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

Objective. Understanding how best to estimate state-level policy effects is important, and several unanswered questions remain, particularly about optimal methods for disentangling the effects of concurrently implemented policies. In this paper, we examined the impact of co-occurring policies on the performance of commonly used models in state policy evaluations. Data Sources. Outcome of interest (annual state-specific opioid mortality rate per 100,000) was obtained from 1999-2016 National Vital Statistics System (NVSS) Multiple Cause of Death mortality files. Study Design. We utilized Monte Carlo simulations to assess the effect of concurrent policy enactment on the evaluation of state-level policies. Simulation conditions varied effect sizes of the co-occurring policies as well as the length of time between enactment dates of the co-occurring policies, among other factors. Data Collection. Longitudinal annual state-level data over 18 years from 50 states. Principal Findings. Our results demonstrated high relative bias (>85%) will arise when confounding co-occurring policies are omitted from the analytic model and the co-occuring policies are enacted in rapid succession. Moreover, our findings indicated that controlling for all co-occurring policies will effectively mitigate the threat of confounding bias; however, effect estimates may be relatively imprecise, with larger variance estimates when co-occurring policies were enacted in near succession of each other. We also found that the required length of time between co-occurring policies necessary to obtain robust policy estimates varied across model specifications, being generally shorter for autoregressive (AR) models compared to difference-in-differences (DID) models.