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Diabetes diagnosis based on glucose control levels and time until diagnosis: a regression discontinuity approach to assess the effect on direct healthcare costs

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  • Toni Mora

    (Universitat Internacional de Catalunya (UIC))

  • Beatriz Rodríguez-Sánchez

    (Universidad Complutense de Madrid)

Abstract

We estimate the difference in direct healthcare costs of individuals diagnosed with diabetes depending on their glucose level, considering different timespans and subgroups. Using data from administrative registers of 285,450 individuals in Catalonia from 2013 to 2017, we used a fuzzy regression discontinuity design to estimate the causal effect of being diagnosed with diabetes at a given timespan (based on an average glucose value equal to or above 6.5%, the treated group) vs. not (having an average glucose level below the threshold, the control group) on healthcare costs across different timespans (6, 9, 12, 15, 18, 21, and 24 months after the first laboratory test) and distances, in days, between the laboratory test and the doctor’s diagnosis. When average glucose level was the only independent parameter and the time until diagnosis was 30 days or less, at the cut-off value (6.5%) healthcare costs were between €3,887 and €5,789 lower for the treated group compared to the control group. Smaller differences were reported as the delay in diagnosis increased, even when additionally controlling for sociodemographic characteristics and health status. Our results highlight the importance of prompt diagnosis and might open the debate about the usefulness of the 6.5% reference value in the blood glucose level as the main diagnostic tool in diabetes.

Suggested Citation

  • Toni Mora & Beatriz Rodríguez-Sánchez, 2025. "Diabetes diagnosis based on glucose control levels and time until diagnosis: a regression discontinuity approach to assess the effect on direct healthcare costs," Health Economics Review, Springer, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:spr:hecrev:v:15:y:2025:i:1:d:10.1186_s13561-025-00613-y
    DOI: 10.1186/s13561-025-00613-y
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    References listed on IDEAS

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    1. Megha Bansal & Mona Shah & Brian Reilly & Susan Willman & Max Gill & Francine R. Kaufman, 2018. "Impact of Reducing Glycated Hemoglobin on Healthcare Costs Among a Population with Uncontrolled Diabetes," Applied Health Economics and Health Policy, Springer, vol. 16(5), pages 675-684, October.
    2. Joan Gil & Paolo Li Donni & Eugenio Zucchelli, 2019. "Uncontrolled diabetes and health care utilisation: A bivariate latent Markov model approach," Health Economics, John Wiley & Sons, Ltd., vol. 28(11), pages 1262-1276, November.
    3. Gaggero, Alessio & Gil, Joan & Jiménez-Rubio, Dolores & Zucchelli, Eugenio, 2021. "Health Information and Lifestyle Behaviours: The Impact of a Diabetes Diagnosis," IZA Discussion Papers 14106, Institute of Labor Economics (IZA).
    4. Manel Mata-Cases & Marc Casajuana & Josep Franch-Nadal & Aina Casellas & Conxa Castell & Irene Vinagre & Dídac Mauricio & Bonaventura Bolíbar, 2016. "Direct medical costs attributable to type 2 diabetes mellitus: a population-based study in Catalonia, Spain," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 1001-1010, November.
    5. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell & Roc ́ıo Titiunik, 2017. "rdrobust: Software for regression-discontinuity designs," Stata Journal, StataCorp LLC, vol. 17(2), pages 372-404, June.
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    JEL classification:

    • H0 - Public Economics - - General
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I0 - Health, Education, and Welfare - - General
    • I1 - Health, Education, and Welfare - - Health
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets

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