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Article(id=1311350562621321622, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350363295417204, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202604019, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1774972800000, receivedDateStr=2026-04-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579937843, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579937843, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579937843, creator=13701087609, updateTime=1790579937843, updator=13701087609, issue=Issue{id=1311350363295417204, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='16', pageStart='2881', pageEnd='3072', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790579890321, creator='13701087609', updateTime=1790584112451, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311368072267653663, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350363295417204, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311368072267653664, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350363295417204, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3029, endPage=3033, ext={EN=ArticleExt(id=1311350562826842519, articleId=1311350562621321622, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Trends and age-period-cohort analysis of incidence of gonorrhea, China, 2005-2024, columnId=1228016573156360233, journalTitle=Modern Preventive Medicine, columnName=Disease Control and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the changing trends in gonorrhea incidence in China from 2005 to 2024, evaluate the influence of age, period, and birth cohort factors, and provide a basis for the development of gonorrhea prevention and control strategies.

Methods

Gonorrhea incidence data from 2005 to 2025 were extracted from the China Public Health Science Data Center and the China Health Statistical Yearbook. A Joinpoint regression model was used to calculate the annual percentage change (APC) and the average annual percentage change (AAPC) to describe the incidence trends. An age-period-cohort model was constructed to evaluate the effects of age, period, and birth cohort on the incidence rate.

Results

From 2005 to 2024, the overall incidence rate of gonorrhea in China showed a declining trend (AAPC=-3.47%, P<0.001). The age effect followed an “S-shaped” curve, with an increasing trend in the 15-29-year age group and decreasing trends in the 0 to 14-year and 30-84-year age groups. The period effect decreased over the years. The cohort effect generally showed an “M-shaped” decline, with an increasing trend from 1925-1929, a fluctuating decline from 1930-1994, a rapid increase from 1995—2009 reaching a peak, and a rapid decline from 2010-2020.

Conclusion

The overall incidence rate of gonorrhea in China is declining. Attention should be focused on high-incidence areas such as the Zhejiang Province and Yunnan Province, as well as key populations in the 15-34-year age group. Health education on gonorrhea prevention and control should be strengthened, and active screening in high-risk areas and among key populations should be promoted to achieve early diagnosis and early treatment.

, authors=Ling-ping WANG1, Yu-li TAO2, Bo-jiang DING1, Tao ZHANG1, Xin-zhou ZHANG1, authorsList=Ling-ping WANG, Yu-li TAO, Bo-jiang DING, Tao ZHANG, Xin-zhou ZHANG, authorCompany=null, correspAuthors=Xin-zhou ZHANG, 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=1311350564273877412, articleId=1311350562621321622, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=2005—2024年中国淋病发病趋势及年龄－时期－队列分析, columnId=1228016573844226129, journalTitle=现代预防医学, columnName=疾病预防控制, runingTitle=null, highlight=null, articleAbstract=
目的

分析2005—2024年中国淋病发病率的变化趋势，评估年龄、时期及出生队列因素对其影响，为淋病防控策略的制定提供依据。

方法

提取2005—2025年中国公共卫生科学数据中心和《中国卫生健康统计年鉴》中淋病发病数据。采用Joinpoint回归模型计算年度变化百分比（APC）和平均年度变化百分比（AAPC），描述发病趋势；构建年龄－时期－队列模型，分别评估年龄效应、时期效应及出生队列效应对发病率的影响。

结果

2005—2024年我国淋病发病率总体呈下降趋势（AAPC＝-3.47%，P<0.001）。年龄效应呈“S”型发展变化，在15～29岁年龄段人群呈上升趋势，0～14岁和30～84岁呈下降趋势；时期效应随着年份的推移而下降；队列效应总体呈“M”型下降，1925—1929年呈上升趋势，1930—1994年波动下降，1995—2009年快速上升并达到峰值，2010—2020年迅速回落。

结论

中国淋病的发病率总体呈下降态势，应重点关注浙江省和云南省等高发地区和15～34岁等年龄段重点人群，加强淋病防治健康教育，推进高风险区域及重点人群的主动筛查，实现早诊早治。

