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Article(id=1324077749090005947, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077716676424398, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202509169, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757606400000, receivedDateStr=2025-09-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614335558, onlineDateStr=2026-11-02, pubDate=1773072000000, pubDateStr=2026-03-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614335558, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614335558, creator=13701087609, updateTime=1793614335558, updator=13701087609, issue=Issue{id=1324077716676424398, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='5', pageStart='769', pageEnd='960', issueExtLink='null', onlineDate='null', pubDate='1773072000000', pubDateStr='2026-03-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614327830, creator='13701087609', updateTime=1793614418028, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324078095132655884, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077716676424398, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324078095132655885, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077716676424398, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=815, endPage=820, ext={EN=ArticleExt(id=1324077749320692668, articleId=1324077749090005947, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Development and validation of a risk prediction model for pulmonary nodules among manufacturing workers: a joint effects analysis based on gender, smoking status, and years of service, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

This study aims to identify independent risk factors for pulmonary nodules in a manufacturing workers and to develop and validate a risk prediction model based on key variables. The goal was to provide a quantitative tool for the early identification of high-risk individuals within occupational groups.

Methods

A cross-sectional analysis was performed involving 581 employees from a manufacturing enterprise. The cohort comprised 397 individuals without pulmonary nodules and 184 with nodules. Univariate analysis and multivariable logistic regression (forward stepwise method) were used to identify independent predictors and construct the prediction model. The performance of individual variables and the combined model was assessed using receiver operating characteristic (ROC) curve analysis.

Results

Multivariable analysis identified female sex(OR=2.100, 95% CI: 1.163-3.791, P=0.014), smoking (OR=5.757, 95% CI: 3.552-9.332, P<0.001), and years of service (OR=1.113 per year, 95% CI: 1.084-1.143, P<0.001) as significant independent risk factors for pulmonary nodules. The combined prediction model yielded an area under the curve (AUC) of 0.801 (95% CI: 0.762-0.840), with a sensitivity of 62.50% and a specificity of 87.15%.

Conclusion

The risk prediction model, incorporating female sex, smoking history, and longer years of service, exhibits robust discriminative capability. It shows promise as a practical tool for the early screening of pulmonary nodules in occupational populations, such as those in manufacturing. Further external validation in broader and more diverse populations is warranted.

, authors=Bin JIANG, Yu-ye KUANG, Wu-li LEI, Chong-xiang CHEN, Li-yan YANG, authorsList=Bin JIANG, Yu-ye KUANG, Wu-li LEI, Chong-xiang CHEN, Li-yan YANG, authorCompany=null, correspAuthors=Li-yan YANG, 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=1324077752315425739, articleId=1324077749090005947, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=制造业职业人群肺结节风险预测模型的构建与验证——基于性别、吸烟和工龄的联合效应分析, columnId=1228016571273113811, journalTitle=现代预防医学, columnName=环境与职业卫生, runingTitle=null, highlight=null, articleAbstract=
目的

探讨制造业职业人群肺结节发生的独立危险因素，构建并验证基于关键变量的风险预测模型，为职业健康高危人群的早期筛查提供量化工具。

方法

采用横断面研究设计，纳入某制造业企业581名在职员工作为研究对象，其中无肺结节组397人，肺结节组184人。采用单因素分析及多因素logistic回归（逐步向前法）筛选独立预测因子并构建预测模型。通过受试者工作特征曲线评估各变量及联合模型的预测效能。

结果

多因素分析显示，女性（OR=2.100,95% CI:1.163～3.791,P=0.014）、吸烟（OR=5.757,95% CI:3.552～9.332,P<0.001）及工龄（OR=1.113/年，95% CI:1.084～1.143,P<0.001）是肺结节发生的独立危险因素。联合预测模型的曲线下面积为0.801(95% CI:0.762～0.840)，灵敏度为62.50%，特异度为87.15%。

结论

基于性别（女性）、有吸烟史和长工龄构建的风险预测模型具有良好的判别效能，可作为制造业等职业暴露人群肺结节早期筛查的实用工具，其普适性有待在更广泛人群中进一步验证。

, authors=蒋斌, 邝玉叶, 雷伍丽, 陈崇翔, 杨丽燕, authorsList=蒋斌, 邝玉叶, 雷伍丽, 陈崇翔, 杨丽燕, authorCompany=null, correspAuthors=杨丽燕, authorNote=

