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Article(id=1321170761566503564, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1321170608034009636, articleNumber=null, orderNo=null, doi=10.11847/zgggws1147233, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740499200000, receivedDateStr=2025-02-26, revisedDate=1746460800000, revisedDateStr=2025-05-06, acceptedDate=1754409600000, acceptedDateStr=2025-08-06, onlineDate=1792921255723, onlineDateStr=2026-10-25, pubDate=1765296000000, pubDateStr=2025-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792921255723, onlineIssueDateStr=2026-10-25, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792921255723, creator=13701087609, updateTime=1792921255723, updator=13701087609, issue=Issue{id=1321170608034009636, tenantId=1146029695717560320, journalId=1319373219989389464, year='2025', volume='41', issue='12', pageStart='1409', pageEnd='1536', issueExtLink='null', onlineDate='null', pubDate='1765296000000', pubDateStr='2025-12-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792921219118, creator='13701087609', updateTime=1793011150268, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321547806687474583, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1321170608034009636, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321547806687474584, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1321170608034009636, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1481, endPage=1487, ext={EN=ArticleExt(id=1321170761797190285, articleId=1321170761566503564, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=A prediction model for drug relapse among patients undergoing methadone maintenance treatment: a retrospective cohort study using lasso-logistic regression, columnId=1320427696342020970, journalTitle=Chinese Journal of Public Health, columnName=Epidemiological Research, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the influencing factors of drug relapse among patients undergoing methadone maintenance treatment (MMT) in Fujian province and construct a risk prediction model.

Methods

A retrospective cohort study was conducted. Data were sourced from the Data Information Management System of Drug Maintenance Treatment. Patients who underwent MMT for 180 days or relapsed in Fujian from 2006 to 2023 were included. Lasso-logistic regression was employed to screen the influencing factors, and a risk prediction model was constructed.

Results

The relapse rate of patients undergoing MMT was 75.58% (5 745/7 601). Female gender (OR = 0.801, 95%CI: 0.695–0.923), longer drug use duration (OR = 0.985, 95%CI: 0.974–0.996), and older enrollment age (OR = 0.988, 95%CI: 0.981–0.997) were protective factors for drug relapse. The risk factors for drug relapse were injection drug use in the month before enrollment (OR = 1.156, 95%CI: 1.034–1.293), being employed (OR = 1.184, 95%CI: 1.059–1.326), being referred to MMT by drug-using friends (OR = 1.273, 95%CI: 1.026–1.582), more frequent daily drug use in the month before enrollment (OR = 1.018, 95%CI: 1.005–1.033), longer travel time to the clinic (OR = 1.004, 95%CI: 1.002–1.008), previous participation in MMT (OR = 1.217, 95%CI: 1.095–1.355), coming to MMT by bus (OR = 1.429, 95%CI: 1.193–1.712), and using other means of transportation to MMT (OR = 2.890, 95%CI: 2.033–4.106). The area under curve (AUC) of the constructed risk prediction model was 0.602 (95%CI: 0.588–0.617).

Conclusions

The drug relapse rate of patients undergoing MMT is high in Fujian, and the constructed risk prediction model can be used to identify high-risk populations of drug relapse.

, authors=Yixiang LIN, Jianfeng XIE, Yuefeng QIU, Shaobin WU, authorsList=Yixiang LIN, Jianfeng XIE, Yuefeng QIU, Shaobin WU, authorCompany=null, correspAuthors=Shaobin WU, authorNote=null, correspAuthorsNote=
WU Shaobin, E-mail:
, copyrightStatement=Copyright © 2025 Chinese Journal of Public Health. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321170762971595416, articleId=1321170761566503564, tenantId=1146029695717560320, journalId=1319373219989389464, language=CN, title=美沙酮维持治疗患者复吸毒品风险预测：基于lasso-logistic模型分析的回顾性队列研究, columnId=1320427696509793132, journalTitle=中国公共卫生, columnName=流行病学研究, runingTitle=null, highlight=null, articleAbstract=
目的

分析福建省美沙酮维持治疗（MMT）患者复吸毒品（复吸）影响因素并构建风险预测模型。

方法

采用回顾性队列研究，资料来源于戒毒药物维持治疗数据信息管理系统，纳入2006—2023年福建省参加MMT达到180天或期间复吸的患者。采用lasso-logistic回归筛选影响因素，构建风险预测模型。