, authors=王玲萍1, 陶煜丽2, 丁柏江1, 张涛1, 张新洲1, authorsList=王玲萍, 陶煜丽, 丁柏江, 张涛, 张新洲, authorCompany=null, correspAuthors=张新洲, authorNote=

王玲萍（1983—），女，本科，病案信息技术师，研究方向：卫生政策与信息管理研究

, correspAuthorsNote=
张新洲，E-mail：
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Epidemiological trend and spatial distribution characteristics of gonorrhea in mainland China,2014—2018［J］. Chinese Journal of Disease Control & Prevention, 2021, 25(8): 885-890, 954.(In Chinese), articleTitle=Epidemiological trend and spatial distribution characteristics of gonorrhea in mainland China,2014—2018, refAbstract=null), Reference(id=1311350573228716542, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, doi=null, pmid=null, pmcid=null, year=2021, volume=21, issue=1, pageStart=1077, pageEnd=null, url=null, language=null, rfNumber=[21], rfOrder=36, authorNames=Lu ZZ, Ji WD, Yin Y, journalName=BMC Public Health, refType=null, unstructuredReference=Lu ZZ, Ji WD, Yin Y, et al. Analysis on the trend of AIDS incidence in Zhejiang, China based on the age-period-cohort model (2004–2018)［J］. 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Prediction of the incidence and prevention and control strategies of major sexually transmitted diseases in districts with high density of floating population［J］. 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Spatial-temporal differences and economic driving mechanism of STD epidemic in China: a case study of gonorrhea and syphilis［J］. Tropical Geography, 2016, 36(5): 761-766.(In Chinese), articleTitle=Spatial-temporal differences and economic driving mechanism of STD epidemic in China: a case study of gonorrhea and syphilis, refAbstract=null)], funds=[Fund(id=1311350569009246681, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, awardId=71804002, language=CN, fundingSource=国家自然基金青年科学基金项目(71804002), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1311350564479398309, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, xref=1., ext=[AuthorCompanyExt(id=1311350564487786918, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, companyId=1311350564479398309, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Medical Quality Management Department Organization,Xixi Hospital Affiliated Zhejiang Chinese Medical University, 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and incidence of gonorrhea in China from 2005 to 2024, figureFileSmall=jY1XzC4SHFSRoj6dy1Z4MQ==, figureFileBig=B3DQBbk3ux4+EKj9V0Fagg==, tableContent=null), ArticleFig(id=1311350567935504844, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, language=CN, label=图1, caption=2005—2024年中国淋病发病人数和发病率, figureFileSmall=jY1XzC4SHFSRoj6dy1Z4MQ==, figureFileBig=B3DQBbk3ux4+EKj9V0Fagg==, tableContent=null), ArticleFig(id=1311350568124248525, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, language=EN, label=Fig.2, caption=Average annual incidence rate of gonorrhea across 31 provinces in China from 2005 to 2024, figureFileSmall=yCfkC6MR8PCuqBqT94qdSA==, figureFileBig=CacKPo/vfTbaCLpQI20cJA==, tableContent=null), ArticleFig(id=1311350568191357390, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, language=CN, label=图2, caption=2005—2024年中国31个省（自治区、直辖市）年平均发病率情况, 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figureFileBig=Cs3SCb1J69Ya8NKQu7/DmA==, tableContent=null), ArticleFig(id=1311350568463987154, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, language=CN, label=图4, caption=2005—2024年中国淋病发病率的年龄效应, figureFileSmall=5YP/yh9TDDBeP28IUKXK4A==, figureFileBig=Cs3SCb1J69Ya8NKQu7/DmA==, tableContent=null), ArticleFig(id=1311350568535290323, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, language=EN, label=Fig.5, caption=Period effect of incidence of gonorrhea in China from 2005 to 2024, figureFileSmall=8cqkRDmdgM6Ha5wV3DgVAg==, figureFileBig=jmmdflbdlJvK5P9i4t5BGw==, tableContent=null), ArticleFig(id=1311350568610787796, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, language=CN, label=图5, caption=2005—2024年中国淋病发病率的时期效应, figureFileSmall=8cqkRDmdgM6Ha5wV3DgVAg==, figureFileBig=jmmdflbdlJvK5P9i4t5BGw==, tableContent=null), ArticleFig(id=1311350568690479573, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, language=EN, label=Fig.6, caption=Birth cohort effect of incidence of gonorrhea in China from 2005 to 2024, figureFileSmall=3s/s6fGWamN+KRaCfqJsVg==, figureFileBig=Akdy0wQP/VUS15Oqb9bEpA==, tableContent=null), ArticleFig(id=1311350568770171350, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, language=CN, label=图6, caption=2005—2024年中国淋病发病率的出生队列效应, figureFileSmall=3s/s6fGWamN+KRaCfqJsVg==, figureFileBig=Akdy0wQP/VUS15Oqb9bEpA==, tableContent=null), ArticleFig(id=1311350568841474519, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, language=EN, label=Table 1, caption=

