Faculty Bibliography

Substantial heterogeneity in effects of social policies on health across subgroups may be common, but has not been systematically characterized. Using a sample of 55 contemporary studies on health effects of social policies, we recorded how often heterogeneous treatment effects (HTEs) were assessed, for what subgroups (e.g., male, female), and the subgroup-specific effect estimates expressed as Standardized Mean Differences (SMDs). For each study, outcome, and dimension (e.g., gender), we fit a random-effects meta-analysis. We characterized the magnitude of heterogeneity in policy effects using the standard deviation of the subgroup-specific effect estimates (Ï„). Among the 44% of studies reporting subgroup-specific estimates, policy effects were generally small (<0.1 SMDs) with mixed impacts on health (67% beneficial) and disparities (50% implied narrowing of disparities). Across study-outcome-dimensions, 54% indicated any heterogeneity in effects, and 20% had Ï„ > 0.1 SMDs. For 26% of study-outcome-dimensions, the magnitude of Ï„ indicated that effects of opposite signs were plausible across subgroups. Heterogeneity was more common in policy effects not specified a priori. Our findings suggest social policies commonly have heterogeneous effects on health of different populations; these HTEs may substantially impact disparities. Studies of social policies and health should routinely evaluate HTEs.

IMPORTANCE/UNASSIGNED:Psychosis is a hypothesized consequence of cannabis use. Legalization of cannabis could therefore be associated with an increase in rates of health care utilization for psychosis. OBJECTIVE/UNASSIGNED:To evaluate the association of state medical and recreational cannabis laws and commercialization with rates of psychosis-related health care utilization. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Retrospective cohort design using state-level panel fixed effects to model within-state changes in monthly rates of psychosis-related health care claims as a function of state cannabis policy level, adjusting for time-varying state-level characteristics and state, year, and month fixed effects. Commercial and Medicare Advantage claims data for beneficiaries aged 16 years and older in all 50 US states and the District of Columbia, 2003 to 2017 were used. Data were analyzed from April 2021 to October 2022. EXPOSURE/UNASSIGNED:State cannabis legalization policies were measured for each state and month based on law type (medical or recreational) and degree of commercialization (presence or absence of retail outlets). MAIN OUTCOMES AND MEASURES/UNASSIGNED:Outcomes were rates of psychosis-related diagnoses and prescribed antipsychotics. RESULTS/UNASSIGNED:This study included 63 680 589 beneficiaries followed for 2 015 189 706 person-months. Women accounted for 51.8% of follow-up time with the majority of person-months recorded for those aged 65 years and older (77.3%) and among White beneficiaries (64.6%). Results from fully-adjusted models showed that, compared with no legalization policy, states with legalization policies experienced no statistically significant increase in rates of psychosis-related diagnoses (medical, no retail outlets: rate ratio [RR], 1.13; 95% CI, 0.97-1.36; medical, retail outlets: RR, 1.24; 95% CI, 0.96-1.61; recreational, no retail outlets: RR, 1.38; 95% CI, 0.93-2.04; recreational, retail outlets: RR, 1.39; 95% CI, 0.98-1.97) or prescribed antipsychotics (medical, no retail outlets RR, 1.00; 95% CI, 0.88-1.13; medical, retail outlets: RR, 1.01; 95% CI, 0.87-1.19; recreational, no retail outlets: RR, 1.13; 95% CI, 0.84-1.51; recreational, retail outlets: RR, 1.14; 95% CI, 0.89-1.45). In exploratory secondary analyses, rates of psychosis-related diagnoses increased significantly among men, people aged 55 to 64 years, and Asian beneficiaries in states with recreational policies compared with no policy. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this retrospective cohort study of commercial and Medicare Advantage claims data, state medical and recreational cannabis policies were not associated with a statistically significant increase in rates of psychosis-related health outcomes. As states continue to introduce new cannabis policies, continued evaluation of psychosis as a potential consequence of state cannabis legalization may be informative.

INTRODUCTION/BACKGROUND:Understanding educational patterns in excess mortality during the coronavirus disease 2019 (COVID-19) pandemic may help to identify strategies to reduce disparities. It is unclear whether educational inequalities in COVID-19 mortality have persisted throughout the pandemic, spanned the full range of educational attainment, or varied by other demographic indicators of COVID-19 risks, such as age or occupation. METHODS:This study analyzed individual-level California Department of Public Health data on deaths occurring between January 2016 and February 2021 among individuals aged ≥25 years (1,502,202 deaths). Authors applied ARIMA (autoregressive integrated moving average) models to subgroups defined by the highest level of education and other demographics (age, sex, race/ethnicity, U.S. nativity, occupational sector, and urbanicity). Authors estimated excess deaths (the number of observed deaths minus the number of deaths expected to occur under the counterfactual of no pandemic) and excess deaths per 100,000 individuals. RESULTS:Educational inequalities in excess mortality emerged early in the pandemic and persisted throughout the first year. The greatest per-capita excess occurred among people without high-school diplomas (533 excess deaths/100,000), followed by those with a high-school diploma but no college (466/100,000), some college (156/100,000), and bachelor's degrees (120/100,000), and smallest among people with graduate/professional degrees (101/100,000). Educational inequalities occurred within every subgroup examined. For example, per-capita excess mortality among Latinos with no college experience was 3.7 times higher than among Latinos with at least some college experience. CONCLUSIONS:Pervasive educational inequalities in excess mortality during the pandemic suggest multiple potential intervention points to reduce disparities.

OBJECTIVE:Researchers are often interested in assessing the causal effect of an exposure on an outcome when randomization is not ethical or feasible. Estimating causal effects by controlling for confounders can be unconvincing because important potential confounders remain unmeasured. Study designs leveraging instrumental variables (IVs) offer alternatives to confounder-control methods but are rarely used in stress and trauma research. METHOD/METHODS:We review the conceptual foundations and implementation of IV methods. We discuss strengths and limitations of IV approaches, contrasting with confounder-control methods, and illustrate the relevance of IVs for stress and trauma research. RESULTS:IV approaches leverage an external or exogenous source of variation in the exposure. Instruments are variables that meet three conditions: relevance (variation in the IV is associated with variation in the chance of exposure), exclusion (the IV only affects the outcome through the exposure), and exchangeability (no unmeasured confounding of the IV-outcome relationship). Interpreting estimates from IV analyses requires an additional assumption, such as monotonicity (the instrument does not change the chance of exposure in different directions for any two individuals). Valid IVs circumvent the need to correctly identify, measure, and control for all confounders of the exposure-outcome relationship. The primary challenge is identifying a valid instrument. CONCLUSIONS:IV approaches have strengths and weaknesses compared with confounder-control approaches. IVs offers a promising complementary study design to improve evidence about the causal effects of exposures on outcomes relevant to stress and trauma. Collaboration with scientists who are experienced with identifying and analyzing IVs will support this work. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

BACKGROUND:Cannabis outlets may affect health and health disparities. Local governments can regulate outlets, but little is known about the effectiveness of local policies in limiting outlet densities and discouraging disproportionate placement of outlets in vulnerable neighborhoods. METHODS:For 241 localities in California, we measured seven policies pertaining to density or location of recreational cannabis outlets. We geocoded outlets using web-scraped data from the online finder Weedmaps between 2018 and 2020. We applied Bayesian spatiotemporal models to evaluate associations of local cannabis policies with Census block group-level outlet counts, accounting for confounders and spatial autocorrelation. We assessed whether associations differed by block group median income or racial-ethnic composition. RESULTS:Seventy-six percent of localities banned recreational cannabis outlets. Bans were associated with fewer outlets, particularly in block groups with higher median income, fewer Hispanic residents, and more White and Asian residents. Outlets were disproportionately located in block groups with lower median income [posterior RR (95% credible interval): 0.76 (0.70, 0.82) per $10,000], more Hispanic residents [1.05 (1.02, 1.09) per 5%], and fewer Black residents [0.91 (0.83, 0.98) per 5%]. For the six policies in jurisdictions permitting outlets, two policies were associated with fewer outlets and two with more; two policy associations were uninformative. For these policies, we observed no consistent heterogeneity in associations by median income or racial-ethnic composition. CONCLUSIONS:Some local cannabis policies in California are associated with lower cannabis outlet densities, but are unlikely to deter disproportionate placement of outlets in racial-ethnic minority and low-income neighborhoods.

Violations of the positivity assumption (also called the common support condition) challenge health policy research and can result in significant bias, large variance, and invalid inference. We define positivity in the single- and multiple-timepoint (i.e., longitudinal) health policy evaluation setting, and discuss real-world threats to positivity. We show empirical evidence of the practical positivity violations that can result when attempting to estimate the effects of health policies (in this case, Naloxone Access Laws). In such scenarios, an alternative is to estimate the effect of a shift in law enactment (e.g., the effect if enactment had been delayed by some number of years). Such an effect corresponds to what is called a modified treatment policy, and dramatically weakens the required positivity assumption, thereby offering a means to estimate policy effects even in scenarios with serious positivity problems. We apply the approach to define and estimate the longitudinal effects of Naloxone Access Laws on opioid overdose rates.

Background/UNASSIGNED:The coronavirus disease 2019 (COVID-19) pandemic is co-occurring with a drug addiction and overdose crisis. Methods/UNASSIGNED:We fit overdispersed Poisson models, accounting for seasonality and secular trends, to estimate the excess fatal drug overdoses (i.e., deaths greater than expected), using data on all deaths in California from 2016 to 2020. Findings/UNASSIGNED:Between January 5, 2020 and December 26, 2020, there were 8605 fatal drug overdoses-a 44% increase over the same period one year prior. We estimated 2084 (95% CI: 1925 to 2243) fatal drug overdoses were excess deaths, representing 5·28 (4·88 to 5·68) excess fatal drug overdoses per 100,000 population. Excess fatal drug overdoses were driven by opioids (4·48 [95% CI: 4·18 to 4·77] per 100,000), especially synthetic opioids (2·85 [95% CI: 2·56 to 3·13] per 100,000). The non-Hispanic Black and Other non-Hispanic populations were disproportionately affected with 10·1 (95% CI: 7·6 to 12·5) and 13·26 (95% CI: 11·0 to 15·5) excess fatal drug overdoses per 100,000 population, respectively, compared to 5·99 (95% CI: 5.2 to 6.8) per 100,000 population in the non-Hispanic white population. There was a steep, nonlinear educational gradient with the highest rate among those with only a high school degree. There was a strong spatial patterning with the highest levels of excess mortality in the southernmost region and consistently lower levels at progressively more northern latitudes (7·73 vs 1·96 per 100,000). Interpretation/UNASSIGNED:Fatal drug overdoses disproportionately increased in 2020 among structurally marginalized populations and showed a strong geographic gradient. Local, tailored public health interventions are urgently needed to reduce growing inequities in overdose deaths. Funding/UNASSIGNED:US National Institutes of Health and Department of Veterans Affairs.

PURPOSE/OBJECTIVE:Social policies are important determinants of population health but may have varying effects on subgroups of people. Evaluating heterogeneous treatment effects (HTEs) of social policies is critical to determine how social policies will affect health inequities. Methods for evaluating HTEs are not standardized. Little is known about how often and by what methods HTEs are assessed in social policy and health research. METHODS:A sample of 55 articles from 2019 on the health effects of social policies were evaluated for frequency of reporting HTEs; for what subgroupings HTEs were reported; frequency of a priori specification of intent to assess HTEs; and methods used for assessing HTEs. RESULTS:A total of 24 (44%) studies described some form of HTE assessment, including by age, gender, education, race/ethnicity, and/or geography. Among studies assessing HTEs, 63% specified HTE assessment a priori, and most (71%) used descriptive methods such as stratification; 21% used statistical tests (e.g., interaction terms in a regression); and no studies used data-driven algorithms. CONCLUSIONS:Although understanding HTEs could enhance policy and practice-based efforts to reduce inequities, it is not routine research practice. Increased evaluation of HTEs across relevant subgroups is needed.