0.819～0.828)<0.001
1990—19940.897(0.891～0.902)<0.0011.107(1.081～1.133)<0.0010.842(0.836～0.848)<0.001
1995—19990.927(0.919～0.935)<0.0011.137(1.101～1.174)<0.0010.875(0.867～0.883)<0.001
2000—20040.935(0.925～0.946)<0.0011.110(1.064～1.157)<0.0010.895(0.884～0.906)<0.001
2005—20090.957(0.941～0.973)<0.0011.083(1.028～1.141)0.0030.936(0.918～0.954)<0.001
AIC67.81437.85972.693
BIC5 636.7242 557.7496 198.268
), ArticleFig(id=1241359971015782401, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=CN, label=表1, caption=

1990—2019年中国居民自残发病风险APC模型分析

, figureFileSmall=null, figureFileBig=null, tableContent=
效应全人群男性女性
RR (95% CI)PRR (95% CI)PRR (95% CI)P
年龄效应（岁）
10～140.676(0.664～0.687)<0.0010.777(0.724～0.833)<0.0010.665(0.653～0.677)<0.001
15～191.575(1.552～1.598)<0.0011.650(1.552～1.755)<0.0011.626(1.602～1.651)<0.001
20～241.779(1.757～1.802)<0.0011.672(1.585～1.763)<0.0011.922(1.897～1.947)<0.001
25～291.508(1.492～1.524)<0.0011.342(1.282～1.403)<0.0011.666(1.647～1.684)<0.001
30～341.275(1.263～1.286)<0.0011.210(1.166～1.256)<0.0011.377(1.364～1.389)<0.001
35～391.095(1.087～1.103)<0.0011.200(1.164～1.236)<0.0011.123(1.114～1.132)<0.001
40～441.013(1.007～1.019)<0.0011.277(1.247～1.308)<0.0010.980(0.974～0.987)<0.001
45～490.897(0.893～0.902)<0.0011.201(1.178～1.225)<0.0010.844(0.839～0.849)<0.001
50～540.612(0.609～0.616)<0.0010.767(0.752～0.782)<0.0010.596(0.592～0.599)<0.001
55～590.547(0.543～0.550)<0.0010.687(0.672～0.703)<0.0010.529(0.525～0.533)<0.001
60～640.551(0.547～0.555)<0.0010.658(0.640～0.677)<0.0010.545(0.540～0.550)<0.001
65～690.605(0.599～0.610)<0.0010.673(0.650～0.697)<0.0010.612(0.606～0.618)<0.001
70～740.651(0.644～0.658)<0.0010.680(0.652～0.711)<0.0010.670(0.662～0.678)<0.001
75～790.869(0.858～0.880)<0.0010.791(0.751～0.832)<0.0010.920(0.907～0.932)<0.001
80～841.356(1.337～1.376)<0.0011.205(1.135～1.279)<0.0011.397(1.375～1.418)<0.001
85～891.523(1.496～1.550)<0.0011.220(1.136～1.309)<0.0011.497(1.470～1.526)<0.001
90～941.569(1.529～1.610)<0.0011.064(0.957～1.183)0.2521.405(1.368～1.442)<0.001
≥951.351(1.276～1.431)<0.0010.795(0.595～1.062)0.1201.187(1.120～1.259)<0.001
时期效应 （年份）
1990—19941.383(1.376～1.391)<0.0011.260(1.233～1.288)<0.0011.453(1.445～1.461)<0.001
1995—19991.294(1.290～1.299)<0.0011.160(1.145～1.176)<0.0011.367(1.362～1.372)<0.001
2000—20041.072(1.070～1.075)<0.0011.025(1.020～1.031)<0.0011.100(1.097～1.102)<0.001
2005—20090.897(0.895～0.899)<0.0010.927(0.922～0.932)<0.0010.885(0.883～0.887)<0.001
2010—20140.797(0.794～0.800)<0.0010.863(0.851～0.875)<0.0010.765(0.762～0.768)<0.001
2015—20190.729(0.725～0.733)<0.0010.834(0.815～0.852)<0.0010.676(0.672～0.680)<0.001
队列效应（年份）
1895—18990.717(0.585～0.879)0.0010.929(0.380～2.276)0.8730.640(0.519～0.789)<0.001
1900—19040.807(0.752～0.867)<0.0010.806(0.604～1.076)0.1430.742(0.690～0.799)<0.001
1905—19090.876(0.842～0.911)<0.0010.784(0.675～0.911)0.0020.822(0.789～0.856)<0.001
1910—19140.926(0.898～0.956)<0.0010.836(0.738～0.946)0.0040.885(0.857～0.914)<0.001
1915—19191.022(0.995～1.050)<0.0010.982(0.878～1.098)0.7490.977(0.950～1.005)0.113
1920—19241.093(1.067～1.120)<0.0011.061(0.959～1.175)0.2511.068(1.041～1.095)<0.001
1925—19291.102(1.078～1.127)<0.0011.046(0.954～1.147)0.3381.115(1.089～1.141)<0.001
1930—19341.095(1.074～1.118)<0.0011.033(0.950～1.123)0.4471.139(1.116～1.163)<0.001
1935—19391.105(1.086～1.126)<0.0011.025(0.951～1.104)0.5171.174(1.152～1.196)<0.001
1940—19441.134(1.116～1.152)<0.0011.027(0.961～1.097)0.4321.223(1.202～1.244)<0.001
1945—19491.183(1.166～1.200)<0.0011.042(0.985～1.103)0.1551.285(1.267～1.304)<0.001
1950—19541.196(1.182～1.210)<0.0011.035(0.987～1.086)0.1581.305(1.288～1.321)<0.001
1955—19591.177(1.166～1.189)<0.0011.016(0.977～1.057)0.4261.281(1.267～1.294)<0.001
1960—19641.144(1.135～1.153)<0.0011.001(0.971～1.033)0.9351.234(1.224～1.244)<0.001
1965—19691.125(1.119～1.132)<0.0010.990(0.968～1.012)0.3711.201(1.194～1.208)<0.001
1970—19741.057(1.053～1.061)<0.0010.990(0.976～1.003)0.1351.109(1.104～1.113)<0.001
1975—19790.968(0.966～0.971)<0.0010.995(0.989～1.002)0.1680.980(0.976～0.983)<0.001
1980—19840.890(0.887～0.893)<0.0011.011(1.003～1.018)0.0070.867(0.864～0.870)<0.001
1985—19890.868(0.864～0.872)<0.0011.051(1.035～1.067)<0.0010.823(0.819～0.828)<0.001
1990—19940.897(0.891～0.902)<0.0011.107(1.081～1.133)<0.0010.842(0.836～0.848)<0.001
1995—19990.927(0.919～0.935)<0.0011.137(1.101～1.174)<0.0010.875(0.867～0.883)<0.001
2000—20040.935(0.925～0.946)<0.0011.110(1.064～1.157)<0.0010.895(0.884～0.906)<0.001
2005—20090.957(0.941～0.973)<0.0011.083(1.028～1.141)0.0030.936(0.918～0.954)<0.001
AIC67.81437.85972.693
BIC5 636.7242 557.7496 198.268
), ArticleFig(id=1241359971129028613, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=EN, label=Table 2, caption=