Results of the age-period-cohort model analysis of gonorrhea incidence rates in China from 2005 to 2024

, figureFileSmall=null, figureFileBig=null, tableContent=
维度效应系数（95%CI）P值
年龄（岁）
0～4-1.87(-2.89～-0.85)<0.001
>5～9-2.54(-3.86～-1.23)<0.001
>10～14-3.23(-5.19～-1.28)0.001
>15～190.73(0.19～1.26)0.008
>20～242.21(1.72～2.70)<0.001
>25～292.50(2.03～2.97)<0.001
>30～342.21(1.74～2.68)<0.001
>35～391.72(1.24～2.21)<0.001
>40～441.29(0.78～1.79)<0.001
>45～490.76(0.21～1.31)0.006
>50～540.46(-0.09～1.02)0.101
>55～590.09(-0.48～0.67)0.753
>60～64-0.12(-0.71～0.47)0.694
>65～69-0.52(-1.16～0.11)0.106
>70～74-0.91(-1.63～-0.19)0.014
>75～79-1.33(-2.21～-0.45)0.003
>80～84-1.45(-2.52～-0.39)0.008
时期
2005—20090.38(0.21～0.55)<0.001
2010—2014-0.07(-0.24～0.10)0.415
2015—2019-0.11(-0.28～0.06)0.208
2020—2024-0.20(-0.40～0.00)0.054
出生队列
1925—19290.11(-1.64～1.87)0.901
1930—19340.51(-0.64～1.65)0.385
1935—19390.45(-0.44～1.35)0.322
1940—19440.36(-0.39～1.12)0.346
1945—19490.34(-0.36～1.04)0.340
1950—19540.37(-0.28～1.03)0.264
1955—19590.24(-0.40～0.88)0.461
1960—19640.00(-0.64～0.64)0.997
1965—19690.02(-0.58～0.62)0.955
1970—1974-0.36(-0.95～0.22)0.226
1975—1979-0.56(-1.12～0.00)0.049
1980—1984-0.59(-1.12～-0.05)0.031
1985—1989-0.60(-1.13～-0.07)0.027
1990—1994-0.71(-1.27～-0.16)0.012
1995—1999-0.72(-1.33～-0.11)0.021
2000—20040.06(-0.58～0.69)0.859
2005—20091.03(0.35～1.70)0.003
2010—20140.74(-0.70～2.18)0.312
2015—20190.06(-1.99～2.11)0.954
2020—2024-0.75(-4.81～3.31)0.717
), ArticleFig(id=1311350568916971992, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350562621321622, language=CN, label=表1, caption=

