Disease Management & Health Outcomes

, Volume 15, Issue 5, pp 279–287 | Cite as

Health Plan Employer Data and Information Set (HEDIS®) Criteria to Determine the Quality of Asthma Care in Children

What Are the Limitations?
Current Opinion

Abstract

The Health Plan Employer Data and Information Set (HEDIS®) of the National Committee for Quality Assurance is a set of standardized performance measures, the goal of which is to enable purchasers and consumers to evaluate the quality of different health plans. The HEDIS® ‘Use of Appropriate Medications for People with Asthma’ measure assesses the presence of an asthma controller medication dispensing in patients who meet healthcare utilization criteria that suggest persistent asthma. The HEDIS® asthma measure has been criticized on the basis of poor sensitivity and specificity for identifying persistent asthma because just one asthma controller medication dispensing is unlikely to be effective, and because asthma controller medications are not all the same.

Meeting the HEDIS® criteria may be associated with reductions in asthma crisis care in more adherent population groups; however, in less adherent populations, a paradoxical increase in asthma crisis care has been observed. The ‘asthma medication ratio’ of anti-inflammatory divided by (anti-inflammatory plus bronchodi-lator) canister dispensings has been proposed as an alternative quality-of-care measure, and improvements in the ratio are associated with a reduction in asthma crises. However, this measure has been criticized because of the difficulty in determining dose equivalence among various medications and delivery systems. Medication-based measures of asthma care quality, although associated with clinically important outcomes, may also create adverse incentives for overtreatment. In addition, medication-based measures only assess the level of asthma control indirectly and neglect important parameters of asthma care, including identification and control of asthma triggers, stepping down medication when asthma is well controlled, and the development of a patient/doctor partnership. Although there is utility to medication-based measures of asthma care quality, we need to be cognizant of the limitations of medication-based measures.

Many items that affect asthma control, such as air quality, housing quality, and involuntary smoke exposure, reflect choices of our society. From a societal perspective, quality of care for the uninsured/intermittently insured is as important as for the continuously enrolled. Asthma control reflects not only the quality of medical care delivered, but also broader aspects of the health of our society. Perhaps the future of asthma quality assessment is not just about physician and health plan performance but also about the performance of our communities and nations in protecting the respiratory health of the most vulnerable.

Notes

Acknowledgments

No sources of funding were used in the preparation of this article. Drs Farber and Schatz have been employed by Kaiser Permanente, a health plan that reports HEDIS® measures. Dr Schatz has received honoraria for lecturing in the past year from Genentech and GlaxoSmith Kline and receives research support from GlaxoSmith Kline and sanofi-aventis. These companies make asthma medications that may be included in the HEDIS® asthma measure.

