An individual-based dynamic model to assess interventions to mitigate opioid overdose risk.

BACKGROUND: Illicit opioid overdose continues to rise in North America and is a leading cause of death. Mathematical modeling is a valuable tool to investigate the epidemiology of this public health issue, as it can characterize key features of population outcomes and quantify the broader effect of structural and interventional changes on overdose mortality. The aim of this study is to quantify and predict the impact of key harm reduction strategies at differing levels of scale-up on fatal and nonfatal overdose among a population of people engaging in unregulated opioid use in Toronto. METHODS: An individual-based model for opioid overdose was built featuring demographic and behavioural variation among members of the population. Key individual attributes known to scale the risk of fatal and nonfatal overdose were identified and incorporated into a dynamic modeling framework, wherein every member of the simulated population encompasses a set of distinct characteristics that govern demographics, intervention usage, and overdose incidence. The model was parametrized to fatal and nonfatal overdose events reported in Toronto in 2019. The interventions considered were opioid agonist therapy (OAT), supervised consumption sites (SCS), take-home naloxone (THN), drug-checking, and reducing fentanyl in the drug supply. Harm reduction scenarios were explored relative to a baseline model to examine the impact of each intervention being scaled from 0% use to 100% use on overdose events. RESULTS: Model simulations resulted in 3690.6 nonfatal and 295.4 fatal overdoses, coinciding with 2019 data from Toronto. From this baseline, at full scale-up, 290 deaths were averted by THN, 248 from eliminating fentanyl from the drug supply, 124 from SCS use, 173 from OAT, and 100 by drug-checking services. Drug-checking and reducing fentanyl in the drug supply were the only harm reduction strategies that reduced the number of nonfatal overdoses. CONCLUSIONS: Within a multi-faceted harm reduction approach, scaling up take-home naloxone, and reducing fentanyl in the drug supply led to the largest reduction in opioid overdose fatality in Toronto. Detailed model simulation studies provide an additional tool to assess and inform public health policy on harm reduction.

Patient preferences for treatment attributes in moderate-to-severe atopic dermatitis: a discrete choice experiment.

Purpose: Evidence on treatment preferences of patients with moderate-to-severe atopic dermatitis (AD) in the United States (US) is limited and an assessment of treatment preferences in this group is warranted.Materials and methods: An online discrete choice experiment survey was conducted (June 2023) among US adults with self-reported moderate-to-severe AD or experience with systemic therapy who had inadequate response to topical treatments. Preference weights estimated from conditional logistic regression models were used to calculate willingness to trade off and attributes' relative importance (RI).Results: Participants (N = 300; mean age: 45 years; 70% females; 52% systemic therapy experienced) preferred treatments with higher efficacy, lower risk of adverse events (AEs), and less frequent blood tests (p < .05). Treatment attributes, from high to low RI, were itch control (38%), risk of cancer (23%), risk of respiratory infections (18%), risk of heart problems (11%), sustained improvement in skin appearance (5%), blood test frequency (3%), and frequency and mode of administration (2%); together, AE attributes accounted for more than half of the RI.Conclusions: Participants preferred AD treatments that maximize itch control while minimizing AE risks, whereas mode of administration had little impact on preferences. Understanding patients' preferences may help improve shared decision-making, potentially leading to enhanced patient satisfaction with treatment, increased engagement, and better clinical outcomes.