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Properly planned and conducted randomized clinical trials remain susceptible to a lack of external validity. The authors illustrate a model-based method to standardize observed trial results to a specified target population using a seminal human immunodeficiency virus (HIV) treatment trial, and they provide Monte Carlo simulation evidence supporting the method. The example trial enrolled 1,156 HIV-infected adult men and women in the United States in 1996, randomly assigned 577 to a highly active antiretroviral therapy and 579 to a largely ineffective combination therapy, and followed participants for 52 weeks. The target population was US people infected with HIV in 2006, as estimated by the Centers for Disease Control and Prevention. Results from the trial apply, albeit muted by 12%, to the target population, under the assumption that the authors have measured and correctly modeled the determinants of selection that reflect heterogeneity in the treatment effect. In simulations with a heterogeneous treatment effect, a conventional intent-to-treat estimate was biased with poor confidence limit coverage, but the proposed estimate was largely unbiased with appropriate confidence limit coverage. The proposed method standardizes observed trial results to a specified target population and thereby provides information regarding the generalizability of trial results. [ABSTRACT FROM AUTHOR]

Multiple imputation is an effective method for dealing with missing data, and it is becoming increasingly common in many fields. However, the method is still relatively rarely used in epidemiology, perhaps in part because relatively few studies have looked at practical questions about how to implement multiple imputation in large data sets used for diverse purposes. This paper addresses this gap by focusing on the practicalities and diagnostics for multiple imputation in large data sets. It primarily discusses the method of multiple imputation by chained equations, which iterates through the data, imputing one variable at a time conditional on the others. Illustrative data were derived from 9,186 youths participating in the national evaluation of the Community Mental Health Services for Children and Their Families Program, a US federally funded program designed to develop and enhance community-based systems of care to meet the needs of children with serious emotional disturbances and their families. Multiple imputation was used to ensure that data analysis samples reflect the full population of youth participating in this program. This case study provides an illustration to assist researchers in implementing multiple imputation in their own data. [ABSTRACT FROM AUTHOR]

This study uses Texas's 2017 integration of the state disability and mental health agencies as a case study, combining interviews with Texas agency and advocacy organization leaders to examine perceptions of agency integration and augmented synthetic control analyses of 2014-2020 Medical Expenditure Panel Survey to examine impacts on mental health service use among individuals with co-occurring cognitive disabilities (including intellectual and developmental disabilities) and mental health conditions. Interviewees described the intensive process of agency integration and identified primarily positive (e.g., decreased administrative burden) impacts of integration. Quantitative analyses indicated no effects of integration on receipt of mental health-related services among people with co-occurring conditions. While leaders identified some potentially beneficial impacts of state agency integration, the limited impact of integration beyond the agency suggests that interventions at multiple levels of the service system, including those targeting providers, are needed to better meet the mental health service needs for this population., Competing Interests: Declarations. Competing interests: Authors have no conflicts of interest to report., (© 2024. The Author(s).)

Objective: Intimate partner violence (IPV) affects an estimated 47% of women living in the USA in their lifetime and is associated with increased risk of physical and mental health concerns. Current prevention efforts focus on individual and family-level interventions rather than macrosystem-level policies. Thus, we sought to test the effects of Medicaid expansion on the rates of IPV and violence more broadly., Methods: Present analyses use retrospective longitudinal data from the National Crime Victimization Survey (NCVS). State level rates of total violence and IPV were measured per 1000 population from the NCVS for years 2008-2018 as 3-year averages for each state. A two-way fixed-effects difference-in-differences model was fit to evaluate differences in the change in violence outcomes pre-2014/post-2014 in Medicaid expansion states versus non-expansion states., Results: Comparison states had a significantly higher proportion of residents who were black, living below the federal poverty level and with lower educational attainment. Before Medicaid expansion, comparison states had a significantly lower mean rate of total violence and IPV per 1000 population. In two-way fixed effects difference-in-differences models, there was no statistically significant association between Medicaid expansion and IPV or total violence., Discussion: Despite null findings, our study adds to the evidence base evaluating the impacts of macro-level policies on different forms of violence. The pathways by which Medicaid expansion could contribute to violence reduction are multifaceted with numerous mediators and those pathways may not be sufficiently strong to generate impacts. Additional work is warranted to further probe Medicaid expansion's impact on violence prevention., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Comparisons between randomized trial analyses and observational analyses that attempt to address similar research questions have generated many controversies in epidemiology and the social sciences. There has been little consensus on when such comparisons are reasonable, what their implications are for the validity of observational analyses, or whether trial and observational analyses can be integrated to address effectiveness questions. Here, we consider methods for using observational analyses to complement trial analyses when assessing treatment effectiveness. First, we review the framework for designing observational analyses that emulate target trials and present an evidence map of its recent applications. We then review approaches for estimating the average treatment effect in the target population underlying the emulation, using observational analyses of the emulation data alone and using transportability analyses to extend inferences from a trial to the target population. We explain how comparing treatment effect estimates from the emulation against those from the trial can provide evidence on whether observational analyses can be trusted to deliver valid estimates of effectiveness-a process we refer to as benchmarking-and, in some cases, allow the joint analysis of the trial and observational data. We illustrate different approaches using a simplified example of a pragmatic trial and its emulation in registry data. We conclude that synthesizing trial and observational data-in transportability, benchmarking, or joint analyses-can leverage their complementary strengths to enhance learning about comparative effectiveness, through a process combining quantitative methods and epidemiologic judgments., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Target trial emulation is an approach to designing rigorous nonexperimental studies by "emulating" key features of a clinical trial. Most commonly used outside of policy contexts, this approach is also valuable for policy evaluation as policies typically are not randomly assigned. In this article, we discuss the application of the target trial emulation framework in a policy evaluation context. The policy trial emulation framework includes 7 components: the units and eligibility criteria, definitions of the exposure and comparison conditions, assignment mechanism, baseline ("time zero") and follow-up, outcomes, causal estimand, and statistical analysis and assumptions. Policy evaluations that emulate a randomized trial across these dimensions can yield estimates of the causal effects of the policy on outcomes. Using the policy trial emulation framework to conduct and report on research design and methods supports transparent assessment of threats to causal inference in nonexperimental studies intended to assess the effect of a health policy on clinical or population health outcomes., Competing Interests: Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M23-2440.

