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A Structural Equation Modeling Approach to Examining the Predictive Power of Determinants of Individuals' Health Expenditures

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Abstract

Understanding the determinants of health expenditures is essential for a fair and effective utilization profiling, particularly in the setting of capitation rates in risk-adjustment models. The objective of the study was to examine the relative importance of determinants in predicting future health expenditures, using structural equation modeling. Based on Andersen's behavioral system model, individual determinants along with prior utilization and measures of health status from 1994 are evaluated in a longitudinal design for their predictive power for health expenditures in 1995. A total of 4255 policyholders enrolled in three health plans at Trigon BlueCross/BlueShield of Virginia who responded to a mail survey were included for analysis. Person-level annual charges for health services utilization were used as the dependent variable. Five health scales were excerpted from Health Survey SF-36 to represent an individual's health status. Excluding prior utilization in 1994, health status (γ = −0.19, p < 0.001) and having diabetes (γ = 0.08, p < 0.001) are two statistically significant predictors of health expenditures in 1995. Including prior utilization, both health status (γ = −0.15, p < 0.001) and prior utilization (γ = 0.15, p < 0.001) are the most important predictors, followed by having diabetes (γ = 0.08, p < 0.001). Health status is a powerful predictor of future health expenditures, even when prior utilization is controlled.

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REFERENCES

  1. Polzer, K., The role of risk adjustment in national health reform. Acad. Med. 69:445, 1994.

    Google Scholar 

  2. Lee, C., and Rogal, D., Risk Adjustment: A Key to Changing Incentives in the Health Insurance Market, Alpha Center, Washington, DC, 1997.

    Google Scholar 

  3. Berk, M., and Monheit, A., The concentration of health expenditures: An update. Health Aff. 11(4):145, 1992.

    Google Scholar 

  4. National Center for Health Statistics, Health, United States, 1995, Public Health Service (PHS No. 96–1232), Hyattsville, MD, 1996.

    Google Scholar 

  5. Newhouse, J. P., Risk adjustment: Where are we now? Inquiry 35:122, 1998.

    Google Scholar 

  6. Beck, K., Growing importance of capitation in Switzerland. Health Care Manag. Sci. 3:111, 2000.

    Google Scholar 

  7. Dudley, R. A., Bowers, L. V., and Luft, H. S., Reconciling quality measurement with financial risk adjustment in health plans. Jt. Comm. J. Qual. Improv. 26:137, 2000.

    Google Scholar 

  8. Rice, N., and Smith, P.C., Capitation and risk adjustment in health care [editorial]. Health Care Manag. Sci. 3:73, 2000.

    Google Scholar 

  9. Hornbrook, M. C., and Goodman, M. J., Assessing relative health plan risk with the RAND-36 health survey. Inquiry 32:56, 1995.

    Google Scholar 

  10. Lichtenstein, R. L., and Thomas, J. W., A comparison of self-reported measures of perceived health and functional health in an elderly population. J. Community Health 12:213, 1987.

    Google Scholar 

  11. Lichtenstein, R. L., and Thomas, J. W., Including a measure of health status in Medicare's Health Maintenance Organization capitation formula: Reliability issues. Med. Care 25:100, 1987.

    Google Scholar 

  12. Whitmore, R. W., Paul, J. E., Gibbs, D. A., and Beebe, J. C., Using health indicators in calculating the AAPCC. In Scheffler, R. M., and Rossiter, L. F. (eds.), Advances in Health Economics and Health Services Research, JAI, London, 1989, pp. 75–109.

    Google Scholar 

  13. Bierman, A., Buboltz, T., Fisher, E., and Wasson, J. H., How well does a single question about health predict the financial health of Medicare managed care plans? Eff. Clin. Pract. 2(2):56–62, 1999.

    Google Scholar 

  14. Andersen, R., and Newman, J. F., Societal and individual determinants of medical care utilization in the United States. Milbank Mem. Fund Q. 51:95, 1973.

    Google Scholar 

  15. Andersen, R., Revisiting the behavioral model and access to medical care: Does it matter? J. Health Soc. Behav. 36:1, 1995.

