Faculty Bibliography

Objectives: Tobacco use may be related to financial strain in people with depression. This study explored the relationship among tobacco expenditures, financial distress, and smoking-induced deprivation (SID) in smokers with and without depression. Methods: Adult tobacco users in the United States (N = 234) completed a survey assessing depression (PHQ-8≥10), sociodemographics, tobacco use and spending, financial satisfaction and distress, and SID. Regression models controlling for income compared outcomes between participants with (N = 96) and without (N = 138) depression. Results: Participants with depression were 4 times more likely to report SID (32% vs 8%, p = .00). Smokers with depression had lower financial satisfaction, greater worry about being able to meet monthly living expenses, higher frequency of being unable to afford a leisure activity, higher frequency living paycheck to paycheck, and lower confidence in finding money for an emergency (p < .01). In smokers with depression, higher tobacco expenditures were independently associated with greater odds of SID and higher levels of financial distress (p < .01). Conclusions: About one-third of smokers with depression forego essentials to pay for tobacco. Tobacco interventions may alleviate financial strain in people with depression.

BACKGROUND:Hepatitis C virus (HCV) incidence has increased dramatically in the United States in recent years and is associated with the opioid epidemic due to high HCV transmission risk among people who inject drugs (PWID). HCV transmission knowledge is difficult to ascertain given the lack of psychometrically-tested measures. We developed and validated an HCV injection-risk knowledge scale. METHODS:Analyses used data from 539 New York City opioid users ages 18-29 recruited via respondent-driven sampling in 2014-16. Blood samples yielded HCV antibody status. Principal components analysis (PCA) of nine knowledge items answered true, false, or don't know identified useful scale items. We evaluated internal consistency with the Cronbach's alpha coefficient and assessed construct validity by comparing knowledge levels with those from a previously validated general HCV knowledge scale and by comparing key sub-group knowledge levels. RESULTS:PCA identified one component with five items that explained 45% of the total variance and had high internal consistency (alpha=0.91). All items referred to transmission through drug-injection equipment and practices: sharing cookers, cottons, diluting water, water containers, and cleaning syringes with water. The mean percent correct was 75%, and as expected, was moderately correlated with general HCV knowledge (Spearman's rho=0.55). As hypothesized, knowledge levels were highest for those previously tested for HCV, those with HCV antibody-positive status, PWID, and those who had received harm reduction information in various settings. CONCLUSIONS:The 5-item, validated HCV Injection-Risk Knowledge Scale (HCV-IRKS) may provide educators, care providers, and researchers with critical information for reducing HCV among PWID.

BACKGROUND:Criminal justice involved (CJS) populations with opioid use disorder (OUD) have high rates of relapse, future arrests, and death upon release. While medication for OUD (MOUD) reduces opioid relapse, concerns regarding diversion and stigma limit treatment in CJS populations. Extended release naltrexone (XR-NTX), as an opioid antagonist, may be more acceptable to CJS administrators. However, the impact of XR-NTX on criminal recidivism remains unknown. METHODS:Arrest data from a published randomized trial comparing XR-NTX to treatment as usual (TAU) was captured by self-report and official state arrest records. Comparisons of future arrests, time to first arrest and total number of arrests were performed using chi square tests and multivariable generalized regression models. Secondary outcomes explored differences in arrests by type and severity of crime, use of opioid and other drugs, and study phase. RESULTS:Of 308 participants randomized, 300 had arrest data. The incidence of arrests did not differ between XR-NTX (47.6%) and TAU (42.5%) participants. (ChiSq p = 0.37). Additionally, there was no significant difference in time to first arrest (adjusted HR 1.35, CI 0.96-1.89) and number of arrests per participant (adjusted IR 1.33, CI 0.78-2.27). Controlling for gender, age, previous criminal activity, and use of non-opioid drugs, logistic regression demonstrated no significant difference in incidence of arrests between groups (adjusted OR 1.38, 95% CI 0.85-2.22). CONCLUSIONS:We detected no significant difference in arrests between CJS participants with OUD randomized to XR-NTX or TAU. Despite its efficacy in reducing opioid use, XR-NTX alone may be insufficient to reduce criminal recidivism.

