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

Author

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  • Péter Elek

    (Department of Economics, Eötvös Loránd University, 1112 Budapest, Pázmány Péter sétány 1/a and Health and Population Lendület Research Group, Centre for Economic and Regional Studies, 1097 Budapest, Tóth Kálmán u. 4.)

  • Anikó Bíró

    (Health and Population Lendület Research Group, Institute of Economics, Centre for Economic and Regional Studies, Tóth Kálmán u. 4., H-1097 Budapest, Hungary)

Abstract

We examine regional differences in diabetes within Europe, and relate them to variations in socio-economic conditions, comorbidities, health behaviour and diabetes management. Using SHARE (Survey of Health, Ageing and Retirement in Europe) data, 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 the risk factors on the regional differences in incidence with causal random forests. Compared to Western Europe, the transition odds to diabetes is 2.3-fold in Southern and 2.7-fold in Eastern Europe, which decreases to 2.0 and 2.1 after adjusting for individual characteristics. The remaining differences are explained by country-specific healthcare indicators. Based on the causal forest approach, the adjusted East-West difference is essentially zero for the lowest risk groups (tertiary education, no hypertension, no overweight) and increases substantially with these risk factors, but the South-West difference is much less heterogeneous. The prevalence of diet-related weight loss around the time of diagnosis also exhibits regional variation. The results suggest that more emphasis should be put on diabetes prevention among high-risk individuals in Eastern Europe.

Suggested Citation

  • Péter Elek & Anikó Bíró, 2020. "Regional differences in diabetes across Europe –regression and causal forest analyses," KRTK-KTI WORKING PAPERS 2027, Institute of Economics, Centre for Economic and Regional Studies.
    • Handle: RePEc:has:discpr:2027
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    References listed on IDEAS

    as
    1. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
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    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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