"Concordance between comorbidity data from patient self-report interviews and medical record documentation"

William Corser, Alla Sikorskii, Ade Olomu, Manfred Stommel, Camille Proden, Margaret Holmes-Rovner, William Corser, Alla Sikorskii, Ade Olomu, Manfred Stommel, Camille Proden, Margaret Holmes-Rovner

Abstract

Background: Comorbidity is an important adjustment measure in research focusing on outcomes such as health status and mortality. One recurrent methodological issue concerns the concordance of comorbidity data obtained from different reporting sources. The purpose of these prospectively planned analyses was to examine the concordance of comorbidity data obtained from patient self-report survey interviews and hospital medical record documentation.

Methods: Comorbidity data were obtained using survey interviews and medical record entries from 525 hospitalized Acute Coronary Syndrome patients. Frequencies and descriptive statistics of individual and composite comorbidity data from both sources were completed. Individual item agreement was evaluated with simple and weighted kappas, Spearman Rho coefficients for composite scores.

Results: On average, patients reported more comorbidities during their patient survey interviews (mean = 1.78, SD = 1.99) than providers had documented in medical records (mean = 1.27, SD = 1.43). Higher proportions of positive responses were obtained from self-reports compared to medical records for all conditions except congestive heart failure and renal disease. Older age and higher depressive symptom levels were significantly associated with poorer levels of data concordance.

Conclusion: These results demonstrate that survey comorbidity data from ACS patients may not be entirely concordat with medical record documentation. In the absence of a gold standard, it is possible that hospital records did not include all pre-admission comorbidities and these patient survey interview methods may need to be refined. Self-report methods to facilitate some patients' complete recall of comorbid conditions may need to be refined by health services researchers.

Trial registration: ClinicalTrials.gov NCT00416026.

Figures

Figure 1
Figure 1
Rates of agreement and disagreement of individual comorbidity items from two data sources. NOTE: the relative proportions of comorbid conditions vary.