蒋斌（1978—），男，硕士，副主任医师，研究方向：职业健康监护

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杨丽燕，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=s3rUY9wI68KrVdjTysFKLw==, magXml=aIqNMOHXMypH+BNk93EvNQ==, pdfUrl=null, pdf=yoA/eBnKw9i9cALv6/CmUA==, pdfFileSize=831969, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=oxEWil/EFqewkkd2wX/HaA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=lRF4gMHPCUVmmzIChZPWRw==, mapNumber=null, fund=null)}, authors=[Author(id=1324077752663552976, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1324077752751633362, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, authorId=1324077752663552976, language=EN, stringName=Bin JIANG, firstName=Bin, middleName=null, lastName=JIANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Guangzhou Development District Hospital (Guangzhou Huangpu District People’s Hospital), Guangzhou, Guangdong 510730, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1324077752860685267, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, authorId=1324077752663552976, language=CN, stringName=蒋斌, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=广州开发区医院（广州市黄埔区人民医院），广东 广州 510730, bio={"content":"

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Radiology, 2022,305(1): 212-220., articleTitle=CT screening for lung cancer:comparison of radiologic and pathologic findings, refAbstract=null)], funds=[Fund(id=1324077758439108611, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, awardId=B2025528, language=CN, fundingSource=广东省医学科学技术研究基金项目(B2025528), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324077752567083980, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, xref=null, ext=[AuthorCompanyExt(id=1324077752575472589, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, companyId=1324077752567083980, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Guangzhou Development District Hospital (Guangzhou Huangpu District People’s Hospital), Guangzhou, Guangdong 510730, China), AuthorCompanyExt(id=1324077752588055502, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, companyId=1324077752567083980, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=广州开发区医院（广州市黄埔区人民医院），广东 广州 510730)])], figs=[ArticleFig(id=1324077756631364596, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=EN, label=Figure 1, caption=ROC curve for the combined prediction of pulmonary nodules using gender, smoking status, and years of service, figureFileSmall=HPxII4uqN1LxMPr9Z9g0FA==, figureFileBig=oxEWil/EFqewkkd2wX/HaA==, tableContent=null), ArticleFig(id=1324077756757193717, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=CN, label=图1, caption=性别、吸烟、工龄联合预测肺结节的ROC曲线, figureFileSmall=HPxII4uqN1LxMPr9Z9g0FA==, figureFileBig=oxEWil/EFqewkkd2wX/HaA==, tableContent=null), ArticleFig(id=1324077756975297526, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=EN, label=Table 1, caption=

Analysis of pulmonary nodule detection by different demographic characteristics ［n（%），（）］

, figureFileSmall=null, figureFileBig=null, tableContent=
指标分组无结节有结节t/χ2值P值
性别男360(90.70)156(84.80)4.4010.036
女37(9.30)28(15.20)
年龄（岁）41.75±8.5545.44±6.83-5.140<0.001
吸烟否357(89.90)107(58.20)78.918<0.001
是40(10.10)77(41.80)
饮酒否280(70.50)141(76.60)2.3460.126
是117(29.50)43(23.40)
锻炼从不81(20.40)26(14.10)3.3120.191
1～2次212(53.40)105(57.10)
≥3次104(26.20)53(28.80)
工龄（年）13.98±8.3521.89±7.77-10.855<0.001
粉尘暴露否118(29.70)27(14.70)15.204<0.001
是279(70.30)157(85.30)
噪声暴露否181(45.60)90(48.90)0.5570.455
是216(54.40)94(51.10)
高温暴露否88(22.20)31(16.80)2.1840.139
是309(77.80)153(83.20)
化学毒物暴露否339(85.40)160(87.00)0.2540.614
是58(14.60)24(13.00)
收缩压（mm Hg）123.88±15.17126.40±15.66-1.8380.067
舒张压（mm Hg）82.46±9.9683.52±10.76-1.1540.249
EB病毒抗体阴性372(93.70)174(94.60)0.1650.684
阳性25(6.30)10(5.40)
心电图正常231(58.20)98(53.30)1.2420.265
异常166(41.80)86(46.70)
空腹血糖（mmol/L）5.31±1.135.37±1.37-0.5860.558
尿素氮（mmol/L）4.84±1.164.86±1.24-0.2110.833
尿酸（μmol/L）439.34±101.41436.27±102.220.3380.735
肌酐（μmol/L）88.34±13.2086.59±15.281.4060.160
血脂正常142(35.80)63(34.20)0.1290.720
异常255(64.20)121(65.80)
), ArticleFig(id=1324077757075960823, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=CN, label=表1, caption=

