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Article(id=1241023849970594019, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241023847537897695, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202409101, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1725465600000, receivedDateStr=2024-09-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773812742940, onlineDateStr=2026-03-18, pubDate=1737734400000, pubDateStr=2025-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773812742940, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773812742940, creator=13701087609, updateTime=1773812742940, updator=13701087609, issue=Issue{id=1241023847537897695, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='2', pageStart='193', pageEnd='384', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773812742361, creator=13701087609, updateTime=1773812823817, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241024189247845056, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241023847537897695, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241024189247845057, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241023847537897695, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=211, endPage=219, ext={EN=ArticleExt(id=1241023850687820014, articleId=1241023849970594019, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Bayesian network-based analysis of factors influencing complex multimorbidity and risk inference, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the correlation between complex multimorbidity and their influencing factors and to reveal the interactions between diseases and factors using network inference methods to identify high-risk populations.

Methods

Based on longitudinal data from 2016 to 2022 in the Urumqi public health surveillance database and electronic medical record information database, this study collected information on the occurrence of complex multimorbidity and related variables. The structure of the Bayesian network was learned using the maximum-minimum hill-climbing algorithm combined with prior knowledge, and parameter learning was conducted using Bayesian estimation. Directed acyclic graphs were employed to identify confounding factors and guide the construction of regression models.

Results

A total of 6 938 participants were included in the study, of which 12.96% (899/ 6 938) developed complex multimorbidity over the seven-year period. After screening influencing factors, six predictors were selected for model construction, resulting in a model with 7 nodes and 10 directed edges. The results indicated that age, gender, source, and BMI weredirectly related to the occurrence of complex multimorbidity, all serving as parent nodes in the model. The results of logistic regression based on DAG guidelines showed that the risk of complex multimorbidity would increase by 8.70% [OR=1.087 (95%CI:1.077-1.098)] for each year of age increase in patients with chronic diseases; the OR of complex multimorbidity for rural residents compared to urban residents=0.274 (95%CI: 0.237-0.317); the OR of complex multimorbidity for obese people compared with thenormal weight people=1.019 (95%CI: 1.008-1.504).

Conclusion

Bayesian networks effectively identify the relationships between complex multimorbidity and influencing factors, as well as the interactions among these factors, thus enabling inference about the risk of complex multimorbidity occurrence. Prevention and control of complex multimorbidity requires attention to aging, urban environments, and obesity management.

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目的

探讨复杂共病与影响因素的相关性，并通过网络推理揭示疾病与因素之间的相互作用，以识别高危人群。

方法

基于乌鲁木齐市公共卫生监测数据库和电子病历信息库中2016至2022年纵向数据，获取研究对象复杂共病的发生情况及相关变量信息。通过最大最小爬山算法结合先验知识进行贝叶斯网络结构学习，采用贝叶斯估计法进行参数学习，利用有向无环图识别混杂因素，指导回归模型的构建。

结果

共纳入6 938名研究对象，12.96% (899/ 6 938)在7年内发生了复杂共病。筛选出的6个预测因子用于模型构建，模型包含7个节点和10条有向边。结果显示：年龄、性别、来源以及BMI与复杂共病的发生直接相关，均为复杂共病发生的父节点。基于DAG指导的logistic回归结果显示：慢性病患者年龄每增长一岁，其复杂共病患病风险将增加8.70% [OR=1.087（95%CI:1.077~1.098）]；与城市居民相比，农村居民患复杂共病的OR=0.274（95%CI:0.237~0.317）；与体重正常人群相比，肥胖人群患复杂共病的OR=1.019（95%CI:1.008~1.504）。

结论

贝叶斯网络能够有效识别复杂共病与影响因素之间的关系及各因素间的相互作用，从而实现对复杂共病发生风险的推理。预防和控制复杂共病，需要关注老龄化、城市环境和肥胖管理。

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卢耀勤，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=Qf9cimSGR7GaBZczZ//IdQ==, magXml=x86VGpfxxnxoDq+xHwz8ZA==, pdfUrl=null, pdf=nUGE5qLldAf9EpIyA5RNyQ==, pdfFileSize=1104581, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=AMht5t3srdKNWA4LMx5jtw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=muQtqJfFBS+znA4al5nZTQ==, mapNumber=null, authorCompany=null, fund=null, authors=

周燚然(1999—），女，硕士在读，研究方向：医学大数据挖掘

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周燚然(1999—），女，硕士在读，研究方向：医学大数据挖掘

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周燚然(1999—），女，硕士在读，研究方向：医学大数据挖掘

