Development and Internal Validation of Models Predicting the Health Insurance Status of Participants in the German National Cohort

Background In Germany, all citizens must purchase health insurance, in either statutory (SHI) or private health insurance (PHI). Because of the division into SHI and PHI, person insurance's status is an important variable for studies in the context of public health research. In the German National Cohort (NAKO), the variable on self-reported health insurance status of the participants has a high proportion of missing values (55.4%). The aim of our study was to develop and internally validate models to predict the health insurance status of NAKO baseline survey participants in order to replace missing values. In this respect, our research interest was focused on the question to which extent socio-demographic characteristics are suitable for predicting health insurance status. Methods We developed two prediction models including 53,796 participants to estimate the probability that a participant is either member of a SHI (model 1) or PHI (model 2). We identified eight predictors by literature research: occupation, income, education, sex, age, employment status, residential area, and marital status. The predictive performance was determined in the internal validation considering discrimination and calibration. Discrimination was assessed based on the Area Under the Curve (AUC) and the Receiver Operating Characteristic (ROC) curve and calibration was assessed based on the calibration slope and calibration plot. Results In model 1, the AUC was 0.91 (95% CI: 0.91-0.92) and the calibration slope was 0.97 (95% CI: 0.97-0.97). Model 2 had an AUC of 0.91 (95% CI: 0.90-0.91) and a calibration slope of 0.97 (95% CI: 0.97-0.97). Based on the calculated performance parameters both models turned out to show an almost ideal discrimination and calibration. Employment status and household income and to a lesser extent educational level, age, sex, marital status, and residential area are suitable for predicting health insurance status. Conclusions Socio-demographic characteristics especially employment status and household income assessed at NAKO's baseline were suitable for predicting the statutory and private health insurance status. However, before applying the prediction models in other studies, an external validation in population-based studies is recommended. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project was conducted with data from the German National Cohort (NAKO) (www.nako.de). The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C and 01ER1511D], federal states and the Helmholtz Association with additional financial support by the participating universities and the institutes of the Leibniz Association. We thank all participants who took part in the German National Cohort and the staff in this research program. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study protocol of the NAKO was approved by the ethics committee of the Bavarian State Medical Association (13023 and 13031) and by the locally responsible ethics committees of the institutions of the 18 study centres. All the described investigations were conducted in compliance with national law and in accordance with the declaration of Helsinki (in the latest revised version). All participants have been fully informed and have given their written informed consent to participate in the study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data that support the findings of this study are available from the German National Cohort but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.