Article(id=1321169727905452053, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1321169668631548508, articleNumber=null, orderNo=null, doi=10.11847/zgggws1146713, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1734624000000, receivedDateStr=2024-12-20, revisedDate=1744128000000, revisedDateStr=2025-04-09, acceptedDate=1754928000000, acceptedDateStr=2025-08-12, onlineDate=1792921009279, onlineDateStr=2026-10-25, pubDate=1757433600000, pubDateStr=2025-09-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792921009279, onlineIssueDateStr=2026-10-25, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792921009279, creator=13701087609, updateTime=1792921009279, updator=13701087609, issue=Issue{id=1321169668631548508, tenantId=1146029695717560320, journalId=1319373219989389464, year='2025', volume='41', issue='9', pageStart='1025', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='1757433600000', pubDateStr='2025-09-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792920995147, creator='13701087609', updateTime=1792920995147, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1147, endPage=1152, ext={EN=ArticleExt(id=1321169729427984406, articleId=1321169727905452053, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Policy experience and inspirations from the construction of population health and health services databases in the United States, columnId=1320431897281413811, journalTitle=Chinese Journal of Public Health, columnName=Public Health Forum, runingTitle=null, highlight=null, articleAbstract=

The construction of population health and health services databases is a crucial foundation for modernizing national public health governance capabilities. This study, taking into account the practical needs and development challenges in China′s health sector, draws on the mature experience of the United States in database construction and operation. It proposes a comprehensive analytical framework centered on data governance, exploring key elements of database construction, including data structure, management and operation, sharing mechanisms, and funding guarantees. This study finds that the diversity, high level of data sharing, and establishment of ecological chains in U.S. databases provide important insights for China. On the basis of the experience, this study suggests that China should incorporate the construction of population health and health services databases into national infrastructure projects, formulate a unified health data strategy and legal framework, enrich database structures, strengthen data sharing, increase financial investment, and promote interdisciplinary cooperation to fully unlock the potential of data. These measures will ultimately enable the creation of a robust health data governance system with international competitiveness.

, authors=Gang LI1, Zixin CAO2, Yan LI1, authorsList=Gang LI, Zixin CAO, Yan LI, authorCompany=null, correspAuthors=Yan LI, authorNote=null, correspAuthorsNote=
LI Yan, 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=1321169729818054679, articleId=1321169727905452053, tenantId=1146029695717560320, journalId=1319373219989389464, language=CN, title=美国人口健康与卫生服务数据库建设的政策经验及启示, columnId=1320431897449185973, journalTitle=中国公共卫生, columnName=公共卫生论坛, runingTitle=null, highlight=null, articleAbstract=

人口健康与卫生服务数据库建设是推动国家公共卫生治理能力现代化的重要基础。本文结合我国卫生健康领域的现实需求与发展挑战,借鉴美国在数据库建设与运营方面的成熟经验,提出了一个以数据治理为核心的综合分析框架,探讨了数据库建设的关键要素,包括数据结构、管理运营、共享机制与资金保障等。研究发现,美国数据库的多样性、较高的开放共享程度以及生态链群的构建为我国提供了一定的借鉴。基于这些经验,本文建议我国应将人口健康与卫生服务数据库建设纳入国家基础设施工程范畴,制定统一的健康数据战略与法律框架,丰富数据库类型,强化数据共享,增加资金投入,并推动多学科合作,以充分挖掘数据的潜力,最终构建具有国际竞争力的健康数据治理体系。

, authors=李刚1, 曹梓鑫2, 李岩1, *, *, authorsList=李刚, 曹梓鑫, 李岩, authorCompany=null, correspAuthors=李岩, authorNote=

