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Article(id=1241102743700558132, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102730337505325, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202403024, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1709222400000, receivedDateStr=2024-03-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773831552671, onlineDateStr=2026-03-18, pubDate=1721836800000, pubDateStr=2024-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773831552671, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773831552671, creator=13701087609, updateTime=1773831552671, updator=13701087609, issue=Issue{id=1241102730337505325, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='14', pageStart='2497', pageEnd='2688', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773831549486, creator=13701087609, updateTime=1773831697291, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241103350322745680, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102730337505325, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241103350322745681, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102730337505325, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2649, endPage=2654, ext={EN=ArticleExt(id=1241102745030152632, articleId=1241102743700558132, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Application and research progress of agent-based modeling methods in intervention of behavioral risk factors for chronic diseases, columnId=1228016573156360233, journalTitle=Modern Preventive Medicine, columnName=Disease Control and Prevention, runingTitle=null, highlight=null, articleAbstract=

In recent years, Agent Based Modeling (ABM) has developed rapidly as a "bottom-up" system science method, which has better descriptive power and predictive performance compared to traditional models. It is also widely used in the field of public health. The trend of chronic non communicable diseases is severe worldwide, and interventions targeting behavioral risk factors can effectively prevent the occurrence of chronic diseases. However, there is relatively little research on the causes of health behavior changes and the heterogeneity feedback of intervention targets. It is worth exploring how to use ABM models to open this "black box" and optimize chronic disease prevention strategies. Based on a systematic search of relevant Chinese and English literature, this article summarizes the application examples of ABM method in the intervention of chronic disease behavioral risk factors, summarizes the methodological commonalities and internal logic in the modeling process, and strives to explore the development prospects of this method, providing evidence-based basis for optimizing the NCDs prevention and control system and promoting the construction of a healthy China.

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近年来，基于主体建模（Agent-Based Modeling，ABM）作为“自下而上”的系统科学方法得到快速发展，与传统模型相比展现出更佳的描述能力和预测效果，在公共卫生领域得以广泛应用。当前，慢性非传染性疾病（Non-Communicable Diseases，NCDs）在世界范围内流行趋势日益严峻，针对行为危险因素的干预措施可有效预防慢性病的发生。然而，当前鲜有涉及健康行为变化的诱因以及干预对象异质性反馈的相关研究。如何利用ABM模型打开这一“黑匣子”，从而优化慢性病预防策略值得深入探讨。因此，本文在系统检索相关中英文献的基础上，梳理ABM方法在慢性病行为危险因素干预中的应用实例，总结建模过程中的方法学共性和内在逻辑，力图探索该方法的发展前景，为优化NCDs预防控制体系、推进“健康中国”建设提供循证依据。

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张良文；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=FuPmr+QrMz/7aM0G0V0B0Q==, magXml=yp8ZiA3PEmu62kqun6meMQ==, pdfUrl=null, pdf=Z3VzhwZgPQhvHPRSFN9WeA==, pdfFileSize=808900, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=eGGYsge9w/Q0+MRQEHEekA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=x7z3LxbTx5pxOu8728BMyw==, mapNumber=null, authorCompany=null, fund=null, authors=

杨启帆（2001年—），女，硕士在读，研究方向：老年人口与卫生政策

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2.卫生技术评估福建省高校重点实验室, bio={"content":"

杨启帆（2001年—），女，硕士在读，研究方向：老年人口与卫生政策

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杨启帆（2001年—），女，硕士在读，研究方向：老年人口与卫生政策

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Summary of features of different ABM models included in the literature