    Google Scholar 

  16. Bollen, K., Structural Equations With Latent Variables, Wiley, New York, 1989.

    Google Scholar 

  17. Ware, J. E., and Sherbourne, C. D., The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Med. Care 30:473, 1992.

    Google Scholar 

  18. Ware, J. E., Snow, K. K., Kosinski, M., and Gandek, B., SF-36 Health Survey: Manual and Interpretation Guide, Nimrod Press, Boston, 1993.

    Google Scholar 

  19. Hornbrook, M. C., and Goodman, M. J., Chronic disease, functional health status, and demographics: A multi-dimensional approach to risk adjustment. Health Serv. Res. 31:283, 1996.

    Google Scholar 

  20. Ware, J. E., Kosinski, M., and Keller, S. D., SF-12: How to Score the SF-12 Physical and Mental Summary Scales, 2nd edn., The Health Institute, New England Medical Center, Boston, 1995.

    Google Scholar 

  21. McHorney, C., Ware, J. E., Jr., and Faczek, A., The MOS 36-item short-form health survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. Med. Care 31:247, 1993.

    Google Scholar 

  22. McHorney, C., Ware, J. E., Jr., Rachel, J., and Sherbourne, C., The MOS 36-item short-form health survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Med. Care 32:40, 1994.

    Google Scholar 

  23. Reed, P. J., Medical outcomes study short form 36: Testing and cross-validating a second-order factorial structure for health system employees. Health Serv. Res. 33(5):1361, 1998.

    Google Scholar 

  24. Chern J. Y., Wan, T. T. H., and Pyles, M., The stability of health status measurement (SF-36) in a working population. J. Outcome Meas. 4:455, 2000.

    Google Scholar 

  25. Wan, T. T. H., A behavioral model of health services utilization by older people. In Marcia, O., and Bond, K. (eds.), Aging and Health Care, Routledge, London, 1989.

    Google Scholar 

  26. Pai, C. W., and Wan, T. T. H., Confirmatory analysis of health outcome indicators: The 36-item short-form health survey (SF-36). J. Rehabil. Outcomes Meas. 1(2):48, 1997.

    Google Scholar 

  27. Hayduk, L. A., Structural Equation Modeling with LISREL: Essentials and Advances, Johns Hopkins University Press, Baltimore, 1987.

    Google Scholar 

  28. Arbuckle, J. L., Amos Users' Guide, SPSS, Chicago, 1997.

    Google Scholar 

  29. Wolinsky, F. D., Culler, S. D., Callahan, C. M., and Johnson, R. J., Hospital resource consumption among older adults: A prospective analysis of episodes, length of stay, and charges over a seven-year period. J. Gerontol. Soc. Sci. 49:S240, 1994.

    Google Scholar 

  30. Wan, T. T. H., A behavioral model of health services utilization by older people. In Marcia, O., and Bond, K. (eds.), Aging and Health Care, Routledge, London, 1989.

    Google Scholar 

  31. Gelberg, L., Andersen, R. M., and Leake, B. D., The behavioral model for vulnerable populations: Applications to medical care use and outcomes for homeless people. Health Serv. Res. 34(6):1273, 2000.

    Google Scholar 

  32. Katz, S. J., Hofer, T. P., and Manning, W. G., Hospital utilization in Ontario and the United States: The impact of socioeconomic status and health status. Can. J. Public Health 87:253, 1996.

    Google Scholar 

  33. Stump, T. E., Johnson, R. J., and Wolinsky, F. D., Changes in physician utilization over time among older adults. Journal of Gerontology: Soc. Sci. 50B:S45, 1995.

    Google Scholar 

  34. Epstein, A. M., and Cumella, E. J., Capitation payment: Using prediction of medical utilization to adjust rates. Health Care Financ. Rev. 10:51, 1988.

    Google Scholar 

  35. Fowles, J. B., Weiner, J. P., Knutson, D., Fowler, E., Tucker, A. M., and Ireland M., Taking health status into account when setting capitation rates: A comparison of risk-adjustment methods. JAMA 276:1316, 1996.

