Clinical and translational investigators increasingly rely on complex institutional and national data resources, yet barriers related to data discovery, governance, and access pathways remain common. To address fragmentation in data access, we piloted a Data Navigation Program within the Clinical and Translational Science Institute (CTSI) that established a trained Data Navigator as a centralized first point of contact for investigator data inquiries who provided individualized consultations, facilitated connections to data domain experts and honest broker services, and increased awareness of institutional data assets and regulatory requirements. To better characterize investigator needs, a CTSI-wide survey assessing data sources, governance, and training priorities was conducted in collaboration with the Clinical Translational Data Science (CTDS) Workgroup. Results demonstrated strong demand for structured guidance in data discovery and governance navigation. These findings informed refinement of the program, including development of the Research Data Source Match, a self-service decision-support tool implemented in REDCap that generates customized data access roadmaps based on investigator characteristics and data needs. During the pilot year, the Data Navigator conducted consultations addressing electronic health record (EHR), PCORnet resources, and government datasets. Integrating personalized navigation with scalable self-service tools may reduce barriers and support responsible data use in translational research.

Background and Aims:Metabolic dysfunction drives hepatic progression in metabolic dysfunction-associated steatotic liver disease (MASLD), yet conventional binary metabolic syndrome (MetS) definitions may obscure dose-response risk of fibrosis. We evaluated the association between MetS severity and advanced fibrosis probability in a representative US MASLD cohort. Methods:Adults aged 20-74 years with ultrasound-defined MASLD in the Third National Health and Nutrition Examination Survey were analyzed. Exposure was the validated MetS severity Z-score (quartiles, percentile categories, and continuous percentile with restricted cubic splines). Outcome was the Nonalcoholic Fatty Liver Disease Fibrosis Score (NFS)-defined advanced fibrosis probability (intermediate-to-high: NFS ≥ -1.455; high: NFS > 0.676). Complex survey logistic regression estimated adjusted odds ratios (aORs). Results:Among 3036 participants, 29.6% had intermediate fibrosis probability and 5.2% had high advanced fibrosis probability. High fibrosis probability was most prevalent in non-Hispanic Black adults (8.0%) and least in Mexican Americans (2.6%; P value <.001). Approximately 2.5% of the low MetS severity group had a high probability of advanced fibrosis compared with 11.4% in the very high MetS severity group (P value <.001). Each one-quartile decrease in MetS severity was associated with 21% (aOR = 0.79; 95% confidence interval [CI]: 0.68-0.91) and 32% (aOR = 0.68; 95% CI: 0.48-0.96) lower odds of intermediate-to-high and high probability of advanced fibrosis, respectively. In dose-response analyses, advanced fibrosis risk rose modestly through midrange severities and accelerated beyond the 70th severity percentile, reaching aOR of 3.59 (95% CI: 2.74-4.70) at the 90th vs the 50th percentile. Conclusion:MetS severity demonstrates a graded and nonlinear association with advanced fibrosis probability in MASLD. A continuous metabolic-burden measure may enhance upstream triage and risk stratification alongside existing noninvasive pathways.

Background:Ohio has been severely impacted by the opioid crisis, with opioid overdose (OD) death rates exceeding national averages. Accurate OD death prediction supports proactive prevention and treatment allocation. Existing methods often focus on ZIP Code Tabulation Area (ZCTA)-level prediction for small-area resource allocation; however, performance at this resolution is poor due to substantial fluctuations in OD death counts, which introduce noise. This raises a critical methodological question: what is the optimal population threshold for OD death prediction that balances predictive accuracy with geographic resolution? Methods:We perform a theoretical analysis of variance and error bounds to establish the minimal population required for robust prediction. Building on this analysis, we propose an Area-specific Autoencoder Spatiotemporal Graph Neural Network (AAE-STGNN) framework for OD death count prediction using urine drug test (UDT) data as dynamic features and Social Determinants of Health (SDoH) as static features. The framework consists of two key components: (1) an Area-specific Autoencoder (AAE), which learns latent spatial representations while incorporating the minimal population threshold, and (2) a Spatiotemporal Graph Neural Network (STGNN), which models geographic adjacency between areas and dynamic features across time. Results:Empirical evaluations demonstrate that AAE-STGNN outperforms state-of-the-art (SOTA) approaches, achieving improved accuracy and robustness. We also provide the OD death count trend estimation to support public health decision-making. Conclusions:These findings underscore the importance of selecting an optimal spatial granularity and leveraging spatiotemporal modeling techniques for data-driven public health surveillance and targeted intervention in the opioid crisis.

