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Article(id=1322609031458440124, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322609030938346427, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202506286, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750176000000, receivedDateStr=2025-06-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793264165986, onlineDateStr=2026-10-29, pubDate=1764000000000, pubDateStr=2025-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793264165986, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793264165986, creator=13701087609, updateTime=1793264165986, updator=13701087609, issue=Issue{id=1322609030938346427, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='22', pageStart='4033', pageEnd='4224', issueExtLink='null', onlineDate='null', pubDate='1764000000000', pubDateStr='2025-11-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793264165863, creator='13701087609', updateTime=1793264299902, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322609593264489401, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322609030938346427, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322609593264489402, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322609030938346427, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4033, endPage=4037, ext={EN=ArticleExt(id=1322609031651378109, articleId=1322609031458440124, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Evaluation of the implementation effect of the Xinjiang model of tuberculosis prevention and control in Kashgar region based on Bayesian structural time series, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To quantitatively assess the causal association between tuberculosis（TB）prevention and control models in Xinjiang and the number of TB incidence in Kashgar region, and to provide a scientific basis for optimising TB prevention and control strategies.

Methods

Monthly reported incidence data of tuberculosis in Kashgar region from 2013 to 2023 were collected in this study. The epidemiological profile of TB in Kashgar was described using Joinpoint regression, and a Bayesian structural time series model was used to construct a counterfactual simulation of the number of TB cases in Kashgar to evaluate the effectiveness of the ‘Xinjiang model’ in a multidimensional way.

Results

Joinpoint regression analysis showed that the average annual percentage change（APC）reversed from +21.24% before the policy implementation to -25.06%（P<0.05）, which was highly consistent with the time of policy initiation. The Bayesian structural time series model reflected inter-regional heterogeneity: the effectiveness of the intervention in Kashgar was outstanding（posterior probability 99.4%, P=0.006）, with a cumulative total of 30 659 TB cases prevented.

Conclusion

By using Joinpoint regression to capture the development trend of tuberculosis, and relying on the counterfactual framework to deduct the spatial and temporal heterogeneity, it was confirmed that the Xinjiang model could effectively control the existing tuberculosis epidemic in Kashgar, and provide scientific support for the optimisation of the policy of infectious disease prevention and control, as well as the practice of precision prevention and control in Xinjiang.

, authors=Kuerbanjiang Guliziba, Sen-lu WANG, Ya-ying ZHANG, Aili Abulikemu, Shuang-long YUAN, Chen LIANG, Ming-qin CAO, authorsList=Kuerbanjiang Guliziba, Sen-lu WANG, Ya-ying ZHANG, Aili Abulikemu, Shuang-long YUAN, Chen LIANG, Ming-qin CAO, authorCompany=null, correspAuthors=Ming-qin CAO, 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=1322609033610118100, articleId=1322609031458440124, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于贝叶斯结构时间序列的喀什地区结核病防控“新疆模式”的实施效果评价, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

本研究目的在于定量评估新疆结核病防控模式与喀什地区肺结核报告发病数的因果关联，为优化结核病防控策略提供科学依据。

方法

本研究收集了2013—2023年喀什地区肺结核的月度报告发病数据。使用Joinpoint回归对喀什地区结核病流行概况进行描述，借助贝叶斯结构时间序列模型对喀什地区的肺结核报告发病数构建反事实模拟，多维度评价“新疆模式”的干预成效。

结果

Joinpoint回归分析表明，年均百分比变化率APC由政策实施前的21.24%逆转为-25.06%，P<0.05，这一转折点与政策启动时间高度吻合。贝叶斯结构时间序列模型体现出地区间异质性：喀什地区的干预成效十分突出（后验概率99.4%，P=0.006），累计防止了30 659例结核病例。

结论

通过Joinpoint回归把握结核病发展趋势，依靠反事实框架扣除时空的混杂状况，证实“新疆模式”能有效把控喀什地区现有的结核病疫情情况，为新疆地区传染病防控政策优化及精准防控实践提供了科学支撑。

, authors=古丽孜巴·库尔班江, 王森路, 张雅莹, 阿卜力克木·艾力, 袁双龙, 梁辰, 曹明芹, authorsList=古丽孜巴·库尔班江, 王森路, 张雅莹, 阿卜力克木·艾力, 袁双龙, 梁辰, 曹明芹, authorCompany=null, correspAuthors=曹明芹, authorNote=