2005—2024年中国淋病发病率的年龄－时期－队列模型分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
维度效应系数（95%CI）P值
年龄（岁）
0～4-1.87(-2.89～-0.85)<0.001
>5～9-2.54(-3.86～-1.23)<0.001
>10～14-3.23(-5.19～-1.28)0.001
>15～190.73(0.19～1.26)0.008
>20～242.21(1.72～2.70)<0.001
>25～292.50(2.03～2.97)<0.001
>30～342.21(1.74～2.68)<0.001
>35～391.72(1.24～2.21)<0.001
>40～441.29(0.78～1.79)<0.001
>45～490.76(0.21～1.31)0.006
>50～540.46(-0.09～1.02)0.101
>55～590.09(-0.48～0.67)0.753
>60～64-0.12(-0.71～0.47)0.694
>65～69-0.52(-1.16～0.11)0.106
>70～74-0.91(-1.63～-0.19)0.014
>75～79-1.33(-2.21～-0.45)0.003
>80～84-1.45(-2.52～-0.39)0.008
时期
2005—20090.38(0.21～0.55)<0.001
2010—2014-0.07(-0.24～0.10)0.415
2015—2019-0.11(-0.28～0.06)0.208
2020—2024-0.20(-0.40～0.00)0.054
出生队列
1925—19290.11(-1.64～1.87)0.901
1930—19340.51(-0.64～1.65)0.385
1935—19390.45(-0.44～1.35)0.322
1940—19440.36(-0.39～1.12)0.346
1945—19490.34(-0.36～1.04)0.340
1950—19540.37(-0.28～1.03)0.264
1955—19590.24(-0.40～0.88)0.461
1960—19640.00(-0.64～0.64)0.997
1965—19690.02(-0.58～0.62)0.955
1970—1974-0.36(-0.95～0.22)0.226
1975—1979-0.56(-1.12～0.00)0.049
1980—1984-0.59(-1.12～-0.05)0.031
1985—1989-0.60(-1.13～-0.07)0.027
1990—1994-0.71(-1.27～-0.16)0.012
1995—1999-0.72(-1.33～-0.11)0.021
2000—20040.06(-0.58～0.69)0.859
2005—20091.03(0.35～1.70)0.003
2010—20140.74(-0.70～2.18)0.312
2015—20190.06(-1.99～2.11)0.954
2020—2024-0.75(-4.81～3.31)0.717
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2005—2024年中国淋病发病趋势及年龄－时期－队列分析
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王玲萍 1 , 陶煜丽 2 , 丁柏江 1 , 张涛 1 , 张新洲 1
现代预防医学 | 疾病预防控制 2026,53(16): 3029-3033
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现代预防医学 |疾病预防控制 2026 , 53 (16) : 3029 -3033
2005—2024年中国淋病发病趋势及年龄－时期－队列分析
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王玲萍1, 陶煜丽2, 丁柏江1, 张涛1, 张新洲1
作者信息
  • 1.浙江中医药大学附属杭州市西溪医院医疗质量管理部，浙江 杭州 310023
  • 2.杭州市余杭区仁和街道社区卫生服务中心儿童保健科
通讯作者:
张新洲，E-mail：
作者简介:

王玲萍（1983—），女，本科，病案信息技术师，研究方向：卫生政策与信息管理研究

Trends and age-period-cohort analysis of incidence of gonorrhea, China, 2005-2024
Ling-ping WANG1, Yu-li TAO2, Bo-jiang DING1, Tao ZHANG1, Xin-zhou ZHANG1
Affiliations
  • Medical Quality Management Department Organization,Xixi Hospital Affiliated Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310023, China
出版时间: 2026-08-25 doi: 10.20043/j.cnki.MPM.202604019
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摘要
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目的

分析2005—2024年中国淋病发病率的变化趋势，评估年龄、时期及出生队列因素对其影响，为淋病防控策略的制定提供依据。

方法

提取2005—2025年中国公共卫生科学数据中心和《中国卫生健康统计年鉴》中淋病发病数据。采用Joinpoint回归模型计算年度变化百分比（APC）和平均年度变化百分比（AAPC），描述发病趋势；构建年龄－时期－队列模型，分别评估年龄效应、时期效应及出生队列效应对发病率的影响。

结果

2005—2024年我国淋病发病率总体呈下降趋势（AAPC＝-3.47%，P<0.001）。年龄效应呈“S”型发展变化，在15～29岁年龄段人群呈上升趋势，0～14岁和30～84岁呈下降趋势；时期效应随着年份的推移而下降；队列效应总体呈“M”型下降，1925—1929年呈上升趋势，1930—1994年波动下降，1995—2009年快速上升并达到峰值，2010—2020年迅速回落。

结论

中国淋病的发病率总体呈下降态势，应重点关注浙江省和云南省等高发地区和15～34岁等年龄段重点人群，加强淋病防治健康教育，推进高风险区域及重点人群的主动筛查，实现早诊早治。

淋病  /  发病率  /  Joinpoint回归模型  /  年龄－时期－队列模型
Objective

To analyze the changing trends in gonorrhea incidence in China from 2005 to 2024, evaluate the influence of age, period, and birth cohort factors, and provide a basis for the development of gonorrhea prevention and control strategies.