References

    1. Tuominen U, Blom M, Hirvonen J, Seitsalo S, Lehto M, Paavolainen P, Hietanien K, Rissanen P, Sintonen H. The effect of co-morbidities on health-related quality of life in patients placed on the waiting list for total joint replacement. Health Qual Life Outcomes. 2007;5:16. doi: 10.1186/1477-7525-5-16.
    1. Bayliss EA, Ellis JL, Steiner JF. Subjective Assessments of Comorbidity correlate with Quality of Life Health Outcomes: Initial Validation of a Comorbidity Assessment. Health Qual Life Outcomes. 2005;3:51–59. doi: 10.1186/1477-7525-3-51.
    1. Corser WD. An investigation of patient outcomes related to comorbidity and interdisciplinary hospital discharge planning. Outcomes Mngmt. 2004;8:45–51.
    1. Olomu AB, Corser WD, Holmes-Rovner MM. Self-report Comorbidity data and functional outcomes in acute coronary syndrome patients. (published abstract) 28th National Annual SGIM Meeting; New Orleans, LA J Gen Intern Med. 2005;20:72.
    1. Lash TL, Mor V, Wieland D, Ferrucci L, Satariano W, Silliman RA. Methodology, design, and analytic techniques to address measurement of comorbid disease. J Gerontol. 2007;62:281–285.
    1. Gijsen R, Hoeymans N, Schellevis FG, Ruwaard D, Satariano WA, Geertrudis AMV. Causes and consequences of comorbidity: a review. J Clin Epid. 2001;54:661–674. doi: 10.1016/S0895-4356(00)00363-2.
    1. Kraemer HC. Statistical Issues in Assessing Comorbidity. Stats in Med. 1995;14:721–733. doi: 10.1002/sim.4780140803.
    1. Roos LL, Stranc L, James RC, Li J. Complications, comorbidities, and mortality: improving classification and prediction. Health Serv Res. 1997;32:229–238.
    1. Simpson CF, Boyd CM, Carlson MC, Griswold ME, Guralnik JM, Fried LP. Agreement between self-report of disease diagnoses and medical record validation in disabled older women: factors that modify agreement. J Am Geriatr Soc. 2004;52:123–127. doi: 10.1111/j.1532-5415.2004.52021.x.
    1. Skinner KM, Miller DR, Lincoln E, Lee A, Kazis LE. Concordance between respondent self-reports and medical records for chronic conditions: experience from the veterans health study. J Ambulatory Care Manage. 2005;28:102–110.
    1. Tisnado DM, Adams JL, Liu H, Damberg CL, Chen WP, Hu FA, Carlisle DM, Mangione CM, Kahn K. What is the concordance between the medical record and patient self-report as data sources for ambulatory care. Med Care. 2006;44:132–140. doi: 10.1097/.
    1. Schneeweiss S, Maclure M. Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol. 2000;29:891–898. doi: 10.1093/ije/29.5.891.
    1. Humphries KH, Rankin JM, Carere RG, Buller CE, Kiely FM, Spinelli JJ. Comorbidity data in outcomes research: are clinical data derived from administrative databases a reliable alternative to chart review? J Clin Epidemiol. 2000;53:343–349. doi: 10.1016/S0895-4356(99)00188-2.
    1. Ingram SS, Seo PH, Martell RE, Clipp EC, Doyle ME, Montoya GS, Cohen HJ. Comprehensive assessment of the elderly cancer patient: the feasibility of self-report methodology. J Clin Oncol. 2002;20:770–775. doi: 10.1200/JCO.20.3.770.
    1. Sangha O, Stucki G, Liang MH, Fossel AH, Katz JN. The self-administered comorbidity questionnaire: a new method to assess comorbidity for clinical and health services research. Arthritis Rheum Res. 2003;49:156–163. doi: 10.1002/art.10993.
    1. DeGroot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity: a critical review of available methods. J Clin Epidemiol. 2003;56:221–229. doi: 10.1016/S0895-4356(02)00585-1.
    1. Selim AJ, Fincke G, Ren XS, Lee A, Rogers WH, Miller DR, Skinner KM, Linzer M, Kazis LE. Comorbidity assessments based on patient report: results from the veterans health study. J Ambul Care Manage. 2004;27:281–295.
    1. Byles JE, D'Este C, Parkinson L, O'Connell R, Treloar C. Single index of multimorbidity did not predict multiple outcomes. J Clin Epidemiol. 2005;58:997–1005. doi: 10.1016/j.jclinepi.2005.02.025.
    1. 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;40:373–383. doi: 10.1016/0021-9681(87)90171-8.
    1. Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidem. 1994;47:1245–1251. doi: 10.1016/0895-4356(94)90129-5.
    1. Katz JN, Chang LC, Sangha O, Fossel AH, Bates DW. Can comorbidity be measured by questionnaire rather than medical record review? Med Care. 1996;34:73–84. doi: 10.1097/00005650-199601000-00006.
    1. Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ. Agreement between self-report questionnaires and medical record data substantial for diabetes, hypertension, myocardial infarction, and stroke, but not for heart failure. J Clin Epidemiol. 2004;57:1096–1103. doi: 10.1016/j.jclinepi.2004.04.005.
    1. Holmes-Rovner M, Stommel M, Corser W, Olomu A, Holtrop J, Siddiqi A, Dunn Sl. Does outpatient telephone coaching add to hospital quality improvement? (under review) J Gen Intern Med.
    1. Holtrop JS, Corser WD, Spence-Jones G, Brooks G, Holmes-Rovner M, Stommel M. Health behavior goals of cardiac patients after hospitalization. Amer J Health Behav. 2006;30:387–399.
    1. Dunn SL, Corser WD, Stommel M, Holmes-Rovner M. Hopelessness and depression in the early recovery period after hospitalization for acute coronary syndrome. J Cardiopulm Rehab. 2006;26:152–159. doi: 10.1097/00008483-200605000-00007.
    1. Stommel M, Olomu A, Holmes-Rovner M, Corser W. Changes in practice patterns affecting in-hospital and post-discharge survival among ACS patients. BioMedCrtl-Health Ser Res. 2006;6:140. doi: 10.1186/1472-6963-6-140.
    1. Yang Z, Olomu A, Corser WD, Holmes-Rovner M. Outpatient medication use & health outcomes in post acute coronary syndrome patients. Amer J Mnged Care. 2006;12:581–7.
    1. Hall SF, Groome PA, Streiner DL, Rochen PA. Inter-rater reliability of measurements of comorbid illness should be reported. J Clin Epidemiol. 2006;59:926–933. doi: 10.1016/j.jclinepi.2006.02.006.
    1. Bernadini J, Callen S, Fried L, Piraino B. Inter-rater reliability and annual rescoring of the Charlson Comorbidity Index. Adv Perit Dial. 2004;20:125–127.
    1. Hlatky MA, Boineau RE, Higginbotham MB, Lee KL, Mark DB, Califf RM, Cobb FR, Pryor DB. A brief self-administered questionnaire to determine functional capacity. Am J Card. 1989;64:651–654. doi: 10.1016/0002-9149(89)90496-7.
    1. Devins G. Center for Epidemiologic Studies Depression Scale. Test Crit. 1985;2:144–160.
    1. Agresti A. Categorical Data Analysis. New York:John Wiley & Sons, Inc; 1999.
    1. McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika. 1947;12:153–157. doi: 10.1007/BF02295996.
    1. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2. New York: Wiley; 2000.
    1. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22. doi: 10.1093/biomet/73.1.13.
    1. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42:121–130. doi: 10.2307/2531248.
    1. SAS Institute Inc . (computer program) Cary NC; S.A.S. Software, version 9.1.
    1. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2. New York: Academic Press; 1988.

Source: PubMed

Szponzorok és közreműködők

Egészségi állapot

Kábítószer-beavatkozások

3
Iratkozz fel