References

  1. 1.
    Chowdhury PP, Balluz L, Murphy W, et al. Centers for Disease Control and Prevention (CDC). Surveillance of certain health behaviors among states and selected local areas: United States, 2005. MMWR Surveill Summ 2007; 56(4): 1–160PubMedGoogle Scholar
  2. 2.
    American Lung Association Epidemiology and Statistics Unit. Trends in asthma morbidity and mortality, 2006 Jul [online]. Available from URL: http://www.lungusa.org/site/pp.asp?.c=dvLUK9O0E&b=22884 [Accessed 2007 May 28]
  3. 3.
    National Asthma Education and Prevention Program. Expert panel report: guidelines for the diagnosis and management of asthma [publication 91-3042]. Bethesda (MD): National Institutes of Health, National Heart, Lung, and Blood Institute, 1991Google Scholar
  4. 4.
    The Child Asthma Management Program Research Group. Long-term effects of budesonide or nedocromil in children with asthma. N Engl J Med 2000; 343(15): 1054–63CrossRefGoogle Scholar
  5. 5.
    Suissa S, Ernst P, Benayoun S, et al. Low-dose inhaled corticosteroids and the prevention of death from asthma. N Engl J Med 2000; 343: 332–6PubMedCrossRefGoogle Scholar
  6. 6.
    Donahue JG, Weiss ST, Livingston JM, et al. Inhaled steroids and the risk of hospitalization for asthma. JAMA 1997; 277: 887–91PubMedCrossRefGoogle Scholar
  7. 7.
    Eisner MD, Lieu TA, Chi F, et al. Beta agonists, inhaled steroids, and the risk of intensive care unit admission for asthma. Eur Respir J 2001; 17: 233–40PubMedCrossRefGoogle Scholar
  8. 8.
    Anis AH, Lynd LD, Wang XH, et al. Double trouble: impact of inappropriate use of asthma medication on the use of health care resources. CMAJ 2001; 164: 625–31PubMedGoogle Scholar
  9. 9.
    Suissa S, Blais L, Ernst P. Patterns of increasing beta-agonist use and the risk of fatal or near-fatal asthma. Eur Respir J 1994; 7: 1602–9PubMedCrossRefGoogle Scholar
  10. 10.
    Finkelstein JA, Lozano P, Farber HJ, et al. Under-use of controller medications among Medicaid-insured children with asthma. Arch Pediatr Adolesc Med 2002; 156: 562–7PubMedGoogle Scholar
  11. 11.
    Lieu TA, Lozano P, Finkelstein JA, et al. Racial/ethnic variation in asthma status and management practices among children in managed medicaid. Pediatrics 2002; 109: 857–65 [online]. Available from URL: http://pediatrics.aappublications.org/cgi/content/full/109/5/857 [Accessed 2007 Jan 5]PubMedCrossRefGoogle Scholar
  12. 12.
    Suissa S, Ernst P, Boivin JF, et al. A cohort analysis of excess mortality in asthma and the use of inhaled β-agonists. Am J Respir Crit Care Med 1994; 149: 604–10PubMedGoogle Scholar
  13. 13.
    Hessel P, Mitchell I, Tough S, et al. Risk factors for death from asthma. Prairie Provinces Asthma Study Group. Ann Allergy Asthma Immunol 1999; 83(5): 362–8PubMedCrossRefGoogle Scholar
  14. 14.
    Farber HJ, Chi FW, Capra A, et al. Use of asthma-medication-dispensing patterns to predict risk of adverse health outcomes: a study of Medicaid-insured children in managed care programs. Ann Asthma Allergy Immunol 2004; 92: 319–28CrossRefGoogle Scholar
  15. 15.
    Lieu TA, Quesenberry CP, Sorel ME, et al. Computer-based models to identify high-risk children with asthma. Am J Respir Crit Care Med 1998; 157:1173–80PubMedGoogle Scholar
  16. 16.
    Patrick H, Mitchell I, Tough S, et al. Risk factors for death from asthma. Ann Asthma Allergy Immunol 1999; 83: 362–8CrossRefGoogle Scholar
  17. 17.
    Spitzer WO, Suissa S, Ernst P, et al. The use of β agonists and the risk of death and near death from asthma. N Engl J Med 1992, 506Google Scholar
  18. 18.
    Suissa S, Ernst P, Kezouh A. Regular use of inhaled corticosteroids and the long term prevention of hospitalization for asthma. Thorax 2002; 57: 880–4PubMedCrossRefGoogle Scholar
  19. 19.
    Farber HJ, Johnson C, Beckerman RC. Young inner-city children visiting the emergency room (ER) for asthma: risk factors and chronic care behaviors. J Asthma 1998; 35: 547–52PubMedCrossRefGoogle Scholar
