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.

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.

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.

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.

Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.

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.

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.

Assets and depression in U.S. adults during the COVID-19 pandemic: a systematic review.

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.

Effects of U.S. State Medical Cannabis Laws on Treatment of Chronic Noncancer Pain.

BACKGROUND: State medical cannabis laws may lead patients with chronic noncancer pain to substitute cannabis in place of prescription opioid or clinical guideline-concordant nonopioid prescription pain medications or procedures. OBJECTIVE: To assess effects of state medical cannabis laws on receipt of prescription opioids, nonopioid prescription pain medications, and procedures for chronic noncancer pain. DESIGN: Using data from 12 states that implemented medical cannabis laws and 17 comparison states, augmented synthetic control analyses estimated laws' effects on receipt of chronic noncancer pain treatment, relative to predicted treatment receipt in the absence of the law. SETTING: United States, 2010 to 2022. PARTICIPANTS: 583 820 commercially insured adults with chronic noncancer pain. MEASUREMENTS: Proportion of patients receiving any opioid prescription, nonopioid prescription pain medication, or procedure for chronic noncancer pain; volume of each treatment type; and mean days' supply and mean morphine milligram equivalents per day of prescribed opioids, per patient in a given month. RESULTS: In a given month during the first 3 years of law implementation, medical cannabis laws led to an average difference of 0.05 percentage points (95% CI, -0.12 to 0.21 percentage points), 0.05 percentage points (CI, -0.13 to 0.23 percentage points), and -0.17 percentage points (CI, -0.42 to 0.08 percentage points) in the proportion of patients receiving any opioid prescription, any nonopioid prescription pain medication, or any chronic pain procedure, respectively, relative to what we predict would have happened in that month had the law not been implemented. LIMITATIONS: This study used a strong nonexperimental design but relies on untestable assumptions involving parallel counterfactual trends. Statistical power is limited by the finite number of states. Results may not generalize to noncommercially insured populations. CONCLUSION: This study did not identify important effects of medical cannabis laws on receipt of opioid or nonopioid pain treatment among patients with chronic noncancer pain. PRIMARY FUNDING SOURCE: National Institute on Drug Abuse.

Using Trial and Observational Data to Assess Effectiveness: Trial Emulation, Transportability, Benchmarking, and Joint Analysis.

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 epidemiological judgements.

Identifying Optimal Methods for Addressing Confounding Bias When Estimating the Effects of State-level Policies.

BACKGROUND: Policy evaluation studies that assess how state-level policies affect health-related outcomes are foundational to health and social policy research. The relative ability of newer analytic methods to address confounding, a key source of bias in observational studies, has not been closely examined. METHODS: We conducted a simulation study to examine how differing magnitudes of confounding affected the performance of 4 methods used for policy evaluations: (1) the two-way fixed effects difference-in-differences model; (2) a 1-period lagged autoregressive model; (3) augmented synthetic control method; and (4) the doubly robust difference-in-differences approach with multiple time periods from Callaway-Sant'Anna. We simulated our data to have staggered policy adoption and multiple confounding scenarios (i.e., varying the magnitude and nature of confounding relationships). RESULTS: Bias increased for each method: (1) as confounding magnitude increases; (2) when confounding is generated with respect to prior outcome trends (rather than levels), and (3) when confounding associations are nonlinear (rather than linear). The autoregressive model and augmented synthetic control method had notably lower root mean squared error than the two-way fixed effects and Callaway-Sant'Anna approaches for all scenarios; the exception is nonlinear confounding by prior trends, where Callaway-Sant'Anna excels. Coverage rates were unreasonably high for the augmented synthetic control method (e.g., 100%), reflecting large model-based standard errors and wide confidence intervals in practice. CONCLUSIONS: In our simulation study, no single method consistently outperformed the others, but a researcher's toolkit should include all methodologic options. Our simulations and associated R package can help researchers choose the most appropriate approach for their data.

Three-dimensional-guided and ICE-guided transseptal puncture for cardiac ablations: A propensity score match study.

