Journal: JAMA Network Open, 2022, doi: 10.1001/jamanetworkopen.2021.45691
Authors: Scott G. Weiner, Sanae El Ibrahimi, Michelle A. Hendricks, Sara E. Hallvik, Christi Hildebran, Michael A. Fischer, Roger D. Weiss, Edward W. Boyer, Peter W. Kreiner, Dagan A. Wright, Diana P. Flores & Grant A. Ritter
Abstract:
Importance: The opioid epidemic continues to be a public health crisis in the US.
Objective: To assess the patient factors and early time-varying prescription-related factors associated with opioid-related fatal or nonfatal overdose.
Design, Setting, and Participants: This cohort study evaluated opioid-naive adult patients in Oregon using data from the Oregon Comprehensive Opioid Risk Registry, which links all payer claims data to other health data sets in the state of Oregon. The observational, population-based sample filled a first (index) opioid prescription in 2015 and was followed up until December 31, 2018. Data analyses were performed from March 1, 2020, to June 15, 2021.
Exposures: Overdose after the index opioid prescription.
Main Outcomes and Measures: The outcome was an overdose event. The sample was followed up to identify fatal or nonfatal opioid overdoses. Patient and prescription characteristics were identified. Prescription characteristics in the first 6 months after the index prescription were modeled as cumulative, time-dependent measures that were updated monthly through the sixth month of follow-up. A time-dependent Cox proportional hazards regression model was used to assess patient and prescription characteristics that were associated with an increased risk for overdose events.
Results: The cohort comprised 236 921 patients (133 839 women [56.5%]), of whom 667 (0.3%) experienced opioid overdose. Risk of overdose was highest among individuals 75 years or older (adjusted hazard ratio [aHR], 3.22; 95% CI, 1.94-5.36) compared with those aged 35 to 44 years; men (aHR, 1.29; 95% CI, 1.10-1.51); those who were dually eligible for Medicaid and Medicare Advantage (aHR, 4.37; 95% CI, 3.09-6.18), had Medicaid (aHR, 3.77; 95% CI, 2.97-4.80), or had Medicare Advantage (aHR, 2.18; 95% CI, 1.44-3.31) compared with those with commercial insurance; those with comorbid substance use disorder (aHR, 2.74; 95% CI, 2.15-3.50), with depression (aHR, 1.26; 95% CI, 1.03-1.55), or with 1 to 2 comorbidities (aHR, 1.32; 95% CI, 1.08-1.62) or 3 or more comorbidities (aHR, 1.90; 95% CI, 1.42-2.53) compared with none. Patients were at an increased overdose risk if they filled oxycodone (aHR, 1.70; 95% CI, 1.04-2.77) or tramadol (aHR, 2.80; 95% CI, 1.34-5.84) compared with codeine; used benzodiazepines (aHR, 1.06; 95% CI, 1.01-1.11); used concurrent opioids and benzodiazepines (aHR, 2.11; 95% CI, 1.70-2.62); or filled opioids from 3 or more pharmacies over 6 months (aHR, 1.38; 95% CI, 1.09-1.75).
Conclusions and Relevance: This cohort study used a comprehensive data set to identify patient and prescription-related risk factors that were associated with opioid overdose. These findings may guide opioid counseling and monitoring, the development of clinical decision-making tools, and opioid prevention and treatment resources for individuals who are at greatest risk for opioid overdose.
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Journal: Journal of Addiction Medicine, 2022, doi: 10.1097/ADM.0000000000000811
Authors: Ximena A. Levander, Haven L. Wheelock, Justine Pope, Abby Lee, Kerith Hartmann, Sarah Abuelkhair, Jessica Gregg & Bradley Buchheit
Abstract:
Background: To reduce coronavirus disease 2019 (COVID-19) spread, federal agencies eased telemedicine restrictions including audio-only appointments. These changes permitted clinicians to prescribe buprenorphine to patients with opioid use disorder (OUD) without in-person or audio/video assessment. Our clinic utilized existing community collaborations to implement protocols and extend outreach. We describe 3 patients with OUD who engaged with treatment through outreach with trusted community partners and low-threshold telemedicine.
