[Risk-Adjustment of Return to Work After Medical Rehabilitation: Methodical Advancements in Quality Assurance of the German Pension Insurance]. / Adjustierung des sozialmedizinischen Verlaufs nach medizinischer Rehabilitation: methodische Weiterentwicklung der Reha-Qualitätssicherung der Deutschen Rentenversicherung.

PURPOSE: Besides the quality of life, patients' return to work is one of the most important treatment results of medical rehabilitation paid by the German Pension Insurance. In order to be able to use the return to work as a quality indicator for medical rehabilitation, a risk adjustment strategy for pre-existing characteristics of patients, rehabilitation departments and labour markets had to be developed. METHODS: Multiple regression analyses and cross validation were used to develop a risk adjustment strategy, which mathematically compensates the influence of confounders and thus allows for appropriate comparisons between rehabilitation departments regarding patients' return to work after medical rehabilitation. Under the inclusion of experts, the number of employment days in the first and second year after medical rehabilitation were chosen as an appropriate operationalization of return to work. Methodological challenges in the development of the risk adjustment strategy were the identification of a suitable regression method for the distribution of the dependent variable, modelling the multilevel structure of the data appropriately and selecting relevant confounders for return to work. A user-friendly way of communicating the results was developed. RESULTS: The fractional logit regression was chosen as an appropriate regression method to model the U-shaped distribution of the employment days. Low intraclass correlations indicate that the multilevel structure of the data (cross-classified labour market regions and rehabilitation departments) is statistically negligible. Potential confounding factors were theoretically preselected (medical experts were involved for medical parameters) and tested for their prognostic relevance in each indication area using backwards selection. Cross validations proved the risk adjustment strategy to be stable. Adjustment results were displayed in a user-friendly report, including the users' perspective (focus groups and interviews). CONCLUSIONS: The developed risk adjustment strategy allows for adequate comparisons between rehabilitation departments and thus enables a quality assessment of treatment results. Methodological challenges, decisions and limitations are discussed in details throughout this paper.

Return to work after medical rehabilitation in Germany: influence of individual factors and regional labour market based on administrative data.

Background: The influence of both individual factors and, in particular, the regional labour market on the return to work after medical rehabilitation is to be analyzed based on comprehensive administrative data from the German Pension Insurance and Employment Agencies. Method: For rehabilitation in 2016, pre- and post-rehabilitation employment was determined from German Pension Insurance data for 305,980 patients in 589 orthopaedic rehabilitation departments and 117,386 patients in 202 psychosomatic rehabilitation departments. Labour market data was linked to the district of residence and categorized into 257 labour market regions. RTW was operationalized as the number of employment days in the calendar year after medical rehabilitation. Predictors are individual data (socio-demographics, rehabilitation biography, employment biography) and contextual data (regional unemployment rate, rehabilitation department level: percentage of patients employed before). The estimation method used was fractional logit regression in a cross-classified multilevel model. Results: The effect of the regional unemployment rate on RTW is significant yet small. It is even smaller (orthopaedics) or not significant (psychosomatics) when individual employment biographies (i.e., pre-rehabilitation employment status) are inserted into the model as the most important predictors. The interaction with pre-rehabilitation employment status is not substantial. Conclusions: Database and methods are of high quality, however due to the nonexperimental design, omitted variables could lead to bias and limit causal interpretation. The influence of the labour market on RTW is small and proxied to a large extent by individual employment biographies. However, if no (valid) employment biographies are available, the labour market should be included in RTW analyses. Supplementary Information: The online version contains supplementary material available at 10.1186/s12651-023-00330-1.