INTRODUCTION: Transseptal puncture (TSP) is routinely performed for left atrial ablation procedures. The use of a three-dimensional (3D) mapping system or intracardiac echocardiography (ICE) is useful in localizing the fossa ovalis and reducing fluoroscopy use. We aimed to compare the safety and efficacy between 3D mapping system-guided TSP and ICE-guided TSP techniques. METHODS: We conducted a prospective observational study of patients undergoing TSP for left atrial catheter ablation procedures (mostly atrial fibrillation ablation). Propensity scoring was used to match patients undergoing 3D-guided TSP with patients undergoing ICE-guided TSP. Logistic regression was used to compare the clinical data, procedural data, fluoroscopy time, success rate, and complications between the groups. RESULTS: Sixty-five patients underwent 3D-guided TSP, and 151 propensity score-matched patients underwent ICE-guided TSP. The TSP success rate was 100% in both the 3D-guided and ICE-guided groups. Median needle time was 4.00 min (interquartile range [IQR]: 2.57-5.08) in patients with 3D-guided TSP compared to 4.02 min (IQR: 2.83-6.95) in those with ICE-guided TSP (p = .22). Mean fluoroscopy time was 0.2 min (IQR: 0.1-0.4) in patients with 3D-guided TSP compared to 1.2 min (IQR: 0.7-2.2) in those with ICE-guided TSP (p < .001). There were no complications related to TSP in both group. CONCLUSIONS: Three-dimensional mapping-guided TSP is as safe and effective as ICE-guided TSP without additional cost.

High-deductible Health Plans and Nonfatal Opioid Overdose.

OBJECTIVES: Opioid-related overdose is a public health emergency in the United States. Meanwhile, high-deductible health plans (HDHPs) have become more prevalent in the United States over the last 2 decades, raising concern about their potential for discouraging high-need populations, like those with opioid use disorder (OUD), from engaging in care that may mitigate the probability of overdose. This study assesses the impact of an employer offering an HDHP on nonfatal opioid overdose among commercially insured individuals with OUD in the United States. RESEARCH DESIGN: We used deidentified insurance claims data from 2007 to 2017 with 97,788 person-years. We used an intent-to-treat, difference-in-differences regression framework to estimate the change in the probability of a nonfatal opioid overdose among enrollees with OUD whose employers began offering an HDHP insurance option during the study period compared with the change among those whose employer never offered an HDHP. We also used an event-study model to account for dynamic time-varying treatment effects. RESULTS: Across both comparison and treatment groups, 2% of the sample experienced a nonfatal opioid overdose during the study period. Our primary model and robustness checks revealed no impact of HDHP offer on the probability of a nonfatal overdose. CONCLUSIONS: Our study suggests that HDHP offer was not associated with an observed increase in the probability of nonfatal opioid overdose among commercially insured person-years with OUD. However, given the strong evidence that medications for OUD (MOUD) can reduce the risk of overdose, research should explore which facets of insurance design may impact MOUD use.

Measurement Approaches to Estimating Methadone Continuity in Opioid Use Disorder Care.

BACKGROUND: Long-term treatment with medications for opioid use disorder (OUD), including methadone, is lifesaving. There has been little examination of how to measure methadone continuity in claims data. OBJECTIVES: To develop an approach for measuring methadone continuity in claims data, and compare estimates of methadone versus buprenorphine continuity. RESEARCH DESIGN: Observational cohort study using de-identified commercial claims from OptumLabs Data Warehouse (January 1, 2017-June 30, 2021). SUBJECTS: Individuals diagnosed with OUD, ≥1 methadone or buprenorphine claim and ≥180 days continuous enrollment (N=29,633). MEASURES: OUD medication continuity: months with any use, days of continuous use, and proportion of days covered. RESULTS: 5.4% (N=1607) of the study cohort had any methadone use. Ninety-seven percent of methadone claims (N=160,537) were from procedure codes specifically used in opioid treatment programs. Place of service and primary diagnosis codes indicated that several methadone procedure codes were not used in outpatient OUD care. Methadone billing patterns indicated that estimating days-supply based solely on dates of service and/or procedure codes would yield inaccurate continuity results and that an approach incorporating the time between service dates was more appropriate. Among those using methadone, mean [s.d.] months with any use, days of continuous use, and proportion of days covered were 4.8 [1.8] months, 79.7 [73.4] days, and 0.64 [0.36]. For buprenorphine, the corresponding continuity estimates were 4.6 [1.9], 80.7 [70.0], and 0.73 [0.35]. CONCLUSIONS: Estimating methadone continuity in claims data requires a different approach than that for medications largely delivered by prescription fills, highlighting the importance of consistency and transparency in measuring methadone continuity across studies.