The opioid crisis has severely impacted Ohio, with overdose death rates surpassing national averages and disproportionately affecting rural and Appalachian regions. Accurately predicting county-level opioid overdose (OD) deaths is critical for timely intervention but remains challenging due to the wide differences in opioid OD deaths between large and small counties. We propose a Spatial-Temporal Graph Neural Network (ST-GNN) framework that integrates graph neural networks (GNNs) to capture spatial relationships between counties and Long Short-Term Memory (LSTM) networks to model temporal dynamics. Using quarterly OD death data from Q1 2017 to Q2 2023 for 88 Ohio counties, we incorporate a nine-dimensional dynamic feature set, including naloxone administration events and high-risk opioid prescribing, along with a static Social Determinants of Health (SDoH) index. Compared to traditional statistical models and temporal deep learning baselines, our ST-GNN demonstrates superior performance, particularly in larger counties, while a classification-based strategy improves predictions for small counties, leading to more stable and reliable results. Our findings emphasize the need for spatial-temporal modeling and customized training to enhance public health decision-making in addressing the opioid crisis.

Regulatory and institutional restrictions on access to electronic health records (EHR) result in prolonged delays, often extending several weeks or more, when these records are accessed to assess the feasibility of proposed research. Investigators are restricted from directly querying EHR data servers for feasibility analysis to protect patient privacy, and this responsibility is shifted to independent data analysts. To address this bottleneck, we developed the Patient-Centered Outcomes Research Search Tool (PCORsearch), a web-based set of applications that enables investigators to independently analyze deidentified EHR-derived data while maintaining regulatory compliance. PCORsearch allows users to interactively explore medical terminology codes and data definitions from the PCORnet Common Data Model (CDM) to construct custom cohorts and retrieve feasibility counts. Cohorts are defined using a custom grammar tailored for querying PCORnet CDM data, with attention to usability and security. In addition to cohort discovery, the platform supports the generation of summary statistics and visualizations to further characterize the identified cohorts. Cohorts can be saved and shared between users to aid in collaborative research. The platform ships complete with utility applications to manage and request access to the platform, as well as a companion application where analysts can make use of the web interface and custom grammar to get line-level data access. By streamlining investigator-analyst feasibility assessment workflows, PCORsearch may substantially accelerate early-stage research planning. It enables broader access to large-scale clinical data resources in a modern, secure, compliant manner.

The risk of cardiovascular outcomes following SARS-CoV-2 infection has been reported in adults, but evidence in children and adolescents is limited. This paper assessed the risk of a multitude of cardiac signs, symptoms, and conditions 28-179 days after infection, with outcomes stratified by the presence of congenital heart defects (CHDs), using electronic health records (EHR) data from 19 children's hospitals and health institutions from the United States within the RECOVER consortium between March 2020 and September 2023. The cohort included 297,920 SARS-CoV-2-positive individuals and 915,402 SARS-CoV-2-negative controls. Every individual had at least a six-month follow-up after cohort entry. Here we show that children and adolescents with prior SARS-CoV-2 infection are at a statistically significant increased risk of various cardiovascular outcomes, including hypertension, ventricular arrhythmias, myocarditis, heart failure, cardiomyopathy, cardiac arrest, thromboembolism, chest pain, and palpitations, compared to uninfected controls. These findings were consistent among patients with and without CHDs. Awareness of the heightened risk of cardiovascular disorders after SARS-CoV-2 infection can lead to timely referrals, diagnostic evaluations, and management to mitigate long-term cardiovascular complications in children and adolescents.

The effectiveness of COVID-19 vaccination in children and adolescents with prior SARS-CoV-2 infection remains unclear, particularly for Omicron subvariants. We evaluated vaccine effectiveness against reinfection with Omicron BA.1/2, BA.4/5, XBB, and later subvariants among 5- to 17-year-olds using data from the RECOVER initiative, a national electronic health record database covering 37 U.S. pediatric institutions. We emulated target trials by age group and variant period, comparing previously infected participants between January 2022 and August 2023. During the BA.1/2 period, vaccination reduced the risk of reinfection, with effectiveness rates of 62% in children and 65% in adolescents. During the BA.4/5 period, protection effectiveness in children was 57%, whereas no statistically significant protection was observed in adolescents. During the XBB or later period, no significant protection was observed in either group. In summary, COVID-19 vaccination provided protection against reinfection during early and mid-Omicron periods in previously infected pediatric populations, but effectiveness declined for later variants.

OBJECTIVES:Emergency medical services (EMS) post-overdose outreach programs expand beyond traditional 9-1-1 response to offer overdose survivors linkage to substance use treatment and other related harm-reducing interventions. Although intuitive and increasingly popular, evidence to define expected outcomes is exceedingly limited. We evaluated process and patient outcomes of one large Midwestern post-overdose outreach program to describe outreach characteristics and linkage to substance use treatment. METHODS:This retrospective cohort study used clinical program records of individuals referred to a multidisciplinary post-overdose outreach program following a non-fatal presumed opioid overdose with emergency response. Measures included (i) number of outreach attempts, (ii) modalities of outreach attempts (in-person visit, text message, letter, phone call, or electronic mail), (iii) outcome of outreach (i.e., if the individual was contacted), (iv) interventions provided including linkage to substance use treatment with coordinated admission and transportation. We used descriptive statistics to report patient characteristics, outreach frequency, outreach modality, successful contact, and treatment linkage through the program. RESULTS:From 2020 to 2022, the program attempted outreach to 3,437 individuals. The median age was 37 years (interquartile range, IQR, 30-47). Most individuals were white/non-Hispanic (n = 2,077, 63.1%) and male (n = 2,084, 61.2%). Few were unhoused at the time of outreach (n = 246, 7.2%). The program made a total of 7,935 outreach attempts with a median of 2 outreach attempts (IQR 1-3) per individual. The most common outreach modalities were in-person visit (n = 3,300, 41.6%) and text message (n = 2,776, 35.0%), though phone calls and in-person visits most often resulted in successful contact (52.6% and 23.7%, respectively). Outreach attempts resulted in 743 (21.6%) successful contacts and the program linked 304 individuals (40.9% of all contacted individuals, 8.8% of all attempted outreach) to treatment. Notably, 160 (52.6%) of the 304 individuals linked to treatment required 3 or more outreach attempts before treatment linkage occurred. CONCLUSIONS:Post-overdose outreach initiated by EMS can successfully find and link individuals to substance use treatment following a non-fatal opioid overdose. However, this intervention may be resource intensive, often requiring multiple attempts at outreach and several modalities of interaction to facilitate treatment linkage.

Importance Provisional estimates of fatal drug overdoses in the US are lagging by 6 months. Efforts to estimate the overdose burden for this 6-month lag window require up-to-date data, such as real-time urine drug test (UDT) data, capable of identifying sudden changes in the overdose trajectory, such as the increase in overdose deaths experienced at the beginning of the COVID-19 pandemic. Objective To evaluate the utility of using aggregated UDT data to estimate national-level drug overdose deaths for the 6-month lag window in which overdose data are unavailable. Design, Setting, and Participants This cross-sectional study included 3 135 748 urine samples submitted for UDT by Millennium Health from patients aged 18 years or older in substance use disorder treatment health care facilities across the US between January 1, 2015, and January 31, 2025. Urine drug test results were aggregated to generate monthly positivity rates and mean creatinine-normalized levels of fentanyl and methamphetamine (among the sample testing positive for fentanyl). Monthly, national drug overdose mortality counts were obtained from the Centers for Disease Control and Prevention. Exposures Urine drug testing. Main Outcomes and Measures Drug overdose death totals were estimated for every 6-month period from January to June 2019 through August 2024 to January 2025 by training generalized linear models with a negative binomial distribution on the preceding 4 years of data and using monthly UDT data to generate overdose estimates for the 6-month lag window of interest. Mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) were calculated by comparing projected monthly estimates with observed overdose death totals. Results A total of 3 135 748 UDT specimens (57.2% from men; mean [SD] age, 38.1 [12.4] years) were included in this study. From 2019 to August 2024, 537 104 people died of an overdose in the US, with a substantial increase in early 2020 at the onset of the COVID-19 pandemic. The UDT modeling strategy (MAPE, 7.1%; MAE, 540.9 deaths per month; RMSE, 659.4) outperformed the baseline autoregressive integrated moving average model (MAPE, 9.0%; MAE, 704.9 deaths per month; RMSE, 1075.8) across all metrics. Furthermore, the model detected the sudden increase in overdose deaths at the start of the COVID-19 pandemic. Conclusions and Relevance In this cross-sectional study, findings suggested that aggregated UDT data may be used to estimate up-to-date overdose death trends. Model implementation can be improved by introducing additional exposure variables, such as those related to drug seizures and syndromic surveillance.