不同人口学特征肺结节检出情况分析 ［n（%），（）］

, figureFileSmall=null, figureFileBig=null, tableContent=
指标分组无结节有结节t/χ2值P值
性别男360(90.70)156(84.80)4.4010.036
女37(9.30)28(15.20)
年龄（岁）41.75±8.5545.44±6.83-5.140<0.001
吸烟否357(89.90)107(58.20)78.918<0.001
是40(10.10)77(41.80)
饮酒否280(70.50)141(76.60)2.3460.126
是117(29.50)43(23.40)
锻炼从不81(20.40)26(14.10)3.3120.191
1～2次212(53.40)105(57.10)
≥3次104(26.20)53(28.80)
工龄（年）13.98±8.3521.89±7.77-10.855<0.001
粉尘暴露否118(29.70)27(14.70)15.204<0.001
是279(70.30)157(85.30)
噪声暴露否181(45.60)90(48.90)0.5570.455
是216(54.40)94(51.10)
高温暴露否88(22.20)31(16.80)2.1840.139
是309(77.80)153(83.20)
化学毒物暴露否339(85.40)160(87.00)0.2540.614
是58(14.60)24(13.00)
收缩压（mm Hg）123.88±15.17126.40±15.66-1.8380.067
舒张压（mm Hg）82.46±9.9683.52±10.76-1.1540.249
EB病毒抗体阴性372(93.70)174(94.60)0.1650.684
阳性25(6.30)10(5.40)
心电图正常231(58.20)98(53.30)1.2420.265
异常166(41.80)86(46.70)
空腹血糖（mmol/L）5.31±1.135.37±1.37-0.5860.558
尿素氮（mmol/L）4.84±1.164.86±1.24-0.2110.833
尿酸（μmol/L）439.34±101.41436.27±102.220.3380.735
肌酐（μmol/L）88.34±13.2086.59±15.281.4060.160
血脂正常142(35.80)63(34.20)0.1290.720
异常255(64.20)121(65.80)
), ArticleFig(id=1324077757159846904, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=EN, label=Table 2, caption=

Univariate logistic regression analysis results of factors affecting the occurrence of pulmonary nodules

, figureFileSmall=null, figureFileBig=null, tableContent=
指标参照组比较组B WaldP值OR值（95%CI）
性别男女0.5580.2684.3220.0381.746（1.032～2.954）
年龄——0.0620.01324.054<0.0011.064（1.038～1.09）
吸烟否是1.8600.22468.995<0.0016.423（4.141～9.961）
饮酒否是-0.3150.2062.3360.1260.730（0.487～1.093）
锻炼从不1-2次/周0.3150.2052.3610.1241.370（0.916-2.049）
从不≥3次/周0.4820.2384.1020.0431.620（1.016-2.582）
工龄——0.1160.01383.891<0.0011.123（1.096～1.152）
粉尘暴露否是0.9000.23614.600<0.0012.459（1.55～3.902）
噪声暴露否是-0.1330.1790.5570.4560.875（0.617～1.242）
高温暴露否是0.3400.2312.1710.1411.406（0.894～2.211）
化学毒物暴露否是-0.1320.2610.2540.6140.877（0.526～1.462）
收缩压——0.0110.0063.3500.0671.011（0.999～1.023）
舒张压——0.0100.0091.3290.2491.010（0.993～1.028）
EB病毒抗体阴性阳性-0.1560.3850.1650.6850.855（0.402～1.82）
心电图正常异常0.2000.1791.2400.2651.221（0.859～1.736）
空腹血糖——0.0420.0710.3410.5591.043（0.906～1.199）
尿素氮——0.0160.0750.0440.8331.016（0.877～1.177）
尿酸——0.0000.0010.1150.7351.000（0.998～1.001）
肌酐——-0.0090.0071.9680.1610.991（0.978～1.004）
血脂正常异常0.0670.1870.1290.7201.070（0.741～1.544）
), ArticleFig(id=1324077757294064633, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=CN, label=表2, caption=

肺结节发生影响因素的单因素logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标参照组比较组B WaldP值OR值（95%CI）
性别男女0.5580.2684.3220.0381.746（1.032～2.954）
年龄——0.0620.01324.054<0.0011.064（1.038～1.09）
吸烟否是1.8600.22468.995<0.0016.423（4.141～9.961）
饮酒否是-0.3150.2062.3360.1260.730（0.487～1.093）
锻炼从不1-2次/周0.3150.2052.3610.1241.370（0.916-2.049）
从不≥3次/周0.4820.2384.1020.0431.620（1.016-2.582）
工龄——0.1160.01383.891<0.0011.123（1.096～1.152）
粉尘暴露否是0.9000.23614.600<0.0012.459（1.55～3.902）
噪声暴露否是-0.1330.1790.5570.4560.875（0.617～1.242）
高温暴露否是0.3400.2312.1710.1411.406（0.894～2.211）
化学毒物暴露否是-0.1320.2610.2540.6140.877（0.526～1.462）
收缩压——0.0110.0063.3500.0671.011（0.999～1.023）
舒张压——0.0100.0091.3290.2491.010（0.993～1.028）
EB病毒抗体阴性阳性-0.1560.3850.1650.6850.855（0.402～1.82）
心电图正常异常0.2000.1791.2400.2651.221（0.859～1.736）
空腹血糖——0.0420.0710.3410.5591.043（0.906～1.199）
尿素氮——0.0160.0750.0440.8331.016（0.877～1.177）
尿酸——0.0000.0010.1150.7351.000（0.998～1.001）
肌酐——-0.0090.0071.9680.1610.991（0.978～1.004）
血脂正常异常0.0670.1870.1290.7201.070（0.741～1.544）
), ArticleFig(id=1324077757449253882, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=EN, label=Table 3, caption=

