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Abstract BackgroundAdolescents in foster care report high rates of mental health needs, yet intervention access remains limited. Substance use commonly co-occurs with mental health symptoms, but availability of substance use services for foster youth is even more scant than mental health services. Technology has advanced access to behavioral health care across the lifespan, but only for certain sectors of the population. Little research focuses on leveraging technology to advance access for foster youth. We report open trial findings, as a precursor to launching a large-scale implementation science trial, on how a U.S. nationwide serving support system for foster youth, Court Appointed Special Advocates (CASA), might be leveraged to expand access to substance use prevention resources via the FostrSpace app. FostrSpace provides asynchronous resources and synchronous navigator, peer support, and direct clinical intervention. A concurrent 6-session ECHO® substance use prevention telementoring curriculum was co-developed as a FostrSpace implementation strategy with a 6-member CASA Advisory Board. MethodsSeven youth-CASA dyads enrolled in the open trial. We used a mixed-methods design (quantitative assessment and qualitative exit interviews) to assess feasibility and acceptability of ECHO® sessions (CASA-only) and the usability of the FostrSpace app (youth-only). ResultsSix of seven of the youth accessed the app at least once, but a majority reported the app log-in process was burdensome and unappealing, thereby limiting them from frequently using the app. All youth rated the app features, design and content as appealing, helpful and relevant. ECHO® -FostrSpace session attendance was high (most attended 5 or more sessions) and CASAs found the content highly engaging and useful, especially regarding CASA-youth substance use communication skills. ConclusionsTechnological barriers, such as log-in burden, can prevent youth in need from accessing relevant services and must be regularly assessed and resolved. Substance use education and skills-building for CASAs is novel and a viable implementation strategy to increase foster youth access to digital behavioral health services innovations. Substance use prevention content should be integrated within discussions on youth mental health and trauma to be most engaging and relevant. Findings are informing the subsequent hybrid implementation-effectiveness trial design of FostrSpace with 400 youth-CASA dyads across 10 CASA programs in California. Trial registrationRetrospectively registered. 

Abstract ONEAudit provides more efficient risk-limiting audits than other extant methods when the voting system cannot report a cast-vote record linked to each cast card. It obviates the need for re-scanning; it is simpler and more efficient than ‘hybrid’ audits; and it is far more efficient than batch-level comparison audits. There may be room to improve the efficiency of ONEAudit further by tuning the statistical tests it uses and by using stratified sampling. We show that tuning the tests by optimizing for the reported batch-level tallies or integrating over a distribution reduces expected workloads by 70–85% compared to the current ONEAudit implementation across a range of simulated elections. The improved tests reduce the expected workload to audit the 2024 Mayoral race in San Francisco, California, by half—from about 200 cards to about 100 cards. In contrast, stratified sampling does not help: it increases workloads by about 25% on average. 

ImportanceAbortion bans may lead to births among those who are unable to overcome barriers to abortion. The population-level effects of these policies, particularly their unequal impacts across subpopulations in the US, remain unclear. ObjectiveTo assess heterogeneity in the association of abortion bans with changes in fertility in the US, within and across states. Design, Setting, and ParticipantsDrawing from birth certificate and US Census Bureau data from 2012 through 2023 for all 50 states and the District of Columbia, this study used a bayesian panel data model to evaluate state-by-subgroup-specific changes in fertility associated with complete or 6-week abortion bans in 14 US states. The average percent and absolute change in the fertility rate among females aged 15 through 44 years was estimated overall and by state, and within and across states by age, race and ethnicity, marital status, education, and insurance payer. ExposureComplete or 6-week abortion ban. Main outcome and MeasuresFertility rate (births per 1000 reproductive-aged females) overall and by subgroups. ResultsThere were an estimated 1.01 (95% credible interval [CrI], 0.45-1.64) additional births above expectation per 1000 females aged 15 through 44 years (reproductive age) in states following adoption of abortion bans (60.55 observed vs 59.54 expected; 1.70% increase; 95% CrI, 0.75%-2.78%), equivalent to 22 180 excess births, with evidence of variation by state and subgroup. Estimated differences above expectation were largest for racially minoritized individuals (≈2.0%), unmarried individuals (1.79%), individuals younger than 35 years (≈2.0%), Medicaid beneficiaries (2.41%), and those without college degrees (high school diploma, 2.36%; some college, 1.58%), particularly in southern states. Differences in race and ethnicity and education across states explain most of the variability in the state-level association between abortion bans and fertility rates. Conclusion and RelevanceThese findings provide evidence that fertility rates in states with abortion bans were higher than would have been expected in the absence of these policies, with the largest estimated differences among subpopulations experiencing the greatest structural disadvantages and in states with among the worst maternal and child health and well-being outcomes. 

Abstract When there are multiple outcome series of interest, Synthetic Control analyses typically proceed by estimating separate weights for each outcome. In this paper, we instead propose estimating a common set of weights across outcomes, by balancing either a vector of all outcomes or an index or average of them. Under a low-rank factor model, we show that these approaches lead to lower bias bounds than separate weights, and that averaging leads to further gains when the number of outcomes grows. We illustrate this via a re-analysis of the impact of the Flint water crisis on educational outcomes. 