Effects of Opioid Prescribing Cap Laws on Opioid and Other Pain Treatments Among Persons with Chronic Pain.

BACKGROUND: Many states have adopted laws that limit the amount or duration of opioid prescriptions. These limits often focus on prescriptions for acute pain, but there may be unintended consequences for those diagnosed with chronic pain, including reduced opioid prescribing without substitution of appropriate non-opioid treatments. OBJECTIVE: To evaluate the effects of state opioid prescribing cap laws on opioid and non-opioid treatment among those diagnosed with chronic pain. DESIGN: We used a difference-in-differences approach that accounts for staggered policy adoption. Treated states included 32 states that implemented a prescribing cap law between 2017 and 2019. POPULATION: A total of 480,856 adults in the USA who were continuously enrolled in medical and pharmacy coverage from 2013 to 2019 and diagnosed with a chronic pain condition between 2013 and 2016. MAIN MEASURES: Among individuals with chronic pain in each state: proportion with at least one opioid prescription and with prescriptions of a specific duration or dose, average number of opioid prescriptions, average opioid prescription duration and dose, proportion with at least one non-opioid chronic pain prescription, average number of such prescriptions, proportion with at least one chronic pain procedure, and average number of such procedures. KEY RESULTS: State laws limiting opioid prescriptions were not associated with changes in opioid prescribing, non-opioid medication prescribing, or non-opioid chronic pain procedures among patients with chronic pain diagnoses. CONCLUSIONS: These findings do not support an association between state opioid prescribing cap laws and changes in the treatment of chronic non-cancer pain.

Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity.

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data.

Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population.

Extending (i.e., generalizing or transporting) causal inferences from a randomized trial to a target population requires assumptions that randomized and nonrandomized individuals are exchangeable conditional on baseline covariates. These assumptions are made on the basis of background knowledge, which is often uncertain or controversial, and need to be subjected to sensitivity analysis. We present simple methods for sensitivity analyses that directly parameterize violations of the assumptions using bias functions and do not require detailed background knowledge about specific unknown or unmeasured determinants of the outcome or modifiers of the treatment effect. We show how the methods can be applied to non-nested trial designs, where the trial data are combined with a separately obtained sample of nonrandomized individuals, as well as to nested trial designs, where the trial is embedded within a cohort sampled from the target population.

Calibrated meta-analysis to estimate the efficacy of mental health treatments in target populations: an application to paliperidone trials for treatment of schizophrenia.

BACKGROUNDS: Meta-analyses can be a powerful tool but need to calibrate potential unrepresentativeness of the included trials to a target population. Estimating target population average treatment effects (TATE) in meta-analyses is important to understand how treatments perform in well-defined target populations. This study estimated TATE of paliperidone palmitate in patients with schizophrenia using meta-analysis with individual patient trial data and target population data. METHODS: We conducted a meta-analysis with data from four randomized clinical trials and target population data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Efficacy was measured using the Positive and Negative Syndrome Scale (PANSS). Weights to equate the trial participants and target population were calculated by comparing baseline characteristics between the trials and CATIE. A calibrated weighted meta-analysis with random effects was performed to estimate the TATE of paliperidone compared to placebo. RESULTS: A total of 1,738 patients were included in the meta-analysis along with 1,458 patients in CATIE. After weighting, the covariate distributions of the trial participants and target population were similar. Compared to placebo, paliperidone palmitate was associated with a significant reduction of the PANSS total score under both unweighted (mean difference 9.07 [4.43, 13.71]) and calibrated weighted (mean difference 6.15 [2.22, 10.08]) meta-analysis. CONCLUSIONS: The effect of paliperidone palmitate compared with placebo is slightly smaller in the target population than that estimated directly from the unweighted meta-analysis. Representativeness of samples of trials included in a meta-analysis to a target population should be assessed and incorporated properly to obtain the most reliable evidence of treatment effects in target populations.

