Journal: JAMA Network Open, 2022, doi:10.1001/jamanetworkopen.2022.16194
Authors: Shivani Mathur Gaiha, Crystal Lin, Lauren Kass Lempert & Bonnie Halpern-Felsher
Abstract:
Importance: Nonnicotine e-cigarettes contain chemicals, flavorants, and solvents that have known health harms and/or have not been proven safe for inhalation.
Objective: To evaluate nonnicotine e-cigarette use patterns, including common flavors, brands, and ingredients.
Design, Setting, and Participants: This cross-sectional study included a convenience sample of US residents aged 13 to 40 years who completed an online survey in November and December 2021. Quota sampling was used for an equal proportion of participants aged 13 to 17 years, 18 to 20 years, and 21 to 40 years, balanced for sex, race, and ethnicity per the latest US Census.
Main Outcomes and Measures: Nonnicotine e-cigarette use (ever, past 30- and past 7-day, number of times used, time taken to finish); co-use with nicotine e-cigarettes; age at first try; and flavors, brands, and ingredients used.
Results: Overall, 6131 participants (mean [SD] age, 21.9 [6.8] years; range, 13-40 years; 3454 [56.3%] identifying as female) completed the survey (55.1% completion rate). Among all participants, 1590 (25.9%) had ever used a nonnicotine e-cigarette, 1021 (16.7%) used one in the past 30 days, and 760 (12.4%) used one in the past 7 days. By age group, 227 of 1630 participants aged 13 to 17 years (13.9%), 497 of 2033 participants aged 18 to 20 years (24.4%), 399 of 1041 participants aged 21 to 24 years (38.3%), and 467 of 1427 participants aged 25 to 40 years (32.7%) had ever used nonnicotine e-cigarettes. Among 1590 participants who had ever used a nonnicotine e-cigarette, 549 (34.5%) had used one more than 10 times; 1017 (63.9%) finished 1 nonnicotine e-cigarette in less than 1 week. Co-use of nonnicotine with nicotine e-cigarettes was reported by 1155 participants (18.8%), 1363 (22.2%) exclusively used nicotine e-cigarettes, and 431 (7.0%) exclusively used nonnicotine e-cigarettes. Most-used flavors were sweet, dessert, or candy (578 [36.3%]); fruit (532 [33.4%]); and mint or menthol (321 [20.2%]); similar flavor patterns were observed for the top 2 flavors among those who used nonnicotine e-cigarettes in the past 30 days, followed by combinations of coffee, alcohol, flower, plant, and mint or menthol flavors by age group. Participants most reported using tetrahydrocannabinol (587 [36.9%]), cannabidiol (537 [33.7%]), melatonin (438 [27.5%]), caffeine (428 [26.9%]), and essential oils (364 [22.9%]) in their nonnicotine e-cigarettes.
Conclusions and Relevance: In this study of adolescents, young adults, and adults, a sizeable proportion reported having used nonnicotine e-cigarettes and co-using them with nicotine e-cigarettes. Surveillance studies should further assess nonnicotine e-cigarette use patterns and regulations, and prevention should be developed to address youth appeal, unsubstantiated health claims, and possible health harms.
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Journal: Harm Reduction Journal, 2022, doi: 10.1186/s12954-022-00624-6
Authors: Alexander Hrycko, Pedro Mateu-Gelabert, Courtney Ciervo, Rebecca Linn-Walton & Benjamin Eckhardt
Abstract:
Background: In the context of the current U.S. injection drug use epidemic, targeted public health harm reduction strategies have traditionally focused on overdose prevention and reducing transmission of blood-borne viral infections. Severe bacterial infections (SBI) associated with intravenous drug use have been increasing in frequency in the U.S. over the last decade. This qualitative study aims to identify the risk factors associated with SBI in hospitalized individuals with recent injection drug use.
Methods: Qualitative analysis (n = 15) was performed using an in-depth, semi-structured interview of participants admitted to Bellevue Hospital, NYC, with SBI and recent history of injection drug use. Participants were identified through a referral from either the Infectious Diseases or Addition Medicine consultative services. Interviews were transcribed, descriptively coded, and analyzed for key themes.