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Disease Primers, refType=null, unstructuredReference=Skou ST, Mair FS, Fortin M, et al. Multimorbidity[J]. Nature Reviews. Disease Primers, 2022, 8(1): 48., articleTitle=Multimorbidity, refAbstract=null), Reference(id=1241023863572722529, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2022, volume=12, issue=4, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[2], rfOrder=1, authorNames=Ho ISS, Azcoaga-Lorenzo A, Akbari A, journalName=BMJ Open, refType=null, unstructuredReference=Ho ISS, Azcoaga-Lorenzo A, Akbari A, et al. Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies[J]. BMJ Open, 2022, 12(4): e057017., articleTitle=Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies, refAbstract=null), Reference(id=1241023863639831405, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2020, volume=28, issue=1, pageStart=14, pageEnd=19, url=null, language=null, rfNumber=[3], rfOrder=2, authorNames=曹新西, 徐晨婕, 侯亚冰, journalName=中国慢性病预防与控制, refType=null, unstructuredReference=曹新西，徐晨婕，侯亚冰，等．1990—2025年我国高发慢性病的流行趋势及预测[J].中国慢性病预防与控制，2020, 28(1): 14-19., articleTitle=1990—2025年我国高发慢性病的流行趋势及预测, refAbstract=null), Reference(id=1241023863732106103, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2020, volume=28, issue=1, pageStart=14, pageEnd=19, url=null, language=null, rfNumber=[3], rfOrder=3, authorNames=Cao XX, Xu CJ, Hou YB, journalName=Chinese Journal of Prevention and Control of Chronic Diseases, refType=null, unstructuredReference=Cao XX, Xu CJ, Hou YB, et al. The epidemic trend and prediction of chronic diseases with high incidence in China from 1990 to 2025[J]. Chinese Journal of Prevention and Control of Chronic Diseases, 2020, 28(1): 14-19. (In Chinese), articleTitle=The epidemic trend and prediction of chronic diseases with high incidence in China from 1990 to 2025, refAbstract=null), Reference(id=1241023863841158023, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2022, volume=19, issue=15, pageStart=9091, pageEnd=null, url=null, language=null, rfNumber=[4], rfOrder=4, authorNames=Sinha A, Kerketta S, Ghosal S, journalName=International Journal of Environmental Research and Public Health, refType=null, unstructuredReference=Sinha A, Kerketta S, Ghosal S, et al. Multimorbidity and complex multimorbidity in India: findings from the 2017-2018 longitudinal ageing study in India (LASI)[J]. International Journal of Environmental Research and Public Health, 2022, 19(15): 9091., articleTitle=Multimorbidity and complex multimorbidity in India: findings from the 2017-2018 longitudinal ageing study in India (LASI), refAbstract=null), Reference(id=1241023864042484634, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2019, volume=105, issue=null, pageStart=142, pageEnd=146, url=null, language=null, rfNumber=[5], rfOrder=5, authorNames=Nicholson K, Makovski TT, Griffith LE, journalName=Journal of Clinical Epidemiology, refType=null, unstructuredReference=Nicholson K, Makovski TT, Griffith LE, et al. Multimorbidity and comorbidity revisited: refining the concepts for international health research[J]. Journal of Clinical Epidemiology, 2019, 105: 142-146., articleTitle=Multimorbidity and comorbidity revisited: refining the concepts for international health research, refAbstract=null), Reference(id=1241023864134759329, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2014, volume=4, issue=7, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[6], rfOrder=6, authorNames=Harrison C, Britt H, Miller G, journalName=BMJ Open, refType=null, unstructuredReference=Harrison C, Britt H, Miller G, et al. Examining different measures of multimorbidity, using a large prospective cross-sectional study in Australian general practice[J]. BMJ Open, 2014, 4(7): e004694., articleTitle=Examining different measures of multimorbidity, using a large prospective cross-sectional study in Australian general practice, refAbstract=null), Reference(id=1241023864260588463, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2021, volume=21, issue=1, pageStart=418, pageEnd=null, url=null, language=null, rfNumber=[7], rfOrder=7, authorNames=Wang ZJ, Peng WJ, Li MY, journalName=BMC Public Health, refType=null, unstructuredReference=Wang ZJ, Peng WJ, Li MY, et al. Association between multimorbidity patterns and disability among older People covered by long-term care insurance in Shanghai, China[J]. BMC Public Health, 2021, 21(1): 418., articleTitle=Association between multimorbidity patterns and disability among older People covered by long-term care insurance in Shanghai, China, refAbstract=null), Reference(id=1241023864369640375, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2023, volume=13, issue=1, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[8], rfOrder=8, authorNames=Rodrigues APDS, Batista SRR, Santos ASEA, journalName=Scientific Reports, refType=null, unstructuredReference=Rodrigues APDS, Batista SRR, Santos ASEA, et al. Multimorbidity and complex multimorbidity in Brazilians with severe obesity[J]. Scientific Reports, 2023, 13(1): 16629., articleTitle=Multimorbidity and complex multimorbidity in Brazilians with severe obesity, refAbstract=null), Reference(id=1241023864482886593, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2021, volume=18, issue=19, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[9], rfOrder=9, authorNames=Kato D, Kawachi I, Saito J, journalName=International Journal of Environmental Research and Public Health, refType=null, unstructuredReference=Kato D, Kawachi I, Saito J, et al. Complex multimorbidity and incidence of Long-Term care needs in Japan: a prospective cohort study[J]. International Journal of Environmental Research and Public Health, 2021, 18(19): 10523., articleTitle=Complex multimorbidity and incidence of Long-Term care needs in Japan: a prospective cohort study, refAbstract=null), Reference(id=1241023864612910028, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2019, volume=14, issue=11, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[10], rfOrder=10, authorNames=Petarli GB, Cattafesta M, Sant’anna MM, journalName=PLOS One, refType=null, unstructuredReference=Petarli GB, Cattafesta M, Sant’anna MM, et al. Multimorbidity and complex multimorbidity in Brazilian rural workers[J]. PLOS One, 2019, 14(11): e0225416., articleTitle=Multimorbidity and complex multimorbidity in Brazilian rural workers, refAbstract=null), Reference(id=1241023864726156248, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2022, volume=19, issue=11, pageStart=6553, pageEnd=null, url=null, language=null, rfNumber=[11], rfOrder=11, authorNames=Kato D, Kawachi I, Kondo N, journalName=International Journal of Environmental Research and Public Health, refType=null, unstructuredReference=Kato D, Kawachi I, Kondo N. Complex multimorbidity and working beyond retirement age in Japan:a prospective propensity-matched analysis[J]. International Journal of Environmental Research and Public Health, 2022, 19(11): 6553., articleTitle=Complex multimorbidity and working beyond retirement age in Japan:a prospective propensity-matched analysis, refAbstract=null), Reference(id=1241023864826819554, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2022, volume=10, issue=4, pageStart=253, pageEnd=263, url=null, language=null, rfNumber=[12], rfOrder=12, authorNames=Kivimäki M, Strandberg T, Pentti J, journalName=The Lancet. Diabetes &Endocrinology, refType=null, unstructuredReference=Kivimäki M, Strandberg T, Pentti J, et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study[J]. The Lancet. Diabetes &Endocrinology, 2022, 10(4): 253-263., articleTitle=Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study, refAbstract=null), Reference(id=1241023864948454383, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2022, volume=23, issue=1, pageStart=258, pageEnd=null, url=null, language=null, rfNumber=[13], rfOrder=13, authorNames=Sugiyama Y, Mutai R, Aoki T, journalName=BMC Primary Care, refType=null, unstructuredReference=Sugiyama Y, Mutai R, Aoki T, et al. Multimorbidity and complex multimorbidity, their prevalence, and associated factors on a remote island in Japan: a cross-sectional study[J]. BMC Primary Care, 2022, 23(1): 258., articleTitle=Multimorbidity and complex multimorbidity, their prevalence, and associated factors on a remote island in Japan: a cross-sectional study, refAbstract=null), Reference(id=1241023865053311996, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2023, volume=50, issue=20, pageStart=3649, pageEnd=3655,3662, url=null, language=null, rfNumber=[14], rfOrder=14, authorNames=王齐里, 宋文柱, 张岩波, journalName=现代预防医学, refType=null, unstructuredReference=王齐里，宋文柱，张岩波，等．贝叶斯网络在老年抑郁症危险因素中的应用——基于CHARLS数据库的实证分析[J].现代预防医学，2023, 50(20): 3649-3655,3662., articleTitle=贝叶斯网络在老年抑郁症危险因素中的应用——基于CHARLS数据库的实证分析, refAbstract=null), Reference(id=1241023865141391361, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2023, volume=50, issue=20, pageStart=3649, pageEnd=3655,3662, url=null, language=null, rfNumber=[14], rfOrder=15, authorNames=Wang QL, Song WZ, Zhang YB, journalName=Modern Preventive Medicine, refType=null, unstructuredReference=Wang QL, Song WZ, Zhang YB, et al. Applications of Bayesian network in risk factors of senile depression-empirical analysis based on CHARLS database[J]. Modern Preventive Medicine, 2023, 50(20): 3649-3655,3662. (In Chinese), articleTitle=Applications of Bayesian network in risk factors of senile depression-empirical analysis based on CHARLS database, refAbstract=null), Reference(id=1241023865237860362, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2022, volume=9, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[15], rfOrder=16, authorNames=Song WZ, Gong H, Wang QL, journalName=Frontiers in Cardiovascular Medicine, refType=null, unstructuredReference=Song WZ, Gong H, Wang QL, et al. Usingbayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity[J]. Frontiers in Cardiovascular Medicine, 2022, 9: 984883., articleTitle=Usingbayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity, refAbstract=null), Reference(id=1241023865367883796, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2022, volume=22, issue=1, pageStart=326, pageEnd=null, url=null, language=null, rfNumber=[16], rfOrder=17, authorNames=Ke XJ, Keenan K, Smith VA, journalName=BMC Medical Research Methodology, refType=null, unstructuredReference=Ke XJ, Keenan K, Smith VA. Treatment of missing data in Bayesian network structure learning: an application to linked biomedical and social survey data[J]. BMC Medical Research Methodology, 2022, 22(1): 326., articleTitle=Treatment of missing data in Bayesian network structure learning: an application to linked biomedical and social survey data, refAbstract=null), Reference(id=1241023865502101533, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2021, volume=9, issue=1, pageStart=5, pageEnd=6, url=null, language=null, rfNumber=[17], rfOrder=18, authorNames=Zhu Z, Xing W, Hu Y, journalName=Asia-Pacific Journal of Oncology Nursing, refType=null, unstructuredReference=Zhu Z, Xing W, Hu Y, et al. Paradigm shift:Moving from symptom clusters to symptom networks[J]. Asia-Pacific Journal of Oncology Nursing, 2021, 9(1): 5-6., articleTitle=Paradigm shift:Moving from symptom clusters to symptom networks, refAbstract=null), Reference(id=1241023865615347750, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2020, volume=4, issue=null, pageStart=436, pageEnd=443, url=null, language=null, rfNumber=[18], rfOrder=19, authorNames=Sieswerda MS, Bermejo I, Geleijnse G, journalName=JCO Clinical Cancer Informatics, refType=null, unstructuredReference=Sieswerda MS, Bermejo I, Geleijnse G, et al. Predicting lung cancer survival using probabilistic reclassification of TNM editions with a Bayesian network[J]. JCO Clinical Cancer Informatics, 2020, 4: 436-443., articleTitle=Predicting lung cancer survival using probabilistic reclassification of TNM editions with a Bayesian network, refAbstract=null), Reference(id=1241023865736982572, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2020, volume=22, issue=10, pageStart=1142, pageEnd=null, url=null, language=null, rfNumber=[19], rfOrder=20, authorNames=Guo ZG, Constantinou AC, journalName=Entropy, refType=null, unstructuredReference=Guo ZG, Constantinou AC. Approximate learning of high dimensional bayesian network structures via pruning of candidate parent Sets[J]. Entropy, 2020, 22(10): 1142., articleTitle=Approximate learning of high dimensional bayesian network structures via pruning of candidate parent Sets, refAbstract=null), Reference(id=1241023865896366139, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2022, volume=142, issue=null, pageStart=264, pageEnd=267, url=null, language=null, rfNumber=[20], rfOrder=21, authorNames=Digitale JC, Martin JN, Glymour MM, journalName=Journal of Clinical Epidemiology, refType=null, unstructuredReference=Digitale JC, Martin JN, Glymour MM. Tutorial on directed acyclic graphs[J]. Journal of Clinical Epidemiology, 2022, 142: 264-267., articleTitle=Tutorial on directed acyclic graphs, refAbstract=null), Reference(id=1241023866051555394, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2021, volume=17, issue=1, pageStart=53, pageEnd=61, url=null, language=null, rfNumber=[21], rfOrder=22, authorNames=Moe SJ, Carriger JF, Glendell M, journalName=Integrated Environmental Assessment and Management, refType=null, unstructuredReference=Moe SJ, Carriger JF, Glendell M. Increased use of bayesian network models has improved environmental risk assessments[J]. Integrated Environmental Assessment and Management, 2021, 17(1): 53-61., articleTitle=Increased use of bayesian network models has improved environmental risk assessments, refAbstract=null), Reference(id=1241023867603447886, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2025, volume=28, issue=1, pageStart=65, pageEnd=70, url=null, language=null, rfNumber=[22], rfOrder=23, authorNames=李丽萍, 廖婧, 高鑫源, journalName=中国全科医学, refType=null, unstructuredReference=李丽萍，廖婧，高鑫源，等．中国共病加权指数与老年人卫生服务利用的关联性研究[J].中国全科医学，2025, 28(1): 65-70., articleTitle=中国共病加权指数与老年人卫生服务利用的关联性研究, refAbstract=null), Reference(id=1241023867712499793, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2025, volume=28, issue=1, pageStart=65, pageEnd=70, url=null, language=null, rfNumber=[22], rfOrder=24, authorNames=Li LP, Liao J, Gao XY, journalName=Chinese General Practice, refType=null, unstructuredReference=Li LP, Liao J, Gao XY, et al. Association between the Chinese multimorbidity-weighted index and health service utilization among the elderly in China[J]. Chinese General Practice, 2025, 28(1): 65-70. (In Chinese), articleTitle=Association between the Chinese multimorbidity-weighted index and health service utilization among the elderly in China, refAbstract=null), Reference(id=1241023867804774488, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2023, volume=1425, issue=null, pageStart=477, pageEnd=484, url=null, language=null, rfNumber=[23], rfOrder=25, authorNames=Petri M, Messinis L, Patrikelis P, journalName=Advances in Experimental Medicine and Biology, refType=null, unstructuredReference=Petri M, Messinis L, Patrikelis P, et al. Illiteracy, neuropsychological assessment, and cognitive rehabilitation: a narrative review[J]. Advances in Experimental Medicine and Biology, 2023, 1425: 477-484., articleTitle=Illiteracy, neuropsychological assessment, and cognitive rehabilitation: a narrative review, refAbstract=null), Reference(id=1241023867947380830, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2002, volume=15, issue=1, pageStart=83, pageEnd=96, url=null, language=null, rfNumber=[24], rfOrder=26, authorNames=Zhou BF, Cooperative Meta-Analysis Group of the Working Group on Obesity inChina, journalName=Biomedical and Environmental Sciences, refType=null, unstructuredReference=Zhou BF, Cooperative Meta-Analysis Group of the Working Group on Obesity inChina. Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults[J]. Biomedical and Environmental Sciences, 2002, 15(1): 83-96., articleTitle=Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults, refAbstract=null), Reference(id=1241023868073209959, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2023, volume=null, issue=3, pageStart=63, pageEnd=66, url=null, language=null, rfNumber=[25], rfOrder=27, authorNames=吴孟泽, journalName=科学技术创新, refType=null, unstructuredReference=吴孟泽．有向无环图在构建logistic预测模型中的应用研究[J].科学技术创新，2023,(3):63-66., articleTitle=有向无环图在构建logistic预测模型中的应用研究, refAbstract=null), Reference(id=1241023868186456171, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2023, volume=null, issue=3, pageStart=63, pageEnd=66, url=null, language=null, rfNumber=[25], rfOrder=28, authorNames=Wu MZ, journalName=Scientific and Technological Innovation, refType=null, unstructuredReference=Wu MZ. Research on the application of directed acyclic graph in the construction of logistic prediction model[J]. Scientific and Technological Innovation, 2023, (3): 63-66. (In Chinese), articleTitle=Research on the application of directed acyclic graph in the construction of logistic prediction model, refAbstract=null), Reference(id=1241023868308090999, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2023, volume=31, issue=6, pageStart=1491, pageEnd=1495, url=null, language=null, rfNumber=[26], rfOrder=29, authorNames=李承龙, 郭海辉, 陈维, journalName=中国临床心理学杂志, refType=null, unstructuredReference=李承龙，郭海辉，陈维，等．青少年黑暗三人格的网络结构：基于高斯图和有向无环图的探索[J].中国临床心理学杂志，2023, 31(6): 1491-1495., articleTitle=青少年黑暗三人格的网络结构：基于高斯图和有向无环图的探索, refAbstract=null), Reference(id=1241023868408754302, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2023, volume=31, issue=6, pageStart=1491, pageEnd=1495, url=null, language=null, rfNumber=[26], rfOrder=30, authorNames=Li CL, Guo HH, Chen W, journalName=Chinese Journal of Clinical Psychology, refType=null, unstructuredReference=Li CL, Guo HH, Chen W, et al. The network structure of the dark triad in adolescents:an exploration based on Gaussian and directed acyclic graphs[J]. Chinese Journal of Clinical Psychology, 2023, 31(6): 1491-1495. (In Chinese), articleTitle=The network structure of the dark triad in adolescents:an exploration based on Gaussian and directed acyclic graphs, refAbstract=null), Reference(id=1241023868509417602, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2023, volume=62, issue=14, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[27], rfOrder=31, authorNames=Ikemoto K, Takahashi K, Ozawa T, journalName=Angewandte Chemie (International ed. in English), refType=null, unstructuredReference=Ikemoto K, Takahashi K, Ozawa T, et al. Akaike’s information criterion for stoichiometry inference of supramolecular complexes[J]. Angewandte Chemie (International ed. in English), 2023, 62(14): e202219059., articleTitle=Akaike’s information criterion for stoichiometry inference of supramolecular complexes, refAbstract=null), Reference(id=1241023868740104341, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2020, volume=17, issue=2, pageStart=241, pageEnd=266, url=null, language=null, rfNumber=[28], rfOrder=32, authorNames=Selig K, Shaw P, Ankerst D, journalName=The International Journal of Biostatistics, refType=null, unstructuredReference=Selig K, Shaw P, Ankerst D. Bayesian information criterion approximations to Bayes factors for univariate and multivariate logistic regression models[J]. The International Journal of Biostatistics, 2020, 17(2): 241-266., articleTitle=Bayesian information criterion approximations to Bayes factors for univariate and multivariate logistic regression models, refAbstract=null), Reference(id=1241023868832379034, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2022, volume=22, issue=1, pageStart=342, pageEnd=null, url=null, language=null, rfNumber=[29], rfOrder=33, authorNames=Shi JK, Guo YB, Li Z, journalName=BMC Public Health, refType=null, unstructuredReference=Shi JK, Guo YB, Li Z, et al. Sociodemographic and behavioral influences on multimorbidity among adult residents of northeastern China[J]. BMC Public Health, 2022, 22(1): 342., articleTitle=Sociodemographic and behavioral influences on multimorbidity among adult residents of northeastern China, refAbstract=null), Reference(id=1241023868945625251, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2022, volume=12, issue=2, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[30], rfOrder=34, authorNames=Fleitas alfonzo L, King T, You E, journalName=BMJ Open, refType=null, unstructuredReference=Fleitas alfonzo L, King T, You E, et al. Theoretical explanations for socioeconomic inequalities in multimorbidity: a scoping review[J]. BMJ Open, 2022, 12(2): e055264., articleTitle=Theoretical explanations for socioeconomic inequalities in multimorbidity: a scoping review, refAbstract=null), Reference(id=1241023869054677163, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, doi=null, pmid=null, pmcid=null, year=2016, volume=388, issue=10046, pageStart=776, pageEnd=786, url=null, language=null, rfNumber=[31], rfOrder=35, authorNames=Global BMI Mortality Collaboration, Diangelantonio E, Bhupathiraju S, journalName=Lancet, refType=null, unstructuredReference=Global BMI Mortality Collaboration, Diangelantonio E, Bhupathiraju S, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents[J]. 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Comparison of baseline status of patients with and without complexmultimorbidity

