Drugs & Aging

, Volume 29, Issue 11, pp 891–897 | Cite as

Effect of External Variables on the Performance of the Geriatric Comorbidity Score Derived from Prescription Claims in the Community-Dwelling Elderly

  • Sarah-Gabrielle Béland
  • Cara Tannenbaum
  • Thierry Ducruet
  • Michel Préville
  • Djamal Berbiche
  • Yola Moride
Original Research Article

Abstract

Background

Comorbidity scores based on medical or prescription claims data are frequently used to control for confounding in pharmacoepidemiological studies. Performance of such scores in predicting the risk of death in community-dwelling elderly adults may be compromised by the absence of mental health and socioeconomic characteristics not captured in claims data.

Objective

The aim of the study was to assess the impact of adding mental health status and sociodemographic characteristics to the Geriatric Comorbidity Score (GCS), a score derived from prescription claims data in the Quebec community-dwelling elderly population.

Methods

We used the cohort study from the longitudinal Quebec Seniors’ Health Survey (n = 1,494) conducted between 2005 and 2006. For each participant, we obtained mental health and socioeconomic characteristics through validated questionnaires, which we linked with the medical and prescription claims databases of the Quebec Health Insurance Agency [Régie de l’assurance maladie du Québec (RAMQ)]. The main study outcome was death within 1 year, ascertained using the Quebec death registry. The GCS was calculated from prescription claims data, with the c statistic as a measure of performance. Using backward stepwise selection, external variables (marital status, region, family income, social support, daily hassles, perceived physical and mental health status, presence of mental health disorders) were added to the logistic regression model and the marginal effect assessed by comparing the c statistic with and without each covariate.

Results

Over 1 year, 77 deaths (5.15 %) were reported. The c statistic for the GCS was calculated as 0.67 (95 % confidence interval 0.64, 0.70). Addition of sex and age to the score yielded a 2.4 % increase. The variable with the greatest impact on the c statistic was marital status (6.1 % increase). Though important contributors, social support and perceived mental health status did not significantly improve performance of the score.

Conclusions

While sex, age and marital status significantly improved performance of a predictive score in the community-dwelling elderly population, the absence of data on mental and physical health status did not appear to compromise the validity of claims-based scores. Combining comorbidity scores with other methods to control for confounding thus remains a useful tool in pharmacoepidemiological research.

Notes

Acknowledgments

The authors are grateful to Danielle Buch, medical writer, for revision and editing of the manuscript. The authors also wish to thank the Régie de l’assurance maladie du Québec (RAMQ). The authors wish to thank the Canadian Institute of Health Research (CIHR) for the doctoral fellowship received by S.G.B., and the Reseau Quebecois de recherche sur le vieillissement, the Reseau Quebecois de recherche sur l’Utilisation du Medicament and the Fonds de recherché en Sante du Quebec for their funding contributions to the study. Y.M. has received several grants from, and has acted as a consultant for, organizations in the pharma industry and has served on two data and safety monitoring boards.

Conflict of interest

The authors have no conflict of interest to declare that are directly relevant to the content of this study.