李刚(1993—),助理研究员,博士,研究方向:公共卫生体系、健康场理论、健康数字治理

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李岩,E-mail:
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JAMA, 2023, 329(19): 1682-1692., articleTitle=null, refAbstract=null), Reference(id=1321169734310154308, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=14, rfOrder=13, authorNames=null, journalName=null, refType=null, unstructuredReference=康力, 王慧. 国内外数字公共卫生领域研究热点与前沿探究[J]. 中国公共卫生, 2024, 40(9): 1103-1109., articleTitle=null, refAbstract=null), Reference(id=1321169734410817605, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=15, rfOrder=14, authorNames=null, journalName=null, refType=null, unstructuredReference=The Trustees of Dartmouth College. Dartmouth atlas of health care[EB/OL]. (2025-03-01)[2025-03-28]. https://www.dartmouthatlas.org/., articleTitle=null, refAbstract=null), Reference(id=1321169734498897990, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=16, rfOrder=15, authorNames=null, journalName=null, refType=null, unstructuredReference=University of North Carolina, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention. China health and nutrition survey[EB/OL]. (2025-03-01)[2025-03-28]. https://www.cpc.unc.edu/projects/china., articleTitle=null, refAbstract=null), Reference(id=1321169734582784071, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=17, rfOrder=16, authorNames=null, journalName=null, refType=null, unstructuredReference=For Healthy Aging and Center. Chinese Longitudinal Healthy Longevity Survey (CLHLS) Community Datasets (1998 - 2014)[Z]. Peking University Open Research Data Platform, 2017., articleTitle=null, refAbstract=null), Reference(id=1321169734658281544, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=18, rfOrder=17, authorNames=null, journalName=null, refType=null, unstructuredReference=Zhao Y H, Strauss J, Chen X X, et al. China health and retirement longitudinal study wave 4 user′s guide[EB/OL]. (2020-09-01)[2025-03-28]. https://charls.pku.edu.cn/en/data/User2018.pdf., articleTitle=null, refAbstract=null), Reference(id=1321169734721196105, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=19, rfOrder=18, authorNames=null, journalName=null, refType=null, unstructuredReference=凌鋆超, 曹瑞芳, 李亦学, 等. 多组学大数据共享平台研究进展[J]. 生命科学, 2023, 35(12): 1553-1560., articleTitle=null, refAbstract=null), Reference(id=1321169734784110666, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=20, rfOrder=19, authorNames=null, journalName=null, refType=null, unstructuredReference=U. S. Census Bureau. American community survey (ACS)[EB/OL]. (2025-03-01)[2025-03-28]. https://www.census.gov/programs-surveys/acs., articleTitle=null, refAbstract=null), Reference(id=1321169734859608139, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=21, rfOrder=20, authorNames=null, journalName=null, refType=null, unstructuredReference=U. S. Census Bureau. TIGER/Line geodatabases[EB/OL]. (2025-03-01)[2025-03-28]. https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-geodatabase-file.html., articleTitle=null, refAbstract=null), Reference(id=1321169734930911308, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=22, rfOrder=21, authorNames=null, journalName=null, refType=null, unstructuredReference=Alberto I R I, Alberto N R I, Ghosh A K, et al. The impact of commercial health datasets on medical research and health-care algorithms[J]. The Lancet Digital Health, 2023, 5(5): e288-e294., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1321169730002604056, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, xref=1, ext=[AuthorCompanyExt(id=1321169730010992665, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, companyId=1321169730002604056, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China), AuthorCompanyExt(id=1321169730023575578, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, companyId=1321169730002604056, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1上海交通大学医学院公共卫生学院,上海 200025)]), AuthorCompany(id=1321169730099073051, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, xref=2, ext=[AuthorCompanyExt(id=1321169730107461660, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, companyId=1321169730099073051, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2School of Political Science and Public Administration, Wuhan University, Wuhan 430072, China), AuthorCompanyExt(id=1321169730115850269, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, companyId=1321169730099073051, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2武汉大学政治与公共管理学院,湖北 武汉 430072)])], figs=[ArticleFig(id=1321169731541913653, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, language=EN, label=Tab.1, caption=

Basic information of the population health and health services databases included in this study