The collinearity diagnostic results for the included variables

, figureFileSmall=null, figureFileBig=null, tableContent=
变量容忍度VIFR2值
工龄0.3572.800.643
粉尘暴露0.3852.600.615
年龄0.2913.440.709
吸烟0.9021.110.098
性别0.9341.070.066
), ArticleFig(id=1324077757524751355, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=CN, label=表3, caption=

纳入变量的共线性诊断结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量容忍度VIFR2值
工龄0.3572.800.643
粉尘暴露0.3852.600.615
年龄0.2913.440.709
吸烟0.9021.110.098
性别0.9341.070.066
), ArticleFig(id=1324077757650580476, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=EN, label=Table 4, caption=

Multivariable logistic regression analysis results of factors influencing pulmonary nodules

, figureFileSmall=null, figureFileBig=null, tableContent=
指标参照组B值 WaldP值OR值（95%CI）
性别男性0.7420.3016.0640.0142.100（1.163～3.791）
吸烟无1.7500.24650.454<0.0015.757（3.552～9.332）
工龄—0.1070.01363.359<0.0011.113（1.084～1.143）
常量-3.2180.291122.023<0.0010.040
), ArticleFig(id=1324077757726077949, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=CN, label=表4, caption=

肺结节影响因素的多因素logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标参照组B值 WaldP值OR值（95%CI）
性别男性0.7420.3016.0640.0142.100（1.163～3.791）
吸烟无1.7500.24650.454<0.0015.757（3.552～9.332）
工龄—0.1070.01363.359<0.0011.113（1.084～1.143）
常量-3.2180.291122.023<0.0010.040
), ArticleFig(id=1324077757839324158, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=EN, label=Table 5, caption=

Additive interaction analysis of smoking and years of service on pulmonary nodule occurrence

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露组合病例数/总人数OR值(95%CI)参照组
不吸烟&工龄<25年30/1501.00（参照）—
吸烟&工龄<25年28/703.20(2.15～4.70)组1
不吸烟&工龄≥25年36/1202.50(1.85～3.24)组1
吸烟&工龄≥25年90/2417.52(5.02～10.11)组1
相加交互作用指标估计值（95%CI）
RERI1.82(0.37～3.27)
AP0.18(0.04～0.33)
S1.25(0.95～1.65)
), ArticleFig(id=1324077757927404543, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=CN, label=表5, caption=

吸烟与工龄对肺结节发生的相加交互作用分析

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露组合病例数/总人数OR值(95%CI)参照组
不吸烟&工龄<25年30/1501.00（参照）—
吸烟&工龄<25年28/703.20(2.15～4.70)组1
不吸烟&工龄≥25年36/1202.50(1.85～3.24)组1
吸烟&工龄≥25年90/2417.52(5.02～10.11)组1
相加交互作用指标估计值（95%CI）
RERI1.82(0.37～3.27)
AP0.18(0.04～0.33)
S1.25(0.95～1.65)
), ArticleFig(id=1324077758007096320, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=EN, label=Table 6, caption=

Analysis results of the predictive capability of three variables for pulmonary nodules

, figureFileSmall=null, figureFileBig=null, tableContent=
指标AUC95%CI灵敏度（%）特异度（%）约登指数
性别0.5290.478～0.58115.2290.680.059
吸烟0.6590.608～0.71041.8589.920.318
工龄0.7430.701～0.785———
联合指标0.8010.762～0.84062.5087.150.497
), ArticleFig(id=1324077758128730112, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=CN, label=表6, caption=

三个变量预测肺结节能力分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标AUC95%CI灵敏度（%）特异度（%）约登指数
性别0.5290.478～0.58115.2290.680.059
吸烟0.6590.608～0.71041.8589.920.318
工龄0.7430.701～0.785———
联合指标0.8010.762～0.84062.5087.150.497
), ArticleFig(id=1324077758216810497, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=EN, label=Table 7, caption=

Stability analysis of the gender factor

, figureFileSmall=null, figureFileBig=null, tableContent=
分析模型人群/变量B值 P值OR值（95%CI）
分层分析
男性亚组（n=516)吸烟（是vs.否）1.7620.250<0.0015.821（3.542～9.565）
工龄（每增加1年）0.1060.014<0.0011.112（1.082～1.143）
女性亚组（n=65)吸烟（是vs.否）————
工龄（每增加1年）————
IP加权回归模型
全人群（加权后）性别（女vs.男）0.6150.3050.0431.850（1.018～3.361）
吸烟（是vs.否）1.7220.248<0.0015.595（3.439～9.103）
工龄（每增加1年）0.1050.013<0.0011.111（1.083～1.140）
), ArticleFig(id=1324077758309085186, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077749090005947, language=CN, label=表7, caption=

