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Large data sets comprising diagnoses about chronic conditions are becomingincreasingly available for research purposes. In Germany, it is planned thataggregated claims data including medical diagnoses from the statutory healthinsurance with roughly 70 million insurants will be published on a regularbasis. Validity of the diagnoses in such big data sets can hardly be assessed.In case the data set comprises prevalence, incidence and mortality, it ispossible to estimate the proportion of false positive diagnoses usingmathematical relations from the illness-death model. We apply the method toage-specific aggregated claims data from 70 million Germans about type 2diabetes in Germany stratified by sex and report the findings in terms of theratio of false positive diagnoses of type 2 diabetes (FPR) in the data set. Theage-specific FPR for men and women changes with age. In men, the FPR increaseslinearly from 1 to 3 per mil in the age 30 to 50. For ages between 50 to 80years, FPR remains below 4 per mil. After 80 years of age, we have an increaseto about 5 per mil. In women, we find a steep increase from age 30 to 60, thepeak FPR is reached at about 12 per mil between 60 and 70 years of age. Afterage 70, the FPR of women drops tremendously. In all age-groups, the FPR ishigher in women than in men. In terms of absolute numbers, we find that thereare 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 (andnegative) diagnoses should appropriately be dealt with in claims data, e.g., byinclusion of age- and sex-specific error terms in statistical models, to avoidpotentially biased or wrong conclusions.

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