, figureFileSmall=null, figureFileBig=null, tableContent=
数据库名称建立时间建设主体
国家健康访谈调查
(National Health Interview Survey, NHIS)
1957美国国家卫生统计中心
国家健康和营养评估调查
(National Health and Nutrition Examination Survey, NHANES)
1960美国国家卫生统计中心
联邦医疗保险和医疗补助数据中心
(Centers for Medicare & Medicaid Data)
1965联邦医疗保险和医疗补助服务中心
(Centers for Medicare & Medicaid Services)
国家药物使用和健康调查
(National Survey on Drug Use and Health, NSDUH)
1971药物滥用与精神健康管理局
(Substance Abuse and Mental Health Services Administratio)
行为风险因素监控系统
(Behavioral Risk Factor Surveillance System, BRFSS)
1984美国疾病控制与预防中心
老龄化纵向研究
(Longitudinal Studies of Aging, LSOA)
1984美国疾病控制与预防中心
医疗保健成本和利用项目
(Healthcare Cost and Utilization Project, HCUP)
1988医疗保健研究和质量署
(Agency for Healthcare Research and Quality)
妊娠风险评估监控系统
(Pregnancy Risk Assessment Monitoring System, PRAMS)
1988美国疾病控制与预防中心
州健康事实
(State Health Facts)
1990凯撒家庭基金会
( Kaiser Family Foundation)
青少年风险行为监控系统
(Youth Risk Behavior Surveillance System, YRBSS)
1991美国疾病控制与预防中心
达特茅斯卫生保健网站档案
(The Dartmouth Atlas of Health Care Website Archive)
1992达特茅斯卫生政策与临床实践研究所
(Dartmouth Institute for Health Policy and Clinical Practice)
国家免疫调查
(National Immunization Surveys, NIS)
1994美国疾病控制与预防中心
医疗保健提供者和系统消费者评估
(Consumer Assessment of Healthcare Providers & Systems, CAHPS)
1995Medicare和Medicaid服务中心
(Centers for Medicare & Medicaid Services, CMS)
美国社区调查
(American Community Survey, ACS)
1996美国人口普查局
(U.S. Census Bureau)
医疗支出面板调查
(Medical Expenditure Panel Survey, MEPS)
1996医疗保健研究和质量署
(Agency for Healthcare Research and Quality)
长期护理调查
(Long Term Care, LTC-Focus)
2000布朗大学老年病学和医疗保健研究中心
(Brown University Center for Gerontology and Healthcare Research)
国家老年病学研究所
(National Institute on Aging)
国家HIV行为监控
(National HIV Behavioral Surveillance, NHBS)
2003美国疾病控制与预防中心
健康和退休研究
(Health and Retirement Study, HRS)
2005密歇根大学社会研究所
(University of Michigan′s Institute for Social Research)
全支付方索赔数据库
(All-payer Claims Database)
2007新罕布什尔大学卫生政策与实践研究所
(Institute for Health Policy and Practice at the University of New Hampshire)
国家卫生数据组织协会
(National Association of Health Data Organizations, NAHDO)
哈佛催化剂政策地图集
(Harvard Catalyst Policy Atlas)
2008哈佛大学学院及其附属学术医疗中心
(Harvard University′s schools and affiliate academic healthcare centers)
法律地图集政策监控计划
(LawAtlas Policy Surveillance Program)
2009费城天普大学比斯利法学院公共卫生法律研究中心
(Center for Public Health Law Research at Temple University′s Beasley School
of Law)
IBM MarketScan研究数据库
(IBM MarketScan Research Databases)
2010IBM
县级健康排名与路线图
(County Health Rankings & Roadmaps, CHR&R)
2010威斯康星大学人口健康研究所
(University of Wisconsin Population Health Institute)
国家医院护理调查
(National Hospital Care Survey, NHCS)
2011美国疾病控制与预防中心
Optum索赔数据
(Optum Claims Data)
2011Optum公司
医疗保健成本研究所数据库
(Health Care Cost Institute Data)
2011医疗保健成本研究所
(Health Care Cost Institute, HCCI)
国家健康和老龄化趋势研究
(National Health and Aging Trends Study, NHATS)
2011约翰霍普金斯大学布隆伯格公共卫生学院
(Johns Hopkins University Bloomberg School of Public Health)
密歇根大学社会研究所
(University of Michigan′s Institute for Social Research)
健康和医疗保健档案
(Health and Medical Care Archive, HCMA)
2013罗伯特·伍德·约翰逊基金会
(Robert Wood Johnson Foundation, RWJF)
密歇根大学大学间政治和社会研究联合会
(University of Michigan, Inter-University Consortium for Political and
Social Research)
解决健康差距的联邦数据集简编
(Compendium of Federal Datasets Addressing Health Disparities)
2016国家层面的跨部门健康公平协作组织
(Interdepartmental Health Equity Collaborative, IHEC)
), ArticleFig(id=1321169731621605430, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321169727905452053, language=CN, label=表1, caption=