Methods

Gonorrhea incidence data from 2005 to 2025 were extracted from the China Public Health Science Data Center and the China Health Statistical Yearbook. A Joinpoint regression model was used to calculate the annual percentage change (APC) and the average annual percentage change (AAPC) to describe the incidence trends. An age-period-cohort model was constructed to evaluate the effects of age, period, and birth cohort on the incidence rate.

Results

From 2005 to 2024, the overall incidence rate of gonorrhea in China showed a declining trend (AAPC=-3.47%, P<0.001). The age effect followed an “S-shaped” curve, with an increasing trend in the 15-29-year age group and decreasing trends in the 0 to 14-year and 30-84-year age groups. The period effect decreased over the years. The cohort effect generally showed an “M-shaped” decline, with an increasing trend from 1925-1929, a fluctuating decline from 1930-1994, a rapid increase from 1995—2009 reaching a peak, and a rapid decline from 2010-2020.

Conclusion

The overall incidence rate of gonorrhea in China is declining. Attention should be focused on high-incidence areas such as the Zhejiang Province and Yunnan Province, as well as key populations in the 15-34-year age group. Health education on gonorrhea prevention and control should be strengthened, and active screening in high-risk areas and among key populations should be promoted to achieve early diagnosis and early treatment.

Gonorrhea  /  incidence rate  /  Joinpoint regression model  /  Age-period-cohort model
王玲萍, 陶煜丽, 丁柏江, 张涛, 张新洲. 2005—2024年中国淋病发病趋势及年龄－时期－队列分析. 现代预防医学, 2026 , 53 (16) : 3029 -3033 . DOI: 10.20043/j.cnki.MPM.202604019
Ling-ping WANG, Yu-li TAO, Bo-jiang DING, Tao ZHANG, Xin-zhou ZHANG. Trends and age-period-cohort analysis of incidence of gonorrhea, China, 2005-2024[J]. Modern Preventive Medicine, 2026 , 53 (16) : 3029 -3033 . DOI: 10.20043/j.cnki.MPM.202604019
正文
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淋病是最常见的性传播疾病之一，是造成女性盆腔炎、宫外孕和不孕症以及男性附睾炎、前列腺炎等泌尿系统疾病的危险因素，同时还增加了人类免疫缺陷病毒（HIV）感染的风险［1-2］。世界卫生组织数据显示，全球每年报告的淋病病例约为8 200万例［3］。另据《2025年中国卫生健康统计年鉴》数据显示，2024年，中国淋病发病人数为101 199例，发病率为7.19/10万［4］。由此可见，淋病所导致的疾病负担依然沉重，加强其预防与控制工作刻不容缓。基于此，本研究采用Joinpoint回归模型和年龄－时期－队列模型（age-period-cohort model，APC模型），对2005—2024年我国淋病发病趋势进行分析，并进一步探讨年龄、时期及出生队列因素对淋病发病风险的影响，以期为淋病防治工作提供参考。