结果

共纳入MMT患者7 601例，复吸率为75.58%。女性（OR=0.801，95%CI=0.695～0.923）、吸毒年限越长（OR=0.985，95%CI=0.974～0.996）、入组年龄越大（OR=0.988，95%CI=0.981～0.997）是复吸的保护因素；入组前1个月有注射吸毒行为（OR=1.156，95%CI=1.034～1.293）、有工作（OR=1.184，95%CI=1.059～1.326）、吸毒朋友介绍来参加MMT（OR=1.273，95%CI=1.026～1.582）、入组前1个月平均每天吸毒次数越多（OR=1.018，95%CI=1.005～1.033）、来门诊路上花费时间越长（OR=1.004，95%CI=1.002～1.008）、参加过MMT（OR=1.217，95%CI=1.095～1.355）、乘公共汽车来MMT（OR=1.429，95%CI=1.193～1.712）和其他交通工具来MMT（OR=2.890，95%CI=2.033～4.106）是复吸的危险因素。风险预测模型的曲线下面积（AUC）为0.602（95%CI=0.588～0.617）。

结论

福建省MMT患者复吸率较高，构建的风险预测模型可用于识别复吸高危人群。

, authors=林毅翔, 谢剑锋, 邱月锋, 吴韶彬*, *, authorsList=林毅翔, 谢剑锋, 邱月锋, 吴韶彬, authorCompany=null, correspAuthors=吴韶彬, authorNote=

林毅翔（1996—），医师，硕士，研究方向：艾滋病防控与美沙酮维持治疗

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吴韶彬，E-mail：
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Journal of Substance Use, 2021, 26(1): 60-66., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1321170764791923353, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, xref=null, ext=[AuthorCompanyExt(id=1321170764804506266, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, companyId=1321170764791923353, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China), AuthorCompanyExt(id=1321170764812894875, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, companyId=1321170764791923353, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=福建省疾病预防控制中心，福州 350012)])], figs=[ArticleFig(id=1321170766700331706, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=EN, label=Fig.1, caption=Variable screening by the lasso regression model, figureFileSmall=eeXokKOBErB+JU63TE+rYw==, figureFileBig=X33ONQqJmOXVsa/0yM2qMQ==, tableContent=null), ArticleFig(id=1321170766780023483, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=CN, label=图1, caption=lasso回归模型的变量筛选

注：A：交叉验证参数λ的选择过程图；B：lasso筛选变量系数变化过程图。

, figureFileSmall=eeXokKOBErB+JU63TE+rYw==, figureFileBig=X33ONQqJmOXVsa/0yM2qMQ==, tableContent=null), ArticleFig(id=1321170766993932988, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=EN, label=Fig.2, caption=Risk prediction nomogram for drug relapse of patients undergoing MMT, figureFileSmall=ee/KQdjxJkXyPGHMa++4ig==, figureFileBig=BZrvUsOBX9L8OFz/lznzaw==, tableContent=null), ArticleFig(id=1321170767056847549, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=CN, label=图2, caption=MMT患者复吸风险预测列线图, figureFileSmall=ee/KQdjxJkXyPGHMa++4ig==, figureFileBig=BZrvUsOBX9L8OFz/lznzaw==, tableContent=null), ArticleFig(id=1321170767157510846, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=EN, label=Fig.3, caption=ROC curve of the risk prediction model, figureFileSmall=91SvI4aJVk7o8p8x+2Ripw==, figureFileBig=7iSFsCVErq8Mzaa3d/5YqQ==, tableContent=null), ArticleFig(id=1321170767258174143, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=CN, label=图3, caption=风险预测模型ROC曲线, figureFileSmall=91SvI4aJVk7o8p8x+2Ripw==, figureFileBig=7iSFsCVErq8Mzaa3d/5YqQ==, tableContent=null), ArticleFig(id=1321170767342060224, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=EN, label=Tab.1, caption=

Comparison of qualitative data between the relapse group and the non-relapse group of patients undergoing MMT

