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Background
In combination with systematic routine screening, brief alcohol interventions have the potential to promote population health. Little is known on the optimal screening interval. Therefore, this study pursued 2 research questions: (i) How stable are screening results for at‐risk drinking over 12 months? (ii) Can the transition from low‐risk to at‐risk drinking be predicted by gender, age, school education, employment, or past week alcohol use?
Methods
A sample of 831 adults (55% female; mean age = 30.8 years) from the general population was assessed 4 times over 12 months. The Alcohol Use Disorders Identification Test—Consumption was used to screen for at‐risk drinking each time. Participants were categorized either as low‐risk or at‐risk drinkers at baseline, 3, 6, and 12 months later. Stable and instable risk status trajectories were analyzed descriptively and graphically. Transitioning from low‐risk drinking at baseline to at‐risk drinking at any follow‐up was predicted using a logistic regression model.
Results
Consistent screening results over time were observed in 509 participants (61%). Of all baseline low‐risk drinkers, 113 (21%) received a positive screening result in 1 or more follow‐up assessments. Females (vs. males; OR = 1.66; 95% confidence intervals [95% CI] = 1.04; 2.64), 18‐ to 29‐year‐olds (vs. 30‐ to 45‐year‐olds; OR = 2.30; 95% CI = 1.26; 4.20), and those reporting 2 or more drinking days (vs. less than 2; OR = 3.11; 95% CI = 1.93; 5.01) and heavy episodic drinking (vs. none; OR = 2.35; 95% CI = 1.06; 5.20) in the week prior to the baseline assessment had increased odds for a transition to at‐risk drinking.
Conclusions
Our findings suggest that the widely used time frame of 1 year may be ambiguous regarding the screening for at‐risk alcohol use although generalizability may be limited due to higher‐educated people being overrepresented in our sample.
Background
Few studies have assessed trajectories of alcohol use in the general population, and even fewer studies have assessed the impact of brief intervention on the trajectories. Especially for low-risk drinkers, it is unclear what trajectories occur, whether they benefit from intervention, and if so, when and how long. The aims were first, to identify alcohol use trajectories among at-risk and among low-risk drinkers, second, to explore potential effects of brief alcohol intervention and, third, to identify predictors of trajectories.
Methods
Adults aged 18-64 years were screened for alcohol use at a municipal registration office. Those with alcohol use in the past 12 months (N = 1646; participation rate: 67%) were randomized to assessment plus computer-generated individualized feedback letters or assessment only. Outcome was drinks/week assessed at months 3, 6, 12, and 36. Alcohol risk group (at-risk/low-risk) was determined using the Alcohol Use Disorders Identification Test–Consumption. Latent class growth models were estimated to identify alcohol use trajectories among each alcohol risk group. Sex, age, school education, employment status, self-reported health, and smoking status were tested as predictors.
Results
For at-risk drinkers, a light-stable class (46%), a medium-stable class (46%), and a high-decreasing class (8%) emerged. The light-stable class tended to benefit from intervention after 3 years (Incidence Rate Ratio, IRR=1.96; 95% Confidence Interval, CI: 1.14–3.37). Male sex, higher age, more years of school, and current smoking decreased the probability of belonging to the light-stable class (p-values<0.05). For low-risk drinkers, a very light-slightly increasing class (72%) and a light-increasing class (28%) emerged. The very light-slightly increasing class tended to benefit from intervention after 6 months (IRR=1.60; 95% CI: 1.12–2.28). Male sex and more years of school increased the probability of belonging to the light-increasing class (p-value < 0.05).
Conclusion
Most at-risk drinkers did not change, whereas the majority of low-risk drinkers increased alcohol use. There may be effects of alcohol feedback, with greater long-term benefits among persons with low drinking amounts. Our findings may help to identify refinements in the development of individualized interventions to reduce alcohol use.
Background
Missing data are ubiquitous in randomised controlled trials. Although sensitivity analyses for different missing data mechanisms (missing at random vs. missing not at random) are widely recommended, they are rarely conducted in practice. The aim of the present study was to demonstrate sensitivity analyses for different assumptions regarding the missing data mechanism for randomised controlled trials using latent growth modelling (LGM).
Methods
Data from a randomised controlled brief alcohol intervention trial was used. The sample included 1646 adults (56% female; mean age = 31.0 years) from the general population who had received up to three individualized alcohol feedback letters or assessment-only. Follow-up interviews were conducted after 12 and 36 months via telephone. The main outcome for the analysis was change in alcohol use over time. A three-step LGM approach was used. First, evidence about the process that generated the missing data was accumulated by analysing the extent of missing values in both study conditions, missing data patterns, and baseline variables that predicted participation in the two follow-up assessments using logistic regression. Second, growth models were calculated to analyse intervention effects over time. These models assumed that data were missing at random and applied full-information maximum likelihood estimation. Third, the findings were safeguarded by incorporating model components to account for the possibility that data were missing not at random. For that purpose, Diggle-Kenward selection, Wu-Carroll shared parameter and pattern mixture models were implemented.
Results
Although the true data generating process remained unknown, the evidence was unequivocal: both the intervention and control group reduced their alcohol use over time, but no significant group differences emerged. There was no clear evidence for intervention efficacy, neither in the growth models that assumed the missing data to be at random nor those that assumed the missing data to be not at random.
Conclusion
The illustrated approach allows the assessment of how sensitive conclusions about the efficacy of an intervention are to different assumptions regarding the missing data mechanism. For researchers familiar with LGM, it is a valuable statistical supplement to safeguard their findings against the possibility of nonignorable missingness.