纳入研究的人口健康与卫生服务数据库基本情况

, figureFileSmall=null, figureFileBig=null, tableContent=
数据库名称建立时间建设主体
国家健康访谈调查
(National Health Interview Survey, NHIS)
1957美国国家卫生统计中心
国家健康和营养评估调查
(National Health and Nutrition Examination Survey, NHANES)
1960美国国家卫生统计中心
联邦医疗保险和医疗补助数据中心
(Centers for Medicare & Medicaid Data)
1965联邦医疗保险和医疗补助服务中心
(Centers for Medicare & Medicaid Services)
国家药物使用和健康调查
(National Survey on Drug Use and Health, NSDUH)
1971药物滥用与精神健康管理局
(Substance Abuse and Mental Health Services Administratio)
行为风险因素监控系统
(Behavioral Risk Factor Surveillance System, BRFSS)
1984美国疾病控制与预防中心
老龄化纵向研究
(Longitudinal Studies of Aging, LSOA)
1984美国疾病控制与预防中心
医疗保健成本和利用项目
(Healthcare Cost and Utilization Project, HCUP)
1988医疗保健研究和质量署
(Agency for Healthcare Research and Quality)
妊娠风险评估监控系统
(Pregnancy Risk Assessment Monitoring System, PRAMS)
1988美国疾病控制与预防中心
州健康事实
(State Health Facts)
1990凯撒家庭基金会
( Kaiser Family Foundation)
青少年风险行为监控系统
(Youth Risk Behavior Surveillance System, YRBSS)
1991美国疾病控制与预防中心
达特茅斯卫生保健网站档案
(The Dartmouth Atlas of Health Care Website Archive)
1992达特茅斯卫生政策与临床实践研究所
(Dartmouth Institute for Health Policy and Clinical Practice)
国家免疫调查
(National Immunization Surveys, NIS)
1994美国疾病控制与预防中心
医疗保健提供者和系统消费者评估
(Consumer Assessment of Healthcare Providers & Systems, CAHPS)
1995Medicare和Medicaid服务中心
(Centers for Medicare & Medicaid Services, CMS)
美国社区调查
(American Community Survey, ACS)
1996美国人口普查局
(U.S. Census Bureau)
医疗支出面板调查
(Medical Expenditure Panel Survey, MEPS)
1996医疗保健研究和质量署
(Agency for Healthcare Research and Quality)
长期护理调查
(Long Term Care, LTC-Focus)
2000布朗大学老年病学和医疗保健研究中心
(Brown University Center for Gerontology and Healthcare Research)
国家老年病学研究所
(National Institute on Aging)
国家HIV行为监控
(National HIV Behavioral Surveillance, NHBS)
2003美国疾病控制与预防中心
健康和退休研究
(Health and Retirement Study, HRS)
2005密歇根大学社会研究所
(University of Michigan′s Institute for Social Research)
全支付方索赔数据库
(All-payer Claims Database)
2007新罕布什尔大学卫生政策与实践研究所
(Institute for Health Policy and Practice at the University of New Hampshire)
国家卫生数据组织协会
(National Association of Health Data Organizations, NAHDO)
哈佛催化剂政策地图集
(Harvard Catalyst Policy Atlas)
2008哈佛大学学院及其附属学术医疗中心
(Harvard University′s schools and affiliate academic healthcare centers)
法律地图集政策监控计划
(LawAtlas Policy Surveillance Program)
2009费城天普大学比斯利法学院公共卫生法律研究中心
(Center for Public Health Law Research at Temple University′s Beasley School
of Law)
IBM MarketScan研究数据库
(IBM MarketScan Research Databases)
2010IBM
县级健康排名与路线图
(County Health Rankings & Roadmaps, CHR&R)
2010威斯康星大学人口健康研究所
(University of Wisconsin Population Health Institute)
国家医院护理调查
(National Hospital Care Survey, NHCS)
2011美国疾病控制与预防中心
Optum索赔数据
(Optum Claims Data)
2011Optum公司
医疗保健成本研究所数据库
(Health Care Cost Institute Data)
2011医疗保健成本研究所
(Health Care Cost Institute, HCCI)
国家健康和老龄化趋势研究
(National Health and Aging Trends Study, NHATS)
2011约翰霍普金斯大学布隆伯格公共卫生学院
(Johns Hopkins University Bloomberg School of Public Health)
密歇根大学社会研究所
(University of Michigan′s Institute for Social Research)
健康和医疗保健档案
(Health and Medical Care Archive, HCMA)
2013罗伯特·伍德·约翰逊基金会
(Robert Wood Johnson Foundation, RWJF)
密歇根大学大学间政治和社会研究联合会
(University of Michigan, Inter-University Consortium for Political and
Social Research)
解决健康差距的联邦数据集简编
(Compendium of Federal Datasets Addressing Health Disparities)
2016国家层面的跨部门健康公平协作组织
(Interdepartmental Health Equity Collaborative, IHEC)
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美国人口健康与卫生服务数据库建设的政策经验及启示
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李刚 1 , 曹梓鑫 2 , 李岩 1, *, *
中国公共卫生 | 公共卫生论坛 2025,41(9): 1147-1152
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中国公共卫生 |公共卫生论坛 2025 , 41 (9) : 1147 -1152
美国人口健康与卫生服务数据库建设的政策经验及启示
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李刚1, 曹梓鑫2, 李岩1, *, *
作者信息
  • 1上海交通大学医学院公共卫生学院,上海 200025
  • 2武汉大学政治与公共管理学院,湖北 武汉 430072
通讯作者:
李岩,E-mail:
作者简介:

李刚(1993—),助理研究员,博士,研究方向:公共卫生体系、健康场理论、健康数字治理

Policy experience and inspirations from the construction of population health and health services databases in the United States
Gang LI1, Zixin CAO2, Yan LI1
Affiliations
  • 1School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
  • 2School of Political Science and Public Administration, Wuhan University, Wuhan 430072, China
  • Corresponding Author:
    LI Yan, E-mail:
出版时间: 2025-09-10 doi: 10.11847/zgggws1146713
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人口健康与卫生服务数据库建设是推动国家公共卫生治理能力现代化的重要基础。本文结合我国卫生健康领域的现实需求与发展挑战,借鉴美国在数据库建设与运营方面的成熟经验,提出了一个以数据治理为核心的综合分析框架,探讨了数据库建设的关键要素,包括数据结构、管理运营、共享机制与资金保障等。研究发现,美国数据库的多样性、较高的开放共享程度以及生态链群的构建为我国提供了一定的借鉴。基于这些经验,本文建议我国应将人口健康与卫生服务数据库建设纳入国家基础设施工程范畴,制定统一的健康数据战略与法律框架,丰富数据库类型,强化数据共享,增加资金投入,并推动多学科合作,以充分挖掘数据的潜力,最终构建具有国际竞争力的健康数据治理体系。

