A Dyadic Analysis of Criminal Justice Involvement and Sexual HIV Risk Behaviors Among Drug-Involved Men in Community Corrections and Their Intimate Partners in New York City: Implications for Prevention, Treatment and Policies

People in community corrections have rates of HIV and sexual risk behaviors that are much higher than the general population. Prior literature suggests that criminal justice involvement is associated with increased sexual risk behaviors, yet these studies focus on incarceration and use one-sided study designs that only collect data from one partner. To address gaps in the literature, this study used the Actor Partner-Interdependence Model with Structural Equation Modeling (SEM), to perform a dyadic analysis estimating individual (actor-only) partner-only, and dyadic patterns (actor-partner) of criminal justice involvement and greater sexual risks in a sample of 227 men on probation and their intimate partners in New York City, United States. Standard errors were bootstrapped with 10,000 replications to reduce bias in the significance tests. Goodness of fit indices suggested adequate or better model fit for all the models. Significant actor-only relationships included associations between exposures to arrest, misdemeanor convictions, time spent in jail or prison, felony convictions, lifetime number of incarceration events, prior conviction for disorderly conduct and increased sexual risk behaviors. Partner only effects included significant associations between male partners conviction for a violent crime and their female partners' sexual risk behaviors. Men's encounters with police and number of prior misdemeanors were associated with their own and intimate partners' sexual risk behaviors. Women's prior arrest was associated with their own and intimate partners' sexual risk behaviors. The results from the present study suggest that men on probation and their intimate partners' criminal justice involvement are associated with increased engagement in sexual risk behaviors. It is necessary to conduct greater research into developing dyadic sexual risk reduction and HIV/STI prevention interventions for people who are involved in the criminal justice system

Practice pattern changes and improvements in hematopoietic cell transplantation for primary immunodeficiencies

Allogeneic HCT practice patterns for PID changed between 1974–2016. Three-year survival improved to >70% for SCID and non-SCID patients after 1999, and further increased to 94% for SCID patients transplanted 2010–2016 following newborn screening diagnosis

Qualitative System Dynamics Modeling to Support Community Planning in Opioid Overdose Prevention

Purpose: We aimed to help community stakeholders develop a shared understanding of the opioid crisis through qualitative system dynamics (SD) modeling to inform local strategies for prevention and treatment. Methods: As part of the HEALing Communities Study-New York State, we used secondary qualitative data from community stakeholder interviews and coalition meeting notes to develop qualitative SD models that elucidate the interdependencies and feedback structures underlying the opioid epidemic in each community. Results: The synthesized model revealed multiple balancing and reinforcing feedback loops that influenced the adoption and reach of evidence-based practices to reduce opioid overdose and fatality. Conclusion: SD modeling is a novel approach to helping community stakeholders to see the inter-connectedness of actors, factors and sectors and the need for multiple mutually reinforcing strategies to avert opioid overdose and fatality. Social workers could play a key role in linking actions across sectors in such a complex system

Embedding Big Qual and Team Science Into Qualitative Research: Lessons From a Large-Scale, Cross-Site Research Study

Background: A major part of the HEALing Communities Study (HCS), launched in 2019 to address the growing opioid epidemic, is evaluating the study’s intervention implementation process through an implementation science (IS) approach. One component of the IS approach involves teams with more than 20 researchers collaborating across four research sites to conduct in-depth qualitative interviews with over 300 participants at four time points. After completion of the first two rounds of data collection, we reflect upon our qualitative data collection and analysis approach. We aim to share our lessons learned about designing and applying qualitative methods within an implementation science framework. Methods: The HCS evaluation is based on the RE-AIM/PRISM framework and incorporates interviews at four timepoints. At each timepoint, the core qualitative team of the Intervention Work Group drafts an interview guide based on the framework and insights from previous round(s) of data collection. Researchers then conduct interviews with key informants and coalition members within their respective states. Data analysis involves drafting, iteratively refining, and finalizing a codebook in a cross-site and within-site consensus processes. Interview transcripts are then individually coded by researchers within their respective states. Results: Successes in the evaluation process include having structured procedures for communication, data collection, and analysis, all of which are critical for ensuring consistent data collection and for achieving consensus during data analysis. Challenges include recognizing and accommodating the diversity of training and knowledge among researchers, as well as establishing reliable ways to securely store, manage, and share the large volumes of data. Conclusion: Qualitative methods using a Team Science approach have been limited in their application in large, multi-site randomized controlled trials of health interventions. Our experience provides practical guidance for future studies with large teams that are experientially and disciplinarily diverse and that are seeking to incorporate qualitative or mixed-methods components for their evaluations

A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program.

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses

Impact of the Communities That HEAL Intervention on Buprenorphine-Waivered Practitioners and Buprenorphine Prescribing: A Prespecified Secondary Analysis of the HCS Randomized Clinical Trial

Buprenorphine significantly reduces opioid-related overdose mortality. From 2002 to 2022, the Drug Addiction Treatment Act of 2000 (DATA 2000) required qualified practitioners to receive a waiver from the Drug Enforcement Agency to prescribe buprenorphine for treatment of opioid use disorder. During this period, waiver uptake among practitioners was modest; subsequent changes need to be examined. To determine whether the Communities That HEAL (CTH) intervention increased the rate of practitioners with DATA 2000 waivers and buprenorphine prescribing. This prespecified secondary analysis of the HEALing Communities Study, a multisite, 2-arm, parallel, community-level, cluster randomized, open, wait-list-controlled comparison clinical trial was designed to assess the effectiveness of the CTH intervention and was conducted between January 1, 2020, to December 31, 2023, in 67 communities in Kentucky, Massachusetts, New York, and Ohio, accounting for approximately 8.2 million adults. The participants in this trial were communities consisting of counties (n = 48) and municipalities (n = 19). Trial arm randomization was conducted using a covariate constrained randomization procedure stratified by state. Each state was balanced by community characteristics including urban/rural classification, fatal opioid overdose rate, and community population. Thirty-four communities were randomized to the intervention and 33 to wait-list control arms. Data analysis was conducted between March 20 and September 29, 2023, with a focus on the comparison period from July 1, 2021, to June 30, 2022. Waiver trainings and other educational trainings were offered or supported by the HEALing Communities Study research sites in each state to help build practitioner capacity. The rate of practitioners with a DATA 2000 waiver (overall, and stratified by 30-, 100-, and 275-patient limits) per 100 000 adult residents aged 18 years or older during July 1, 2021, to June 30, 2022, were compared between the intervention and wait-list control communities. The rate of buprenorphine prescribing among those waivered practitioners was also compared between the intervention and wait-list control communities. Intention-to-treat and per-protocol analyses were performed. A total of 8 166 963 individuals aged 18 years or older were residents of the 67 communities studied. There was no evidence of an effect of the CTH intervention on the adjusted rate of practitioners with a DATA 2000 waiver (adjusted relative rate [ARR], 1.04; 95% CI, 0.94-1.14) or the adjusted rate of practitioners with a DATA 2000 waiver who actively prescribed buprenorphine (ARR, 0.97; 95% CI, 0.86-1.10). In this randomized clinical trial, the CTH intervention was not associated with increases in the rate of practitioners with a DATA 2000 waiver or buprenorphine prescribing among those waivered practitioners. Supporting practitioners to prescribe buprenorphine remains a critical yet challenging step in the continuum of care to treat opioid use disorder. ClinicalTrials.gov Identifier: NCT04111939

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative

Objective In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Materials and Methods We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. Conclusion By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require