BACKGROUND:Current data suggest that opioid misuse or opioid use disorder (OUD) may be over represented among tobacco users. However, this association remains understudied in primary care settings. A better understanding of the extent of heterogeneity in opioid misuse among primary care patients who use tobacco may have implications for improved primary care-based screening, prevention, and intervention approaches. METHODS:Data were derived from a sample of 2000 adult (aged ≥18) primary care patients across 5 distinct clinics. Among past-year tobacco users (n = 882), we assessed the prevalence of opioid misuse and OUD by sociodemographic characteristics and past-year polysubstance use. Latent class analysis (LCA) was used to identify heterogeneous subgroups of tobacco users according to past-year polysubstance use patterns. Multinomial logistic regression was used to examine variables associated with LCA-defined class membership. RESULTS:Past-year tobacco use was reported by >84% of participants who reported past-year opioid misuse or OUD. Among those reporting past-year tobacco use, the prevalence of past-year opioid misuse and OUD was 14.0% and 9.5%, respectively. The prevalence of opioid misuse or OUD was highest among tobacco users who were male or unemployed. Three LCA-defined classes among tobacco users were identified including a tobacco-minimal drug use group (78.0%), a tobacco-cannabis use group (10.1%), and a tobacco-opioid/polydrug use group (11.9%). Class membership differed by sociodemographic characteristics. CONCLUSIONS:Results from this study support the benefit of more comprehensive assessment of and/or monitoring for opioid misuse among primary care patients who use tobacco, particularly for those who are male, unemployed, or polydrug users.

Objective/UNASSIGNED:To examine the association between state-level tobacco control measures and current use estimates of both e-cigarettes and cigarettes, while accounting for socio-demographic correlates. Methods/UNASSIGNED:Using the 2012-2013 and 2013-2014 National Adult Tobacco Survey (NATS), we assessed prevalence estimates of US adults' e-cigarette and cigarette current use. Four state groups were created based on the combined state-specific prevalence of both products: low cigarette/e-cigarette (n = 15), high cigarette/e-cigarette (n = 16), high cigarette/low e-cigarette (n = 11), and low cigarette/high e-cigarette) (n = 9). To evaluate the implementation of state-level tobacco control measures, Tobacco Control Index (TCI) was calculated using the State of Tobacco Control annual reports for 2012 and 2013. Multinomial logistic regression models were used to examine differences among the four groups on socio-demographic factors and TCI. Low cigarette/e-cigarette group was used as the referent group. Results/UNASSIGNED:Current use estimates of each product varied substantially by state; current e-cigarette use was highest in Oklahoma (10.3%) and lowest in Delaware (2.7%), and current cigarette use was highest in West Virginia (26.1%), and lowest in Vermont (12.6%). Compared to low cigarette/e-cigarette, all other US-state categories had significantly lower TCI scores (high cigarette/e-cigarette: adjusted Relative Risk Ratio [aRRR] = 0.61; 95% confidence interval [CI]: 0.60-0.61, high cigarette/low e-cigarette: aRRR = 0.74; 95% CI: 0.73-0.74, and low cigarette/high e-cigarette: aRRR = 0.72; 95% CI: 0.71-073). Conclusions/UNASSIGNED:Enforcing existing tobacco control measures likely interacts with e-cigarette use despite being cigarette-focused. Continuing to monitor e-cigarette use is critical to establish baseline use and evaluate future e-cigarette specific federal and state-level tobacco regulatory actions while accounting for the existing tobacco control environment. Implications/UNASSIGNED:This study investigates state-level current use estimates of e-cigarettes and cigarettes among US adults; and their association with four existing tobacco control measures. The overall score of these measures was negatively associated with state-level current use estimates such that states with low current e-cigarette and cigarette use had the highest mean overall score. This study assesses the potential relationship between existing state-level tobacco control measures and e-cigarette use and calls for improving the enforcement of the known-to-work tobacco control measures across all US states, while developing evidence-based regulations and interventions specific to e-cigarettes within the existing US tobacco use environment.