Case Presentations: Patient 1—a 40-year-old man with severe OUD who injected heroin and was living outside. A weekend harm reduction organization volunteer the patient previously knew used her mobile phone to facilitate an audio-only intake appointment during clinic hours. He completed outpatient buprenorphine initiation. Patient 2—a 48-year-old man with severe opioid and methamphetamine use disorders who injected both and was living in his recreational vehicle. He engaged regularly with syringe services program (SSP), but utilized no other healthcare services. Initially, an SSP worker connected him to our clinic for audio-only appointment using their landline to initiate buprenorphine; a harm reduction volunteer coordinated follow-up. Patient 3—a 66-year-old man with moderate OUD used non-prescribed pill opioids without prior buprenorphine experience. He lived over 5 hours away in a rural town. He underwent virtual appointment and completed home buprenorphine initiation.
Conclusion: These 3 cases illustrate examples of how policy changes allowing for telemedicine buprenorphine prescribing can expand availability of addiction services for patients with OUD who were previously disengaged for reasons including geography, lack of housing, transportation difficulties, and mistrust of traditional healthcare systems.
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Journal: JAMA Network Open, 2022, doi:10.1001/jamanetworkopen.2021.45653
Authors: Greg Marchand, Ahmed Taher Masoud, Malini Govindan, Kelly Ware, Alexa King, Stacy Ruther, Giovanna Brazil, Hollie Ulibarri, Julia Parise, Amanda Arroyo, Catherine Coriell, Sydnee Goetz, Amitis Karrys & Katelyn Sainz
Abstract:
Importance: While some studies have found an association between marijuana use and adverse neonatal outcomes, results have not been consistent across all trials.
Objective: To assess available data on neonatal outcomes in marijuana-exposed pregnancies.
Data Sources: PubMed, Medline, ClinicalTrials.gov, Cochrane, Scopus, and Web of Science were searched from each database’s inception until August 16, 2021.
Study Selection: All interventional and observational studies that included pregnant women who were exposed to marijuana compared with pregnant women who were not exposed to marijuana and that reported neonatal outcomes were included.
Data Extraction and Synthesis: Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline. Data were extracted by 2 authors for all outcomes, which were pooled using a random-effects model as mean difference or risk ratio (RR) and 95% CI. Data were analyzed from August through September 2021.
Main Outcomes and Measures: All outcomes were formulated prior to data collection. Outcomes included incidence of birth weight less than 2500 g, small for gestational age (defined as less than the fifth percentile fetal weight for gestational age), rate of preterm delivery (defined as before 37 weeks’ gestation), gestational age at time of delivery, birth weight, incidence of neonatal intensive care unit (NICU) admission, Apgar score at 1 minute, Apgar score at 5 minutes, incidence of an Apgar score less than 7 at 5 minutes, fetal head circumference, and fetal length.
Results: Among 16 studies including 59 138 patients, there were significant increases in 7 adverse neonatal outcomes among women who were exposed to marijuana during pregnancy vs those who were not exposed during pregnancy. These included increased risk of birth weight less than 2500 g (RR, 2.06 [95% CI, 1.25 to 3.42]; P = .005), small for gestational age (RR, 1.61 [95% CI, 1.44 to 1.79]; P < .001), preterm delivery (RR, 1.28 [95% CI, 1.16 to 1.42]; P < .001), and NICU admission (RR, 1.38 [95% CI, 1.18 to 1.62]; P < .001), along with decreased mean birth weight (mean difference, −112.30 [95% CI, −167.19 to −57.41] g; P < .001), Apgar score at 1 minute (mean difference, −0.26 [95% CI, −0.43 to −0.09]; P = .002), and infant head circumference (mean difference, −0.34 [95% CI, −0.63 to −0.06] cm; P = .02).
