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Local Interventions for Reducing Cardiovascular Disease Risks: What Can They Achieve?

机译:减少心血管疾病风险的本地干预措施:它们可以实现什么?

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Public documents identify broad strategies for reducing the burden of cardiovascular disease (CVD) in the U.S., but they do not specify how best to allocate limited resources. Such specific guidance is lacking in part because of gaps in data on intervention costs and effect sizes, but also because the many factors contributing to cardiovascular risk interact through pathways and stock-flow structures that defy simple calculation. The U.S. Centers for Disease Control and Prevention, with support from the National Institutes of Health, is using SD modeling to better understand these complexities and to evaluate potential intervention strategies in terms of their impacts on adverse events and costs over the coming decades. The project considers interventions that might be undertaken at a city or county level, including interventions to improve health care, physical activity, nutrition, mental health, tobacco control, and indoor and outdoor air quality. Construction of the model has involved working with subject matter experts as well as collaborating with the Austin/Travis County, Texas, health department, which has gathered a broad spectrum of local data on population health and interventions over the past several years. This collaborative effort is helping to translate the science of cardiovascular disease into a form that is policy relevant and that can help many communities do a better job of allocating their public health resources.
机译:公开文件确定了减轻美国心血管疾病(CVD)负担的广泛策略,但未指定如何最好地分配有限的资源。之所以缺乏这样的具体指导,部分是因为有关干预成本和效应大小的数据存在差距,而且还因为许多导致心血管疾病风险的因素通过阻碍简单计算的途径和库存流结构相互作用。在美国国立卫生研究院(National Institutes of Health)的支持下,美国疾病控制与预防中心正在使用SD模型更好地了解这些复杂性,并根据其对未来几十年内对不良事件和成本的影响来评估潜在的干预策略。该项目考虑了可能在城市或县级进行的干预措施,包括改善医疗保健,体育锻炼,营养,心理健康,烟草控制以及室内和室外空气质量的干预措施。该模型的构建涉及与主题专家合作以及与德克萨斯州奥斯汀/特拉维斯县卫生部门的合作,该部门在过去几年中收集了有关人口健康和干预措施的广泛本地数据。这项合作正在帮助将心血管疾病科学转化为与政策相关的形式,并且可以帮助许多社区更好地分配公共卫生资源。

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