Purpose of review Recent literature focused on prescription opioids has neglected gender differences in use. Here we evaluated the recent literature (since 2015) examining gender differences in prescription opioid use. Recent findings Between 2015 and 2016, our review found only eight articles addressing gender differences in prescription opioid use mostly opioid misuse in North America among individuals with chronic pain. Risk factors included depression, pain, and poly-drug use. In addition to that review, we had the opportunity to further address gender differences in, and risk factors for, prescription opioid use through a community engagement program, HealthStreet. Among the sample (n=8,525, Mage = 43.7 years, 58.6% female), approximately half reported use of prescription opioids. Women were significantly more likely to report lifetime use (54.9% vs 42.2%; p <.0001) and report cancer compared to men, yet, women with cancer had a significantly reduced risk of using opioids compared to men with cancer (OR: 0.46; 95% CI, 0.36–0.59). Summary Only a few recently published studies analyzed gender differences related to prescription opioid use. Findings from the literature and our data suggest women are more likely to use prescription opioids compared to men. There is limited information on gender differences in opioid use risk factors and outcomes and more research in this area is warranted.

Background Non-medical use (NMU) of prescription opioids in youth is of concern since they may continue this pattern into adulthood and become addicted or divert medications to others. Research into risk factors for NMU can help target interventions to prevent non-medical use of opioids in youth. Method The National Monitoring of Adolescent Prescription Stimulants Study (N-MAPSS) was conducted from 2008 to 2011. Participants 10-18 years of age were recruited from entertainment venues in urban, rural and suburban areas of 10 US cities. Participants completed a survey including questions on their use of prescription opioids. NMU was defined as a non-labeled route of administration or using someone else’s prescription. Information on age, gender, alcohol, marijuana and tobacco use was also collected. Summary descriptive and chi-square statistics were calculated using SAS 9.4. Results Of the 10,965 youth who provided information about past 30 day prescription opioid use, prevalence of reported opioid use was 4.8% with 3.2% reported as NMU (n=345) and 1.6% as medical use (MU) only (n=180). More males than females (55.7% vs 44.4%) reported opioid NMU (p<0.0001). Logistic regression revealed that among males (comparing NMU to MU only), current smokers were 4.4 times more likely to report opioid NMU than non-smokers (95% CI: 1.8, 10.7). Among females (comparing NMU to MU only), current smokers and alcohol users were more likely to report opioid NMU than those who had never smoked or used alcohol (OR=3.2, 95% CI:1.4, 7.0 and OR=4.1, 95% CI: 1.7, 10.4, respectively). Conclusions These results suggest that further research on gender differences in opioid NMU is needed; interventions for opioid NMU may need to be gender specific to obtain the best results.

Objectives Cocaine use is increasing and many cocaine users engage in polysubstance use. Within polysubstance use, relationships among use of individual substances are necessarily complex. To address this complexity, we used latent class analysis (LCA) to identify patterns of polysubstance use among lifetime cocaine users and examine associations among these patterns, demographics, and risk profiles. Methods Members of HealthStreet, an ongoing community engagement program, were asked about lifetime and past 30-day use of cocaine, alcohol, tobacco, marijuana, and prescription medications, mental health conditions, recent Emergency Department (ED) visits and demographics. LCA was used to identify classes of past 30-day polysubstance use among individuals who endorsed lifetime cocaine use. Multinomial logistic regression identified factors associated with these classes. Results Among 1797 lifetime cocaine users, a five-class LCA model was identified: 1) past 30-day tobacco use only (45%), 2) past 30-day alcohol, marijuana and tobacco use (31%), 3) past 30-day tobacco, prescription opioid and sedative use (13%), 4) past 30-day cocaine, alcohol, marijuana and tobacco use (9%), 5) past 30-day cocaine and multiple polysubstance use (2%). Demographics, ED visits and mental health conditions were associated with class membership. Conclusions Approximately 11% of lifetime cocaine users used cocaine in the past 30 days with two different concurrent substance use patterns. Prescription medication (opioids and sedatives) and complex polysubstance use patterns were stronger indicators of negative outcomes than current cocaine use. Cocaine was not used frequently with other stimulants. In addition to polysubstance use, prescription medication use should be targeted for intervention among lifetime cocaine users.

Objective The current analysis examines whether opioid use is associated with insomnia in a community sample, as the consequences of the growing epidemic of prescription opioid use continue to cause public health concern. Study Design A cross-sectional study including 8,433 members in a community outreach program, HealthStreet, in Northeast Florida. Methods Community Health Workers (CHWs) assessed health information, including use of opioids (i.e., Vicodin®, Oxycodone, Codeine, Demerol®, Morphine, Percocet®, Darvon®, Hydrocodone) from community members during field outreach. Insomnia was determined based on self-report: "Have you ever been told you had, or have you ever had a problem with insomnia?" Summary descriptive statistics were calculated and logistic regression modelling was used to calculate adjusted odds ratios (ORs) with 95% confidence intervals for insomnia, by opioid use status, after adjustment for demographics and other covariates. Results Among 8,433 community members recruited (41% male; 61% black), 2,115 (25%) reported insomnia, and 4,200 (50.3%) reported use of opioids. After adjusting for covariates, opioid users were significantly more likely to report insomnia than non-users (adjusted OR, 1.42; 95% CI, 1.25 –1.61). Conclusion Insomnia was 42% more likely among those who reported using prescription opioids compared to those who did not. With one half of the sample reporting prescription opioid use, and a fourth reporting insomnia it is important to further investigate the relationship between the two. Findings provide useful preliminary information from which to conduct further analyses.