古丽孜巴·库尔班江（1999—），女，硕士在读，研究方向：肺结核相关研究工作

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曹明芹，E-mail：
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注：图中从上到下依次是预测黑色虚线与实际观测值黑色实线对比图、每月预测与实际观测值差异图及累积差异图，其中阴影表示95%可信区间，图A为原始数据图；图B为点状效应图；图C为累积效应图。

, figureFileSmall=P11XEw0Z+O4Hp/clPIrL9A==, figureFileBig=7YiaOXNXptM0/oYYdvP3OQ==, tableContent=null), ArticleFig(id=1322609040216145936, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609031458440124, language=EN, label=Table 1, caption=

Relative reduction between the actual observed and predicted values of reported tuberculosis cases in Kashgar region（January 2018-December 2023）

, figureFileSmall=null, figureFileBig=null, tableContent=
时间实际报告发病数预测报告发病数（95% CI)差值（95% CI）相对减少比率（95% CI）
201833 53513 926（8 533~19 692）-19 609（-5 393~5 766）137.43（-38.38~41.28）
201918 66311 915（4 460~20 039）6 748（-7 455~6 886）-44.06（-62.10~65.94）
202011 15911 004（3 861~17 890）-155（-7 143~6 886）-5.76（-65.22~62.98）
20218 56810 769（3 489~17 718）-2 201（-7 281~6 949）-22.02（-67.76~64.65）
20225 64010 622（3 305~17 592）-4 982（-7 317~6 970）-48.01（-69.04~65.72）
20236 98910 701（3 496~17 560）-3 712（-7 205~-6 858）-35.13（-67.37~64.18）
), ArticleFig(id=1322609040279060497, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322609031458440124, language=CN, label=表1, caption=

2018年1月—2023年12月喀什地区肺结核报告发病数实际观测值与预测值的相对减少量

, figureFileSmall=null, figureFileBig=null, tableContent=
时间实际报告发病数预测报告发病数（95% CI)差值（95% CI）相对减少比率（95% CI）
201833 53513 926（8 533~19 692）-19 609（-5 393~5 766）137.43（-38.38~41.28）
201918 66311 915（4 460~20 039）6 748（-7 455~6 886）-44.06（-62.10~65.94）
202011 15911 004（3 861~17 890）-155（-7 143~6 886）-5.76（-65.22~62.98）
20218 56810 769（3 489~17 718）-2 201（-7 281~6 949）-22.02（-67.76~64.65）
20225 64010 622（3 305~17 592）-4 982（-7 317~6 970）-48.01（-69.04~65.72）
20236 98910 701（3 496~17 560）-3 712（-7 205~-6 858）-35.13（-67.37~64.18）
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基于贝叶斯结构时间序列的喀什地区结核病防控“新疆模式”的实施效果评价
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古丽孜巴·库尔班江 , 王森路 , 张雅莹 , 阿卜力克木·艾力 , 袁双龙 , 梁辰 , 曹明芹
现代预防医学 | 流行病与统计方法 2025,52(22): 4033-4037
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现代预防医学 |流行病与统计方法 2025 , 52 (22) : 4033 -4037
基于贝叶斯结构时间序列的喀什地区结核病防控“新疆模式”的实施效果评价
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古丽孜巴·库尔班江（1999—），女，硕士在读，研究方向：肺结核相关研究工作

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古丽孜巴·库尔班江（1999—），女，硕士在读，研究方向：肺结核相关研究工作

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古丽孜巴·库尔班江, 王森路, 张雅莹, 阿卜力克木·艾力, 袁双龙, 梁辰, 曹明芹
作者信息
  • 新疆医科大学公共卫生学院流行病与卫生统计学教研室，新疆　乌鲁木齐　830017
通讯作者:
曹明芹，E-mail：
作者简介:

古丽孜巴·库尔班江（1999—），女，硕士在读，研究方向：肺结核相关研究工作

Evaluation of the implementation effect of the Xinjiang model of tuberculosis prevention and control in Kashgar region based on Bayesian structural time series
Kuerbanjiang Guliziba, Sen-lu WANG, Ya-ying ZHANG, Aili Abulikemu, Shuang-long YUAN, Chen LIANG, Ming-qin CAO
Affiliations
  • Department of Epidemiology and Health Statistics, School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang 830017, China
出版时间: 2025-11-25 doi: 10.20043/j.cnki.MPM.202506286
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摘要
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目的