Conclusions and Relevance: This study found that women exposed to marijuana in pregnancy were at a significantly increased risk of some adverse neonatal outcomes. These findings suggest that increasing awareness about these risks may be associated with improved outcomes.
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Journal: International Journal of Environmental Research and Public Health, 2022, doi: 10.3390/ijerph19031134
Authors: David C. Wheeler, Joseph Boyle, D. Jeremy Barsell, Trevin Glasgow, F. Joseph McClernon, Jason A. Oliver & Bernard F. Fuemmeler
Abstract:
Tobacco causes 29% of cancer-related deaths while alcohol causes 5.5% of cancer-related deaths. Reducing the consumption of these cancer-causing products is a special priority area for the National Cancer Institute. While many factors are linked to tobacco and alcohol use, the placement and density of retail outlets within neighborhoods may be one community-level risk factor contributing to greater use of these products. To elucidate associations between tobacco, alcohol, and tobacco and alcohol retail outlets (TRO, ARO, and TARO) and neighborhood disadvantage over a large geographic area, we employed a novel Bayesian index modeling approach to estimate a neighborhood disadvantage index (NDI) and its associations with rates of the three types of retailers across block groups in the state of North Carolina. We used a novel extension of the Bayesian index model to include a shared component for the spatial pattern common to all three types of outlets and NDI effects that varied by outlet type. The shared component identifies areas that are elevated in risk for all outlets. The results showed significant positive associations between neighborhood disadvantage and TROs (relative risk (RR) = 1.12, 95% credible interval (CI = 1.09, 1.14)) and AROs (RR = 1.15, 95% CI = 1.11, 1.17), but the association was greatest for TAROs (RR = 1.21, 95% CI = 1.18, 1.24). The most important variables in the NDI were percent renters (i.e., low home ownership), percent of homes built before 1940 (i.e., old housing stock), and percent without a high school diploma (i.e., low education).
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Journal: Preventive Medicine Reports, 2022, doi: 10.1016/j.pmedr.2022.101715.
Authors: Delvon T. Mattingly, Akash Patel, Jana L. Hirschtick & Nancy L. Fleischer
Abstract:
Nicotine and cannabis vaping has increased over the past several years. While patterns of cigarette and cannabis co-use are well-documented, less is known about the intersection between nicotine and cannabis vaping, especially among adults. Thus, we categorized nicotine and cannabis vaping among adults (18+) who currently (past 30-day) used electronic vapor products (EVPs) from Wave 4 of the Population Assessment of Tobacco and Health Study (n = 3795) as: 1) nicotine only, 2) cannabis only, 3) nicotine and cannabis, and 4) non-nicotine/non-cannabis e-liquid. We calculated vaping pattern proportions overall and by sociodemographic characteristics. Adjusted multinomial logistic regression models assessed associations between sociodemographic characteristics and vaping categories relative to nicotine-only vaping. Approximately half (54.2%) of adults who currently used EVPs vaped nicotine only, 7.4% vaped cannabis only, 23.8% vaped nicotine and cannabis, and 14.6% vaped non-nicotine/non-cannabis e-liquid. Young adults (aged 18–24) (vs. adults aged 35+) had at least three-fold greater odds of vaping cannabis only, nicotine and cannabis, and non-nicotine/non-cannabis e-liquid, compared to nicotine only. Hispanic and non-Hispanic Black (vs. non-Hispanic White) adults had 2.5–3 times greater odds of vaping cannabis only and non-nicotine/non-cannabis e-liquid, compared to nicotine only. Sexual minority adults (vs. heterosexual adults) had 1.5 times greater odds of vaping nicotine and cannabis, compared to nicotine only. Nearly half of adults who vaped EVPs consumed something other than nicotine only, and nicotine/cannabis vaping patterns differed by sociodemographic groups. Vaping and nicotine reduction efforts must recognize that adults who currently vape may be vaping cannabis, or neither nicotine nor cannabis.
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