Display options
Share it on

J Diabetes Sci Technol. 2008 Jan;2(1):24-32. doi: 10.1177/193229680800200105.

Longitudinal approaches to evaluate health care quality and outcomes: the Veterans Health Administration diabetes epidemiology cohorts.

Journal of diabetes science and technology

Donald R Miller, Leonard Pogach

Affiliations

  1. Center for Health Quality, Outcomes and Economic Research, Bedford VA Medical Center, Bedford, Massachusetts, USA.

PMID: 19885174 PMCID: PMC2769712 DOI: 10.1177/193229680800200105

Abstract

OBJECTIVE: The Institute of Medicine proposed recently that, while current pay for performance measures should target multiple dimensions of care, including measures of technical quality, they should transition toward longitudinal and health-outcome measures across systems of care. This article describes the development of the Diabetes Epidemiology Cohorts (DEpiC), which facilitates evaluation of intermediate "quality of care" outcomes and surveillance of adverse outcomes for veterans with diabetes served by the Veterans Health Administration (VHA) over multiple years.

METHODS: The Diabetes Epidemiology Cohorts is a longitudinal research database containing records for all diabetes patients in the VHA since 1998. It is constructed using data from a variety of national computerized data files in the VHA (including medical encounters, prescriptions, laboratory tests, and mortality files), Medicare claims data for VHA patients, and large patient surveys conducted by the VHA. Rigorous methodology is applied in linking and processing data into longitudinal patient records to assure data quality.

RESULTS: Validity is demonstrated in the construction of the DEpiC. Adjusted comparisons of disease prevalence with general population estimates are made. Further analyses of intermediate outcomes of care demonstrate the utility of the database. In the first example, using growth curve models, we demonstrated that hemoglobin A1c trends exhibit marked seasonality and that serial cross-sectional outcomes overestimate the improvement in population A1c levels compared to longitudinal cohort evaluation. In the second example, the use of individual level data enabled the mapping of regional performance in amputation prevention into four quadrants using calculated observed to expected major and minor amputation rates. Simultaneous evaluation of outliers in all categories of amputation may improve the oversight of foot care surveillance programs.

CONCLUSIONS: The use of linked, patient level longitudinal data resolves confounding case mix issues inherent in the use of serial cross-sectional data. Policy makers should be aware of the limitations of cross-sectional data and should make use of longitudinal patient databases to evaluate clinical outcomes.

Keywords: A1c; amputations; databases; diabetes; registry

References

  1. Jt Comm J Qual Improv. 2002 Oct;28(10):555-65 - PubMed
  2. Health Aff (Millwood). 2007 Mar-Apr;26(2):w156-68 - PubMed
  3. Womens Health Issues. 2006 Nov-Dec;16(6):361-71 - PubMed
  4. Lancet. 1998 Sep 12;352(9131):837-53 - PubMed
  5. Med Care. 2005 Jan;43(1):4-11 - PubMed
  6. Diabetes Care. 2003 Nov;26(11):3042-7 - PubMed
  7. Health Serv Res. 2006 Apr;41(2):564-80 - PubMed
  8. J Gen Intern Med. 2006 Mar;21 Suppl 3:S47-53 - PubMed
  9. Diabetes Care. 2004 May;27 Suppl 2:B90-4 - PubMed
  10. Am J Hypertens. 2006 Feb;19(2):161-9 - PubMed
  11. Lancet. 2000 Jan 22;355(9200):253-9 - PubMed
  12. Diabetes Care. 2007 Feb;30(2):245-51 - PubMed
  13. Arch Intern Med. 2005 Dec 12-26;165(22):2631-8 - PubMed
  14. N Engl J Med. 1993 Sep 30;329(14):977-86 - PubMed
  15. Diabetes Care. 2004 May;27 Suppl 2:B10-21 - PubMed
  16. Med Care. 2006 Aug;44(8):779-87 - PubMed
  17. Diabetes Care. 2007 Jul;30(7):1689-93 - PubMed
  18. Int J Qual Health Care. 2006 Sep;18 Suppl 1:26-30 - PubMed
  19. Med Care. 2007 Apr;45(4):308-14 - PubMed
  20. Med Care. 2005 Jan;43(1):88-92 - PubMed
  21. Med Care. 2000 Jun;38(6 Suppl 1):I38-48 - PubMed
  22. Diabetes Care. 2005 Aug;28(8):1890-7 - PubMed
  23. Arch Intern Med. 2003 Apr 28;163(8):922-8 - PubMed
  24. Am J Epidemiol. 2005 Mar 15;161(6):565-74 - PubMed
  25. Diabetes Care. 2003 Mar;26(3):917-32 - PubMed
  26. N Engl J Med. 2006 Nov 2;355(18):1845-7 - PubMed
  27. Ann Intern Med. 2006 Apr 4;144(7):465-74 - PubMed
  28. Popul Health Metr. 2006 Jul 06;4:7 - PubMed
  29. Am J Manag Care. 2005 Dec;11(12):797-804 - PubMed
  30. Med Care Res Rev. 2003 Jun;60(2):253-67 - PubMed
  31. Lancet. 2003 Jun 14;361(9374):2005-16 - PubMed
  32. Med Care. 2006 May;44(5):439-45 - PubMed
  33. Diabetes Care. 2005 Apr;28(4):950-5 - PubMed
  34. MMWR Morb Mortal Wkly Rep. 2003 Sep 5;52(35):833-7 - PubMed
  35. Am J Manag Care. 2007 Feb;13(2):73-9 - PubMed
  36. JAMA. 2007 Feb 7;297(5):520-3 - PubMed
  37. Am J Manag Care. 2004 Feb;10(2 Pt 2):171-80 - PubMed
  38. Int J Qual Health Care. 2007 Dec;19(6):368-76 - PubMed
  39. Am J Manag Care. 2004 Nov;10(11 Pt 2):886-92 - PubMed
  40. Diabetes Care. 2003 Nov;26(11):3017-23 - PubMed
  41. Diabetologia. 2004 Dec;47(12):2051-8 - PubMed
  42. Popul Health Metr. 2006 Apr 10;4:2 - PubMed
  43. Am J Manag Care. 2007 Mar;13(3):133-40 - PubMed
  44. Health Serv Res. 2005 Dec;40(6 Pt 1):1818-35 - PubMed
  45. Jt Comm J Qual Patient Saf. 2006 Apr;32(4):206-13 - PubMed

Publication Types