APC model analysis of mortality risk of self-harm in China, from 1990 to 2019

, figureFileSmall=null, figureFileBig=null, tableContent=
效应全人群男性女性
RR(95% CI)PRR(95% CI)PRR(95% CI)P
年龄效应（岁）
10～140.204(0.199～0.209)<0.0010.164(0.157～0.171)<0.0010.254(0.247～0.262)<0.001
15～190.553(0.543～0.563)<0.0010.489(0.472～0.507)<0.0010.616(0.602～0.630)<0.001
20～240.758(0.746～0.769)<0.0010.678(0.658～0.699)<0.0010.821(0.806～0.837)<0.001
25～290.734(0.724～0.744)<0.0010.633(0.617～0.650)<0.0010.816(0.803～0.830)<0.001
30～340.712(0.704～0.721)<0.0010.623(0.609～0.637)<0.0010.781(0.770～0.793)<0.001
35～390.651(0.644～0.658)<0.0010.658(0.645～0.670)<0.0010.619(0.610～0.628)<0.001
40～440.601(0.595～0.607)<0.0010.637(0.627～0.648)<0.0010.537(0.530～0.544)<0.001
45～490.596(0.590～0.602)<0.0010.622(0.613～0.632)<0.0010.539(0.532～0.546)<0.001
50～540.655(0.649～0.661)<0.0010.668(0.658～0.678)<0.0010.604(0.596～0.613)<0.001
55～590.715(0.708～0.722)<0.0010.743(0.732～0.755)<0.0010.632(0.623～0.641)<0.001
60～640.796(0.787～0.804)<0.0010.817(0.802～0.832)<0.0010.708(0.697～0.718)<0.001
65～690.974(0.962～0.986)<0.0010.959(0.939～0.980)<0.0010.905(0.890～0.919)<0.001
70～741.475(1.456～1.495)<0.0011.391(1.357～1.426)<0.0011.435(1.411～1.459)<0.001
75～791.986(1.956～2.016)<0.0011.848(1.796～1.902)<0.0011.968(1.931～2.005)<0.001
80～842.835(2.787～2.884)<0.0012.655(2.569～2.745)<0.0012.857(2.797～2.918)<0.001
85～893.855(3.778～3.935)<0.0014.353(4.187～4.525)<0.0013.545(3.457～3.636)<0.001
90～943.265(3.167～3.366)<0.0014.561(4.307～4.830)<0.0013.324(3.204～3.448)<0.001
≥952.536(2.360～2.725)<0.0013.389(2.901～3.958)<0.0012.923(2.695～3.171)<0.001
时期效应（年份）
1990—19941.396(1.386～1.405)<0.0011.239(1.223～1.255)<0.0011.566(1.552～1.579)<0.001
1995—19991.313(1.306～1.320)<0.0011.150(1.140～1.161)<0.0011.503(1.493～1.513)<0.001
2000—20041.066(1.061～1.071)<0.0011.042(1.035～1.049)<0.0011.103(1.095～1.110)<0.001
2005—20090.956(0.952～0.960)<0.0010.980(0.974～0.986)<0.0010.944(0.937～0.950)<0.001
2010—20140.775(0.771～0.780)<0.0010.853(0.845～0.861)<0.0010.704(0.698～0.710)<0.001
2015—20190.691(0.686～0.696)<0.0010.806(0.795～0.816)<0.0010.580(0.574～0.586)<0.001
队列效应（年份）
1895—18991.471(1.178～1.836)0.0011.662(1.030～2.684)0.0371.195(0.930～1.536)0.164
1900—19041.490(1.378～1.612)<0.0011.603(1.382～1.859)<0.0011.290(1.176～1.414)<0.001
1905—19091.411(1.352～1.473)<0.0011.556(1.435～1.687)<0.0011.317(1.251～1.388)<0.001
1910—19141.447(1.398～1.498)<0.0011.614(1.508～1.727)<0.0011.367(1.312～1.424)<0.001
1915—19191.463(1.420～1.508)<0.0011.623(1.528～1.725)<0.0011.404(1.355～1.456)<0.001
1920—19241.522(1.481～1.564)<0.0011.671(1.581～1.766)<0.0011.482(1.435～1.531)<0.001
1925—19291.572(1.534～1.612)<0.0011.680(1.597～1.766)<0.0011.564(1.518～1.611)<0.001
1930—19341.532(1.498～1.568)<0.0011.579(1.509～1.653)<0.0011.562(1.520～1.605)<0.001
1935—19391.488(1.457～1.519)<0.0011.481(1.421～1.543)<0.0011.562(1.523～1.603)<0.001
1940—19441.424(1.397～1.451)<0.0011.387(1.337～1.439)<0.0011.511(1.476～1.548)<0.001
1945—19491.412(1.388～1.436)<0.0011.330(1.287～1.374)<0.0011.544(1.511～1.578)<0.001
1950—19541.387(1.366～1.408)<0.0011.217(1.184～1.252)<0.0011.635(1.603～1.667)<0.001
1955—19591.251(1.234～1.267)<0.0011.090(1.064～1.117)<0.0011.468(1.442～1.494)<0.001
1960—19641.069(1.056～1.082)<0.0010.952(0.933～0.972)<0.0011.203(1.183～1.223)<0.001
1965—19691.036(1.026～1.047)<0.0010.887(0.872～0.902)<0.0011.201(1.184～1.218)<0.001
1970—19740.923(0.915～0.932)<0.0010.788(0.777～0.799)<0.0011.067(1.053～1.081)<0.001
1975—19790.768(0.761～0.775)<0.0010.697(0.688～0.706)<0.0010.834(0.823～0.846)<0.001
1980—19840.618(0.611～0.624)<0.0010.624(0.615～0.633)<0.0010.597(0.587～0.607)<0.001
1985—19890.550(0.543～0.557)<0.0010.581(0.572～0.591)<0.0010.504(0.494～0.513)<0.001
1990—19940.496(0.489～0.504)<0.0010.524(0.513～0.536)<0.0010.449(0.439～0.460)<0.001
1995—19990.452(0.442～0.462)<0.0010.467(0.453～0.482)<0.0010.408(0.395～0.422)<0.001
2000—20040.406(0.393～0.420)<0.0010.398(0.380～0.417)<0.0010.394(0.374～0.415)<0.001
2005—20090.330(0.306～0.357)<0.0010.349(0.316～0.385)<0.0010.290(0.255～0.328)<0.001
AIC55.02231.51758.633
BIC4 413.4301 950.4044 883.410
), ArticleFig(id=1241359971225497609, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241342456118178785, language=CN, label=表2, caption=