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第一作者及年份干预措施结局变量
建模目的一：探索健康行为改变诱因
Gouri suresh 2020[18]健康教育；媒体宣传健康饮食率
Garcia 2018[19]未设置干预情景，仅进行预测运动率
Van woudenberg 2019[21]重点干预中心度高的个体；随机选择干预对象运动率
Stankov 2019[22]调整酒精税酗酒率
Blok 2018[23]增加运动设施数量；健康教育；改善社区安全水平；联合干预运动率
Hammond 2020[24]缩减香烟购买渠道吸烟率
Atkinson 2018[25]缩减酒类购买渠道；提供急救服务；联合干预酗酒相关恶性事件发生率
Castillo-Carniglia 2019[26]缩减酒类购买渠道轻度、重度饮酒率
Keyes 2019[27]调整酒精税酗酒率
建模目的二：评价NCDs行为干预效果
Garney 2022[28]实施全面禁烟政策吸烟率、脑卒中、糖尿病发病率
Li 2016[29]拓宽健康食品购买渠道；降低健康食品价格脑卒中、糖尿病发病率
Schauder 2020[30]免费向学龄儿童提供水果蔬菜；调整该政策开始时间与持续时段蔬果消费量
Luo 2019[31]降低吸烟率；提高运动率；提高健康饮食率肥胖率
Kasman 2019[32]全社区传播预防肥胖相关知识糖尿病发病率
), ArticleFig(id=1241102753687196579, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102743700558132, language=CN, label=表1, caption=

纳入文献中不同ABM模型的特征汇总

, figureFileSmall=null, figureFileBig=null, tableContent=
第一作者及年份干预措施结局变量
建模目的一：探索健康行为改变诱因
Gouri suresh 2020[18]健康教育；媒体宣传健康饮食率
Garcia 2018[19]未设置干预情景，仅进行预测运动率
Van woudenberg 2019[21]重点干预中心度高的个体；随机选择干预对象运动率
Stankov 2019[22]调整酒精税酗酒率
Blok 2018[23]增加运动设施数量；健康教育；改善社区安全水平；联合干预运动率
Hammond 2020[24]缩减香烟购买渠道吸烟率
Atkinson 2018[25]缩减酒类购买渠道；提供急救服务；联合干预酗酒相关恶性事件发生率
Castillo-Carniglia 2019[26]缩减酒类购买渠道轻度、重度饮酒率
Keyes 2019[27]调整酒精税酗酒率
建模目的二：评价NCDs行为干预效果
Garney 2022[28]实施全面禁烟政策吸烟率、脑卒中、糖尿病发病率
Li 2016[29]拓宽健康食品购买渠道；降低健康食品价格脑卒中、糖尿病发病率
Schauder 2020[30]免费向学龄儿童提供水果蔬菜；调整该政策开始时间与持续时段蔬果消费量
Luo 2019[31]降低吸烟率；提高运动率；提高健康饮食率肥胖率
Kasman 2019[32]全社区传播预防肥胖相关知识糖尿病发病率
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基于主体建模方法在慢性病行为危险因素干预中的应用与研究进展
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杨启帆 1, 2 , 段颖 1, 2 , 方亚 1, 2 , 张良文 1, 2
现代预防医学 | 疾病预防控制 2024,51(14): 2649-2654
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现代预防医学 | 疾病预防控制 2024, 51(14): 2649-2654
基于主体建模方法在慢性病行为危险因素干预中的应用与研究进展
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杨启帆1, 2, 段颖1, 2, 方亚1, 2, 张良文1, 2
作者信息
  • 1.厦门大学公共卫生学院老年健康研究中心，福建 厦门 361000
  • 2.卫生技术评估福建省高校重点实验室

    • 杨启帆（2001年—），女，硕士在读，研究方向：老年人口与卫生政策

通讯作者:

张良文；E-mail：
Application and research progress of agent-based modeling methods in intervention of behavioral risk factors for chronic diseases
Qi-fan YANG1, 2, Ying DUAN1, 2, Ya FANG1, 2, Liang-wen ZHANG1, 2
Affiliations
  • Elderly Health Research Center of Public Health School, Xiamen University，Xiamen, Fujian 361000,China
出版时间: 2024-07-25 doi: 10.20043/j.cnki.MPM.202403024
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摘要
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近年来，基于主体建模（Agent-Based Modeling，ABM）作为“自下而上”的系统科学方法得到快速发展，与传统模型相比展现出更佳的描述能力和预测效果，在公共卫生领域得以广泛应用。当前，慢性非传染性疾病（Non-Communicable Diseases，NCDs）在世界范围内流行趋势日益严峻，针对行为危险因素的干预措施可有效预防慢性病的发生。然而，当前鲜有涉及健康行为变化的诱因以及干预对象异质性反馈的相关研究。如何利用ABM模型打开这一“黑匣子”，从而优化慢性病预防策略值得深入探讨。因此，本文在系统检索相关中英文献的基础上，梳理ABM方法在慢性病行为危险因素干预中的应用实例，总结建模过程中的方法学共性和内在逻辑，力图探索该方法的发展前景，为优化NCDs预防控制体系、推进“健康中国”建设提供循证依据。