Cancer survivors face an increased risk of cardiovascular disease (CVD) due to treatment-related toxicity, lifestyle factors, and comorbidities. Addressing CV health is crucial for improving quality of life and long-term outcomes. The American Heart Association's Life's Essential 8 framework highlights modifiable determinants of CV health, emphasizing early detection and monitoring. Mobile health (mHealth) technologies, such as wearables and smartphone apps, offer continuous tracking, yet their applications in cancer survivorship remain unclear. This review systematically characterizes the types of mHealth technologies used to monitor CV health in cancer survivors, focusing on the specific data collected (major adverse CV events, CV risk factors, and surrogate endpoints) and the use of active versus passive collection methods. A systematic search of PubMed, Scopus, Embase, and Web of Science identified studies published between January 1, 2016, and June 13, 2024. Eligible studies included observational and interventional designs assessing at least one CV outcome using mHealth. Data were extracted on design, technology type, and outcomes. Risk of bias was evaluated using the Cochrane RoB-2 and ROBINS-I tools. Fourteen studies (13 interventional, one observational) met criteria. Physical activity was the most monitored risk factor, followed by HR. The most common technologies were mobile apps and commercial wearables. Passive methods typically captured PA and HR, while active methods captured PA, symptom tracking, and diet. A key finding was the lack of integration with electronic medical records, highlighting a gap in clinical implementation. mHealth provides scalable tools to track CV health indicators in cancer survivors. Findings highlight the potential to support practice by enabling remote oversight of risk-reducing behaviors and guiding lifestyle interventions. However, we also identified gaps, including the underutilization of biomarkers (e.g., HRV) and the lack of integration with electronic records. Future research must address these gaps to translate real-time data into clinical insights and optimize survivorship care.

INTRODUCTION:Deaths related to drug overdose and suicide in the USA have increased 500% and 35%, respectively, over the last two decades. The human and economic costs to society associated with these 'deaths of despair' are immense. Great efforts and substantial investments have been made in treatment and prevention, yet these efforts have not abated these increasing trajectories of deaths over time. The COVID-19 pandemic has exacerbated and highlighted these problems. Notably, some geographical areas (eg, Appalachia, farmland) and some communities (eg, low-income persons, 'essential workers', minoritised populations) have been disproportionately affected. Risk factors have been identified for substance use and suicide deaths: forms of adversity, neglect, opportunity indexes and trauma. Yet, the biological, psychological and social mechanisms driving risk are not uniform. Notably, most people exposed to risk factors do not become symptomatic and could broadly be considered resilient. Achieving a better understanding of biological, psychological and social mechanisms underlying both pathology and resilience will be crucial for improving approaches for prevention and treatment and creating precision medicine approaches for more efficient and effective treatment. METHODS AND ANALYSIS:The State of Ohio Adversity and Resilience (SOAR) study is a prospective, longitudinal, multimodal, integrated familial study designed to identify biological, psychological and social risk and resilience factors and processes leading to mental health disorders, substance use disorders, substance overdose, suicide and associated psychological/medical comorbidities which reduce life expectancy and quality of life. It includes two nested longitudinal samples: (1) WD Survey: an address-based random population epidemiological sample of 15 000 individuals (unique households) representative of the state of Ohio assessed for psychosocial, psychiatric, behavioural health and substance use factors and (2) Brain Health Study: a family-based, multimodal, deep-phenotyping study conducted in 1200 families (up to 3600 persons aged 12-72 years) including MRI, electroencephalography, blood biomarkers and psychiatric diagnostic interviews, as well as neuropsychological, psychosocial functioning and family/community history, dynamics and support assessments. SOAR is designed to discover, develop and deploy advanced predictive analytics and interventions to transform mental health prevention, diagnosis, treatment and recovery. ETHICS AND DISSEMINATION:All participants will provide written informed consent (or parental permission and assent for minors). The study was approved by The Ohio State University Institutional Review Board (study numbers 2023H0316 (Brain Health) and 2023H0350 (Wellness Survey). The Brain Health study was also approved by institutional review boards at each partnering institution involved in conducting participant assessments. Findings will be disseminated to academic peers, clinicians and healthcare consumers, policymakers and the general public, using local and international academic channels (academic journals, evidence briefs and conferences) and outreach (workshops and seminars).

