A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.

BACKGROUND: Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. METHODS: To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. RESULTS: We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. CONCLUSION: The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

Serious illness communication skills training for emergency physicians and advanced practice providers: a multi-method assessment of the reach and effectiveness of the intervention.

BACKGROUND: EM Talk is a communication skills training program designed to improve emergency providers' serious illness conversational skills. Using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, this study aims to assess the reach of EM Talk and its effectiveness. METHODS: EM Talk consisted of one 4-h training session during which professional actors used role-plays and active learning to train providers to deliver serious/bad news, express empathy, explore patients' goals, and formulate care plans. After the training, emergency providers filled out an optional post-intervention survey, which included course reflections. Using a multi-method analytical approach, we analyzed the reach of the intervention quantitatively and the effectiveness of the intervention qualitatively using conceptual content analysis of open-ended responses. RESULTS: A total of 879 out of 1,029 (85%) EM providers across 33 emergency departments completed the EM Talk training, with the training rate ranging from 63 to 100%. From the 326 reflections, we identified meaning units across the thematic domains of improved knowledge, attitude, and practice. The main subthemes across the three domains were the acquisition of Serious Illness (SI) communication skills, improved attitude toward engaging qualifying patients in SI conversations, and commitment to using these learned skills in clinical practice. CONCLUSION: Our study showed the extensive reach and the effectiveness of the EM Talk training in improving SI conversation. EM Talk, therefore, can potentially improve emergency providers' knowledge, attitude, and practice of SI communication skills. TRIAL REGISTRATION: Clinicaltrials.gov: NCT03424109; Registered on January 30, 2018.

Developing a Bayesian hierarchical model for a prospective individual patient data meta-analysis with continuous monitoring.

BACKGROUND: Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques. METHODS: We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power. RESULTS: The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions. CONCLUSION: This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.

A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis.

BACKGROUND: Missing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to address this issue. In this study, for the first time, the function of the longitudinal regression tree algorithm as a non-parametric method after imputing missing data using SI and MI was investigated using simulated and real data. METHOD: Using different simulation scenarios derived from a real data set, we compared the performance of cross, trajectory mean, interpolation, copy-mean, and MI methods (27 approaches) to impute missing longitudinal data using parametric and non-parametric longitudinal models and the performance of the methods was assessed in real data. The real data included 3,645 participants older than 18 years within six waves obtained from the longitudinal Tehran cardiometabolic genetic study (TCGS). The data modeling was conducted using systolic and diastolic blood pressure (SBP/DBP) as the outcome variables and included predictor variables such as age, gender, and BMI. The efficiency of imputation approaches was compared using mean squared error (MSE), root-mean-squared error (RMSE), median absolute deviation (MAD), deviance, and Akaike information criteria (AIC). RESULTS: The longitudinal regression tree algorithm outperformed based on the criteria such as MSE, RMSE, and MAD than the linear mixed-effects model (LMM) for analyzing the TCGS and simulated data using the missing at random (MAR) mechanism. Overall, based on fitting the non-parametric model, the performance of the 27 imputation approaches was nearly similar. However, the SI traj-mean method improved performance compared with other imputation approaches. CONCLUSION: Both SI and MI approaches performed better using the longitudinal regression tree algorithm compared with the parametric longitudinal models. Based on the results from both the real and simulated data, we recommend that researchers use the traj-mean method for imputing missing values of longitudinal data. Choosing the imputation method with the best performance is widely dependent on the models of interest and the data structure.

Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials.

