Dataset Information

Nonmedical prescription opioids and pathways of drug involvement in the US: Generational differences.

ABSTRACT: BACKGROUND:This study sought to specify (1) the position of nonmedical prescription opioids (NMPO) in drug initiation sequences among Millennials (1979-96), Generation X (1964-79), and Baby Boomers (1949-64) and (2) gender and racial/ethnic differences in sequences among Millennials. METHODS:Data are from the 2013-2014 National Surveys on Drug Use and Health (n?=?73,026). We identified statistically significant drug initiation sequences involving alcohol/cigarettes, marijuana, NMPO, cocaine, and heroin using a novel method distinguishing significant sequences from patterns expected only due to correlations induced by common liability among drugs. RESULTS:Alcohol/cigarettes followed by marijuana was the most common sequence. NMPO or cocaine use after marijuana, and heroin use after NMPO or cocaine, differed by generation. Among successively younger generations, NMPO after marijuana and heroin after NMPO increased. Millennials were more likely to initiate NMPO than cocaine after marijuana; Generation X and Baby Boomers were less likely (odds ratios?=?1.4;0.3;0.2). Millennials were more likely than Generation X and Baby Boomers to use heroin after NMPO (hazards ratios?=?7.1;3.4;2.5). In each generation, heroin users were far more likely to start heroin after both NMPO and cocaine than either alone. Sequences were similar by gender. Fewer paths were significant among African-Americans. CONCLUSIONS:NMPOs play a more prominent role in drug initiation sequences among Millennials than prior generations. Among Millennials, NMPO use is more likely than cocaine to follow marijuana use. In all generations, transition to heroin from NMPO significantly occurs only when both NMPO and cocaine have been used. Delineation of drug sequences suggests optimal points in development for prevention and treatment efforts.

SUBMITTER: Wall M

PROVIDER: S-EPMC5870126 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Nonmedical prescription opioids and pathways of drug involvement in the US: Generational differences.

Wall Melanie M Cheslack-Postava Keely K Hu Mei-Chen MC Feng Tianshu T Griesler Pamela P Kandel Denise B DB

Drug and alcohol dependence 20171127

<h4>Background</h4>This study sought to specify (1) the position of nonmedical prescription opioids (NMPO) in drug initiation sequences among Millennials (1979-96), Generation X (1964-79), and Baby Boomers (1949-64) and (2) gender and racial/ethnic differences in sequences among Millennials.<h4>Methods</h4>Data are from the 2013-2014 National Surveys on Drug Use and Health (n = 73,026). We identified statistically significant drug initiation sequences involving alcohol/cigarettes, marijuana, NMP ...[more]

PMID: 29220668

Similar Datasets

Project description:BackgroundThe availability of central nervous system stimulants has risen in recent years, along with increased dispensing of stimulants for treatment of, for example, parent-reported attention-deficit/hyperactivity disorder in children and new diagnoses during adulthood. Typologies of drug use, as has been done with opioids, fail to include a sufficient range of behavioral factors to contextualize person-centric circumstances surrounding drug use. Understanding these patterns across drug classes would bring public health and regulatory practices toward precision public health.ObjectiveThe objective of this study was to quantitatively delineate the unique behavioral profiles of adults who currently nonmedically use stimulants and opioids using a latent class analysis and to contrast the differences in findings by class. We further evaluated whether the subgroups identified were associated with an increased Drug Abuse Screening Test-10 (DAST-10) score, which is an indicator of average problematic drug use.MethodsThis study used a national cross-sectional web-based survey, using 3 survey launches from 2019 to 2020 (before the COVID-19 pandemic). Data from adults who reported nonmedical use of prescription stimulants (n=2083) or prescription opioids (n=6127) in the last 12 months were analyzed. A weighted latent class analysis was used to identify the patterns of use. Drug types, motivations, and behaviors were factors in the model, which characterized unique classes of behavior.ResultsFive stimulant nonmedical use classes were identified: amphetamine self-medication, network-sourced stimulant for alertness, nonamphetamine performance use, recreational use, and nondiscriminatory behaviors. The drug used nonmedically, acquisition through a friend or family member, and use to get high were strong differentiators among the stimulant classes. The latter 4 classes had significantly higher DAST-10 scores than amphetamine self-medication (P<.001). In addition, 4 opioid nonmedical use classes were identified: moderate pain with low mental health burden, high pain with higher mental health burden, risky behaviors with diverse motivations, and nondiscriminatory behaviors. There was a progressive and significant increase in DAST-10 scores across classes (P<.001). The potency of the opioid, pain history, the routes of administration, and psychoactive effect behaviors were strong differentiators among the opioid classes.ConclusionsA more precise understanding of how behaviors tend to co-occur would improve efficacy and efficiency in developing interventions and supporting the overall health of those who use drugs, and it would improve communication with, and connection to, those at risk for severe drug outcomes.

