大数据中诊断准确性的重要性：7000万德国人健康保险索赔数据中2型糖尿病的假阳性诊断
Importance of diagnostic accuracy in big data: False-positive diagnoses of type 2 diabetes in health insurance claims data of 70 million Germans
摘要：
包括慢性病诊断的大型数据集越来越多地可用于研究目的。在德国，计划定期公布包括约7000万被保险人的法定健康保险的医疗诊断在内的汇总索赔数据。在这样的大数据集中，诊断的有效性很难评估。如果数据集包括流行率、发病率和死亡率，则可以利用疾病-死亡模型的数学关系来估计假阳性诊断的比例。我们将该方法应用于按性别分层的7000万德国人关于2型糖尿病的特定年龄汇总索赔数据，并报告了数据集中2型糖尿病假阳性诊断（FPR）的比例。男性和女性的特定年龄FPR随年龄而变化。在男性中，在30至50岁期间，FPR从每千分之1线性增加至每千分之3。对于50至80岁之间的年龄，FPR保持在每百万人4以下。80岁后，我们增加到每百万人约5。在女性中，我们发现从30岁到60岁急剧增加，FPR峰值在60至70岁之间达到每百万人约12。70岁以后，女性的FPR大幅下降。在所有年龄组中，妇女的FPR都高于男子。从绝对数字来看，我们发现数据集中有21.7万人出现假阳性诊断（95%置信区间，CI：204 ~ 229），绝大多数为女性（17.2万，95% CI：162 ~ 180）。我们的工作表明，可能的假阳性（和阴性）诊断应在索赔数据中适当处理，例如，通过在统计模型中纳入年龄和性别特异性误差项，以避免可能存在偏倚或错误的结论。
英文摘要：
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.
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