References

  1. 1.
    Iezzoni LI, Shwartz M, Ash AS, et al. Risk adjustment methods can affect perceptions of outcomes. Am J Med Qual. 1994;9(2):43–8.PubMedCrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Von Korff M, Wagner EH, Saunders K. A chronic disease score from automated pharmacy data. JJ Clin Epidemiol. 1992;45(2):197–203.CrossRefGoogle Scholar
  4. 4.
    Schneeweiss S, Seeger JD, Maclure M, et al. Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol. 2001;154(9):854–64.PubMedCrossRefGoogle Scholar
  5. 5.
    Fowles JB, Weiner JP, Knutson D, et al. Taking health status into account when setting capitation rates: a comparison of risk-adjustment methods. JAMA. 1996;276(16):1316–21.PubMedCrossRefGoogle Scholar
  6. 6.
    Hornbrook MC, Goodman MJ. Chronic disease, functional health status, and demographics: a multi-dimensional approach to risk adjustment. Health Serv Res. 1996;31(3):283–307.PubMedGoogle Scholar
  7. 7.
    Fan VS, Au D, Heagerty P, et al. Validation of case-mix measures derived from self-reports of diagnoses and health. J Clin Epidemiol. 2002;55(4):371–80.PubMedCrossRefGoogle Scholar
  8. 8.
    Gruenberg L, Kaganova E, Hornbrook MC. Improving the AAPCC (adjusted average per capita cost) with health-status measures from the MCBS (Medicare Current Beneficiary Survey). Health Care Financ Rev. 1996;17(3):59–75.PubMedGoogle Scholar
  9. 9.
    Covinsky KE, Justice AC, Rosenthal GE, et al. Measuring prognosis and case mix in hospitalized elders. The importance of functional status. J Gen Intern Med. 1997;12(4):203–8.PubMedGoogle Scholar
  10. 10.
    Selim AJ, Fincke G, Ren XS, et al. Comorbidity assessments based on patient report: results from the Veterans Health Study. J Ambul Care Manage. 2004;27(3):281–95.PubMedGoogle Scholar
  11. 11.
    Mayo NE, Nadeau L, Levesque L, et al. Does the addition of functional status indicators to case-mix adjustment indices improve prediction of hospitalization, institutionalization, and death in the elderly? Med Care. 2005;43(12):1194–202.PubMedCrossRefGoogle Scholar
  12. 12.
    Grenier S, Préville M, Boyer R, et al. The impact of DSM-IV symptom and clinical significance criteria on the prevalence estimates of subthreshold and threshold anxiety in the older adult population. Am J Geriat Psychiat. 2010;19(4):316–26.Google Scholar
  13. 13.
    Steffens DC, Skoog I, Norton MC, et al. Prevalence of depression and its treatment in an elderly population: the Cache County study. Arch Gen Psychiatry. 2000;57(6):601–7.PubMedCrossRefGoogle Scholar
  14. 14.
    Préville M, Boyer R, Grenier S, Dube M, Voyer P, Punti R, et al. The epidemiology of psychiatric disorders in Quebec’s older adult population. Can J Psychiatry Revue canadienne de psychiatrie. 2008;53(12):822–32.Google Scholar
  15. 15.
    Steinbach U. Social networks, institutionalization, and mortality among elderly people in the United States. J Gerontol. 1992;47(4):S183–90.PubMedCrossRefGoogle Scholar
  16. 16.
    Boyle PJ, Feng Z, Raab GM. Does widowhood increase mortality risk?: testing for selection effects by comparing causes of spousal death. Epidemiology. 2011;22(1):1–5.Google Scholar
  17. 17.
    Beland SG, Ducruet T, Tannenbaum C, et al. Development and validation of an Geriatric Comorbidity Score based of drug use. Pharmacoepidem DrS, vol. 20 (Issue Supplement S1); 2011. p. S1–382.Google Scholar
  18. 18.
    Quebec Prescription Drug Insurance Plan. Prescription claims database [data used: January 2011]. Québec: Régie de l’Assurance Maladie du Quebec; 2012.Google Scholar
  19. 19.
    Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–9.PubMedCrossRefGoogle Scholar
  20. 20.
    Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1993;46(10):1075–9 (discussion 81–90).Google Scholar
  21. 21.
    Ghali WA, Hall RE, Rosen AK, et al. Searching for an improved clinical comorbidity index for use with ICD-9-CM administrative data. J Clin Epidemiol. 1996;49(3):273–8.PubMedCrossRefGoogle Scholar
  22. 22.
    Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36(1):1–10.PubMedCrossRefGoogle Scholar
  23. 23.
    Death records. Québec: Institut de la statistique du Québec; 2010.Google Scholar
  24. 24.
    International classification of disease. 9th ed. Geneva: World health Organization; 1977.Google Scholar
  25. 25.
    Kanner AD, Coyne JC, Schaefer C, Lazarus RS. Comparison of two modes of stress measurement: daily hassles and uplifts versus major life events. J Behav Med. 1981;4(1):1–39.PubMedCrossRefGoogle Scholar
  26. 26.
    Vézina JGL. L’Échelle des Embêtements: une étude de validation française du “Hassles Scale” pour les personnes âgées. Communication presented at the 49th Annual Meeting of the Canadian Psychological Association. 1988; Montreal.Google Scholar
  27. 27.
    Préville M, Hebert R, Boyer R, Bravo G. Correlates of psychotropic drug use in the elderly compared to adults aged 18–64: results from the Quebec Health Survey. Aging Ment Health. 2001;5(3):216–24.PubMedCrossRefGoogle Scholar
  28. 28.
    Hosmer D, Lemeshow S. Applied logistic regression. 2nd ed. Wiley Series in probability statistics. Hoboken: Wiley; 2000.Google Scholar
  29. 29.
    American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington, D.C: American Psychiatric Press; 1994.Google Scholar
  30. 30.
    Manzoli L, Villari P, Pirone GM, Boccia A. Marital status and mortality in the elderly: a systematic review and meta-analysis. Soc Sci Med. 2007;64(1):77–94.Google Scholar
  31. 31.
    Berkman LF, Glass T, Brissette I, Seeman TE. From social integration to health: Durkheim in the new millennium. Soc Sci Med. 2000;51(6):843–57.PubMedCrossRefGoogle Scholar
  32. 32.
    House JS. Social isolation kills, but how and why? Psychosom Med. 2001;63(2):273–4.PubMedGoogle Scholar
  33. 33.
    Ford ES, Loucks EB, Berkman LF. Social integration and concentrations of C-reactive protein among US adults. Ann Epidemiol. 2006;16(2):78–84.PubMedCrossRefGoogle Scholar
  34. 34.
    Loucks EB, Berkman LF, Gruenewald TL, Seeman TE. Relation of social integration to inflammatory marker concentrations in men and women 70 to 79 years. Am J Cardiol. 2006;97(7):1010–6.PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2012

Authors and Affiliations

  • Sarah-Gabrielle Béland
    • 1
    • 2
  • Cara Tannenbaum
    • 1
    • 3
  • Thierry Ducruet
    • 1
    • 2
  • Michel Préville
    • 4
  • Djamal Berbiche
    • 4
  • Yola Moride
    • 1
    • 2
  1. 1.Faculty of PharmacyUniversity of MontrealMontrealCanada
  2. 2.Pharmacoepidemiology UnitResearch Centre of the University of Montreal Hospital Centre (CRCHUM)MontrealCanada
  3. 3.Research CentreUniversity of Montreal Geriatrics InstituteMontrealCanada
  4. 4.Charles LeMoyne Hospital Research CentreUniversity of SherbrookeLongueuilCanada

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