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基线特征调查人数
占比/%
复吸未复吸χ2值P值
人数占比/%人数占比/%
性别
　男性636483.73484384.301 52181.955.681<0.05
　女性1 23716.2790215.7033518.05
民族
　汉族733496.49554096.431 79496.660.2150.643
　其他民族2673.512053.57623.34
文化程度
　文盲2313.041823.17492.643.0110.566
　小学208927.481 58227.5450727.32
　初中422455.573 19055.531 03455.71
　高中或中专96312.6771612.4624713.31
　大专及以上941.24751.31191.02
婚姻状况
　未婚3 17241.732 42242.1675040.413.0360.552
　已婚有配偶370948.802 78948.5592049.57
　离异6929.105128.911808.70
　丧偶180.24150.2630.16
　其他100.1370.1230.16
居住状况
　独居7079.305519.591568.413.9100.271
　与家人/亲属639884.17483184.091 56784.43
　与朋友4826.343526.131307.00
　其他140.18110.1930.16
入组前半年生活费来源
　固定工资95612.5875213.0920410.9912.723<0.05
　家人朋友供给430156.593 21255.911 08958.67
　临时工资2 00626.391 53926.7946725.16
　其他2843.742073.60774.15
　社会福利540.71350.61191.02
入组前半年吸毒方式
　单纯口吸或烫吸2 85437.55213537.1671938.744.3510.226
　注射和其他方式混用4696.173425.951276.84
　单纯注射421955.513 22456.1299553.61
　其他590.78440.77150.81
入组前1个月是否有注射吸毒行为
　否2 94738.772 18438.0276341.115.658<0.05
　是465461.23356161.981 09358.89
是否有工作
　否474462.41353761.571 20765.037.183<0.05
　是2 85737.592 20838.4364934.97
参加治疗的途径
　非吸毒亲朋介绍4606.053315.761296.9536.016<0.05
　公安部门介绍5226.873756.531477.92
　社区/媒体宣传1 14815.1083114.4631717.08
　吸毒朋友介绍520468.46403170.171 17363.20
　其他2673.511773.08904.85
每天来门诊的交通方式
　步行7399.724958.6224413.1583.031<0.05
　骑自行车2613.431913.32703.77
　乘公共汽车457160.14355561.881 01654.74
　骑摩托车1 08814.3177313.4631516.97
　自己开车5907.764257.401658.89
　其他3524.633065.33462.48
入组前与吸毒的朋友交往的次数/（次/周）
　<12373.121632.84743.996.140<0.05
　1～6409753.903 10654.0699153.39
　≥73 26742.982 47643.1079142.62
是否参加过MMT
　否3 39044.602 50843.6688247.528.486<0.05
　是421155.403 23756.3497452.48
入组前3个月是否被公安部门拘留
　否737396.99549495.631 77995.850.1650.685
　是3284.312514.37774.15
入组前3个月因吸毒而有违法犯罪
　否752899.04568899.011 84099.140.2500.617
　是730.96570.99160.86
是否单纯使用海洛因
　否2473.25556296.811 79296.550.3080.579
　是735496.751833.19643.45
), ArticleFig(id=1321170767421752001, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=CN, label=表1, caption=

MMT患者复吸组与未复吸组定性资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
基线特征调查人数
占比/%
复吸未复吸χ2值P值
人数占比/%人数占比/%
性别
　男性636483.73484384.301 52181.955.681<0.05
　女性1 23716.2790215.7033518.05
民族
　汉族733496.49554096.431 79496.660.2150.643
　其他民族2673.512053.57623.34
文化程度
　文盲2313.041823.17492.643.0110.566
　小学208927.481 58227.5450727.32
　初中422455.573 19055.531 03455.71
　高中或中专96312.6771612.4624713.31
　大专及以上941.24751.31191.02
婚姻状况
　未婚3 17241.732 42242.1675040.413.0360.552
　已婚有配偶370948.802 78948.5592049.57
　离异6929.105128.911808.70
　丧偶180.24150.2630.16
　其他100.1370.1230.16
居住状况
　独居7079.305519.591568.413.9100.271
　与家人/亲属639884.17483184.091 56784.43
　与朋友4826.343526.131307.00
　其他140.18110.1930.16
入组前半年生活费来源
　固定工资95612.5875213.0920410.9912.723<0.05
　家人朋友供给430156.593 21255.911 08958.67
　临时工资2 00626.391 53926.7946725.16
　其他2843.742073.60774.15
　社会福利540.71350.61191.02
入组前半年吸毒方式
　单纯口吸或烫吸2 85437.55213537.1671938.744.3510.226
　注射和其他方式混用4696.173425.951276.84
　单纯注射421955.513 22456.1299553.61
　其他590.78440.77150.81
入组前1个月是否有注射吸毒行为
　否2 94738.772 18438.0276341.115.658<0.05
　是465461.23356161.981 09358.89
是否有工作
　否474462.41353761.571 20765.037.183<0.05
　是2 85737.592 20838.4364934.97
参加治疗的途径
　非吸毒亲朋介绍4606.053315.761296.9536.016<0.05
　公安部门介绍5226.873756.531477.92
　社区/媒体宣传1 14815.1083114.4631717.08
　吸毒朋友介绍520468.46403170.171 17363.20
　其他2673.511773.08904.85
每天来门诊的交通方式
　步行7399.724958.6224413.1583.031<0.05
　骑自行车2613.431913.32703.77
　乘公共汽车457160.14355561.881 01654.74
　骑摩托车1 08814.3177313.4631516.97
　自己开车5907.764257.401658.89
　其他3524.633065.33462.48
入组前与吸毒的朋友交往的次数/（次/周）
　<12373.121632.84743.996.140<0.05
　1～6409753.903 10654.0699153.39
　≥73 26742.982 47643.1079142.62
是否参加过MMT
　否3 39044.602 50843.6688247.528.486<0.05
　是421155.403 23756.3497452.48
入组前3个月是否被公安部门拘留
　否737396.99549495.631 77995.850.1650.685
　是3284.312514.37774.15
入组前3个月因吸毒而有违法犯罪
　否752899.04568899.011 84099.140.2500.617
　是730.96570.99160.86
是否单纯使用海洛因
　否2473.25556296.811 79296.550.3080.579
　是735496.751833.19643.45
), ArticleFig(id=1321170767497249474, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=EN, label=Tab.2, caption=

