论文标题
部分可观测时空混沌系统的无模型预测
Importance of diagnostic accuracy in big data: False-positive diagnoses of type 2 diabetes in health insurance claims data of 70 million Germans
论文作者
论文摘要
包括有关慢性疾病的诊断的大型数据集越来越多地用于研究目的。在德国，计划定期出版大约7,000万保险公司的法定健康保险的医疗诊断，包括法定健康保险的医疗诊断。在此类大数据集中诊断的有效性几乎无法评估。如果数据集包括患病率，发病率和死亡率，则可以使用来自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来自7000万德国人的特定年龄汇总的索赔数据，该数据对性别分层的德国2型糖尿病，并根据数据集中2型糖尿病（FPR）的假阳性诊断比率报告结果。男性和女性的特定年龄FPR随着年龄的增长而变化。在男性中，FPR在30岁时线性地增加到每Mil的1升增加到50岁。在50至80岁之间，FPR保持在每ML的4米以下。 80岁以后，我们的增加到每公里约5。在女性中，我们发现从30岁到60岁的急剧增长，FPR达到了60至70岁之间约12个MIL的峰值。 70岁以后，女性FPR跌幅巨大。在所有年龄段，女性的FPR都比男性高。就绝对数量而言，我们发现数据集中有21.7万人有假阳性诊断（95％置信区间，CI：204至229），绝大多数女性（1.72千，95％CI：162至180）。我们的工作表明，应在索赔数据中适当处理可能的假阳性（和阴性）诊断，例如，通过在统计模型中包含年龄和性别的错误术语，以避免可能有偏见或错误的结论。
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.
