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Evaluating the Health Impact of Large-Scale Public Policy Changes: Classical and Novel Approaches
Annual Review of Public Health ( IF 20.8 ) Pub Date : 2017-04-06 00:00:00 , DOI: 10.1146/annurev-publhealth-031816-044208
Sanjay Basu 1, 2, 3 , Ankita Meghani 2 , Arjumand Siddiqi 4, 5
Affiliation  

Large-scale public policy changes are often recommended to improve public health. Despite varying widely—from tobacco taxes to poverty-relief programs—such policies present a common dilemma to public health researchers: how to evaluate their health effects when randomized controlled trials are not possible. Here, we review the state of knowledge and experience of public health researchers who rigorously evaluate the health consequences of large-scale public policy changes. We organize our discussion by detailing approaches to address three common challenges of conducting policy evaluations: distinguishing a policy effect from time trends in health outcomes or preexisting differences between policy-affected and -unaffected communities (using difference-in-differences approaches); constructing a comparison population when a policy affects a population for whom a well-matched comparator is not immediately available (using propensity score or synthetic control approaches); and addressing unobserved confounders by utilizing quasi-random variations in policy exposure (using regression discontinuity, instrumental variables, or near-far matching approaches).

中文翻译:


评估大规模公共政策变化对健康的影响:经典方法和新颖方法

通常建议进行大规模的公共政策变革以改善公共卫生。尽管从烟草税到扶贫计划等政策差异很大,但这些政策给公共卫生研究人员带来了一个共同的困境:当不可能进行随机对照试验时,如何评估其对健康的影响。在这里,我们回顾了公共卫生研究人员的知识和经验状况,他们严格评估大规模公共政策变化对健康的影响。我们通过详细介绍政策评估的三个常见挑战的方法来组织我们的讨论:区分政策效果与健康结果的时间趋势或受政策影响和未受影响社区之间预先存在的差异(使用双重差异方法);当某项政策影响到无法立即获得匹配的比较对象的人群时,构建比较人群(使用倾向评分或综合控制方法);通过利用政策风险的准随机变化(使用不连续性回归、工具变量或远近匹配方法)来解决未观察到的混杂因素。

更新日期:2017-04-06
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