Joshua A. Barocas

Background and aim: Health officials in the United States (USA) are seeking strategies to mitigate overdose mortality. Medications for opioid use disorder (MOUD) are effective but underused in residential treatment centers. We used a simulation model to investigate population‐level health and health economic impacts of requiring residential treatment centers to offer MOUD to patients with opioid use disorder (OUD). Design, setting and participants: We used a cohort state‐transition model in Massachusetts, USA, simulating 4 strategies: (1) status quo – a small fraction of residential centers offers MOUD; (2) ‘Improved MOUD’ – all residential centers routinely offer MOUD to admitted patients; (3) ‘Improved MOUD with linkage’ – adding linkage to outpatient MOUD for those discharged on MOUD; and (4) ‘Improved MOUD with extended linkage’ – adding linkage for all patients being discharged. We used two complementary simulation approaches: a closed cohort estimating per‐person outcomes for residential treatment patients and an open cohort estimating state‐level impacts across the Massachusetts OUD population. Measurements: Outcomes included person‐time on MOUD, overdose fatalities, costs (2024 US dollars), cost‐effectiveness and budgetary impact. Results: In the closed cohort, compared with the status quo, the ‘Improved MOUD’ strategy increased person‐time in MOUD by 36% and prevented 5 [95% uncertainty interval (UI) = 1–6] overdose deaths per 10 000 individuals at one year. Over a lifetime horizon, this strategy extended life expectancy by 0.0041 life‐years (LY) and discounted quality‐adjusted life expectancy by 0.0034 quality‐adjusted life‐years (QALY) at an incremental lifetime cost per person of $309. ‘Improved MOUD with extended linkage’ was the preferred strategy, providing more LY (0.0096) and QALY (0.0080) at lower incremental cost ($198). The incremental cost‐effectiveness ratio compared with the status quo was $40 195/QALY gained. At the state level, this strategy prevented 2.64 (95% UI = 0.49–4.76) overdose deaths per 10 000 individuals over 10 years (a 0.40% reduction). The 5‐year budgetary impact to the Massachusetts health care system of providing MOUD in residential treatment was $9.65 million. Findings were robust in sensitivity analyses; however, there is some risk of unintended consequences should facilities choose to close rather than offer MOUD. In a scenario in which 21% of sites closed (reflecting centers that discourage MOUD) outcomes were worse than under the status quo. Summary: Residential treatment programs for opioid use disorder appear to be an effective and cost‐effective venue for expanding the use of medications for opioid use disorder, with the greatest value achieved when care navigation links patients to community‐based treatment upon discharge.

Background: Pneumonia and sepsis remain leading causes of hospitalization and death among people with HIV (PWH) despite widespread antiretroviral therapy. Although disparities in critical illness have been described, it remains unclear whether the association between HIV status and mortality differs across racial groups. Setting: Retrospective cohort study of adults hospitalized with pneumonia and/or sepsis using the Healthcare Cost and Utilization Project State Inpatient Databases from seven U.S. states (2018–2021). Methods: We compared crude in-hospital mortality by HIV status and race. Multilevel logistic regression models adjusted for demographics, comorbidities, acute organ failures present on admission, do-not-resuscitate status, surgical status, and hospital-level clustering. We evaluated race as an effect modifier using an HIV × race interaction model to estimate race-specific adjusted odds ratios. Complementary analyses evaluated racial disparities separately among patients with and without HIV. Results: Among 3,949,906 hospitalizations, PWH had lower crude mortality than patients without HIV (8.4% vs. 10.2%; absolute risk difference, 1.8%, 95% CI, 1.6–2.0). A significant HIV × race interaction was observed (P=0.0033). HIV infection was associated with increased mortality among patients identified as White (aOR, 1.09; 95% CI, 1.03–1.16) and Asian (aOR, 1.53; 95% CI, 1.13–2.08), but not among patients identified as Black or Other race. Racial disparities also differed according to HIV status. Conclusions: The association between HIV infection and pneumonia- and sepsis-related mortality differs across racial groups, indicating important heterogeneity masked by average population estimates. An intersectionality framework may provide insight into structural mechanisms underlying disparities in critical illness among PWH.

