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Understanding how best to estimate state-level policy effects is important,and several unanswered questions remain, particularly about the ability ofstatistical models to disentangle the effects of concurrently enacted policies.In practice, many policy evaluation studies do not attempt to control foreffects of co-occurring policies, and this issue has not received extensiveattention in the methodological literature to date. In this study, we utilizedMonte Carlo simulations to assess the impact of co-occurring policies on theperformance of commonly-used statistical models in state policy evaluations.Simulation conditions varied effect sizes of the co-occurring policies andlength of time between policy enactment dates, among other factors. Outcomedata (annual state-specific opioid mortality rate per 100,000) were obtainedfrom 1999-2016 National Vital Statistics System (NVSS) Multiple Cause of Deathmortality files, thus yielding longitudinal annual state-level data over 18years from 50 states. When co-occurring policies are ignored (i.e., omittedfrom the analytic model), our results demonstrated that high relative bias(>85%) arises, particularly when policies are enacted in rapid succession.Moreover, as expected, controlling for all co-occurring policies willeffectively mitigate the threat of confounding bias; however, effect estimatesmay be relatively imprecise (i.e., larger variance) when policies are enactedin near succession. Our findings highlight several key methodological issuesregarding co-occurring policies in the context of opioid-policy research yetalso generalize more broadly to evaluation of other state-level policies, suchas policies related to firearms or COVID-19, showcasing the need to thinkcritically about co-occurring policies that are likely to influence the outcomewhen specifying analytic models.

Methodological considerations for estimating policy effects in the context of co-occurring policies