Cluster-randomized trials (CRTs) often allocate intact clusters of participants to treatment or control conditions and are increasingly used to evaluate healthcare delivery interventions. While previous studies have developed sample size methods for testing confirmatory hypotheses of treatment effect heterogeneity in CRTs (i.e., targeting the difference between subgroup-specific treatment effects), sample size methods for testing the subgroup-specific treatment effects themselves have not received adequate attention-despite a rising interest in health equity considerations in CRTs. In this article, we develop formal methods for sample size and power analyses for testing subgroup-specific treatment effects in parallel-arm CRTs with a continuous outcome and a binary subgroup variable. We point out that the variances of the subgroup-specific treatment effect estimators and their covariance are given by weighted averages of the variance of the overall average treatment effect estimator and the variance of the heterogeneous treatment effect estimator. This analytical insight facilitates an explicit characterization of the requirements for both the omnibus test and the intersection-union test to achieve the desired level of power. Generalizations to allow for subgroup-specific variance structures are also discussed. We report on a simulation study to validate the proposed sample size methods and demonstrate that the empirical power corresponds well with the predicted power for both tests. The design and setting of the Umea Dementia and Exercise (UMDEX) CRT in older adults are used to illustrate our sample size methods.

Prospective individual patient data meta-analysis: Evaluating convalescent plasma for COVID-19.

As the world faced the devastation of the COVID-19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID-19 encountered at participating sites. It has become clear that it might take several more COVID-19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient-level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta-analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID-19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.

Data from emergency medicine palliative care access (EMPallA): a randomized controlled trial comparing the effectiveness of specialty outpatient versus telephonic palliative care of older adults with advanced illness presenting to the emergency department.

BACKGROUND: The Emergency Medicine Palliative Care Access (EMPallA) trial is a large, multicenter, parallel, two-arm randomized controlled trial in emergency department (ED) patients comparing two models of palliative care: nurse-led telephonic case management and specialty, outpatient palliative care. This report aims to: 1) report baseline demographic and quality of life (QOL) data for the EMPallA cohort, 2) identify the association between illness type and baseline QOL while controlling for other factors, and 3) explore baseline relationships between illness type, symptom burden, and loneliness. METHODS: Patients aged 50+ years with advanced cancer (metastatic solid tumor) or end-stage organ failure (New York Heart Association Class III or IV heart failure, end stage renal disease with glomerular filtration rate < 15 mL/min/m2, or Global Initiative for Chronic Obstructive Lung Disease Stage III, IV, or oxygen-dependent chronic obstructive pulmonary disease defined as FEV1 < 50%) are eligible for enrollment. Baseline data includes self-reported demographics, QOL measured by the Functional Assessment of Cancer Therapy-General (FACT-G), loneliness measured by the Three-Item UCLA Loneliness Scale, and symptom burden measured by the Edmonton Revised Symptom Assessment Scale. Descriptive statistics were used to analyze demographic variables, a linear regression model measured the importance of illness type in predicting QOL, and chi-square tests of independence were used to quantify relationships between illness type, symptom burden, and loneliness. RESULTS: Between April 2018 and April 3, 2020, 500 patients were enrolled. On average, end-stage organ failure patients had lower QOL as measured by the FACT-G scale than cancer patients with an estimated difference of 9.6 points (95% CI: 5.9, 13.3), and patients with multiple conditions had a further reduction of 7.4 points (95% CI: 2.4, 12.5), when adjusting for age, education level, race, sex, immigrant status, presence of a caregiver, and hospital setting. Symptom burden and loneliness were greater in end-stage organ failure than in cancer. CONCLUSIONS: The EMPallA trial is enrolling a diverse sample of ED patients. Differences by illness type in QOL, symptom burden, and loneliness demonstrate how distinct disease trajectories manifest in the ED. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT03325985 . Registered October 30, 2017.

Early Emotion Knowledge and Later Academic Achievement Among Children of Color in Historically Disinvested Neighborhoods.

This study examined longitudinal relations between emotion knowledge (EK) in pre-kindergarten (pre-K; Mage  = 4.8 years) and math and reading achievement 1 and 3 years later in a sample of 1,050 primarily Black children (over half from immigrant families) living in historically disinvested neighborhoods. Participants were part of a follow-up study of a cluster randomized controlled trial. Controlling for pre-academic skills, other social-emotional skills, sociodemographic characteristics, and school intervention status, higher EK at the end of pre-K predicted higher math and reading achievement test scores in kindergarten and second grade. Moderation analyses suggest that relations were attenuated among children from immigrant families. Findings suggest the importance of enriching pre-K programs for children of color with EK-promotive interventions and strategies.

