IDEAS home Printed from https://ideas.repec.org/a/eee/retrec/v103y2024ics0739885924000064.html
   My bibliography  Save this article

Predicting trips to health care facilities: A binary logit and receiver operating characteristics (ROC) approach

Author

Listed:
  • McCarthy, Patrick

Abstract

This paper discusses an overarching framework that integrates latent variable models and prediction success with receiver operator characteristics (ROC) curves. In three illustrative examples that focus on trip purpose, health market indicators, and resources, the paper employs binary logit models and ROC methodology to identify factors that best discriminate individuals' trips to out-patient health care facilities. Data for the examples are a three-year longitudinal survey of persons 45 years and older in China. The study contributes to the sparse empirical economics literature using ROC methodologies and more broadly to the transportation and health literatures. The ROC applications provide new insights on health care trips, finding that out-patient trips for treatment, paying out-of-pocket costs, and lack of monetary resources are salient discriminators in one's trip choice decisions.

Suggested Citation

  • McCarthy, Patrick, 2024. "Predicting trips to health care facilities: A binary logit and receiver operating characteristics (ROC) approach," Research in Transportation Economics, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:retrec:v:103:y:2024:i:c:s0739885924000064
    DOI: 10.1016/j.retrec.2024.101411
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0739885924000064
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.retrec.2024.101411?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Christian Pierdzioch, 2016. "Using ROC techniques to measure the effectiveness of foreign exchange market interventions," Applied Economics Letters, Taylor & Francis Journals, vol. 23(6), pages 389-393, April.
    2. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03045837, HAL.
    3. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 199-244, June.
    4. Stephan Dreiseitl & Lucila Ohno-Machado & Michael Binder, 2000. "Comparing Three-class Diagnostic Tests by Three-way ROC Analysis," Medical Decision Making, , vol. 20(3), pages 323-331, July.
    5. Stein, Roger M., 2005. "The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing," Journal of Banking & Finance, Elsevier, vol. 29(5), pages 1213-1236, May.
    6. Takeshi Amemiya, 1975. "Qualitative Response Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 4, number 3, pages 363-372, National Bureau of Economic Research, Inc.
    7. R. John Irwin & Timothy C. Irwin, 2013. "Appraising Credit Ratings: Does The Cap Fit Better Than The Roc?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 18(4), pages 396-408, October.
    8. Zhenqiu Liu & Ming Tan, 2008. "ROC-Based Utility Function Maximization for Feature Selection and Classification with Applications to High-Dimensional Protease Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1155-1161, December.
    9. Aigner, Dennis J. & Hsiao, Cheng & Kapteyn, Arie & Wansbeek, Tom, 1984. "Latent variable models in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 23, pages 1321-1393, Elsevier.
    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. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    2. O. Vasiurenko & V. LYASHENKO, 2020. "Wavelet coherence as a tool for retrospective analysis of bank activities," Economy and Forecasting, Valeriy Heyets, issue 2, pages 43-60.
    3. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    4. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
    5. Tim Meyer, 2019. "On the Directional Accuracy of United States Housing Starts Forecasts: Evidence from Survey Data," The Journal of Real Estate Finance and Economics, Springer, vol. 58(3), pages 457-488, April.
    6. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    7. Petra P. Šimović & Claire Y. T. Chen & Edward W. Sun, 2023. "Classifying the Variety of Customers’ Online Engagement for Churn Prediction with a Mixed-Penalty Logistic Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 451-485, January.
    8. Petra Posedel v{S}imovi'c & Davor Horvatic & Edward W. Sun, 2021. "Classifying variety of customer's online engagement for churn prediction with mixed-penalty logistic regression," Papers 2105.07671, arXiv.org, revised Jul 2021.
    9. João Gabriel Moraes Souza & Daniel Tavares Castro & Yaohao Peng & Ivan Ricardo Gartner, 2024. "A Machine Learning-Based Analysis on the Causality of Financial Stress in Banking Institutions," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1857-1890, September.
    10. Martina Mokrišová & Jarmila Horváthová, 2023. "Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach," Risks, MDPI, vol. 11(11), pages 1-18, November.
    11. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    12. Magdalena Brygała, 2022. "Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data," Risks, MDPI, vol. 10(2), pages 1-13, January.
    13. Philippe Jardin, 2025. "Designing Ensemble-Based Models Using Neural Networks and Temporal Financial Profiles to Forecast Firms’ Financial Failure," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 149-209, January.
    14. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    15. Laura Márquez-Ramos & Inmaculada Martínez-Zarzoso & Celestino Suárez-Burguet, 2011. "Determinants of Deep Integration: Examining Socio-political Factors," Open Economies Review, Springer, vol. 22(3), pages 479-500, July.
    16. Riillo, Cesare Fabio Antonio & Peroni, Chiara, 2022. "Immigration and entrepreneurship in Europe: cross-country evidence," MPRA Paper 114580, University Library of Munich, Germany.
    17. Simon Freyaldenhoven & Christian Hansen & Jorge Perez Perez & Jesse Shapiro, 2021. "Visualization, Identification, and stimation in the Linear Panel Event-Study Design," Working Papers 21-44, Federal Reserve Bank of Philadelphia.
    18. Alain Coen & Francois-Éric Racicot, 2006. "A New Approach Based on Cumulants for Estimating Financial Regression Models with Errors in the Variables: the Fama and French Model Revisited," RePAd Working Paper Series UQO-DSA-wp142006, Département des sciences administratives, UQO.
    19. Andrew Cherlin & Aphichat Chamratrithirong, 1988. "Variations in marriage patterns in central Thailand," Demography, Springer;Population Association of America (PAA), vol. 25(3), pages 337-353, August.
    20. Dongwoo Kim & Daniel Wilhelm, 2024. "Powerful t-tests in the presence of nonclassical measurement error," Econometric Reviews, Taylor & Francis Journals, vol. 43(6), pages 345-378, July.

    More about this item

    Keywords

    Receiver operating characteristics; ROC; Prediction success; Binary logit; Out-patient trips; Aging; Longitudinal; China; CHARLS;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

    Statistics

    Access and download statistics

    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:eee:retrec:v:103:y:2024:i:c:s0739885924000064. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/620614/description#description .

    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.