Alex S. Bennett

Background: Popular discourse often frames prescription stimulants Ritalin and Adderall as drugs for teens and emerging adults with greater financial resources (eg, students or young professionals), while illicit stimulants such as crack or methamphetamine are often considered drugs for older adults with lower incomes. Crack and methamphetamine are also frequently used in combination with opioids to offset the sedating effects of adulterants such as fentanyl and xylazine in the unregulated drug supply. This combination of stimulant and opioid use creates additional overdose risks and may require new public health approaches. Our team posited that creating effective messages to protect against overdose in the context of stimulant use entailed first developing a better understanding of which stimulants people are using in combination with opioids. Objective: While conducting formative research to develop a new intervention, our team sought to examine prescription stimulant use among participants, including middle-aged, lower-income, and homeless people. This entailed first ascertaining the prevalence of Ritalin and Adderall use among participants, and then asking people why they used them. We also sought to implement a novel AI-assisted coding methodology and to write up the steps we took as a replicable model that other research teams could readily use to facilitate their own data analysis. Methods: We collected substance use screenings during 3 waves of data collection in 2025 (N=102 participants). In early 2026, we conducted 16 qualitative interviews with people who reported using Ritalin or Adderall. We then conducted mixed methods analyses to examine reported substance use, including the use of opioids and Ritalin or Adderall in combination, as well as potential relationships between using opioids with prescription stimulants and reporting a desire to stop using drugs. After conducting the interviews, we implemented a new hybrid methodology using AI tools to code interview transcripts and generate detailed reports accompanied by supporting quotes. Results: Participants described using Ritalin or Adderall to manage negative effects of increasingly powerful opioids, including oversedation and withdrawal symptoms. A subset of participants who reported using both Ritalin or Adderall and opioids were 3.5 times more likely to agree or strongly agree with the statement “I want to stop using drugs but I need help to do that” compared to those who did not report using these substances in combination (19/22, 86.4% vs 18/28, 64.3%; odds ratio 3.52, 95% CI 0.83-14.89; P =.09). Although not statistically significant, this difference may prove practically significant and merits further study. Conclusions: The use of prescription stimulants appears to be increasingly common among new populations of people who use opioids, including those who report very low incomes and even homelessness. Additional research is warranted to examine how this type of polysubstance use may require different types of prevention strategies.

BACKGROUND Naloxone remains a critical life-saving intervention, but overdose reversals may also involve withdrawal and stigmatizing encounters with professional responders. Such experiences may shape future willingness to engage with overdose response resources. OBJECTIVE To understand interpersonal nature of overdose response to inform trainings and develop compassionate response overdose protocols. METHODS We conducted a cross-sectional, community-engaged survey of overdose survivors in New York City between June and August 2024. Eligible participants had experienced a naloxone-reversed overdose within the prior 12 months. Measures included overdose context, naloxone dosing, withdrawal experiences, responder interactions, and willingness to engage with naloxone. Descriptive analyses characterized overdose experiences, and hypothesis-driven analyses examined associations between interpersonal quality of professional response and future willingness to receive naloxone, administer naloxone to others, and call 911. RESULTS Severe or “pretty bad” withdrawal was reported by 62% of participants, and negative treatment by professional responders was common. More favorable EMT/police treatment was associated with greater willingness to administer naloxone to another person (ρ=.267, p=.044) and to call 911 during a future overdose (ρ=.514, p<.001). More favorable hospital treatment was also associated with greater willingness to call 911 (ρ=.359, p=.006). In sensitivity analyses, negative hospital treatment was associated with approximately threefold greater odds of reluctance to administer naloxone to another person (OR=3.13, 95% CI 1.01–9.66). CONCLUSIONS The interpersonal quality of overdose response may have consequences extending beyond the immediate reversal event. Respectful, non-stigmatizing treatment of overdose survivors may help preserve trust and willingness to engage with naloxone and emergency response during subsequent overdoses. Findings support incorporating compassionate care principles into overdose-response training and practice. CLINICALTRIAL na

The principle “Nothing about us without us” underscores the ethical imperative to involve people with lived and living expertise in shaping harm reduction praxis. While the United States Veterans Health Administration (VHA) has expanded harm reduction initiatives, such as syringe services and naloxone distribution, over the past four years, Veterans with lived and living expertise (VWLLE) have not been meaningfully integrated into policy and program design. In response, VHA clinicians and advocates established the Harm Reduction Veteran Engagement Board (HR-VEB), a peer-supported initiative that positions VWLLE as partners in harm-reduction reforms. This perspective emphasizes structural barriers faced by Veterans who use drugs, highlights the leadership and work of HR-VEB members, and outlines policy and practice recommendations for designing more equitable care. Through the voices and work of Veterans, we argue that peer-led harm reduction is both evidence-based and a matter of social justice.

Despite efforts to blanket high-mortality communities with no-cost naloxone, drug checking technologies, and alternatives to using opioids alone, there rem

Objectives: To evaluate the performance of modified Poisson regression using cluster-weighted generalized estimating equations (MP-CWGEE) for estimating risk ratios in longitudinal data with informative cluster sizes (ICSs). Methods: Using a comprehensive Monte Carlo simulation study, we evaluated the finite-sample performance of MP-CWGEE relative to five competing approaches: (1) modified Poisson regression using (unweighted) generalized estimating equations (MP-GEE), (2) log-binomial regression using cluster-weighted generalized estimating equations (LB-CWGEE), (3) log-binomial regression using (unweighted) generalized estimating equations (LB-GEE), (4) log-binomial mixed effects model (LB-MM), and (5) Poisson mixed effects model (Pois-MM). We conducted 1,000 simulations across 324 simulation scenarios, spanning different ICS mechanisms, informativeness degrees, sample sizes, exposure effects, within-person correlations, and exposure-covariate associations. Performance metrics included convergence rate, empirical bias, standard error ratio, coverage rate, and empirical Type I error rate. MP-CWGEE’s practical application was demonstrated in a cohort study of 423 people who used illicit opioids in New York City. Results: MP-CWGEE recovered the individual-averaged marginal risk ratio with negligible bias, near-nominal coverage and Type I error, and perfect convergence across all scenarios, including under high ICS. The unweighted marginal estimators were biased under ICS, and the log-binomial approaches converged less reliably. The Pois-MM performed comparably to MP-CWGEE, but requires a random-effects specification and yields a conditional (cluster-specific) rather than a marginal interpretation. Conclusions: MP-CWGEE offers a robust modeling approach for estimating risk ratios in longitudinal data with ICSs. Implementation code for R, Stata, and SAS is provided to facilitate wider adoption.

Background Despite efforts to blanket high-mortality communities with no-cost naloxone, drug checking technologies, and alternatives to using opioids alone, there remains a considerable lack of knowledge about how exposure to these low-threshold interventions may impact rates of naloxone protection. Methods People who used illicit opioids in New York City were surveyed between April 2019–April 2022. Naloxone protection was defined as the proportion of opioid use events in the past 30 days when opioid use occurred with naloxone present and someone to administer it. Mixed-effect Poisson models examined correlates (syringe service program utilization, behavioral, and experiential factors) of naloxone protection among the overall sample and stratified by gender. Results Among 428 study participants (mean age 49 years, 64% cisgender male, 35% Black, 41% Latinx), several factors were significantly associated with naloxone protection levels. Visiting a syringe service program (SSP) in the last three months was significantly associated with higher naloxone protection in the full sample (IRR = 1.94, 95% CI: 1.38–2.74) and among males (IRR = 1.84, 95% CI: 1.29–2.64). Significantly lower naloxone protection was also observed among Latinx participants compared to White individuals in the full sample (IRR = 0.64, 95% CI: 0.44–0.94) and among females aged 57–72 years compared to those aged 20–40 years (IRR = 0.39, 95% CI: 0.16–0.95). Participants living with someone who does not use opioids (vs. living with someone who uses opioids) showed significantly lower naloxone protection levels in the full sample (IRR = 0.54, 95% CI: 0.37–0.80) and among males (IRR = 0.46, 95% CI: 0.29–0.73). Other factors associated with lower naloxone protection in the full sample included any opioid withdrawal in the last 30 days, higher pain severity, and more negative life events. Conversely, receiving social support from another person who uses opioids was significantly associated with higher naloxone protection in the full sample (IRR = 1.14, 95% CI: 1.07–1.22) and among females (IRR = 1.30 95% CI 1.16–1.46). Additional factors associated with greater naloxone protection in the full sample included: concurrent opioid and stimulant use, heroin/injection use (1–15 days), fentanyl test strip use, lifetime opioid overdose, and number of lifetime arrests. Conclusion Findings suggest positive impacts of syringe service program engagement and use of harm reduction best-practices and interventions on promoting naloxone protection and highlight differential associations. These results can be used to inform culturally-sensitive and tailored outreach to people at risk of overdose.