慢性病  /  基于主体建模（ABM）  /  行为干预

In recent years, Agent Based Modeling (ABM) has developed rapidly as a "bottom-up" system science method, which has better descriptive power and predictive performance compared to traditional models. It is also widely used in the field of public health. The trend of chronic non communicable diseases is severe worldwide, and interventions targeting behavioral risk factors can effectively prevent the occurrence of chronic diseases. However, there is relatively little research on the causes of health behavior changes and the heterogeneity feedback of intervention targets. It is worth exploring how to use ABM models to open this "black box" and optimize chronic disease prevention strategies. Based on a systematic search of relevant Chinese and English literature, this article summarizes the application examples of ABM method in the intervention of chronic disease behavioral risk factors, summarizes the methodological commonalities and internal logic in the modeling process, and strives to explore the development prospects of this method, providing evidence-based basis for optimizing the NCDs prevention and control system and promoting the construction of a healthy China.

Chronic disease  /  Agent based modeling(ABM)  /  Behavioral intervention
杨启帆, 段颖, 方亚, 张良文. 基于主体建模方法在慢性病行为危险因素干预中的应用与研究进展. 现代预防医学, 2024 , 51 (14) : 2649 -2654 . DOI: 10.20043/j.cnki.MPM.202403024
Qi-fan YANG, Ying DUAN, Ya FANG, Liang-wen ZHANG. Application and research progress of agent-based modeling methods in intervention of behavioral risk factors for chronic diseases[J]. Modern Preventive Medicine, 2024 , 51 (14) : 2649 -2654 . DOI: 10.20043/j.cnki.MPM.202403024
正文
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随着人口老龄化、城镇化、工业化进程加快及行为危险因素的流行，慢性非传染疾病（Non-Communicable Diseases，NCDs）已成为人群最主要的死因之一。2002年至2019年间中国NCDs的负担不断增加，NCDs导致的死亡从2002年的80%增加到了2019年的88.5%，超过世界平均水平[1-2]。日益增加的患者数量为其家庭及社会带来沉重负担，亟需扭转当前慢性病高发态势，改善人群健康水平。既往研究表明，60%的NCDs是由吸烟、过量饮酒、体力活动不足、不健康的膳食习惯、肥胖等不良行为生活方式引发的[3]。行为干预作为WHO认定的最具成本效益的NCDs预防措施，同样是《“健康中国2030”规划纲要》中实现健康中国战略的首要任务[4]。
