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在治愈社区研究中,从社区参与,数据驱动的基于证据的实践策略选择中吸取教训.

Peter Balvanz1, Daniel Harris, Ramona Olvera

  • 1Author Affiliations: General Internal Medicine, Boston Medical Center, Boston, MA (Mr Balvanz, Ms Bridden, Ms Damato-MacPherson, Ms Kosakowski, Dr Larochelle); Institute for Biomedical Informatics, University of Kentucky, Lexington, Kentucky (Dr Harris); Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research (CATALYST), College of Medicine, The Ohio State University, Columbus, Ohio (Dr Olvera); School of Social Work, Columbia University, New York, New York (Dr Sabounchi, Mr David, Dr Hunt, Dr Lounsbury, Dr Wu); Families First Parenting Programs, Watertown, Massachusetts (Ms Carpenter); Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio (Dr Fareed); College of Medicine, The Ohio State University, HEALing Communities Study, Columbus, Ohio (Dr Olvera, Dr Fareed, Dr Huerta, Ms Plagens, Dr Chase); Tufts Clinical and Translational Science Institute, Boston, Massachusetts (Ms Gibson); Boston University Chobanian & Avedisian School of Medicine (Dr Larochelle); Berkshire Regional Planning Commission, Pittsfield, Massachusetts (Ms Lewis); Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California (Ms Smeltzer) and Division of Program Coordination, Planning, and Strategic Initiatives, National Institute on Drug Abuse, Bethesda, Maryland (Dr Villani).

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概括

治愈社区研究 (HCS) 使用数据驱动方法帮助社区减少阿片类药物过量服用. 这种方法指导了基于证据的策略的选择,以获得更大的影响.

关键词:
临床试验临床试验临床试验临床试验临床试验社区参与 社区参与以数据为导向的决策.基于证据的实践.在阿片类药物阿片类药物.

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科学领域:

  • 公共卫生 公共卫生
  • 医疗保健服务研究 医疗服务研究
  • 基于社区的干预措施

背景情况:

  • 对于公共卫生领域的社区参与,数据驱动的决策存在有限的实际指导.
  • "治愈社区研究" (HEALing Communities Study,简称HCS) 的目的是解决受严重影响的社区中阿片类药物过量流行病的问题.
  • 现有的公共卫生数据和工具往往缺乏社区使用的有效实施策略.

研究的目的:

  • 评估社区参与,数据驱动干预对减少致命阿片类药物过量服用的影响.
  • 描述治愈社区 (HCS) 干预的方法.
  • 确定实施公共卫生倡议数据驱动决策的最佳实践和障碍.

主要方法:

  • 一个随机的,等候名单的受控试验,涉及美国四个州的67个社区.
  • 社区实施的HEAL干预,一个分阶段的方法.
  • 核心数据驱动的步骤包括数据选择,访问,可视化和联盟参与,以确定干预机会.

主要成果:

  • 在HCS干预中,采用分阶段,联盟参与,数据驱动的方法来选择基于证据的策略.
  • 工作人员参与了社区联盟,以评估资源缺口和干预机会,使用选定的指标和可视化.
  • 干预后的研讨会确定了数据驱动决策中遇到的关键最佳实践和挑战.

结论:

  • 结构化,社区参与,数据驱动的方法是可行的指导公共卫生干预措施.
  • 该HCS模型为利用数据为社区层面的过量减少策略提供了框架.
  • 学到的经验教训为未来数据驱动公共卫生倡议的实施提供了指导.