  20. 20.
    Farber HJ, Boyette M. Control your child’s asthma: a breakthrough program for the treatment and management of childhood asthma. New York: Henry Holt, 2001Google Scholar
  21. 21.
    Global Initiative for Asthma. Global strategy for asthma management and prevention 2006 [online]. Available from URL: http://www.ginasthma.org/Guidelineitem.asp?.?ll=2&12=l&intId=60 [Accessed 2007 Jan 5]
  22. 22.
    National Committee for Quality Assurance. The Health Plan Employer Data and Information Set (HEDIS®) [online]. Available from URL: http://www.ncqa.org/programs/hedis/ [Accessed 2007 Feb 20]
  23. 23.
    The State of Health Care Quality 2006. National Committee for Quality Assurance 2006, Washington, DC [online]. Available from URL: http://www.ncqa.org/Communications/SOHC2006/SOHC_2006.pdf [Accessed 2007 Jan 5]
  24. 24.
    Renner P, Weiss K, Gayles Kim M. MPH Improvements in identifying persistent asthma patients using HEDIS data [letter]. Am J Manag Care 2006; 12: 118PubMedGoogle Scholar
  25. 25.
    HEDIS 2007, Vol. 2. Technical specifications. Washington, DC: National Committee for Quality Assurance, 2007Google Scholar
  26. 26.
    Mosen DM, Macy E, Schatz M, et al. How well do the HEDIS asthma inclusion criteria identify persistent asthma. Am J Manag Care 2005; 11: 650–4PubMedGoogle Scholar
  27. 27.
    Berger WE, Legooretta AP, Blaiss MS, et al. The utility of the Health Plan Employer Data and Information Set (HEDIS) asthma measure to predict asthma-related outcomes. Ann Allergy Asthma Immunol 2004; 93: 538–45PubMedCrossRefGoogle Scholar
  28. 28.
    Fuhlbrigge AL, Carey VJ, Finkelstein JA, et al. Validity of the HEDIS criteria to identify children with persistent asthma and sustained high utilization. Am J Manag Care 2005; 11: 325–30PubMedGoogle Scholar
  29. 29.
    Dombkowski KJ, Wasilivich E, Lyon-Callo SK. Pediatric asthma surveillance using Medicaid claims. Pub Health Rep 2005; 120: 515–24Google Scholar
  30. 30.
    Armstrong SC. Limits of the Health Plan Employer Data Information Set (HEDIS) criteria in determining asthma severity for children, applied to an impoverished urban population [letter]. Pediatrics 2005; 115: 1453PubMedCrossRefGoogle Scholar
  31. 31.
    Cabana MD, Slish KK, Nan B, et al. Limits of the HEDIS criteria in determining asthma severity for children. Pediatrics 2004; 114: 1049–55PubMedCrossRefGoogle Scholar
  32. 32.
    Wakefield DB, Cloutier MM. Modifications to HEDIS and CSTE algorithms improve case recognition of pediatric asthma. Pediatr Pulmonol 2006; 41: 962–71PubMedCrossRefGoogle Scholar
  33. 33.
    Glauber JH. Does the HEDIS asthma measure go far enough. Am J Manag Care 2001; 7: 575–9PubMedGoogle Scholar
  34. 34.
    Farber HJ, Capra AM, Lozano P, et al. Misunderstanding of asthma controller medications: association with nonadherence. J Asthma 2003; 40: 17–25PubMedCrossRefGoogle Scholar
  35. 35.
    Milgrom H, Bender B, Ackerson L, et al. Noncompliance and treatment failure in children with asthma. J Allergy Clin Immunol 1996; 98(6 Pt 1): 1051–7PubMedCrossRefGoogle Scholar
  36. 36.
    Adams RJ, Fuhlbrigge A, Finkelstein JA, et al. Use of inhaled anti-inflammatory medication in children with asthma in managed care settings. Arch Pediatr Adolesc Med 2001; 155: 501–7PubMedGoogle Scholar
  37. 37.
    Schatz M, Cook EF, Nakahiro R, et al. Inhaled corticosteroids and allergy specialty care reduce emergency hospital use for asthma. J Allergy Clin Immunol 2003; 111: 503–8PubMedCrossRefGoogle Scholar
  38. 38.
    Meltzer EO, Lockey RF, Friedman BF, et al. Fluticasone Propionate Clinical Research Study Group. Efficacy and safety of low-dose fluticasone propionate compared with montelukast for maintenance treatment of persistent asthma. Mayo Clin Proc 2002; 77: 437–45PubMedGoogle Scholar
  39. 39.
