Journal of General Internal Medicine

, Volume 16, Issue 8, pp 525–530 | Cite as

Risk screening in a Medicare/Medicaid population

Administrative data versus self report
  • Christopher L. Vojta
  • Deneen D. Vojta
  • Thomas R. TenHave
  • Miguel Amaya
  • Risa Lavizzo-Mourey
  • David A. Asch
Original Articles

Abstract

OBJECTIVE: To compare the abilities of two validated indices, one survey-based and the other database-derived, to prospectively identify high-cost, dual-eligible Medicare/Medicaid members.

DESIGN: A longitudinal cohort study.

SETTING: A Medicaid health maintenance organization in Philadelphia, Pa.

PARTICIPANTS: HMO enrollees (N=558) 65 years and older eligible for both Medicare and Medicaid.

MEASUREMENTS AND MAIN RESULTS: Two hundred ninety six patients responded to a survey containing the Probability of Repeat Admission Questionnaire (Pra) between October and November 1998. Using readily available administrative data, we created an administrative proxy for the Pra. Choosing a cut point of 0.40 for both indices maximized sensitivity at 55% for the administrative proxy and 50% for the survey Pra. This classification yielded 103 high-risk patients by administrative proxy and 73 by survey Pra. High-cost patients averaged at least 2.3 times the resource utilization during the 6-month follow-up. Correlation between the two scores was 0.53, and the scales disagreed on high-cost risk in 78 patients (54 high-cost by administrative proxy only, and 24 high-cost by survey Pra only). These two discordant groups utilized intermediate levels of resources, $2,171 and $2,794, that were not statistically significantly different between the two groups (probability>χ2=.66). Receiver operating characteristic curve areas (0.68 for survey Pra and administrative proxy for respondents, and 0.67 by administrative proxy for nonrespondents) revealed similar overall discriminative abilities for the two instruments for costs.

CONCLUSIONS: The Medicaid/Medicare dual-eligible population responded to the survey Pra at a rate of 53%, limiting its practical utility as a screening instrument. Using a cut point of 0.40, the administrative proxy performed as well as the survey Pra in this population and was equally applicable to nonrespondents. The time lag inherent in database screening limits its applicability for new patients, but combining database-driven and survey-based approaches holds promise for targeting patients who might benefit from case management intervention.