Results: Participants reported a basic understanding of prevention of blood-borne viral transmission but limited understanding of SBI risk. Participants described engagement in high risk injection behaviors prior to hospitalization with SBI. These practices included polysubstance use, repetitive tissue damage, nonsterile drug diluting water and multipurpose use of water container, lack of hand and skin hygiene, re-use of injection equipment, network sharing, and structural factors leading to an unstable drug injection environment. Qualitative analysis led to the proposal of an Ecosocial understanding of SBI risk, detailing the multi-level interplay between individuals and their social and physical environments in producing risk for negative health outcomes.
Conclusions: Structural factors and injection drug use networks directly impact drug use, injection drug use practices, and harm reduction knowledge, ultimately resulting in tissue damage and inoculation of bacteria into the host and subsequent development of SBI. Effective healthcare and community prevention efforts targeted toward reducing risk of bacterial infections could prevent long-term hospitalizations, decrease health care expenditures, and reduce morbidity and mortality.
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Journal: JAMA Network Open, 2022, doi: 10.1001/jamanetworkopen.2022.11634
Authors: Carrie M. Mintz, Kevin Y. Xu, Ned J. Presnall, Sarah M. Hartz, Frances R. Levin, Jeffrey F. Scherrer, Laura J. Bierut & Richard A. Grucza
Abstract:
Importance: Stimulant medication use is common among individuals receiving buprenorphine for opioid use disorder (OUD). Associations between prescription stimulant use and treatment outcomes in this population have been understudied.
Objectives: To investigate whether use of prescription stimulants was associated with (1) drug-related poisoning and (2) buprenorphine treatment retention.
Design, setting, and participants: This retrospective, recurrent-event cohort study with a case-crossover design used a secondary analysis of administrative claims data from IBM MarketScan Commercial and Multi-State Medicaid databases from January 1, 2006, to December 31, 2016. Primary analyses were conducted from March 1 through August 31, 2021. Individuals aged 12 to 64 years with an OUD diagnosis and prescribed buprenorphine who experienced at least 1 drug-related poisoning were included in the analysis. Unit of observation was the person-day.
Exposures: Days of active stimulant prescriptions.
Main outcomes and measures: Primary outcomes were drug-related poisoning and buprenorphine treatment retention. Drug-related poisonings were defined using International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, codes; treatment retention was defined by continuous treatment claims until a 45-day gap was observed.
Results: There were 13 778 567 person-days of observation time among 22 946 individuals (mean [SD] age, 32.8 [11.8] years; 50.3% men) who experienced a drug-related poisoning. Stimulant treatment days were associated with 19% increased odds of drug-related poisoning (odds ratio [OR], 1.19 [95% CI, 1.06-1.34]) compared with nontreatment days; buprenorphine treatment days were associated with 38% decreased odds of poisoning (OR, 0.62 [95% CI, 0.59-0.65]). There were no significant interaction effects between use of stimulants and buprenorphine. Stimulant treatment days were associated with decreased odds of attrition from buprenorphine treatment (OR, 0.64 [95% CI, 0.59-0.70]), indicating that stimulants were associated with 36% longer mean exposure to buprenorphine and its concomitant protection.
Conclusions and relevance: Among persons with OUD, use of prescription stimulants was associated with a modest increase in per-day risk of drug-related poisoning, but this risk was offset by the association between stimulant use and improved retention to buprenorphine treatment, which is associated with protection against overdose.
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Journal: Journal of Medical Internet Research: Formative Research, 2022, doi: 10.2196/33037
Authors: Patrick Dulin, Robyn Mertz, Alexandra Edwards & Diane King
Abstract:
Background: Mobile apps have shown considerable promise for reducing alcohol consumption among problem drinkers, but like many mobile health apps, they frequently report low utilization, which is an important limitation, as research suggests that effectiveness is related to higher utilization. Interactive chatbots have the ability to provide a conversational interface with users and may be more engaging and result in higher utilization and effectiveness, but there is limited research into this possibility.