Importance: Syringe service programs (SSPs) deliver evidence-based harm reduction interventions to persons who inject drugs to reduce morbidity and mortality in this population, including syringe exchange, naloxone distribution, linkage to medications for opioid use disorder, and other services. In July 2025, a federal executive order threatened federal support for SSPs in the US. Objective: To estimate the potential long-term outcomes of halting federal funding for SSPs. Design, Setting, and Participants: This decision analytical model study used a closed cohort microsimulation model of the natural history of injection drug use and health outcomes among persons who inject drugs in the US from August 1, 2025, to August 2030. The model was populated with data from the Centers for Disease Control and Prevention’s National HIV Behavioral Surveillance system and published data to create representative cohorts of persons who inject drugs nationwide. Exposure: Cases in which total funding for SSPs was reduced by 11% and 80% were modeled. Within each case, 3 potential scenarios related to federal funding disruptions due to an executive order threatening funding for SSPs were modeled: (1) funding disruptions remain in place for 5 years, through August 2030; (2) funding returns to previous levels after 1 year, in August 2026; and (3) funding returns to 100% of previous levels in January 2029. Main Outcomes: The primary outcome was 5-year all-cause and overdose mortality and nonfatal overdoses. Results: In a hypothetical study population of 3 694 500 persons who inject drugs (57.0% male; mean [SD] age, 49.5 [17.5] years), all-cause mortality increased by 0.1% (95% credible interval [CrI], 0-0.2%) to 5.0% (95% CrI, 0-0.8%), overdose mortality increased by 0.2% (95% CrI, −0.1% to 0.4%) to 6.9% (95% CrI, −4.3% to 14.4%), and nonfatal overdoses decreased by 0.1% (95% CrI, −0.2% to 0) to 4.2% (95% CrI, −7.8% to 0.1%) during 5 years across all scenarios. The worst-case scenario, in which there was sustained high levels of service disruption, resulted in 39 600 additional deaths and 15 600 additional overdose deaths among persons who inject drugs in the US. All-cause mortality and overdose mortality increased in most sensitivity analyses. Conclusions and Relevance: In this decision analytical model study estimating the effects of reducing federal funding for SSPs, the findings suggest that mortality will increase among persons who inject drugs during the 5 years after loss of funding. Future studies are needed to understand clinical effects of funding changes.

Little is known about antibiotic use and antimicrobial resistance among people who inject drugs and people who experience homelessness. Here, we identify key antibiotic stewardship challenges unique to these important patient populations and outline concrete street stewardship interventions to better understand and/or ameliorate these challenges.

Background and aims: The opioid crisis is still a public health emergency in the United States, despite recent declines in opioid overdose deaths (OODs) and increased availability of evidence‐based practices (EBPs) for opioid use disorder (OUD). The geographic variability in OODs drives the need for localized decision‐making, where interventions are tailored to the unique needs of communities. This study aimed to develop and calibrate a simulation model that evaluates the impact of enhanced implementation of EBP on OODs at the community‐level. Design: We developed OPSiM (Opioid Policy Simulation Model), a community‐level microsimulation model that simulates the course of opioid use, OUD, treatment, recovery and overdose‐related events. The model was parameterized with data from the HEALing Communities Study and looked at six scenarios of EBPs implemented in 2025 with sustainment through 2030: (1) maintain 2024 EBP levels (status quo); (2) increase initiation of medications for opioid use disorder (MOUD); (3) increase MOUD retention; (4) increase MOUD initiation and retention; (5) increase distribution of naloxone; and (6) both scenarios 4 and 5. Setting: Twenty‐nine communities in Massachusetts, New York, and Ohio, USA. Participants: Simulated community residents with non‐prescribed opioid use or OUD. Measurements: Estimated number of OODs per 100 000 individuals between 2025 and 2030 in each community, averaged across the 26 communities. Findings: Under the status quo, the model projected 158 OODs (range across communities: 39–468) per 100 000 individuals between 2025 and 2030. Increasing medications for the treatment of OUD (MOUD) retention alone reduced OODs by 6% (range: 3–15%), while increasing MOUD initiation alone reduced OODs by 9% (range: 8–12%). Increasing both MOUD initiation and retention had a synergistic effect, reducing OODs by 21% (range: 15–31%). Reduction in OODs in response to increased MOUD initiation and/or retention was similar across urban and rural communities. The effect of increasing naloxone distribution varied substantially across communities due to differing saturation levels; in some communities, additional naloxone kits provided only marginal benefits. Rural communities were further from saturation whereas most urban communities were at or close to saturation. Conclusions: A tailored, multi‐pronged approach that scales up medications for opioid use disorder alongside widespread naloxone distribution, and that addresses community‐specific needs and capacities, will be most effective at reducing opioid overdose deaths in the United States.