Impact of Administering Buprenorphine to Overdose Survivors Using Emergency Medical Services.

STUDY OBJECTIVE: To evaluate the efficacy and safety of utilizing emergency medical services units to administer high dose buprenorphine after an overdose to treat withdrawal symptoms, reduce repeat overdose, and provide a next-day substances use disorder clinic appointment to initiate long-term treatment. METHODS: This was a retrospective matched cohort study of patients who experienced an overdose and either received emergency medical services care from a buprenorphine-equipped ambulance or a nonbuprenorphine-equipped ambulance in Camden, New Jersey, an urban community with high overdose rates. There were 117 cases and 123 control patients in the final sample. RESULTS: Compared with a nonbuprenorphine-equipped ambulance, exposure to a buprenorphine-equipped ambulance was associated with greater odds of engaging in opioid use disorder treatment within 30 days of an emergency medical services encounter (unadjusted odds ratio: 5.62, 95% confidence interval, 2.36 to 13.39). Buprenorphine-equipped ambulance engagement did not decrease repeat overdose compared to the comparison group. Patients who received buprenorphine experienced a decrease in withdrawal symptoms. Their clinical opiate withdrawal scale score decreased from an average of 9.27 to 3.16. buprenorphine-equipped ambulances increased on-scene time by 6.12 minutes. CONCLUSION: Patients who encountered paramedics trained to administer buprenorphine and able to arrange prompt substance use disorder treatment after an acute opioid overdose demonstrated a decrease in opioid withdrawal symptoms, an increase in outpatient addiction follow-up care, and showed no difference in repeat overdose. Patients receiving buprenorphine in the out-of-hospital setting did not experience precipitated withdrawal. Expanded out-of-hospital treatment of opiate use disorder is a promising model for rapid access to buprenorphine after an overdose in a patient population that often has limited contact with the health care system.

Effects of State Opioid Prescribing Laws on Use of Opioid and Other Pain Treatments Among Commercially Insured U.S. Adults.

BACKGROUND: There is concern that state laws to curb opioid prescribing may adversely affect patients with chronic noncancer pain, but the laws' effects are unclear because of challenges in disentangling multiple laws implemented around the same time. OBJECTIVE: To study the association between state opioid prescribing cap laws, pill mill laws, and mandatory prescription drug monitoring program query or enrollment laws and trends in opioid and guideline-concordant nonopioid pain treatment among commercially insured adults, including a subgroup with chronic noncancer pain conditions. DESIGN: Thirteen treatment states that implemented a single law of interest in a 4-year period and unique groups of control states for each treatment state were identified. Augmented synthetic control analyses were used to estimate the association between each state law and outcomes. SETTING: United States, 2008 to 2019. PATIENTS: 7 694 514 commercially insured adults aged 18 years or older, including 1 976 355 diagnosed with arthritis, low back pain, headache, fibromyalgia, and/or neuropathic pain. MEASUREMENTS: Proportion of patients receiving any opioid prescription or guideline-concordant nonopioid pain treatment per month, and mean days' supply and morphine milligram equivalents (MME) of prescribed opioids per day, per patient, per month. RESULTS: Laws were associated with small-in-magnitude and non-statistically significant changes in outcomes, although CIs around some estimates were wide. For adults overall and those with chronic noncancer pain, the 13 state laws were each associated with a change of less than 1 percentage point in the proportion of patients receiving any opioid prescription and a change of less than 2 percentage points in the proportion receiving any guideline-concordant nonopioid treatment, per month. The laws were associated with a change of less than 1 in days' supply of opioid prescriptions and a change of less than 4 in average monthly MME per day per patient prescribed opioids. LIMITATIONS: Results may not be generalizable to non-commercially insured populations and were imprecise for some estimates. Use of claims data precluded assessment of the clinical appropriateness of pain treatments. CONCLUSION: This study did not identify changes in opioid prescribing or nonopioid pain treatment attributable to state laws. PRIMARY FUNDING SOURCE: National Institute on Drug Abuse.

Estimating subgroup effects in generalizability and transportability analyses.

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 non-randomized 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 non-randomized subset, and 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 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.