Among the 5777 stage III colon cancer patients and the subset of 4003 individuals in the mortality cohort, nearly 3 quarters were 80 year olds or younger, more than 80% were white, and about half were married (). About 70% of the study population lived in census tracts in which over 3 quarters of the population had graduated from high school. Within 2 years, 39% of the overall study population had died.
Characteristics of the Study Population
displays the proportion of the study population with at least 1 comorbid condition using the 4 comorbidity measures and demonstrates how adding claims sources increases the identification of individuals as having comorbid disease. Using only prior inpatient claims, less than 20% of individuals were identified as having comorbid conditions in the Elixhauser and Klabunde measures. This proportion at least doubled when diagnoses from prior outpatient claims were added, and increased substantially again with the addition of cancer resection inpatient claims. Even using only prior inpatient and outpatient claims, the DCG measure identifies a large proportion of individuals as having comorbidity, since it includes all diagnosis codes. Overall, the Klabunde and major ADG measures identified fewer individuals with comorbidity, as they used fewer ICD-9-CM diagnoses.
Percentage of patients with any comorbidity identified from different claims sources (n = 5777).
In , we report frequencies for the 4 comorbidity measures and the degree to which these measures predict study outcomes. We report only those Elixhauser conditions significantly associated with at least one of our study outcomes. (A complete regression model is available from the authors upon request.) In general, individuals with comorbidity are less likely to receive chemotherapy, and more likely to die of a condition other than cancer within 2 years. A notable exception is that a prior diagnosis of hypertension in the Elixhauser measure increases rather than decreases an individual’s likelihood of receiving chemotherapy.
Frequencies of Different Comorbidity Measures: Crude Rates and Adjusted Odds Ratios (ORs) of Study Outcomes
reports the change in deviance (−2 log likelihood) from the baseline model to illustrate the improvement in goodness of fit as additional claims sources for identifying comorbidity are added. For both comorbidity measures, the addition of comorbid diagnoses from the prior inpatient claims to the baseline model significantly improves the fit of the regression models for both receipt of chemotherapy and non-cancer death. However, the addition of comorbidity diagnoses from prior outpatient claims significantly improves the fit of both regression models only for noncancer death. For receipt of chemotherapy, the addition of prior outpatient claims diagnoses significantly improves model fit for the Elixhauser measure only. Addition of diagnoses from the cancer resection hospitalization improves the fit of the receipt of chemotherapy and noncancer death models for both measures.
FIGURE 2 A, Influence of different claim sources on logistic regression model fit by comorbidity measure where outcome is receipt of chemotherapy.† B, influence of different claim sources on logistic regression model fit by comorbidity measure where outcome (more ...)
To compare the 4 comorbidity measures, we used a standard model, including previous inpatient and previous outpatient claims. All 4 measures significantly improved the fit of the regression model beyond the baseline model as measured by a statistically significant change in the likelihood ratio (not shown). presents the c-statistics and AICs for the models examining receipt of chemotherapy and noncancer death as outcomes. The c-statistic suggests that the Elixhauser measure produces the best fitting model for both study outcomes. However, the AIC measure, which penalizes the model by the number of covariates used, shows the Elixhauser model has a poorer fit, making it difficult to conclude that any 1 model outperforms the others for either outcome. The small increase in c-statistic after adjusting for each of the 4 comorbidity measures suggests that comorbidity has a relatively minor influence on chemotherapy receipt after controlling for other sociodemographic and environmental variables.
A, Performance of different comorbidity measures where outcome is receipt of chemotherapy. B, performance of different comorbidity measures where outcome is noncancer death within 24 months of diagnosis.
We conducted several subanalyses to test the robustness of our study findings (not shown). First, because questions have been raised about the validity of the cause of death variable, we examined all-cause death as an outcome using the overall study sample. We found that, as for noncancer death, there was a significant association between each of the comorbidity measures and all-cause death, although the measures were more powerful predictors of noncancer death (ie, odds ratios farther from one) than all-cause death. Second, to ensure that our results were generalizable to a broader patient population, we included stage I–III colon cancer patients rather than only stage III colon cancer cases in the regression model using noncancer death as the outcome and obtained similar results. Each of the measures significantly improved the fit of both models. Third, to explore the degree to which the high performance of the Elixhauser measure was related to its use of a large number of individual indicator variables, we aggregated the individual Elixhauser condition indicators into a variable that counted the number of conditions. We divided them into 4 groups—0, 1, 2, 3 or more—and created indicator variables for each group. The c-statistics for the regression model using this Elixhauser condition count (final c-statistic 0.710 for noncancer death outcome; 0.775 for receipt of chemotherapy outcome) were lower than both those using the individual Elixhauser conditions (c-statistic 0.755, 0.788 respectively) and those using the Klabunde measure (c-statistic 0.731, 0.776, respectively).