Method: Participants were 396 college students who reported any alcohol use in the past 90 days and were aged 18 years or older. We conducted see more factor analyses to determine whether a one- or two-factor model provided a better fit to the AUD criteria. IRT analyses estimated item severity and discrimination parameters for each criterion. Multivariate analyses examined differences among the DSM-V diagnostic cut-off (AUD vs. No AUD) and severity qualifiers (no diagnosis, moderate, severe) across several
validating measures of alcohol use.
Results: A dominant single-factor model provided the best fit to the AUD criteria. IRT analyses indicated that abuse and dependence criteria were intermixed along the latent continuum. The “”legal problems”" criterion had the highest severity parameter and the tolerance criterion had the lowest severity parameter. The abuse criterion “”social/interpersonal problems”" TGF-beta inhibitor review and dependence criterion “”activities to
obtain alcohol”" had the highest discrimination parameter estimates. Multivariate analysis indicated that the DSM-V cut-off point, and severity qualifier groups were distinguishable on several measures of alcohol consumption, drinking consequences, and drinking restraint.
Discussion: Findings suggest that the AUD criteria reflect a latent variable that Rapamycin molecular weight represents a primary disorder and provide support for the proposed DSM-V AUD criteria in a sample of college students. Continued research in other high-risk samples of college students is needed. (C) 2011 Elsevier Ireland Ltd. All rights reserved.”
“QUESTIONS UNDER STUDY: The FIRE Project established a standardised data collection to facilitate research and quality improvement projects in Swiss primary care. The project is based on the concept
of merging clinical and administrative data. Since chronic conditions and multimorbidity are major challenges in primary care, in this study we investigated the agreement between different approaches to identify patients with chronic and multimorbid conditions in electronic medical records (EMRs).
METHODS: A total of 60 primary care physicians were included and data were collected between October 2008 and June 2011. In total, data from 509594 consultations derived from 98152 patients were analysed. Chronic and multimorbid conditions were identified either by ICPC-2 codes or by the type of prescribed medication. We compared these different approaches regarding the completeness of the data to describe chronic conditions and multimorbidity of patients in primary care practices.