The Revolution Will Be Hard to Evaluate: How Co-Occurring Policy Changes Affect Research on the Health Effects of Social Policies. Issue 1 (8th October 2021)

• Record Type: Journal Article
• Authors: Matthay, Ellicott C; Hagan, Erin; Joshi, Spruha; Tan, May Lynn; Vlahov, David; Adler, Nancy; Glymour, M Maria
• Is Part Of: Epidemiologic reviews. Volume 43:Issue 1(2021)
• Page Start: 19
• Page End: 32
• Publication Date: 2021-10-08
• Subjects: epidemiologic methods -- policy analysis -- population health -- public policy -- research design -- social determinants; Epidemiology -- Periodicals; 614.405
• DOI: 10.1093/epirev/mxab009
• Languages: English
• ISSNs: 0193-936X

Abstract

Abstract: Extensive empirical health research leverages variation in the timing and location of policy changes as quasi-experiments. Multiple social policies may be adopted simultaneously in the same locations, creating co-occurrence that must be addressed analytically for valid inferences. The pervasiveness and consequences of co-occurring policies have received limited attention. We analyzed a systematic sample of 13 social policy databases covering diverse domains including poverty, paid family leave, and tobacco use. We quantified policy co-occurrence in each database as the fraction of variation in each policy measure across different jurisdictions and times that could be explained by covariation with other policies. We used simulations to estimate the ratio of the variance of effect estimates under the observed policy co-occurrence to variance if policies were independent. Policy co-occurrence ranged from very high for state-level cannabis policies to low for country-level sexual minority-rights policies. For 65% of policies, greater than 90% of the place-time variation was explained by other policies. Policy co-occurrence increased the variance of effect estimates by a median of 57-fold. Co-occurring policies are common and pose a major methodological challenge to rigorously evaluating health effects of individual social policies. When uncontrolled, co-occurring policies confound one another, and when controlled, resulting positivity violations may substantially inflate the variance of estimated effects. Tools to enhance validity and precision for evaluating co-occurring policies are needed.