Transition probabilities for four states of alcohol use in adolescence and young adulthood: what factors matter when?

Long-term drinking stability in the open-access self-administration monkey model

The Non-Human Primate (NHP) model for the study of Alcohol Use Disorders (AUD) as developed in our laboratories is critical to our understanding of the pathophysiology of voluntary, chronic, ethanol consumption. Previous work in this model established categories of ethanol consumption that parallel reported categories of human consumption across a spectrum spanning low drinking, binge drinking, heavy drinking, and very heavy drinking, albeit at generally higher daily intakes across categories than documented in people. Original categories assigned to ethanol consumption patterns were established using a limited cohort of rhesus macaques. This study revisits the validity of categorical drinking using an additional 28 monkeys. In addition to finding categorical representations consistent with the original 2014 report, our findings demonstrate that drinking categories remain stable across the observed 12 months of nearly consistent access to ethanol (22 h/day), termed "open access". Animals occupying the two ends of the spectrum, "low" and "very heavy" drinkers, exhibit the largest stability. The findings also indicate a slight escalatory drift over time, with very heavy drinking animals experiencing fatigue near the end of open access.

Behavioral stability of alcohol consumption and socio-demographic correlates of change among a nationally representative cohort of US adults

AIMS

To estimate the probability of transitioning between different categories of alcohol use (drinking states) among a nationally representative cohort of United States (US) adults and to identify the effects of socio-demographic characteristics on those transitions.

DESIGN, SETTING AND PARTICIPANTS

Secondary analysis of data from the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC), a prospective cohort study conducted in 2001-02 and 2004-05; a US nation-wide, population-based study. Participants included 34 165 adults (mean age = 45.1 years, standard deviation = 17.3; 52% women).

MEASUREMENTS

Alcohol use was self-reported and categorized based on the grams consumed per day: (1) non-drinker (no drinks in past 12 months), (2) category I (women = ≤ 20; men = ≤ 40), (3) category II (women = 21-40; men = 41-60) and (4) category III (women = ≥ 41; men = ≥ 61). Multi-state Markov models estimated the probability of transitioning between drinking states, conditioned on age, sex, race/ethnicity and educational attainment. Analyses were repeated with alcohol use categorized based on the frequency of heavy episodic drinking.

FINDINGS

The highest transition probabilities were observed for staying in the same state; after 1 year, the probability of remaining in the same state was 90.1% [95% confidence interval (CI) = 89.7%, 90.5%] for non-drinkers, 90.2% (95% CI = 89.9%, 90.5%) for category I, 31.8% (95% CI = 29.7, 33.9%) category II and 52.2% (95% CI = 46.0, 58.5%) for category III. Women, older adults, and non-Hispanic Other adults were less likely to transition between drinking states, including transitions to lower use. Adults with lower educational attainment were more likely to transition between drinking states; however, they were also less likely to transition out of the 'weekly HED' category. Black adults were more likely to transition into or stay in higher use categories, whereas Hispanic/Latinx adults were largely similar to White adults.

CONCLUSIONS

In this study of alcohol transition probabilities, some demographic subgroups appeared more likely to transition into or persist in higher alcohol consumption states.

Prevention, screening, and treatment for heavy drinking and alcohol use disorder

Heavy drinking and alcohol use disorder are major public health problems. Practitioners not specialising in alcohol treatment are often unaware of the guidelines for preventing, identifying, and treating heavy drinking and alcohol use disorder. However, a consensus exists that clinically useful and valuable tools are available to address these issues. Here, we review existing information and developments from the past 5 years in these areas. We also include information on heavy drinking and alcohol use disorder among individuals with co-occurring psychiatric disorders, including drug use disorders. Areas covered include prevention; screening, brief intervention, and referral for treatment; evidence-based behavioural interventions; medication-assisted treatment; technology-based interventions (eHealth and mHealth); and population-level interventions. We also discuss the key topics for future research.

Trajectories of Substance Use Frequency among Adolescents Seen in Primary Care: Implications for Screening

OBJECTIVE

To identify trajectories of substance use in a prospective cohort of adolescent primary care patients one year after a clinic visit.

STUDY DESIGN

We recruited 12- to 18-year-olds from 9 New England practices between 2005 and 2008 and identified 5 trajectories of substance use. We first distinguished adolescents with no past-year use at a baseline clinic visit and at 12-month follow-up (trajectory A). For adolescents who used substances, we assessed past 90-day use at both timepoints, and identified the remaining 4 trajectories based on frequency of use. Trajectories included less than monthly use at both timepoints (trajectory B), less than monthly use increasing to monthly or more often (trajectory C), monthly or more often use decreasing to less than monthly (trajectory D), and monthly or more often use at both timepoints (trajectory E). Using multiple logistic regression, we then examined associations with substance-involved parents, siblings, and peers.

RESULTS

Among 860 adolescents (mean age 15.4 years; 60.9% female; 65.6% non-Hispanic white), more than one-half (52.7%) abstained (trajectory A). The remainder were classified into trajectories B (23.8%), C (9.5%), D (5.7%), and E (8.3%). Those who abstained were least likely to have substance-involved parents (aOR 0.58; 95% CI 0.46-0.72), siblings (aOR 0.49; 95% CI 0.40-0.60), or peers (aOR 0.44; 95% CI 0.37-0.52). Those increasing from less than monthly use to using monthly or more often were more likely to have substance-involved siblings (aOR 1.58; 95% CI 1.23-2.03) or peers (aOR 1.51; 95% CI 1.06-2.17).

CONCLUSIONS

Most adolescent primary care patients remained abstinent or infrequent users over 1 year, but 1 in 5 showed frequent use, with substance-involved siblings and peers predicting escalation of use.