本研究目的在于定量评估新疆结核病防控模式与喀什地区肺结核报告发病数的因果关联，为优化结核病防控策略提供科学依据。

方法

本研究收集了2013—2023年喀什地区肺结核的月度报告发病数据。使用Joinpoint回归对喀什地区结核病流行概况进行描述，借助贝叶斯结构时间序列模型对喀什地区的肺结核报告发病数构建反事实模拟，多维度评价“新疆模式”的干预成效。

结果

Joinpoint回归分析表明，年均百分比变化率APC由政策实施前的21.24%逆转为-25.06%，P<0.05，这一转折点与政策启动时间高度吻合。贝叶斯结构时间序列模型体现出地区间异质性：喀什地区的干预成效十分突出（后验概率99.4%，P=0.006），累计防止了30 659例结核病例。

结论

通过Joinpoint回归把握结核病发展趋势，依靠反事实框架扣除时空的混杂状况，证实“新疆模式”能有效把控喀什地区现有的结核病疫情情况，为新疆地区传染病防控政策优化及精准防控实践提供了科学支撑。

结核病  /  实施效果  /  Joinpoint回归  /  贝叶斯结构时间序列
Objective

To quantitatively assess the causal association between tuberculosis（TB）prevention and control models in Xinjiang and the number of TB incidence in Kashgar region, and to provide a scientific basis for optimising TB prevention and control strategies.

Methods

Monthly reported incidence data of tuberculosis in Kashgar region from 2013 to 2023 were collected in this study. The epidemiological profile of TB in Kashgar was described using Joinpoint regression, and a Bayesian structural time series model was used to construct a counterfactual simulation of the number of TB cases in Kashgar to evaluate the effectiveness of the ‘Xinjiang model’ in a multidimensional way.

Results

Joinpoint regression analysis showed that the average annual percentage change（APC）reversed from +21.24% before the policy implementation to -25.06%（P<0.05）, which was highly consistent with the time of policy initiation. The Bayesian structural time series model reflected inter-regional heterogeneity: the effectiveness of the intervention in Kashgar was outstanding（posterior probability 99.4%, P=0.006）, with a cumulative total of 30 659 TB cases prevented.

Conclusion

By using Joinpoint regression to capture the development trend of tuberculosis, and relying on the counterfactual framework to deduct the spatial and temporal heterogeneity, it was confirmed that the Xinjiang model could effectively control the existing tuberculosis epidemic in Kashgar, and provide scientific support for the optimisation of the policy of infectious disease prevention and control, as well as the practice of precision prevention and control in Xinjiang.