Key words

geriatrics risk screening Medicaid dual eligible health maintenance organization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Waid MO. Brief summaries of Medicare & Medicaid. Title XVIII and Title XIX of The Social Security Act as of June 25, 1998. Baltimore, Md: Health Care Financing Administration [online] 1998 June 25 [cited 2000 mar 22]; [21 screens]. Available from URL:http://www.hcfa.gov/medicare/Google Scholar
  2. 2.
    Coleman EA, Wagner EH, Grothaus LC, Hecht J, Savarino J, Buchner DM. Predicting hospitalization and functional decline in health plan enrollees: are administrative data as accurate as self-report? J Am Geriatr Soc. 1998;46:419–25.PubMedGoogle Scholar
  3. 3.
    Boult C, Dowd B, McCaffery D, Boult L, Hernandez R, Krulewitch H. Screening elders at risk for hospital admission. J Am Geriatr Soc. 1993;41:811–7.PubMedGoogle Scholar
  4. 4.
    Pacala JT, Boult C, Boult L. Predictive validity of a questionnaire that identifies older persons at risk for hospital admission. J Am Geriatr Soc. 1995;43:374–77.PubMedGoogle Scholar
  5. 5.
    Pacala JT, Boult C, Reed R, Aliberti E. Predictive validity of the Pra instrument among older recipients of managed care. J Am Geriatr Soc. 1997;45:614–7.PubMedGoogle Scholar
  6. 6.
    Pacala JT, Boult C, Boult L. Predictive validity of a questionnaire that identifies older persons at risk for hospital admission. J Am Geriatr Soc. 1995;45:373–7.Google Scholar
  7. 7.
    Pacala JT, Boult C, Reed R, Aliberti E. Predictive validity of the Pra instrument among older recipients of managed care. J Am Geriatr Soc. 1997;45:614–7.PubMedGoogle Scholar
  8. 8.
    Fillit HM, Picariello G, Warburton SW. Health risk appraisal in the elderly: results from a survey of 70,000 Medicare HMO members. J Clin Outcomes Manage. 1997;4:23–9.Google Scholar
  9. 9.
    Hertzog AR, Rodgers WL. Age and response rates to interview sample surveys. J Gerontol. 1988;43S:200–5.Google Scholar
  10. 10.
    Launer LJ, Wind AW, Deeg DJ. Nonresponse pattern and bias in a community-based cross-sectional study of cognitive functioning among the elderly. Am J Epidemiol. 1994;139:803–12.PubMedGoogle Scholar
  11. 11.
    Vojta CL, Amaya M, Browngoehl K, Coburn K, Vojta DD. A home-based asthma education program in managed Medicaid. J Clin Outcomes Manage. 1999;6:30–4.Google Scholar
  12. 12.
    Root J, Stableford S. Easy-to-read consumer communications: a missing link in Medicaid managed care. J Health Polit Policy Law. 1999;24(1):1–26.PubMedGoogle Scholar
  13. 13.
    Coleman EA, Wagner EH, Grothaus LC, Hecht J, Savarino J, Buchner DM. Predicting hospitalization and functional decline in health plan enrollees: are administrative data as accurate as self-report? J Am Geriatr Soc. 1998;46:419–25.PubMedGoogle Scholar
  14. 14.
    Pacala JT, Boult C, Boult L. Predictive validity of a questionnaire that identifies older persons at risk for hospital admission. J Am Geriatr Soc. 1995;45:373–7.Google Scholar
  15. 15.
    Coleman EA, Wagner EH, Grothaus LC, Hecht J, Savarino J, Buchner DM. Predicting hospitalization and functional decline in health plan enrollees: are administrative data as accurate as self-report? J Am Geriatr Soc. 1998;46:419–25.PubMedGoogle Scholar
  16. 16.
    Chao J, Gillanders WG, Flocke SA, Goodwin MA, Kikano GE, Strange KC. Billing for physician services: a comparison of actual billing with CPT codes assigned by direct observation. J Fam Pract. 1998;47(5):335–6.Google Scholar
  17. 17.
    Robinson JR, Young TK, Roos LL, Gelskey DE. Estimating the burden of disease. Comparing administrative data and self-reports. Med Care. 1997;35(9):932–47.PubMedCrossRefGoogle Scholar
  18. 18.
    Herbert PL, Geiss LS, Tierey EF, Engelgau MM, Yawn BP, McBean AM. Identifying persons with diabetes using Medicare claims data. Am J Med Quality. 1999;14(6):270–7.Google Scholar
  19. 19.
    Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40(5):373–83.PubMedCrossRefGoogle Scholar
  20. 20.
    Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Outcomes Clin Epidemiol. 1992;45(6):613–9.CrossRefGoogle Scholar
  21. 21.
    Metz CE, Herman BA, Roe CA. Statistical comparison of two ROC curve estimates obtained from-partially-paired datasets. Med Decis Making. 1998;18:110–21.PubMedCrossRefGoogle Scholar
  22. 22.
    Pacala JT, Boult C, Boult L. Predictive validity of a questionnaire that identifies older persons at risk for hospital admission. J Am Geriatr Soc. 1995;45:373–7.Google Scholar
  23. 23.
    Fillit HM, Picariello G, Warburton SW. Health risk appraisal in the elderly: results from a survey of 70,000 Medicare HMO members. J Clin Outcomes Manage. 1997;4:23–39.Google Scholar
  24. 24.
    Robinson JR, Young TK, Roos LL, Gelskey DE. Estimating the burden of disease. Comparing administrative data and self-reports. Med Care. 1997;35(9):932–47.PubMedCrossRefGoogle Scholar
  25. 25.
    Boult C, Boult L, Murphy C, Ebbitt B, Luptak M, Kane RL. A controlled trial of outpatient geriatric evaluation and management. J Am Geriatr Soc. 1994;42:465–70.PubMedGoogle Scholar
  26. 26.
    Burns LR, Lamb GS, Wholey DR. Impact of integrated community nursing services on hospital utilization and costs in a Medicare risk plan. Inquiry. 1996;33:30–41.PubMedGoogle Scholar
  27. 27.
    Forman SA, Kelliher M, Wood G. Clinical improvement with bottom-line impact. Custom care planning for patients with acute and chronic illness in a managed care setting. Am J Managed Care. 1997;3:1039–48.Google Scholar
  28. 28.
    Boult C, Rassen J, Rassen A, Moore RJ, Robison S. The effect of case management on the costs of health care for enrollees in Medicare Plus Choice plans: a randomized trial. J Am Geriatr Soc. 2000;48:996–1001.PubMedGoogle Scholar

Copyright information

© Blackwell Science Inc 2001

Authors and Affiliations

  • Christopher L. Vojta
    • 1
    • 3
  • Deneen D. Vojta
    • 4
    • 6
    • 5
  • Thomas R. TenHave
    • 2
  • Miguel Amaya
    • 6
  • Risa Lavizzo-Mourey
    • 1
    • 3
    • 4
    • 7
    • 5
  • David A. Asch
    • 2
    • 3
    • 4
    • 7
    • 5
  1. 1.Received from the Division of GeriatricsPhiladelphia
  2. 2.the Center for Clinical Epidemiology and BiostatisticsPhiladelphia
  3. 3.the Institute on AgingPhiladelphia
  4. 4.Department of Medicine at the University of PennsylvaniaPhiladelphia
  5. 5.the Leonard Davis Institute of Health EconomicsPhiladelphia
  6. 6.the Division of Medical AffairsPhiladelphia
  7. 7.Health Partners; and the Philadelphia Veterans Affairs Medical CenterPhiladelphia

Personalised recommendations