Comparison of quantitative data between the relapse group and the non-relapse group of patients undergoing MMT

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基线特征复吸未复吸t值P值
入组年龄/岁34.28±7.84435.43±7.8515.498<0.05
吸毒年限/年9.358±7.809.978±7.804.261<0.05
入组前1个月平均每天吸毒次数/（次/天）4.08±5.863.65±5.863.485<0.05
入组前1个月平均每天毒品花费/元410.85±317.92391.27±318.112.306<0.05
每天来门诊参加治疗的路上需要花费时间/分30.03±22.1226.44±19.106.756<0.05
), ArticleFig(id=1321170769128833731, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=CN, label=表2, caption=

MMT患者复吸组与未复吸组定量资料比较

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基线特征复吸未复吸t值P值
入组年龄/岁34.28±7.84435.43±7.8515.498<0.05
吸毒年限/年9.358±7.809.978±7.804.261<0.05
入组前1个月平均每天吸毒次数/（次/天）4.08±5.863.65±5.863.485<0.05
入组前1个月平均每天毒品花费/元410.85±317.92391.27±318.112.306<0.05
每天来门诊参加治疗的路上需要花费时间/分30.03±22.1226.44±19.106.756<0.05
), ArticleFig(id=1321170771100156612, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=EN, label=Tab.3, caption=

Multivariate logistic analysis model for drug relapse in patients undergoing MMT

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变量名称比较组对照组B值 $ S_{\overline{x}} $Wald χ2值OR值95%CIP值
性别女性男性–0.2210.0729.3680.8010.695～0.923<0.05
入组前1个月是否有注射吸毒行为是否0.1450.0576.4491.1561.034～1.293<0.05
是否有工作是否0.1690.0578.7331.1841.059～1.326<0.05
参加治疗的途径公安部门介绍非吸毒亲朋介绍–0.0200.1440.0200.9800.739～1.3000.889
社区/媒体宣传0.0260.1250.0451.0270.804～1.3110.832
吸毒朋友介绍0.2410.1104.7871.2731.026～1.582<0.05
其他–0.1970.1691.3540.8210.590～1.1440.245
每天来门诊的交通方式骑自行车步行0.3140.1623.7481.3690.996～1.8810.053
乘公共汽车0.3570.09215.0191.4291.193～1.712<0.05
骑摩托车0.0680.1050.4201.0710.871～1.3160.517
自己开车0.0070.1260.0031.0070.787～1.2880.958
其他1.0610.17935.0282.8902.033～4.106<0.05
是否参加过MMT是否0.1960.05413.1121.2171.095～1.355<0.05
入组前1个月平均每天吸毒次数/（次/天）0.0180.0076.7411.0181.005～1.033<0.05
每天来门诊参加治疗的路上需要花费时间/分0.0040.00110.2831.0041.002～1.008<0.05
吸毒年限/年–0.0140.0056.6520.9850.974～0.996<0.05
入组年龄/岁–0.0110.0047.9660.9880.981～0.997<0.05
), ArticleFig(id=1321170771213402821, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, language=CN, label=表3, caption=

