This research demonstrates that using longitudinal comorbidity data, when available, is associated with the best fit of Cox regression models predicting survival. Our analyses suggest that a combination of baseline comorbidity and the last year’s rolling comorbidity is the best longitudinal comorbidity measure. These findings are consistent with a limited literature examining use of longitudinal comorbidity data for predicting survival.9,10,24
Grunau et al. found that among individuals with acute myocardial infarction, adjusting for comorbidity after the index hospitalization improved the prediction of survival more than solely adjusting for comorbidity identified at the index hospitalization. Stukenborg et al. found that for five medical conditions, adjustment for comorbidity prior to the index hospitalization and comorbidity at the time of the index hospitalization somewhat improved the regression model’s ability to predict probability of death.
In this study, the difference in the likelihood ratio test and AIC between the regression models with and without baseline comorbidity is small. Differences are even smaller for the models restricted to individuals with at least 2 years of continuous enrollment. One could argue that using only the prior year rolling comorbidity variable is sufficient, though the addition of an easily computed baseline comorbidity requires minimal effort.
The “rolling” version of the prior year comorbidity variable performs better in predicting survival than the comorbidity variable based only on the prior year’s diagnoses. This suggests that many chronic conditions are not consistently recorded in administrative data, yet have an influence on outcomes. Among the 44,016 individuals with continuous enrollment, 1,578 had a recorded diabetes diagnosis in 1992 but only 70% had this recorded diagnosis in 1993. Thus, using a “rolling” comorbidity variable for chronic conditions can improve on the inconsistencies of administrative data.
Another important methodological question is whether to impute missing comorbidity data, as in model (I), which used a LOCF imputation method, or (K) which uses a RC imputation method. Imputation is justified if the imputation method is valid and the standard deviation is properly adjusted. An alternative method is to project a linear trajectory for comorbidity and impute using the predicted values.25
We did not use this strategy since the longitudinal comorbidity measures do not appear to fit a linear model well. While improper imputation may cause bias, ignoring missing comorbidity can cause bias as well. This study’s results suggest that imputing missing comorbidity data via either the LOCF or RC imputation method for a sample with non-continuous enrollment is an effective data analysis method. Research using RC as an imputation method21–23
suggests that the LOCF and RC perform equally well due largely to the moderate relative hazard parameter for comorbidity. When the relative hazard parameter is large, other more complicated statistical methods26,27
should be considered.
An alternative modeling approach uses baseline and change in comorbidity, or change in rolling comorbidity, as covariates. We did not include this modeling approach because it is a reparameterization of model (D). The coefficient estimate for the change in comorbidity is equivalent to the coefficient estimate for the last year’s comorbidity, while the coefficient estimate of the baseline comorbidity for the change model is the summation of the coefficient estimates of the baseline comorbidity and last year’s comorbidity from model (D). Likewise, modeling longitudinal comorbidity using baseline and change in rolling comorbidity will be a reparameterization of model (E).
Our ability to compare across all of our modeling approaches was limited. Because models (B) – (E) exclude observation years for which prior year comorbidity is unavailable, they include fewer observation years than models (F) – (K), making it difficult to compare the likelihood ratios and AICs between these model sets. Second, we chose to examine only a few approaches to modeling the comorbidity-survival relationship. There are many more complicated approaches to modeling longitudinal comorbidity, but we focused on practical models that can be readily applied in health services research.
We chose to use the Charlson index in this study because it is commonly used by health services researchers and widely available. However, use of other comorbidity measures, such as ACGs and DxCGs,5,7,28
would allow modeling with continuous comorbidity variables in addition to discrete comorbidity groups to determine the robustness of our findings. Other comorbidity measures may not permit calculation of rolling comorbidity scores, though. This study is also limited in its use of the less intuitive likelihood ratio and AIC measures for discriminating between different Cox regression models. Further research developing more intuitive measures of model discrimination for survival analysis would be of benefit to health services researchers.
This research provides health services researchers with guidance on the most appropriate methods for including longitudinal comorbidity data in models predicting survival outcomes. We conclude that investigators should make use of the longitudinal data available to them.8
This study’s findings suggest that a combination of baseline comorbidity and time-dependent comorbidity variables should be included in survival models. We identify how researchers can improve on less consistent administrative data by acknowledging the nature of chronic conditions through development of a “rolling” comorbidity measure, and through imputation of missing data by carrying forward the most recent available information. Further research using different study populations, modeling additional comorbidity measures, and extending to outcomes other than survival is needed to confirm these findings and expand the literature in the largely understudied area of longitudinal comorbidity measurement.