关于性别因素稳定性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
分析模型人群/变量B值 P值OR值（95%CI）
分层分析
男性亚组（n=516)吸烟（是vs.否）1.7620.250<0.0015.821（3.542～9.565）
工龄（每增加1年）0.1060.014<0.0011.112（1.082～1.143）
女性亚组（n=65)吸烟（是vs.否）————
工龄（每增加1年）————
IP加权回归模型
全人群（加权后）性别（女vs.男）0.6150.3050.0431.850（1.018～3.361）
吸烟（是vs.否）1.7220.248<0.0015.595（3.439～9.103）
工龄（每增加1年）0.1050.013<0.0011.111（1.083～1.140）
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制造业职业人群肺结节风险预测模型的构建与验证——基于性别、吸烟和工龄的联合效应分析
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蒋斌 , 邝玉叶 , 雷伍丽 , 陈崇翔 , 杨丽燕
现代预防医学 | 环境与职业卫生 2026,53(5): 815-820
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现代预防医学 |环境与职业卫生 2026 , 53 (5) : 815 -820
制造业职业人群肺结节风险预测模型的构建与验证——基于性别、吸烟和工龄的联合效应分析
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蒋斌, 邝玉叶, 雷伍丽, 陈崇翔, 杨丽燕
作者信息
  • 广州开发区医院（广州市黄埔区人民医院），广东 广州 510730
通讯作者:
杨丽燕，E-mail:
作者简介:

蒋斌（1978—），男，硕士，副主任医师，研究方向：职业健康监护

Development and validation of a risk prediction model for pulmonary nodules among manufacturing workers: a joint effects analysis based on gender, smoking status, and years of service
Bin JIANG, Yu-ye KUANG, Wu-li LEI, Chong-xiang CHEN, Li-yan YANG
Affiliations
  • Guangzhou Development District Hospital (Guangzhou Huangpu District People’s Hospital), Guangzhou, Guangdong 510730, China
出版时间: 2026-03-10 doi: 10.20043/j.cnki.MPM.202509169
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摘要
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目的

探讨制造业职业人群肺结节发生的独立危险因素，构建并验证基于关键变量的风险预测模型，为职业健康高危人群的早期筛查提供量化工具。

方法

采用横断面研究设计，纳入某制造业企业581名在职员工作为研究对象，其中无肺结节组397人，肺结节组184人。采用单因素分析及多因素logistic回归（逐步向前法）筛选独立预测因子并构建预测模型。通过受试者工作特征曲线评估各变量及联合模型的预测效能。

结果

多因素分析显示，女性（OR=2.100,95% CI:1.163～3.791,P=0.014）、吸烟（OR=5.757,95% CI:3.552～9.332,P<0.001）及工龄（OR=1.113/年，95% CI:1.084～1.143,P<0.001）是肺结节发生的独立危险因素。联合预测模型的曲线下面积为0.801(95% CI:0.762～0.840)，灵敏度为62.50%，特异度为87.15%。

结论

基于性别（女性）、有吸烟史和长工龄构建的风险预测模型具有良好的判别效能，可作为制造业等职业暴露人群肺结节早期筛查的实用工具，其普适性有待在更广泛人群中进一步验证。

职业人群  /  肺结节  /  风险预测模型  /  工龄  /  吸烟  /  性别差异
Objective

This study aims to identify independent risk factors for pulmonary nodules in a manufacturing workers and to develop and validate a risk prediction model based on key variables. The goal was to provide a quantitative tool for the early identification of high-risk individuals within occupational groups.

Methods

A cross-sectional analysis was performed involving 581 employees from a manufacturing enterprise. The cohort comprised 397 individuals without pulmonary nodules and 184 with nodules. Univariate analysis and multivariable logistic regression (forward stepwise method) were used to identify independent predictors and construct the prediction model. The performance of individual variables and the combined model was assessed using receiver operating characteristic (ROC) curve analysis.

Results

Multivariable analysis identified female sex(OR=2.100, 95% CI: 1.163-3.791, P=0.014), smoking (OR=5.757, 95% CI: 3.552-9.332, P<0.001), and years of service (OR=1.113 per year, 95% CI: 1.084-1.143, P<0.001) as significant independent risk factors for pulmonary nodules. The combined prediction model yielded an area under the curve (AUC) of 0.801 (95% CI: 0.762-0.840), with a sensitivity of 62.50% and a specificity of 87.15%.

Conclusion

The risk prediction model, incorporating female sex, smoking history, and longer years of service, exhibits robust discriminative capability. It shows promise as a practical tool for the early screening of pulmonary nodules in occupational populations, such as those in manufacturing. Further external validation in broader and more diverse populations is warranted.

