Predictors of Treatment Outcome Among Asian Pathological Gamblers (PGs): Clinical,Behavioural, Demographic,and Treatment Process Factors

Research on predictors of treatment outcome among pathological gamblers (PGs) is inconclusive and dominated by studies from Western countries. Using a prospective longitudinal design, the current study examined demographic, clinical, behavioural and treatment programme predictors of gambling frequency at 3, 6 and 12-months, among PGs treated at an addiction clinic in Singapore. Measures included the Hospital anxiety and depression scale, gambling symptom assessment scale (GSAS), personal well-being index (PWI), treatment perception questionnaire and gambling readiness to change scale. Treatment response in relation to changes in symptom severity, personal wellbeing and abstinence were also assessed. Abstinence rates were 38.6, 46.0 and 44.4 % at 3, 6 and 12-months respectively. Significant reductions in gambling frequency, GSAS, and improvement in PWI were reported between baseline and subsequent outcome assessments, with the greatest change occurring in the initial three months. No demographic, clinical, behavioural or treatment programme variable consistently predicted outcome at all three assessments, though treatment satisfaction was the most frequent significant predictor. However, being unemployed, having larger than average debts, poor treatment satisfaction and attending fewer sessions at the later stages of treatment were associated with significantly poorer outcomes, up to 1-year after initiating treatment. These findings show promise for the effectiveness of a CBT-based treatment approach for the treatment of predominantly Chinese PGs. Clinical implications and suggestions for future research are discussed. Taken together, the findings suggest early treatment satisfaction is paramount in improving short-term outcomes, with baseline gambling behaviour and treatment intensity playing a more significant role in the longer term.     

Conditional Gaussian mixture modelling for dietary pattern analysis

Summary.  Free-living individuals have multifaceted diets and consume foods in numerous combinations. In epidemiological studies it is desirable to characterize individual diets not only in terms of the quantity of individual dietary components but also in terms of dietary patterns. We describe the conditional Gaussian mixture model for dietary pattern analysis and show how it can be adapted to take account of important characteristics of self-reported dietary data. We illustrate this approach with an analysis of the 2000–2001 National Diet and Nutrition Survey of adults. The results strongly favoured a mixture model solution allowing clusters to vary in shape and size, over the standard approach that has been used previously to find dietary patterns.     

Short Notices

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