Differing Behaviors Around Adult Nonmedical Use of Prescription Stimulants and Opioids: Latent Class Analysis
The 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 d...
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Published in | Journal of medical Internet research Vol. 25; no. 3; p. e46742 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Toronto
Journal of Medical Internet Research
20.09.2023
Gunther Eysenbach MD MPH, Associate Professor JMIR Publications |
Subjects | |
Online Access | Get full text |
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Abstract | The 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. The 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. This 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. Five 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. A 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. |
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AbstractList | The 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. The 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. This 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. Five 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. A 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. 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. Background:The 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.Objective:The 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.Methods:This 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.Results:Five 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.Conclusions:A 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. |
Audience | Academic |
Author | Iwanicki, Janetta L Olson, Richard Black, Joshua C Dart, Richard C Rockhill, Karilynn M |
AuthorAffiliation | 1 Rocky Mountain Poison and Drug Safety Denver Health and Hospital Authority Denver, CO United States |
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Cites_doi | 10.1592/phco.26.10.1501 10.1001/jama.2016.1464 10.1001/jamanetworkopen.2021.41995 10.1016/j.jsat.2006.08.002 10.1002/pds.5260 10.1016/j.drugalcdep.2014.05.022 10.1016/j.jaac.2013.09.005 10.1016/j.drugalcdep.2010.11.004 10.1016/j.drugalcdep.2020.108297 10.1080/10550887.2012.759858 10.1016/0306-4603(82)90005-3 10.1001/jamainternmed.2020.7850 10.2196/15830 10.4159/9780674041318 10.15288/jsad.2018.79.799 10.1177/0706743715620143 10.1177/0095798420930932 10.1016/j.smrv.2004.03.002 10.1016/j.drugpo.2022.103584 10.1111/add.12347 10.1016/j.amepre.2015.08.031 10.1111/bmsp.12227 10.1016/j.drugalcdep.2009.08.011 10.1186/s12888-017-1463-3 10.1016/j.addbeh.2020.106757 10.15585/mmwr.mm7006a4 10.1080/10705510701575602 10.1016/j.jaac.2019.06.012 10.1192/bjp.bp.107.048827 10.1080/10826084.2022.2040029 |
ContentType | Journal Article |
Copyright | COPYRIGHT 2023 Journal of Medical Internet Research 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Karilynn M Rockhill, Richard Olson, Richard C Dart, Janetta L Iwanicki, Joshua C Black. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.09.2023. 2023 |
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References | ref13 Collins, LM (ref24) 2010 ref12 ref15 Nagin, D (ref27) 2005 ref14 ref31 ref30 ref11 ref33 ref10 ref32 ref2 ref1 ref17 ref16 ref19 ref18 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref29 ref8 ref7 Zablotsky, B (ref3) 2020 ref9 ref4 ref6 ref5 |
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Snippet | The availability of central nervous system stimulants has risen in recent years, along with increased dispensing of stimulants for treatment of, for example,... Background The availability of central nervous system stimulants has risen in recent years, along with increased dispensing of stimulants for treatment of, for... Background:The availability of central nervous system stimulants has risen in recent years, along with increased dispensing of stimulants for treatment of, for... BACKGROUNDThe availability of central nervous system stimulants has risen in recent years, along with increased dispensing of stimulants for treatment of, for... BackgroundThe availability of central nervous system stimulants has risen in recent years, along with increased dispensing of stimulants for treatment of, for... |
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SubjectTerms | Adults Alertness Amphetamines Analysis Attention deficit hyperactivity disorder Behavior Central nervous system Cocaine COVID-19 Drug abuse Drug overdose Drug use Drugs Efficacy Fentanyl Health behavior Health status Hyperactivity Internet Latent class analysis Medical screening Mental health Methamphetamine Morphine Multimedia Narcotics Nervous system Opioids Original Paper Pain Pandemics Polls & surveys Prescription drugs Public health Risk taking Self-medication Sleep apnea syndromes Stimulants Substance abuse |
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Title | Differing Behaviors Around Adult Nonmedical Use of Prescription Stimulants and Opioids: Latent Class Analysis |
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