Tuberculosis  /  Implementation effectiveness  /  Joinpoint regression  /  Bayesian Structural Time Series
古丽孜巴·库尔班江, 王森路, 张雅莹, 阿卜力克木·艾力, 袁双龙, 梁辰, 曹明芹. 基于贝叶斯结构时间序列的喀什地区结核病防控“新疆模式”的实施效果评价. 现代预防医学, 2025 , 52 (22) : 4033 -4037 . DOI: 10.20043/j.cnki.MPM.202506286
Kuerbanjiang Guliziba, Sen-lu WANG, Ya-ying ZHANG, Aili Abulikemu, Shuang-long YUAN, Chen LIANG, Ming-qin CAO. Evaluation of the implementation effect of the Xinjiang model of tuberculosis prevention and control in Kashgar region based on Bayesian structural time series[J]. Modern Preventive Medicine, 2025 , 52 (22) : 4033 -4037 . DOI: 10.20043/j.cnki.MPM.202506286
正文
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肺结核是由结核分枝杆菌所引起的慢性呼吸道传染性疾病，是全球十大死因之一，对全球公共卫生造成持续性威胁。根据世界卫生组织最新《全球结核报告（2024）》统计，2023年全球新发肺结核患者约1 080万例。中国位列结核病高负担国家之一，始终将结核病防控置于公共卫生优先领域[1]。自国家“十二五”规划以来，我国构建起“三位一体”的结核病防治服务体系，通过规范诊疗流程、强化病例等管理措施，显著降低了全国肺结核报告率并减轻了结核患者负担[2]。然而，新疆维吾尔自治区的肺结核疫情仍居高不下，尤其以南疆四地州和田、阿克苏、喀什及克孜勒苏柯尔克孜自治州最为严峻，在2018年报告发病率高达624.34/10万，远超同期全国报告平均水平59.3/10万[3]。为应对结核挑战，新疆于2018年7月创新性实施“主动筛查+传染期患者住院治疗”与“集中服药+营养早餐”相结合的结核防控策略简称“新疆模式”。该模式通过主动筛查并扩大病例发现范围，依托住院隔离阻断结核传播链，并借助集中服药与营养干预提升患者的治疗依从性[4]。
科学评估政策干预效果对结核病防治至关重要，但随机对照试验常难以实施，导致效果评估易受干扰。传统方法难以区分政策干预与自然趋势的因果关联，需采用准实验设计量化效果，避免资源错配或盲目推广。基于观测数据的因果推断方法通过模拟RCT环境，控制干扰因素，为政策评估提供更真实的因果效应分析[5]。本研究聚焦于喀什地区2013—2023年间肺结核报告发病数据，通过Joinpoint回归对喀什地区肺结核报告发病数的流行概况进行描述，采用贝叶斯结构时间序列生成反事实预测，扣除潜在发病风险，从而分离政策干预的因果效应。
1　资料与方法
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1.1　数据来源
2013年1月—2023年12月喀什、阿克苏地区以及克孜勒苏柯尔克孜自治州的肺结核患者月度报告发病数据来源于中国疾病预防控制结核病信息管理系统。
1.2　方法
1.2.1　Joinpoint回归
Joinpoint回归模型通过识别趋势组合来评估发病率趋势的变化[6]，这些组合与用泊松回归或时间序列模型拟合的单一趋势线相比，在统计学上能够更好地拟合数据序列。该程序倾向于确定连接点的数量，这些连接点足以评估随时间推移的发病率趋势的重大变化，该技术旨在确定时间趋势中发生统计上显著突变的连接点[7]。该方法适用于评估公共卫生干预措施实施前后疾病流行趋势的阶段性变化，为政策效果评价提供了一定的量化依据[8]。采用Joinpoint回归描述喀什地区肺结核报告发病数的时间趋势变化，其分析结果相较于传统方法而言，能更灵敏地检测出“新疆模式”实施后的趋势转折。本研究以肺结核报告发病数为结局变量，各年度时间作为自变量构建Joinpoint回归模型：
其中，E为第i个时间段时yi的期望值（预测值），yi为第i个时间段的肺结核报告发病数，xi为第i个时间段，e为自然底数，β0为截距项，β1表示第一个时间段的斜率（趋势系数），δ1表示第一个转折点之后的斜率变化量，k为转折点个数，δk为第k分段函数的回归系数，τk表示未知的转折点。
Joinpoint回归模型通过网格搜索算法系统性地评估所有可能的转折点组合，以最小化残差平方和为目标函数，结合Monte Carlo置换检验和修正贝叶斯信息准则（BIC）进行模型选择。最终模型的最优转折点数量及位置通过Bonferroni校正确定统计学显著性。Joinpoint回归模型的核心指标包括年度变化百分比（Annual Percent Change, APC），APC用于量化各分段区间内的时间趋势变化特征，当模型未检测到显著转折点时（即连接点数为0），APC反映整体时间趋势。从计量学角度，APC为无量纲的相对变化指标，其数学符号表征趋势变化方向（负值表示下降趋势，正值表示上升趋势），绝对值反映变化速率。基于公式1，APC的计算公式可表示为：