System Changes to Implement the Joint Commission Tobacco Treatment (TOB) Performance Measures for Improving the Treatment of Tobacco Use Among Hospitalized Patients.

BACKGROUND: In 2012 The Joint Commission implemented new Tobacco Treatment (TOB) performance measures for hospitals. A study evaluated the impact of a hospital-based electronic health record (EHR) intervention on adherence to the revised TOB measures. METHODS: The study was conducted in two acute care hospitals in New York City. Data abstracted from the EHR were analyzed retrospectively from 4,871 smokers discharged between December 2012 and March 2015 to evaluate the impact of two interventions: an order set to prompt clinicians to prescribe pharmacotherapy and a nurse-delivered counseling module that automatically populated the nursing care plan for all smokers. The study estimated the relative odds of a patient being prescribed medication and/or receiving smoking cessation counseling in the intervention period compared to the baseline time period. RESULTS: There was a modest increase in medication orders (odds ratio [OR], 1.35). In contrast, rates of counseling increased 10-fold (OR, 10.54). Patients admitted through surgery were less likely to receive both counseling and medication compared with the medicine service. CONCLUSION: Hospitalization presents an important opportunity to engage smokers in treatment for primary and secondary prevention of tobacco-related illnesses. EHRs can be leveraged to facilitate integration of TOB measure requirements into routine inpatient care; however, the smaller effect on prescribing patterns suggests limitations in this approach alone in changing clinician behavior to meet this measure. The success of the nurse-focused EHR-driven intervention suggests an effective tool for integrating the cessation counseling component of the new measures and the importance of nursing's role in achieving the Joint Commission measure targets.

Psychiatric Comorbidity and Substance Use Outcomes in an Office-Based Buprenorphine Program Six Months Following Hurricane Sandy.

BACKGROUND: On October 2012, Hurricane Sandy struck New York City, resulting in unprecedented damages, including the temporary closure of Bellevue Hospital Center and its primary care office-based buprenorphine program. OBJECTIVES: At 6 months, we assessed factors associated with higher rates of substance use in buprenorphine program participants that completed a baseline survey one month post-Sandy (i.e. shorter length of time in treatment, exposure to storm losses, a pre-storm history of positive opiate urine drug screens, and post-disaster psychiatric symptoms). METHODOLOGY: Risk factors of interest extracted from the electronic medical records included pre-disaster diagnosis of Axis I and/or II disorders and length of treatment up to the disaster. Factors collected from the baseline survey conducted approximately one month post-Sandy included self-reported buprenorphine supply disruption, health insurance status, disaster exposure, and post-Sandy screenings for PTSD and depression. Outcome variables reviewed 6 months post-Sandy included missed appointments, urine drug results for opioids, cocaine, and benzodiazepines. RESULTS: 129 (98%) patients remained in treatment at 6 months, and had no sustained increases in opioid-, cocaine-, and benzodiazepine-positive urine drug tests in any sub-groups with elevated substance use in the baseline survey. Contrary to our initial hypothesis, diagnosis of Axis I and/or II disorders pre-Sandy were associated with significantly less opioid-positive urine drug findings in the 6 months following Sandy compared to the rest of the clinic population. CONCLUSION: These findings demonstrate the adaptability of a safety net buprenorphine program to ensure positive treatment outcomes despite disaster-related factors.

Emergency department visits for heart failure and subsequent hospitalization or observation unit admission.

