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Analysis of Health Care Costs in Elderly Patients with Multiple Chronic Conditions Using a Finite Mixture of Generalized Linear Models

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  • Matthias Eckardt
  • Christian Brettschneider
  • Hendrik van den Bussche
  • Hans‐Helmut König
  • MultiCare Study Group

Abstract

In this paper we analysed healthcare costs in a sample of elderly patients suffering from multimorbidity. On the one hand, multimorbid individuals consume a disproportionally large share of healthcare resources. On the other hand, the patient specific number and combination of co‐occurring single diseases result in inhomogeneous data leading to biased estimates when using traditional regression techniques. Therefore, we applied a mixture of regressions in order to control for unobserved heterogeneity focussing on the identification of multimorbidity patterns. We used a subsample of N = 1050 patients from a multicentre prospective cohort study of randomly selected multimorbid primary care patients aged 65 to 85 years in Germany (ISRCTN 89818205) who completed a detailed questionnaire on healthcare utilization during the 6‐month period preceding the interview. Disease combinations of 1047 were included. We detected four different groups of patients with regard to total costs. These groups corresponded largely to findings from the epidemiological literature. The effect of the presence of an additional disease on costs differed between groups. Moreover, two diametrically opposed cost trends were detected with respect to the number of co‐occurring diseases. While in one group costs increased with the number of co‐occurring diseases, in a second group cost tended to decrease. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Matthias Eckardt & Christian Brettschneider & Hendrik van den Bussche & Hans‐Helmut König & MultiCare Study Group, 2017. "Analysis of Health Care Costs in Elderly Patients with Multiple Chronic Conditions Using a Finite Mixture of Generalized Linear Models," Health Economics, John Wiley & Sons, Ltd., vol. 26(5), pages 582-599, May.
  • Handle: RePEc:wly:hlthec:v:26:y:2017:i:5:p:582-599
    DOI: 10.1002/hec.3334
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    References listed on IDEAS

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    1. Galina Besstremyannaya, 2012. "Estimating income equity in social health insurance system," Working Papers w0172, Center for Economic and Financial Research (CEFIR).
    2. Galina Besstremyannaya, 2012. "Heterogeneous effect of coinsurance rate on the demand for health care: a finite mixture approach," Working Papers w0163, Center for Economic and Financial Research (CEFIR).
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    Cited by:

    1. Kwok Tai Chui & Wadee Alhalabi & Sally Shuk Han Pang & Patricia Ordóñez de Pablos & Ryan Wen Liu & Mingbo Zhao, 2017. "Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications," Sustainability, MDPI, vol. 9(12), pages 1-23, December.
    2. Brettschneider, Christian & Hajek, Andre & Röhr, Susanne & Fuchs, Angela & Weeg, Dagmar & Mamone, Silke & Werle, Jochen & Heser, Kathrin & Mallon, Tina & Stein, Janine & Pentzek, Michael & Bickel, Hor, 2019. "Determinants of health-care costs in the oldest-old in Germany," The Journal of the Economics of Ageing, Elsevier, vol. 14(C).
    3. Anna Kollerup & Jakob Kjellberg & Rikke Ibsen, 2022. "Ageing and health care expenditures: the importance of age per se, steepening of the individual-level expenditure curve, and the role of morbidity," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(7), pages 1121-1149, September.

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