Background and Aims: Extended-release naltrexone (XR-NTX) and sublingual buprenorphine (SL-BUP) are both approved for opioid use disorder (OUD) treatment in any medical setting. We aimed to compare the real-world effectiveness of XR-NTX and SL-BUP., Design and Setting: This was an observational active comparator, new user cohort study of Medicaid claims records for patients in New Jersey and California, USA, 2016-19., Participants/cases: The participants were adult Medicaid patients aged 18-64 years who initiated XR-NTX or SL-BUP for maintenance treatment of OUD and did not use medications for OUD in the 90 days before initiation. Our cohort included 1755 XR-NTX and 9886 SL-BUP patients., Measurements: We examined two outcomes up to 180 days after medication initiation: (1) composite of medication discontinuation and death and (2) composite of overdose and death., Findings: In adjusted analyses, treatment with XR-NTX was more likely to result in discontinuation or death by the end of follow-up than treatment with SL-BUP: cumulative risk 75.9% [95% confidence interval (CI) = 73.9%, 77.9%] versus 62.2% (95% CI = 61.2%, 63.2%), respectively (risk difference = 13.7 percentage points, 95% CI = 11.4, 16.0). There was minimal difference in the cumulative risk of overdose or death by the end of follow-up: XR-NTX 3.9% (95% CI = 3.0%, 4.8%) versus SL-BUP 3.3% (95% CI = 2.9%, 3.7%); risk difference = 0.5 percentage points, 95% CI = -0.4, 1.5. Results were consistent across sensitivity analyses., Conclusions: Medicaid patients in California and New Jersey, USA, receiving treatment for opioid use disorder stayed in treatment longer on sublingual buprenorphine than on extended-release naltrexone, but the risk of overdose was similar. Most patients in this study discontinued medication within 6 months, regardless of which medication was initiated., (© 2024 Society for the Study of Addiction.)

Objectives: Heterogeneity of treatment effect (HTE) is a concern in substance use disorder (SUD) treatments but has not been rigorously examined. This exploratory study applied a causal forest approach to examine HTE in psychosocial SUD treatments, considering multiple covariates simultaneously., Methods: Data from 12 randomized controlled trials of nine psychosocial treatments were obtained from the National Institute on Drug Abuse Clinical Trials Network. Using causal forests, we estimated the conditional average treatment effect (CATE) on drug abstinence. To assess HTE, we compared CATE variance against total outcome variability, conducted an omnibus test, and applied the Rank-Weighted Average Treatment Effect (RATE)., Results: Across nine interventions, CATE variance was lower than total outcome variability, indicating lack of strong evidence of HTE with respect to the baseline covariates considered. The omnibus test and RATE analysis generally support this finding. However, the RATE analysis identified potential HTE in a motivational interviewing trial; this could be a false positive given the multiple analyses; replication is needed to confirm this., Conclusions: While causal forests show utility in exploring HTE in SUD interventions, limited baseline assessments in most trials suggest a cautious interpretation. The RATE findings for motivational interviewing highlight potential subgroup-specific treatment benefits, warranting further research., (© 2024 The Author(s). International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.)

This study aims to understand availability of school-based infectious disease surveillance data (e.g., COVID-19 cases, student absences) based on experiences during the COVID-19 pandemic using a national sample of public K-12 schools (n = 1,602). Based on surveys administered to school administrators throughout the 2021-2022 school year, we found high levels of missingness data for school-level COVID-19 cases, quarantines, and student absenteeism, increasing missingness over time, and variations in missingness by school characteristics (e.g., school size) and protocols (e.g., having a school-based system to report at-home COVID-19 tests). For the same sample of schools, using data requests to health departments, we found similarly high levels of missingness of school-level COVID-19 case data and varying approaches in data collection. Developing nationally standardized case definitions-and systems to surveil or collect and monitor school-based infectious disease outcomes early in a public health emergency-may be helpful in producing actionable data., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2024.)

In epidemiology and the social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness. However, how to appropriately use multiple imputation for propensity score analysis is not completely clear. This paper aims to bring clarity on the consistency (or lack thereof) of methods that have been proposed, focusing on the "within" approach (where the effect is estimated separately in each imputed dataset and then the multiple estimates are combined) and the "across" approach (where typically propensity scores are averaged across imputed datasets before being used for effect estimation). We show that the within method is valid and can be used with any causal effect estimator that is consistent in the full-data setting. Existing across methods are inconsistent, but a different across method that averages the inverse probability weights across imputed datasets is consistent for propensity score weighting. We also comment on methods that rely on imputing a function of the missing covariate rather than the covariate itself, including imputation of the propensity score and of the probability weight. Based on consistency results and practical flexibility, we recommend generally using the standard within method. Throughout, we provide intuition to make the results meaningful to the broad audience of applied researchers., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Propensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always result in optimal bias reduction in the treatment effect estimate. To address this issue, evaluating covariate balance through prognostic scores, which account for the relationship between covariates and the outcome, has been proposed. Similarly, using a typical model averaging approach for PS estimation that minimizes prediction error for treatment status and covariate imbalance does not necessarily optimize PS-based confounding adjustment. As an alternative approach, using the averaged PS model that minimizes inter-group differences in the prognostic score may further reduce bias in the treatment effect estimate. Moreover, since the prognostic score is also an estimated quantity, model averaging in the prognostic scores can help identify a better prognostic score model. Utilizing the model-averaged prognostic scores as the balance metric for constructing the averaged PS model can contribute to further decreasing bias in treatment effect estimates. This paper demonstrates the effectiveness of the PS model averaging approach based on prognostic score balance and proposes a method that uses the model-averaged prognostic score as a balance metric, evaluating its performance through simulations and empirical analysis., Methods: We conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. In our examination, we separately provid four candidate estimates for the PS and prognostic score models using traditional regression and machine learning methods. The model averaging of PS based on these candidate estimators is performed to either maximize the prediction accuracy of the treatment or to minimize intergroup differences in covariate distributions or prognostic scores. We also utilize not only the prognostic scores from each candidate model but also an averaged score that best predicted the outcome, for the balance assessment., Results: The simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across various scenarios compared to existing methods. Specifically, using the optimally averaged prognostic scores as a balance metric significantly improves the robustness of the weighted treatment effect estimates., Discussion: The prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a balance metric can produce more robust estimates. Since our results are obtained under relatively simple conditions, applying them to real data analysis requires adjustments to obtain accurate estimates according to the complexity and dimensionality of the data., Conclusions: Using the prognostic score as the balance metric for the PS model averaging enhances the performance of the treatment effect estimator, which can be recommended for a wide variety of situations. When applying the proposed method to real-world data, it is important to use it in conjunction with techniques that mitigate issues arising from the complexity and high dimensionality of the data., (© 2024. The Author(s).)