Working population  /  Pulmonary nodules  /  Risk prediction model  /  Years of service  /  Smoking  /  Sex differences
蒋斌, 邝玉叶, 雷伍丽, 陈崇翔, 杨丽燕. 制造业职业人群肺结节风险预测模型的构建与验证——基于性别、吸烟和工龄的联合效应分析. 现代预防医学, 2026 , 53 (5) : 815 -820 . DOI: 10.20043/j.cnki.MPM.202509169
Bin JIANG, Yu-ye KUANG, Wu-li LEI, Chong-xiang CHEN, Li-yan YANG. Development and validation of a risk prediction model for pulmonary nodules among manufacturing workers: a joint effects analysis based on gender, smoking status, and years of service[J]. Modern Preventive Medicine, 2026 , 53 (5) : 815 -820 . DOI: 10.20043/j.cnki.MPM.202509169
正文
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肺结节是肺癌早期的重要影像学标志，在职业暴露人群中检出率显著高于一般人群[1-2]。长期暴露于粉尘、化学毒物等职业有害因素可通过慢性炎症与DNA损伤促进结节形成甚至恶性转化[3-4]。我国制造业劳动者规模庞大，职业性呼吸系统疾病风险突出[5]，肺结节隐匿性强，亟待建立针对性的早期筛查策略。
目前肺结节风险评估多依赖低剂量CT（low-dose computed tomography，LDCT）检查，虽敏感性高，但存在假阳性率高、辐射暴露及成本较大等问题。基于人口学与行为特征的预测模型可弥补上述不足，然而现有模型（如Brock、Mayo模型）[6-7]主要针对普通人群，预测变量集中于年龄、吸烟史、肿瘤家族史等，普遍缺乏对职业暴露累积效应（如工龄）及职业-行为交互作用（如吸烟协同粉尘）的量化评估，导致其在职业人群中的应用效能受限。
与经典模型相比，本研究旨在构建一个适用于职业人群的简易风险预测模型，核心创新在于引入“工龄”作为累积职业暴露的量化指标，并重点探索职业暴露（以工龄为代表）与吸烟行为之间的联合效应，揭示职业环境中特有的风险叠加模式。本研究通过横断面调查，筛选肺结节的独立危险因素，重点分析工龄、吸烟及性别的联合效应，构建并验证简易预测模型，为职业人群肺结节的精准筛查与一级预防提供依据。
1　对象与方法
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1.1　对象
采用整群抽样法，选取2023年1月—12月某大型制造业企业全体在职员工581人作为研究队列，该企业主要从事汽车零部件制造，员工主要工种包括焊接、冲压、涂装、组装及相关辅助岗位。纳入标准：年龄≥18岁；在职工龄≥1年；签署知情同意书并完成LDCT检查。排除标准：既往肺癌或其他恶性肿瘤病史；严重呼吸系统疾病（如慢性阻塞性肺疾病Ⅲ～Ⅳ级、肺结核活动期）；孕期或哺乳期女性。本研究通过广州开发区医院医学伦理委员会审核批准（批件编号：F2025-027），且获得研究对象知情同意。
1.2　方法
采用128层LDCT 进行检查。检查体位：仰卧位，双侧手臂上举放置于头顶部；吸气末屏气时完成扫描，范围从双肺尖部至双侧肋膈角。扫描参数：管电压120 KV，管电流40 mA，层厚1 mm，层间距1 mm。扫描后完成图像重建。发现肺结节后需对肺结节进行三维重建。
1.3　实验室检查
所有受试者禁食禁水8 h以上于次日清晨采集静脉血5 ml，采用罗氏cobas8000C702自动生化仪完成空腹血糖、血脂（总胆固醇、甘油三酯、高密度脂蛋白、低密度脂蛋白）、血尿酸等指标的检测。
1.4　问卷调查
采用经预调查验证的信效度良好的问卷（Cronbach α=0.85，KMO=0.78）进行调查，内容涵盖基本情况、职业史、生活习惯、既往史及家族史等。经培训的调查员指导填写并回收审核问卷。吸烟定义为每日吸烟≥1支且持续≥6个月，饮酒定义为平均每周饮酒≥1次且持续≥1年。数据采用Excel双录入并核对。
1.5　职业病危害因素的确定
研究对象接触的职业病危害因素包括粉尘（如煤尘、矽尘、石灰石粉尘等）、噪声、高温及多种化学毒物（一氧化碳、二氧化碳、一氧化氮、二氧化氮、氨等）。
1.6　统计学分析
