The two worlds of alcohol problems: Who is in treatment and who is not?

Abstract

In the study “Women and Men in Swedish Alcohol and Drug Treatment,” it is possible to compare alcohol consumption and problems among respondents in the general population with those in clients entering alcohol treatment. The differences between these groups have led researchers to talk about the “two worlds” of alcohol problems–in general and in clinical populations. The aim of this article is to study the relative strength of factors in predicting entering and the clinical population. The studied factors are demographics and marginalization; volume and frequency of drinking; alcohol dependence; social response to drinking (suggestions to cut down or seek treatment by informal actors, e.g. family and friends, and formal actors such as employer, the social services or judicial system); and treatment history. The client sample includes 1202 clients (71% men) interviewed face-to-face when entering inpatient and outpatient treatment facilities in Stockholm. In the general population survey, 3557 persons aged 18–75 years were interviewed. The two samples differ significantly. As expected, clients were older, more marginalized and reported more severe alcohol problems, and many reported previous treatment experiences and social responses. Logistic regression analyses show that previous treatment, unemployment/institutionalization and having an unstable living situation are the strongest predictors of who is in treatment, followed by age, alcohol dependence and frequency of drinking. Formal pressures to cut down or seek treatment are also important and males are more likely to be in treatment. The results support a notion of the treatment system as a place for handling marginalized people, beyond and beside their extent of drinking.

• General and clinical populations
• treatment
• alcohol
• marginalization

Notes

Notes

1. None of the respondents in the general population sample were in treatment at the time of the interview.

2. The screening included various alternative criteria by which the respondent could be screened in. The alcohol use disorder identification test (AUDIT) for the detection of problem drinking (Babor et al. 1992) was used as a score (cut off at 6 among women and 8 among men. The last two AUDIT items were coded at 0, 1, 2 instead of 0, 2, 4). Further, respondents were screened in if they answered positively to any of three individual items: weekly consumption of six drinks, or a drink in the morning to get going, or concerns by friends or health workers with respect to drinking. Another criterion for screening in was reported life area problems in the last 12 months related to drinking. The respondent had to report two of the six following: alcohol had a harmful effect on friendships or social life; physical health; home life or marriage/relation to partner; studies or possibilities at work or occupation; finances; and/or drinking driving. Additionally, those who had used cannabis weekly or used another drug at least five times in the last year were screened in. Finally, those who thought they had alcohol problems, had considered seeking treatment, received treatment and/or those who reported that someone had suggested they should seek treatment were also screened in.

3. Analyses comparable to Table II were run in STATA (not shown) with the two samples weighted equally (“client weights”) as a sensitivity test on the influence of the extreme disparity in weights between cases. The size of relationships and odds ratios were not much affected. However, when using the “client weights” instead of the “population weights”, the two oldest age categories, retirement as work status, social response and previous treatment yield slightly higher odds ratios (informal response is a significant predictor). On the contrary, sex is not significant in the models equivalent to Model 4–5. The alcohol frequency measure and dependence yield somewhat lower odds ratios when using “client weights”. Most differences, when using the two different weightings, were found in the last Model, where sex, elementary school, fairly stable living situation, high alcohol volume and informal response change from significant to not significant and vice versa.

4. These differences were in fact obvious when, for the present analysis, we tried to find cut-off points and categorizations which would be appropriate for both the clients and the general population.

5. In fact, an unpublished comparison show that those who paid for treatment at a Minnesota Model institution in Sweden themselves reported more severe alcohol problems (ASI Composite scores), were older, a higher percentage were men, had accommodation, were married/cohabiting and had children compared to clients who got their treatment financed by the social services (Maria Bodin, unpublished).