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从模型到行动:运用慢性病风险系统动力学模型协调社区行动。

From model to action: using a system dynamics model of chronic disease risks to align community action.

作者信息

Loyo Heather Karina, Batcher Cynthia, Wile Kristina, Huang Philip, Orenstein Diane, Milstein Bobby

机构信息

Texas Department of State Health Services, Georgetown, TX, USA.

出版信息

Health Promot Pract. 2013 Jan;14(1):53-61. doi: 10.1177/1524839910390305. Epub 2012 Apr 9.

Abstract

Health planners in Austin, Texas, are using a System Dynamics Model of Cardiovascular Disease Risks (SD model) to align prevention efforts and maximize the effect of limited resources. The SD model was developed using available evidence of disease prevalence, risk factors, local contextual factors, resulting health conditions, and their impact on population health. Given an interest in understanding opportunities for upstream health protection, the SD model focused on the portion of the population that has never had a cardiovascular event. Leaders in Austin used this interactive simulation model as a catalyst for convening diverse stakeholders in thinking about their strategic directions and policy priorities. Health officials shared insights from the model with a range of organizations in an effort to align actions and leverage assets in the community to promote healthier conditions for all. This article summarizes the results from several simulated intervention scenarios focusing specifically on conditions in East Travis County, an area marked by higher prevalence of adverse living conditions and related chronic diseases. The article also describes the formation of a new Chronic Disease Prevention Coalition in Austin, along with shifts in its members' perceived priorities for intervention both before and after interactions with the SD model.

摘要

得克萨斯州奥斯汀市的卫生规划者正在使用心血管疾病风险系统动力学模型(SD模型)来协调预防工作,并最大限度地发挥有限资源的作用。该SD模型是利用疾病患病率、风险因素、当地背景因素、由此产生的健康状况及其对人群健康的影响等现有证据开发的。鉴于对了解上游健康保护机会的兴趣,SD模型关注的是从未发生过心血管事件的人群部分。奥斯汀市的领导人将这个交互式模拟模型作为催化剂,召集不同的利益相关者思考他们的战略方向和政策重点。卫生官员与一系列组织分享了该模型的见解,以协调行动并利用社区资产,为所有人促进更健康的状况。本文总结了几个模拟干预情景的结果,这些情景特别关注东特拉维斯县的情况,该地区以恶劣生活条件和相关慢性病的较高患病率为特征。文章还描述了奥斯汀市一个新的慢性病预防联盟的形成,以及其成员在与SD模型互动前后对干预优先事项的认知变化。

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