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The HBCD Study aims to reflect the sociodemographic diversity of pregnant individuals in the U.S. The study will also oversample individuals who use substances during pregnancy and enroll similar individuals who do not use to allow for generalizable inferences of the impact of prenatal substance use on trajectories of child development. Without probability sampling or a randomization-based design, the study requires innovation during enrollment, close monitoring of group differences, and rigorous evaluation of external and internal validity across the enrollment period. In this article, we discuss the HBCD Study recruitment and enrollment data collection processes and potential analytic strategies to account for sources of heterogeneity and potential bias. First, we introduce the adaptive design and enrollment monitoring indices to assess and enhance external and internal validity. Second, we describe the visit schedule for in-person and remote data collection where dyads are randomly assigned to visit windows based on a jittered design to optimize longitudinal trajectory estimation. Lastly, we provide an overview of analytic procedures planned for estimating trajectories., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: To model the potential impact of mobile methadone unit implementation in Louisiana on net medication for opioid use disorder (MOUD) treatment rates., Data Sources/study Setting: We use secondary Louisiana Medicaid claims data between 2020 and 2021., Study Design: We simulate the impact of mobile methadone units in Louisiana using two approaches: (1) a "Poisson regression approach," which predicts the number of opioid use disorder (OUD) patients that might use methadone at mobile locations based on the underlying association between methadone use and proximity to a brick-and-mortar methadone clinic; (2) a "policy approach," which leverages local treatment uptake rates following the expansion of methadone coverage to Louisiana Medicaid beneficiaries in 2020 to estimate methadone use following mobile unit implementation. Models were run in cases where mobile methadone operators could choose their operation locations freely and in a separate instance where they were restricted to serving rural locations., Data Collection: Our analytic sample includes 43,341 Louisiana Medicaid beneficiaries with one or more primary or secondary diagnoses for opioid dependence., Principal Findings: We predict that 10 new mobile methadone units in Louisiana would increase the net MOUD treatment rate in the state by 0.54-2.39 percentage points. If these mobile units delivered Methadone exclusively to rural areas, they could increase rural MOUD treatment by 8.54-13.67 percentage points. Further, roughly 20% of all beneficiaries residing in rural areas being treated with methadone would be an average of 24 miles closer to a methadone treatment provider following mobile unit implementation., Conclusions: Mobile methadone units represent a promising innovation in the delivery of methadone that is likely to increase methadone use, especially in underserved rural locations. However, we find significant variation in their impact conditional on where they choose to operate, and so careful location planning will be required to maximize their benefit., (© 2023 Health Research and Educational Trust.)

The study of treatment effects is often complicated by noncompliance and missing data. In the one-sided noncompliance setting where of interest are the complier and noncomplier average causal effects, we address outcome missingness of the latent missing at random type (LMAR, also known as latent ignorability). That is, conditional on covariates and treatment assigned, the missingness may depend on compliance type. Within the instrumental variable (IV) approach to noncompliance, methods have been proposed for handling LMAR outcome that additionally invoke an exclusion restriction-type assumption on missingness, but no solution has been proposed for when a non-IV approach is used. This article focuses on effect identification in the presence of LMAR outcomes, with a view to flexibly accommodate different principal identification approaches. We show that under treatment assignment ignorability and LMAR only, effect nonidentifiability boils down to a set of two connected mixture equations involving unidentified stratum-specific response probabilities and outcome means. This clarifies that (except for a special case) effect identification generally requires two additional assumptions: a specific missingness mechanism assumption and a principal identification assumption. This provides a template for identifying effects based on separate choices of these assumptions. We consider a range of specific missingness assumptions, including those that have appeared in the literature and some new ones. Incidentally, we find an issue in the existing assumptions, and propose a modification of the assumptions to avoid the issue. Results under different assumptions are illustrated using data from the Baltimore Experience Corps Trial., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Personalized medicine promises the ability to improve patient outcomes by tailoring treatment recommendations to the likelihood that any given patient will respond well to a given treatment. It is important that predictions of treatment response be validated and replicated in independent data to support their use in clinical practice. In this paper, we propose and test an approach for validating predictions of individual treatment effects with continuous outcomes across samples that uses matching in a test (validation) sample to match individuals in the treatment and control arms based on their predicted treatment response and their predicted response under control. To examine the proposed validation approach, we conducted simulations where test data is generated from either an identical, similar, or unrelated process to the training data. We also examined the impact of nuisance variables. To demonstrate the use of this validation procedure in the context of predicting individual treatment effects in the treatment of alcohol use disorder, we apply our validation procedure using data from a clinical trial of combined behavioral and pharmacotherapy treatments. We find that the validation algorithm accurately confirms validation and lack of validation, and also provides insights into cases where test data were generated under similar, but not identical conditions. We also show that the presence of nuisance variables detrimentally impacts algorithm performance, which can be partially reduced though the use of variable selection methods. An advantage of the approach is that it can be widely applied to different predictive methods., (© 2024 John Wiley & Sons Ltd.)