BACKGROUND: Treatment of acute heart failure in the emergency department (ED) or observation unit is an alternative to hospitalization. Both ED management and observation unit management have been associated with reduced costs and may be used to avoid penalties related to rehospitalizations. The purpose of this study was to examine trends in ED visits for heart failure and disposition following such visits. METHODS: We used the National Hospital Ambulatory Medical Care Survey, a representative sample of ED visits in the United States, to estimate rates and characteristics of ED visits for heart failure between 2002 and 2010. The primary outcome was the discharge disposition from the ED. Regression models were fit to estimate trends and predictors of hospitalization and admission to an observation unit. RESULTS: The number of ED visits for heart failure remained stable over the period, from 914,739 in 2002 to 848,634 in 2010 (annual change -0.7%, 95% CI -3.7% to +2.5%). Of these visits, 74.2% led to hospitalization, wheras 3.1% led to observation unit admission. The likelihood of hospitalization did not change during the period (adjusted prevalence ratio 1.00, 95% CI 0.99-1.01 for each additional year), whereas admission to the observation unit increased annually (adjusted prevalence ratio 1.12, 95% CI 1.01-1.25). We observed significant regional differences in likelihood of hospitalization and observation admission. CONCLUSIONS: The number of ED visits for heart failure and the high proportion of ED visits with subsequent inpatient hospitalization have not changed in the last decade. Opportunities may exist to reduce hospitalizations by increasing short-term management of heart failure in the ED or observation unit.

Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes.

Background: Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. Methods: To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative treatment benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. Results: We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analysis demonstrates the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. Conclusion: The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs.

The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel-arm cluster randomized design. While statistical methods have been developed for designing cluster randomized crossover trials, they have exclusively focused on testing the overall average treatment effect, with little attention to differential treatment effects across subpopulations. Recently, interest has grown in understanding whether treatment effects may vary across pre-specified patient subpopulations, such as those defined by demographic or clinical characteristics. In this article, we consider the two-treatment two-period cluster randomized crossover design under either a cross-sectional or closed-cohort sampling scheme, where it is of interest to detect the heterogeneity of treatment effect via an interaction test. Assuming a patterned correlation structure for both the covariate and the outcome, we derive new sample size formulas for testing the heterogeneity of treatment effect with continuous outcomes based on linear mixed models. Our formulas also address unequal cluster sizes and therefore allow us to analytically assess the impact of unequal cluster sizes on the power of the interaction test in cluster randomized crossover designs. We conduct simulations to confirm the accuracy of the proposed methods, and illustrate their application in two real cluster randomized crossover trials.

Psychometric Properties of the Functional Assessment of Cancer Therapy-General for Evaluating Quality of Life in Patients With Life-Limiting Illness in the Emergency Department.

Background: The Functional Assessment of Cancer Therapy-General (FACT-G) is a widely used quality-of-life measure. However, no studies have examined the FACT-G among patients with life-limiting illnesses who present to emergency departments (EDs). Objective: The goal of this study was to examine the psychometric properties of the FACT-G among patients with life-limiting illnesses who present to EDs in the United States. Methods: This cross-sectional study pooled data from 12 EDs between April 2018 and January 2020 (n = 453). Patients enrolled in the study were adults with one or more of the four life-limiting illnesses: advanced cancer, Congestive Heart Failure, Chronic Obstructive Pulmonary Disease, or End-Stage Renal Disease. We conducted item, exploratory, and confirmatory analyses (exploratory factor analysis [EFA] and confirmatory factor analysis [CFA]) to determine the psychometric properties of the FACT-G. Results: The FACT-G had good internal consistency (Cronbach's alpha α = 0.88). The simplest EFA model was a six-factor structure. The CFA supported the six-factor structure, evidenced by the adequate fit indices (comparative fit index = 0.93, Tucker-Lewis index = 0.92, root-mean-square error of approximation = 0.05; 90% confidence interval: 0.04 - 0.06). The six-factor structure comprised the physical, emotional, work and daily activities-related functional well-being, and the family and friends-related social well-being domains. Conclusions: The FACT-G is a reliable measure of health-related quality of life among patients with life-limiting illnesses who present to the ED. Clinical Trial Registration: NCT03325985.

Predictors of Specialty Outpatient Palliative Care Utilization Among Persons With Serious Illness.

CONTEXT: Outpatient Palliative Care (OPC) benefits persons living with serious illness, yet barriers exist in utilization. OBJECTIVES: To identify factors associated with OPC clinic utilization. METHODS: Emergency Medicine Palliative Care Access is a multicenter, randomized control trial comparing two models of palliative care for patients recruited from the Emergency Department (ED): nurse-led telephonic case management and OPC (one visit a month for six months). Patients were aged 50+ with advanced cancer or end-stage organ failure and recruited from 19 EDs. Using a mixed effects hurdle model, we analyzed patient, provider, clinic and healthcare system factors associated with OPC utilization. RESULTS: Among the 603 patients randomized to OPC, about half (53.6%) of patients attended at least one clinic visit. Those with less than high school education were less likely to attend an initial visit than those with a college degree or higher (aOR 0.44; CI 0.23, 0.85), as were patients who required considerable assistance (aOR 0.45; CI 0.25, 0.82) or had congestive heart failure only (aOR 0.46; CI 0.26, 0.81). Those with higher symptom burden had a higher attendance at the initial visit (aOR 1.05; CI 1.00, 1.10). Reduced follow up visit rates were demonstrated for those of older age (aRR 0.90; CI 0.82, 0.98), female sex (aRR 0.84; CI 0.71, 0.99), and those that were never married (aRR 0.62; CI 0.52, 0.87). CONCLUSION: Efforts to improve OPC utilization should focus on those with lower education, more functional limitations, older age, female sex, and those with less social support. Trial Registration ClinicalTrials.gov Identifier: NCT03325985.

Jail-based medication for opioid use disorder and patterns of reincarceration and acute care use after release: A sequence analysis.

BACKGROUND: Treatment with methadone and buprenorphine medications for opioid use disorder (MOUD) during incarceration may lead to better community re-entry, but evidence on these relationships have been mixed. We aimed to identify community re-entry patterns and examine the association between in-jail MOUD and a pattern of successful reentry defined by rare occurrence of reincarceration and preventable healthcare utilization. METHODS: Data came from a retrospective, observational cohort study of 6066 adults with opioid use disorder who were incarcerated in New York City jails and released to the community during 2011-14. An outcome was community re-entry patterns identified by sequence analysis of 3-year post-release reincarceration, emergency department visits, and hospitalizations. An exposure was receipt of in-jail MOUD versus out-of-treatment (42 % vs. 58 %) for the last 3 days before discharge. The study accounted for differences in baseline demographic, clinical, behavioral, housing, and criminal legal characteristics between in-jail MOUD and out-of-treatment groups via propensity score matching. RESULTS: This study identified five re-entry patterns: stability (64 %), hospitalization (23 %), delayed reincarceration (7 %), immediate reincarceration (4 %), and continuous incarceration (2 %). After addressing confounding, 64 % and 57 % followed the stability pattern among MOUD and out-of-treatment groups who were released from jail in 2011, respectively. In 2012-14, the prevalence of following the stability pattern increased year-by-year while a consistently higher prevalence was observed among those with in-jail MOUD. CONCLUSIONS: Sequence analysis helped define post-release stability based on health and criminal legal system involvement. Receipt of in-jail MOUD was associated with a marker of successful community re-entry.

Impact of jail-based methadone or buprenorphine treatment on non-fatal opioid overdose after incarceration.

BACKGROUND: Non-fatal overdose is a leading predictor of subsequent fatal overdose. For individuals who are incarcerated, the risk of experiencing an overdose is highest when transitioning from a correctional setting to the community. We assessed if enrollment in jail-based medications for opioid use disorder (MOUD) is associated with lower risk of non-fatal opioid overdoses after jail release among individuals with opioid use disorder (OUD). METHODS: This was a retrospective, observational cohort study of adults with OUD who were incarcerated in New York City jails and received MOUD or did not receive any MOUD (out-of-treatment) within the last three days before release to the community in 2011-2017. The outcome was the first non-fatal opioid overdose emergency department (ED) visit within 1 year of jail release during 2011-2017. Covariates included demographic, clinical, incarceration-related, and other characteristics. We performed multivariable cause-specific Cox proportional hazards regression analysis to compare the risk of non-fatal opioid overdose ED visits within 1 year after jail release between groups. RESULTS: MOUD group included 8660 individuals with 17,119 incarcerations; out-of-treatment group included 10,163 individuals with 14,263 incarcerations. Controlling for covariates and accounting for competing risks, in-jail MOUD was associated with lower non-fatal opioid overdose risk within 14 days after jail release (adjusted HR=0.49, 95% confidence interval=0.33-0.74). We found no significant differences 15-28, 29-56, or 57-365 days post-release. CONCLUSION: MOUD group had lower risk of non-fatal opioid overdose immediately after jail release. Wider implementation of MOUD in US jails could potentially reduce post-release overdoses, ED utilization, and associated healthcare costs.

COVID-19 Convalescent Plasma Therapy: Long-term Implications.

Background: The long-term effect of coronavirus disease 2019 (COVID-19) acute treatments on postacute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) is unknown. The CONTAIN-Extend study explores the long-term impact of COVID-19 convalescent plasma (CCP) therapy on postacute sequelae of SARS-CoV-2 infection (PASC) symptoms and general health 18 months following hospitalization. Methods: The CONTAIN-Extend study examined 281 participants from the original CONTAIN COVID-19 trial (CONTAIN-RCT, NCT04364737) at 18 months post-hospitalization for acute COVID-19. Symptom surveys, global health assessments, and biospecimen collection were performed from November 2021 to October 2022. Multivariable logistic and linear regression estimated associations between the randomization arms and self-reported symptoms and Patient-Reported Outcomes Measurement Information System (PROMIS) scores and adjusted for covariables, including age, sex, race/ethnicity, disease severity, and CONTAIN enrollment quarter and sites. Results: There were no differences in symptoms or PROMIS scores between CCP and placebo (adjusted odds ratio [aOR] of general symptoms, 0.95; 95% CI, 0.54-1.67). However, females (aOR, 3.01; 95% CI, 1.73-5.34), those 45-64 years (aOR, 2.55; 95% CI, 1.14-6.23), and April-June 2020 enrollees (aOR, 2.39; 95% CI, 1.10-5.19) were more likely to report general symptoms and have poorer PROMIS physical health scores than their respective reference groups. Hispanic participants (difference, -3.05; 95% CI, -5.82 to -0.27) and Black participants (-4.48; 95% CI, -7.94 to -1.02) had poorer PROMIS physical health than White participants. Conclusions: CCP demonstrated no lasting effect on PASC symptoms or overall health in comparison to the placebo. This study underscores the significance of demographic factors, including sex, age, and timing of acute infection, in influencing symptom reporting 18 months after acute hypoxic COVID-19 hospitalization.

Transitions of care between jail-based medications for opioid use disorder and ongoing treatment in the community: A retrospective cohort study.

BACKGROUND: Offering medications for opioid use disorder (MOUD) in carceral settings significantly reduces overdose. However, it is unknown to what extent individuals in jails continue MOUD once they leave incarceration. We aimed to assess the relationship between in-jail MOUD and MOUD continuity in the month following release. METHODS: We conducted a retrospective cohort study of linked NYC jail-based electronic health records and community Medicaid OUD treatment claims for individuals with OUD discharged from jail between 2011 and 2017. We compared receipt of MOUD within 30 days of release, among those with and without MOUD at release from jail. We tested for effect modification based on MOUD receipt prior to incarceration and assessed factors associated with treatment discontinuation. RESULTS: Of 28,298 eligible incarcerations, 52.8 % received MOUD at release. 30 % of incarcerations with MOUD at release received community-based MOUD within 30 days, compared to 7 % of incarcerations without MOUD (Risk Ratio: 2.62 (2.44-2.82)). Most (69 %) with MOUD claims prior to incarceration who received in-jail MOUD continued treatment in the community, compared to 9 % of those without prior MOUD. Those who received methadone (vs. buprenorphine), were younger, Non-Hispanic Black and with no history of MOUD were less likely to continue MOUD following release. CONCLUSIONS: MOUD maintenance in jail is strongly associated with MOUD continuity upon release. Still, findings highlight a gap in treatment continuity upon-reentry, especially among those who initiate MOUD in jail. In the wake of worsening overdose deaths and troubling disparities, improving MOUD continuity among this population remains an urgent priority.