其中，yx+1和分别为第x+1和第x个时间段的肺结核报告发病数。
1.2.2　贝叶斯结构时间序列
贝叶斯结构时间序列（Bayesian Structural Time Series Model, BSTS）是一种基于反事实推断的计量方法，该模型基于卡尔曼滤波算法对时间序列的趋势项与季节项进行参数估计，结合Spike-and-slab回归的稀疏变量选择技术和贝叶斯模型平均BMA的集成预测框架，生成无政策干预状态下的反事实预测轨迹，并通过后验概率分布计算效应显著性的统计证据[9]。相比于传统政策评估模型侧重于宏观趋势变化的识别，BSTS模型的核心创新体现在三方面：通过建立时空交互效应的综合控制机制，将未观测混杂因素对结局变量的影响纳入潜在变量空间，有效校正遗漏变量偏倚；采用贝叶斯分层建模框架实现预测变量的动态筛选与权重优化，提升反事实模拟的稳健性；基于马尔可夫链蒙特卡洛MCMC算法对参数不确定性进行概率化表征，为政策效应的偶然性检验提供量化依据[10]。在本研究中，以2013—2023年喀什地区结核病累计报告月发病例数作为结局变量，以2013年1月—2018年6月的数据作为训练集，通过BSTS模型拟合结核病发病的基线趋势；2018年7月—2023年12月的数据作为测试集，用于评估喀什地区政策干预后的反事实预测与实际观测值的差异。
其公式为：
其中，Yt为肺结核报告发病数时间序列在第t月中的观测值，且遵循泊松分布。μt表示潜在状态随时间演化的t，动态斜率为δt，τt则被解释为季节分量。χt表示南疆其他两地州肺结核报告发病数据，βT表示回归系数，εt表示观测误差项，μt和vt是随机扰动项，分别用于描述潜在趋势和动态斜率的随机变化。
1.2.3　新疆肺结核防控策略实施阶段与因果效应评估
自2013年—2018年6月：采用三位一体的结核病防治模式；2018年7月—2023年：在三位一体的模式基础，创新并实施针对新疆的结核病防治模式。以报告的喀什地区肺结核月度报告发病数作为结局变量，分别建立了Joinpoint回归以及贝叶斯结构时间序列模型。在南疆四地州当中喀什地区实施“新疆模式”的覆盖率大，投入的财力也多，为进一步深入考察喀什地区的发病走向及政策效果，将喀什地区作为实验组，政策覆盖率较低的阿克苏与克州地区作为对照组，创建贝叶斯结构时间序列模型，得到更精准的预测数值，进而为评估喀什地区开展的“新疆模式”实际因果关系提供数据支持，协助政策持续改进与实施。
1.2.4　统计学处理
研究使用Excel进行数据的收集与整理，在Joinpoint 5.4.0软件中进行肺结核报告发病数变化趋势的绘制，采用R 4.4.2中的“bsts”与“CausalImpact”包用于喀什地区贝叶斯结构时间序列模型的构建。
2　结果
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2.1　Joinpoint回归
基于Joinpoint回归分析，喀什地区2013—2023年肺结核月度报告发病数呈现显著的阶段性变化趋势P<0.05，见图1。在2013—2018年即政策实施前，APC为21.24%，表明肺结核报告发病数呈快速上升趋势；而2018—2023年即政策实施后，APC转为-25.06%，发病数显著下降。趋势转折点出现在2018年，与新疆结核病防治政策的实施时间2018年7月高度一致，提示该政策的介入可能对疫情控制产生了积极影响。
2.2　贝叶斯结构时间序列
2013—2023年，喀什地区结核病累计报告的发病例数是144 411，况且于2018年报告的发病例数达到最高，2022年报告发病的病例数量最低，见图2。
2013—2023年喀什地区肺结核报告发病数的季节性分析显示，结核病流行呈现显著的季节性波动特征。肺结核报告发病数在5月最高11.1%，表明该时期结核病传播风险较高；2月最低5.6%。季节指数最高为133%，最低为78%，见图3。
本研究以2013年1月—2018年6月期间的肺结核报告发病数构建训练数据集，2018年7月—2023年12月期间的肺结核报告发病数构建测试数据集，采用MCMC算法进行参数估计，在模型设定中季节效应参数被固定为12个月，从而有效模拟肺结核报告发病数的月度周期性变化。经过1 500次迭代模拟后，模型达到平稳状态，收敛诊断显示后验分布的尾部面积概率P<0.05，表明参数估计结果具有统计学显著性，后验分布收敛良好，模型拟合效果达到预期要求。
喀什地区2018—2019年的实际报告发病数高出预测水平，2020年开始呈稳定的下降趋势，且均低于预测水平。与三位一体的结核病防治模式相比，2018年7月开始实施的新疆模式在喀什地区共避免30 659例病例，见图4、表1。政策的干预对喀什地区发病数影响显著且稳健：有99.4%的概率表明干预有效P=0.006，后验分布收敛良好。政策实施后，平均每月报告发病数增加225例，增幅达25%，效应量可观。