Objective: Dual use of combustible cigarettes and e-cigarettes is common among U.S. tobacco users, yet mis-perceptions about the harm of dual use persist, often oversimplifying its multifaceted exposure and health impacts. To address this gap, we evaluated the association of prolonged dual use (consecutive use for more than 1 year) with psychosocial factors, including perceptions of absolute and relative harm of e-cigarettes, social norms, and intentions to quit smoking, among U.S. adult smokers over time., Methods: Using the data from Waves 1 to 5 (2013-2019) from the Population Assessment of Tobacco and Health (PATH) Study, we characterized dual use and prolonged dual use by sociodemographics and psychosocial factors among U.S. adult smokers. We examined the association between dual use, including prolonged dual use, and psychosocial factors over time using logistic regression., Results: Dual use of smokers decreased from 19.8 % in 2013 to 16.4 % in 2019, and prolonged dual use among dual users decreased from 40.0 % in 2013 to 27.4 % in 2019. Prolonged dual users, independent of frequency of use, presented significantly higher cigarette dependence than temporary dual users. The perception of absolute e-cigarette harm (perceiving e-cigarettes as very or extremely harmful) was negatively associated with prolonged dual use. No significant association was found between prolonged dual use and perception of relative e-cigarette harm (perceiving e-cigarettes are less harmful than cigarettes) as well as with intentions to quit smoking and beliefs that most people disapprove of e-cigarette use., Conclusion: Increased perceptions of absolute harm of e-cigarettes, rather than relative harm, appear to decrease prolonged dual use. Public health strategies should consider further emphasis in educating users of the absolute harm, as opposed to endorsing e-cigarette use as a harm reduction alternative, in their tobacco cessation efforts to further discourage dual use.

An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one-sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome-mean-given-covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI-based main analysis methods, including outcome regression, influence function (IF) based and weighting methods. We discuss range selection for the sensitivity parameter. We illustrate the sensitivity analyses with several outcome types from the JOBS II study. This application estimates nuisance functions parametrically - for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF-based estimators to be asymptotically normal - with a view to inform nonparametric inference., (© 2024 John Wiley & Sons Ltd.)

Background: The COVID-19 pandemic upended contexts for families; relatively little work has studied the influence of rapidly changing contexts on the mental health of parents. We aimed to assess the relation between financial strain and schooling modality with the mental health of adults living with school-age children across the pandemic., Methods: Using a large, national sample from the COVID-19 Trends and Impact Surveys (N=1 485 072 responses from November 2020 through June 2022), we used weighted multiple logistic regression with interactions for school semester to estimate changes in the association of frequent feelings of depression and anxiety, respectively, with financial strain and schooling modality, controlling for demographics and state, across time., Results: In all time periods, financial strain was associated with reporting frequent feelings of depression and anxiety, respectively. The association grew over time (p<0.001) from adjusted OR (aOR) 2.25 (95% CI 2.19, 2.32)/aOR 2.63 (95% CI 2.54, 2.73) in Autumn 2020 to aOR 3.11 (95% CI 3.01, 3.22)/aOR 3.79 (95% CI 3.64, 3.95) in Spring 2022. Living with children in fully online versus in-person schooling was associated with frequent feelings of anxiety and depression symptoms in all time periods, and increased from aOR 1.08 (1.05, 1.11)/aOR 1.06 (1.02, 1.10) in Autumn 2020 to aOR 1.20 (1.10, 1.32)/aOR 1.28 (1.16, 1.42) in Spring 2022., Conclusion: Associations between financial strain and online-only schooling with poor mental health increased during the COVID-19 pandemic. Policies to support parents in the face of external stressors, such as economic instability and school closures, may improve overall population mental health., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Area-level credit scores may capture aspects of a neighborhood's resources and history that may affect population mental health beyond income and other demographic determinants. Using a sample of 511,363 adults in 1,438 Pennsylvania ZIP codes who completed the COVID-19 Trends and Impact Survey, we assessed the relationship between area-level VantageScores (nine categories from <675 to ≥850) and individual mental health. We estimate odds and predicted probability of depression and anxiety symptoms, adjusting for demographics and median household income. Given a history of discrimination and unequal access to the benefits of assets, we assess whether the relationship between area credit scores and individual mental health was different for different gender, age, race and ethnicity, and education groups. Persons who lived in ZIP codes with average credit scores of 700-725 reported 1.31 (95% CI 1.13, 1.52) and 1.22 (95% CI 1.07, 1.40) times the odds of frequent feelings of depression and anxiety symptoms, respectively, relative to persons living in ZIP codes with an average area level credit score ≥850. This translated to a difference of 2.8 and 2.5 percentage points in the predicted probability of symptoms of depression and anxiety, respectively, for persons living in lower versus higher area credit score ZIP codes. Stratified models appeared to show differences in the association of depression and anxiety with credit scores across demographic groups, but tests of interaction did not show significant differences between most groups. Findings suggest that area credit scores may capture assets that shape mental health over and above income and other demographic determinants., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Importance: Prior observational research has shown that infants born in states with more abortion restrictions are more likely to die during infancy. It is unclear how recent and more severe abortion bans in the US have impacted infant mortality., Objective: To examine whether Texas Senate Bill 8 (SB8), which banned abortions after embryonic cardiac activity and did not allow exemptions for congenital anomalies, is associated with infant mortality in the state of Texas., Design, Setting, and Participants: This population-based cohort study of all recorded infant deaths from the state of Texas and 28 comparison states used a comparative interrupted time series analysis with an augmented synthetic control approach and national birth certificate data from January 1, 2018, to December 31, 2022, to estimate the difference between the number of observed and expected infant and neonatal deaths and death rates among monthly cohorts exposed to Texas' SB8., Exposure: Deaths in March 2022 were treated as the first cohort exposed to the Texas' SB8 abortion policy because these infants (if born full term) were approximately 10 to 14 weeks' gestation when SB8 went into effect on September 1, 2021. The exposure period was thus March through December 2022., Main Outcomes and Measures: Our outcomes were monthly counts and rates of infant (aged <1 year) and neonatal (aged <28 days) deaths in the exposure period in Texas. In secondary analyses, annual changes in cause-specific infant deaths between 2021 and 2022 in Texas and the rest of the US were examined., Results: Between 2018 and 2022, there were 102 391 infant deaths in the US, with 10 351 of these deaths occurring in the state of Texas. Between 2021 and 2022, infant deaths in Texas increased from 1985 to 2240, or 255 additional deaths. This corresponds to a 12.9% increase, whereas the rest of the US experienced a comparatively lower 1.8% increase. On the basis of the counterfactual analysis that used data from Texas and eligible comparison states, an excess of 216 infant deaths (95% CI, -122 to 554) was observed from March to December 2022, or a 12.7% increase above expectation. At the monthly level, significantly greater-than-expected counts were observed for 4 months between March and December 2022: April, July, September, and October. An analysis of neonatal deaths found somewhat similar patterns, with significantly greater-than-expected neonatal deaths in April and October 2022. Descriptive statistics by cause of death showed that infant deaths attributable to congenital anomalies in 2022 increased more for Texas (22.9% increase) but not the rest of the US (3.1% decrease)., Conclusions and Relevance: This study found that Texas' 2021 ban on abortion in early pregnancy was associated with unexpected increases in infant and neonatal deaths in Texas between 2021 and 2022. Congenital anomalies, which are the leading cause of infant death, also increased in Texas but not the rest of the US. Although replication and further analyses are needed to understand the mechanisms behind these findings, the results suggest that restrictive abortion policies may have important unintended consequences in terms of trauma to families and medical cost as a result of increases in infant mortality. These findings are particularly relevant given the recent Dobbs v Jackson Women's Health Organization US Supreme Court decision and subsequent rollbacks of reproductive rights in many US states.