分类变量采用χ2检验，连续变量采用独立样本t检验。将单因素分析中P<0.05的变量纳入多因素logistic回归模型，采用逐步向前法（入选标准α=0.05，剔除标准α=0.10）筛选变量，并进行共线性诊断［方差膨胀因子（variance inflation factor，VIF）<5为可接受标准］。基于最终模型的回归系数建立个体肺结节发生风险的预测概率模型。通过绘制受试者工作特征（receiver operating characteristic,ROC）曲线计算曲线下面积（area under the curve,AUC）以评估模型的整体区分能力，并根据最大约登指数确定最佳风险概率截断值，进而计算该截断值下的灵敏度与特异度。采用bootstrap法（1 000次重复抽样）进行内部验证以评估模型的稳定性。为探究吸烟与工龄的联合效应，在模型中引入乘积项评估相乘交互作用，并将工龄二分类化（以约登指数最大化确定切点）后，计算相对超额危险度（relative excess risk due to interaction,RERI）、归因比和协同指数以评估相加交互作用。为评估样本性别比例失衡对主要结果的潜在影响，后续进行了分层分析（按性别）和基于逆概率加权（inverse probability weighting,IPW）的敏感性分析。
2　结果
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2.1　基本情况
本研究共纳入581名研究对象，其中男性516人（88.81%），女性65人（11.18%）。肺结节检出率为31.67%（184/581），其中男性占84.8%，女性占15.2%。χ2检验与t检验结果显示，性别、吸烟、粉尘暴露、年龄及工龄在有无肺结节两组间差异具有统计学意义（P<0.05），其余指标组间差异均无统计学意义（P>0.05）。见表1。
2.2　影响肺结节发生的单因素分析
研究对象的个人基本情况、个人生活习惯以及职业史与肺结节发生的相关性，采用单因素logistic回归分析结果显示，性别、吸烟、粉尘、年龄和工龄与是否有结节存在相关性（P<0.05）。血压、尿酸、尿素氮、肌酐、血脂、空腹血糖、饮酒情况、EB病毒抗体、噪声暴露、高温暴露、化学毒物暴露、锻炼与肺结节是否发生没有统计学意义（P>0.05）。见表2。
2.3　肺结节独立危险因素的多因素分析
将单因素分析中P<0.05的变量（性别、年龄、吸烟、工龄、粉尘）纳入多因素logistic回归。共线性诊断显示，工龄与粉尘暴露（r=0.801,P<0.001）及年龄（r=0.720,P<0.001）均高度相关，但各变量VIF均<5（工龄2.80，粉尘2.60），未达到严重共线性，见表3。采用逐步向前法（入选α=0.05，剔除α=0.10）筛选变量后，粉尘暴露及年龄被剔除，最终性别（女性）、吸烟和工龄保留为独立危险因素。见表4。基于回归系数建立预测模型：
2.4　吸烟与工龄交互作用分析
为分析吸烟与工龄对肺结节发生的联合效应，本研究以工龄为连续变量，分别评估了二者的相乘与相加交互作用。结果显示，吸烟与工龄的相乘交互作用无统计学意义（P=0.152）。然而，相加交互作用分析显示存在显著正向交互效应：RERI=1.82（95% CI: 0.37～3.27），归因比=0.18（95% CI: 0.04～0.33），协同指数=1.25（95% CI:0.95～1.65）。这表明吸烟与工龄的联合效应主要表现为风险的叠加，而非效应的倍数放大。见表5。
2.5　性别、吸烟和工龄三个变量预测肺结节能力分析
性别、吸烟和工龄三个指标联合对肺结节发生的AUC为0.801（95% CI:0.762～0.840），说明三个变量联合预测肺结节能力较好。根据ROC曲线确定的最佳风险概率截断值为0.315，此时模型的灵敏度为62.50%，特异度为87.15%（Youden指数=0.497），见表6和图1。
2.6　关于性别因素稳定性分析
为评估性别比例失衡的影响，进行了分层分析与IPW分析。结果显示，在男性亚组中，吸烟与工龄仍是稳定且显著的独立危险因素，效应值与总模型一致；而女性亚组因样本量过小，模型无法收敛，未能获得可靠估计。IPW分析平衡混杂后，女性性别仍与肺结节风险显著相关（OR=1.85），但效应值低于原始模型（OR=2.100），提示原始估计可能存在一定高估，且受小样本量限制。见表7。
3　讨论
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本研究基于制造业职业人群横断面数据，构建并验证了一个包含性别、吸烟史和工龄的肺结节风险预测模型。最终模型表明，性别（女性）、有吸烟史和长工龄是职业人群肺结节发生的独立危险因素，联合预测模型展现出良好的判别能力（AUC=0.801）。
本模型的优势在于专门针对制造业职业人群，变量简单易得（仅3个），具有良好的判别效能与实用性，尤其为缺乏精细暴露监测数据的中小企业提供了可行的筛查工具。然而，需要关注的是模型灵敏度为62.5%，提示存在漏检风险。这可能源于两方面：一是模型尚未覆盖所有关键风险因素；二是在确定风险阈值时，为控制假阳性、节约医疗资源，优先保障了较高的特异度（87.15%）。在实际应用中，可根据筛查目的调整风险阈值以平衡灵敏度与特异度。未来可通过纳入影像组学等特征进一步提升模型性能。
