Probability and predictors of remission from life-time prescription drug use disorders: Results from the National Epidemiologic Survey on Alcohol and Related Conditions

Blanco, Carlos; Villa, Roberto Secades; García–Rodríguez, Olaya; Labrador, Marta; Wang, Shuai; Schwartz, Robert P.

doi:10.1016/j.jpsychires.2012.08.019

Cited by 22 publications

(24 citation statements)

References 41 publications

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“…Age of substance use onset was determined by asking respondents about the age at which they first used: cannabis, sedatives, tranquilizers, analgesics, stimulants, cocaine or crack, hallucinogens, inhalants/solvents, heroin, and other. Consistent with prior reports (Blanco et al, 2007; Blanco et al, 2013; Martins et al., 2012) non-medical use of a prescription drug was defined to respondents as using a prescription drug (sedatives, tranquilizers, analgesics, and stimulants) “without a prescription, in greater amounts, more often, or longer than prescribed, or for a reason other than a doctor said you should use them”. After the initial probe item, the respondent was given an extensive list of examples of prescription drugs and asked if s/he used any of the prescription drugs on the list or similar drugs ‘nonmedically’.…”

Section: Methodsmentioning

confidence: 93%

Probability and predictors of the cannabis gateway effect: A national study

Villa

García–Rodríguez

Jin

et al. 2015

International Journal of Drug Policy

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135

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Background While several studies have shown a high association between cannabis use and use of other illicit drugs, the predictors of progression from cannabis to other illicit drugs remain largely unknown. This study aims to estimate the cumulative probability of progression to illicit drug use among individuals with lifetime history of cannabis use, and to identify predictors of progression from cannabis use to other illicit drugs use. Methods Analyses were conducted on the sub-sample of participants in Wave 1of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) who started cannabis use before using any other drug (n= 6,624). Estimated projections of the cumulative probability of progression from cannabis use to use of any other illegal drug use in the general population were obtained by the standard actuarial method. Univariate and multivariable survival analyses with time-varying covariates were implemented to identify predictors of progression to any drug use. Results Lifetime cumulative probability estimates indicated that 44.7% of individuals with lifetime cannabis use progressed to other illicit drug use at some time in their lives. Several sociodemographic characteristics, internalizing and externalizing psychiatric disorders and indicators of substance use severity predicted progression from cannabis use to other illicit drugs use. Conclusion A large proportion of individuals who use cannabis go on to use other illegal drugs. The increased risk of progression from cannabis use to other illicit drugs use among individuals with mental disorders underscores the importance of considering the benefits and adverse effects of changes in cannabis regulations and of developing prevention and treatment strategies directed at curtailing cannabis use in these populations.

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Section: Methodsmentioning

confidence: 93%

Probability and predictors of the cannabis gateway effect: A national study

Villa

García–Rodríguez

Jin

et al. 2015

International Journal of Drug Policy

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135

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“…Second, we examined patterns of treatment-seeking among all individuals with SUD. Because many individuals achieve remission without accessing treatment (Blanco et al, 2013c; Lopez-Quintero et al, 2011), it is possible that some individuals with SUD may not require treatment, although recent data suggest that treatment for SUD increases the probability of remission and decreases the probability of new onset of other SUDs (Blanco et al, 2014a). There is a need to systematically examine how to most appropriately define objective need for the treatment.…”

Section: Discussionmentioning

confidence: 99%

“…Substance use disorders (SUDs) are pervasive in the general population and result in critical threats to health and well-being, substantial family distress, and a massive societal economic burden (Blanco et al, 2013c; Compton et al, 2007; Hasin et al, 2007; Mokdad et al, 2004; Rubio et al, 2014, 2013). Alcohol consumption ranks third among preventable causes of death (Mokdad et al, 2004) and drug offenses are the leading cause of incarceration with half of federal inmates reporting illegal drug use in the month before their offense (Mumola and Karberg, 2004).…”