MMT患者复吸情况多因素logistic分析模型

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名称比较组对照组B值 $ S_{\overline{x}} $Wald χ2值OR值95%CIP值
性别女性男性–0.2210.0729.3680.8010.695～0.923<0.05
入组前1个月是否有注射吸毒行为是否0.1450.0576.4491.1561.034～1.293<0.05
是否有工作是否0.1690.0578.7331.1841.059～1.326<0.05
参加治疗的途径公安部门介绍非吸毒亲朋介绍–0.0200.1440.0200.9800.739～1.3000.889
社区/媒体宣传0.0260.1250.0451.0270.804～1.3110.832
吸毒朋友介绍0.2410.1104.7871.2731.026～1.582<0.05
其他–0.1970.1691.3540.8210.590～1.1440.245
每天来门诊的交通方式骑自行车步行0.3140.1623.7481.3690.996～1.8810.053
乘公共汽车0.3570.09215.0191.4291.193～1.712<0.05
骑摩托车0.0680.1050.4201.0710.871～1.3160.517
自己开车0.0070.1260.0031.0070.787～1.2880.958
其他1.0610.17935.0282.8902.033～4.106<0.05
是否参加过MMT是否0.1960.05413.1121.2171.095～1.355<0.05
入组前1个月平均每天吸毒次数/（次/天）0.0180.0076.7411.0181.005～1.033<0.05
每天来门诊参加治疗的路上需要花费时间/分0.0040.00110.2831.0041.002～1.008<0.05
吸毒年限/年–0.0140.0056.6520.9850.974～0.996<0.05
入组年龄/岁–0.0110.0047.9660.9880.981～0.997<0.05
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美沙酮维持治疗患者复吸毒品风险预测：基于lasso-logistic模型分析的回顾性队列研究
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林毅翔 , 谢剑锋 , 邱月锋 , 吴韶彬 *, *
中国公共卫生 | 流行病学研究 2025,41(12): 1481-1487
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中国公共卫生 |流行病学研究 2025 , 41 (12) : 1481 -1487
美沙酮维持治疗患者复吸毒品风险预测：基于lasso-logistic模型分析的回顾性队列研究
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[Author(id=1321170764880003741, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, 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=1321170764955501215, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, authorId=1321170764880003741, language=EN, stringName=Yixiang LIN, firstName=Yixiang, middleName=null, lastName=LIN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321170765018415776, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170761566503564, authorId=1321170764880003741, language=CN, stringName=林毅翔, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=福建省疾病预防控制中心，福州 350012, bio={"content":"

林毅翔（1996—），医师，硕士，研究方向：艾滋病防控与美沙酮维持治疗

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林毅翔（1996—），医师，硕士，研究方向：艾滋病防控与美沙酮维持治疗

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林毅翔, 谢剑锋, 邱月锋, 吴韶彬*, *
作者信息
  • 福建省疾病预防控制中心，福州 350012
通讯作者:
吴韶彬，E-mail：
作者简介:

林毅翔（1996—），医师，硕士，研究方向：艾滋病防控与美沙酮维持治疗

A prediction model for drug relapse among patients undergoing methadone maintenance treatment: a retrospective cohort study using lasso-logistic regression
Yixiang LIN, Jianfeng XIE, Yuefeng QIU, Shaobin WU
Affiliations
  • Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China
    • Corresponding Author:
    WU Shaobin, E-mail:
出版时间: 2025-12-10 doi: 10.11847/zgggws1147233
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摘要
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目的

分析福建省美沙酮维持治疗（MMT）患者复吸毒品（复吸）影响因素并构建风险预测模型。

方法

采用回顾性队列研究，资料来源于戒毒药物维持治疗数据信息管理系统，纳入2006—2023年福建省参加MMT达到180天或期间复吸的患者。采用lasso-logistic回归筛选影响因素，构建风险预测模型。

结果

共纳入MMT患者7 601例，复吸率为75.58%。女性（OR=0.801，95%CI=0.695～0.923）、吸毒年限越长（OR=0.985，95%CI=0.974～0.996）、入组年龄越大（OR=0.988，95%CI=0.981～0.997）是复吸的保护因素；入组前1个月有注射吸毒行为（OR=1.156，95%CI=1.034～1.293）、有工作（OR=1.184，95%CI=1.059～1.326）、吸毒朋友介绍来参加MMT（OR=1.273，95%CI=1.026～1.582）、入组前1个月平均每天吸毒次数越多（OR=1.018，95%CI=1.005～1.033）、来门诊路上花费时间越长（OR=1.004，95%CI=1.002～1.008）、参加过MMT（OR=1.217，95%CI=1.095～1.355）、乘公共汽车来MMT（OR=1.429，95%CI=1.193～1.712）和其他交通工具来MMT（OR=2.890，95%CI=2.033～4.106）是复吸的危险因素。风险预测模型的曲线下面积（AUC）为0.602（95%CI=0.588～0.617）。

结论

福建省MMT患者复吸率较高，构建的风险预测模型可用于识别复吸高危人群。

美沙酮维持治疗  /  复吸毒品  /  预测模型  /  影响因素  /  福建省
Objective

To analyze the influencing factors of drug relapse among patients undergoing methadone maintenance treatment (MMT) in Fujian province and construct a risk prediction model.

Methods

A retrospective cohort study was conducted. Data were sourced from the Data Information Management System of Drug Maintenance Treatment. Patients who underwent MMT for 180 days or relapsed in Fujian from 2006 to 2023 were included. Lasso-logistic regression was employed to screen the influencing factors, and a risk prediction model was constructed.