1990—2019年中国居民自残死亡风险APC模型分析

, figureFileSmall=null, figureFileBig=null, tableContent=
效应全人群男性女性
RR(95% CI)PRR(95% CI)PRR(95% CI)P
年龄效应（岁）
10～140.204(0.199～0.209)<0.0010.164(0.157～0.171)<0.0010.254(0.247～0.262)<0.001
15～190.553(0.543～0.563)<0.0010.489(0.472～0.507)<0.0010.616(0.602～0.630)<0.001
20～240.758(0.746～0.769)<0.0010.678(0.658～0.699)<0.0010.821(0.806～0.837)<0.001
25～290.734(0.724～0.744)<0.0010.633(0.617～0.650)<0.0010.816(0.803～0.830)<0.001
30～340.712(0.704～0.721)<0.0010.623(0.609～0.637)<0.0010.781(0.770～0.793)<0.001
35～390.651(0.644～0.658)<0.0010.658(0.645～0.670)<0.0010.619(0.610～0.628)<0.001
40～440.601(0.595～0.607)<0.0010.637(0.627～0.648)<0.0010.537(0.530～0.544)<0.001
45～490.596(0.590～0.602)<0.0010.622(0.613～0.632)<0.0010.539(0.532～0.546)<0.001
50～540.655(0.649～0.661)<0.0010.668(0.658～0.678)<0.0010.604(0.596～0.613)<0.001
55～590.715(0.708～0.722)<0.0010.743(0.732～0.755)<0.0010.632(0.623～0.641)<0.001
60～640.796(0.787～0.804)<0.0010.817(0.802～0.832)<0.0010.708(0.697～0.718)<0.001
65～690.974(0.962～0.986)<0.0010.959(0.939～0.980)<0.0010.905(0.890～0.919)<0.001
70～741.475(1.456～1.495)<0.0011.391(1.357～1.426)<0.0011.435(1.411～1.459)<0.001
75～791.986(1.956～2.016)<0.0011.848(1.796～1.902)<0.0011.968(1.931～2.005)<0.001
80～842.835(2.787～2.884)<0.0012.655(2.569～2.745)<0.0012.857(2.797～2.918)<0.001
85～893.855(3.778～3.935)<0.0014.353(4.187～4.525)<0.0013.545(3.457～3.636)<0.001
90～943.265(3.167～3.366)<0.0014.561(4.307～4.830)<0.0013.324(3.204～3.448)<0.001
≥952.536(2.360～2.725)<0.0013.389(2.901～3.958)<0.0012.923(2.695～3.171)<0.001
时期效应（年份）
1990—19941.396(1.386～1.405)<0.0011.239(1.223～1.255)<0.0011.566(1.552～1.579)<0.001
1995—19991.313(1.306～1.320)<0.0011.150(1.140～1.161)<0.0011.503(1.493～1.513)<0.001
2000—20041.066(1.061～1.071)<0.0011.042(1.035～1.049)<0.0011.103(1.095～1.110)<0.001
2005—20090.956(0.952～0.960)<0.0010.980(0.974～0.986)<0.0010.944(0.937～0.950)<0.001
2010—20140.775(0.771～0.780)<0.0010.853(0.845～0.861)<0.0010.704(0.698～0.710)<0.001
2015—20190.691(0.686～0.696)<0.0010.806(0.795～0.816)<0.0010.580(0.574～0.586)<0.001
队列效应（年份）
1895—18991.471(1.178～1.836)0.0011.662(1.030～2.684)0.0371.195(0.930～1.536)0.164
1900—19041.490(1.378～1.612)<0.0011.603(1.382～1.859)<0.0011.290(1.176～1.414)<0.001
1905—19091.411(1.352～1.473)<0.0011.556(1.435～1.687)<0.0011.317(1.251～1.388)<0.001
1910—19141.447(1.398～1.498)<0.0011.614(1.508～1.727)<0.0011.367(1.312～1.424)<0.001
1915—19191.463(1.420～1.508)<0.0011.623(1.528～1.725)<0.0011.404(1.355～1.456)<0.001
1920—19241.522(1.481～1.564)<0.0011.671(1.581～1.766)<0.0011.482(1.435～1.531)<0.001
1925—19291.572(1.534～1.612)<0.0011.680(1.597～1.766)<0.0011.564(1.518～1.611)<0.001
1930—19341.532(1.498～1.568)<0.0011.579(1.509～1.653)<0.0011.562(1.520～1.605)<0.001
1935—19391.488(1.457～1.519)<0.0011.481(1.421～1.543)<0.0011.562(1.523～1.603)<0.001
1940—19441.424(1.397～1.451)<0.0011.387(1.337～1.439)<0.0011.511(1.476～1.548)<0.001
1945—19491.412(1.388～1.436)<0.0011.330(1.287～1.374)<0.0011.544(1.511～1.578)<0.001
1950—19541.387(1.366～1.408)<0.0011.217(1.184～1.252)<0.0011.635(1.603～1.667)<0.001
1955—19591.251(1.234～1.267)<0.0011.090(1.064～1.117)<0.0011.468(1.442～1.494)<0.001
1960—19641.069(1.056～1.082)<0.0010.952(0.933～0.972)<0.0011.203(1.183～1.223)<0.001
1965—19691.036(1.026～1.047)<0.0010.887(0.872～0.902)<0.0011.201(1.184～1.218)<0.001
1970—19740.923(0.915～0.932)<0.0010.788(0.777～0.799)<0.0011.067(1.053～1.081)<0.001
1975—19790.768(0.761～0.775)<0.0010.697(0.688～0.706)<0.0010.834(0.823～0.846)<0.001
1980—19840.618(0.611～0.624)<0.0010.624(0.615～0.633)<0.0010.597(0.587～0.607)<0.001
1985—19890.550(0.543～0.557)<0.0010.581(0.572～0.591)<0.0010.504(0.494～0.513)<0.001
1990—19940.496(0.489～0.504)<0.0010.524(0.513～0.536)<0.0010.449(0.439～0.460)<0.001
1995—19990.452(0.442～0.462)<0.0010.467(0.453～0.482)<0.0010.408(0.395～0.422)<0.001
2000—20040.406(0.393～0.420)<0.0010.398(0.380～0.417)<0.0010.394(0.374～0.415)<0.001
2005—20090.330(0.306～0.357)<0.0010.349(0.316～0.385)<0.0010.290(0.255～0.328)<0.001
AIC55.02231.51758.633
BIC4 413.4301 950.4044 883.410
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1990—2019年中国居民自残疾病负担变化趋势及预测研究
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蒙元洁 1 , 王朝才 2 , 刘燕 1
现代预防医学 | 疾病预防控制 2024,51(4): 728-735
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现代预防医学 | 疾病预防控制 2024, 51(4): 728-735
1990—2019年中国居民自残疾病负担变化趋势及预测研究
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蒙元洁1, 王朝才2 , 刘燕1
作者信息
  • 1.青海大学医学部公共卫生系，青海 西宁 810000
  • 2.青海省疾病预防控制中心传染病预防控制所

