Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2011, Article ID 857892, 14 pages
http://dx.doi.org/10.1155/2011/857892
Research Article

Medical Cost Trajectories and Onsets of Cancer and NonCancer Diseases in US Elderly Population

1Center for Population Health and Aging, Duke University, Durham, NC 27708, USA
2Duke Cancer Institute, Duke University, Durham, NC 27705, USA

Received 4 January 2011; Accepted 3 March 2011

Academic Editor: Thierry Busso

Copyright © 2011 Igor Akushevich et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. HI, SMI, “Annual Report of the Boards of Trustees of the Federal Hospital Insurance and Federal Supplementary Medical Insurance Trust Funds,” Washington, DC, USA, 2010, https://www.cms.gov/ReportsTrustFunds/downloads/tr2010.pdf.
  2. HI, SMI, “Annual Report of the Boards of Trustees of the Federal Hospital Insurance and Federal Supplementary Medical Insurance Trust Funds,” Washington, DC, USA, 2009, http://www.cms.gov/ReportsTrustFunds/downloads/tr2009.pdf.
  3. B. S. Klees et al., “Brief summaries of Medicare and Medicaid,” Health Care Financing Review/2009 Statistical Supplement, November 2009.
  4. D. P. Goldman, “Health status and medical treatment of the future elderly,” Final Reports, Rand Corporation, RAND, Santa Monica, Calif, USA, 2004. View at Google Scholar
  5. H. Pardes, K. C. Manton, E. S. Lander, H. D. Tolley, A. D. Ulllan, and H. Palmer, “Effects of medical research on health care and the economy,” Science, vol. 283, no. 5398, pp. 36–37, 1999. View at Publisher · View at Google Scholar · View at Scopus
  6. D. P. Goldman, D. M. Cutler, B. Shang, and G. F. Joyce, “The Value of Elderly Disease Prevention,” 2006, http://works.bepress.com/dana_goldman/42/.
  7. D. P. Goldman, B. Shang, J. Bhattacharya et al., “Consequences of health trends and medical innovation for the future elderly,” Health Affairs, vol. 24, pp. W5R5–W5R17, 2005. View at Google Scholar · View at Scopus
  8. D. P. Goldman, Y. Zheng, F. Girosi et al., “The benefits of risk factor prevention in Americans aged 51 years and older,” American Journal of Public Health, vol. 99, no. 11, pp. 2096–2101, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Lubitz, “Health, technology, and medical care spending,” Health Affairs, vol. 24, pp. W5r81–W5r85, 2005. View at Google Scholar · View at Scopus
  10. J. D. Lubitz and G. F. Riley, “Trends in Medicare payments in the last year of life,” New England Journal of Medicine, vol. 328, no. 15, pp. 1092–1096, 1993. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Miller, “Increasing longevity and medicare expenditures,” Demography, vol. 38, no. 2, pp. 215–226, 2001. View at Google Scholar · View at Scopus
  12. I. Akushevich, A. Kulminski, and K. G. Manton, “Life tables with covariates: dynamic model for nonlinear analysis of longitudinal data,” Mathematical Population Studies, vol. 12, no. 2, pp. 51–80, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. K. G. Manton, E. Stallard, and B. Singer, “Projecting the future size and health status of the US elderly population,” International Journal of Forecasting, vol. 8, no. 3, pp. 433–458, 1992. View at Google Scholar · View at Scopus
  14. K. G. Manton, X. Gu, and V. L. Lamb, “Change in chronic disability from 1982 to 2004/2005 as measured by long-term changes in function and health in the U.S. elderly population,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 48, pp. 18374–18379, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. A. B. Nattinger, P. W. Laud, R. Bajorunaite, R. A. Sparapani, and J. L. Freeman, “An algorithm for the use of medicare claims data to identify women with incident breast cancer,” Health Services Research, vol. 39, no. 6, pp. 1733–1749, 2004. View at Google Scholar · View at Scopus
