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. 2024 Dec 2;7(12):e2448732.
doi: 10.1001/jamanetworkopen.2024.48732.

Agent-Based Model of Combined Community- and Jail-Based Take-Home Naloxone Distribution

Eric Tatara  1   2 , Jonathan Ozik  1   2   3   4 , Harold A Pollack  3   5   6 , John A Schneider  3   5   7   8 , Samuel R Friedman  9   10 , Nina T Harawa  11   12   13 , Basmattee Boodram  14 , Elizabeth Salisbury-Afshar  15 , Anna Hotton  7   8 , Larry Ouellet  16 , Mary Ellen Mackesy-Amiti  14 , Nicholson Collier  1   2 , Charles M Macal  1   2   4
Affiliations

Agent-Based Model of Combined Community- and Jail-Based Take-Home Naloxone Distribution

Eric Tatara et al. JAMA Netw Open. .

Abstract

Importance: Opioid-related overdose accounts for almost 80 000 deaths annually across the US. People who use drugs leaving jails are at particularly high risk for opioid-related overdose and may benefit from take-home naloxone (THN) distribution.

Objective: To estimate the population impact of THN distribution at jail release to reverse opioid-related overdose among people with opioid use disorders.

Design, setting, and participants: This study developed the agent-based Justice-Community Circulation Model (JCCM) to model a synthetic population of individuals with and without a history of opioid use. Epidemiological data from 2014 to 2020 for Cook County, Illinois, were used to identify parameters pertinent to the synthetic population. Twenty-seven experimental scenarios were examined to capture diverse strategies of THN distribution and use. Sensitivity analysis was performed to identify critical mediating and moderating variables associated with population impact and a proxy metric for cost-effectiveness (ie, the direct costs of THN kits distributed per death averted). Data were analyzed between February 2022 and March 2024.

Intervention: Modeled interventions included 3 THN distribution channels: community facilities and practitioners; jail, at release; and social network or peers of persons released from jail.

Main outcomes and measures: The primary outcome was the percentage of opioid-related overdose deaths averted with THN in the modeled population relative to a baseline scenario with no intervention.

Results: Take-home naloxone distribution at jail release had the highest median (IQR) percentage of averted deaths at 11.70% (6.57%-15.75%). The probability of bystander presence at an opioid overdose showed the greatest proportional contribution (27.15%) to the variance in deaths averted in persons released from jail. The estimated costs of distributed THN kits were less than $15 000 per averted death in all 27 scenarios.

Conclusions and relevance: This study found that THN distribution at jail release is an economical and feasible approach to substantially reducing opioid-related overdose mortality. Training and preparation of proficient and willing bystanders are central factors in reaching the full potential of this intervention.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Ozik reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study. Dr Pollack reported receiving grants from the NIH National Institute on Drug Abuse (NIDA) Justice Community Opioid Innovation Network (JCOIN) Methodology and Advanced Analytics Resource Center (MAARC) during the conduct of the study and being a paid expert witness outside the submitted work. Dr Friedman reported receiving NIDA grants through NYU Grossman School of Medicine via the University of Chicago during the conduct of the study. Dr Harawa reported receiving grants from the NIDA during the conduct of the study, personal fees from Gilead Sciences, an HIV Prevention Center grant from Gilead Sciences, and funding from the California HIV/AIDS Research Program outside the submitted work. Prof Boodram reported receiving grants from the NIH during the conduct of the study. Dr Salisbury-Afshar reported receiving grants from the NIDA during the conduct of the study. Prof Hotton reported receiving grants from the NIH during the conduct of the study. Prof Ouellet reported receiving personal fees from the NIH during the conduct of the study. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Justice-Community Circulation Model Synthetic Population Subgroups by Opioid Use and Criminal-Legal-System Involvement (CLI)
CLI includes current or prior incarceration and risk of future incarceration. IOU indicates injection opioid user; NIOU, noninjection opioid use; OU, opioid user. The social network of potential bystanders at overdose events was modeled using stochastic parameter of bystander probability.
Figure 2.
Figure 2.. Flowchart of Bystander Behavior Logic During a Witnessed Opioid-Related Overdose Event Showing Modeled Decisions of the Bystander and Outcomes for Person Experiencing the Overdose
Without a bystander, the survival probability for the person is modeled by the fatality probability per overdose. When a bystander is present and willing to intervene, the bystander may choose to call emergency medical services (EMS) and administer naloxone if available. The bystander logic separately considers the probability of naloxone availability at the overdose location and the probability that it is actually or effectively used.
Figure 3.
Figure 3.. Percentage of Opioid-Related Overdose Deaths Averted for the Selected Community and Jail Naloxone Distribution Scenarios
Each circle indicates a unique combination of nonintervention parameters averaged over 10 stochastic runs, and each color indicates the value of bystander probability for the individual run. Right and left sides of the boxes represent the 25th and 75th percentiles, respectively; the vertical line inside boxes represents the median; and whiskers represent minimum and maximum values.
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Comment in

  • doi: 10.1001/jamanetworkopen.2024.48667

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