title: [Prediction of premature pension after stationary rehabilitation due to adipositas--a prognostic model based on routinely collected data of the State Insurance Institution of Baden-Württemberg].
abstract: Up to 60 % of the German population can be marked as obese. Due is to its frequency and its associated diseases like cardiovascular disorders and disorders of the musculoskeletal system adipositas is a severe burden on the German health care system. This burden is caused by costs of the disease and costs due to premature pensioning. In this study logistic regression modelling has been performed by means of routinely collected data of patients of the regional statutory pension insurance institute Landesversicherungsanstalt Baden-Württemberg (LVA-BW) rehabilitated due to adipositas (n = 599). The aim was to detect influential variables for the prognosis of premature pensioning (n = 135). The data of the patients were obtained from a research database of the "RehaNet" project which includes data of the standardized discharge report of the Federation of German Pension Insurance Institutes and quality assurance questionnaires of the LVA-BW. Three variables remain in the model after a step-down procedure for modelling by logistic regression. The selected variables are age (in years), the physician's statement about the patients limitations of movement after rehabilitation (yes/no) and about the patients ability to work in future (more/less than half-day). After internal validation of the model by bootstrap methods the model achieves a sensitivity of 73 %, a specificity of 87 %, a positive and a negative predictive value of 57 and 93 % respectively. The area under the curve (AUC) of the ROC analysis is 0.87, so the model achieves a good prognostic value. Thus, this model is a valuable test for the exclusion of possible premature pension while or after rehabilitation due to adipositas. It was found that the situation of "no premature pensioning" of patients rehabilitated due to adipositas can be predicted quite accurately with little information (three variables). This reveals a perspective for further research in the possibility of an early, risk-adapted and individualised intervention after stationary rehabilitation for adipositas to keep employment.
authors.affiliation: Universität Ulm, Abteilung Biometrie und Medizinische Dokumentation.
authors.forename: D
authors.initials: D
authors.lastname: Mosshammer
authors.forename: C
authors.initials: C
authors.lastname: Ring
authors.forename: R
authors.initials: R
authors.lastname: Kaluscha
authors.forename: R
authors.initials: R
authors.lastname: Muche
mesh_terms: D016208:Databases, Factual; D004185:Disability Evaluation; D005858:Germany / Q000453:epidemiology; D007297:Inpatients / Q000706:statistics & numerical data*; D015233:Models, Statistical; D009313:National Health Programs / Q000706:statistics & numerical data*; D009765:Obesity / Q000175:diagnosis* / Q000453:epidemiology*; D009784:Occupational Diseases / Q000453:epidemiology / Q000534:rehabilitation; D010415:Pensions / Q000706:statistics & numerical data*; D015995:Prevalence; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors