Accounting for individual differences and timing of events: estimating the effect of treatment on criminal convictions in heroin users

Abstract

BackgroundThe reduction of crime is an important outcome of opioid maintenance treatment (OMT). Criminal intensity and treatment regimes vary among OMT patients, but this is rarely adjusted for in statistical analyses, which tend to focus on cohort incidence rates and rate ratios. The purpose of this work was to estimate the relationship between treatment and criminal convictions among OMT patients, adjusting for individual covariate information and timing of events, fitting time-to-event regression models of increasing complexity.MethodsNational criminal records were cross linked with treatment data on 3221 patients starting OMT in Norway 1997–2003. In addition to calculating cohort incidence rates, criminal convictions was modelled as a recurrent event dependent variable, and treatment a time-dependent covariate, in Cox proportional hazards, Aalen’s additive hazards, and semi-parametric additive hazards regression models. Both fixed and dynamic covariates were included.ResultsDuring OMT, the number of days with criminal convictions for the cohort as a whole was 61% lower than when not in treatment. OMT was associated with reduced number of days with criminal convictions in all time-to-event regression models, but the hazard ratio (95% CI) was strongly attenuated when adjusting for covariates; from 0.40 (0.35, 0.45) in a univariate model to 0.79 (0.72, 0.87) in a fully adjusted model. The hazard was lower for females and decreasing with older age, while increasing with high numbers of criminal convictions prior to application to OMT (all p < 0.001). The strongest predictors were level of criminal activity prior to entering into OMT, and having a recent criminal conviction (both p < 0.001). The effect of several predictors was significantly time-varying with their effects diminishing over time.ConclusionsAnalyzing complex observational data regarding to fixed factors only overlooks important temporal information, and naïve cohort level incidence rates might result in biased estimates of the effect of interventions. Applying time-to-event regression models, properly adjusting for individual covariate information and timing of various events, allows for more precise and reliable effect estimates, as well as painting a more nuanced picture that can aid health care professionals and policy makers.

Associations between methadone maintenance treatment and crime: a 17‐year longitudinal cohort study of Canadian provincial offenders

“…The multivariable Cox model included methadone and interaction terms with time as well as the controlling variables (potential confounders): age at enrollment (quadratic term), gender, ethnicity, education level, calendar year (continuous variable) and number of offences in the previous year (continuous variable). Confounders were selected based on their associations with future offending as well as to account for changes in policy and policing behavior across decades.…”

How handling missing data may impact conclusions: A comparison of six different imputation methods for categorical questionnaire data

“…Reduction in criminal activity is an important aspect of OMT 43 and has been studied extensively. 44–48 Whether the patient had been arrested or charged with criminal activity within the last 12 months was selected as the binary outcome variable in this study. Four binary explanatory variables were included in the analyses: gender (male/female), type of substitution medication (methadone/buprenorphine), use of stimulant drugs (yes/no) and illicit drug use during the last 12 months (yes/no).…”

History of the discovery, development, and FDA-approval of buprenorphine medications for the treatment of opioid use disorder

“…MOUD combined with psychosocial interventions is the most effective treatment option for patients suffering from OUD ( ASAM, 2020 ; Connock et al., 2007 ; Dugosh et al., 2016 ). MOUD has been shown to be associated with positive outcomes for reducing drug use, criminal activity ( Gisev et al., 2019 ; Gossop et al., 2000 ; Oliver et al., 2010 ; Røislien et al., 2014 ), risk behaviors and HIV-transmission ( Marsch 1998 ), overdoses, and overall mortality ( Gisev et al., 2015 ; Sordo et al., 2017 ) as well as improving rates of retention in treatment ( Lim et al., 2022 ).…”