Vicki Osborne

Non-medical opioid use in youth: Gender differences in risk factors and prevalence

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 10-18years 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 elses prescription. Information on age, gender, alcohol, marijuana and tobacco use was also collected. Summary descriptive, chi-square statistics and logistic regression were conducted using SAS 9.4. RESULTS Of the 10,965 youth who provided information about past 30day 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.

Impact of survival bias on opioid-related outcomes when using death as an exclusion criterion

In the recent article by Hayes et al.1, the authors explored the influence of propoxyphene withdrawal on opioid use in veterans. This was a very interesting paper that used a pre-period and post-period methodology in an attempt to quantify the effect of the withdrawal. However, I was concerned by the exclusion of patients who died during the post-period with no explanation as to the reason for this decision. Although the limitations section acknowledged the possibility of survival bias occurring, there was no consideration as to the importance of this for interpretation of results. No information was provided in the paper as to the number that was excluded as a result of these criteria, so the extent of the impact is unclear. A flow diagram outlining numbers included and excluded would have been useful to clarify any exclusion. In addition, no explanation is provided as to why this was an exclusion criterion—are data removed from the database on patients who died? If information was available on patients who died, then I believe this should have been included for completeness and transparency. One of the reasons that survival bias may be a very important issue in this study is that the secondary analysis focuses on changes in the number of emergency department visits and opioid-related outcomes between the pre-period and the post-period. Considering that the most serious of these outcomes would result in death (e.g. overdose), the study design is likely to result in under ascertainment of outcomes and so the conclusion that there is no difference in such outcomes between the two periods may not reflect the true difference. Under ascertainment also appears likely considering that propoxyphene users are at a higher risk of medication related toxicity deaths in an out-of-hospital setting than non-users.2 Did the authors consider conducting a sensitivity analysis to include all patients, censoring at date of death rather than excluding those who died, to determine if this may have affected the study results? I believe this is an important question to consider given that one of the key points of the study is “removal of propoxyphene does not appear to contribute to a decrease or increase in adverse events”.

Sex differences in patterns of prescription opioid non-medical use among 10–18 year olds in the US

BACKGROUND Non-medical use (NMU) of prescription opioids is a public health concern and sex differences in prevalence of NMU have been observed previously. Little is known about how youth are obtaining and using these drugs. While any regular use could be problematic, NMU is particularly concerning. More information is needed on NMU patterns among youth and how these patterns might differ by sex. METHODS The National Monitoring of Adolescent Prescription Stimulants Study (N-MAPSS) recruited youth 10-18 years of age from 10 US metropolitan areas from 2008 to 2011 with a final sample of 11,048 youth. The cross-sectional survey included questions on past 30 day use of prescription opioids (10,965 provided responses), with NMU defined as non-oral use and/or use of someone elses opioids. NMU through use of a patients own prescription orally for a reason other than prescribed could not be identified, though this is usually contained within the standard definition of NMU. RESULTS Among the 10,965 youth, past 30 day prevalence of NMU of prescription opioids was 3.1% (n = 345) with 59.7% (n = 206) using someone elses opioids only, 5.2% (n = 18) having non-oral use only and 35.1% (n = 121) having both. In total, seven sources and three routes of administration were assessed. The most common source among males was someone from school (n = 111, 60.0%), with no highly prevalent second source. Among females, there were two prevalent sources of prescription opioids; a parent (n = 59,41.6%) and someone from school (n = 53,37.3%). For non-oral use, snorting prescription opioids was more frequent among males compared to females (n = 85, 31.8% and n = 44, 17.1%; p < .01). CONCLUSIONS Based on these findings, to combat the current opioid crisis, implementation of strategies to prevent youth from sharing opioids, especially with friends from school, should be considered and tested.

Safety of Atrovent® CFC‐free inhaler: respiratory events reported from an observational cohort study in England

The aim of the study was to identify any unexpected clinical events associated with starting the new CFC‐free formulation of Atrovent® MDI in general practice in England.

The 9th Biennial Conference on Signal Detection and Interpretation in Pharmacovigilance.

Since its inception in 2001, the Biennial Conference on Signal Detection and Interpretation in Pharmacovigilance has aimed to keep up to date with continuous developments in the field of signal detection, and this ninth conference in June 2017 was no exception. The proceedings from the first conference have been published previously and covered topics such as screening algorithms [1], data mining [2] and causal association in pharmacovigilance [3]. Many conferences run annually and risk a lack of progress from year to year, whereas a biennial format ensures progress has always been made since the previous meeting. Expert speakers from around the world participated this year, providing relevant updates for anyone working in the signal detection field in addition to a global perspective on pharmacovigilance practices. A pre-conference tutorial covered the importance of signal detection in pharmacovigilance, an introduction to disproportionality analysis and the responsibilities of marketing authorisation holders (MAHs). The full meeting programme is available on the official conference website: http://www.dsru.org/courses/ international-conferences/. 2 Signal Detection in the Age of Big Data

One Step at a Time: A Latent Transitional Analysis on Changes in Substance Use, Exposure to Violence, and HIV/AIDS Risk Behaviors among Female Offenders

The aim of this analysis is to identify latent subgroups of women based on substance use, exposure to violence, and risky sexual behaviors and quantify discrete stages of behavior change over time. Data comes from 317 women recruited from a Municipal Drug Court System in the Midwest. All participants were interviewed regarding their substance use and sexual behaviors, as well as their exposure to violence at baseline, a 4th-month follow-up, and an 8th-month follow-up. A latent transitional analysis (LTA), a longitudinal extension of a latent class analysis (LCA), was used to quantify discrete stages of behavior change. The results of our analyses revealed 4 distinct behavioral profiles in our sample: 1) women with high probabilities of risky sexual behaviors, exposure to violence, and crack/cocaine use, 2) women with a high probability of exposure to violence, and moderate sexual risk taking, 3) women characterized solely by a high probability of crack/cocaine use, 4) women with low probabilities of all factors. The proportion of women in latent statuses characterized by a high probability of crack/cocaine use did not substantially decrease over time. Women who experienced child sexual abuse, had a greater number of lifetime arrests, were older, and believed they had risky drug using behavior that needed changing at baseline were significantly more likely to be in higher-risk latent statuses. Targeted interventions tailored to crack/cocaine users, as well as a wide-spread need for trauma-informed interventions among females involved in the criminal justice system, are needed.

The association between insomnia and prescription opioid use: results from a community sample in Northeast Florida

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 8433 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 modeling 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 8433 community members recruited (41% male; 61% Black), 2115 (25%) reported insomnia, and 4200 (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.