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The economic burden of diabetes to French national health insurance: a new cost-of-illness method based on a combined medicalized and incremental approach

Author

Listed:
  • Grégoire Lagasnerie

    (National Health Insurance (CNAMTS))

  • Anne-Sophie Aguadé

    (National Health Insurance (CNAMTS))

  • Pierre Denis

    (National Health Insurance (CNAMTS))

  • Anne Fagot-Campagna

    (National Health Insurance (CNAMTS))

  • Christelle Gastaldi-Menager

    (National Health Insurance (CNAMTS))

Abstract

A better understanding of the economic burden of diabetes constitutes a major public health challenge in order to design new ways to curb diabetes health care expenditure. The aim of this study was to develop a new cost-of-illness method in order to assess the specific and nonspecific costs of diabetes from a public payer perspective. Using medical and administrative data from the major French national health insurance system covering about 59 million individuals in 2012, we identified people with diabetes and then estimated the economic burden of diabetes. Various methods were used: (a) global cost of patients with diabetes, (b) cost of treatment directly related to diabetes (i.e., ‘medicalized approach’), (c) incremental regression-based approach, (d) incremental matched-control approach, and (e) a novel combination of the ‘medicalized approach’ and the ‘incremental matched-control’ approach. We identified 3 million individuals with diabetes (5% of the population). The total expenditure of this population amounted to €19 billion, representing 15% of total expenditure reimbursed to the entire population. Of the total expenditure, €10 billion (52%) was considered to be attributable to diabetes care: €2.3 billion (23% of €10 billion) was directly attributable, and €7.7 billion was attributable to additional reimbursed expenditure indirectly related to diabetes (77%). Inpatient care represented the major part of the expenditure attributable to diabetes care (22%) together with drugs (20%) and medical auxiliaries (15%). Antidiabetic drugs represented an expenditure of about €1.1 billion, accounting for 49% of all diabetes-specific expenditure. This study shows the economic impact of the assumption concerning definition of costs on evaluation of the economic burden of diabetes. The proposed new cost-of-illness method provides specific insight for policy-makers to enhance diabetes management and assess the opportunity costs of diabetes complications’ management programs.

Suggested Citation

  • Grégoire Lagasnerie & Anne-Sophie Aguadé & Pierre Denis & Anne Fagot-Campagna & Christelle Gastaldi-Menager, 2018. "The economic burden of diabetes to French national health insurance: a new cost-of-illness method based on a combined medicalized and incremental approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(2), pages 189-201, March.
  • Handle: RePEc:spr:eujhec:v:19:y:2018:i:2:d:10.1007_s10198-017-0873-y
    DOI: 10.1007/s10198-017-0873-y
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    References listed on IDEAS

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    1. Albouy, Valerie & Davezies, Laurent & Debrand, Thierry, 2010. "Health expenditure models: A comparison using panel data," Economic Modelling, Elsevier, vol. 27(4), pages 791-803, July.
    2. Eberechukwu Onukwugha & Jacquelyn McRae & Alex Kravetz & Stefan Varga & Rahul Khairnar & C. Mullins, 2016. "Cost-of-Illness Studies: An Updated Review of Current Methods," PharmacoEconomics, Springer, vol. 34(1), pages 43-58, January.
    3. Till Seuring & Olga Archangelidi & Marc Suhrcke, 2015. "The Economic Costs of Type 2 Diabetes: A Global Systematic Review," PharmacoEconomics, Springer, vol. 33(8), pages 811-831, August.
    4. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
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    Cited by:

    1. Sonsoles Fuentes & Emmanuel Cosson & Laurence Mandereau-Bruno & Anne Fagot-Campagna & Pascale Bernillon & Marcel Goldberg & Sandrine Fosse-Edorh, 2019. "Identifying diabetes cases in health administrative databases: a validation study based on a large French cohort," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(3), pages 441-450, April.
    2. Alexandre Vimont & Henri Leleu & Isabelle Durand-Zaleski, 2022. "Machine learning versus regression modelling in predicting individual healthcare costs from a representative sample of the nationwide claims database in France," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(2), pages 211-223, March.
    3. J. Pöhlmann & K. Norrbacka & K. S. Boye & W. J. Valentine & H. Sapin, 2020. "Costs and where to find them: identifying unit costs for health economic evaluations of diabetes in France, Germany and Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(8), pages 1179-1196, November.
    4. Claudiu Morgovan & Smaranda Adina Cosma & Madalina Valeanu & Anca Maria Juncan & Luca Liviu Rus & Felicia Gabriela Gligor & Anca Butuca & Delia Mirela Tit & Simona Bungau & Steliana Ghibu, 2020. "An Exploratory Research of 18 Years on the Economic Burden of Diabetes for the Romanian National Health Insurance System," IJERPH, MDPI, vol. 17(12), pages 1-16, June.
    5. Rodríguez-Sánchez, Beatriz & Cantarero-Prieto, David, 2019. "Socioeconomic differences in the associations between diabetes and hospital admission and mortality among older adults in Europe," Economics & Human Biology, Elsevier, vol. 33(C), pages 89-100.

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    More about this item

    Keywords

    Diabetes; Cost of illness; Econometrics; Health administrative databases;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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