    • 蒙元洁（2001—），女，硕士在读，研究方向：卫生统计学

通讯作者:

刘燕，E-mail：；
王朝才，E-mail：
Analyses on the trends and projections of disease burden of self-harm in China, 1990-2019
Yuan-jie MENG1, Zhao-cai WANG2 , Yan LIU1
Affiliations
  • Department of Public Health, Faculty of Medicine, Qinghai University, Xining, Qinghai 810000, China
出版时间: 2024-02-25 doi: 10.20043/j.cnki.MPM.202308526
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摘要
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目的

分析1990—2019年中国居民自残发病率和死亡率变化趋势并进行预测研究，为相关部门制定自残行为防治措施提供依据。

方法

收集 2019 年全球疾病负担（GBD）数据库中中国居民自残发病和死亡相关数据，使用年龄-时期-队列（APC）模型分析 1990—2019 年中国居民自残发病和死亡情况，采用贝叶斯年龄-时期-队列（BAPC）方法对2020—2030年中国居民自残的发病率和死亡率进行预测。

结果

2019年中国居民自残年龄标准化发病率（ASIR）为51.24/10万，年龄标准化死亡率（ASMR）为6.8/10万。1990—2019年，男女性自残发病率和死亡率年估计百分比变化（EAPC）均<0，均呈现下降趋势。APC分析结果显示，年龄效应中20～24岁和70岁以上呈现发病风险高峰，70岁以上人群死亡风险急速上升。时期效应结果显示，1990—2019年中国居民发病和死亡风险整体呈现下降趋势。出生队列结果显示男女性死亡和发病风险均随着出生年份的变化先缓慢上升再逐渐下降，1950—1954年出生的人群具有较高的发病和死亡风险。到2030年，男性自残ASIR预计降低为28.38/10万，女性ASIR为57.02/10万；男性ASMR降低至7.53/10万，女性ASMR降低至3.90/10万。

结论

中国居民自残疾病负担虽然整体呈现下降趋势，但仍是高疾病负担疾病之一，将20～24岁年轻人群和70岁以上老年人群作为重点对象进行生活帮扶和心理疏导，并着重关注自杀未遂女性身心健康，降低自残带来的疾病负担。

自残  /  疾病负担  /  年龄-时期-队列（APC）模型  /  预测分析
Objective

To analyze and forecast the epidemic trends and disease burden of self-harm in China from 1990 to 2019, and to provide data for evaluating and formulating self-harm related prevention and treatment measures.

Methods

We used age-period-cohort modeling to estimate age, period, and cohort effects on self-harm incidence rates and mortality rates in China between 1990 and 2019. We projected the rates of self-harm in China from 2020 to 2030 using Bayesian age-period-cohort analysis.