人口健康与卫生服务数据库  /  数据治理  /  健康大数据  /  健康信息系统

The construction of population health and health services databases is a crucial foundation for modernizing national public health governance capabilities. This study, taking into account the practical needs and development challenges in China′s health sector, draws on the mature experience of the United States in database construction and operation. It proposes a comprehensive analytical framework centered on data governance, exploring key elements of database construction, including data structure, management and operation, sharing mechanisms, and funding guarantees. This study finds that the diversity, high level of data sharing, and establishment of ecological chains in U.S. databases provide important insights for China. On the basis of the experience, this study suggests that China should incorporate the construction of population health and health services databases into national infrastructure projects, formulate a unified health data strategy and legal framework, enrich database structures, strengthen data sharing, increase financial investment, and promote interdisciplinary cooperation to fully unlock the potential of data. These measures will ultimately enable the creation of a robust health data governance system with international competitiveness.

population health and health services research datasets  /  data governance  /  health big data  /  health information systems
李刚, 曹梓鑫, 李岩. 美国人口健康与卫生服务数据库建设的政策经验及启示. 中国公共卫生, 2025 , 41 (9) : 1147 -1152 . DOI: 10.11847/zgggws1146713
Gang LI, Zixin CAO, Yan LI. Policy experience and inspirations from the construction of population health and health services databases in the United States[J]. Chinese Journal of Public Health, 2025 , 41 (9) : 1147 -1152 . DOI: 10.11847/zgggws1146713
随着数字化、网络化与智能化的不断推进,数据已成为推动社会各领域变革与创新的核心生产要素[1]。在医学与公共卫生领域,健康数据的价值愈加凸显,不仅为疾病预防与控制提供支持,还在科学研究与政策制定中发挥关键作用。2016年以来,我国在卫生健康领域的数字化转型虽取得了一定进展[2],但仍面临数据标准化不足、共享机制不健全等挑战。特别是在新型冠状病毒感染疫情期间,因缺乏系统化、全面的健康数据及相关领域数据库支持,精准决策和防控措施受到一定制约,进一步凸显了健全健康数据基础设施、提升数据治理能力的紧迫性。相较之下,美国自20世纪70年代起即启动健康信息化进程,并基于此发展出多个全国性健康数据库,这些数据库在公共卫生政策制定、医疗资源优化和疾病防控等方面发挥了重要作用[3]。鉴于此,本研究以美国人口健康与卫生服务数据库为研究对象,分析其发展历程与现状,并结合我国的具体国情,探讨如何借鉴其经验,以进一步推动我国人口健康与卫生服务数据库的建设和发展。