Results

The relapse rate of patients undergoing MMT was 75.58% (5 745/7 601). Female gender (OR = 0.801, 95%CI: 0.695–0.923), longer drug use duration (OR = 0.985, 95%CI: 0.974–0.996), and older enrollment age (OR = 0.988, 95%CI: 0.981–0.997) were protective factors for drug relapse. The risk factors for drug relapse were injection drug use in the month before enrollment (OR = 1.156, 95%CI: 1.034–1.293), being employed (OR = 1.184, 95%CI: 1.059–1.326), being referred to MMT by drug-using friends (OR = 1.273, 95%CI: 1.026–1.582), more frequent daily drug use in the month before enrollment (OR = 1.018, 95%CI: 1.005–1.033), longer travel time to the clinic (OR = 1.004, 95%CI: 1.002–1.008), previous participation in MMT (OR = 1.217, 95%CI: 1.095–1.355), coming to MMT by bus (OR = 1.429, 95%CI: 1.193–1.712), and using other means of transportation to MMT (OR = 2.890, 95%CI: 2.033–4.106). The area under curve (AUC) of the constructed risk prediction model was 0.602 (95%CI: 0.588–0.617).

Conclusions

The drug relapse rate of patients undergoing MMT is high in Fujian, and the constructed risk prediction model can be used to identify high-risk populations of drug relapse.