Background: The rise in prevalence of high deductible health plans (HDHPs) in the United States may raise concerns for high-need, high-utilization populations such as those with comorbid chronic conditions. In this study, we examine changes in total and out-of-pocket (OOP) spending attributable to HDHPs for enrollees with comorbid substance use disorder (SUD) and cardiovascular disease (CVD)., Methods: We used de-identified administrative claims data from 2007 to 2017. SUD and CVD were defined using algorithms of ICD 9 and 10 codes and HEDIS guidelines. The main outcome measures of interest were spending measure for all non-SUD/CVD-related services, SUD-specific services, and CVD-specific services, for all services and medications specifically. We assessed both total and OOP spending. We used an intent-to-treat two-part model approach to model spending and computed the marginal effect of HDHP offer as both the dollar change and percent change in spending attributable to HDHP offer., Results: Our sample included 33,684 enrollee-years and was predominantly white and male with a mean age of 53 years. The sample had high demonstrated substantial healthcare utilization with 94% using any non-SUD/CVD services, and 84% and 78% using SUD and CVD services, respectively. HDHP offer was associated with a 17.0% (95% CI = [0.07, 0.27] increase in OOP spending for all non-SUD/CVD services, a 21.1% (95% CI = [0.11, 0.31]) increase in OOP spending for all SUD-specific services, and a 13.1% (95% CI = [0.04, 0.23]) increase in OOP spending for all CVD-specific services. HDHP offer was also associated with a significant increase in OOP spending on non-SUD/CVD-specific medications and SUD-specific medications, but not CVD-specific medications., Conclusions: This study suggests that while HDHPs do not change overall levels of annual spending among enrollees with comorbid CVD and SUD, they may increase the financial burden of healthcare services by raising OOP costs, which could negatively impact this high-need and high-utilization population., (© 2024. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Importance: Given that the Patient Health Questionnaire (PHQ) item 9 is commonly used to screen for risk of self-harm and suicide, it is important that clinicians recognize circumstances when at-risk adolescents may go undetected., Objective: To understand characteristics of adolescents with a history of depression who do not endorse the PHQ item 9 before a near-term intentional self-harm event or suicide., Design, Setting, and Participants: This was a retrospective cohort study design using electronic health record and claims data from January 2009 through September 2017. Settings included primary care and mental health specialty clinics across 7 integrated US health care systems. Included in the study were adolescents aged 13 to 17 years with history of depression who completed the PHQ item 9 within 30 or 90 days before self-harm or suicide. Study data were analyzed September 2022 to April 2023., Exposures: Demographic, diagnostic, treatment, and health care utilization characteristics., Main Outcome(s) and Measure(s): Responded "not at all" (score = 0) to PHQ item 9 regarding thoughts of death or self-harm within 30 or 90 days before self-harm or suicide., Results: The study included 691 adolescents (mean [SD] age, 15.3 [1.3] years; 541 female [78.3%]) in the 30-day cohort and 1024 adolescents (mean [SD] age, 15.3 [1.3] years; 791 female [77.2%]) in the 90-day cohort. A total of 197 of 691 adolescents (29%) and 330 of 1024 adolescents (32%), respectively, scored 0 before self-harm or suicide on the PHQ item 9 in the 30- and 90-day cohorts. Adolescents seen in primary care (odds ratio [OR], 1.5; 95% CI, 1.0-2.1; P = .03) and older adolescents (OR, 1.2; 95% CI, 1.0-1.3; P = .02) had increased odds of scoring 0 within 90 days of a self-harm event or suicide, and adolescents with a history of inpatient hospitalization and a mental health diagnosis had twice the odds (OR, 2.0; 95% CI, 1.3-3.0; P = .001) of scoring 0 within 30 days. Conversely, adolescents with diagnoses of eating disorders were significantly less likely to score 0 on item 9 (OR, 0.4; 95% CI, 0.2-0.8; P = .007) within 90 days., Conclusions and Relevance: Study results suggest that older age, history of an inpatient mental health encounter, or being screened in primary care were associated with at-risk adolescents being less likely to endorse having thoughts of death and self-harm on the PHQ item 9 before a self-harm event or suicide death. As use of the PHQ becomes more widespread in practice, additional research is needed for understanding reasons why many at-risk adolescents do not endorse thoughts of death and self-harm.