Variation in binary outcomes over time by cluster size arises across various biomedical disciplines, including reproductive health, dental medicine, and psychiatric epidemiology. This study formally integrates modified Poisson regression with cluster-weighted generalized estimating equations (MP-CWGEE) for computing risk ratios in longitudinal studies with informative cluster sizes. Using a comprehensive Monte-Carlo simulation study, we empirically evaluated MP-CWGEE’s statistical properties against alternative modeling approaches: MP-GEE, log-binomial CWGEE (LB-CWGEE), and log-binomial GEE (LB-GEE). We conducted 1,000 simulations across varying sample sizes, risk ratios, and informativeness degrees. MP-CWGEE demonstrated superior performance in model convergence, empirical bias, average estimated standard error, coverage, and Type 1 error control. While LB-CWGEE showed comparable results, its convergence rates were slightly inferior. The benefits of cluster-weighted models (MP-CWGEE and LB-CWGEE) over unweighted models (MP-GEE and LB-GEE) were pronounced in scenarios with informative cluster sizes. We demonstrated MP-CWGEE’s practical application to a cohort study of people who used illicit opioids in New York City. We also provided implementation code for R, Stata, and SAS to facilitate wider adoption of the MP-CWGEE approach.

Background: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use research.Objective: To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes.Methods: We simulated data for 200 individuals. Using the Vale and Maurelli method, we simulated multivariate non-normal drug exposure data: xylazine (mean = 300 ng/mL, SD = 100 ng/mL), fentanyl (mean = 200 ng/mL, SD = 71 ng/mL), benzodiazepine (mean = 300 ng/mL, SD = 55 ng/mL), and nitazene (mean = 200 ng/mL, SD = 141 ng/mL) concentrations. We performed 10,000 MCMC sampling iterations with three Markov chains. Model diagnostics included trace plots, r-hat values, and effective sample sizes. We also provided visual relationships of the univariate and bivariate exposure-response and the overall mixture effect.Results: Higher levels of fentanyl and nitazene concentrations were associated with higher levels of the simulated health outcome, controlling for age. Trace plots, r-hat values, and effective sample size statistics demonstrated BKMR stability across multiple Markov chains.Conclusions: Our understanding of drug mixtures tends to be limited to studies of single-drug models. BKMR offers an innovative way to discern which substances pose a greater health risk than other substances and can be applied to assess univariate, bivariate, and cumulative drug effects on health outcomes. Antecedentes: Se necesitan enfoques anal & iacute;ticos innovadores para estudios de medicamentos que permitan comprender mejor el consumo simult & aacute;neo de opioides y no opioides entre personas que usan drogas il & iacute;citas. Un enfoque es la regresi & oacute;n bayesiana de m & aacute;quina kernel (BKMR), ampliamente utilizada en epidemiolog & iacute;a ambiental para estudiar mezclas de exposiciones, pero que ha recibido mucha menos atenci & oacute;n en la investigaci & oacute;n sobre el uso de sustancias.Objetivo: Describir la utilidad del enfoque BKMR para estudiar los efectos de las mezclas de sustancias en los resultados de salud.M & eacute;todos: Simulamos datos para 200 individuos. Utilizando el m & eacute;todo de Vale y Maurelli, simulamos datos de exposici & oacute;n a drogas multivariadas no normales: concentraciones de xilazina (media = 300 ng/mL, SD = 100 ng/mL), fentanilo (media = 200 ng/mL, SD = 71 ng/mL), benzodiazepina (media = 300 ng/mL, SD = 55 ng/mL) y nitazeno (media = 200 ng/mL, SD = 141 ng/mL). Realizamos 10,000 iteraciones de muestreo MCMC con tres cadenas de Markov. Los diagn & oacute;sticos del modelo incluyeron gr & aacute;ficos de trazado, valores r-hat y tama & ntilde;os efectivos de muestra. Tambi & eacute;n proporcionamos relaciones visuales del efecto exposici & oacute;n-respuesta univariada y bivariada y del efecto global de la mezcla.Resultados: Niveles m & aacute;s altos de concentraciones de fentanilo y nitazeno se asociaron con niveles m & aacute;s altos del resultado de salud simulado, controlando la edad. Los gr & aacute;ficos de trazado, los valores r-hat y las estad & iacute;sticas del tama & ntilde;o efectivo de muestra demostraron la estabilidad de BKMR en m & uacute;ltiples cadenas de Markov.Conclusiones: Nuestra comprensi & oacute;n de las mezclas de medicamentos tiende a limitarse a estudios de modelos de un solo medicamento. BKMR ofrece una manera innovadora de discernir qu & eacute; sustancias representan un mayor riesgo para la salud que otras y se puede aplicar para evaluar los efectos univariados, bivariados y acumulativos de los medicamentos en los resultados de salud.