Description
Version-of-record in Injury Prevention
Too little is known about the effectiveness of efforts to prevent firearm violence. We evaluated California’s Armed and Prohibited Persons System (APPS), which identifies legal purchasers of firearms who have become prohibited persons and seeks to recover all firearms and ...
Background Too little is known about the effectiveness of efforts to prevent firearm violence. We evaluated California’s Armed and Prohibited Persons System (APPS), which identifies legal purchasers of firearms who have become prohibited persons and seeks to recover all firearms and ammunition to which they have access.
Design and methods This cluster-randomised pragmatic trial was made possible by APPS’s expansion from a small pilot to a continuing statewide programme. We included 363 California cities, allocated to early (n=187) or later (n=176) intervention in blocks stratified by region within the state, and within region by population and violent crime rate. The study period began 1 February 2015; region-specific end dates ranged from 1 May 2015 to 1 February 2016. Analysis was on an intention-to-treat, difference-in-difference basis using generalised linear mixed models and generalised estimating equations with robust SEs. The population-level primary outcome measures were monthly city-level counts of firearm-related homicides, robberies and aggravated assaults. The primary model was adjusted for stratification variables; city-level population, population density, socioeconomic status and firearm purchasing; year; and month.
Findings Allocation groups were well balanced on baseline characteristics and implementation measures. In adjusted models, allocation to early intervention was not associated with statistically significant differences in any primary outcome measure; these findings were robust to multiple sensitivity analyses. There was some heterogeneity across regions.
Conclusions The APPS intervention directly affects a very small percentage of the population, limiting its potential for population-level effects. Individual-level analyses may provide a better estimate of the intervention’s effectiveness.