Results

In 2019, the age-standardized incidence rate of self-harm in China was 51.24/100 000 and the age-standardized mortality rate was 6.8/100 000. From 1990 to 2019, the disease burden of self-harm showed a decreasing trend in both male and female (EAPC<0). The results of APC analysis showed that in the age effect, the RR value of incidence peaked in 20-24 years old and over 70 years old, and the RR value of mortality increased rapidly in people over 70 years old.The period effect results showed that the overall morbidity and mortality risk of Chinese residents showed a downward trend from 1990 to 2019.Birth cohort results showed that the risk of morbidity and mortality for both male and female increased slowly and then decreased gradually with the change of birth year, and the population born between 1950 and 1954 had a higher risk of morbidity and mortality. By 2030, the ASIR of male self-harm is expected to decrease to 28.38/100 000 and that of female is 57.02/100 000. The ASMR of male decreased to 7.53/100 000 and that of female decreased to 3.90/100 000.

Conclusion

Although the burden of self-harm disease among Chinese residents shows an overall downward trend, it is still one of the diseases with a high burden of disease. Young people aged 20-24 and elderly people over 70 years old are targeted for life assistance and psychological counseling, and attention is paid to the physical and mental health of women who have attempted suicide, so as to reduce the burden of disease caused by self-harm.

