Importance of diagnostic accuracy in big data: False-positive diagnoses of type 2 diabetes in health insurance claims data of 70 million Germans (2202.13150v1)

Abstract: Large data sets comprising diagnoses about chronic conditions are becoming increasingly available for research purposes. In Germany, it is planned that aggregated claims data including medical diagnoses from the statutory health insurance with roughly 70 million insurants will be published on a regular basis. Validity of the diagnoses in such big data sets can hardly be assessed. In case the data set comprises prevalence, incidence and mortality, it is possible to estimate the proportion of false positive diagnoses using mathematical relations from the illness-death model. We apply the method to age-specific aggregated claims data from 70 million Germans about type 2 diabetes in Germany stratified by sex and report the findings in terms of the ratio of false positive diagnoses of type 2 diabetes (FPR) in the data set. The age-specific FPR for men and women changes with age. In men, the FPR increases linearly from 1 to 3 per mil in the age 30 to 50. For ages between 50 to 80 years, FPR remains below 4 per mil. After 80 years of age, we have an increase to about 5 per mil. In women, we find a steep increase from age 30 to 60, the peak FPR is reached at about 12 per mil between 60 and 70 years of age. After age 70, the FPR of women drops tremendously. In all age-groups, the FPR is higher in women than in men. In terms of absolute numbers, we find that there are 217 thousand people with a false-positive diagnosis in the data set (95% confidence interval, CI: 204 to 229), the vast majority women (172 thousand, 95% CI: 162 to 180). Our work indicates that possible false positive (and negative) diagnoses should appropriately be dealt with in claims data, e.g., by inclusion of age- and sex-specific error terms in statistical models, to avoid potentially biased or wrong conclusions.