Abstract
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 SEERMedicare-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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 601-609 |
| Number of pages | 9 |
| Journal | Clinical Epidemiology |
| Volume | 9 |
| DOIs | |
| State | Published - Nov 20 2017 |
| Externally published | Yes |
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Keywords
- Administrative health care data
- Cancer registry
- Data linkages
- Hospital discharge data
- Population-based
- Survival
- Validation
ASJC Scopus subject areas
- Epidemiology
Cite this
Comorbidity index in central cancer registries : The value of hospital discharge data. / Lichtensztajn, Daphne Y.; Giddings, Brenda M.; Morris, Cyllene R.; Parikh-Patel, Arti; Kizer, Kenneth W.
In: Clinical Epidemiology, Vol. 9, 20.11.2017, p. 601-609.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Comorbidity index in central cancer registries
T2 - The value of hospital discharge data
AU - Lichtensztajn, Daphne Y.
AU - Giddings, Brenda M.
AU - Morris, Cyllene R.
AU - Parikh-Patel, Arti
AU - Kizer, Kenneth W
PY - 2017/11/20
Y1 - 2017/11/20
N2 - 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 SEERMedicare-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.
AB - 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 SEERMedicare-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.
KW - Administrative health care data
KW - Cancer registry
KW - Data linkages
KW - Hospital discharge data
KW - Population-based
KW - Survival
KW - Validation
UR - http://www.scopus.com/inward/record.url?scp=85036475045&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85036475045&partnerID=8YFLogxK
U2 - 10.2147/CLEP.S146395
DO - 10.2147/CLEP.S146395
M3 - Article
AN - SCOPUS:85036475045
VL - 9
SP - 601
EP - 609
JO - Clinical Epidemiology
JF - Clinical Epidemiology
SN - 1179-1349
ER -