Abstract Sociologists use the concept of narrative as an analytical tool and theoretical concept to understand the stories that people tell and their role in social and cultural life. A key tenet of prior research on narratives is their capacity to shape the audience’s understanding and evaluation of the narrator. In this mixed-method study, we investigate the role of narratives in destigmatization through the case of criminal record stigma in the labor market. Based on evidence from a survey experiment in which people with managerial experience were randomly assigned to job applicants with different narratives, we show that evaluations differ across reentry narratives. Drawing on prior theorizations and qualitative interviews with employers, we identify and describe three processes through which narratives impact evaluation and destigmatization: moral justification, social affinity signaling, and information salience. 

Abstract We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights—weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients that combine those of the original outcome model with those from unpenalized ordinary least-squares (OLS). Under certain choices of regularization parameters, the augmented estimator in fact collapses to the OLS estimator alone. We then extend these results to specific outcome and weighting models. We first show that the augmented estimator that uses (kernel) ridge regression for both outcome and weighting models is equivalent to a single, undersmoothed (kernel) ridge regression—implying a novel analysis of undersmoothing. When the weighting model is instead lasso-penalized, we demonstrate a familiar ‘double selection’ property. Our framework opens the black box on this increasingly popular class of estimators, bridges the gap between existing results on the semiparametric efficiency of undersmoothed and doubly robust estimators, and provides new insights into the performance of augmented balancing weights. 

ObjectivesThis study aimed to examine concurrent polysubstance use of alcohol, tobacco and marijuana and determine correlations with access to marijuana, friend and familial drug use habits, risk perception and the age of initiation associated with the singular use of these substances. DesignA secondary data analysis. SettingUsed the Jamaica National Drug Prevalence Survey 2016 dataset. ParticipantsInvolved the entire dataset comprising 4623 randomly selected respondents between 12 and 65 years old. Outcome measuresPrimary outcome: concurrent polysubstance use recorded as using two or more of alcohol, tobacco and marijuana. Predictor variables include risk perception and age of initiation of singular alcohol, tobacco and marijuana use, ease of marijuana access and family and friend alcohol and illegal drug use. ResultsApproximately 58%–66% of respondents commenced singular alcohol, tobacco or marijuana use under 17. Participants commencing marijuana use at 11 years and under and between 12 and 17 were 3.346 and 4.560 times more likely to report past month concurrent polysubstance use (p=0.030 and p<0.001). Respondents who did not believe that smoking tobacco sometimes (p=0.049), and smoking marijuana sometimes and often was harmful, had increased odds of concurrent polysubstance use (p=0.047 and p<0.001, respectively). Respondents who indicated access to marijuana as easy were significantly more likely to report past month concurrent polysubstance use compared with those who reported access as difficult (p=0.002). Participants who indicated that friends or family members get drunk and take illegal drugs were associated with 1.722 and 1.864 increased odds of reporting past month concurrent polysubstance use (p=0.004 and p=0.017, respectively). ConclusionsDecreased perceived risk, childhood and adolescent age of initiation and easy access to marijuana were significantly associated with polysubstance use among Jamaicans. The influence of friends and family members’ drug and alcohol use behaviours on individuals developing polysubstance use habits further endorses the need for interventions. 

Abstract Research documents that Black patients experience worse general surgery outcomes than White patients in the U.S. In this paper, we focus on an important but less-examined category: the surgical treatment of emergency general surgery (EGS) conditions, which refers to medical emergencies where the injury is internal, such as a burst appendix. Our goal is to assess racial disparities in outcomes after EGS treatment using administrative data. We also seek to understand the extent to which differences are attributable to patient-level risk factors vs. hospital-level factors, as well as to the decision to operate on EGS patients. To do so, we develop a class of linear weighting estimators that reweight White patients to have a similar distribution of baseline characteristics to Black patients. This framework nests many common approaches, including matching and linear regression, but offers important advantages over these methods in terms of controlling imbalance between groups, minimizing extrapolation, and reducing computation time. Applying this approach to the claims data, we find that disparities estimates that adjust for the admitting hospital are substantially smaller than estimates that adjust for patient baseline characteristics only, suggesting that hospital-specific factors are important drivers of racial disparities in EGS outcomes. 

Abstract This paper explores the intersection of two major trends in the United States over the last forty years: a substantial investment in local law enforcement and the diversification of suburbia. While previous research on police spending has focused almost exclusively on large central cities, this study broadens this perspective to assess how these dynamics play out in outer-ring suburbs. I construct a unique panel dataset of over 200 California municipalities and find that the drivers of police spending vary across the metropolis in significant ways. Fixed-effects models that control for unobserved heterogeneity across place suggest that suburbs with growing shares of renters spend more on police. Elaborating on the concept of renter threat, I show how increases in renter households are associated with increases in police expenditures across a range of model specifications in suburbia. I point to suburban homeowner concerns about crime and property values as well as the history of racial exclusion in suburbia that is often couched in economic terms as potential explanations for these findings. Results point to the enduring role of police as a contemporary mechanism of both social control and inequality in California suburbs.