Importance: Given the expiration of expanded unemployment and other benefits during the COVID-19 pandemic, it is important to understand the association between pandemic income or job loss and long-term implications on mental health., Objective: To evaluate the association between income or job loss due to the COVID-19 pandemic and later psychological distress., Design, Setting, and Participants: This cohort study used 5 waves of nationally representative, longitudinal survey data (September 16, 2019, through September 18, 2022) from the Pew Research Center's American Trends Panel. Doubly robust propensity score-weighted quasi-Poisson models were used to estimate the association of self or household income or job loss during the early phase of the COVID-19 pandemic with later psychological distress, while controlling for pre-income loss characteristics (demographics, finances, and psychological distress). The study sample comprised US working-age adults (aged 18-64 years) who had not experienced income or job loss due to the COVID-19 pandemic by March 24, 2020., Exposure: Participant-reported self or household income or job loss (ie, reduced hours or demand for work) due to the COVID19 pandemic between March 24, 2020, and August 16, 2020., Main Outcome and Measures: Psychological distress was measured using a composite scale of 0 to 15 based on participants' reported frequency of feeling depressed, on edge, sleepless, lonely, and hopeless in the past week in March 2020, February 2021, and September 2022., Results: Of 1392 working-age adults (survey weighted 52.7% male and 47.7% aged 30-49 years) who had not reported income or job loss before March 24, 2020, a survey weighted 35.7% reported job or income loss between March 24 and August 16, 2020. Early-phase pandemic income or job loss was associated with higher distress in February 2021 (estimated ratio, 1.09; 95% CI, 1.01-1.18; P = .03) and September 2022 (estimated ratio, 1.11; 95% CI, 1.02-1.22; P = .02) among participants who experienced job or income loss between March 24 and August 16, 2020, compared with the propensity score-weighted mean in the group who did not experience income loss., Conclusions and Relevance: These small but significant within-person associations between early-phase pandemic household income or job loss and psychological distress up to 29 months later suggest that policies are needed to support people with income or job loss to help mitigate the long-term adverse mental health outcomes of economic disruption.

Objective: To assess how changes in depressive symptoms influence physical function over time among those with radiographic knee osteoarthritis (OA)., Methods: Participants from the Osteoarthritis Initiative with radiographic knee OA (n = 2,212) and complete data were identified at baseline. Depressive symptoms were assessed as a time-varying score at baseline and the first three annual follow-up visits using the Center for Epidemiological Studies Depression Scale (CES-D) Scale. Physical function was measured at the first four follow-up visits using 20-meter gait speed meters per second. The following two marginal structural models were fit: one assessing the main effect of depressive symptoms on gait speed and another assessing time-specific associations., Results: Time-adjusted results indicated that higher CES-D scores were significantly associated with slower gait speed (-0.0048; 95% confidence interval -0.0082 to -0.0014), and time-specific associations of CES-D were largest during the first follow-up interval (-0.0082; 95% confidence interval -0.0128 to -0.0035). During subsequent follow-up time points, the influence of depressive symptoms on gait speed diminished., Conclusion: The negative effect of depressive symptoms on physical function may decrease over time as knee OA progresses., (© 2024 American College of Rheumatology.)

Competing Interests: Declarations of interests Related to the submitted work, JB received support from the National Health and Medical Research Council as a Leadership Fellow (investigator grant: IG GNT1176620). All conflicts are outside of the submitted work. EEM has received grants from the National Institutes of Health (NIH; National Institute on Drug Abuse [NIDA], National Institute of Mental Health [NIMH], and National Heart, Lung and Blood Institute [NHLBI]), the Centers for Disease Control and Prevention (CDC), the Commonwealth Fund, and Arnold Ventures; is an appointed member of the Agency for Healthcare Research and Quality (AHRQ) National Integration Academy; and has received consulting fees from the Implementation Research Institute and the Stanford Center for Dissemination and Implementation. RSB has received grants from NIH (NIMH, NHLBI, National Cancer Institute, and National Institute of Nursing Research) and been a co-investigator on grants from the Patient Centered Outcomes Research Institute, CDC, and the National Psoriasis Foundation; is principal at Implementation Science & Practice; is an appointed member of the National Advisory Mental Health Council and the NASEM study, “Blueprint for a national prevention infrastructure for behavioral health disorders”; and serves on the scientific advisory board for AIM Youth Mental Health Foundation and the Klingenstein Third Generation Foundation. RSB has received consulting fees from United Behavioral Health and OptumLabs, previously served on the scientific and advisory board for Optum Behavioral Health, and has received royalties from Oxford University Press. JB has received support air travel from the International Society for Quality in Health Care and conferences for invited keynote addresses; serves as Chair of the Board of the International Society for Quality in Health Care; and as an unpaid member for OECD and WHO committees. LK received a grant from NIMH. DLL received grants from NIDA and AHRQ and reports payment for expert testimony in US litigation against opioid manufacturers and distributors. NM received a grant from AHRQ and is a member of the American Heart Associate Implementation Science Committee. HAP received a grant from the National Center for Advancing Translational Sciences; has received consulting fees from the National Committee on Quality Assurance; and received support for attending meetings from the FORE Foundation and Kaiser Permanente. RS is supported by the DBT-Wellcome Trust India Alliance Intermediate Fellowship in Clinical and Public Health Research and a recipient (co-investigator) of a NIH and National Institute of Aging grant on Yoga for Healthy Ageing; and reports payment Cambridge University Press for serving as senior editors of the journal Cambridge Prisms: Global Mental Health. SJS received grants from the VA, NIMHD, and NSF, consulting fees from Harvard Medical School, and teaching honoraria from Intermountain Health. MDE received grants from NIDA, NINR, the National Institute of Child Health and Human Development, Arnold Ventures, and the Commonwealth Fund. All other authors declare no competing interests.

Policy implementation is a key component of scaling effective chronic disease prevention and management interventions. Policy can support scale-up by mandating or incentivizing intervention adoption, but enacting a policy is only the first step. Fully implementing a policy designed to facilitate implementation of health interventions often requires a range of accompanying implementation structures, like health IT systems, and implementation strategies, like training. Decision makers need to know what policies can support intervention adoption and how to implement those policies, but to date research on policy implementation is limited and innovative methodological approaches are needed. In December 2021, the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness and the Johns Hopkins Center for Mental Health and Addiction Policy convened a forum of research experts to discuss approaches for studying policy implementation. In this report, we summarize the ideas that came out of the forum. First, we describe a motivating example focused on an Affordable Care Act Medicaid health home waiver policy used by some US states to support scale-up of an evidence-based integrated care model shown in clinical trials to improve cardiovascular care for people with serious mental illness. Second, we define key policy implementation components including structures, strategies, and outcomes. Third, we provide an overview of descriptive, predictive and associational, and causal approaches that can be used to study policy implementation. We conclude with discussion of priorities for methodological innovations in policy implementation research, with three key areas identified by forum experts: effect modification methods for making causal inferences about how policies' effects on outcomes vary based on implementation structures/strategies; causal mediation approaches for studying policy implementation mechanisms; and characterizing uncertainty in systems science models. We conclude with discussion of overarching methods considerations for studying policy implementation, including measurement of policy implementation, strategies for studying the role of context in policy implementation, and the importance of considering when establishing causality is the goal of policy implementation research., (© 2023. The Author(s) 2023.)

Purpose: Mental health is shaped by social and economic contexts, which were altered during the COVID-19 pandemic. No study has systematically reviewed the literature on the relation between different assets and depression during the COVID-19 pandemic., Methods: We conducted a systematic review of the literature on financial (e.g. income/savings), physical (e.g., home ownership), and social (e.g., marital status, educational attainment) assets and depression in U.S. adults. For each asset type, we created binary comparisons to report on the direction of the relationship and described if each study reported insignificant, positive, negative, or mixed associations., Results: Among the 41 articles identified, we found that income was the most studied asset (n=34), followed by education (n=25), marital status (n=18), home ownership (n=5), and savings (n=4). 88%, 100%, and 100% of articles reported a significant association of higher income, home ownership, and higher savings, respectively, with less depression. The association between marital status and education with depression was more nuanced: 72% (13 of 18) studies showed that unmarried persons had greater risk of depression than married or cohabitating persons and 52% (13 of 25) of studies reported no significant difference in depression across educational groups., Conclusion: This work adds to the literature a deeper understanding of how different assets relate to depression. In the context of largescale traumatic events, policies that maintain and protect access to social, physical, and financial assets may help to protect mental health., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Background: People with cognitive disabilities such as intellectual and developmental disabilities face significant barriers to accessing high-quality health care services. Barriers may be exacerbated for those with co-occurring mental health conditions., Objective: This study compares patient experiences of health care services between adults with and without cognitive disabilities and, among people with a cognitive disability, those with and without co-occurring mental health conditions., Methods: Cross-sectional analyses were conducted using 2021 Medical Expenditure Panel Survey data, a national U.S. survey, to examine differences in Consumer Assessment of Healthcare Providers and Systems measures., Results: Adults with cognitive disabilities reported lower satisfaction with health care services compared to the general population (7.62 (95% confidence interval (CI): 7.41-7.83) vs. 8.33 (95% CI: 8.29-8.38) on scale from 0 to 10). Adults with cognitive disabilities were less likely to report that providers listened carefully to them (odds ratio (OR): 0.55, 95% CI: 0.42-0.71), explained things in a way that was easy to understand (OR: 0.48, 95% CI: 0.35-0.66), showed respect for what they had to say (OR: 0.38, 95% CI: 0.29-0.51), spent enough time with them (OR: 0.52, 95% CI: 0.40-0.69), or gave advice that was easy to understand (OR: 0.40, 95% CI: 0.28-0.58) compared to the general population. Among adults with cognitive disabilities, there were no differences based on co-occurring mental health conditions., Conclusions: Adults with cognitive disabilities report lower satisfaction with health care services driven by worse experiences with the health care system. Policies to increase provider capacity to support this population should be prioritized., Competing Interests: Declaration of competing interest Authors have no conflicts of interest to report., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to better estimate heterogeneous treatment effects. This article discusses several nonparametric approaches for estimating heterogeneous treatment effects using data from multiple trials. We extend single-study methods to a scenario with multiple trials and explore their performance through a simulation study, with data generation scenarios that have differing levels of cross-trial heterogeneity. The simulations demonstrate that methods that directly allow for heterogeneity of the treatment effect across trials perform better than methods that do not, and that the choice of single-study method matters based on the functional form of the treatment effect. Finally, we discuss which methods perform well in each setting and then apply them to four randomized controlled trials to examine effect heterogeneity of treatments for major depressive disorder., (© 2024 John Wiley & Sons Ltd.)

Purpose: The COVID-19 pandemic disrupted pediatric health care in the United States, and this disruption layered on existing barriers to health care. We sought to characterize disparities in unmet pediatric health care needs during this period., Methods: We analyzed data from Wave 1 (October through November 2020) and Wave 2 (March through May 2021) of the COVID Experiences Survey, a national longitudinal survey delivered online or via telephone to parents of children aged 5 through 12 years using a probability-based sample representative of the US household population. We examined 3 indicators of unmet pediatric health care needs as outcomes: forgone care and forgone well-child visits during fall 2020 through spring 2021, and no well-child visit in the past year as of spring 2021. Multivariate models examined relationships of child-, parent-, household-, and county-level characteristics with these indicators, adjusting for child's age, sex, and race/ethnicity., Results: On the basis of parent report, 16.3% of children aged 5 through 12 years had forgone care, 10.9% had forgone well-child visits, and 30.1% had no well-child visit in the past year. Adjusted analyses identified disparities in indicators of pediatric health care access by characteristics at the level of the child (eg, race/ethnicity, existing health conditions, mode of school instruction), parent (eg, childcare challenges), household (eg, income), and county (eg, urban-rural classification, availability of primary care physicians). Both child and parent experiences of racism were also associated with specific indicators of unmet health care needs., Conclusions: Our findings highlight the need for continued research examining unmet health care needs and for continued efforts to optimize the clinical experience to be culturally inclusive., (© 2024 Annals of Family Medicine, Inc.)