传统行为干预研究未探索干预对象的健康行为改变诱因，由此提出的政策建议“公式化”趋势明显，无法聚焦个体实际情况进行精准干预。因此，如何突破同质、线性和稳态系统等传统模型中假设条件的限制，进一步为NCDs预防策略提供优化路径，已成为国内外研究的热点议题。近年来系统科学方法以其整体性、动态性与反馈性的特征引起关注[5]，在公共卫生领域，基于主体建模（Agent-Based Modeling ，ABM）早期多用于传播机制明显的传染病干预研究[6]，而在慢性病行为干预研究中应用较少。为了对ABM模型在NCDs行为危险因素干预中的应用提供经验支持，本文详细阐述ABM的特征、优势，总结概括ABM模型在慢性病行为干预领域内的应用成果，在归纳建模目的、干预情景与结局指标的基础上，详细阐述模型的优势与局限，为ABM模型在该领域的进一步推广提供理论支撑，并为提升全民健康素养水平奠定基础。
1　ABM模型简介
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ABM模型作为一种“自下而上”的计算机仿真模型，由主体、环境以及主体间的交互作用三要素构成，最早在20世纪80年代此模型被应用于经济学、社会科学等领域[7]，探索种族隔离、文化传播、金融危机等现象的成因。环境为主体提供决策信息与活动空间。主体具有感知能力与适应性，能够接收环境信息并做出决策。交互作用仅存在于关系紧密或距离较近的主体之间。该模型基本原理是通过计算机平台模拟微观层面的主体有限理性行为和交互模式[8]，试图重现宏观层面的复杂现象并预测其发展态势。ABM模型的独特优势在于具有较高的可控性与便利性，可为卫生政策研究者提供模拟干预的“实验室”，通过开展仿真实验分析问题，找出关键变量以评价不同干预情景的效果，或为现有卫生干预策略评价提供理论依据。
2　ABM模型构建过程
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为总结ABM模型建模过程中的方法学共性和内在逻辑，本文简要概括NCDs行为危险因素干预领域中ABM模型的具体构建过程（见图1）。首先，明确研究目的，确定模型主体、理论框架与参数。如图2所示，根据效用最大化理论、社会网络理论构建理论框架[9]，主体健康相关行为决策机制受到人口学因素、认知因素与外部环境因素的共同影响，从而做出“是否吸烟、饮酒”等决策。其中，通过主体当前健康行为状态估计NCDs发病率一般有两种途径：一是通过文献中NCDs与健康行为的比值比（OR）计算，二是利用权威NCDs发病风险评估工具计算，如Framingham心血管疾病风险计算器[10]。其次，确定参数来源。例如年龄、性别分布特征参数可通过清洗权威数据库、大型队列数据获取，社会网络密度、中心度以及地理环境等参数可采用查阅文献、抽样调查等方法搜集。进而，提炼建模规则。根据主体的行为逻辑与交互模式建立具体的规则公式，并转写为代码构建模型。最后，进行参数优化与模型验证。定性访谈与专家咨询法可用于确定参数初始范围[11]，网格搜索法可用于参数优化。模型验证包括概念模型验证与历史数据验证等，前者确保概念模型可反应现实问题与研究目的，后者用于度量模拟值与历史数据的一致性。ABM模型的结果验证多采用t检验、卡方检验、贝叶斯假设检验等静态数据验证方法。
综上，ABM模型的构建过程与传统统计模型类似，需经历理论框架构建、代码撰写、模型验证等固定步骤[12]，但存在以下几点不同。第一，研究者依托权威行为理论构建ABM模型的理论框架[13]，通过模拟每一主体在环境影响下的动态变化过程，进而模拟人群特征的涌现迭代。第二，ABM模型初始化过程需提前设定大量初始参数，其精确度取决于基础数据的样本量与代表性，因此更依赖于大样本、长随访的个体数据。第三，ABM模型能够模拟主体及环境随时间变化的动态过程，主体特征具有时变性，如在吸烟状态与戒烟状态之间转变。与马尔可夫模型不同，发病率不仅仅取决于主体当前状态，更加符合NCDs受早期行为危险因素影响这一真实情况[14]，因此具有更好的模拟效果。第四，模型内的行为决策机制缺乏实证数据支持，导致模型验证环节较为困难。
3　ABM模型在NCDs行为危险因素干预中的应用
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3.1　探索健康行为改变诱因
