A comprehensive multivariate model of biopsychosocial factors associated with opioid misuse and use disorder in a 2017-2018 United States national survey
| Autor: | Bonita Salmeron, Faustine Williams, Philip R. McNab, Tamika D. Gilreath, Francisco A. Montiel Ishino |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Biopsychosocial model medicine.medical_specialty Adolescent Heroin 03 medical and health sciences Opioid misuse 0302 clinical medicine Biopsychosocial factors Epidemiology Odds Ratio medicine Humans Comprehensive risk 030212 general & internal medicine Prescription Drug Misuse Substance dependence business.industry lcsh:Public aspects of medicine Public health Public Health Environmental and Occupational Health lcsh:RA1-1270 Opioid use disorder Odds ratio Opioid-Related Disorders medicine.disease United States Analgesics Opioid Opioids Logistic Models District of Columbia Biostatistics business 030217 neurology & neurosurgery Research Article medicine.drug Demography |
| Zdroj: | BMC Public Health BMC Public Health, Vol 20, Iss 1, Pp 1-16 (2020) |
| DOI: | 10.21203/rs.3.rs-16830/v3 |
| Popis: | Background Few studies have comprehensively and contextually examined the relationship of variables associated with opioid use. Our purpose was to fill a critical gap in comprehensive risk models of opioid misuse and use disorder in the United States by identifying the most salient predictors. Methods A multivariate logistic regression was used on the 2017 and 2018 National Survey on Drug Use and Health, which included all 50 states and the District of Columbia of the United States. The sample included all noninstitutionalized civilian adults aged 18 and older (N = 85,580; weighted N = 248,008,986). The outcome of opioid misuse and/or use disorder was based on reported prescription pain reliever and/or heroin use dependence, abuse, or misuse. Biopsychosocial predictors of opioid misuse and use disorder in addition to sociodemographic characteristics and other substance dependence or abuse were examined in our comprehensive model. Biopsychosocial characteristics included socioecological and health indicators. Criminality was the socioecological indicator. Health indicators included self-reported health, private health insurance, psychological distress, and suicidality. Sociodemographic variables included age, sex/gender, race/ethnicity, sexual identity, education, residence, income, and employment status. Substance dependence or abuse included both licit and illicit substances (i.e., nicotine, alcohol, marijuana, cocaine, inhalants, methamphetamine, tranquilizers, stimulants, sedatives). Results The comprehensive model found that criminality (adjusted odds ratio [AOR] = 2.58, 95% confidence interval [CI] = 1.98–3.37, p p p p p p = 0.004), and other substance dependence or abuse were significant predictors of opioid misuse and/or use disorder. Substances associated were nicotine (AOR = 3.01, 95% CI = 2.30–3.93, p p = 0.038), marijuana (AOR = 2.24, 95% CI = 1.40–3.58, p = 0.001), cocaine (AOR = 3.92, 95% CI = 2.14–7.17, p p p p = 0.044). Conclusions Biopsychosocial characteristics such as socioecological and health indicators, as well as other substance dependence or abuse were stronger predictors of opioid misuse and use disorder than sociodemographic characteristics. |
| Databáze: | OpenAIRE |
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