Objective: The authors aimed to assess differences in appointment completion rates between telepsychiatry and in-person outpatient psychiatric care for patients with depression in an academic health system., Methods: Electronic health records of encounters for patients (ages ≥10) with a depression diagnosis and at least one scheduled outpatient psychiatric appointment (N=586,266 appointments; November 2017-October 2022) were assessed for appointment volume and completion of telepsychiatry versus in-person sessions., Results: Telepsychiatry became the dominant care modality after the onset of the COVID-19 pandemic, although the number of telepsychiatry and in-person appointments nearly converged by October 2022. Logistic regression showed that telepsychiatry appointments (July 2020-October 2022) were more likely (OR=1.30, 95% CI=1.27-1.34) to be completed than in-person appointments., Conclusions: Telepsychiatry appointments were less likely to be canceled or missed than in-person appointments, suggesting that telepsychiatry improved efficiency and continuity of care. As in-person operations resume following the pandemic, maintaining telepsychiatry services may optimize hospital-level and patient outcomes., Competing Interests: Dr. Mojtabai reports having received royalties from UpToDate and Medscape and providing expert consultation regarding social media litigation on behalf of the plaintiffs. The other authors report no financial relationships with commercial interests.

Background: Depressive symptoms are common in knee osteoarthritis (OA), exacerbate knee pain severity and may influence outcomes of oral analgesic treatments. The aim was to assess whether oral analgesic effectiveness in knee OA varies by fluctuations in depressive symptoms., Methods: The sample included Osteoarthritis Initiative (OAI) participants not treated with oral analgesics at enrolment (n = 1477), with radiographic disease at the first follow-up visit (defined as the index date). Oral analgesic treatment and depressive symptoms, assessed with the Center for Epidemiological Studies Depression [(CES-D) score ≥16] Scale, were measured over three annual visits. Knee pain severity was measured at visits adjacent to treatment and modifier using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale (rescaled range = 0-100). Structural nested mean models (SNMMs) estimated causal mean differences in knee pain severity comparing treatment versus no treatment., Results: The average causal effects of treated versus not treated for observations without depressive symptoms showed negligible differences in knee pain severity. However, causal mean differences in knee pain severity comparing treatment versus no treatment among observations with depressive symptoms increased over time from -0.10 [95% confidence interval (CI): -9.94, 9.74] to -16.67 (95% CI: -26.33, -7.01). Accordingly, the difference in average causal effects regarding oral analgesic treatment for knee pain severity between person-time with and without depressive symptoms was largest (-16.53; 95% CI: -26.75, -6.31) at the last time point. Cumulative treatment for 2 or 3 years did not yield larger causal mean differences., Conclusions: Knee OA patients with persistent depressive symptoms and chronic pain may derive more analgesic treatment benefit than those without depressive symptoms and less pain., (© The Author(s) 2023; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Methods for extending-generalizing or transporting-inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is sufficient for rendering the randomized and nonrandomized groups exchangeable. Yet, decision makers are often interested in examining treatment effects in subgroups of the target population defined in terms of only a few discrete covariates. Here, we propose methods for estimating subgroup-specific potential outcome means and average treatment effects in generalizability and transportability analyses, using outcome model--based (g-formula), weighting, and augmented weighting estimators. We consider estimating subgroup-specific average treatment effects in the target population and its nonrandomized subset, and we provide methods that are appropriate both for nested and non-nested trial designs. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study (North America, 1975-1996) to compare the effect of surgery plus medical therapy versus medical therapy alone for chronic coronary artery disease in subgroups defined by history of myocardial infarction., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Importance: The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population., Objective: To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults., Design, Setting, and Participants: This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023., Interventions: Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years., Main Outcomes and Measures: For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality., Results: The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%)., Conclusions and Relevance: Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.

In population neuroscience, samples are not often selected with equal or known probability from an underlying population of interest; in other words, samples are not often formally representative of a specified underlying population. This chapter provides an overview of an epidemiological approach to considering the implications of selective participation on the value of our results for population health. We discuss definitions of generalizability and transportability, given the growing recognition that generalizability and transportability are central for interpreting data that are aiming to be population-based. We provide evidence that differences in the prevalence of effect measure modifiers between a study sample and a target population will lead to a lack of generalizability and transportability. We provide an example of an association between a poly-genetic risk score and depression, showing how an internally valid association can differ based on the prevalence of effect measure modifiers. We show that when estimating associations, inferences from a study sample to a population can depend on clearly defining a target population. Given that representative sampling from explicitly defined target populations may not be feasible or realistic in many situations, especially given the sample sizes needed for statistical power for many exposures of interest (and especially when interactions are being tested), researchers should be well versed in tools available to enhance the interpretability of samples regarding target populations., (© 2024. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Multi-site randomized controlled trials (RCTs) provide unbiased estimates of the average impact in the study sample. However, their ability to accurately predict the impact for individual sites outside the study sample, to inform local policy decisions, is largely unknown. To extend prior research on this question, we analyzed six multi-site RCTs and tested modern prediction methods-lasso regression and Bayesian Additive Regression Trees (BART)-using a wide range of moderator variables. The main study findings are that: (1) all of the methods yielded accurate impact predictions when the variation in impacts across sites was close to zero (as expected); (2) none of the methods yielded accurate impact predictions when the variation in impacts across sites was substantial; and (3) BART typically produced "less inaccurate" predictions than lasso regression or than the Sample Average Treatment Effect. These results raise concerns that when the impact of an intervention varies considerably across sites, statistical modelling using the data commonly collected by multi-site RCTs will be insufficient to explain the variation in impacts across sites and accurately predict impacts for individual sites.

Objective: The authors examined trends in opioid use disorder treatment and in-person and telehealth modalities before and after COVID-19 pandemic onset among patients who had received treatment prepandemic., Methods: The sample included 13,113 adults with commercial insurance or Medicare Advantage and receiving opioid use disorder treatment between March 2018 and February 2019. Trends in opioid use disorder outpatient treatment, treatment with medications for opioid use disorder (MOUD), and in-person and telehealth modalities were examined 1 year before pandemic onset and 2 years after (March 2019-February 2022)., Results: From March 2019 to February 2022, the proportion of patients with opioid use disorder outpatient and MOUD visits declined by 2.8 and 0.3 percentage points, respectively. Prepandemic, 98.6% of outpatient visits were in person; after pandemic onset, at least 34.9% of patients received outpatient care via telehealth., Conclusions: Disruptions in opioid use disorder outpatient and MOUD treatments were marginal during the pandemic, possibly because of increased telehealth utilization., Competing Interests: The authors report no financial relationships with commercial interests.