Assessing Comorbidity Using Claims Data

Abstract

Comorbidity, additional disease beyond the condition under study that increases a patient's total burden of illness, is one dimension of health status. For investigators working with observational data obtained from administrative databases, comorbidity assessment may be a useful and important means of accounting for differences in patients' underlying health status. There are multiple ways of measuring comorbidity. This paper provides an overview of current approaches to and issues in assessing comorbidity using claims data, with a particular focus on established indices and the SEER-Medicare database. In addition, efforts to improve measurement of comorbidity using claims data are described, including augmentation of claims data with medical record, patient self-report, or health services utilization data; incorporation of claims data from sources other than inpatient claims; and exploration of alternative conditions, indices, or ways of grouping conditions. Finally, caveats about claims data and areas for future research in claims-based comorbidity assessment are discussed. Although the use of claims databases such as SEER-Medicare for health services and outcomes research has become increasingly common, investigators must be cognizant of the limitations of comorbidity measures derived from these data sources in capturing and controlling for differences in patient health status. The assessment of comorbidity using claims data is a complex and evolving area of investigation.

Fall-related injuries in elderly cancer patients treated with neurotoxic chemotherapy: A retrospective cohort study

“…Comorbidity was calculated using an unweighted count of diagnostic codes for conditions contained in the Charlson comorbidity index, 20 as previously described. [21][22][23] Similar to the accounting of FRI by diagnostic codes, a condition was considered present if a code was found in the inpatient file or if 2 codes were present in the outpatient file in the 12 months prior to cancer diagnosis. We also used the Cox proportional hazards model to measure the risk of a FRI between the cohorts and adjusted the model for the same covariates that were included in the multivariate logistic regression model.…”

Change in Comorbidity Prevalence with Advancing Age Among Persons with Heart Failure

“…We are limited by the use of population-based data to providing associations rather than describing causality between comorbidity and age. The use of administrative data sources for estimating the prevalence of conditions is at risk for underestimating the true burden of disease 38 ; however, we believe that our use of the CCW, designed specifically to mitigate the risk of underestimation through the use of evidence-based algorithms to indicate the presence of a disease, facilitated the accurate reporting of disease prevalence. Moreover, a prior study that utilized chart review methods to identify comorbid conditions among older adults with heart failure resulted in similar prevalence estimates 20 , providing support for the CCW algorithms as an alternative to more costly and time-consuming methods for identifying comorbidity.…”

Use of Annual Mammography Among Older Women with Ductal Carcinoma In Situ

“…We also excluded women who did not have Medicare part A and B coverage or who were members of a health maintenance organization (HMO) for any time after diagnosis until date of death or end of study, December 31, 2007, because these individuals may not have complete claims in SEER-Medicare (N = 5,326). 20 Finally, we excluded women who were not eligible to receive a mammogram during the first 22 months after diagnosis due to death, end of the study period, second or bilateral mastectomy, or attainment of age 86 during the time interval (N = 531), for a final sample of 8,853 women.…”