Depressive symptoms as a predictor of alcohol relapse after residential treatment programs for alcohol use disorder

Alcohol use disorder (AUD) and depressive disorders often co-occur. Findings on the effects of major depressive disorder (MDD) or depressive symptoms on posttreatment alcohol relapse are controversial. The study's aim is to examine the association of MDD and depressive symptoms with treatment outcomes after residential AUD programs. In a naturalistic-prospective, multisite study with 12 residential AUD treatment programs in the German-speaking part of Switzerland, 64 patients with AUD with MDD, 283 patients with AUD with clinically significant depressive symptoms at admission, and 81 patients with AUD with such problems at discharge were compared with patients with AUD only on alcohol use, depressive symptoms, and treatment service utilization. MDD was provisionally identified at admission and definitively defined at discharge. Whereas patients with MDD did not differ from patients with AUD only at 1-year follow-up, patients with AUD with clinically significant depressive symptoms had significantly shorter time-to-first-drink and a lower abstinence rate. These patients also had elevated AUD indices and treatment service utilization for psychiatric disorders. Our results suggest that clinically significant depressive symptoms are a substantial risk factor for relapse so that it may be important to treat them during and after residential AUD treatment programs.

Treatment outcomes of an integrated residential programme for patients with schizophrenia and substance use disorder

About half of all schizophrenic patients have a co-occurring substance use disorder, leading to poorer social and functional outcomes than obtained in non-abusing patients. To improve outcomes, integrated treatments have been designed that address the two conditions simultaneously. Results are, however, conflicting because the available effect studies are hampered by various methodological issues, among which are heterogeneous patient samples.

A prediction model for assessing residential radon concentration in Switzerland

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.

Self-efficacy as a predictor of outcome after residential treatment programs for alcohol dependence: simply ask the patient one question!

Self-efficacy has been identified as one of the most consistent variables that predict the outcome of alcohol treatment. However, many previous studies in this field failed to control for other important predictors (e.g., dependences severity, psychiatric symptoms, and treatment goal). Our study's first goal was to evaluate the predictive value of self-efficacy when most other relevant variables were statistically controlled. The second goal was to compare the predictive values of self-efficacy assessed with the Situational Confidence Questionnaire (SCQ), and general self-efficacy assessed with a single question.