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Cost-of-illness studies based on massive data: a prevalence-based, top-down regression approach

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Abstract

Despite the increasing availability of routine data, no analysis method has yet been presented for cost-of-illness (COI) studies based on massive data. We aim, first, to present such a method and, second, to assess the relevance of the associated gain in numerical efficiency. We propose a prevalence-based, top-down regression approach consisting of five steps: aggregating the data; fitting a generalized additive model (GAM); predicting costs via the fitted GAM; comparing predicted costs between prevalent and non-prevalent subjects; and quantifying the stochastic uncertainty via error propagation. To demonstrate the method, it was applied to aggregated data in the context of chronic lung disease to German sickness funds data (from 1999), covering over 7.3 million insured. To assess the gain in numerical efficiency, the computational time of the innovative approach has been compared with corresponding GAMs applied to simulated individual-level data. Furthermore, the probability of model failure was modeled via logistic regression. Applying the innovative method was reasonably fast (19 min). In contrast, regarding patient-level data, computational time increased disproportionately by sample size. Furthermore, using patient-level data was accompanied by a substantial risk of model failure (about 80 % for 6 million subjects). The gain in computational efficiency of the innovative COI method seems to be of practical relevance. Furthermore, it may yield more precise cost estimates.

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Acknowledgments

Financial support for this study was provided by the Helmholtz Zentrum München, German Research Centre for Environmental Health (HMGU) and the Institute of Health Economics and Clinical Epidemiology, University of Cologne, Germany. The funding agreement enabled the authors to design the study, interpret the data, and write and publish the manuscript. The following authors are employed by the sponsors: Björn Stollenwerk (HMGU), Stephanie Stock (University of Cologne). We thank Heather Hynd for proofreading the manuscript.

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Authors and Affiliations

  1. Institute of Health Economics and Health Care Management, Helmholtz Zentrum München (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany

    Björn Stollenwerk, Thomas Welchowski & Matthias Vogl

  2. Institut für Medizinische Biometrie, Informatik und Epidemiologie (IMBIE), Universitätsklinikum Bonn, Sigmund-Freud-Straße 25, 53105, Bonn, Germany

    Thomas Welchowski

  3. Institute of Health Economics and Clinical Epidemiology, University of Cologne, Gleueler Straße 176-178, 50935, Cologne, Germany

    Stephanie Stock

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  1. Björn Stollenwerk
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Correspondence to Björn Stollenwerk.

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Stollenwerk, B., Welchowski, T., Vogl, M. et al. Cost-of-illness studies based on massive data: a prevalence-based, top-down regression approach. Eur J Health Econ 17, 235–244 (2016). https://doi.org/10.1007/s10198-015-0667-z

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