1　资料与方法
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1.1　资料来源
利用2005—2020年中国公共卫生科学数据中心网站和2022—2025年《中国卫生健康统计年鉴》，分别提取2005—2024年中国31个省（自治区、直辖市）及各年龄别淋病发病例数及发病率数据［5-8］，两者淋病相关数据均来源于中国疾病预防控制中心的法定报告传染病统计资料，与历年淋病数据保持一致。此外，本研究统计数据未包括香港特别行政区、澳门特别行政区和台湾省数据。
1.2　统计学分析
1.2.1　Joinpoint回归模型
本研究借助Joinpoint 回归模型计算2005—2024年我国淋病发病率的年度变化百分比（annual percent change，APC）和平均年度变化百分比（average annual percent change，AAPC）［9-10］。其中，当APC或AAPC>0，提示该时段发病率呈现上升趋势；当APC/AAPC<0，表示该时期发病率呈现下降趋势［11］。
1.2.2　年龄－时期－队列模型
年龄－时期－队列模型（age-period-cohort model，APC模型）通过内生因子算法（intrinsic estimator,IE）有效解决了年龄、时期以及出生队列三者之间的共线性问题，常用于人口学、社会学和流行病学等研究领域［12］。在本研究中，由于85岁以上年龄段发病率是开口数据，不符合年龄－时期－队列模型要求，因此，本研究纳入 0～84岁人群，以每5年为一个年龄组，共分为17组。此外，分别以2005年、2010年、2015年和2020年单年的淋病发病率，替代相应五年时期的平均发病率进行分析，以消除相邻出生队列的信息重叠，模型公式为：Y=log(M)μ+αagei+βperiodj+γcohortk+ε。其中，M代表淋病的发病率；α、β、γ分别为年龄、时期、出生队列的效应系数，分别反映特定年龄组、特定时期及同一出生队列人群的发病风险；μ为截距，ε为随机误差［13］。
2　结果
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2.1　淋病发病概况
2005—2024年我国淋病累计发病人数为2 365 139例，平均发病率为8.71/10万，其中，2005年发病数和发病率最高，发病人数达180 316例，发病率为13.87/10万；2012年发病人数和发病率最低，发病人数为91 853例，发病率为6.82/10万。见图1。
2005—2024年各省（自治区、直辖市）淋病平均发病率见图2，其中，浙江省、上海市、广东省、海南省、广西壮族自治区、福建省、江苏省、重庆市、北京市和云南省等10个省（自治区、直辖市）淋病平均发病率高于全国平均水平。见图2。
2.2　2005—2024年淋病发病率变化趋势
Joinpoint模型结果显示，2005—2024年我国淋病发病率呈下降趋势（AAPC＝-3.47%，P<0.001），其中，2005—2012年呈快速下降（APC＝-10.06%，P<0.001），2012-2017呈快速上升趋势（APC＝7.43%，P=0.033），2017—2024年又呈迅速下降趋势（APC＝-4.01%，P =0.007）。见图3。
2.3　年龄－时期－队列模型分析
年龄效应呈“S”字型发展变化，在15～29岁年龄段人群呈上升趋势，0～14岁和30～84岁呈下降趋势。其中，总人群在25～29岁年龄效应最大，在10～14岁年龄效应最低。与0～4岁年龄组进行比较，年龄效应最大的组别其效应系数增加了4.37，即对应的发病风险倍数为79.04(RR=e4.37）。见图4和表1。
我国淋病的发病风险随着年份的变化呈下降趋势。2005—2009年时期效应表现为快速下降，2010—2014年下降速度变缓，2015—2020年继续呈下降趋势。见图5和表1。
发病风险的出生队列效应呈“M”字型趋势，1925—1929年呈上升趋势，1930—1994年出生队列组发病风险呈波动下降趋势，1995—2009年呈快速上升趋势并达到峰值，2010—2020年呈快速下降趋势。见图6和表1。
3　讨论
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本研究发现，2005—2024年我国淋病发病率总体呈下降趋势，与既往研究保持一致［14-15］。浙江省、上海市、广东省、广西壮族自治区、重庆市和云南省等10个省（自治区、直辖市）年平均发病率高于全国平均水平，这可能与浙江省、上海市和广东省等地区的经济水平较为发达、娱乐行业多和流动人口较大有关，同时这些地区淋球菌染色镜检、淋球菌培养和核酸检测开展率高，都直接或间接影响淋病发病率［16-17］；此外，广西壮族自治区、重庆市和云南省等地区近年来随着医疗检测机构的不断增多、淋病实验室检测能力的不断提高使得更多的淋病病例被检出［16,18］。