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变量合计(n=6 938)否(n=6 039)是(n=899)统计量P
年龄（岁）[M(P25，P75)]61.00(52.00,70.00)59.000(51.000,69.000)70.000(63.000,75.000)Z=-20.945<0.001
年龄分组（岁）[n(%)]χ2=367.721<0.001
15~<2527(0.389)27(0.447)0(0.00)
25~<35199(2.868)198(3.279)1(0.111)
35~<45481(6.933)472(7.816)9(1.001)
45~<551 525(21.980)1 438(23.812)87(9.677)
55~<651 795(25.872)1 623(26.875)172(19.132)
≥652 911(41.957)2 281(37.771)630(70.078)
性别n(%)χ2=4.6250.032
男2 973(42.851)2 558(42.358)415(46.162)
女3 965(57.149)3 481(57.642)484(53.838)
来源n(%)χ2=329.869<0.001
城市1 553(22.384)1 140(18.877)413(45.940)
农村5 385(77.616)4 899(81.123)486(54.060)
受教育水平n(%)χ2=52.344<0.001
文盲594(8.562)479(7.932)115(12.792)
小学3 000(43.240)2 583(42.772)417(46.385)
中学2 868(41.338)2 581(42.739)287(31.924)
高等教育476(6.861)396(6.557)80(8.899)
婚姻状况n(%)χ2=36.531<0.001
未婚138(1.989)128(2.120)10(1.112)
已婚6 183(89.118)5 410(89.584)773(85.984)
离异87(1.254)81(1.341)6(0.667)
丧偶530(7.639)420(6.955)110(12.236)
日吸烟量分组（支）[n(%)]-0.147a
<56 462(93.139)5 606(92.830)856(95.217)
5~1069(0.995)59(0.977)10(1.112)
10~<15140(2.018)128(2.120)12(1.335)
15~<2036(0.519)34(0.563)2(0.222)
20~<25197(2.839)181(2.997)16(1.780)
25~<306(0.086)6(0.099)0(0.00)
≥3028(0.404)25(0.414)3(0.334)
BMI分组n(%)χ2=2.1210.346
正常2 414(34.794)2 120(35.105)294(32.703)
超重2 902(41.828)2 510(41.563)392(43.604)
肥胖1 622(23.378)1 409(23.332)213(23.693)
近视n(%)χ2=58.656<0.001
是4 547(65.538)3 856(63.852)691(76.863)
否2 391(34.462)2 183(36.148)208(23.137)
龋齿n(%)χ2=25.934<0.001
是196(2.825)147(2.434)49(5.451)
否6 742(97.175)5 892(97.566)850(94.549)
糖尿病家族史n(%)χ2=4.9160.027
是337(4.857)280(4.637)57(6.340)
否6 601(95.143)5 759(95.363)842(93.660)
高血压家族史n(%)χ2=1.1580.282
是1 223(17.628)1 076(17.818)147(16.352)
否5 715(82.372)4 963(82.182)752(83.648)
), ArticleFig(id=1241023861001614040, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, language=CN, label=表1, caption=