  16. F. A. Sloan, D. S. Brown, E. S. Carlisle, J. Ostermann, and P. P. Lee, “Estimates of incidence rates with longitudinal claims data,” Archives of Ophthalmology, vol. 121, no. 10, pp. 1462–1468, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. A. B. Nattinger, P. W. Laud, R. Bajorunaite, R. A. Sparapani, and J. L. Freeman, “Erratum: clarification note to an algorithm for the use of medicare claims data to identify women with incident breast cancer (Health Services Research vol. 39, no. 6, pp. 1733–1750, 2004),” Health Services Research, vol. 41, no. 1, p. 302, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. K. G. Manton and X. Gu, “Changes in the prevalence of chronic disability in the United States black and nonblack population above age 65 from 1982 to 1999,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 11, pp. 6354–6359, 2001. View at Publisher · View at Google Scholar · View at Scopus
  19. M. E. Charlson, P. Pompei, K. A. Ales, and C. R. MacKenzie, “A new method of classifying prognostic comorbidity in longitudinal studies: development and validation,” Journal of Chronic Diseases, vol. 40, no. 5, pp. 373–383, 1987. View at Google Scholar · View at Scopus
  20. H. Quan, V. Sundararajan, P. Halfon et al., “Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data,” Medical Care, vol. 43, no. 11, pp. 1130–1139, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Z. Nagi, “An epidemiology of disability among adults in the United States,” Milbank Memorial Fund Quarterly, Health and Society, vol. 54, no. 4, pp. 439–467, 1976. View at Google Scholar · View at Scopus
  22. BLS, “Consumer Price Index,” 2009, http://www.bls.gov/cpi/.
  23. V. De Groot, H. Beckerman, G. J. Lankhorst, and L. M. Bouter, “How to measure comorbidity: a critical review of available methods,” Journal of Clinical Epidemiology, vol. 56, no. 3, pp. 221–229, 2003. View at Publisher · View at Google Scholar · View at Scopus
  24. CDC, “The Burden of Chronic Diseases and Their Risk Factors,” US Department of Health and Human Services, Centers for Disease Control and Prevention, 2004.
  25. K. R. Yabroff, J. L. Warren, J. Banthin et al., “Comparison of approaches for estimating prevalence costs of care for cancer patients: what is the impact of data source?” Medical Care, vol. 47, no. 7, pp. S64–S69, 2009. View at Google Scholar · View at Scopus
  26. K. Noyes, H. Liu, and H. Temkin-Greener, “Medicare capitation model, functional status, and multiple comorbidities: model accuracy,” American Journal of Managed Care, vol. 14, no. 10, pp. 679–690, 2008. View at Google Scholar · View at Scopus
  27. K. R. Yabroff, E. B. Lamont, A. Mariotto et al., “Cost of care for elderly cancer patients in the United States,” Journal of the National Cancer Institute, vol. 100, no. 9, pp. 630–641, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. M. T. Forthman, H. G. Dove, and L. D. Wooster, “Episode Treatment Groups (ETGs): a patient classification system for measuring outcomes performance by episode of illness,” Topics in Health Information Management, vol. 21, no. 2, pp. 51–61, 2000. View at Google Scholar · View at Scopus
  29. G. C. Pope, J. Kautter, R. P. Ellis et al., “Risk adjustment of medicare capitation payments using the CMS-HCC model,” Health Care Financing Review, vol. 25, no. 4, pp. 119–141, 2004. View at Google Scholar · View at Scopus
  30. I. Akushevich and A. I. Yashin, “Circulatory diseases and aging,” in International Encyclopedia of Public Health, Vol. 1, K. Heggenhougen and S. Quah, Eds., Academic Press, San Diego, Calif, USA, 2008. View at Google Scholar
  31. I. Akushevich et al., “Age Patterns of Disease Incidences in the U.S. Elderly: Population-Based Analysis,” 2006, http://www.psc.isr.umich.edu/pubs/pdf/tr06-6.pdf.