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Evaluating the Health Impact of Large-Scale Public Policy Changes: Classical and Novel Approaches

机译:评估大规模公共政策变化的健康影响:古典和新的方法

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摘要

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. Weorganize 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).
机译:通常建议改善公共卫生的大规模公共政策变化。尽管从贫困方案中广泛变化,但这些政策对公共卫生研究人员呈现出常见的困境:如何在不可能进行随机对照试验时评估其健康效果。在这里,我们审查了公共卫生研究人员的知识和经验状况,严格评估大规模公共政策变化的健康后果。通过详细说明涉及进行政策评估的三个共同挑战的方法来拓展我们的讨论:区分政策趋势的政策效应或政策影响和 - 不受关区社区之间的差异(使用差异差异方法);当政策影响一个匹配良好的比较者的人口时,构建比较种群(使用倾向评分或合成控制方法);通过利用策略曝光中的准随机变化(使用回归不连续,仪器变量或近距离匹配方法)来解决未观察的混乱。

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