复杂共病与非复杂共病患者基线特征比较

, figureFileSmall=null, figureFileBig=null, tableContent=
变量合计(n=6 938)否(n=6 039)是(n=899)统计量P
年龄（岁）[M(P25，P75)]61.00(52.00,70.00)59.000(51.000,69.000)70.000(63.000,75.000)Z=-20.945<0.001
年龄分组（岁）[n(%)]χ2=367.721<0.001
15~<2527(0.389)27(0.447)0(0.00)
25~<35199(2.868)198(3.279)1(0.111)
35~<45481(6.933)472(7.816)9(1.001)
45~<551 525(21.980)1 438(23.812)87(9.677)
55~<651 795(25.872)1 623(26.875)172(19.132)
≥652 911(41.957)2 281(37.771)630(70.078)
性别n(%)χ2=4.6250.032
男2 973(42.851)2 558(42.358)415(46.162)
女3 965(57.149)3 481(57.642)484(53.838)
来源n(%)χ2=329.869<0.001
城市1 553(22.384)1 140(18.877)413(45.940)
农村5 385(77.616)4 899(81.123)486(54.060)
受教育水平n(%)χ2=52.344<0.001
文盲594(8.562)479(7.932)115(12.792)
小学3 000(43.240)2 583(42.772)417(46.385)
中学2 868(41.338)2 581(42.739)287(31.924)
高等教育476(6.861)396(6.557)80(8.899)
婚姻状况n(%)χ2=36.531<0.001
未婚138(1.989)128(2.120)10(1.112)
已婚6 183(89.118)5 410(89.584)773(85.984)
离异87(1.254)81(1.341)6(0.667)
丧偶530(7.639)420(6.955)110(12.236)
日吸烟量分组（支）[n(%)]-0.147a
<56 462(93.139)5 606(92.830)856(95.217)
5~1069(0.995)59(0.977)10(1.112)
10~<15140(2.018)128(2.120)12(1.335)
15~<2036(0.519)34(0.563)2(0.222)
20~<25197(2.839)181(2.997)16(1.780)
25~<306(0.086)6(0.099)0(0.00)
≥3028(0.404)25(0.414)3(0.334)
BMI分组n(%)χ2=2.1210.346
正常2 414(34.794)2 120(35.105)294(32.703)
超重2 902(41.828)2 510(41.563)392(43.604)
肥胖1 622(23.378)1 409(23.332)213(23.693)
近视n(%)χ2=58.656<0.001
是4 547(65.538)3 856(63.852)691(76.863)
否2 391(34.462)2 183(36.148)208(23.137)
龋齿n(%)χ2=25.934<0.001
是196(2.825)147(2.434)49(5.451)
否6 742(97.175)5 892(97.566)850(94.549)
糖尿病家族史n(%)χ2=4.9160.027
是337(4.857)280(4.637)57(6.340)
否6 601(95.143)5 759(95.363)842(93.660)
高血压家族史n(%)χ2=1.1580.282
是1 223(17.628)1 076(17.818)147(16.352)
否5 715(82.372)4 963(82.182)752(83.648)
), ArticleFig(id=1241023861131637481, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, language=EN, label=Table 2, caption=

Results of single and multifactorial logistic regression analysis of influential factors associated with complex multimorbidity

, figureFileSmall=null, figureFileBig=null, tableContent=
变量单因素
βSEZPOR(95%CI)
年龄（岁）0.0840.00516.976<0.0011.087(1.077~1.098)
性别
男（参照）1.000
女-0.1540.072-2.1490.0320.857(0.745~0.987)
来源
城市（参照）1.000
农村-1.2950.075-17.370<0.0010.274(0.237~0.317)
受教育水平
文盲（参照）1.000
小学-0.3970.116-3.407<0.0010.672(0.535~0.845)
中学-0.7700.121-6.358<0.0010.463(0.365~0.587)
高等教育-0.1730.161-1.0750.2830.841(0.614~1.153)
婚姻状况
未婚（参照）1.000
已婚0.6040.3311.8260.0681.829(0.957~3.496)
离异-0.0530.536-0.0990.9210.948(0.332~2.709)
丧偶1.2100.3453.502<0.0013.352(1.704~6.597)
每日吸烟量（支）-0.0280.010-2.7170.0070.972(0.952~0.992)
BMI分组
正常（参照）1.000
超重0.1190.0831.4380.1501.126(0.958~1.324)
肥胖0.0860.0960.8950.3711.090(0.903~1.317)
近视
否（参照）1.000
是0.6320.0847.565<0.0011.881(1.597~2.215)
龋齿
否（参照）1.000
是0.8380.1694.956<0.0012.311(1.659~3.218)
糖尿病家族史
否（参照）1.000
是0.3310.1502.2080.0271.392(1.038~1.868)
高血压家族史
否（参照）1.000
是-0.1040.096-1.0760.2820.902(0.747~1.089)
变量多因素a
βSEZPOR(95%CI)
年龄（岁）0.0660.00611.675<0.0011.068(1.057~1.080)
性别
男（参照）1.000
女-0.2360.080-2.9490.0030.790(0.675~0.924)
来源
城市（参照）1.000
农村-0.9220.084-11.002<0.0010.398(0.337~0.469)
受教育水平
文盲（参照）1.000
小学-0.1460.123-1.1920.2330.864(0.679~1.099)
中学-0.2820.133-2.1230.0340.754(0.581~0.979)
高等教育-0.1450.177-0.8180.4130.865(0.611~1.224)
婚姻状况
未婚（参照）1.000
已婚-0.9000.364-2.4730.0130.407(0.199~0.830)
离异-1.4280.563-2.5350.0110.240(0.080~0.723)
丧偶-0.8570.386-2.2180.0270.424(0.199~0.905)
每日吸烟量（支）-0.0110.011-1.0410.2980.989(0.968~1.010)
BMI分组
正常（参照）1.000
超重0.1860.0872.1460.0321.205(1.016~1.429)
肥胖0.2760.1022.7110.0071.318(1.080~1.610)
近视
否（参照）1.000
是0.1680.0911.8410.0661.183(0.989~1.415)
龋齿
否（参照）1.000
是0.6500.1813.589<0.0011.915(1.343~2.730)
糖尿病家族史
否（参照）1.000
是0.5400.1673.2380.0011.715(1.237~2.378)
高血压家族史
否（参照）1.000
是0.0050.1060.0520.9591.006(0.816~1.238)
), ArticleFig(id=1241023861265855227, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, language=CN, label=表2, caption=

复杂共病相关影响因素单、多因素logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量单因素
βSEZPOR(95%CI)
年龄（岁）0.0840.00516.976<0.0011.087(1.077~1.098)
性别
男（参照）1.000
女-0.1540.072-2.1490.0320.857(0.745~0.987)
来源
城市（参照）1.000
农村-1.2950.075-17.370<0.0010.274(0.237~0.317)
受教育水平
文盲（参照）1.000
小学-0.3970.116-3.407<0.0010.672(0.535~0.845)
中学-0.7700.121-6.358<0.0010.463(0.365~0.587)
高等教育-0.1730.161-1.0750.2830.841(0.614~1.153)
婚姻状况
未婚（参照）1.000
已婚0.6040.3311.8260.0681.829(0.957~3.496)
离异-0.0530.536-0.0990.9210.948(0.332~2.709)
丧偶1.2100.3453.502<0.0013.352(1.704~6.597)
每日吸烟量（支）-0.0280.010-2.7170.0070.972(0.952~0.992)
BMI分组
正常（参照）1.000
超重0.1190.0831.4380.1501.126(0.958~1.324)
肥胖0.0860.0960.8950.3711.090(0.903~1.317)
近视
否（参照）1.000
是0.6320.0847.565<0.0011.881(1.597~2.215)
龋齿
否（参照）1.000
是0.8380.1694.956<0.0012.311(1.659~3.218)
糖尿病家族史
否（参照）1.000
是0.3310.1502.2080.0271.392(1.038~1.868)
高血压家族史
否（参照）1.000
是-0.1040.096-1.0760.2820.902(0.747~1.089)
变量多因素a
βSEZPOR(95%CI)
年龄（岁）0.0660.00611.675<0.0011.068(1.057~1.080)
性别
男（参照）1.000
女-0.2360.080-2.9490.0030.790(0.675~0.924)
来源
城市（参照）1.000
农村-0.9220.084-11.002<0.0010.398(0.337~0.469)
受教育水平
文盲（参照）1.000
小学-0.1460.123-1.1920.2330.864(0.679~1.099)
中学-0.2820.133-2.1230.0340.754(0.581~0.979)
高等教育-0.1450.177-0.8180.4130.865(0.611~1.224)
婚姻状况
未婚（参照）1.000
已婚-0.9000.364-2.4730.0130.407(0.199~0.830)
离异-1.4280.563-2.5350.0110.240(0.080~0.723)
丧偶-0.8570.386-2.2180.0270.424(0.199~0.905)
每日吸烟量（支）-0.0110.011-1.0410.2980.989(0.968~1.010)
BMI分组
正常（参照）1.000
超重0.1860.0872.1460.0321.205(1.016~1.429)
肥胖0.2760.1022.7110.0071.318(1.080~1.610)
近视
否（参照）1.000
是0.1680.0911.8410.0661.183(0.989~1.415)
龋齿
否（参照）1.000
是0.6500.1813.589<0.0011.915(1.343~2.730)
糖尿病家族史
否（参照）1.000
是0.5400.1673.2380.0011.715(1.237~2.378)
高血压家族史
否（参照）1.000
是0.0050.1060.0520.9591.006(0.816~1.238)
), ArticleFig(id=1241023861416850194, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023849970594019, language=EN, label=Table 3, caption=

Integration of results from logistic regression modeling of influences related to complex multimorbidity based on directed acyclic graphs

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变量βSEZ值P值OR值 (95%CI)
年龄（岁）a0.0840.00516.980<0.0011.087 (1.077~1.098)
性别b
男（参照）1.000
女-14.230.074-1.9260.0540.867 (0.751~1.003)
来源c
城市（参照）1.000
农村-1.2950.075-17.370<0.0010.274 (0.237~0.317)
受教育水平d
文盲（参照）1.000
小学-0.3060.120-2.5440.0110.736 (0.583~0.935)
中学-0.7550.125-6.046<0.0010.471 (0.369~0.602)
高等教育-0.5780.167-3.453<0.0010.561 (0.403~0.309)
婚姻状况e
未婚（参照）1.000
已婚-1.1300.363-3.1120.0020.323 (0.165~0.695)
离异-1.6010.562-2.8490.0040.202 (0.063~0.595)
丧偶-0.9920.385-2.5770.0100.371 (0.181~0.828)
BMI分组f
正常（参照）1.000
超重0.1600.0851.8790.0601.173 (0.994~1.386)
肥胖0.2140.0992.1540.0311.019 (1.008~1.504)
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基于有向无环图的复杂共病相关影响因素logistic回归模型结果整合

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变量βSEZ值P值OR值 (95%CI)
年龄（岁）a0.0840.00516.980<0.0011.087 (1.077~1.098)
性别b
男（参照）1.000
女-14.230.074-1.9260.0540.867 (0.751~1.003)
来源c
城市（参照）1.000
农村-1.2950.075-17.370<0.0010.274 (0.237~0.317)
受教育水平d
文盲（参照）1.000
小学-0.3060.120-2.5440.0110.736 (0.583~0.935)
中学-0.7550.125-6.046<0.0010.471 (0.369~0.602)
高等教育-0.5780.167-3.453<0.0010.561 (0.403~0.309)
婚姻状况e
未婚（参照）1.000
已婚-1.1300.363-3.1120.0020.323 (0.165~0.695)
离异-1.6010.562-2.8490.0040.202 (0.063~0.595)
丧偶-0.9920.385-2.5770.0100.371 (0.181~0.828)
BMI分组f
正常（参照）1.000
超重0.1600.0851.8790.0601.173 (0.994~1.386)
肥胖0.2140.0992.1540.0311.019 (1.008~1.504)
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基于贝叶斯网络的复杂共病影响因素分析及风险推理
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周燚然 1 , 苏银霞 3 , 殷峰 4 , 古丽加衣娜·艾肯 1 , 卢耀勤 2, 1
现代预防医学 | 流行病与统计方法 2025,52(2): 211-219
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现代预防医学 | 流行病与统计方法 2025, 52(2): 211-219
基于贝叶斯网络的复杂共病影响因素分析及风险推理
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周燚然1, 苏银霞3, 殷峰4, 古丽加衣娜·艾肯1, 卢耀勤2, 1
作者信息
  • 1．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　830054
  • 2．乌鲁木齐市疾病预防控制中心（市卫生监督所）
  • 3．新疆医科大学医学工程技术学院
  • 4．乌鲁木齐市第一人民医院

    • 周燚然(1999—），女，硕士在读，研究方向：医学大数据挖掘

通讯作者:

卢耀勤，E-mail：
Bayesian network-based analysis of factors influencing complex multimorbidity and risk inference
Yi-ran ZHOU1, Yin-xia SU3, Feng YIN4, Guligiayina·Aiken1, Yao-qin LU2, 1
Affiliations
  • Department of Epidemiology and Health Statistics, School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang 830054, China
出版时间: 2025-01-25 doi: 10.20043/j.cnki.MPM.202409101
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摘要
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目的

探讨复杂共病与影响因素的相关性，并通过网络推理揭示疾病与因素之间的相互作用，以识别高危人群。

方法

基于乌鲁木齐市公共卫生监测数据库和电子病历信息库中2016至2022年纵向数据，获取研究对象复杂共病的发生情况及相关变量信息。通过最大最小爬山算法结合先验知识进行贝叶斯网络结构学习，采用贝叶斯估计法进行参数学习，利用有向无环图识别混杂因素，指导回归模型的构建。

结果

共纳入6 938名研究对象，12.96% (899/ 6 938)在7年内发生了复杂共病。筛选出的6个预测因子用于模型构建，模型包含7个节点和10条有向边。结果显示：年龄、性别、来源以及BMI与复杂共病的发生直接相关，均为复杂共病发生的父节点。基于DAG指导的logistic回归结果显示：慢性病患者年龄每增长一岁，其复杂共病患病风险将增加8.70% [OR=1.087（95%CI:1.077~1.098）]；与城市居民相比，农村居民患复杂共病的OR=0.274（95%CI:0.237~0.317）；与体重正常人群相比，肥胖人群患复杂共病的OR=1.019（95%CI:1.008~1.504）。

结论

贝叶斯网络能够有效识别复杂共病与影响因素之间的关系及各因素间的相互作用，从而实现对复杂共病发生风险的推理。预防和控制复杂共病，需要关注老龄化、城市环境和肥胖管理。

复杂共病  /  贝叶斯网络  /  风险推理
Objective

To explore the correlation between complex multimorbidity and their influencing factors and to reveal the interactions between diseases and factors using network inference methods to identify high-risk populations.

Methods

Based on longitudinal data from 2016 to 2022 in the Urumqi public health surveillance database and electronic medical record information database, this study collected information on the occurrence of complex multimorbidity and related variables. The structure of the Bayesian network was learned using the maximum-minimum hill-climbing algorithm combined with prior knowledge, and parameter learning was conducted using Bayesian estimation. Directed acyclic graphs were employed to identify confounding factors and guide the construction of regression models.

Results

A total of 6 938 participants were included in the study, of which 12.96% (899/ 6 938) developed complex multimorbidity over the seven-year period. After screening influencing factors, six predictors were selected for model construction, resulting in a model with 7 nodes and 10 directed edges. The results indicated that age, gender, source, and BMI weredirectly related to the occurrence of complex multimorbidity, all serving as parent nodes in the model. The results of logistic regression based on DAG guidelines showed that the risk of complex multimorbidity would increase by 8.70% [OR=1.087 (95%CI:1.077-1.098)] for each year of age increase in patients with chronic diseases; the OR of complex multimorbidity for rural residents compared to urban residents=0.274 (95%CI: 0.237-0.317); the OR of complex multimorbidity for obese people compared with thenormal weight people=1.019 (95%CI: 1.008-1.504).

Conclusion

Bayesian networks effectively identify the relationships between complex multimorbidity and influencing factors, as well as the interactions among these factors, thus enabling inference about the risk of complex multimorbidity occurrence. Prevention and control of complex multimorbidity requires attention to aging, urban environments, and obesity management.