人口健康和卫生服务数据库(population health and health services research datasets)是一系列综合的数据集,提供了关于个体和群体健康状况、医疗服务使用、健康结果、医疗保险、卫生政策等领域的详细信息。这些数据集通常来源于医疗记录、健康调查、公共卫生监测系统和行政数据等,旨在支持研究人员、政策制定者和医疗专业人员评估健康趋势、识别健康差异、制定干预措施,并提高整体健康服务质量和效率[4]。在本研究中,数据库的筛选遵循以下3个步骤。首先,通过系统文献检索收集与美国人口健康和卫生服务数据库相关的研究成果和文献,确定主要的数据库来源。其次,通过专家访谈确认各个数据库的实际应用价值和影响力,尤其是其在公共卫生政策、医疗服务质量改善等方面的作用。最后,根据数据库的影响力、调查人群规模、数据可获取性和时间跨度等因素,综合考量并最终纳入30个美国人口健康和卫生服务数据库进行分析,如表1所示。
纳入的30个美国人口健康与卫生服务数据库呈现出以下显著特征:(1)数据库的主题涵盖了多个研究领域,展现出较强的多样性。其中,公共卫生与社会服务领域占比最高,共有12个数据库,约占总数的40%;其次是医疗成本与质量相关的数据库,共7个,占比23%;此外,还涉及卫生政策、健康公平、医疗索赔等领域的数据库。这一多样性反映了美国健康数据研究的广泛应用,能够支持从公共卫生干预到卫生政策制定的多维度研究。(2)数据库的建设时间较早,显示出美国在该领域的先发优势。最早的国家健康访谈调查(National Health Interview Survey, NHIS)可以追溯到1957年,其中有14个数据库是在2000年之前建立的[5]。这一时间上的领先为美国在人口健康与卫生服务领域积累了大量长期数据资源,为相关研究奠定了坚实的基础。(3)大部分数据库可公开获取,确保了研究者、政策制定者和医疗专业人员能够广泛获取数据。在所纳入的30个数据库中,仅有5个数据库需通过收费获取数据,其余均为免费公开获取。这种开放性增强了数据的可用性与研究的透明度,推动了该领域的科学研究和公共卫生政策制定。(4)数据库的覆盖范围较为广泛,绝大多数数据库的数据收集范围覆盖全国层面,这为研究者提供了全面的人口健康数据,并确保了分析结果的代表性。数据库的规模和数据维度也反映在变量的数量上,微观调查数据库中的变量数普遍较多。以医疗支出面板调查(Medical Expenditure Panel Survey, MEPS)为例,调查变量数已累计接近66 000个[6]。国家健康与营养调查(National Health and Nutrition Examination Survey, NHANES)数据包含约4 200个变量[7],而行为风险因素监测系统(Behavioral Risk Factor Surveillance System, BRFSS)也拥有约200个变量[8]。这种丰富的变量设置使得数据库在多领域、多角度的研究中具有较高的挖掘潜力。此外,通过对数据库讨论主题的文本分析,常见的关键词包括“健康”“护理”“服务”“医疗保健”“医生”“疫苗”等。这些关键词之间的关系网络揭示了健康与社会、健康保险、儿童、家庭、心理健康、医疗服务等领域之间的密切关联,也进一步表明和验证了数据库的研究重点主要集中在公共卫生、医疗服务和健康公平等领域。
美国的人口健康与卫生服务数据库建设主体呈现出高度的多元化特征,涵盖政府部门、高校及研究机构、私人基金会和企业。根据主导机构的不同,数据库的建设模式可以大致分为三类:(1)由政府部门主导的数据库建设。这类数据库在美国占据重要地位,通常依赖政府机构的直接支持,并享有充足的财政预算。约半数的数据库由美国卫生和公众服务部(Department of Health and Human Services, HHS)下属的各主要机构牵头建设和管理,包括美国疾病控制与预防中心(Centers for Disease Control and Prevention, CDC)、联邦医疗保险和医疗补助服务中心(Centers for Medicare & Medicaid, CMS)以及美国医疗保健研究与质量署(Agency for Healthcare Research and Quality, AHRQ)等。这些机构通过提供资金、政策指导以及技术支持,推动了各类健康与卫生数据的收集、管理和应用,保障了数据库的长期运行与可持续性。(2)由高校和研究机构主导的数据库建设。高校和研究机构凭借其丰富的科研资源和多学科优势,在数据库建设中占据了独特的地位。例如,美国国家健康和老龄化趋势研究(National Health and Aging Trends Study, NHATS)由约翰霍普金斯大学布隆伯格公共卫生学院主导[9],高校作为数据库的主要建设者,不仅为学术界提供了大量原始数据,还通过深入分析直接推动了公共卫生领域的研究进展。(3)由私人基金会和企业资助或主导的数据库建设。这些组织不仅提供资金支持,还参与数据库的实际开发和维护。例如,比尔及梅琳达·盖茨基金会资助的全球健康数据交换平台(Global Health Data Exchange, GHDx)[10],集成并共享全球范围内的健康数据,是全球疾病负担研究(Global Burden of Disease, GBD)等重要研究项目的数据来源。罗伯特·伍德·约翰逊基金会(Robert Wood Johnson Foundation, RWJF)是美国健康数据库建设的重要推动者,其核心理念是推动健康公平[11]。
