Data Sources for Measuring Comorbidity: A Comparison of Hospital Records and Medicare Claims for Cancer Patients

Background:Identifying appropriate comorbidity data sources is a key consideration in health services and outcomes research. Objective:Using cancer patients as an example, we compared comorbid conditions identified: 1) on the discharge facesheet versus full hospital medical record and 2) in the hospital record versus Medicare claims, both precancer diagnosis and associated with a cancer treatment-related index hospitalization. Methods:We used data from 1995 Surveillance, Epidemiology and End Results patterns of care studies for 1382 patients. Comorbid conditions were ascertained from the hospital record associated with the most definitive cancer treatment and Medicare claims. We calculated the prevalence for and assessed concordances among 12 conditions derived from the hospital record facesheet; full hospital record; Medicare claims precancer diagnosis, with and without a rule-out algorithm applied; and Medicare claims associated with an index hospitalization. Results:The proportion of patients with one or more comorbid conditions varied by data source, from 21% for the facesheet to 85% for prediagnosis Medicare claims without the rule-out algorithm. Condition prevalences were substantially lower for the facesheet compared with the full hospital record. For prediagnosis Medicare claims, condition prevalences were more than 1.7 times greater in the absence of an algorithm to screen for rule-out diagnoses. Measures assessing concordance between the full hospital record and prediagnosis Medicare claims (rule-out algorithm applied) showed modest agreement. Conclusions:The hospital record and Medicare claims are complementary data sources for identifying comorbid conditions. Comorbidity is greatly underascertained when derived only from the facesheet of the hospital record. Investigators using Part B Medicare claims to measure comorbidity should remove conditions that are listed for purposes of generating bills but are not true comorbidities.

[1]  C. Newschaffer,et al.  Comorbidity measurement in elderly female breast cancer patients with administrative and medical records data. , 1997, Journal of clinical epidemiology.

[2]  Hsia Dc,et al.  Accuracy of Diagnostic Coding for Medicare Patients under the Prospective-Payment System , 1988 .

[3]  S. Leatherman,et al.  Using Claims Data for Epidemiologic Research: The Concordance of Claims-Based Criteria With the Medical Record and Patient Survey for Identifying a Hypertensive Population , 1993, Medical care.

[4]  A R Feinstein,et al.  THE PRE-THERAPEUTIC CLASSIFICATION OF CO-MORBIDITY IN CHRONIC DISEASE. , 1970, Journal of chronic diseases.

[5]  J. Spinelli,et al.  Co-morbidity data in outcomes research: are clinical data derived from administrative databases a reliable alternative to chart review? , 2000, Journal of clinical epidemiology.

[6]  C. Bombardier,et al.  Accuracy of administrative data for assessing outcomes after knee replacement surgery. , 1997, Journal of clinical epidemiology.

[7]  D McLerran,et al.  Using administrative data to describe casemix: a comparison with the medical record. , 1994, Journal of clinical epidemiology.

[8]  R. Tamblyn,et al.  Validation of diagnostic codes within medical services claims. , 2004, Journal of clinical epidemiology.

[9]  Jonathan Klein,et al.  Reliability in Adolescent Reporting of Clinician Counseling, Health Care Use, and Health Behaviors , 2002, Medical care.

[10]  F. Schellevis,et al.  Prevalence estimates of asthma or COPD from a health interview survey and from general practitioner registration: what's the difference? , 2006, European journal of public health.

[11]  M. J. Hall,et al.  Trend data on medical encounters: tracking a moving target. , 2001, Health affairs.

[12]  E. Fisher,et al.  Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality? , 1992, JAMA.

[13]  W. M. Krushat,et al.  Accuracy of diagnostic coding for Medicare patients under the prospective-payment system. , 1988, The New England journal of medicine.

[14]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[15]  J. Jollis,et al.  A comparison of administrative versus clinical data: coronary artery bypass surgery as an example. Ischemic Heart Disease Patient Outcomes Research Team. , 1994, Journal of clinical epidemiology.

[16]  N. Goldman,et al.  Do health interview surveys yield reliable data on chronic illness among older respondents? , 2000, American journal of epidemiology.

[17]  J L Warren,et al.  Development of a comorbidity index using physician claims data. , 2000, Journal of clinical epidemiology.

[18]  J A Knottnerus,et al.  Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. , 1998, Journal of clinical epidemiology.

[19]  Trends in Prevalence, Awareness, Treatment, and Control of Hypertension in the United States, 1988-2000 , 2003 .

[20]  Ihab Hajjar,et al.  Trends in prevalence, awareness, treatment, and control of hypertension in the United States, 1988-2000. , 2003, JAMA.

[21]  B. Reeve,et al.  Do Patients Consistently Report Comorbid Conditions Over Time?: Results From the Prostate Cancer Outcomes Study , 2005, Medical care.

[22]  Sven E. Wilson,et al.  Do panel surveys make people sick? US arthritis trends in the Health and Retirement Study. , 2005, Social science & medicine.

[23]  Christianna S. Williams,et al.  Risk adjustment for older hospitalized persons: a comparison of two methods of data collection for the Charlson index. , 2001, Journal of clinical epidemiology.

[24]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.