3　讨论
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本次研究发现，2013—2023年期间，喀什地区季节分析结果显示，发病高峰为5月，发病低谷为2月，这与我国的季节性趋势春秋季高发、冬夏季低发一致[11]。河南省肺结核在3—5月达发病高峰，2月、10月出现低谷等[12-13]，其原因可能来自3个方面：（1）2月正是春节假期，医疗机构服务时间缩短及患者就诊延迟导致病例报告减少，形成就诊低谷；而3月就诊需求集中释放，表现为发病数回升；（2）春节期间大规模人口流动与室内聚集活动增加，叠加干燥低温气候条件，显著提升了结核分枝杆菌的传播效率；（3）2月实际天数较其他月份少，导致月度报告病例数低[14]。
喀什地区的结核病报告发病例数的转折点发生在2018年，2018年之前呈上升趋势，政策对于发病数有显著的影响，与疾病的发生有关[15]。而这种变化的原因可能是“新疆模式”通过主动病例发现显著提升了结核病检出率，与Chukwu等[16]研究一致，该策略促进了早期诊断，减少传播风险[17]。2018年之后结核病报告发病例数呈下降趋势，Joinpoint回归分析表明，APC由政策实施前的21.24%逆转为-25.06%，P<0.05，这一转折点与政策启动时间高度吻合。更为关键的是，贝叶斯结构时间序列模型的反事实预测表明，若无政策干预，2018—2023年间喀什地区将多发生30 659例结核病，精准量化了“新疆模式”的防控成效，其99.4%的后验概率P=0.006进一步证实了干预效果的有效性。
本研究联合Joinpoint回归与贝叶斯结构时间序列评估结核病防控政策“新疆模式”的效果。Joinpoint回归通过分段线性建模精准识别2018年7月这一关键转折点P<0.05，与泊松回归或时间序列模型拟合的单一趋势线相比，在统计学上显著优于数据序列[18]；BSTS模型则通过卡尔曼滤波算法构建反事实轨迹，有效控制喀什地区的季节性混杂。值得注意的是，BSTS中Spike-and-slab回归自动筛选预测变量，降低过拟合风险[19]，喀什地区结核病防控工作取得显著成效，共避免30 659例病例。凭借设立对照组开展反事实预测，将阿克苏与克州地区作为对照组，处理了喀什地区的环境混杂因素（如气候因素）干扰，相较于传统政策评估模型BSTS多把焦点放在宏观趋势变化的识别，BSTS模型的特点主要呈现在其基于反事实推断的因果识别框架[20]。此模型集成有三大核心模块[21]：卡尔曼滤波进行时间序列潜在状态的递归估计；Spike-and-slab回归通过引入二元潜在变量来达成预测因子的稀疏化甄选；贝叶斯模型整合多个模型的预测结果，减少模型设定的差错，此模块化架构使BSTS模型在公共卫生干预优化等场景中呈现出卓越的预测效果。
综上，“新疆模式”与喀什地区肺结核报告发病数之间存在动态因果关系，政策实施后发病数呈现持续下降态势。基于反事实框架的因果推断表明，这一干预措施有效降低了结核病的疾病负担，为新疆高负担地区的结核病防控工作提供了重要参考。研究成果不仅验证了“主动筛查+集中治疗”策略的科学性和有效性，也为公共卫生政策的精准实施和优化调整提供了量化依据，对完善我国新疆地区的结核病防控体系具有重要的实践意义。
基金
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  • 国家自然科学基金项目(82460669)
文献
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2025年第52卷第22期
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doi: 10.20043/j.cnki.MPM.202506286
  • 接收时间：2025-06-18
  • 首发时间：2026-10-29
  • 出版时间：2025-11-25
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  • 收稿日期：2025-06-18
基金
国家自然科学基金项目(82460669)
作者信息
    新疆医科大学公共卫生学院流行病与卫生统计学教研室，新疆　乌鲁木齐　830017

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曹明芹，E-mail：
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https://castjournals.cast.org.cn/joweb/xdyfyx/CN/10.20043/j.cnki.MPM.202506286
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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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