Section: Introductionmentioning

confidence: 99%

Probability and predictors of treatment-seeking for substance use disorders in the U.S

Blanco

Iza

Rodríguez-Fernández

et al. 2015

Drug and Alcohol Dependence

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120

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Background Little is known about to what extent treatment-seeking behavior varies across individuals with alcohol abuse, alcohol dependence, drug abuse, and drug dependence. Methods The sample included respondents from the Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) who reported a lifetime diagnosis alcohol abuse, alcohol dependence, drug abuse, or drug dependence. Unadjusted and adjusted hazard ratios are presented for time to first treatment contact by sociodemographic characteristics and comorbid psychiatric disorders. Individuals were censored from the analyses if their condition remitted prior to seeking treatment. Results In the first year after disorder onset, rates of treatment-seeking were 13% for drug dependence, 5% for alcohol dependence, 2% for drug abuse, and 1% for alcohol abuse. The lifetime probability of seeking treatment among individuals who did not remit was also highest for drug dependence (90%), followed by drug abuse (60%), alcohol dependence (54%), and alcohol abuse (16%). Having had previous treatment contact for a substance use disorder (SUD) increased the probability of seeking treatment for another SUD. By contrast, an early age of SUD onset, belonging to an older cohort, and a higher level of education decreased the lifetime probability of treatment contact for SUD. The role of comorbid mental disorders was more complex, with some disorders increasing and other decreasing the probability of seeking treatment. Conclusions Given high rates of SUD and their substantial health and economic burden, these patterns suggest the need for innovative approaches to increase treatment access for individuals with SUD.

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“…The overall survey response rate was 70.2%, reflecting 34,653 completed interviews (Grant et al 2009). The Wave 2 NESARC data were weighted to reflect design characteristics of the NESARC survey and to account for oversampling and be representative of the U.S. civilian population based on the 2000 census (Blanco et al 2013). The research protocol, including written informed consent procedures, received full human subjects review and approval from the U.S. Census bureau and the Office of Management and Budget (Grant et al 2009).…”

Section: Methodsmentioning

confidence: 99%

Childhood maltreatment and risk of intimate partner violence: A national study

McMahon

Hoertel

Wall

et al. 2015

Journal of Psychiatric Research

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Objective Prior research indicates that different types of childhood maltreatment frequently co-occur and confer risk for adulthood intimate partner violence (IPV). However, it is unknown whether the risk of IPV is due to specific type(s) of maltreatment or to their shared association or both. Although these competing explanations have different implications for intervention, they have never been evaluated empirically. Method Data were drawn from a nationally representative survey of 34,653 US adults, the 2004–2005 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Structural equation modeling was used to simultaneously examine the shared and specific effects of five types of childhood maltreatment (i.e., sexual abuse, physical and emotional abuse and neglect) on the risk of different IPV behaviors (i.e., perpetration, victimization and reciprocal violence). Analyses were stratified by sex and adjusted for sociodemographic characteristics (i.e., age, personal income, educational background and race/ethnicity). Results Most types of childhood maltreatment increased the risk of victimization, perpetration and reciprocal violence. Effects of maltreatment types on each IPV behavior were exerted mostly through a latent factor representing the shared effect across all different types of maltreatment in both sexes (CFI=0.990, TLI=0.990, RMSEA=0.023), although sexual abuse had an additional effect on victimization. Conclusions Because childhood maltreatment types increase the risk of each intimate partner violence behavior mainly through a general maltreatment dimension, underlying biological and developmental-ecological mechanisms should be considered important targets of prevention for both victimization and perpetration of abuse in adult relationships.

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Probability and predictors of remission from life-time prescription drug use disorders: Results from the National Epidemiologic Survey on Alcohol and Related Conditions

Cited by 22 publications

References 41 publications

Probability and predictors of the cannabis gateway effect: A national study

Probability and predictors of the cannabis gateway effect: A national study

Probability and predictors of treatment-seeking for substance use disorders in the U.S

Childhood maltreatment and risk of intimate partner violence: A national study

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