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Comorbidity index in central cancer registries: the value of hospital discharge data

Authors Lichtensztajn DY, Giddings BM, Morris CR, Parikh-Patel A, Kizer KW

Received 14 July 2017

Accepted for publication 26 September 2017

Published 20 November 2017 Volume 2017:9 Pages 601—609


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Henrik Toft Sørensen

Daphne Y Lichtensztajn,1 Brenda M Giddings,2 Cyllene R Morris,2 Arti Parikh-Patel,2 Kenneth W Kizer2

1Greater Bay Area Cancer Registry, Cancer Prevention Institute of California, CA, USA; 2California Cancer Reporting and Epidemiologic Surveillance Program, Institute for Population Health Improvement, UC Davis Health, CA, USA

Background: The presence of comorbid medical conditions can significantly affect a cancer patient’s treatment options, quality of life, and survival. However, these important data are often lacking from population-based cancer registries. Leveraging routine linkage to hospital discharge data, a comorbidity score was calculated for patients in the California Cancer Registry (CCR) database.
Methods: California cancer cases diagnosed between 1991 and 2013 were linked to statewide hospital discharge data. A Deyo and Romano adapted Charlson Comorbidity Index was calculated for each case, and the association of comorbidity score with overall survival was assessed with Kaplan–Meier curves and Cox proportional hazards models. Using a subset of Medicare-enrolled CCR cases, the index was validated against a comorbidity score derived using Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data.
Results: A comorbidity score was calculated for 71% of CCR cases. The majority (60.2%) had no relevant comorbidities. Increasing comorbidity score was associated with poorer overall survival. In a multivariable model, high comorbidity conferred twice the risk of death compared to no comorbidity (hazard ratio 2.33, 95% CI: 2.32–2.34). In the subset of patients with a SEER-Medicare-derived score, the sensitivity of the hospital discharge-based index for detecting any comorbidity was 76.5. The association between overall mortality and comorbidity score was stronger for the hospital discharge-based score than for the SEER-Medicare-derived index, and the predictive ability of the hospital discharge-based score, as measured by Harrell’s C index, was also slightly better for the hospital discharge-based score (C index 0.62 versus 0.59, P<0.001).
Conclusions: Despite some limitations, using hospital discharge data to construct a comorbidity index for cancer registries is a feasible and valid method to enhance registry data, which can provide important clinically relevant information for population-based cancer outcomes research.

Keywords: administrative health care data, data linkages, population-based, validation, cancer registry, hospital discharge data, survival

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