The effects of comorbidity on colorectal cancer mortality in an Australian cancer population

Abstract

This study estimated the absolute risk of colorectal cancer (CRC) specific and other-cause mortality using data from the population-based South Australian Cancer Registry. The impact of competing risks on the absolute and relative risks of mortality in cases with and without comorbidity was also investigated. The study included 7115 staged, primary CRC cases diagnosed between 2003 and 2012 with at least one year of follow-up. Comorbidities were classified according to Charlson, Elixhauser and C3 comorbidity indices, using hospital inpatient diagnoses occurring five years before CRC diagnosis. To estimate the differences in measures of association, the subdistribution hazard ratios (sHR) for the effect of comorbidity on mortality from the Fine and Gray model were compared to the cause-specific hazards (HR) from Cox regression model. CRC was most commonly diagnosed in people aged ≧ 70 years. In cases without comorbidity, the 10-year cumulative probability of CRC and other cause mortality were 37.1% and 17.2% respectively. In cases with Charlson comorbidity scores ≥2, the 10-year cumulative probability of CRC-specific and other cause mortality was 45.5% and 32.2%, respectively. Comorbidity was associated with increased CRC-specific and other cause mortality and the effect differed only marginally based on comorbidity index used.

Prevalence of diabetes in Japanese patients with cancer

“…The present findings suggest that healthcare professionals should consider multimorbidity in patients with cancer in the aged population. As cancer patients with coexisting diabetes might have a comparatively poorer life prognosis than their non-diabetic counterparts [6][7][8] , the increased recognition of multimorbidity might lead to better medical management and possibly improve the prognosis of such patients. The major strength of the present study was its use of the best available data on diabetes prevalence, summary estimates of relative risks and cancer incidence, and survival, all of which are representative of the Japanese population.…”

“…Second, we were unable to differentiate the overall survival of cancer patients with pre-existing diabetes from that of patients with no co-existing morbidity, because of a lack of information on medical history in the cancer registry data. Because it is well known that cancer patients with diabetes experience poorer prognosis than those without diabetes [6][7][8] , the differences in overall survival might operate toward overestimation of the prevalence. Similarly, we were unable to consider other comorbidities that are commonly found in cancer patients, although it has been reported that patients with multiple co-existing diseases are likely to experience higher mortality than patients with a single co-existing disease [6][7][8] .…”

“…For instance, cancer accounted for approximately 38% of overall causes of death in diabetes patients in Japan between 2001 and 2010 5 . Cancer patients with diabetes are known to experience a poorer prognosis than those without diabetes [6][7][8] , which will considerably impact treatment practices and cancer survivorship.…”

Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

“…Additionally, evidence indicates that a weighted variable better reduces type I errors than dummy variables while addressing multicollinearity concerns in regression analysis and organizing multiple highly correlated variables into more meaningful information [23,21]. The weight assigned to each comorbidity re ects a higher, lower or neutral risk of mortality [24]. For example, for hospitalized patients, metastatic cancer entails a considerably higher risk of death than obesity.…”

Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

“…Additionally, evidence indicates that a weighted variable reduces type I errors compared to dummy variables while addressing multicollinearity concerns in regression analysis and organizing multiple highly correlated variables into more meaningful information [23,21]. The weight assigned to each comorbidity re ects a higher, lower or neutral risk of mortality [24]. Practically, mortality risk scores can help to identify high-risk cases for special management and to assess provider services whose patients perform better or worse than expected from the summary measure of the morbidity burden.…”