Self-harm  /  Burden of disease  /  Age-period-cohort (APC)model  /  Prediction
蒙元洁, 王朝才, 刘燕. 1990—2019年中国居民自残疾病负担变化趋势及预测研究. 现代预防医学, 2024 , 51 (4) : 728 -735 . DOI: 10.20043/j.cnki.MPM.202308526
Yuan-jie MENG, Zhao-cai WANG, Yan LIU. Analyses on the trends and projections of disease burden of self-harm in China, 1990-2019[J]. Modern Preventive Medicine, 2024 , 51 (4) : 728 -735 . DOI: 10.20043/j.cnki.MPM.202308526
正文
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自残是世界范围内的一个主要公共健康问题，不仅影响自残者，还通过增加资源成本和生产力损失影响家庭成员和更广泛的社会[1]。自残被美国国家健康和护理卓越研究所（NICE）指南（CG16和133）定义为“一个人实施的任何自我中毒或自残行为，无论动机如何”[2]。自残和自杀通常与心理健康问题有关，虽然自残和自杀可被视为两种不同的行为，但自残是自杀的主要风险因素[3]。中国的自杀率一直受到国内外学者的关注。本世纪初，中国人群的自杀率为 23/10 万，曾是我国全人口五大死因之一，而同期全球的自杀率约为 11/10万[4]。本文利用全球疾病负担数据库（global burden of disease study，GBD）全面分析 1990—2019 年中国居民自残发病和死亡变化趋势，并对 2020—2030 年年龄标准化发病率和年龄标准化死亡率进行预测，为我国相关部门制定自残相关防治政策和措施提供依据。
1　资料与方法
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1.1　数据来源
本研究数据来源于2019年全球疾病负担（GBD2019）研究，GBD 2019是一项多国合作研究，包括1990—2019年间204个国家和地区的369种疾病和伤害[5]。GBD 数据库中国发病数据来自中国国家伤害监测系统（NISS）和中国珠海伤害患者随访研究，死亡数据来自中国疾病监测点（DSP）系统、中国疾病预防控制中心（CDC）收集的生命登记、癌症登记数据以及澳门和香港的死因医学证明[6]。本文对 GBD 数据库进行数据筛选，选择地区为“China”，疾病为“self-harm”，年份选择 1990—2019 年全部年份，年龄选择10～14岁，15～19岁，20～24岁，……，90～94岁和≥95岁年龄组。
1.2　指标计算方法
（1）基本指标：本研究采用年龄标准化发病率（age-standardized incidence rate，ASIR）和年龄标准化死亡率（age-standardized mortality rate，ASMR）来描述发病和死亡情况。标化人口选择GBD 研究通过贝叶斯层次队列模型对来自1 250次人口普查和747个人口登记年的数据进行测算得出标准化人口结构[7]。
（2）变化趋势指标：选择年估计百分比变化（estimated annual percentage change，EAPC）来描述发病率和死亡率的变化趋势，根据 EAPC的95% CI 判断趋势变化是否有统计学意义：如果EAPC及其95% CI的下限均大于0，则其对应的年龄标准化率呈上升趋势，反之亦然[8]。
1.3　模型介绍
（1）年龄-时期-队列（age-period-cohort model，APC）模型基于泊松分布，根据年龄、时期和队列效应描述自残发病率和死亡率的趋势。本研究将效应系数进行自然对数转换，计算相对危险度（relative risk, RR）以观察年龄、时期、出生队列对自残发病率和死亡率的影响。由于分离年龄、时期和队列效应的根本困难是它们之间存在完美的线性关系，即年龄=时期-队列，因此不能使用线性回归分析等常规统计方法。故本研究引入了内生因子 (intrinsic estimator, IE)算法来克服参数不可估计的问题。应用赤池信息标准（akaike information criterion, AIC）和贝叶斯信息标准（bayesian information criterion, BIC）来观察模型的拟合程度[9-10]。
（2）贝叶斯年龄-时期-队列（bayesian age-period-cohort analysis, BAPC）模型是在APC模型的基础上增加贝叶斯模型，本研究采用集成嵌套拉普拉斯近似（integrated nested Laplace approximation，INLA）法对2020—2030年中国居民自残年龄标化发病率和年龄标化死亡率进行估计，该模型显示出比其他预测方法更好的覆盖率和准确性[11]。
1.4　统计学分析
