A framework to determine naloxone saturation with simulation modeling and cost‐effectiveness methods
Zongbo Li, Leah C. Shaw, Carolyn J. Park, Alexander Y. Walley, Czarina N. Behrends, Bruce R. Schackman, Brandon D. L. Marshall, Xiao ZangAbstract
Background and aims
Jurisdictions lack practical tools to determine data‐informed naloxone saturation targets that account for local overdose dynamics and resource constraints. We aimed to introduce a generalizable framework that integrates simulation modeling with cost‐effectiveness principles to identify local naloxone saturation targets, using Rhode Island, USA, as a case study.
Design
We applied a previously developed simulation model to project annual opioid overdose deaths (OODs) in 2026 under a range of annual distribution scenarios. We then used these projections to calculate the number of additional kits required to avert one OOD and compared this metric against hypothetical decision‐maker thresholds.
Setting
Rhode Island, USA.
Participants
Simulated population at risk for OOD.
Measurements
Analogous to cost‐effectiveness analysis, we defined the ‘cost‐effectiveness’ of naloxone distribution as the number of additional kits required (analogous to ‘incremental cost’) to avert one additional OOD (analogous to ‘incremental effect’). Projected annual OODs in 2026; additional number of naloxone kits required to avert one additional OOD.
Results
In the case study, kits‐per‐death‐averted increased with distribution levels, from 761 kits per OOD averted at 60 000 kits distributed annually to 16 836 kits per OOD averted at 280 000 kits distributed annually. Hypothetical thresholds of 5000 and 10 000 kits per OOD averted corresponded to saturation targets of approximately 194 000 and 243 000 kits distributed annually, respectively. A hypothetical cost‐per‐life‐saved threshold of $1 500 000 per life saved corresponds to 238 800 kits distributed annually.
Summary
This framework provides a scalable, data‐driven approach to set evidence‐based naloxone saturation targets based on US state‐specific budgetary constraints and public health considerations.