    Zeiger RS, Szefler SJ, Phillips BR, et al. Childhood Asthma Research and Education Network of the National Heart, Lung, and Blood Institute. Response profiles to fluticasone and montelukast in mild-to-moderate persistent childhood asthma. J Allergy Clin Immunol 2006 Jan; 117: 45–52PubMedCrossRefGoogle Scholar
  40. 40.
    Busse W, Raphael GD, Galant S, et al. Fluticasone Proprionate Clinical Research Study Group. Low-dose fluticasone propionate compared with montelukast for first-line treatment of persistent asthma: a randomized clinical trial. J Allergy Clin Immunol 2001; 107: 461–8PubMedCrossRefGoogle Scholar
  41. 41.
    Lazarus SC, Boushey HA, Fahy JV, et al. Asthma Clinical Research Network for the National Heart, Lung, and Blood Institute. Long-acting beta2-agonist monotherapy vs continued therapy with inhaled corticosteroids in patients with persistent asthma: a randomized controlled trial. JAMA 2001; 285: 2583–93PubMedCrossRefGoogle Scholar
  42. 42.
    Fuhlbrigge A, Carey VJ, Adams RJ, et al. Evaluation of asthma prescription measures and health system performance based on emergency department utilization. Med Care 2004; 42: 465–71PubMedCrossRefGoogle Scholar
  43. 43.
    Schatz M, Zeiger RS, Vollmer WM, et al. The controller-to-total asthma medication ratio is associated with patient-centered as well as utilization outcomes. Chest 2006; 130: 43–50PubMedCrossRefGoogle Scholar
  44. 44.
    Schatz M, Nakahiro R, Crawford W, et al. Asthma quality-of-care markers using administrative data. Chest 2005; 128: 1968–73PubMedCrossRefGoogle Scholar
  45. 45.
    Glauber JH, Fuhlbrigge AL. Stratifying asthma populations by medication use: how you count counts. Ann Allergy Asthma Immunol 2002; 88: 451–6PubMedCrossRefGoogle Scholar
  46. 46.
    Boushey HA, Sorkness CA, King TS, et al. National Heart, Lung, and Blood Institute’s Asthma Clinical Research Network. Daily versus as-needed cortico-steroids for mild persistent asthma. N Engl J Med 2005; 352: 1519–28PubMedCrossRefGoogle Scholar
  47. 47.
    Boushey HA. Daily inhaled corticosteroid treatment should not be prescribed for mild persistent asthma. Con. Am J Respir Crit Care Med 2005; 172: 412–5PubMedCrossRefGoogle Scholar
  48. 48.
    Farber HJ. Risk of readmission to hospital forpediatric asthma. J Asthma 1998; 35: 95–9PubMedCrossRefGoogle Scholar
  49. 49.
    Belessis Y, Dixon S, Thomsen A, et al. Risk factors for an intensive care unit admission in children with asthma. Pediatr Pulmonol 2004; 37: 201–9PubMedCrossRefGoogle Scholar
  50. 50.
    Schatz M, Cook EF, Joshua A, et al. Risk factors for asthma hospitalizations in a managed care organization: development of a clinical prediction rule. Am J Manag Care 2003; 9: 538–47PubMedGoogle Scholar
  51. 51.
    Macias CG, Caviness C, Sockrider M, et al. The effect of acute and chronic asthma severity on pediatric emergency department utilization. Pediatrics 2006; 117: S86–95PubMedGoogle Scholar
  52. 52.
    Schatz M, Zeiger RS, Vollmer WM, et al. Validation of a β-agonist long-term asthma control scale derived from computerized pharmacy data. J Allergy Clin Immunol 2006; 117: 995–1000PubMedCrossRefGoogle Scholar
  53. 53.
    Lieu TA, Capra AM, Quesenberry CP, et al. Computer-based models to identify high-risk adults with asthma: is the glass half empty or half full?. J Asthma 1999; 36: 359–70PubMedCrossRefGoogle Scholar
  54. 54.
    Liu AH, Zeiger R, Sorkness C, et al. Development and cross-sectional validation of the Childhood Asthma Control Test. J Allergy Clin Immunol 2007; 119:817–25PubMedCrossRefGoogle Scholar
  55. 55.
    Schatz M, Sorkness CA, Li JT, et al. Asthma Control Test: reliability, validity, and responsiveness in patients not previously followed by asthma specialists. J Allergy Clin Immunol 2006; 117: 549–56PubMedCrossRefGoogle Scholar
  56. 56.
    Skinner EA, Diette GB, Algatt-Bergstrom PJ, et al. The Asthma Therapy Assessment Questionnaire (ATAQ) for children and adolescents. Dis Manag 2004; 7: 305–13PubMedCrossRefGoogle Scholar
