To determine how the city-run New Haven syringe exchange program (SEP) expedited requests, for entry into treatment, records of the SEP's drug treatment coordinator were analyzed. During the study period, a majority of those requesting treatment did not use the SEP to obtain sterile syringes. Comparisons between the people requesting treatment and those exchanging syringes revealed that those requesting treatment were more likely female and less like White. Factors associated with failure to enter treatment included long lag times, worse insurance, cocaine use, and requesting primary detoxification only. Majorities of the requests, appointments, and entries came from individuals whose treatment was to be paid through city welfare. A managed care initiative from the city welfare department, which excluded the SEP, halved requests or entries. Subsequent addition of the SEP to the initiative did not significantly increase requests or entries. Although the SEP initially acted as a conduit to treatment its effectiveness was curtailed by the imposition of bureaucratic restrictions.

Automatic stepwise subset selection methods in linear regression often perform poorly, both in terms of variable selection and estimation of coefficients and standard errors, especially when number of independent variables is large and multicollinearity is present. Yet, stepwise algorithms remain the dominant method in medical and epidemiological research. Performance of stepwise (backward elimination and forward selection algorithms using AIC, BIC, and Likelihood Ratio Test, p = 0.05 (LRT)) and alternative subset selection methods in linear regression, including Bayesian model averaging (BMA) and penalized regression (lasso, adaptive lasso, and adaptive elastic net) was investigated in a dataset from a cross-sectional study of drug users in St. Petersburg, Russia in 2012–2013. Dependent variable measured health-related quality of life, and independent correlates included 44 variables measuring demographics, behavioral, and structural factors. In our case study all methods returned models of different size and composition varying from 41 to 11 variables. The percentage of significant variables among those selected in final model varied from 100 % to 27 %. Model selection with stepwise methods was highly unstable, with most (and all in case of backward elimination: BIC, forward selection: BIC, and backward elimination: LRT) of the selected variables being significant (95 % confidence interval for coefficient did not include zero). Adaptive elastic net demonstrated improved stability and more conservative estimates of coefficients and standard errors compared to stepwise. By incorporating model uncertainty into subset selection and estimation of coefficients and their standard deviations, BMA returned a parsimonious model with the most conservative results in terms of covariates significance. BMA and adaptive elastic net performed best in our analysis. Based on our results and previous theoretical studies the use of stepwise methods in medical and epidemiological research may be outperformed by alternative methods in cases such as ours. In situations of high uncertainty it is beneficial to apply different methodologically sound subset selection methods, and explore where their outputs do and do not agree. We recommend that researchers, at a minimum, should explore model uncertainty and stability as part of their analyses, and report these details in epidemiological papers.

Opioid overdose is a leading cause of death among injection drug users. Over half of injection drug users report at least one nonfatal overdose during their lifetime. Death from opioid overdose rarely occurs instantaneously, but rather over the course of one to three hours, allowing ample time for providing life-saving measures. In response to the prevalence of overdoses in the U.S., there are a growing number of overdose prevention and naloxone distribution programs targeting the injection drug using community.