IDEAS home Printed from https://ideas.repec.org/a/inm/orijds/v3y2024i1p49-67.html
   My bibliography  Save this article

Cost Patterns of Multiple Chronic Conditions: A Novel Modeling Approach Using a Condition Hierarchy

Author

Listed:
  • Lida Anna Apergi

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • Margrét Vilborg Bjarnadóttir

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • John S. Baras

    (Institute for Systems Research, University of Maryland, College Park, Maryland 20740)

  • Bruce L. Golden

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

Abstract

Healthcare cost predictions are widely used throughout the healthcare system. However, predicting these costs is complex because of both uncertainty and the complex interactions of multiple chronic diseases: chronic disease treatment decisions related to one condition are impacted by the presence of the other conditions. We propose a novel modeling approach inspired by backward elimination, designed to minimize information loss. Our approach is based on a cost hierarchy: the cost of each condition is modeled as a function of the number of other, more expensive chronic conditions the individual member has. Using this approach, we estimate the additive cost of chronic diseases and study their cost patterns. Using large-scale claims data collected from 2007 to 2012, we identify members that suffer from one or more chronic conditions and estimate their total 2012 healthcare expenditures. We apply regression analysis and clustering to characterize the cost patterns of 69 chronic conditions. We observe that the estimated cost of some conditions (for example, organic brain problem) decreases as the member’s number of more expensive chronic conditions increases. Other conditions, such as obesity and paralysis, demonstrate the opposite pattern; their contribution to the overall cost increases as the member’s number of other more serious chronic conditions increases. The modeling framework allows us to account for the complex interactions of multimorbidity and healthcare costs and, therefore, offers a deeper and more nuanced understanding of the cost burden of chronic conditions, which can be utilized by practitioners and policy makers to plan, design better intervention, and identify subpopulations that require additional resources. More broadly, our hierarchical model approach captures complex interactions and can be applied to improve decision making when the enumeration of all possible factor combinations is not possible, for example, in financial risk scoring and pay structure design.

Suggested Citation

  • Lida Anna Apergi & Margrét Vilborg Bjarnadóttir & John S. Baras & Bruce L. Golden, 2024. "Cost Patterns of Multiple Chronic Conditions: A Novel Modeling Approach Using a Condition Hierarchy," INFORMS Joural on Data Science, INFORMS, vol. 3(1), pages 49-67, April.
  • Handle: RePEc:inm:orijds:v:3:y:2024:i:1:p:49-67
    DOI: 10.1287/ijds.2022.0010
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijds.2022.0010
    Download Restriction: no

    File URL: https://libkey.io/10.1287/ijds.2022.0010?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
    ---><---