年龄效应显示，淋病发病的高峰年龄段为20～34岁，即在此年龄段发病风险最高，且在25～29岁达到峰值。淋病传播的主要途径是通过性行为，而25～29岁年龄段的人群发生性行为的频率较高，容易感染淋病，由此造成淋病的互相传播［16］。此外，15～24岁年龄段淋病发病率呈快速攀升趋势，这提示淋病有着年轻化的倾向，青少年群体是高发群体［16］。因此，年龄是淋病发病的重要危险因素之一，要重点加强对15～34岁年龄段等高风险人群的关注。
时期效应是指在特定时间段内社会或经济等环境决定因素的改变对发病风险的影响［19］。时期效应结果发现，我国淋病发病的时期效应随着年份的推移呈下降趋势，主要分为两个阶段：2005—2010年和2011—2020年。可以看出，2005—2010年淋病的发病时期效应下降的速度高于2011—2020年。2010年是我国淋病时期效应的拐点，可能是我国淋病防治相关政策和工作取得了一定的成效。自2004年起全国启用了传染病网络直报系统，收集各地淋病病例数据。2009年，国家工商管理总局发布《关于在整治互联网低俗之风专项行动中严厉打击非法“性药品”广告和性病治疗广告的通知》（工商明电〔2009〕1号），加强互联网性病广告整治。2012年，我国修订《性病防治管理办法》并于2013年实施，进一步加强了性病防治的宣教和行为干预等，提出了更具针对性的防控措施，这一系列举措加快了我国淋病防治工作的进程［20］。此外，我国实施的艾滋病和梅毒综合防治策略（如艾滋病防治示范区建设、国际合作项目以及《中国预防与控制梅毒规划（2010—2020年）》）等举措也对淋病发病的控制产生积极影响［15］。另有研究指出，新型冠状病毒肺炎大流行期间性病的检测和报告减少，也在一定程度上造成了淋病发病率的下降［15］。
队列效应是指同一出生队列暴露于某种疾病危险因素的条件下个体发病的风险［21］。本研究发现，淋病发病的出生队列效应随着时间的推移总体呈下降趋势，但1995—2009年淋病发病的出生队列效应出现快速上升，可能与流动人口规模扩大及经济发展因素有关。2000年后中国流动人口地理活跃度显著增强，人口流动加速了淋病传播［22］；同时，部分地区经济发展对淋病疫情呈现正向驱动效应，即经济越发展，疫情越严重［23］。
综上所述，2005—2024年我国淋病的总体发病率呈下降趋势。从地区发病情况看，浙江省、上海市、广东省、广西壮族自治区、重庆市和云南省等10个省（自治区、直辖市）年平均发病率高于全国平均水平。此外，25～29岁年龄段淋病发病的年龄效应最大，15～24岁年龄段的年龄效应增长速度最快；淋病发病的时期效应总体表现为随着年份的变化呈下降趋势；淋病发病的出生队列效应总体也随着时间的推移呈下降趋势。因此，要密切关注浙江省、上海市、广东省、广西壮族自治区、重庆市和云南省等10个高风险地区和15～34岁青少年及年轻群体等重点人群，加强淋病防治的健康教育，提高居民对淋病的认知水平。此外，强化高风险地区和重点人群淋病主动筛查工作，积极推进淋病早期筛查和早诊早治，进而从整体上降低淋病的发病率。
本研究存在一定的局限性，由于中国公共卫生科学数据中心和《中国卫生健康统计年鉴》数据库的限制，未能对不同性别、城乡等特征人群的淋病发病的情况进行亚组分析。
基金
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  • 国家自然基金青年科学基金项目(71804002)
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2026年第53卷第16期
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doi: 10.20043/j.cnki.MPM.202604019
  • 接收时间：2026-04-01
  • 首发时间：2026-09-28
  • 出版时间：2026-08-25
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  • 收稿日期：2026-04-01
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国家自然基金青年科学基金项目(71804002)
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    1.浙江中医药大学附属杭州市西溪医院医疗质量管理部，浙江 杭州 310023
    2.杭州市余杭区仁和街道社区卫生服务中心儿童保健科

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张新洲，E-mail：
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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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