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Regional differences in diabetes across Europe – regression and causal forest analyses

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  • Elek, Péter
  • Bíró, Anikó

Abstract

We examine regional differences in diabetes within Europe, and relate them to variations in socio-economic conditions, comorbidities, health behaviour and diabetes management. We use the SHARE (Survey of Health, Ageing and Retirement in Europe) data of 15 European countries and 28,454 individuals, who participated both in the 4th and 7th (year 2011 and 2017) waves of the survey. First, we estimate multivariate regressions, where the outcome variables are diabetes prevalence, diabetes incidence, and weight loss due to diet as an indicator of management. Second, we study the heterogeneous impact of demographic, socio-economic, health and lifestyle indicators on the regional differences in diabetes incidence with causal random forests.

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  • Elek, Péter & Bíró, Anikó, 2021. "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, vol. 40(C).
    • Handle: RePEc:eee:ehbiol:v:40:y:2021:i:c:s1570677x20302185
    DOI: 10.1016/j.ehb.2020.100948
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    References listed on IDEAS

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    3. 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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    8. Richard Heijink & Xander Koolman & Gert Westert, 2013. "Spending more money, saving more lives? The relationship between avoidable mortality and healthcare spending in 14 countries," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(3), pages 527-538, June.
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    Cited by:

    1. Hayakawa, Kazunobu & Keola, Souknilanh & Silaphet, Korrakoun & Yamanouchi, Kenta, 2022. "Estimating the impacts of international bridges on foreign firm locations: a machine learning approach," IDE Discussion Papers 847, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    2. Patrick Rehill & Nicholas Biddle, 2024. "Causal machine learning in public policy evaluation -- an application to the conditioning of cash transfers in Morocco," Papers 2401.07075, arXiv.org.

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    More about this item

    Keywords

    Causal forest; Diabetes; Europe; Health behaviour; SHARE data;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality

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