以探索、分析健康行为改变诱因为建模目的ABM模型均以健康行为发生率作为结局指标，如吸烟率、健康饮食率、酗酒率等等。ABM与其他模拟干预模型相比，最重要的区别是其研究视角从干预对象出发，关注干预对象对于不同干预措施的反馈[15]，因此适用于分析其健康相关行为决策机制与影响因素，探索针对大众而非NCDs患者的全民性行为干预策略。整理所检索文献见表1，多数ABM模型所依据的理论框架将健康相关行为决策机制与影响因素分为内因与外因两部分，后者指主体所处环境，分为社交环境与物质环境两大类。
3.1.1　内部因素
研究表明，影响健康相关行为的因素主要包括社会人口学因素与认知因素[16]。其中人口学因素主要指性别、年龄、经济收入和文化程度等。鞠牛[17]分析中国民众健康不平等的形成过程，发现社会经济地位主要通过健康行为影响人群健康水平。而ABM模型可进一步分析在健康行为干预过程中，健康不平等现象的存在有何作用，学者Gouri suresh等[18]发现即使通过健康教育、媒体宣传等途径提高穷人对健康食品的偏好，收入隔离的存在仍会阻碍其购买健康食品，可见贫富差距带来的负向影响可能会抵消健康行为干预的正向效果。此外，认知因素也对健康相关行为决策机制有重要影响，主要包括自我效能与健康信念。如Garcia[19]以连续意见与离散行为理论为框架建模，量化运动参与的意图作为二级结局指标，并在模型验证环节将人群运动意图水平是否呈U形分布作为验证标准之一，体现了ABM模型的独有验证优势。
3.1.2　外部因素
在社交环境方面，部分学者构建的ABM模型认为在人群社交网络中，可以通过传递健康知识、模仿健康技能来促进主体健康行为的改变[20]，体现了该模型在描述主体间交互作用的优势。ABM模型结合社交网络可比较不同社交网络特征个体对干预措施的反馈差异，精准定位干预对象。如Van woudenberg等人[21]利用MyMovez项目中收集的真实数据构建社交网络，建立包含中心度、密度等社交环境参数的模型，结果发现干预对象的网络中心度与运动率呈正相关。此外，吸烟作为成瘾行为极易受到社交环境的影响，但在检索到的戒烟干预ABM模型均未构建社交网络，可能会低估干预效果。Stankov等人[22]建立的ABM模型关注同样具有成瘾性的饮酒行为，结果显示，针对抑郁且社会参与程度低的老人进行重点戒酒干预效果更佳。
在物质环境方面，多数学者利用ABM模型分析健康行为相关设施的分布、价格等因素对干预效果的影响[23-27]，体现了该模型描述主体与环境交互过程的优势。Blok等[23]发现在增加运动场所数量、降低价格2个干预情景下人群运动率均有上升，尤其是社会经济地位较低的人群提升更为明显，这提示针对物质环境的干预策略可在一定程度上缓解健康不平等。Hammond等[24]建立名为Tobacco Town的ABM模型，发现减少香烟售卖点的对香烟消费行为有明显作用，并在建模过程中通过设计多个行为决策函数弥补相关实证研究数据缺乏的不足。而酒类消费行为有明显的价格弹性[25]，相应干预情景以调整酒精税为主。如Keyes的团队[26-27]先后建立ABM模型并调整优化，估计调整酒精税对酒类消费行为的影响，发现酒精税提高10%即可将酗酒者数量减少2.6%，效果显著。
3.2　评价NCDs行为干预效果
以评价NCDs行为危险因素干预效果建模目的ABM模型均以NCDs发病率作为结局指标，如糖尿病、脑卒中、肥胖等[28-32]。作为“政策仿真实验室”，利用ABM模型可突破现实条件限制，模拟高强度干预措施带来的健康效益。如Garney等人[28]建立ABM模型模拟美国两个社区中推行全面禁烟政策的长期影响，预测结果表明人群心肌梗死、脑卒中和糖尿病的发病率均显著下降。利用ABM模型可详细描述不同主体对于同一干预措施效果的反馈差异，分析实际干预效果与预期存在偏差的原因。如Li等人[29]建立ABM模型模拟了健康教育对不同类型干预对象每日蔬果摄入量的影响，发现男性、文化水平较低的居民干预效果较差。利用ABM模型可探索既定政策是否存在优化空间，如Schauder等人[30]利用ABM模型剖析美国儿童在不同年龄阶段享受“新鲜水果蔬菜计划”政策的效果差异，结果显示在儿童生命早期接受干预更有利于养成健康的饮食习惯。NCDs的行为危险因素常以特定组合的形式呈现，且可能存在协同作用[33]，模拟不同干预措施的组合效果是ABM模型的独有优势。如Luo等人[31]建立ABM模型评估健康行为变化对于糖尿病发病率变化的影响，结果表明戒烟、饮食干预和运动干预的联合干预效果明显优于单因素干预。
3.3　小结