本研究将工龄作为累积职业暴露的代理变量纳入预测模型，其风险预测效能显著（OR=1.113/年，95% CI:1.084～1.143），印证了长期职业暴露通过慢性炎症与DNA损伤促进肺结节形成的观点[4]。工龄每增加1年，肺结节风险相应升高11.3%，与安顺地区矿工研究结论一致[9]。虽然粉尘变量因与工龄共线性（VIF=2.8）在多因素分析中被取代，但其单因素分析仍有意义（OR=2.459,P<0.001），说明工龄是暴露时长的有效量化指标，尤其适用于缺乏精确暴露数据的制造业人群。作为连续变量，工龄较二分类的粉尘暴露更能准确反映剂量-效应关系（工龄R2=0.643 vs.粉尘R2=0.615），为职业暴露评估提供了时间维度指标。工龄的引入突破了传统模型[6-7]，仅关注年龄、吸烟等因素的局限，为职业性肺部疾病的早期筛查提供了可行的量化依据。
吸烟在模型中独立风险值最高（OR=5.757），且与长工龄（≥25年）存在显著相加交互作用（RERI=1.82,95% CI:0.37～3.27），表明同时暴露者的肺结节风险较单一暴露者升高约182%，提示职业暴露与吸烟具有协同效应[10]。其机制可能与烟草烟雾增强粉尘毒性、协同诱导DNA损伤并抑制修复有关[11-12]。本研究吸烟者肺结节检出率达41.8%，远高于非吸烟者（10.1%），该差距远超普通人群报道[13-14]，进一步证实职业暴露与吸烟的叠加放大效应[15-16]。因此，制造业企业应将控烟，尤其针对长工龄员工，纳入职业健康管理的核心措施。
本研究结果显示，女性肺结节粗检出率低于男性（15.2% vs. 84.8%），但校正工龄、吸烟等混杂因素后，女性表现出更高的独立风险（原始模型OR=2.100）。加权分析进一步表明，平衡协变量后女性风险仍显著升高（OR=1.85），但效应值低于原始模型，提示原始估计可能因小样本量而被高估。因此，“女性OR= 2.100”应视为需谨慎解读的初步信号。该现象可能源于：男性平均工龄更长（21.89年 vs. 14.26年）、吸烟率更高（41.8% vs. 2.1%），导致粗检出率“虚高”；女性肺泡巨噬细胞对粉尘的炎性应答更强（IL-6分泌量为男性的1.8倍），更易形成肉芽肿[17]；此外，未充分评估的家庭油烟暴露[18]及雌激素通路影响[19]也可能加剧女性患病风险。
值得注意的是，性别作为单一预测变量时效能较低（AUC=0.529），这进一步印证了男性高暴露导致的粗检出率虚高现象；但校正混杂因素后，女性仍显示出显著升高的独立风险（OR=2.100），凸显了职业健康评估中纳入性别特异性因素的必要性（如雌激素通路调控及家庭油烟暴露）。这些发现提示，在职业健康评估工作中，需构建性别特异性预测模型，并整合家庭环境暴露等相关指标，以更精准地识别高危人群。
本研究存在一定局限性。第一，样本性别比例失衡，女性样本量小（n=65），虽加权分析提示女性风险增加关联稳健，但点估计（OR=2.100）不稳定，其确切效应需在更大规模、性别均衡的队列中验证。第二，研究对象仅来自单一制造业企业，未包含矿工、纺织工等其他高危职业人群，限制了模型外推性，需通过多中心研究进一步验证。第三，暴露评估精度不足，目前仅对粉尘、毒物进行二分类判定，未来可引入个体暴露浓度数据，构建“工龄×暴露指数”复合指标。第四，研究未区分结节良恶性，下一步可结合影像组学特征优化对恶性结节的预测。此外，本研究目前仅通过bootstrap法进行了内部验证，模型在更广泛的制造业及其他高危职业人群中的普适性，仍有待独立外部队列验证，这也是模型投入实际应用前的必要步骤。
综上所述，本研究明确了工龄、吸烟和性别是影响职业人群肺结节风险的三大核心因素。对于工龄>25年且吸烟的制造业工人，建议纳入职业病重点监护范围并实施年度LDCT筛查。所构建的三变量（工龄、吸烟、性别）预测模型效能良好（AUC>0.80），填补了职业人群肺结节早期筛查的工具缺口，为从结节防治向肺癌一级预防提供了依据。未来需通过精细化暴露评估、扩大人群覆盖和分层恶性风险，进一步提升模型的普适性，助力职业健康行动目标的实现。
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  • 广东省医学科学技术研究基金项目(B2025528)
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2026年第53卷第5期
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doi: 10.20043/j.cnki.MPM.202509169
  • 接收时间：2025-09-12
  • 首发时间：2026-11-02
  • 出版时间：2026-03-10
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  • 收稿日期：2025-09-12
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广东省医学科学技术研究基金项目(B2025528)
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    广州开发区医院（广州市黄埔区人民医院），广东 广州 510730

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