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

Understanding how best to estimate state-level policy effects is important, and several unanswered questions remain, particularly about the ability of statistical models to disentangle the effects of concurrently enacted policies. In practice, many policy evaluation studies do not attempt to control for effects of co-occurring policies, and this issue has not received extensive attention in the methodological literature to date. In this study, we utilized Monte Carlo simulations to assess the impact of co-occurring policies on the performance of commonly-used statistical models in state policy evaluations. Simulation conditions varied effect sizes of the co-occurring policies and length of time between policy enactment dates, among other factors. Outcome data (annual state-specific opioid mortality rate per 100,000) were obtained from 1999 to 2016 National Vital Statistics System (NVSS) Multiple Cause of Death mortality files, thus yielding longitudinal annual state-level data over 18 years from 50 states. When co-occurring policies are ignored (i.e., omitted from the analytic model), our results demonstrated that high relative bias (> 82%) arises, particularly when policies are enacted in rapid succession. Moreover, as expected, controlling for all co-occurring policies will effectively mitigate the threat of confounding bias; however, effect estimates may be relatively imprecise (i.e., larger variance) when policies are enacted in near succession. Our findings highlight several key methodological issues regarding co-occurring policies in the context of opioid-policy research yet also generalize more broadly to evaluation of other state-level policies, such as policies related to firearms or COVID-19, showcasing the need to think critically about co-occurring policies that are likely to influence the outcome when specifying analytic models.

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

Understanding how best to estimate state-level policy effects is important, and several unanswered questions remain, particularly about the ability of statistical models to disentangle the effects of concurrently enacted policies. In practice, many policy evaluation studies do not attempt to control for effects of co-occurring policies, and this issue has not received extensive attention in the methodological literature to date. In this study, we utilized Monte Carlo simulations to assess the impact of co-occurring policies on the performance of commonly-used statistical models in state policy evaluations. Simulation conditions varied effect sizes of the co-occurring policies and length of time between policy enactment dates, among other factors. Outcome data (annual state-specific opioid mortality rate per 100,000) were obtained from 1999 to 2016 National Vital Statistics System (NVSS) Multiple Cause of Death mortality files, thus yielding longitudinal annual state-level data over 18 years from 50 states. When co-occurring policies are ignored (i.e., omitted from the analytic model), our results demonstrated that high relative bias (> 82%) arises, particularly when policies are enacted in rapid succession. Moreover, as expected, controlling for all co-occurring policies will effectively mitigate the threat of confounding bias;however, effect estimates may be relatively imprecise (i.e., larger variance) when policies are enacted in near succession. Our findings highlight several key methodological issues regarding co-occurring policies in the context of opioid-policy research yet also generalize more broadly to evaluation of other state-level policies, such as policies related to firearms or COVID-19, showcasing the need to think critically about co-occurring policies that are likely to influence the outcome when specifying analytic models.

Factors Associated With Parental COVID-19 Vaccination Acceptance.

As the coronavirus pandemic continues to impact families and children, understanding parental attitudes and likely acceptance of the COVID-19 vaccine is essential. We conducted a statewide survey with a representative sample of parents in Tennessee focused on COVID-19 and influenza vaccine acceptance and perspectives. Data from 1066 parents were analyzed using weighted survey methods to generalize results to the state of Tennessee. About 53% of parents reported a likelihood to vaccinate their children against COVID-19, and 45% were likely to vaccinate their child against COVID-19 and influenza. Female parents were less likely to vaccinate their children against COVID-19, but the strongest predictor of likely COVID-19 vaccine acceptance was influenza vaccine acceptance (adjusted odds ratio = 5.46; 95% confidence interval: 3.20-9.30). Parental acceptance of COVID-19 vaccines for children is closely tied to influenza vaccine acceptance. Public health approaches to maximize vaccine uptake could focus on children who have not been receiving influenza vaccines.

Well-being of Parents and Children During the COVID-19 Pandemic: A National Survey.

BACKGROUND: As the coronavirus disease pandemic spread across the United States and protective measures to mitigate its impact were enacted, parents and children experienced widespread disruptions in daily life. Our objective with this national survey was to determine how the pandemic and mitigation efforts affected the physical and emotional well-being of parents and children in the United States through early June 2020. METHODS: In June 2020, we conducted a national survey of parents with children age <18 to measure changes in health status, insurance status, food security, use of public food assistance resources, child care, and use of health care services since the pandemic began. RESULTS: Since March 2020, 27% of parents reported worsening mental health for themselves, and 14% reported worsening behavioral health for their children. The proportion of families with moderate or severe food insecurity increased from 6% before March 2020 to 8% after, employer-sponsored insurance coverage of children decreased from 63% to 60%, and 24% of parents reported a loss of regular child care. Worsening mental health for parents occurred alongside worsening behavioral health for children in nearly 1 in 10 families, among whom 48% reported loss of regular child care, 16% reported change in insurance status, and 11% reported worsening food security. CONCLUSIONS: The coronavirus disease pandemic has had a substantial tandem impact on parents and children in the United States. As policy makers consider additional measures to mitigate the health and economic effects of the pandemic, they should consider the unique needs of families with children.