  57. 57.
    Peters D, Chen C, Markson LE, et al. Using an asthma control questionnaire and administrative data to predict health-care utilization. Chest 2006; 129: 918–24PubMedCrossRefGoogle Scholar
  58. 58.
    Schatz M, Zeiger RS, Drane A, et al. Reliability and predictive validity of the Asthma Control Test administered by telephone calls using speech recognition technology. J Allergy Clin Immunol 2007; 119: 336–43PubMedCrossRefGoogle Scholar
  59. 59.
    Centers for Disease Control. National Center for Health Statitistics. Asthma prevalence, health care use and mortality, 2002. Hyattsville (MD): US Department of Health and Human Services, 2007 Jan [online]. Available from URL: http://www.cdc.gov/nchs/products/pubs/pubd/hestats/asthma/asthma.htm [Accessed 2007 May 28]Google Scholar
  60. 60.
    Lang DM, Polansk M. Patterns of asthma mortality in Philadelphia from 1969 to 1991. N Engl J Med 1994; 331: 1542–6PubMedCrossRefGoogle Scholar
  61. 61.
    Almqvist C, Pershagen G, Wickman M. Low socioeconomic status as a risk factor for asthma, rhinitis and sensitization at 4 years in a birth cohort. Clin Exp Allergy 2005; 35: 612–8PubMedCrossRefGoogle Scholar
  62. 62.
    de Vries MP, van den Bemt L, Lince S, et al. Factors associated with asthma control. J Asthma 2005; 42: 659–65PubMedCrossRefGoogle Scholar
  63. 63.
    Adams RJ, Fuhlbrigge A, Guilbert T, et al. Inadequate use of asthma medication in the United States: results of the asthma in America national population survey. J Allergy Clin Immunol 2002; 110: 58–64PubMedCrossRefGoogle Scholar
  64. 64.
    Yawn BP, Wollan P, Kurland M, et al. A longitudinal study of the prevalence of asthma in a community population of school-age children. J Pediatr 2002; 140: 576–81PubMedCrossRefGoogle Scholar
  65. 65.
    Perry T, Matsui E, Merriman B, et al. The prevalence of rat allergen in inner-city homes and its relationship to sensitization and asthma morbidity. J Allergy Clin Immunol 2003; 112: 346–52PubMedCrossRefGoogle Scholar
  66. 66.
    Rosenstreich DL, Eggleston P, Kattan M, et al. The role of cockroach allergy and exposure to cockroach allergen in causing morbidity among inner-city children with asthma. N Engl J Med 1997; 336: 1356–63PubMedCrossRefGoogle Scholar
  67. 67.
    Rauh VA, Chew GR, Garfinkel RS. Deteriorated housing contributes to high cockroach allergen levels in inner-city households. Environ Health Perspect 2002; 110 Suppl. 2: 323–7PubMedCrossRefGoogle Scholar
  68. 68.
    Lanphear BP, Aligne CA, Auinger P, et al. Residential exposures associated with asthma in US children. Pediatrics 2001; 107: 505–11PubMedCrossRefGoogle Scholar
  69. 69.
    American Lung Association. Urban air pollution and health inequities: a workshop report. Environ Health Perspect 2001; 109 Suppl. 3: 357–74CrossRefGoogle Scholar
  70. 70.
    Eggleston PA, Butz A, Rand C, et al. Home environmental intervention in inner-city asthma: a randomized controlled clinical trial. Ann Allergy Asthma Immunol 2005; 95: 518–24PubMedCrossRefGoogle Scholar
  71. 71.
    Davis TC, Mayeaux EJ, Fredrickson D, et al. Reading ability of parents compared with reading level of pediatric patient education materials. Pediatrics 1994; 93: 460–8PubMedGoogle Scholar
  72. 72.
    Forbis SG, Aligne CA. Poor readability of written asthma management plans found in national guidelines of asthma. Pediatrics 2002; 109: e52 [online]. Available from URL: http://www.pediatrics.Org/cgi/content/full/109/4/e52 [Accessed 2007 Feb 20]PubMedCrossRefGoogle Scholar
  73. 73.
    Klingbeil C, Speece MW, Schubiner H. Readability of pediatric patient education materials: current perspectives on an old problem. Clin Pediatr (Phila) 1995; 34: 96–102CrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2007

Authors and Affiliations

  1. 1.Kaiser Permanente Vallejo Medical CenterVallejoUSA
  2. 2.Kaiser Permanente San Diego Medical CenterSan DiegoUSA

Personalised recommendations