美国人口健康与卫生服务数据库的良好运营依赖于一套成熟的、系统化的管理体系,涵盖数据收集和质量控制、组织结构与协调机制、数据应用与技术支持以及资金保障与可持续性等关键方面。这些机制通过紧密的协同关系,确保了数据库的高效运行和资源的最大化利用。首先,数据收集和质量控制是数据库良好运营的基础。在美国,统一的国家标准和规范在数据采集阶段起到了至关重要的作用,确保了数据的一致性和可比性。例如,美国CDC负责主导的多个数据库,严格遵循数据清洗、校验和抽样验证等质量控制措施,确保了数据的准确性和可靠性,从而为后续的公共卫生研究和政策制定提供了高质量的数据支持。其次,组织结构与协调机制是数据库运营的另一关键要素。美国的人口健康与卫生服务数据库通常由多个机构共同运作,形成了一个高度协同的管理体系。例如,NHATS由约翰霍普金斯大学和密歇根大学共同主导,但同时接受美国国家老龄研究所(National Institute on Aging, NIA)和国家卫生研究院(National Institutes of Health, NIH)的监督和支持。而NHANES和NHIS则由美国CDC统一管理,以确保数据库在数据制式和标准化方面遵循高标准。这种多层次的管理体系不仅促进了机构间的协作,还提高了数据管理和使用的效率,同时加强了数据的科学性和可持续性。在数据应用与技术支持方面,为了保证数据库的有效存储、处理和分析,美国广泛采用先进的技术系统。例如,卫生计量与评估研究所(Institute for Health Metrics and Evaluation, IHME)通过其平台定期发布基于数据库的研究报告,并利用数据可视化工具展示关键数据,使研究人员和政策制定者能够迅速获取和分析关键信息。此外,诸如MEPS和BRFSS等数据库,提供了开放的数据申请渠道,允许研究人员、政府机构和公众访问下载数据。这种数据库的开放性与共享机制,促进了跨机构的数据合作,实现了数据的应用价值最大化。此外,资金保障是确保数据库长期可持续运营的核心因素之一。美国的人口健康与卫生服务数据库主要依赖于政府拨款,以确保数据库的持续发展和维护。以MEPS数据库为例,据2024年预算报告显示,美国政府为AHRQ的各类项目提供了约4亿美元的资金,其中超过7 000万美元用于支持MEPS的运营和更新[12]。与此同时,美国的数据库通常设有合理的收费机制,对部分数据库用户收取使用费用,这为数据库的运营提供了额外的资金保障。通过定期的预算审核与资金报告,确保了资金的透明使用,从而提高了数据库的运营效率和资源配置的合理性。
数据资源的潜在价值只有在被充分挖掘和利用时才能得以体现。美国多样性的人口健康与卫生服务数据库为研究者提供了丰富的原始数据,广泛支持流行病学、卫生管理等多个领域的研究,极大地推动了科研创新与公共卫生学科的发展。这些数据库通常集成在统一平台上,研究者可以方便快捷地获取所需信息,并能够基于多个数据库联合进行大规模分析,大幅度地提升了研究效率并拓展了研究边界。随着循证决策方法在公共管理中的重要性日益增强,数据的综合分析为政策制定者提供了更加全面和精准的决策支持。例如,通过联合分析MEPS和BRFSS数据,政策制定者能够更好地理解不同群体的健康状况和需求,从而更科学地优化资源配置和健康服务,提高政策决策的针对性与有效性[13]。数据库管理机构定期发布的数据报告和白皮书进一步提高了数据库的实际应用价值。例如,美国CDC和AHRQ每年发布基于其数据库的年度报告和专题研究报告,汇总重要发现,提供综合分析结果、数据亮点及政策建议。这一持续发布机制不仅加强了数据库对科研的支持,也推动了循证决策的发展。为了让健康数据的利用惠及更广泛的受众,多个数据库还建设了数据可视化平台,将复杂的数据转化为易于理解的图表和模型。数据可视化不仅帮助研究人员和政策制定者快速获取关键信息,也提升了公众对健康数据的理解与互动性,进而促进健康行为的改善[14–15]。
自21世纪以来,我国在人口健康与卫生服务数据库建设方面取得了显著进展。早期的大型全国性调查数据库可追溯至1989年,其中中国健康与营养调查(1989年启动)作为一项国际合作项目,由美国北卡罗来纳大学与中国疾病预防控制中心等机构联合发起[16]。随着研究体系的不断完善,我国逐步建立了一系列高质量的微观调查数据库,以中国老年健康影响因素跟踪调查(1998年启动)[17]和中国健康与养老追踪调查(2011年启动)[18]等为代表。这些数据库依托长期追踪调查,系统构建了涵盖人口健康状况、医疗服务利用、健康行为等多个维度的数据体系,并积累了大量的跨时空健康数据。这些数据库在科学设计和数据标准化方面具有较高规范性,使研究者能够基于不同国家和地区的数据进行横向对比分析,从而推动我国健康数据体系与国际健康研究的接轨。同时,其数据的系统性和长期性为健康影响因素分析及政策评估提供了重要的实证支持,进一步促进了全球健康研究的深入发展。
尽管我国已构建多个具有国际影响力的健康数据库,但仍缺乏统一的国家级大型数据库,尤其是在生命科学、精准医学等领域,未能形成覆盖多层次、多组学的大规模综合数据库。目前,大量高价值的健康数据(如细胞、蛋白测序、临床组学、微生物组数据)分散在各医疗机构、科研院所和企业[19],但或因机构利益,或因标准不一、数据孤岛问题严重,未能实现有效整合和共享。这不仅限制了这些数据在精准医学研究、疾病预防和治疗中的潜在应用价值,也影响了我国在全球健康数据研究中的竞争力。此外,我国现有数据库的长期可持续运行机制尚未完全建立,部分数据库依赖短期科研项目支持,缺乏稳定的资金投入,导致数据的持续更新和维护面临挑战,进一步削弱了数据库的长期价值。
