IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i15p9686-d881653.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/15/9686/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/15/9686/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Youmi Suk, 2024. "A Within-Group Approach to Ensemble Machine Learning Methods for Causal Inference in Multilevel Studies," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 61-91, February.
    2. Youmi Suk & Hyunseung Kang, 2022. "Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 310-343, March.
    3. Sandra Jaworeck & Peter Kriwy, 2021. "It’s Sunny, Be Healthy? An International Comparison of the Influence of Sun Exposure and Latitude Lines on Self-Rated Health," IJERPH, MDPI, vol. 18(8), pages 1-19, April.
    4. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    5. William Arbour, 2021. "Can Recidivism be Prevented from Behind Bars? Evidence from a Behavioral Program," Working Papers tecipa-683, University of Toronto, Department of Economics.
    6. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Dimitris Bertsimas & Agni Orfanoudaki & Rory B. Weiner, 2020. "Personalized treatment for coronary artery disease patients: a machine learning approach," Health Care Management Science, Springer, vol. 23(4), pages 482-506, December.
    8. JANSSENS, Jochen & DE CORTE, Annelies & SÖRENSEN, Kenneth, 2016. "Water distribution network design optimisation with respect to reliability," Working Papers 2016007, University of Antwerp, Faculty of Business and Economics.
    9. Eichengreen, Barry & Aksoy, Cevat Giray & Saka, Orkun, 2021. "Revenge of the experts: Will COVID-19 renew or diminish public trust in science?," Journal of Public Economics, Elsevier, vol. 193(C).
    10. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    11. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Raymond Hernandez & Elizabeth A. Pyatak & Cheryl L. P. Vigen & Haomiao Jin & Stefan Schneider & Donna Spruijt-Metz & Shawn C. Roll, 2021. "Understanding Worker Well-Being Relative to High-Workload and Recovery Activities across a Whole Day: Pilot Testing an Ecological Momentary Assessment Technique," IJERPH, MDPI, vol. 18(19), pages 1-17, October.
    13. Christopher Hassall & Michael Nisbet & Evan Norcliffe & He Wang, 2024. "The Potential Health Benefits of Urban Tree Planting Suggested through Immersive Environments," Land, MDPI, vol. 13(3), pages 1-12, February.
    14. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
    15. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    16. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    17. Elisabeth Beckmann & Lukas Olbrich & Joseph Sakshaug, 2024. "Multivariate assessment of interviewer-related errors in a cross-national economic survey (Lukas Olbrich, Elisabeth Beckmann, Joseph W. Sakshaug)," Working Papers 253, Oesterreichische Nationalbank (Austrian Central Bank).
    18. Stephen Jarvis & Olivier Deschenes & Akshaya Jha, 2022. "The Private and External Costs of Germany’s Nuclear Phase-Out," Journal of the European Economic Association, European Economic Association, vol. 20(3), pages 1311-1346.
    19. F J Heather & D Z Childs & A M Darnaude & J L Blanchard, 2018. "Using an integral projection model to assess the effect of temperature on the growth of gilthead seabream Sparus aurata," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-19, May.
    20. 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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9686-:d:881653. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.