Objective: This study aimed to develop a chatbot alcohol intervention based on an empirically supported app (Step Away) for reducing drinking and to conduct a pilot trial of the 2 interventions. Included participants met the criteria for hazardous drinking and were interested in reducing alcohol consumption. The study assessed utilization patterns and alcohol outcomes across the 2 technology conditions, and a waitlist control group.
Methods: Participants were recruited using Facebook advertisements. Those who met the criteria for hazardous consumption and expressed an interest in changing their drinking habits were randomly assigned to three conditions: the Step Away app, Step Away chatbot, and waitlist control condition. Participants were assessed on the web using the Alcohol Use Disorders Identification Test, Adapted for Use in the United States, Readiness to Change Questionnaire, Short Inventory of Problems-Revised, and Timeline Followback at baseline and at 12 weeks follow-up.
Results: A total of 150 participants who completed the baseline and follow-up assessments were included in the final analysis. ANOVA results indicated that participants in the 3 conditions changed their drinking from baseline to follow-up, with large effect sizes noted (ie, η2=0.34 for change in drinks per day across conditions). However, the differences between groups were not significant across the alcohol outcome variables. The only significant difference between conditions was in the readiness to change variable, with the bot group showing the greatest improvement in readiness (F2,147=5.6; P=.004; η2=0.07). The results suggested that the app group used the app for a longer duration (mean 50.71, SD 49.02 days) than the bot group (mean 27.16, SD 30.54 days; P=.02). Use of the interventions was shown to predict reduced drinking in a multiple regression analysis (β=.25, 95% CI 0.00-0.01; P=.04).
Conclusions: Results indicated that all groups in this study reduced their drinking considerably from baseline to the 12-week follow-up, but no differences were found in the alcohol outcome variables between the groups, possibly because of a combination of small sample size and methodological issues. The app group reported greater use and slightly higher usability scores than the bot group, but the bot group demonstrated improved readiness to change scores over the app group. The strengths and limitations of the app and bot interventions as well as directions for future research are discussed.
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Journal: PLoS One, 2022, doi: 10.1371/journal.pone.0268464
Authors: Meghan E Morean, Krysten W Bold, Danielle R Davis, Grace Kong, Suchitra Krishnan-Sarin & Deepa R. Camenga
Abstract:
Background: “Tobacco-free” nicotine (TFN) e-cigarettes and nicotine pouches containing synthetic nicotine are increasingly available. The term TFN may lead to reduced risk perceptions and increased use intentions relative to tobacco-derived nicotine products. Effectively communicating messages about TFN may depend on the public’s ability to differentiate TFN from tobacco-derived nicotine. Our goals were to examine knowledge about the source(s) of nicotine in commonly used products and beliefs about what TFN means.
Methods: In 2021 we surveyed 2464 young adults (18-25 years) online. Participants reported whether cigarettes, smokeless tobacco, e-cigarettes, and nicotine pouches contain nicotine that comes from tobacco (always, sometimes, never). Correct responses were “always” for cigarettes/smokeless and “sometimes” for e-cigarettes/pouches. Participants also reported “what [they] think TFN e-cigarettes/vapes contain” (nicotine only; tobacco only; both nicotine and tobacco; neither nicotine nor tobacco). We ran unadjusted and adjusted models examining correct responses for nicotine source and TFN contents by past-month product use status (cigarettes, smokeless, e-cigarettes, pouches).
Results: Rates of correctly identifying nicotine source were modest (23.6% pouches-61.9% cigarettes). Except smokeless tobacco, using a given product was associated with identifying its nicotine source correctly in unadjusted models. Participants reported “TFN” means a product contains nicotine only (57.8%), tobacco only (10.8%), both (14.1%), or neither (17.1%).
Conclusions: There is confusion about the source of nicotine in products, and many young adults incorrectly interpreted TFN to mean something other than containing nicotine but no tobacco. Regulatory efforts may be needed to restrict using the term “tobacco-free nicotine” on product labeling and advertising.
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