Association between prolonged dual use of cigarettes and E-cigarettes and psychosocial factors among U.S. Smokers: Secondary data analysis from the 2013 to 2019 PATH study.

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.

Advancing high quality longitudinal data collection: Implications for the HEALthy Brain and Child Development (HBCD) Study design and recruitment.

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.

Comparative effectiveness of extended-release naltrexone and sublingual buprenorphine for treatment of opioid use disorder among Medicaid patients.

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.

Effects of Texas State Agency Integration on Mental Health Service Use Among Individuals with Co-occurring Cognitive Disabilities and Mental Health Conditions.

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.

Area-level credit scores and symptoms of depression and anxiety in adults.

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.

Financial strain, schooling modality and mental health of US adults living with children during the COVID-19 pandemic.

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.

Validation of predicted individual treatment effects in out of sample respondents.

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.

Income or Job Loss and Psychological Distress During the COVID-19 Pandemic.

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.

Infant Deaths After Texas' 2021 Ban on Abortion in Early Pregnancy.

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.

Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects.

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.

Multiple imputation for propensity score analysis with covariates missing at random: some clarity on within and across methods.

In epidemiology and 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.

Identification of complier and noncomplier average causal effects in the presence of latent missing-at-random (LMAR) outcomes: a unifying view and choices of assumptions.

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.

Adolescents Who Do Not Endorse Risk via the Patient Health Questionnaire Before Self-Harm or Suicide.

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.

Population Neuroscience: Understanding Concepts of Generalizability and Transportability and Their Application to Improving the Public's Health.

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.

Disparities in Unmet Health Care Needs Among US Children During the COVID-19 Pandemic.

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.

Changes in Healthcare Spending Attributable to High Deductible Health Plan Offer Among Enrollees with Comorbid Substance Use Disorder and Cardiovascular Disease.

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.

Using a Multi-Site RCT to Predict Impacts for a Single Site: Do Better Data and Methods Yield More Accurate Predictions?

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.

Comparative effectiveness of extended release naltrexone and sublingual buprenorphine for treatment of opioid use disorder among Medicaid patients.

Aims: To compare the real-world effectiveness of extended release naltrexone (XR-NTX) and sublingual buprenorphine (SL-BUP) for the treatment of opioid use disorder (OUD). Design: An observational active comparator, new user cohort study. Setting: Medicaid claims records for patients in New Jersey and California, 2016-2019. Participants/Cases: 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. Comparators: New initiation with XR-NTX versus SL-BUP for the treatment of OUD. 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: Our cohort included 1,755 XR-NTX and 9,886 SL-BUP patients. 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 76% (95% confidence interval [CI] 75%, 78%) versus 62% (95% CI 61%, 63%), respectively (risk difference 14 percentage points, 95% CI 13, 16). There was minimal difference in the cumulative risk of overdose or death by the end of follow-up: XR-NTX 3.8% (95% CI 2.9%, 4.7%) versus SL-BUP 3.3% (95% 2.9%, 3.7%); risk difference 0.5 percentage points, 95%CI -0.5, 1.5. Results were consistent across sensitivity analyses. Conclusions: Longer medication retention is important because risks of negative outcomes are elevated after discontinuation. Our results support selection of SL-BUP over XR-NTX. However, most patients discontinued medication by 6 months indicating that more effective tools are needed to improve medication retention, particularly after initiation with XR-NTX, and to identify which patients do best on which medication.