Basu Sanjay, Meghani Ankita, Siddiqi Arjumand
Centers for Health Policy, Primary Care and Outcomes Research; Center on Poverty and Inequality; and Institute for Economic Policy Research, Stanford University, Stanford, California 94305; email:
Department of Medicine, Stanford University, Stanford, California 94305; email:
Annu Rev Public Health. 2017 Mar 20;38:351-370. doi: 10.1146/annurev-publhealth-031816-044208.
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).
人们经常建议进行大规模的公共政策变革以改善公众健康。尽管这些政策千差万别——从烟草税到扶贫项目——但它们给公共卫生研究人员带来了一个共同的难题:当无法进行随机对照试验时,如何评估其对健康的影响。在此,我们回顾了那些严格评估大规模公共政策变革对健康影响的公共卫生研究人员的知识和经验状况。我们通过详细阐述应对政策评估三个常见挑战的方法来组织讨论:将政策效果与健康结果的时间趋势或政策影响社区与未受影响社区之间的既有差异区分开来(使用差分法);当一项政策影响到一个没有立即可用的匹配良好的对照人群时,构建一个对照人群(使用倾向得分或合成控制法);以及通过利用政策暴露中的准随机变化来处理未观察到的混杂因素(使用断点回归、工具变量或远近匹配法)。