Analytic Methods for Constructing Cross-Sectional Profiles of Health Care Providers

  • Mary Beth LandrumEmail author
  • Susan E. Bronskill
  • Sharon-Lise T. Normand


National effort is currently directed toward developing and disseminating comparative information involving both outcomes and processes of care for health care providers. Univariate provider-specific comparative indices based on posterior summaries as well as indices based on maximum likelihood estimates have been developed for use in the cross-sectional setting. A remaining issue in the dissemination of cross-sectional profiles relates to the multivariate nature of the indices: often many performance measures are used to assess quality for a particular provider. Because this information can often be contradictory and overwhelming, there is a need for measures that summarize quality at a provider level. This article proposes the use of latent variable models for comparing health care providers in the cross-sectional setting when each provider is measured on more than one dimension of care. By combining information across dimensions of care within a provider, an integrated analysis can produce a composite measure of quality and has more statistical power to detect differences among providers. As the number of individual performance measures grows over time, composite measures will become increasingly important tools to support decision making by consumers, payors, and providers.

variation processes of care acute myocardial infarction hierarchical models latent variable models posterior summaries 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. O. Aguilar and M. West. “Analysis of hospital quality monitors using hierarchical time series models,” in Case Studies in Bayesian Statistics, Vol IV, C. Gatsonis, R. E. Kass, B. Carlin, A. Carriquiry, A. Gelman, I. Verdinelli, and M. West, eds. Springer-Verlag: New York, 1999.Google Scholar
  2. F. B. Baker. Item Response Theory: Parameter Estimation Techniques, New York: Marcel Dekker, 1992.Google Scholar
  3. K. Bandeen-Roche, D. L. Miglioretti, S. L. Zeger and P. J. Rathouz. “Latent variable regression for multiple discrete outcomes.” Journal of the American Statistical Association, Vol. 92, pp. 1375-1385, 1997.Google Scholar
  4. R. D. Bock and R. D. Gibbons, “High-dimensional multivariate probit analysis.” Biometrics, Vol. 52, pp. 1183-1194, 1996.Google Scholar
  5. A. S. Bryk and S. W. Raudenbush. Hierarchical Linear Models: Applications and Data Analysis Methods, Sage Publications: Newbury Park, 1992.Google Scholar
  6. M. R. Chassin, R. H. Brook, R. E. Park and J. Keesey et al. “Variations in the use of medical and surgical services by the Medicare population.” New England Journal of Medicine, Vol. 314, pp. 285-290, 1986.Google Scholar
  7. J. Chen, M. J. Radford, Y. Wang, T. A. Marciniak and H. M. Krumholz. “Do America's best hospitals perform better for acute myocardial infarction?” New England Journal of Medicine, Vol. 340, pp. 286-292, 1999.Google Scholar
  8. C. L. Christiansen and C. N. Morris. “Improving the statistical approach to health care provider profiling.” Annals of Internal Medicine, Vol. 127, pp. 764-768, 1997.Google Scholar
  9. Cleveland Health Quality Choice. “The greater Cleveland consumer report on hospital performance.” Quality Information Management Corporation, June 10, 1998.Google Scholar
  10. M. J. Daniels and C. Gatsonis. “Hierarchical generalized linear models in the analysis of variations in health care utilization.” Journal of the American Statistical Association, Vol. 94, pp. 29-42, 1999.Google Scholar
  11. M. J. Daniels and M. Hughes. “Meta-Analysis for the evaluation of potential surrogate markers.” Statistics in Medicine, Vol. 16, pp. 1965-1982, 1997.Google Scholar
  12. P. Diehr, K. Cain, F. Connell and E. Volinn. “What is too much variation? The null hypothesis in small-area analysis.” Health Services Research, Vol. 24, pp. 741-771, 1990.Google Scholar
  13. A. M. Epstein. “Rolling down the runway. The challenges ahead for quality report cards.” Journal of the American Medical Association, Vol. 279, pp. 1691-1696, 1998.Google Scholar
  14. A. V. Eye and C. C. Clogg, eds Latent Variable Modeling: Applications for Developmental Research, Thousand Oaks, Sage: Thousand Oaks, California, 1994.Google Scholar
  15. A. Gelman and D. B. Rubin. “Inference from iteration simulation using multiple sequences.” Statistical Science, Vol. 7, pp. 457-511, 1992.Google Scholar
  16. R. D. Gibbons and V. Wilcox-Gok. “Health service utilization and insurance coverage: A multivariate probit analysis.” Journal of the American Statistical Association, Vol. 93, pp. 63-72, 1998.Google Scholar
  17. W. R. Gilks, A. Thomas and D. J. Spiegelhalter. “A language and program for complex Bayesian modelling.” The Statistician, Vol. 43, pp. 169-178, 1994.Google Scholar
  18. K. Gillis, J. Hixson. “Efficacy of statistical outlier analysis for monitoring quality of care.” Journal of Business and Economic Statistics, Vol. 9, pp. 241-252, 1991.Google Scholar
  19. H. Goldstein and D. J. Spiegelhalter. “League tables and their limitations: Statistical issues in comparisons of institutional performance” (with discussion). Journal of the Royal Statistical Society, Ser. A, Vol. 159, pp. 385-444, 1996.Google Scholar
  20. J. Green, N. Wintfeld, M. Krasner and C. Wells. “In search of America's best hospitals: The promise and reality of quality assessment.” Journal of American Medical Association, Vol. 227, pp. 1152-1155, 1997.Google Scholar
