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A New Approach for Constructing a Health Care Index including the Subjective Level

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  • Sandra Jaworeck

    (Institute of Sociology, Chemnitz University of Technology, 09107 Chemnitz, Germany
    Institute of Labor Sciences, Ruhr-University Bochum, 44801 Bochum, Germany)

Abstract

Until now, health care systems have been compared by means of macro criteria, an approach that might have its shortcomings in assessing the actual benefits that health care systems may provide for people. Therefore, a new health care index is presented which combines individual assessments of health care systems with objective macro health care system criteria. Two steps are taken for furthering this approach: First, a data-driven procedure is used to determine the influence of self-rated health on confidence in the health care system through macro criteria of health care systems. The macro indicators are weighted accordingly and created into an index, which adds to the subjective level of the link. In a second step, the constructed health care index is tested in a multilevel model with self-rated health being the dependent variable, to avoid tautological conclusions. The index is able to reduce country differences, decrease explained variability and has a statistically significant effect without affecting other estimates.

Suggested Citation

  • Sandra Jaworeck, 2022. "A New Approach for Constructing a Health Care Index including the Subjective Level," IJERPH, MDPI, vol. 19(15), pages 1-16, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9686-:d:881653
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    References listed on IDEAS

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    1. 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.
    2. Handler, A. & Issel, M. & Turnock, B., 2001. "A conceptual framework to measure performance of the public health system," American Journal of Public Health, American Public Health Association, vol. 91(8), pages 1235-1239.
    3. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    4. Mohseni, Mohabbat & Lindstrom, Martin, 2007. "Social capital, trust in the health-care system and self-rated health: The role of access to health care in a population-based study," Social Science & Medicine, Elsevier, vol. 64(7), pages 1373-1383, April.
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