    References listed on IDEAS

    as
    1. Marcella Alsan & Marianne Wanamaker, 2018. "Tuskegee and the Health of Black Men," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 407-455.
    2. Wullianallur Raghupathi & Viju Raghupathi, 2018. "An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health," IJERPH, MDPI, vol. 15(3), pages 1-24, March.
    3. Dimitris Bertsimas & Margrét V. Bjarnadóttir & Michael A. Kane & J. Christian Kryder & Rudra Pandey & Santosh Vempala & Grant Wang, 2008. "Algorithmic Prediction of Health-Care Costs," Operations Research, INFORMS, vol. 56(6), pages 1382-1392, December.
    4. I. Duncan & M. Loginov & M. Ludkovski, 2016. "Testing Alternative Regression Frameworks for Predictive Modeling of Health Care Costs," North American Actuarial Journal, Taylor & Francis Journals, vol. 20(1), pages 65-87, January.
    5. Blough, David K. & Madden, Carolyn W. & Hornbrook, Mark C., 1999. "Modeling risk using generalized linear models," Journal of Health Economics, Elsevier, vol. 18(2), pages 153-171, April.
    6. Amal Malehi & Fatemeh Pourmotahari & Kambiz Angali, 2015. "Statistical models for the analysis of skewed healthcare cost data: a simulation study," Health Economics Review, Springer, vol. 5(1), pages 1-16, December.
    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. Yeongah Choi & Jiho An & Seiyoung Ryu & Jaekyeong Kim, 2022. "Development and Evaluation of Machine Learning-Based High-Cost Prediction Model Using Health Check-Up Data by the National Health Insurance Service of Korea," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    2. 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.
    3. Trottmann, Maria & Zweifel, Peter & Beck, Konstantin, 2012. "Supply-side and demand-side cost sharing in deregulated social health insurance: Which is more effective?," Journal of Health Economics, Elsevier, vol. 31(1), pages 231-242.
    4. Buntin, Melinda Beeuwkes & Zaslavsky, Alan M., 2004. "Too much ado about two-part models and transformation?: Comparing methods of modeling Medicare expenditures," Journal of Health Economics, Elsevier, vol. 23(3), pages 525-542, May.
    5. Courbage, Christophe & Rey, Béatrice, 2012. "Priority setting in health care and higher order degree change in risk," Journal of Health Economics, Elsevier, vol. 31(3), pages 484-489.
    6. Kristensen, Frederikke Frehr & Sharp, Paul, 2021. "Disease Surveillance, Mortality and Race: The Case of HIV/AIDS in the United States," CAGE Online Working Paper Series 553, Competitive Advantage in the Global Economy (CAGE).
    7. A. Mukasheva & N. Saparkhojayev & Z. Akanov & A. Algazieva, 2019. "The Prevalence of Diabetes in the Republic of Kazakhstan Based on Regression Analysis Methods," International Journal of Health and Medical Sciences, Mohammad A. H. Khan, vol. 5(1), pages 8-16.
    8. Carole Roan Gresenz & Jeanette A. Rogowski & Jose Escarce, 2004. "Healthcare Markets, the Safety Net and Access to Care Among the Uninsured," NBER Working Papers 10799, National Bureau of Economic Research, Inc.
    9. Partha Deb & Murat K. Munkin & Pravin K. Trivedi, 2006. "Bayesian analysis of the two‐part model with endogeneity: application to health care expenditure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 1081-1099, November.
    10. Michelle Nichols & Ronald Teufel & Sarah Miller & Mohan Madisetti & Christine San Giovanni & Katherine Chike-Harris & Lacy Jones & Margaret Prentice & Kenneth Ruggiero & Teresa Kelechi, 2020. "Managing Asthma and Obesity Related Symptoms (MATADORS): An mHealth Intervention to Facilitate Symptom Self-Management among Youth," IJERPH, MDPI, vol. 17(21), pages 1-15, October.
    11. Onur Başer & Joseph C. Gardiner & Cathy J. Bradley & Hüseyin Yüce & Charles Given, 2006. "Longitudinal analysis of censored medical cost data," Health Economics, John Wiley & Sons, Ltd., vol. 15(5), pages 513-525, May.
    12. Balat, Jorge & Papageorge, Nicholas W. & Qayyum, Shaiza, 2017. "Positively Aware? Conflicting Expert Reviews and Demand for Medical Treatment," IZA Discussion Papers 10919, Institute of Labor Economics (IZA).
    13. SangA Lee & Deogwoon Kim & Haeok Lee, 2022. "Examine Race/Ethnicity Disparities in Perception, Intention, and Screening of Dementia in a Community Setting: Scoping Review," IJERPH, MDPI, vol. 19(14), pages 1-19, July.
    14. Keane, Michael & Stavrunova, Olena, 2016. "Adverse selection, moral hazard and the demand for Medigap insurance," Journal of Econometrics, Elsevier, vol. 190(1), pages 62-78.
    15. Matthias Flückiger & Markus Ludwig & Ali Sina Önder, 2019. "Ebola and State Legitimacy," The Economic Journal, Royal Economic Society, vol. 129(621), pages 2064-2089.
    16. Marcel Bilger & Willard G. Manning, 2015. "Measuring Overfitting In Nonlinear Models: A New Method And An Application To Health Expenditures," Health Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 75-85, January.
    17. Jay Dev Dubey, 2021. "Measuring Income Elasticity of Healthcare-Seeking Behavior in India: A Conditional Quantile Regression Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(4), pages 767-793, December.
    18. Donn L Feir & Rob Gillezeau & Maggie E C Jones, 2024. "The Slaughter of the Bison and Reversal of Fortunes on the Great Plains," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(3), pages 1634-1670.
    19. Lennon, Conor, 2021. "Are the costs of employer-sponsored health insurance passed on to workers at the individual level?," Economics & Human Biology, Elsevier, vol. 41(C).
    20. Manning, Willard G. & Basu, Anirban & Mullahy, John, 2005. "Generalized modeling approaches to risk adjustment of skewed outcomes data," Journal of Health Economics, Elsevier, vol. 24(3), pages 465-488, May.

    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:inm:orijds:v:3:y:2024:i:1:p:49-67. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.