Hu Yannan, van Lenthe Frank J, Hoffmann Rasmus, van Hedel Karen, Mackenbach Johan P
Erasmus MC, P.O. Box 2040, Rotterdam, 3000 CA, The Netherlands.
European University Institute, Florence, Italy.
BMC Med Res Methodol. 2017 Apr 20;17(1):68. doi: 10.1186/s12874-017-0317-5.
The scientific evidence-base for policies to tackle health inequalities is limited. Natural policy experiments (NPE) have drawn increasing attention as a means to evaluating the effects of policies on health. Several analytical methods can be used to evaluate the outcomes of NPEs in terms of average population health, but it is unclear whether they can also be used to assess the outcomes of NPEs in terms of health inequalities. The aim of this study therefore was to assess whether, and to demonstrate how, a number of commonly used analytical methods for the evaluation of NPEs can be applied to quantify the effect of policies on health inequalities.
We identified seven quantitative analytical methods for the evaluation of NPEs: regression adjustment, propensity score matching, difference-in-differences analysis, fixed effects analysis, instrumental variable analysis, regression discontinuity and interrupted time-series. We assessed whether these methods can be used to quantify the effect of policies on the magnitude of health inequalities either by conducting a stratified analysis or by including an interaction term, and illustrated both approaches in a fictitious numerical example.
All seven methods can be used to quantify the equity impact of policies on absolute and relative inequalities in health by conducting an analysis stratified by socioeconomic position, and all but one (propensity score matching) can be used to quantify equity impacts by inclusion of an interaction term between socioeconomic position and policy exposure.
Methods commonly used in economics and econometrics for the evaluation of NPEs can also be applied to assess the equity impact of policies, and our illustrations provide guidance on how to do this appropriately. The low external validity of results from instrumental variable analysis and regression discontinuity makes these methods less desirable for assessing policy effects on population-level health inequalities. Increased use of the methods in social epidemiology will help to build an evidence base to support policy making in the area of health inequalities.
应对健康不平等问题的政策的科学证据基础有限。自然政策实验（NPE）作为评估政策对健康影响的一种手段，越来越受到关注。有几种分析方法可用于从总体人群健康的角度评估NPE的结果，但尚不清楚它们是否也可用于从健康不平等的角度评估NPE的结果。因此，本研究的目的是评估一些常用的NPE评估分析方法是否能够以及如何用于量化政策对健康不平等的影响。
我们确定了七种评估NPE的定量分析方法：回归调整、倾向得分匹配、差异分析、固定效应分析、工具变量分析、断点回归和中断时间序列分析。我们评估了这些方法是否可通过进行分层分析或纳入交互项来量化政策对健康不平等程度的影响，并在一个虚拟数值示例中展示了这两种方法。
通过按社会经济地位进行分层分析，所有七种方法均可用于量化政策对健康方面绝对和相对不平等的公平影响，除一种方法（倾向得分匹配）外，其他所有方法均可通过纳入社会经济地位与政策暴露之间的交互项来量化公平影响。
经济学和计量经济学中常用的评估NPE的方法也可用于评估政策的公平影响，我们的示例为如何恰当地做到这一点提供了指导。工具变量分析和断点回归结果的外部有效性较低，使得这些方法不太适合评估政策对人群层面健康不平等的影响。在社会流行病学中更多地使用这些方法将有助于建立一个证据基础，以支持健康不平等领域的政策制定。