methadone maintenance treatment  /  drug relapse  /  prediction model  /  influencing factor  /  Fujian
林毅翔, 谢剑锋, 邱月锋, 吴韶彬. 美沙酮维持治疗患者复吸毒品风险预测：基于lasso-logistic模型分析的回顾性队列研究. 中国公共卫生, 2025 , 41 (12) : 1481 -1487 . DOI: 10.11847/zgggws1147233
Yixiang LIN, Jianfeng XIE, Yuefeng QIU, Shaobin WU. A prediction model for drug relapse among patients undergoing methadone maintenance treatment: a retrospective cohort study using lasso-logistic regression[J]. Chinese Journal of Public Health, 2025 , 41 (12) : 1481 -1487 . DOI: 10.11847/zgggws1147233
正文
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自2006年福建省开展美沙酮维持治疗（methadone maintenance treatment, MMT）门诊服务以来，在控制毒品使用、减少犯罪及恢复成瘾者社会功能等方面成绩斐然[1-2]。然而，复吸毒品（复吸）问题却在一定程度上成为MMT效果提升的阻碍，严重影响MMT患者的康复进程与社会的和谐稳定[3-4]。若有合适的工具帮助门诊医务人员提前识别高复吸风险患者，及时开展针对性的干预措施，降低复吸率，可进一步提升福建省MMT门诊的治疗质量。本研究纳入2006—2023年福建省参加MMT达到180天或期间复吸的患者。采用lasso-logistic回归分析MMT患者复吸影响因素并构建风险预测模型，为早期识别MMT患者复吸高危人群及减少复吸提供依据。
1　对象与方法
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1.1　对象
按《滥用阿片类物质成瘾者社区药物维持治疗工作方案》[5]纳入2006—2023年在福建省参加MMT达到180天或期间出现复吸的患者，剔除不符合要求的患者。剔除标准：（1）入组时间在2023年12月31日之后；（2）数据记录不全或不明确者；（3）治疗时间未满180 d退出，且治疗期间未观察到复吸者。本研究经福建省疾病预防控制中心伦理委员会批准（编号：2023-010）。并获得所有调查对象的知情同意。
1.2　方法
采用回顾性队列研究，随访起点为研究对象入组MMT的时间，随访终点为入组后的第180天。资料来源为戒毒药物维持治疗数据信息管理系统。采用国家制定的调查问卷对患者进行调查。内容包括研究对象的基本信息、参加治疗情况、药物滥用情况等信息。随访期间，符合以下情形之一者判定为复吸：（1）尿液毒品检测结果呈阳性；（2）因偷吸行为被执行强制隔离戒毒措施。
1.3　统计分析
使用Excel 2007建立数据库，使用SPSS 21.0、R 4.4.2软件进行数据库整理和分析，定量资料以$ \overline{x}\pm s $描述，定性资料以率、构成比表示。使用χ2检验、t检验进行单因素分析，采用lasso-logistic回归筛选影响因素，构建风险预测模型。采用bootstrap法对模型进行内部验证，并绘制受试者工作特征（receiver operator characteristic, ROC）曲线评价预测效能。绘制列线图实现模型的可视化。检验水准为双侧α=0.05。
2　结　果
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2.1　两组资料比较（表1、2）
本研究共纳入MMT患者7 601例，复吸人数5 745例，复吸率为75.58%。复吸组男性、入组前1个月有注射吸毒行为、有工作、参加过MMT的占比均高于未复吸组（P<0.05）；复吸组的平均入组年龄、吸毒年限均低于未复吸组（P<0.05）；复吸组的入组前1个月平均每天吸毒次数、入组前1个月平均每天毒品花费、来门诊路上花费时间均高于未复吸组（P<0.05）。两组间入组前半年生活费来源、参加治疗的途径、每天来门诊的交通方式、职业、入组前1个月内和吸毒的朋友交往次数的分布不同。差异均有统计学意义（P<0.05）。
2.2　lasso回归筛选预测变量（图1）
将上述13个单因素分析差异有统计学意义的变量纳入到lasso模型中进行筛选。图1A展示了10折交叉验证确定最优λ值的过程，随着λ增大，模型复杂度降低，对应的曲线下面积（area under the curve, AUC）逐渐下降且下降幅度趋缓。其中，lambda.min为使AUC最大的λ值，而lambda.1se为lambda.min对应AUC的1个标准误差范围内的最大λ值。选择最优λ值为lambda.1se=0.00912671，此时既能保证模型具有较好的预测效能，又能提升模型的精简性便于门诊使用；图1B呈现了各候选变量在lasso回归筛选过程的系数变化轨迹，随着λ值增大，模型压缩作用增强，纳入模型的变量减少。最终筛选出10个非零系数的相关因素：性别、入组前1个月是否有注射吸毒行为、是否有工作、参加治疗的途径、入组前1个月平均每天吸毒次数、来门诊路上花费时间、吸毒年限、入组年龄、每天来门诊的交通方式、是否接受过MMT。
2.3　风险预测模型的构建（表3、图2）
将lasso回归筛选出的10个变量作为自变量，以MMT患者是否复吸（否=0，是=1）为因变量，构建多因素logistic回归模型并绘制复吸风险预测列线图。结果显示，女性（OR=0.801，95%CI=0.695～0.923）、吸毒年限越长（OR=0.985，95%CI=0.974～0.996）、入组年龄越大（OR=0.988，95%CI=0.981～0.997）是复吸的保护因素；入组前1个月有注射吸毒行为（OR=1.156，95%CI=1.034～1.293）、有工作（OR=1.184，95%CI=1.059～1.326）、吸毒朋友介绍来参加MMT（OR=1.273，95%CI= 1.026～1.582）、入组前1个月平均每天吸毒次数越多（OR=1.018，95%CI=1.005～1.033）、来门诊路上花费时间越长（OR=1.004，95%CI=1.002～1.008）、参加过MMT（OR=1.217，95%CI=1.095～1.355）、乘公共汽车来MMT（OR=1.429，95%CI=1.193～1.712）和其他交通工具来MMT（OR=2.890，95%CI=2.033～4.106）是复吸的危险因素。绘制的列线图包含10个影响因素及对应的分值轴，通过累加各因素分值得到总分，再对应至预测概率轴，用于直观预测MMT患者的复吸风险。下面以1例研究对象为例，说明如何使用该列线图进行风险预测。该研究对象性别为男性、入组前1个月有注射吸毒行为、有工作、吸毒朋友介绍来治疗、入组前1个月平均每天吸毒次数1次、来门诊路上花费时间20 min、吸毒年限10年、入组年龄35岁、乘坐公交车来门诊、接受过MMT。将这些条件代入列线图中，各因素对应分值分别为13、9、10、26、1、6、22、30、21、12分，总分150分，对应预测概率约0.81。该模型的最佳分类阈值为0.76，即预测概率>0.76的患者，判断为复吸高危人群，否则为非高危险人群。由于该研究对象的预测概率> 0.76，因此判定为复吸高危人群。
