Predictive modeling of nontuberculous mycobacterial pulmonary disease epidemiology using German health claims data

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.

Keywords

• Epidemiology
• Insurance claims analysis
• Machine learning
• Nontuberculous mycobacteria
• Nontuberculous mycobacterium infections
• Probability learning