  21. C. A. Hill, K. L. Winfrey, and B. A. Rudolph. “Best hospitals: A description of the methodology for the index of hospital quality.” Inquiry, Vol. 34, pp. 80-90, 1997.Google Scholar
  22. V. A. Kazandjian, P. W. Durance and M. A. Schork. “The extremal quotient in small area variation analysis.” Health Services Research, Vol. 24, pp. 665-684, 1989.Google Scholar
  23. M. B. Landrum and S. L. Normand. “Applying Bayesian ideas to the development of medical guidelines.” Statistics in Medicine, Vol. 18, pp. 117-137, 1999.Google Scholar
  24. D. V. Lindley and A. F. M. Smith. “Bayes estimates for the linear model” (with discussion). Journal of the Royal Statistical Society, Ser. B, Vol. 34, pp. 1-41, 1972.Google Scholar
  25. N. Longford. Random Coefficient Models, Oxford University Press: Oxford, 1993.Google Scholar
  26. K.-Y. Liang and S. L. Zeger. “Longitudinal data analysis using generalized linear models.,” Biometrika, Vol. 73, pp. 13-22, 1986.Google Scholar
  27. T. A. Marciniak, E. F. Ellerbeck, M. J. Radford, T. F. Kresowik and J. A. Gold, et al. “Improving the quality of care for Medicare patients with acute myocardial infarction. Results from the Cooperative Cardiovascular Project.” Journal of the American Medical Association, Vol. 279, pp. 1351-1357, 1998.Google Scholar
  28. K. McPherson, J. Wennberg, O. B. Hovind and P. Clifford. “Small-area variation in the use of common surgical procedures: an international comparison of New England, England and Norway.” New England Journal of Medicine, Vol. 307, pp. 1310-1314, 1982.Google Scholar
  29. C. N. Morris. “Parametric empirical Bayes inference: theory and applications.” Journal of the American Statistical Association, Vol. 78, pp. 47-65, 1983.Google Scholar
  30. S. L. Normand, M. E. Glickman and C. A. Gatsonis. “Statistical methods for profiling providers of medical care: issues and applications.” Journal of the American Statistical Association, Vol. 92, pp. 803-814, 1997.Google Scholar
  31. S. L. Normand, M. E. Glickman, R. Sharma and B. J. McNeil. “Using admission characteristics to predict short-term mortality from acute myocardial infarction in the elderly: results from the Cooperative Cardiovascular Project.” Journal of the American Medical Association, Vol. 275, pp. 1322-1328, 1996.Google Scholar
  32. G. T. O'Connor, H. B. Quinton, N. D. Traven, L. D. Ramunno, T. A. Dodds, T. A. Marciniak and J. E. Wennberg. “Geographic variation in the treatment of acute myocardial infarction: The Cooperative Cardiovascular Project.” Journal of the American Medical Association, Vol. 281, pp. 627-633, 1999.Google Scholar
  33. P. Paul-Shaheen, J. D. Clark and D. Williams. “Small Area Analysis: a review and analysis of the North American Literature.” Journal of Health Politics, Policy, and Law, vol. 12, pp. 741-808, 1987.Google Scholar
  34. Quality Compass 1998. National Committee for Quality Assurance. 1998. ( Scholar
  35. G. E. Rosenthal. “Weak associations between hospital mortality rates for individual diagnoses: Implications for profiling hospital quality.” American Journal of Public Health, Vol. 87, pp. 429-433, 1997.Google Scholar
  36. G. E. Rosenthal, A. Shah, L. E. Way and D. L. Harper. “Variations in standardized hospital mortality rates for six common medial diagnoses: Implications for profiling hospital quality.” Medical Care, Vol. 36, pp. 955-964, 1998.Google Scholar
  37. M. D. Sammel, L. M. Ryan and J. M. Legler. “Latent variable models for mixed discrete and continuous outcomes.” Journal of the Royal Statistical Society, Ser. B. Vol. 59, pp. 667-678, 1997.Google Scholar
  38. J. H. Silber, P. R. Rosenbaum and R. N. Ross. “Comparing the contributions of groups of predictors which outcomes vary with hospital rather than patient characteristics.” Journal of the American Statistical Association, Vol. 90, pp. 7-18.Google Scholar
  39. T. A. Stukel, R. J. Glynn, E. S. Fisher, S. M. Sharp, G. Lu-Yao and J. E. Wennberg. “Standardized rates of recurrent outcomes.” Statistics in Medicine, Vol. 13, pp. 1781-1791, 1994.Google Scholar
  40. J. W. Thomas and T. P. Hofer. “Research evidence of the validity of risk-adjusted mortality rate as a measure of quality of care.” Medical Care Research and Review, Vol. 55, pp. 371-404, 1998.Google Scholar
  41. N. Thomas, N. Longford and J. Rolph. “Empirical Bayes methods for estimating hospital-specific mortality rates.” Statistics in Medicine, Vol. 13, pp. 889-903, 1994.Google Scholar
  42. U.S. News and World Reports. “American's Top HMO's.” U.S. News and World Reports, October 5, 1998. ( Scholar
  43. W. J. van der Linden and R. K. Hambleton, eds. Handbook of Modern Item Response Theory, Springer: New York, 1997.Google Scholar
  44. J. Wennberg, A. Gittelsohn. “Small area variations in health care delivery.” Science, Vol. 182, pp. 1102-1108, 1973.Google Scholar
  45. J. Wennberg, A. Gittelsohn. “Variations in medical care among small areas.” Scientific American, Vol. 246, pp. 120-135, 1982.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Mary Beth Landrum
    • 1
    • 2
    Email author
  • Susan E. Bronskill
    • 1
    • 2
  • Sharon-Lise T. Normand
    • 1
    • 2
  1. 1.The Department of Health Care PolicyHarvard Medical School (M.B.L., S.E.B., S-L.T.N.)USA
  2. 2.Department of BiostatisticsHarvard School of Public Health (S-L.T.N.)BostonUSA

Personalised recommendations