    Google Scholar 

  36. Ash, A., Porell, F., Gruenberg, L., Sawitz, E., and Beiser, A., Adjusting Medicare capitation payments using prior hospitalization data. Health Care Financ. Rev. 10:17, 1989.

    Google Scholar 

  37. Newhouse, J. P., Manning W. G., Keeler, E. B., and Sloss, E. M., Adjusting capitation rates using objective health measures and prior utilization. Health Care Financ. Rev. 10:41, 1989.

    Google Scholar 

  38. Manning, W. G., Keeler, E. B., Newhouse, J. P., Sloss, E. M., and Wassermann, J., The Costs of Poor Health Habits, Harvard University Press, Cambridge, MA, 1991.

    Google Scholar 

  39. Yen, L. T., Edington, D. W., and Witting, P., Associations between health risk appraisal scores and employee medical claims costs in a manufacturing company. Am. J. Health Promot. 6:46, 1991.

    Google Scholar 

  40. Hayes, S. T., Demographic risk factors derived from HMO data. In Scheffler, R. M., and Rossiter, L. F. (eds.), Advances in Health Economics and Health Services Research, JAI, London, 1991, pp. 177–196.

    Google Scholar 

  41. Howland, J., Stokes, J. I., Crane, S. C., and Belanger, A. J., Adjusting capitation using chronic disease risk factors: A preliminary study. Health Care Financ. Rev. 9:15, 1987.

    Google Scholar 

  42. Van Vliet, R., and Van de Ven, W., Towards a capitation formula for competing health insurers: An empirical analysis. Soc. Sci. Med. 34:1035, 1992.

    Google Scholar 

  43. Hornbrook, M. C., and Goodman, M. J., Health plan case mix: Definition, measurement, and use. In Scheffler, R. M., and Rossiter, L. F. (eds.), Advances in Health Economics and Health Services Research, JAI, London, 1991, pp. 111–148.

    Google Scholar 

  44. Osmond, D. H., Vranizan, K., Schillinger, D., Stewart, A. L., and Bindman, A. B., Measuring the need for medical care in an ethnically diverse population. Health Serv. Res. 31:551, 1996.

    Google Scholar 

  45. Wolinsky, F. D., Aguirre, B. E., Fann, L. J., Keith, V. M., Arnold, C. L., Niederhauer, J. C., and Dietrich, K., Ethnic differences in the demand for physician and hospital utilization among older adults in major American cities: Conspicuous evidence of considerable inequalities. Milbank Q. 67:412, 1989.

    Google Scholar 

  46. Robinson, J. C., Lft, H. S., Gardner, L. B., and Morrison, E. M., A method for risk-adjusting employer contributions to competing health insurance plans. Inquiry 28:107–116, 1991.

    Google Scholar 

  47. Porell, F. W., and Gruenberg, L., Discretionary hospital use and diagnostic risk adjustment of Medicare HMO capitation rates. Inquiry 37(2):162, 2000.

    Google Scholar 

  48. Hornbrook, M. C., Goodman, M. J., and Bennett, M. D., Assessing health plan case mix in employed populations: Ambulatory morbidity and prescribed drug models. In Scheffler, R. M., and Rossiter, L. F. (eds.), Advances in Health Economics and Health Services Research, JAI, London, 1991, pp. 197–232.

    Google Scholar 

  49. Chern, J. Y., Rossiter, L. F., and Wan, T. T. H., Examining the real effect of prior utilization on subsequent utilization. In Kronenfeld, J. J. (ed.), Research in the Sociology of Health Care, JAI, Stamford, CT, 2000, pp. 237–249.

    Google Scholar 

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Correspondence to Thomas T. H. Wan.

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Chern, JY., Wan, T.T.H. & Begun, J.W. A Structural Equation Modeling Approach to Examining the Predictive Power of Determinants of Individuals' Health Expenditures. Journal of Medical Systems 26, 323–336 (2002). https://doi.org/10.1023/A:1015868720789

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