Methodology and Attribution Success of a Data Linkage of Clinical Registry Data with Health Insurance Data

Abstract

The aim of the project "Effectiveness of care in oncological centres" (WiZen), funded by the innovation fund of the federal joint committee, is to investigate the effectiveness of certification in oncology. The project uses nationwide data from the statuory health insurance AOK and data from clinical cancer registries from three different federal states from 2006-2017. To combine the strengths of both data sources, these will be linked for eight different cancer entities in compliance with data protection regulations. Data linkage was performed using indirect identifiers and validated using the health insurance's patient ID ("Krankenversichertennummer") as a direct identifier and gold standard. This enables quantification of the quality of different linkage variants. Sensitivity and specificity as well as hit accuracy and a score addressing the quality of the linkage were used as evaluation criteria. The distributions of relevant variables resulting from the linkage were validated against the original distributions in the individual datasets. Depending on the combination of indirect identifiers, we found a range of 22,125 to 3,092,401 linkage hits. An almost perfect linkage could be achieved by combining information on cancer type, date of birth, gender and postal code. A total of 74,586 one-to-one linkages were achieved with these characteristics. The median hit quality for the different entities was more than 98%. In addition, both the age and sex distributions and the dates of death, if any, showed a high degree of agreement. SHI and cancer registry data can be linked with high internal and external validity at the individual level. This robust linkage enables completely new possibilities for analysis through simultaneous access to variables from both data sets ("the best of both worlds"): Information on the UICC stage that stems from the registries can now be combined, for instance, with comorbidities from the SHI data at the individual level. Due to the use of readily available variables and the high success of the linkage, our procedure constitutes a promising method for future linkage processes in health care research.