Complex multimorbidity  /  Bayesian network  /  Risk inference
周燚然, 苏银霞, 殷峰, 古丽加衣娜·艾肯, 卢耀勤. 基于贝叶斯网络的复杂共病影响因素分析及风险推理. 现代预防医学, 2025 , 52 (2) : 211 -219 . DOI: 10.20043/j.cnki.MPM.202409101
Yi-ran ZHOU, Yin-xia SU, Feng YIN, Guligiayina·Aiken, Yao-qin LU. Bayesian network-based analysis of factors influencing complex multimorbidity and risk inference[J]. Modern Preventive Medicine, 2025 , 52 (2) : 211 -219 . DOI: 10.20043/j.cnki.MPM.202409101
正文
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慢性病已成为全球主要死因之一[1]，根据2021年数据，慢性病已导致全球超过4 300万人死亡[2]。随着经济社会发展和人口老龄化，慢性病患病率逐年上升[3]，患者病种累积数目不断增加。研究显示，慢性病累多个器官系统时，患者更容易出现急诊入院、计划外住院和多重用药[4]，从而给个人、社会和医疗系统带来巨大经济负担。为反映身体各系统中常见疾病的异质性[5]，识别治疗和护理需求较高的患者，Harrison等[6]提出了复杂共病（complex multimorbidity）的概念，即同一个体同时患有三种或三种以上影响不同身体系统的慢性病。复杂共病往往伴随着更沉重的疾病负担[7]和更恶劣的健康结局[8]，因此，关注复杂共病及其影响因素，有助于识别高危人群并减缓疾病累积。
既往研究常采用logistic回归[4, 9-11]、Cox比例风险回归[12]、Poisson回归等方法[8, 13]探讨了复杂共病的影响因素，但这些研究存在一定局限性。首先，疾病的影响因素通常并非独立，这违反了模型假设[14]；其次，这些模型无法有效呈现疾病与因素、因素与因素之间的复杂网络关系，缺乏因果证据；最后，这些研究大多集中于发达国家，对于发展中国家，尤其是我国的相关探索相对不足。
在大数据分析中，贝叶斯网络（Bayesian Network，BN）的适用性和重要价值得到了广泛认可[15-17]，因其能直观解决不确定性建模和推理问题[18]。BN通过有向无环图（Directed Acyclic Graph，DAG）编码一组反映随机变量间概率依赖关系的条件概率分布表（Conditional Probability Table, CPT）[19]，DAG被认为是因果关系、中介和相互作用相关研究的标准语言[20]，DAG与CPT的结合使得BN能以定量和定性的方式描述变量之间的因果关系[21]。
本研究拟采用logistic回归筛选复杂共病影响因素，利用BN探索复杂共病与这些因素之间的潜在因果关系，基于DAG进行混杂因素识别后，再次进行logistic回归建模，并对结果进行综合分析。这一研究将有助于识别复杂共病的高危人群，减缓疾病的累积，并为制定相关干预措施提供理论参考。
1　对象与方法
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1.1　研究对象
本研究参照李丽萍等[22]的研究设计，从新疆省乌鲁木齐市公共卫生监测数据库和电子病历信息库中筛选出2016年1月1日至2022年12月31日期间首次诊断为纳入慢性病的患者。参与者需前往二级及以上医疗机构就诊或体检，以参与随访。收集的数据包括人口学特征、行为习惯、家族史等基本信息及慢性病诊断信息。纳入标准为：①年龄≥15岁；②2016至2022年期间至少参与过一次随访；③随访期间首次诊断至少患有一种本研究纳入的慢性病。排除标准为：①基本信息缺失者；②慢性病诊断记录不全者。最终，共纳入6 938例慢性病患者作为研究对象，所有参与者均已签署知情同意书。
1.2　调查内容
通过对乌鲁木齐市公共卫生监测数据库与电子病历信息库内多个表单的关联，获取以下信息：年龄（岁）、性别（男性；女性）、来源（城市；农村）、受教育水平、婚姻状况（未婚；已婚；离异；丧偶）、每日吸烟量（支）、身体质量指数（Body Mass Index, BMI）、糖尿病家族史（是；否）、高血压家族史（是；否）、龋齿（是；否）及近视（是；否）患病情况。其中，受教育水平被划分为文盲（年满十五周岁且不能识字[23]），小学（受教育年限≤6年），中学（6<受教育年限≤9年），高等教育（受教育年限>9年）；BMI以“体重(kg)/身高(m)2”进行计算，依据中国标准划分为正常（18.5≤BMI<24）、超重（24≤BMI<28）和肥胖（BMI≥28）[24]。
1.3　相关定义
本研究关注的系统及慢性病包括：①运动系统：关节炎、颈椎病、腰椎间盘突出；②消化系统：胆囊炎、胆结石、胃炎、胆管炎、肝囊肿、胆囊息肉；③呼吸系统：结核、支气管炎、肺气肿、哮喘、肺心病、支气管扩张；④泌尿系统：肾囊肿、肾结石、肾积水；⑤循环系统：高血压、冠心病、大动脉粥样硬化、血脂异常、脑卒中；⑥内分泌系统：甲状腺功能亢进、糖尿病；⑦神经系统：严重精神障碍、精神分裂。共涵盖七大系统内的27种常见慢性病，疾病系统划分依据国际疾病分类ICD-10。复杂共病定义为患者同时患有三种或三种以上身体系统内的慢性病。
1.4　贝叶斯网络
BN通常包括具有DAG的BN拓扑结构和在已知DAG基础上从数据中学习估计节点的CPT。DAG能够识别在因果推断框架下回归分析需要控制的协变量[25]，具体识别方法如图1所示：假设存在DAG，其中X为解释变量，Y为结局变量，其余为可能的协变量。路径的关系包括：①X→Y（直接因果关系）；②X→C→Y（链式结构）；③X←A→B→Y（叉式结构）；④X→E←D→Y（对撞结构）。在路径①和②中，所有箭头均指向同一方向，表示因果关系；在路径③和④中，箭头方向不统一，表示非因果关系，可能涉及混杂和/或碰撞器。对于路径②，X通过中介变量C影响Y，此时估计X对Y的总体影响时不应控制C。对于路径③，A作为X与Y的共同原因会引发混淆，控制A或其他相关变量（B）可以消除混淆。对于路径④，碰撞器E会阻塞路径，对其进行调整可能导致新的混杂。
1.5　统计学分析
首先，对于符合正态分布的定量数据，采用均数±标准差描述；非正态分布的定量数据则使用中位数和四分位数。定性资料采用频数和构成比进行描述性统计。通过方差分析和χ2检验比较复杂共病患者与非患者在基线特征上的差异。其次，使用单因素分析和基于前向逐步回归法的多因素二分类logistic回归粗略评估影响因素与复杂共病发生之间的关联，双侧检验α=0.05。第三，根据数据分布及logistic回归分析结果确定贝叶斯网络模型的变量节点，利用R 4.4.0中“bnlearn”包进行贝叶斯网络分析，通过最大最小爬山算法结合先验知识进行网络结构学习，并采用贝叶斯估计法进行参数学习。在结构学习中，为确保网络结构稳定性，生成10 000个网络，通过Bootstrap法计算各边缘及指向的出现频率，边缘保留阈值设为85%，指向保留阈值设为51% [26]，对网络进行平均以生成最终结构。第四，将DAG中各变量节点作为暴露因素，识别需要调整的混杂因素，并在调整混杂因素后进行二次logistic回归建模，选用赤池信息准则（akaike information criterion, AIC）[27]及贝叶斯信息准则（bayesian informationcriterion, BIC）[28]比较两次建模的效果。最后，通过Netica软件（版本号：7.01）实现贝叶斯网络风险推理。
2　结果
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2.1　基线情况
本研究共纳入6 938名研究对象，包含男性2 972 (42.85%)名，女性3965 (57.15)名，中位年龄为61.00(52.00, 70.00)岁，在特征“日吸烟量分组”、“龋齿”及“糖尿病家族史”中存在变量聚集（亚组频数占比>90%）的情况。七年间共有899名慢性病患者发展为复杂共病，其中位年龄远高于非复杂共病群体，70.00 (63.00, 75.00) vs. 59.00 (51.00, 69.00)。复杂共病群体与未患人群在性别、来源、受教育水平、婚姻状况、近视、龋齿及糖尿病家族史方面差异均具有统计学意义（P<0.05）（表1）。
2.2　复杂共病相关影响因素分析
（1）复杂共病的二分类logistic回归模型：由于多分类变量“年龄分组”及“日吸烟量分组”个别亚组中频数过低，在模型中转换为定量资料进行处理。不以单因素分析结果作为多因素分析变量纳入标准，以前向逐步回归法进行探索，结果显示：年龄增长、男性、城市人口、受教育水平低下、未婚、超重及肥胖、存在龋齿、存在糖尿病家族史均会使复杂共病发生风险显著升高（P<0.05）（表2）。
（2）复杂共病的贝叶斯网络模型：结合前文结果，为提高BN模型可靠性，剔除高度聚集变量：“日吸烟量分组”、“龋齿”、“糖尿病家族史”及多因素logistic回归结果不显著变量：“近视”、“高血压家族史”，选取剩余6个变量与复杂共病有关的变量构建BN模型。如图2所示，该模型包含7个节点和10个有向边，有向边表示节点间存在概率依赖，图中展示了各节点定义及其先验概率。结果显示：年龄、性别、来源及BMI为复杂共病发生的父节点，受教育程度及婚姻状况与复杂共病之间间接相连。
（3）基于DAG的logistic回归分析：依据前文所述协变量识别方法分别找出各暴露因素所需控制的协变量并进行建模，整合结果见表3：年龄增长、城市人口、受教育水平低下、未婚、肥胖会使复杂共病发生风险显著升高（P<0.05）。
2.3　复杂共病发生的风险推理
BN能从已知的节点状态推断出未知节点的概率，从而实现复杂共病的风险推理。将前文基于DAG的logistic回归分析中结果显著的节点变量设定为已知（即各节点某定义的先验概率为100%），如图3所示，如果一个慢性病患者特征为：年龄≥65岁、城市人口、未婚、文盲、肥胖，则该患者发生复杂共病的概率会上升至41.40%。
3　讨论
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本研究构建了一个预测慢性病患者复杂共病风险的贝叶斯网络（BN）模型。首先，选择了七大系统中的27种常见慢性病定义复杂共病，并纳入了11个可能影响因素。通过logistic回归模型结合数据分布筛选，确定了6个主要预测因子：年龄、性别、来源、受教育程度、婚姻状况及BMI分组。基于BN的拓扑结构DAG识别了混杂因素，并在此基础上进行logistic回归的二次建模，结果显示年龄、来源、受教育水平、婚姻状况和BMI与复杂共病显著相关。最终，将各节点中最劣势特征的先验概率设定为100%进行风险推理。
研究结果表明，年长的慢性病患者更容易发生复杂共病，这与老龄化密切相关。老龄化是多种疾病的重要风险因素[29]，随着年龄的增长，身体功能衰退使得慢性病累积风险增加。城市居民发生复杂共病的可能性高于农村居民，这可能与城市生活方式中的不良因素如不健康饮食、久坐不动等有关[30]，提示城市环境中的生活方式干预对于减少慢性病和复杂共病的发生具有重要意义。此外，本研究显示肥胖与复杂共病的发生有显著关联。肥胖不仅与无病寿命的减少3~8年相关，还增加了过早死亡的风险1.3倍[31]。肥胖被认为是多种慢性疾病的风险因素，包括心脏代谢疾病、消化系统疾病、呼吸系统疾病、神经系统疾病、肌肉骨骼疾病以及感染性疾病[12]。肥胖对健康的负面影响通过多种机制作用于身体多个系统，增加了多种慢性病的风险，从而进一步增加复杂共病的发生概率，强调了肥胖管理在慢性病预防和控制中的重要性。
本研究基于DAG识别回归分析中需要控制的协变量，并进行了logistic回归的二次建模，结果显示与一次建模有所不同，这可能归因于DAG对控制变量的准确识别。BN补充了logistic回归的不足，能揭示多因素间的复杂关系，并在已知先验概率的情况下对复杂共病进行风险推理。Logistic回归显示年龄、来源、受教育水平、婚姻状况和BMI与复杂共病相关，而BN发现年龄、性别、来源和BMI直接相关，受教育程度和婚姻状况可能仅间接影响复杂共病。因此，BN能够识别及各因素在疾病发生过程中所起的作用，能在一定程度上为复杂共病的防治提供科学依据。
该研究也存在部分局限性。首先，复杂共病的发生是多因素共同作用的结果。尽管我们根据现有研究选择了一些关键变量，仍可能存在其他未被纳入的变量与复杂共病的发生相关。其次，BN的构建是数据驱动与先验知识结合的结果。尽管我们采用了统计方法尽可能确保模型的稳定性，但由于变量选择的局限性，模型结果可能与真实情况存在一定的偏差。
未来研究应扩大样本规模，收集更全面的因素数据，应用动态BN模型深入探讨因果关系，并关注不同干预措施的实际效果，以提供更精准的风险预测和干预策略，为高危人群识别、公共卫生政策制定和个体化干预提供理论支持。
基金
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  • 新疆维吾尔自治区研究生科研创新项目(XJ2024G170)
  • 新疆维吾尔自治区自然科学基金项目(2024D01E29)
文献
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参考文献 引证文献
排序方式：
[1]
Skou ST, Mair FS, Fortin M, et al. Multimorbidity[J]. Nature Reviews. Disease Primers, 2022, 8(1): 48.
[2]
Ho ISS, Azcoaga-Lorenzo A, Akbari A, et al. Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies[J]. BMJ Open, 2022, 12(4): e057017.
[3]
曹新西，徐晨婕，侯亚冰，等．1990—2025年我国高发慢性病的流行趋势及预测[J].中国慢性病预防与控制，2020, 28(1): 14-19.
Cao XX, Xu CJ, Hou YB, et al. The epidemic trend and prediction of chronic diseases with high incidence in China from 1990 to 2025[J]. Chinese Journal of Prevention and Control of Chronic Diseases, 2020, 28(1): 14-19. (In Chinese)
[4]