综上，ABM模型作为新兴的建模方法，在NCDs行为危险因素干预领域中研究数量不多，但已逐渐得到认可。其优势与适用性主要体现在以下方面：第一，ABM模型适合探索动态、复杂、非线性的系统问题[34]。NCDs的发生发展受到多种因素协同影响，多因素间可能存在交互作用，健康行为状态具有时变性，而传统统计方法假设变量间存在线性关系，且要求满足总体同质性，无法量化多因素动态交互的动态过程。第二，ABM模型作为一个发展、验证理论的强大工具，具有极强的可操作性[35]。研究者可利用模型预测不同干预措施的组合效果，筛选相对最优的干预策略，且不受伦理学、现实条件限制，也可进一步结合成本效果分析方法衡量可推广性。第三，ABM模型将研究问题分为微观与宏观两个层次，如健康行为发生过程与慢性病发病过程，带来独特的验证思路。可利用微观层面数据进行参数校准，利用宏观层面数据评估模型性能。以上优势表明ABM模型有潜力为人群健康问题，尤其是涉及复杂影响因素的NCDs提供新的干预思路。除良好的适用性外，ABM模型在慢性病干预领域内应用也存在一定局限性：第一，行为决策机制相关数据匮乏，若以效用最大化原理建立行为函数，仅能赋予不同影响因素相同的权重[36]，无法模拟决策过程的异质性；第二，以评价NCDs行为危险因素干预效果建模目的ABM模型对健康行为变化过程描述过于粗略，模型优势未得到充分发挥；第三，健康相关行为习惯往往形成于生命早期，多数研究中构建的ABM模型未对比不同生命时期的行为干预效果，针对中老年人进行饮食干预效果是否会削减有待探究。
4　展望
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近年来，我国开展一系列工作推进慢性病防控并取得明显成效，从出台《“健康中国2030”规划纲要》，到实施《健康中国行动（2019—2030年）》；从制定《中国防治慢性病中长期规划（2017—2025年）》，到建设国家慢性病综合防控示范区，NCDs过早死亡率逐年下降，居民的健康意识不断增强，但部分行为危险因素仍未得到有效控制。在此背景下，制定科学可行的干预策略，控制NCDs的行为危险因素流行趋势，实现人群健康效益最大化具有重要现实意义。传统干预研究多忽略宏观环境因素的影响，干预对象仅限于NCDs患者，缺少针对全人群的预防策略。而事实上慢性病的发生发展是多种因素共同演化的系统，需以整体的思维来衡量，更应倡导“治未病”的预防理念。尽管ABM模型作为仿真模型存在着外部有效性不足的局限，但其独特优势有助于拓展慢性病干预领域的分析边界，提高卫生政策制定者认识和解决慢性病干预中复杂问题的能力。未来模型开发可重点从三个方向进行探索：首先，根据现有的ABM模型规范化框架——OOD，严格建立符合标准的高质量模型，积极探索该模型在慢性病干预领域的应用价值。其次，在参数选取过程中需考虑不同研究人群分布的差异性，尽量与模拟目标人群同质，以达到更优的预测效果。最后，模型设计时应尝试纳入真实社交网络，描述社会环境对健康行为、NCDs发病率的影响。通过ABM模型这一“政策仿真平台”，制定推广性强的健康行为准则，为完善当前慢病防控策略、贯彻落实全民健康生活方式行动提供依据，逐步打造自我为主、人际互助、社会支持、政府指导的健康管理模式，助力健康中国建设。
基金
收起
  • 国家重点研发计划资助(2022YFC3603000)
  • 南京大学中国医院改革发展研究院课题项目(NDYG2023034)
  • 福建省社会科学基金项目(FJ2022C047)
  • 福建省卫生健康政策创新研究课题(2024B03)
文献
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2024年第51卷第14期
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文章信息
doi: 10.20043/j.cnki.MPM.202403024
  • 接收时间：2024-03-01
  • 首发时间：2026-03-18
  • 出版时间：2024-07-25
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  • 收稿日期：2024-03-01
基金
国家重点研发计划资助(2022YFC3603000)
南京大学中国医院改革发展研究院课题项目(NDYG2023034)
福建省社会科学基金项目(FJ2022C047)
福建省卫生健康政策创新研究课题(2024B03)
作者信息
    1.厦门大学公共卫生学院老年健康研究中心，福建 厦门 361000
    2.卫生技术评估福建省高校重点实验室

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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