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Hepatitis C virus (HCV) in the U.S. has tripled in the prior five years, and injecting drug use is the primary risk for HCV, with up to 90% of older and former people who inject drugs (PWIDs) testing positive. Laboratory testing of HCV for any PWIDs is the gold standard, however many PWIDs lack access to health treatment or services. Identifying risks of HCV via a data science approach would aid community health workers (CHW) to rapidly link those most at risk of infection with treatment. This study employed a data-science approach to determine the strongest risk factors of HCV in a sample of Mexican-Americans WIDs n = 221 (96 negative/125 positive). Data included 238 demographic and psychosocial predictors. A Random Forest machine learning algorithm demonstrated significant prediction improvement over baseline no information rate comparison. Strongest risks for positive HCV included sharing drug-use equipment and younger age at first heroin use; receiving drug-education during incarceration was protective. A ROC curve fit to the prediction yielded an area under the curve of 0.77. Predictive variables of HCV in the present analysis can be obtained via screening by CHW. Identification of patients most at risk of HCV within community settings can maximize treatment utilization. [ABSTRACT FROM AUTHOR]

Background: Cocaine and methamphetamine have similar withdrawal symptoms and many individuals concurrently use both substances; however, no measures concurrently assess withdrawal from multiple stimulants. Objectives: This study's aim was to explore the Stimulant Selective Severity Assessment (SSSA), a modified version of the Cocaine Selective Severity Assessment (CSSA), in a sample of stimulant users to determine if it can assess withdrawal symptoms in users of one or more stimulants. Methods: Baseline data were analyzed from the STimulant Reduction Intervention using Dosed Exercise trial, a multisite randomized clinical trial that evaluated exercise versus health education on drug use outcomes in individuals with stimulant use disorders. Data were analyzed for internal consistency, construct validity, and scale dimensionality. Results: Internal consistency for the full sample was good (α = 0.81; N = 302), with similar alphas in Cocaine (0.81; n = 177) and Cocaine/Other Stimulant (0.82; n = 92) groups, but with much lower alpha for the group without cocaine use (Other Stimulant, i.e., primarily methamphetamine, α = 0.66; n = 32). Support for construct validity was evidenced by significant positive correlations (r = 0.17 to 0.67) with measures of stimulant craving, depressive symptoms, and pain. Four factors were revealed. Conclusions/Importance: The Stimulant Selective Severity Assessment is a new measure that can be used to assess withdrawal symptoms in users of cocaine or cocaine plus methamphetamine, but it should not be administered to users of methamphetamine only. [ABSTRACT FROM AUTHOR]

Objective: The neonatal intensive care unit (NICU) is an ideal setting to intervene with an under served population on secondhand smoke exposure (SHSe). Unfortunately, attrition may compromise outcomes. Baseline characteristics associated with intervention and follow-up attendance were investigated in mothers who participated in a novel SHSe prevention study designed for households with a smoker and a NICU-admitted infant. Methods: Intervention participants received two motivational, NICU-based counseling sessions; usual care participants received pamphlets. Home-based follow-up assessments occurred at 1, 3 and 6 months. Sociodemographic, smoking history, and psychosocial factors were analyzed. Results: Mothers from households with greater numbers of cigarettes smoked and fewer children had higher odds of both intervention and follow-up attendance. Maternal smoking abstinence (lifetime), more adults in the home and higher perceived interpersonal support were also associated with higher odds of follow-up visit completion. Conclusions: Innovative strategies are needed to engage mothers in secondhand smoke interventions, especially mothers who smoke, have lower levels of social support and have greater childcare responsibilities. [ABSTRACT FROM AUTHOR]

Aim: Mothers of infants in the neonatal intensive care unit (NICU) have very low breastfeeding rates and these high-respiratory-risk (HRR) NICU infants may benefit from breastfeeding through decreased risk for respiratory illnesses. This population's increased risk for maternal depression and high rates (22%) of maternal smoking may negatively affect breastfeeding. Objective: This exploratory study investigated associations of breastfeeding with depressive symptoms and maternal smoking in mothers of HRR NICU infants (i.e. presence of one household smoker and birth weight <1500 g or mechanical ventilation ≥12 h). Methods: Breastfeeding, depression and smoking data were collected from 104 mothers in the NICU following delivery. Results: Fifty-five (52.9%) mothers reported breastfeeding, 39 (37.5%) had a Center for Epidemiological Studies Depression Scale (CES-D) score (≥16) suggestive of depression, and 36 (34.6%) reported smoking. Mothers with CES-D scores ≥16 were less likely to breastfeed compared to those with scores <16 (38.5% versus 61.5%; p = 0.02). Breastfeeding and smoking were not significantly associated ( p < 0.10). Mothers of HRR infants with significant depressive symptoms who smoke have significantly lower breastfeeding rates (21%) than mothers who are not depressed and do not smoke (65%). Conclusions: Interventions to improve breastfeeding initiation and continuation that target depression and smoking are necessary. [ABSTRACT FROM AUTHOR]

The gateway drug model is a popular conceptualization of a progression most substance users are hypothesized to follow as they try different legal and illegal drugs. Most forms of the gateway hypothesis are that 'softer' drugs lead to 'harder,' illicit drugs. However, the gateway hypothesis has been notably difficult to directly test-that is, to test as competing hypotheses in a single model that licit drug use might lead to illicit drug use or the reverse. This article presents a novel statistical technique, dual-process discrete-time survival analysis, which enables this comparison. This method uses mixture-modeling software to estimate 2 concurrent time-to-event processes and their effects on each other. Using this method, support for the gateway hypothesis in the National Longitudinal Survey of Youth, 1997, was weak. However, this article was not designed as a strong test of causal direction but more as a technical demonstration and suffered from certain technological limitations. Both these limitations and future directions are discussed. [ABSTRACT FROM AUTHOR]