根据APC模型要求，年龄、时期和出生队列时间间隔要保持一致，先以5年为间隔整理数据，年龄段划分为18组，1990—2019年时期分为6组，出生队列=时期-年龄。预测过程通过R-BAPC包和R-INLA包实现。数据通过RStudio 4.2.1和Stata 16.0进行模型分析和预测，通过GraphPad Prism 9.5.0绘图展示。检验水准α=0.05。
2　结果
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2.1　中国居民自残疾病负担变化情况
1990—2019年，中国居民ASIR总体呈现下降趋势，由1990年的114.88/10万降低至51.24/10万，EAPC为-3.17（95% CI: -3.33～-3.01）。性别差异上，女性ASIR在1990—2019年期间均高于男性，并且下降程度更为明显，由174.77/10万到2019年降低至62.87/10万，女性EAPC为-3.97（95% CI: -4.15～-3.78），男性EAPC为-1.49（95% CI: -1.6～-1.39）。1990—2019年，中国男性居民自残ASMR由20.13/10万降低至8.85/10万，女性由21.53/10万降低至5.03/10万，男女性均呈现下降趋势，男性EAPC为-3.09（95% CI: -3.25～-2.94），女性EAPC为-5.50（95% CI: -5.77～-5.24），女性ASMR降低幅度仍高于男性。全人群ASMR同样呈现下降趋势，EAPC为-4.21（95% CI: -4.40～-4.01）。见图1。
2.2　中国居民自残疾病负担APC模型分析
2.2.1　中国居民自残发病风险APC模型分析
对中国居民1990—2019年自残发病率进行APC模型分析，年龄效应结果显示，自残发病风险总体呈现先上升在20～24岁年龄段达到峰值，后随着年龄的增长缓慢下降，55～59岁年龄段起再次上升，80～89岁年龄段发病风险再次达到峰值的趋势。20～24岁年龄段的女性自残发病风险高于男性，35～39岁年龄段起男性逐渐高于女性，直至70～74岁年龄段女性发病风险再次高于男性。时期效应结果显示，1990—2019年，中国居民发病风险整体呈现下降趋势，2005—2009年之前女性发病风险高于男性，之后男性较高。出生队列效应结果显示，随着出生时期的变化，总体发病风险先缓慢上升再逐渐下降，1950—1954年出生的人群相对具有更高的发病风险，其中男性发病风险的变化趋势较女性更加平稳，下降趋势不明显。见表1，图2。
2.2.2　中国居民自残死亡风险APC模型分析
年龄效应结果显示，中国居民自残死亡风险在10～24岁和60～89岁有两个明显的上升阶段，70～74岁年龄段起，RR值逐渐>1，85～89岁年龄段死亡风险RR值达到3.855（95% CI：3.778～3.935）。性别上死亡风险没有明显差异，80～84岁起，男性高于女性。随着时期的变化，死亡风险RR值呈现单调递减的趋势。出生队列效应结果显示，男女性死亡风险RR值均呈现先缓慢上升再逐渐降低的趋势，男性死亡风险最高的人群出生在1925—1929年，RR值达1.680（95% CI: 1.597～1.766），女性1950—1954年出生的人群死亡风险最高，RR值为1.635（95% CI: 1.603～1.667)。见表2，图3。
2.3　基于贝叶斯APC的中国居民自残的发病率和死亡率预测
根据贝叶斯APC预测结果，2020—2030年，中国居民男女性自残发病率和死亡率均呈现下降趋势。男性ASIR预计从2020年的33.20/10万在2030年降低为28.38/10万，女性ASIR从2020年的57.02/10万到2030年降低至38.15/10万，女性ASIR仍高于男性。见图4。2020—2030年，男性ASMR由11.24/10万降低至7.53/10万，女性ASMR由6.30/10万降低至3.90/10万。见图5。
3　讨论
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本研究利用GBD 2019数据库中中国部分的数据分析了1990—2019年中国居民自残发生和死亡的变化趋势。本研究结果显示，2019年中国居民自残ASIR为51.24/10万、ASMR为6.8/10万。与Duan等[6]研究的2017年中国居民自残疾病负担相比，ASIR有所上升，ASMR有所下降。1990—2019年，男女性自残造成的疾病负担均呈现逐年下降趋势，其中女性降低程度更为明显，这可能与随着社会的发展，越来越多的女性受教育水平提高，经济能力增强有关。但到2019年为止，女性ASIR仍远高于男性，提示预防自残行为应仍然以女性为重点人群，需要继续加强对女性的关爱和社会支持。