IMPORTANCE:Prior studies have demonstrated the effectiveness of COVID-19 vaccines in children and adolescents. However, the benefits of vaccination in these age groups with prior infection remain underexplored. OBJECTIVE:To evaluate the effectiveness of COVID-19 vaccination in preventing reinfection with various Omicron subvariants (BA.1/2, BA.4/5, XBB, and later) among 5- to 17-year-olds with prior SARS-CoV-2 infection. DESIGN:A target trial emulation through nested designs with distinct study periods. SETTING:The study utilized data from the Research COVID to Enhance Recovery (RECOVER) initiative, a national electronic health record (EHR) database comprising 37 U.S. children's hospitals and health institutions. PARTICIPANTS:Individuals aged 5-17 years with a documented history of SARS-CoV-2 infection prior to the study start date during a specific variant-dominant period (Delta, BA.1/2, or BA.4/5) who received a subsequent dose of COVID-19 vaccine during the study periods were compared with those with a documented history of infection who did not receive SARS-CoV-2 vaccine during the study period. Those infected within the Delta-Omicron composite period (December 1, 2021, to December 31, 2021) were excluded. The study period was from January 1, 2022, to August 30, 2023, and focused on adolescents aged 12 to 17 years and children aged 5 to 11 years. EXPOSURES:At least received one COVID-19 vaccination during the study period vs. no receipt of any COVID-19 vaccine during the study period. MAIN OUTCOMES AND MEASURES:The primary outcome is documented SARS-CoV-2 reinfection during the study period (both asymptomatic and symptomatic cases). The effectiveness of the COVID-19 vaccine was estimated as (1- hazard ratio) *100%, with confounders adjusted by a combination of propensity score matching and exact matching. RESULTS:The study analyzed 87,573 participants during the BA.1/2 period, 229,326 during the BA.4/5 period, and 282,981 during the XBB or later period. Among vaccinated individuals, significant protection was observed during the BA.1/2 period, with effectiveness rates of 62% (95% CI: 38%-77%) for children and 65% (95% CI: 32%-81%) for adolescents. During the BA.4/5 period, vaccine effectiveness was 57% (95% CI: 25%-76%) for children, but not statistically significant for adolescents (36%, 95% CI: -16%-65%). For the XBB period, no significant protection was observed in either group, with effectiveness rates of 22% (95% CI: -36%-56%) in children and 34% (95% CI: -10%-61%) in adolescents. CONCLUSIONS AND RELEVANCE:COVID-19 vaccination provides significant protection against reinfection for children and adolescents with prior infections during the early and mid-Omicron periods. This study also highlights the importance of addressing low vaccination rates in pediatric populations to enhance protection against emerging variants.