In 2025, the federal administration proposed approximately $1.7 billion in cuts to the Centers for Disease Control and Prevention (CDC), including eliminating the agency's Domestic HIV Prevention Program [ 1, 2]. Should these proposed cuts be enacted, a range of core human immunodeficiency virus (HIV) prevention activities would be at risk, including federally funded HIV testing. Any funding cuts to HIV testing would cause both measurable and hidden negative impacts. HIV testing is a foundational, cost-effective strategy for ending the HIV epidemic in the United States [ 3–6]. Since the first commercial HIV test was licensed in 1985, HIV testing at state and local health departments, at community-based organizations, and, most recently, for individuals via at-home self-tests has been funded largely by CDC programs [ 7]. Each CDC-funded HIV testing program has been instrumental in diagnosing people with HIV, particularly in jurisdictions with the greatest needs [ 8]. From 2010 to 2021, up to 3 million CDC-funded HIV tests were performed annually across the United States, leading to 24%–33% of all new HIV diagnoses [ 3, 9, 10]. In its first year (2023–2024), Together TakeMeHome, a CDC-funded program to expand at-home HIV self-testing, mailed 440 000 self-test kits, leading to more than 8000 diagnoses [ 11]. Taken together, these data underscore the central role of CDC-funded programs in supporting HIV testing and diagnosis efforts.

This decision analytical model estimates the change in the number of people experiencing homelessness in a year following termination of federal support for Housing First programs.

Background and aims: Addressing the opioid crisis requires opioid use disorder (OUD) prevalence estimates at the community level for targeted interventions. This study presents a new method that utilizes Medicaid claims data and publicly available data to estimate OUD prevalence at the United States (US) county level and compares it with other existing estimates. Design, setting and participants: This study utilized data on OUD diagnoses among Medicaid beneficiaries in combination with national survey data, Census data and published literature to estimate the prevalence of OUD (including undiagnosed OUD) in each of the US state of Ohio's 88 counties for the years 2019 to 2021 among the population aged 12 years and older. Prevalence estimates were adjusted for misclassification in claims data and for variation in healthcare utilization among individuals with OUD. Measurements: Counts and proportions of the population aged 12 and older with OUD at the US state and county level. Findings: OUD prevalence for Ohioans aged 12 years and older was relatively stable at 3.6% [95% uncertainty interval (UI) = 3.5%–3.8%] in 2019 and 3.7% (95% UI = 3.5%–3.9%) in 2021. County‐level prevalence estimates ranged from 0.7% to 14.2% in 2021. Southern counties generally had higher OUD prevalence than northern counties. The prevalence estimates were strongly correlated (Pearson's r = 0.88) with prevalence estimates for 19 Ohio counties from a previous study. Compared with the previous estimates, estimates from the current study tended to be lower for most communities but remained within the 95% credible intervals of previous estimates. Conclusions: This approach for estimating opioid use disorder prevalence within United States communities by using Medicaid claims data and publicly available data is a robust alternative to methods relying on individual‐level data or multiple linked datasets.

Introduction: Homelessness in the United States increased every year since 2016, with a 38% increase from 2023 to 2024. Much of the increase is attributable to rising home and rent costs, economic hardship caused by the recent pandemic, and the ending of protective legislation. Notably, people who experience homelessness have an increased risk of substance use disorders, HIV infection and poorer HIV outcomes than people who are stably housed. The iHouse model aims to develop feasible, effective, and cost-effective tailored approaches to improve health outcomes in this population including life expectancy, overdose, and HIV.Methods and analysis: The study will employ Group Model Building methods and use insights from that process to develop an agent-based model simulating the dynamic processes contributing to HIV incidence and treatment, overdose, and life expectancy among people along the housing and homelessness continuum in Denver, CO and San Francisco, CA. The model will evaluate multiple outcomes from 4 conceptual dimensions: (1) movement along the housing continuum, (2) population health (overdose and HIV incidence and life expectancy), (3) budgetary impact, (4) economic value.Ethics and dissemination: This study has been approved by the Colorado Institutional Review Board at the University of Colorado under protocol 24–0878. The data generated by this protocol, the methodologies used, and the findings will be made available in a timely manner to other researchers. iHOUSE code and parameter values will be published in Git Hub, such that all model analyses can be reproduced by independent investigators. Documentation of all parameter estimates and model results will be published for independent review and confirmation. In addition, supplemental materials and appendices for the model will be shared on a publicly available website.