男女性自残发病风险在10～14岁年龄段和20～24岁年龄段期间急剧上升，这可能是由于青少年时期是身心发展的关键时期，学业压力、人际关系、欺凌与童年期不良经历都会导致青少年非自杀性自伤甚至自杀[12-13]。其中女性自残发病风险较同年龄段男性更高，其主要原因可能是这一年龄段女性经历了从青春期到性成熟期的变化过程，尤其是妊娠带来的心理问题，这些变化都给女性带来了较同年龄段男性更大的挑战[14]。同时研究结果表明，35～39岁年龄段起男性发病风险逐渐高于女性。其主要原因可能在于这个年龄段的男性身体机能逐渐下降，生理上的生命曲线开始从高峰值跌落[14]，同时该年龄段男性面临着更重的经济负担和社会压力。70～74岁起，自残的发病风险和死亡风险急速上升，与武汉[15]和云南[16]等地的研究报道一致。与年轻人群自残特征不同，老年人重复自残和自杀与单身、独居和年龄歧视等在内的社会人口因素相关性更强，并且老年人具有更大的死亡意图以及使用更高致命性的自杀手段，因此老年人经常死于第一次自杀企图[2]。同时队列效应结果显示，我国1975年之后出生的男性自残发病风险高于女性，出生越晚的人群死亡风险越低，与年龄效应结果一致，因此应针对老年人自残特征进行特异性的预防与控制，降低老年人自残的发生与死亡。
1990—2019年间，中国居民自残造成的发病和死亡风险随着时期的变化持续下降，这与社会的发展以及政策的管控有着密切的联系，1997年之前中国农村地区的自残死亡以服食农药和鼠药为主要方式[17]。中国政府在1997年颁布并相继于2001年和2017年修订了《农药管理条例》，加强了对农药售卖、使用的监督管理。此外，鉴于中国自残死亡的人中有很大一部分患有精神疾病，预防自残可能受益于改善精神卫生服务的努力[18]。《国家精神卫生工作计划（2015—2020年）》提出了通过加强对精神卫生问题的宣传和加强对有心理和行为问题的人群的干预来改善精神卫生服务的目标[6]。《健康中国行动（2019—2030）》提出，到2030年，我国精神科执业（助理）医师要达到每10万人4.5名的水平。精神科医生数量的增多，使得更多具有精神疾病的病人得到有效的治疗，一定程度上降低了自残的风险。同时随着社会的发展，居民受教育程度提高，健康素养的提升，使得自残/自杀就诊率提升，越来越多的患者能够得到有效的治疗，降低自残行为带来的死亡风险[19]。
根据本研究预测结果，到2030年中国男女性自残行为ASIR和ASMR均降低，表明现有的防控策略能够持续有效的起到作用。需要关注的是，预测所得女性ASIR高于男性，但ASMR低于男性，表明自残行为发生后存活的女性多于男性，往往这类自杀未遂人群具有较高的重复自残行为发生率[20]，应重点关注这部分人群身心健康，制定针对性的策略加以控制。
同时本研究存在一定的局限性，其它研究结果显示，自杀死亡水平在地理分布上有着差异，并且农村和城市之间的发病率和死亡率也具有明显的差异性[21]，但本研究只获取了中国整体的数据进行研究，缺乏分类比较。其次，应考虑对自残的危险因素进行研究，有助于对建立更加精准的干预措施提供依据。最后，本研究采用的预测模型是仅根据年龄、时期和队列因素进行预测，但影响疾病发生和死亡的不可控因素很多，因此预测结果与实际结果可能会存在一定的偏差。
综上所述，中国居民自残疾病负担虽然呈下降趋势但仍是导致居民死亡的主要原因之一，由于自残是可以预防、可以控制的，应针对不同年龄、不同性别发病和死亡特点制定相应的干预措施，将20～24岁年轻人群和70岁以上老年人群作为重点对象进行生活帮扶和心理疏导，并着重关注自杀未遂女性身心健康，降低自残带来的疾病负担。
基金
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  • 青海大学医学部教育教学改革项目(qyjg202204)
  • 青海省“昆仑英才”行动计划(青人才字（2020）18号)
文献
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参考文献 引证文献
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2024年第51卷第4期
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doi: 10.20043/j.cnki.MPM.202308526
  • 接收时间：2023-09-11
  • 首发时间：2026-03-19
  • 出版时间：2024-02-25
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  • 收稿日期：2023-09-11
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青海大学医学部教育教学改革项目(qyjg202204)
青海省“昆仑英才”行动计划(青人才字（2020）18号)
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    1.青海大学医学部公共卫生系，青海 西宁 810000
    2.青海省疾病预防控制中心传染病预防控制所

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
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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