2.4　风险预测模型评价（图3）
该预测模型AUC为0.602（95%CI=0.588～0.617）。Bootstrap内部验证法计算平均一致性指数（concordance Index, C-index）为0.606（95%CI=0.591～0.621，P<0.001），表明该模型区分度尚可。Hosmer-Lemeshow拟合优度检验显示差异无统计学意义（χ2=3.687，P=0.884），表明模型稳定性良好。
3　讨　论
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本研究结果显示，2006—2023年福建省MMT患者的复吸率为75.58%，高于重庆市（71.2%）[6]、清远市（53.33%）[7]、杭州市的调查结果（56%）[8]。可能原因是福建省地处特殊地区，既是台湾海峡两岸交流的前沿窗口，又是“一带一路”海上丝绸之路的重要节点，客观上加剧了毒品跨区域渗透风险[9-10]，且对于MMT患者的戒毒康复支持较为薄弱[11-12]，导致复吸率较高。
女性（OR=0.801，95%CI=0.695～0.923）复吸风险更低，这与雷红涛[13]、薛秋[14]等的研究结果一致。可能原因是女性在治疗过程中有更高的依从性，同时也更主动寻求心理支持，这些特点有助于降低复吸风险。此外，吸毒年限越长（OR=0.985，95%CI=0.974～0.996），越不易复吸，可能原因是依据《滥用阿片类物质成瘾者社区药物维持治疗工作方案》[5]，患者入组需满足“经过多次戒毒治疗仍不能戒断毒瘾的滥用阿片类物质成瘾者”。对于长期吸毒且多次戒毒失败并有意愿接受MMT的患者，其戒毒动机往往更为强烈，更积极配合各项干预措施。同时由于长期吸毒所导致的经济负担过重，获取毒品存在困难，而MMT费用相对较低，还能获得更多的社会支持，从而降低了复吸风险[15-16]。入组年龄越大（OR=0.988，95%CI=0.981～0.997）患者越不易复吸，可能原因是随着年龄增长，心理成熟度提高，对毒品的危害有更深刻的认识，能更好地应对治疗过程中的心理压力和诱惑，避免因复吸带来严重后果[17]。
入组前1个月有注射吸毒行为（OR=1.156，95%CI= 1.034～1.293）、入组前1个月平均每天吸毒次数越多（OR=1.018，95%CI=1.005～1.033）越易复吸。当采用注射吸毒时，毒品可直接进入血液循环，成瘾速度更快、成瘾程度更深[18]。而吸毒次数增加会强化身体对毒品的适应性和依赖性，进一步增大了复吸的可能性[19]。对此应加强对吸毒危害的宣传教育，优化监管与跟踪服务，完善心理与社会支持体系，降低复吸率。
有工作（OR=1.184，95%CI=1.059～1.326）、吸毒朋友介绍来参加MMT（OR=1.273，95%CI=1.026～1.582）的MMT患者越易复吸，可能原因是有工作的MMT患者具有消费毒品的经济基础，在面对毒品诱惑时，经济阻力较小[20]；而吸毒朋友介绍来参加 MMT 的患者仍处于与吸毒人群有密切联系的社交圈子中。这种环境会增加接触毒品的机会，并且吸毒朋友的行为和态度会对患者产生负面的示范效应，使其更容易受到诱惑而复吸[21]。接受过MMT（OR=1.217，95%CI=1.095～1.355）是复吸的危险因素。有研究表明，多次参与 MMT 治疗的患者，其退出治疗的风险会相应增加，而这种情况可能导致患者复吸[22]。
从预测模型结果可见，在众多复吸影响因素中，交通方式尤其是采用其他交通工具来接受 MMT（OR=2.890，95%CI=2.033～4.106）对复吸风险的提升作用最为显著；其次是乘公共汽车来接受MMT（OR=1.429，95%CI= 1.193～1.712）。上述两个因素的OR值高于其他影响因素，表明交通方式在预测复吸风险时权重最大，是影响 MMT 患者复吸的关键因素。同时来门诊路上花费时间越长（OR=1.004，95%CI= 1.002～1.008）也会增加复吸的风险。有研究指出，较长的路途时间、交通工具不便利可能导致患者对治疗的依从性下降，进而增加复吸的风险[23]。因此，根据本地阿片类成瘾者的数量及分布、戒毒工作需求等实际情况，对 MMT 门诊进行优化调整十分必要。
本研究构建的福建省MMT患者复吸风险预测模型，在一定程度上揭示了复吸的影响因素并具备初步的预测能力。该模型的AUC为0.602，尽管与其他的针对不同疾病或行为预测模型相比，精准度还有提升空间。但考虑到吸毒问题的严重性和复杂性，其在MMT中仍具有不可忽视的价值。吸毒不仅对个人的身心健康造成严重损害，还会引发一系列社会问题，如犯罪率上升、家庭破裂等。只要能精准干预1例患者，避免毒品对其身体的进一步伤害，使其有机会重新回归正常生活，恢复身体健康、重建家庭关系、重新融入社会，这无疑是巨大的收益。
本研究也存在一定局限性。一方面，数据来源于戒毒药物维持治疗数据信息管理系统，部分信息存在记录不完整或不准确的情况，且一些复吸相关因素未纳入数据库，例如患者的心理状态[24]、家庭变化[25]、吸烟[26]等。因此，有必要进一步开展专项调查，纳入更多相关因素。另一方面，研究对象为福建省MMT患者，地域局限性明显，该模型在其他地区的适用性和有效性有待进一步验证。未来还需增加样本的多样性，开展多中心研究，以验证模型的适用性并完善模型，为更广泛地区的MMT患者提供更精准有效的服务。
综上所述，福建省MMT患者的复吸率较高，需要尽早识别复吸高危人群，有针对性地开展综合干预服务，进一步提高MMT患者治疗效果。

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doi: 10.11847/zgggws1147233
  • 接收时间：2025-02-26
  • 首发时间：2026-10-25
  • 出版时间：2025-12-10
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  • 收稿日期：2025-02-26
  • 修回日期：2025-05-06
  • 录用日期：2025-08-06
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    福建省疾病预防控制中心，福州 350012

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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
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