Research News Roundup: September 29, 2022

Journal: BMC Public Health, 2022, doi: 10.1186/s12889-022-14152-2

Authors: Scott Appleton, Helen Cyrus-Miller, Ryan Seltzer, Karin Gilligan & Willie McKinney

Abstract:

Inaccurate labels on some e-cigarette products have prompted calls for routine testing to monitor product label integrity. The objective of this study was to compare label statements of commercial disposable/non-chargeable e-cigarette products for nicotine concentration and e-liquid volume with analytically verified levels. Commercial e-cigarette samples were analyzed for nicotine concentration (N = 51), e-liquid volume and total nicotine content (N = 39). Twenty-three of the 51 samples analyzed for nicotine deviated from their label statements by more than ± 10%. Deviations ranged from -50.1% to + 13.9%. Thirty of the 39 samples analyzed for e-liquid volume deviated from their label statements by more than ± 10%. Deviations ranged from -62.1% to + 13.3%. Only one brand listed total nicotine on the label. In thirty-one of the 39 samples, calculated total nicotine amount in e-liquid deviated from the amounts calculated from the label metrics by more than ± 10%. Deviations ranged from -66.8% to -1.43%. These findings underscore the need for regulatory enforcement of manufacturing quality control and product labeling practices to optimize the harm reduction potential and consumer experience associated with the use of e-cigarette products.

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Journal: JMIR Formative Research, 2022, doi: 10.2196/37483

Authors: Vanessa M. McMahan, Justine Arenander, Tim Matheson, Audrey M. Lambert, Sarah Brennan, Traci C. Green, Alexander Y. Walley & Phillip O. Coffin

Abstract:

Background: Drug overdose mortality continues to increase, now driven by fentanyl. Prevention tools such as naloxone and medications to treat opioid use disorder are not sufficient to control overdose rates; additional strategies are urgently needed.

Objective: We sought to adapt a behavioral intervention to prevent opioid overdose (repeated-dose behavioral intervention to reduce opioid overdose [REBOOT]) that had been successfully piloted in San Francisco, California, United States, to the setting of Boston, Massachusetts, United States, and the era of fentanyl for a full efficacy trial.

Methods: We used the assessment, decision, adaptation, production, topical experts, integration, training, and testing (ADAPT-ITT) framework for intervention adaptation. We first identified opioid overdose survivors who were actively using opioids as the population of interest and REBOOT as the intervention to be adapted. We then performed theater testing and elicited feedback with 2 focus groups (n=10) in Boston in 2018. All participants had used opioids that were not prescribed to them in the past year and experienced an opioid overdose during their lifetime. We incorporated focus group findings into our initial draft of the adapted REBOOT intervention. The adapted intervention was reviewed by 3 topical experts, and their feedback was integrated into a subsequent draft. We trained study staff on the intervention and made final refinements based on internal piloting. This paper describes the overall ADAPT-ITT process for intervention adaptation, as well as a qualitative analysis of the focus groups. Working independently, 2 authors (VMM and JA) reviewed the focus group transcripts and coded them for salient and common themes using the constant comparison method, meeting to discuss any discrepancies until consensus was reached. Codes and themes were then mapped onto the REBOOT counseling steps.

Results: Focus group findings contributed to substantial changes in the counseling intervention to better address fentanyl overdose risk. Participants described the widespread prevalence of fentanyl and said that, although they tried to avoid it, avoidance was becoming impossible. Using alone and lower opioid tolerance were identified as contributors to overdose risk. Slow shots or tester shots were acceptable and considered effective to reduce risk. Naloxone was considered an effective reversal strategy. Although calling emergency services was not ruled out, participants described techniques to prevent the arrival of police on the scene. Expert review and internal piloting improved the intervention manual through increased participant centeredness, clarity, and usability.

