Faculty Bibliography

Despite well-studied associations of state firearm laws with lower state- and county-level firearm homicide, there is a shortage of studies investigating differences in the effects of distinct state firearm law categories on various cities within the same state using identical methods. We examined associations of 5 categories of state firearm laws-pertaining to buyers, dealers, domestic violence, gun type/trafficking, and possession-with city-level firearm homicide, and then tested differential associations by city characteristics. City-level panel data on firearm homicide cases of 78 major cities from 2010 to 2020 was assessed from the Centers for Disease Control and Prevention's National Vital Statistics System. We modeled log-transformed firearm homicide rates as a function of firearm law scores, city, state, and year fixed effects, along with time-varying city-level confounders. We considered effect measure modification by poverty, unemployment, vacant housing, and income inequality. A one z-score increase in state gun type/trafficking, possession, and dealer law scores was associated with 25% (95% confidence interval [CI]:-0.37,-0.1), 19% (95% CI:-0.29,-0.07), and 17% (95% CI:-0.28, -0.4) lower firearm homicide rates, respectively. Protective associations were less pronounced in cities with high unemployment and high housing vacancy, but more pronounced in cities with high income inequality. In large US cities, state-level gun type/trafficking, possession, and dealer laws were associated with lower firearm homicide rates, but buyers and domestic violence laws were not. State firearm laws may have differential effects on firearm homicides based on city characteristics, and city-wide policies to enhance socioeconomic drivers may add benefits of firearm laws.

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).

OBJECTIVE:To demonstrate the use of the alignment method to evaluate whether surveys function similarly (i.e., have evidence of measurement invariance) across culturally diverse intersectional groups. Intersectionality theory recognizes the interconnected nature of social categories such as race, gender, ethnicity, and socioeconomic status. DATA SOURCES:A total of 30,215 American adult's responses to the eight-item Patient Health Questionnaire depression assessment scale (PHQ-8) from the 2019 National Health Interview Survey (NHIS). STUDY DESIGN:Using the alignment method, we examined the measurement invariance (equivalence) of the PHQ-8 depression assessment scale across 16 intersectional subgroups defined at the intersection of age (under 52, 52 and older), gender (male, female), race (Black, non-Black), and education (no bachelor's degree, bachelor's degree). PRINCIPAL FINDINGS:Overall, 24% of the factor loadings and 5% of the item intercepts showed evidence of differential functioning across one or more of the intersectional groups. These levels fall beneath the benchmark of 25% suggested for determining measurement invariance with the alignment method. CONCLUSIONS:The results of the alignment study suggest that the PHQ-8 functions similarly across the intersectional groups examined, despite some evidence of different factor loadings and item intercepts in some groups (i.e., noninvariance). By examining measurement invariance through an intersectional lens, researchers can investigate how a person's multiple identities and social positions possibly contribute to their response behavior on an assessment scale.

BACKGROUND:Public health experts have urged governments around the world to regulate newly legalized cannabis as they do alcohol to effectively and efficiently protect health. However, research evaluating the alignment of alcohol and cannabis policies is sparse. We assessed similarities and differences in local alcohol and cannabis control policies across California, and characterized localities adopting distinct policy approaches. METHODS:Using standard legal epidemiologic techniques, we collected and coded local alcohol and cannabis control policies relevant to public health for 12 California counties and all incorporated cities within them (N=241). We assessed whether localities were equally stringent on alcohol and cannabis policies by comparing overall restrictiveness (summed policy scores) and 9 specific provisions that applied to both substances. We captured distinct local alcohol-cannabis policy approaches using latent class analysis, and examined this classification in relation to local demographic, socioeconomic, political, and retail market characteristics. RESULTS:All 241 localities permitted alcohol sales, while 71% banned cannabis sales. Among those that did not ban cannabis sales, more stringent alcohol policy scores were associated with more stringent cannabis policy scores (linear regression coefficient: 0.16 [95% CI: 0.07, 0.25]). Local governments rarely adopted the same provisions for alcohol and cannabis (e.g., limits on hours of sale, advertising restrictions), and only two regulated the co-location of cannabis and alcohol outlets. Localities that were restrictive on alcohol yet permissive on cannabis (12%) were more urban, politically progressive, and had more low-income and racial/ethnic minority residents. Localities that were more permissive on alcohol and restrictive on cannabis (51%) were more socioeconomically advantaged. CONCLUSION/CONCLUSIONS:We found few similarities between local alcohol and cannabis control policies. California's experience suggests that, as governments around the world legalize cannabis, lessons learned from regulating alcohol are not routinely applied to cannabis, particularly in communities distinguished by high social and economic advantages.

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.

OBJECTIVE:The growing availability of cannabis products through home delivery services may affect cannabis-related health outcomes. However, research is impeded by a lack of data measuring the scale of home delivery. Prior research demonstrated that crowdsourced websites can be used to validly enumerate brick-and-mortar cannabis outlets. We piloted an extension of this method to explore the feasibility of measuring availability of cannabis home delivery. METHOD:We tested implementation of an automated algorithm designed to webscrape data from Weedmaps, the largest crowdsourced website for cannabis retail, to count the number of legal cannabis retailers offering home delivery to the geographic centroid of each Census block group in California. We compared these estimates to the number of brick-and-mortar outlets within each block group. To assess data quality, we conducted follow-up telephone interviews with a subsample of cannabis delivery retailers. RESULTS:We successfully implemented the webscraping. Of the 23,212 block groups assessed, 22,542 (97%) were served by at least one cannabis delivery business. Only 461 block groups (2%) contained one or more brick-and-mortar outlets. In interviews, availability varied dynamically as a function of staffing levels, order sizes, time of day, competition, and demand. CONCLUSIONS:Webscraping crowdsourced websites could be a viable method for quantifying rapidly evolving availability of cannabis home delivery. However, key practical and conceptual challenges must be overcome to conduct a full-scale validation and develop methodological standards. Acknowledging data limitations, cannabis home delivery appears to be nearly universal in California, whereas availability of brick-and-mortar outlets is limited, underscoring the need for research on home delivery.

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.