与之相比,美国在人口与健康数据库建设方面已形成相互支撑的数据库生态体系,不同数据库之间形成互补和协同效应。例如,美国人口普查局的美国社区调查(American Community Survey, ACS)和拓扑集成地理编码和参照(Topology Integrated Geographic Encoding and Referencing,TIGER)地理数据库等[20–21],不仅为研究者提供各地区的人口、经济、社会、住房信息,还结合地理信息数据库,拓展了健康数据的分析维度。这些数据库相互支撑,为疾病流行趋势分析、健康不平等研究、环境与健康的关联分析等提供了强有力的数据支持。这种数据库生态链的建立,使得美国的健康数据研究能够更广泛地融入人口、社会、环境等多领域的信息体系,为精准公共卫生政策和医学研究提供更加全面的支撑。
相较之下,我国仍需进一步加强国家级数据库的统筹建设,不仅要提升健康数据的标准化和共享性,还应借鉴美国经验,推动健康数据库与人口、社会、环境等多领域数据库的互联互通,形成具备综合分析能力的数据库生态系统。这将有助于提升我国在健康数据研究领域的国际竞争力,为精准医学、疾病防治以及国家健康政策的制定提供更有力的科学依据。
为推动我国人口健康与卫生服务数据库的建设,国家应将其纳入基础设施工程的范畴,并制定全面的健康数据战略。这一战略应包括人口健康与卫生服务数据库建设和投入的立法,明确健康数据的法定公开目录,制定统一的数据标准,涵盖数据的收集、管理、分析和共享机制,明确数据格式、质量控制和隐私保护要求,以提高数据的一致性、透明度和可操作性。同时,应设立跨部门的健康数据管理委员会,协调各级政府、医疗卫生机构和科研机构的工作,确保数据管理的透明度和协作性,从而提升整体数据治理能力。
当前我国健康数据库种类仍较为单一,为了更全方位地支持公共卫生研究,建议加强对多种类型健康数据库的建设。例如,除了传统的人口健康数据库外,还应开发涵盖卫生政策、健康地理信息、医疗资源、医疗索赔、医疗成本与质量、慢性病管理和心理健康等领域的数据库。这将有助于全面覆盖健康领域的各个方面,支持多维度的研究和政策制定。此外,还应推动构建与社会经济状况、环境健康、遥感、街景影像等相关的数据库生态链群,形成各数据库间相互支撑的体系。
为实现数据的有效整合,我国应建立统一的健康数据共享平台,鼓励政府行政机构、公共卫生机构、医疗机构与科研单位之间的协作与数据共享。通过制定清晰的数据共享政策,确保各方能够方便、安全地访问和利用相关数据。这一平台应涵盖全国范围内的数据交换标准,以促进不同地区和机构之间的协作,推动健康数据的跨部门共享。建议在现有数据安全法的基础上,进一步制定详细的实施细则。这些细则应明确各部门之间的数据共享目录、共享程度、共享时效以及数据访问权限等,以确保数据治理的规范性和可操作性。
建议政府加大对人口健康与卫生服务数据库建设的资金投入。通过设立专项资金,支持数据库的技术升级、数据更新及维护工作。同时,鼓励私营部门和社会组织参与数据库建设[22],形成多元化的资金支持体系,以确保数据库的可持续发展。
为了提高数据库的应用效果,建议建立动态评估和反馈机制。定期收集和分析用户反馈,评估数据库的使用情况和数据质量。同时,通过数据分析评估公共卫生政策的实施效果,为政策调整提供实时支持。确保数据库在不断变化的公共卫生需求中保持灵活性和适应性,以便更好地服务于政策制定和实施。
推动公共卫生、社会科学、数据科学、临床医学、生命科学等多学科的合作,鼓励研究者共同参与数据库的建设与应用。通过跨学科的协作,能够更加全面地分析健康数据,助力精准医学研究、疾病预防和治疗,并为公共卫生决策提供科学依据,从而释放数据的最大潜力。同时,需要加强相关学科的人才培养,培养具备跨学科知识和技能的专业人才,为数据库的长期发展和实际应用提供持续支持。

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2025年第41卷第9期
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doi: 10.11847/zgggws1146713
  • 接收时间:2024-12-20
  • 首发时间:2026-10-25
  • 出版时间:2025-09-10
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  • 收稿日期:2024-12-20
  • 修回日期:2025-04-09
  • 录用日期:2025-08-12
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    1上海交通大学医学院公共卫生学院,上海 200025
    2武汉大学政治与公共管理学院,湖北 武汉 430072

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2种不同金属材料的力学参数
科
Family
属数
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genus
种数
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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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