[Opioid Maintenance for Patients in Inpatient Long-Term Rehabilitation Treatment - which Patients Benefit?] / Substitution Opiatabhängiger im Rahmen der stationären Rehabilitationsbehandlung ­ welche Patienten profitieren?

OBJECTIVE: Patients from opioid maintenance treatment (OMT) may taper off from opioids during rehab treatment. It is investigated whether this option improves treatment outcomes in particular for those patients who are affected by psychiatric or somatic comorbidity, or by social problems. METHODS: Multicenter study with 4 rehab units. Patient characteristics and treatment outcomes were collected retrospectively. There were n = 227 patients who were admitted to treatment while still receiving OMT, and n = 156 who were opioid free. RESULTS: With regard to regular completion of rehab treatment, an interaction was observed between status at admission and diagnosis of affective, neurotic and anxiety disorders, and between status and previous rehab treatment experience. If admitted in an abstinent status, patients who had never entered rehab, and patients with affective or anxiety disorders showed decreased regular completion rates, while they showed increased completion rates if tapered off during treatment. There was no interaction between initial status and acute life crisis (release from prison, homelessness), somatic diagnoses, or number of stabilizing social factors. CONCLUSION: Admission while still receiving opiate maintenance could be helpful for patients with psychiatric comorbidity and for patients who had previously been reluctant to enter rehab treatment.

A physical descriptive model for predicting bacteria level variation at a dynamic beach.

A rational-based physical descriptive model (PDM) has been developed to predict the levels of Escherichia coli in water at a beach with dynamic conditions in the Greater Toronto Area (GTA), Ontario, Canada. Bacteria loadings in the water were affected not only by multiple physical factors (precipitation, discharge, wind, etc.), but also by cumulative effects, intensity, duration and timing of storm events. These may not be linearly related to the observed variations in bacteria levels, and are unlikely to be properly represented by a widely used multiple linear regression model. In order to account for these complex relationships, the amounts of precipitation and nearby creek discharge, the impact of various time-related factors, lag time between events and sample collection, and threshold for different parameters were used in determining bacteria levels. This new comprehensive PDM approach improved the accuracy of the E. coli level predictions in the studied beach water compared to the previously developed statistical predictive and presently used geometric mean models. In spite of the complexity and dynamic conditions at the studied beach, the PDM achieved 75% accuracy overall for the five case years examined.