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

2024 MAY 01 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from medrxiv.org:

“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 insurance9s 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 NAKO9s 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.”

This preprint has not been peer-reviewed.

For more information on this research see: http://medrxiv.org/content/10.1101/2024.04.09.24305544v1

(Our reports deliver fact-based news of research and discoveries from around the world.)