Background: Nationally, many ASCVD patients fail to achieve an LDL-C <70 mg/dL, and uptake of both statin and non-statin therapies is low. The degree to which this varies across health systems is less clear. Methods: A cross sectional analysis was performed where lipid levels and lipid lowering therapies (LLT) were assessed using electronic health record data in patients with a previous diagnosis of ASCVD. The data was obtained across 14 US healthcare systems between 1/1/2021-12/31/2022. Proportions of patients with an active prescription of any statin, high intensity statin, ezetimibe, PCSK9i, and combination therapy (two or more agents) within 395 days of the most recent LDL-C value (index date) was evaluated overall and by participating site. Additionally, the proportion of patients with an LDL-C <70 mg/dL at the index date was also assessed. Results: Across 14 health systems, 1,118,623 patients with ASCVD were identified (median 61,840 per health system, range 8,161-182,315). Overall, 675,776 (60.4%) had an LDL-C level in the past year (range 39.1% - 70.8%). Of those with a lipid level, achievement of LDL-C <70 mg/dL ranged from 34.6-47.2%. In total, 42.6% were on any statin, 20.1% were on a high intensity statin, 4.3% on ezetimibe, and 1.2% on a PCSK9i. Only 2.9% were on combination therapy of a statin with ezetimibe or a PCSK9i. Variability was seen across health systems in utilization of each of these therapies, however even in the highest performing health systems, LLT uptake and achievement of LDL-C < 70mg/dL remained low ( Figure). Conclusion: Variability in utilization of LLT in ASCVD patients between health systems suggests that system-level factors may impact achieving guideline-based LDL-C goals. Despite the variability, the highest proportion of patients achieving an LDL-C <70mg/dL remained under 50% indicating the need for aggressive implementation efforts.

BACKGROUND Evidence-based practices for reducing opioid-related overdose deaths include overdose education and naloxone distribution, the use of medications for the treatment of opioid use disorder, and prescription opioid safety. Data are needed on the effectiveness of a community-engaged intervention to reduce opioid-related overdose deaths through enhanced uptake of these practices. METHODS In this community-level, cluster-randomized trial, we randomly assigned 67 communities in Kentucky, Massachusetts, New York, and Ohio to receive the intervention (34 communities) or a wait-list control (33 communities), stratified according to state. The trial was conducted within the context of both the coronavirus disease 2019 (Covid-19) pandemic and a national surge in the number of fentanyl-related overdose deaths. The trial groups were balanced within states according to urban or rural classification, previous overdose rate, and community population. The primary outcome was the number of opioid-related overdose deaths among community adults. RESULTS During the comparison period from July 2021 through June 2022, the population-averaged rates of opioid-related overdose deaths were similar in the intervention group and the control group (47.2 deaths per 100,000 population vs. 51.7 per 100,000 population), for an adjusted rate ratio of 0.91 (95% confidence interval, 0.76 to 1.09; P=0.30). The effect of the intervention on the rate of opioid-related overdose deaths did not differ appreciably according to state, urban or rural category, age, sex, or race or ethnic group. Intervention communities implemented 615 evidence-based practice strategies from the 806 strategies selected by communities (254 involving overdose education and naloxone distribution, 256 involving the use of medications for opioid use disorder, and 105 involving prescription opioid safety). Of these evidence-based practice strategies, only 235 (38%) had been initiated by the start of the comparison year. CONCLUSIONS In this 12-month multimodal intervention trial involving community coalitions in the deployment of evidence-based practices to reduce opioid overdose deaths, death rates were similar in the intervention group and the control group in the context of the Covid-19 pandemic and the fentanyl-related overdose epidemic.

Importance The HEALing Communities Study (HCS) evaluated the effectiveness of the Communities That HEAL (CTH) intervention in preventing fatal overdoses amidst the US opioid epidemic. Objective To evaluate the impact of the CTH intervention on total drug overdose deaths and overdose deaths involving combinations of opioids with psychostimulants or benzodiazepines. Design, Setting, and Participants This randomized clinical trial was a parallel-arm, multisite, community-randomized, open, and waitlisted controlled comparison trial of communities in 4 US states between 2020 and 2023. Eligible communities were those reporting high opioid overdose fatality rates in Kentucky, Massachusetts, New York, and Ohio. Covariate constrained randomization stratified by state allocated communities to the intervention or control group. Trial groups were balanced by urban or rural classification, 2016-2017 fatal opioid overdose rate, and community population. Data analysis was completed by December 2023. Intervention Increased overdose education and naloxone distribution, treatment with medications for opioid use disorder, safer opioid prescribing practices, and communication campaigns to mitigate stigma and drive demand for evidence-based interventions. Main Outcomes and Measures The primary outcome was the number of drug overdose deaths among adults (aged 18 years or older), with secondary outcomes of overdose deaths involving specific opioid-involved drug combinations from death certificates. Rates of overdose deaths per 100 000 adult community residents in intervention and control communities from July 2021 to June 2022 were compared with analyses performed in 2023. Results In 67 participating communities (34 in the intervention group, 33 in the control group) and including 8 211 506 participants (4 251 903 female [51.8%]; 1 273 394 Black [15.5%], 603 983 Hispanic [7.4%], 5 979 602 White [72.8%], 354 527 other [4.3%]), the average rate of overdose deaths involving all substances was 57.6 per 100 000 population in the intervention group and 61.2 per 100 000 population in the control group. This was not a statistically significant difference (adjusted rate ratio [aRR], 0.92; 95% CI, 0.78-1.07; P = .26). There was a statistically significant 37% reduction (aRR, 0.63; 95% CI, 0.44-0.91; P = .02) in death rates involving an opioid and psychostimulants (other than cocaine), and nonsignificant reductions in overdose deaths for an opioid with cocaine (6%) and an opioid with benzodiazepine (1%). Conclusion and Relevance In this clinical trial of the CTH intervention, death rates involving an opioid and noncocaine psychostimulant were reduced; total deaths did not differ statistically. Community-focused data-driven interventions that scale up evidence-based practices with communications campaigns may effectively reduce some opioid-involved polysubstance overdose deaths.