Project description:ObjectiveTo examine whether sources of nonmedical use of prescription drugs (NMUPD) involving anxiolytics, opioids, and stimulants were associated with sociodemographic characteristics, NMUPD characteristics (eg, frequency), and other substance use.MethodNationally representative samples of US high school seniors (N = 18,549) were surveyed by self-administered questionnaires (2009-2016). Design-based latent class analysis and Rao-Scott χ2 tests were used to test associations among sociodemographic characteristics, NMUPD characteristics, other substance use behaviors (eg, binge drinking, cigarette smoking, marijuana use), and NMUPD sources.ResultsApproximately 11.0% of high school seniors reported past-year NMUPD (n = 1,917). A substantial proportion of nonmedical users obtained the prescription drugs from multiple sources (44.2%). Latent class analysis identified 5 subgroups of NMUPD sources (friend/relative sources, friend/purchased sources, own leftover prescription, multiple sources, and other sources). Nonmedical users who obtained prescription drugs from friend/purchased sources were more likely to be adolescent boys, whereas those who used their own leftover prescriptions were more likely to be adolescent girls. Nonmedical users who obtained prescription drugs from multiple sources were more involved in substance use. In contrast, adolescent nonmedical users who used their own leftover prescriptions were less involved in substance use.ConclusionGrowing evidence indicates that different NMUPD sources are associated with different substance use behaviors. All NMUPD sources should be discouraged, because they place individuals, families, and communities at risk. Patients and their families should receive education on how to manage and properly dispose of controlled medications to avoid diversion into the community. Prescribers are encouraged to check prescription monitoring programs and screen adolescents for substance use/misuse when prescribing controlled medications.

Project description:BACKGROUND:Nonmedical prescription psychiatric drug use (NMPDU) is an increasing global health problem, with recent concern focusing on darknet cryptomarkets as sources of procurement. There is a shortage of evidence regarding comparative worldwide NMPDU trends, due in part to data collection difficulties. This problem is particularly marked for non-opioid drugs, particularly those psychiatric drugs which act on the central nervous system (CNS) and have high misuse potential and are associated with high levels of dependency and fatal overdose. This paper therefore has two goals: 1) to report on the kinds of psychiatric prescription drugs available on cryptomarkets, and 2) to use this data to uncover temporal and geographical trends in sales of these products, potentially informing policy regarding NMPDU more generally. METHOD:Digital trace data collected from 31 cryptomarkets in operation between September 2013 and July 2016 was analysed by country of origin descriptively and for trends in the sales for 7 psychiatric drug groupings, based on their main indication or intended use in psychiatric practice. RESULTS:Sedatives (such as diazepam and alprazolam) and CNS stimulants (mainly Adderall, modafinil and methylphenidate) had the greatest share of sales, but usage and trends varied by location. The UK has high and rising levels of sedative sales, whilst the USA has the greatest stimulant sales and increasing sedative rates. Sales of drugs used in the treatment of opioid dependency are also substantial in the USA. The picture is less clear in mainland Europe with high sales levels reported in unexpected Central and Northern European countries. There is evidence of a move towards the more potent sedative alprazolam - already implicated as a source of problematic NMPDU in the USA - in Australia and the UK. Sales of drugs such as antidepressants, antipsychotics, mood stabilisers and antidementia drugs - all drugs with limited abuse potential - were negligible, indicating minimal levels of online cryptomarket procurement for self-medicating mental health problems. CONCLUSION:Predominantly, psychiatric drugs with potent sedative, stimulant or euphoriant effects are sold on cryptomarkets and this varies by country. With some caveats regarding the limitations of cryptomarket digital trace data taken into account, the study of trends of these products sold online over time may offer a novel and increasingly important window onto wider drug purchasing habits.

Dataset Information

Nonmedical prescription opioids and pathways of drug involvement in the US: Generational differences.

Publications

Nonmedical prescription opioids and pathways of drug involvement in the US: Generational differences.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets