Predictive modeling of nontuberculous mycobacterial pulmonary disease epidemiology using German health claims data. (March 2021)

• Record Type: Journal Article
• Authors: Ringshausen, Felix C.; Ewen, Raphael; Multmeier, Jan; Monga, Bondo; Obradovic, Marko; van der Laan, Roald; Diel, Roland
• Is Part Of: International journal of infectious diseases. Volume 104(2021)
• Page Start: 398
• Page End: 406
• Publication Date: 2021-03
• Subjects: Epidemiology -- Insurance claims analysis -- Machine learning -- Nontuberculous mycobacteria -- Nontuberculous mycobacterium infections -- Probability learning; Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2021.01.003
• Languages: English
• ISSNs: 1201-9712

Abstract

Highlights: Machine learning using historical claims data may predict previously undiagnosed NTM-PD. A random forest model with a risk threshold >99% performed best (AUC 0.847; total error 19.4%). Prevalence increased 5-fold to 19/100, 000 for both coded and non-coded vs. coded cases alone. Correspondingly, incidence increased 9-fold to 15/100, 000 population in 2016. A relevant number of previously unreported NTM-PD cases were identified with high probability. Abstract: Objectives: Administrative claims data are prone to underestimate the burden of non-tuberculous mycobacterial pulmonary disease (NTM-PD). Methods: We developed machine learning-based algorithms using historical claims data from cases with NTM-PD to predict patients with a high probability of having previously undiagnosed NTM-PD and to assess actual prevalence and incidence. Adults with incident NTM-PD were classified from a representative 5% sample of the German population covered by statutory health insurance during 2011–2016 by the International Classification of Diseases, 10th revision code A31.0. Pre-diagnosis characteristics (patient demographics, comorbidities, diagnostic and therapeutic procedures, and medications) were extracted and compared to that of a control group without NTM-PD to identify risk factors. Results: Applying a random forest model (area under the curve 0.847; total error 19.4%) and a risk threshold of >99%, prevalence and incidence rates in 2016 increased 5-fold and 9-fold to 19 and 15 cases/100, 000 population, respectively, for both coded and non-coded vs. coded cases alone. Conclusions: The use of a machine learning-based algorithm applied to German statutory health insurance claims data predicted a considerable number of previously unreported NTM-PD cases with high probabilty.