AbstractClinical trial data underscores the need to improve oncolytic virus (OV) distribution within tumors, a challenge compounded by the lack of predictive biomarkers and limited opportunities for post-treatment analysis. To decipher the factors influencing treatment outcomes, we employed multimodal bioluminescence imaging (MM-BLI) in conjunction with magnetic resonance imaging (MRI) to monitor OV infection, replication, and tumor viability in orthotopic brain glioma mouse models. This approach revealed two distinct therapeutic responses: “Responders” with tumor regression and “Non-Responders” with tumor progression. In-depth analysis of individual brains from both groups uncovered dynamic interactions between the OV and the tumor microenvironment, highlighting the involvement of Iba-1+ microglia and tumor necrosis in hindering OV distribution within the tumor. To address this, we incorporated a CSF-1 receptor inhibitor (PLX3397), which improved tumor control by enhancing OV’s direct cytopathic effects and reducing microglial interference. Our findings highlight microglia as a significant barrier to effective OV therapy, suggesting that targeting brain-resident immune cells could enhance the therapeutic efficacy of OVs in resistant brain tumors.

Opioid-related fatalities are a leading cause of death in Ohio and nationally, with an increasing number of overdoses attributable to fentanyl. Rapid fentanyl test strips can identify fentanyl and some fentanyl analogs in urine samples and are increasingly being used to check illicit drugs for fentanyl before they are used. Fentanyl test strips are a promising harm reduction strategy; however, little is known about the real-world acceptability and impact of fentanyl test strip use. This study investigates fentanyl test strip distribution and education as a harm reduction strategy to prevent overdoses among people who use drugs. The research team will recruit 2400 individuals ≥ 18 years with self-reported use of illicit drugs or drugs purchased on the street within the past 6 months. Recruitment will occur at opioid overdose education and naloxone distribution programs in 16 urban and 12 rural Ohio counties. Participating sites will be randomized at the county level to the intervention or non-intervention study arm. A brief fentanyl test strip educational intervention and fentanyl test strips will be provided to participants recruited from sites in the intervention arm. These participants will be eligible to receive additional fentanyl test strips for 2 years post-enrollment. Participants recruited from sites in the non-intervention arm will not receive fentanyl test strip education or fentanyl test strips. All participants will be followed for 2 years post-enrollment using biweekly, quarterly, and 6-month surveys. Primary outcomes include (1) identification of perceived barriers and facilitating factors associated with incorporating fentanyl test strip education and distribution into opioid overdose education and naloxone distribution programs; (2) differences in knowledge and self-efficacy regarding how to test drugs for fentanyl and strategies for reducing overdose risk between the intervention and non-intervention groups; and (3) differences in non-fatal and fatal overdose rates between the intervention and non-intervention groups. Findings from this cluster randomized controlled trial will contribute valuable information about the feasibility, acceptability, and impact of integrating fentanyl test strip drug checking in rural and urban communities in Ohio and help guide future overdose prevention interventions. ClinicalTrials.gov NCT05463341. Registered on July 19, 2022. https://clinicaltrials.gov/study/NCT05463341