Conclusions: We successfully completed the ADAPT-ITT approach for an overdose prevention intervention, using theater testing with people who use opioids to incorporate the perspectives of people who use drugs into a substance use intervention. In the current crisis, overdose prevention strategies must be adapted to the context of fentanyl, and innovative strategies must be deployed, including behavioral interventions.

Trial Registration: ClinicalTrials.gov NCT03838510; https://clinicaltrials.gov/ct2/show/NCT03838510

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Journal: Medicine, 2022, doi: 10.1097/MD.0000000000029944

Authors: Jordan Westra, Mukaila Raji & Yong-Fang Kuo

Abstract:

Objective: To understand the changes in opioid cessation surrounding the release of CDC guidelines and changes in state Medicaid coverage at the individual patient level.

Methods: This study used a 20% national sample of Medicare beneficiaries between 2013 and 2018 with at least 90 days of consecutive opioid use in the first year of either of 2 study periods (2013-2015 or 2016-2018). Cessation of opioid use was assessed in year 3 of each period by generalized linear mixed models.

Results: Opioid cessation rates were higher in period 2 (11.2%) compared to period 1 (10.1%). Adjusted for beneficiary characteristics, those in period 2 had 1.07 times the odds of cessation (95% CI: 1.05-1.09) compared to those in period 1. Additionally, the increase in opioid cessation over time was larger in states with Medicaid expansion compared to those without.

Conclusion: The increase in opioid cessation after 2016 suggests the potential effects of the CDC guidelines on opioid prescribing and underscores the need for further research on the relationship between opioid cessation and subsequent change in pain control, quality of life, and opioid toxicity.

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Journal: BMC Health Services Research, 2022, doi: 10.1186/s12913-022-08408-1

Authors: Emma D. Ryan, Yanni M. Chang, Malia Oliver, Katharine A. Bradley & Kevin A. Hallgren

Abstract:

Background: Although alcohol use disorder can complicate depression management, there is no standard process for assessing AUD symptoms (i.e., AUD diagnostic criteria) in primary care for patients who screen positive for depression. This study characterizes the association between depressive symptoms and high-risk drinking reported by primary care patients on screening measures in routine care. Then, using data from a novel clinical program, this study characterizes the association between depressive symptoms and AUD symptoms reported by primary care patients with high-risk drinking via an Alcohol Symptom Checklist.

Methods: In this cross-sectional study, electronic health record data were obtained from patients who visited 33 Kaiser Permanente Washington primary care clinics between 03/2018 and 02/2020 and completed depression (PHQ-2) and alcohol consumption (AUDIT-C) screening measures as part of routine care (N = 369,943). Patients who reported high-risk drinking (AUDIT-C scores 7-12) also completed an Alcohol Symptom Checklist where they reported the presence or absence of 11 AUD criteria as defined by the DSM-5 (N = 8,184). Generalized linear models estimated and compared the prevalence of high-risk drinking (AUDIT-C scores 7-12) and probable AUD (2-11 AUD symptoms on Alcohol Symptom Checklists) for patients with and without positive depression screens.

Results: Patients who screened positive for depression had a 131% higher prevalence of high-risk drinking than those who screened negative (5.2% vs. 2.2%; p < 0.001). Among patients with high-risk drinking, positive depression screens were associated with a significantly higher prevalence of probable AUD (69.8% vs. 48.0%; p < 0.001), with large differences in the prevalence of probable AUD observed with increasing PHQ-2 scores (e.g., probable AUD prevalence of 37.6%, 55.3% and 65.2%, for PHQ-2 scores of 0, 1, and 2, respectively). Although the overall prevalence of high-risk drinking was higher for male patients, similar patterns of association between depression screens, high-risk drinking, and AUD symptoms were observed for male and female patients.

Conclusions: Patients with positive depression screens are more likely to have high-risk drinking. Large percentages of patients with positive depression screens and high-risk drinking report symptoms consistent with AUD to healthcare providers when given the opportunity to do so using an Alcohol Symptom Checklist.

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