Sinha A, Kerketta S, Ghosal S, et al. Multimorbidity and complex multimorbidity in India: findings from the 2017-2018 longitudinal ageing study in India (LASI)[J]. International Journal of Environmental Research and Public Health, 2022, 19(15): 9091.
[5]
Nicholson K, Makovski TT, Griffith LE, et al. Multimorbidity and comorbidity revisited: refining the concepts for international health research[J]. Journal of Clinical Epidemiology, 2019, 105: 142-146.
[6]
Harrison C, Britt H, Miller G, et al. Examining different measures of multimorbidity, using a large prospective cross-sectional study in Australian general practice[J]. BMJ Open, 2014, 4(7): e004694.
[7]
Wang ZJ, Peng WJ, Li MY, et al. Association between multimorbidity patterns and disability among older People covered by long-term care insurance in Shanghai, China[J]. BMC Public Health, 2021, 21(1): 418.
[8]
Rodrigues APDS, Batista SRR, Santos ASEA, et al. Multimorbidity and complex multimorbidity in Brazilians with severe obesity[J]. Scientific Reports, 2023, 13(1): 16629.
[9]
Kato D, Kawachi I, Saito J, et al. Complex multimorbidity and incidence of Long-Term care needs in Japan: a prospective cohort study[J]. International Journal of Environmental Research and Public Health, 2021, 18(19): 10523.
[10]
Petarli GB, Cattafesta M, Sant’anna MM, et al. Multimorbidity and complex multimorbidity in Brazilian rural workers[J]. PLOS One, 2019, 14(11): e0225416.
[11]
Kato D, Kawachi I, Kondo N. Complex multimorbidity and working beyond retirement age in Japan:a prospective propensity-matched analysis[J]. International Journal of Environmental Research and Public Health, 2022, 19(11): 6553.
[12]
Kivimäki M, Strandberg T, Pentti J, et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study[J]. The Lancet. Diabetes &Endocrinology, 2022, 10(4): 253-263.
[13]
Sugiyama Y, Mutai R, Aoki T, et al. Multimorbidity and complex multimorbidity, their prevalence, and associated factors on a remote island in Japan: a cross-sectional study[J]. BMC Primary Care, 2022, 23(1): 258.
[14]
王齐里，宋文柱，张岩波，等．贝叶斯网络在老年抑郁症危险因素中的应用——基于CHARLS数据库的实证分析[J].现代预防医学，2023, 50(20): 3649-3655,3662.
Wang QL, Song WZ, Zhang YB, et al. Applications of Bayesian network in risk factors of senile depression-empirical analysis based on CHARLS database[J]. Modern Preventive Medicine, 2023, 50(20): 3649-3655,3662. (In Chinese)
[15]
Song WZ, Gong H, Wang QL, et al. Usingbayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity[J]. Frontiers in Cardiovascular Medicine, 2022, 9: 984883.
[16]
Ke XJ, Keenan K, Smith VA. Treatment of missing data in Bayesian network structure learning: an application to linked biomedical and social survey data[J]. BMC Medical Research Methodology, 2022, 22(1): 326.
[17]
Zhu Z, Xing W, Hu Y, et al. Paradigm shift:Moving from symptom clusters to symptom networks[J]. Asia-Pacific Journal of Oncology Nursing, 2021, 9(1): 5-6.
[18]
Sieswerda MS, Bermejo I, Geleijnse G, et al. Predicting lung cancer survival using probabilistic reclassification of TNM editions with a Bayesian network[J]. JCO Clinical Cancer Informatics, 2020, 4: 436-443.
[19]
Guo ZG, Constantinou AC. Approximate learning of high dimensional bayesian network structures via pruning of candidate parent Sets[J]. Entropy, 2020, 22(10): 1142.
[20]
Digitale JC, Martin JN, Glymour MM. Tutorial on directed acyclic graphs[J]. Journal of Clinical Epidemiology, 2022, 142: 264-267.
[21]
Moe SJ, Carriger JF, Glendell M. Increased use of bayesian network models has improved environmental risk assessments[J]. Integrated Environmental Assessment and Management, 2021, 17(1): 53-61.
[22]
李丽萍，廖婧，高鑫源，等．中国共病加权指数与老年人卫生服务利用的关联性研究[J].中国全科医学，2025, 28(1): 65-70.
Li LP, Liao J, Gao XY, et al. Association between the Chinese multimorbidity-weighted index and health service utilization among the elderly in China[J]. Chinese General Practice, 2025, 28(1): 65-70. (In Chinese)
[23]
Petri M, Messinis L, Patrikelis P, et al. Illiteracy, neuropsychological assessment, and cognitive rehabilitation: a narrative review[J]. Advances in Experimental Medicine and Biology, 2023, 1425: 477-484.
[24]
Zhou BF, Cooperative Meta-Analysis Group of the Working Group on Obesity inChina. Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults[J]. Biomedical and Environmental Sciences, 2002, 15(1): 83-96.
[25]
吴孟泽．有向无环图在构建logistic预测模型中的应用研究[J].科学技术创新，2023,(3):63-66.
Wu MZ. Research on the application of directed acyclic graph in the construction of logistic prediction model[J]. Scientific and Technological Innovation, 2023, (3): 63-66. (In Chinese)
[26]
李承龙，郭海辉，陈维，等．青少年黑暗三人格的网络结构：基于高斯图和有向无环图的探索[J].中国临床心理学杂志，2023, 31(6): 1491-1495.
Li CL, Guo HH, Chen W, et al. The network structure of the dark triad in adolescents:an exploration based on Gaussian and directed acyclic graphs[J]. Chinese Journal of Clinical Psychology, 2023, 31(6): 1491-1495. (In Chinese)
[27]
Ikemoto K, Takahashi K, Ozawa T, et al. Akaike’s information criterion for stoichiometry inference of supramolecular complexes[J]. Angewandte Chemie (International ed. in English), 2023, 62(14): e202219059.
[28]
Selig K, Shaw P, Ankerst D. Bayesian information criterion approximations to Bayes factors for univariate and multivariate logistic regression models[J]. The International Journal of Biostatistics, 2020, 17(2): 241-266.
[29]
Shi JK, Guo YB, Li Z, et al. Sociodemographic and behavioral influences on multimorbidity among adult residents of northeastern China[J]. BMC Public Health, 2022, 22(1): 342.
[30]
Fleitas alfonzo L, King T, You E, et al. Theoretical explanations for socioeconomic inequalities in multimorbidity: a scoping review[J]. BMJ Open, 2022, 12(2): e055264.
[31]
Global BMI Mortality Collaboration, Diangelantonio E, Bhupathiraju S, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents[J]. Lancet, 2016, 388(10046): 776-786.
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doi: 10.20043/j.cnki.MPM.202409101
  • 接收时间：2024-09-05
  • 首发时间：2026-03-18
  • 出版时间：2025-01-25
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  • 收稿日期：2024-09-05
基金
新疆维吾尔自治区研究生科研创新项目(XJ2024G170)
新疆维吾尔自治区自然科学基金项目(2024D01E29)
作者信息
    1．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　830054
    2．乌鲁木齐市疾病预防控制中心（市卫生监督所）
    3．新疆医科大学医学工程技术学院
    4．乌鲁木齐市第一人民医院

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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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