In this analysis, we found that the presence of chronic conditions in veterans between the ages of 55 and 64 years is high, and a significant number of these individuals have two or more conditions. Not surprisingly, the presence of more conditions was associated with a higher risk of 5-year mortality. However, there was a wide range in the mortality rates across clusters with the same number of conditions, indicating the presence of specific diseases within a cluster impacted the risk of 5-year mortality differently.
Previous studies, both on a veteran cohort6
and on a sample of the general U.S. population,5
found that costs increased dramatically as the number of chronic conditions increased. These previous studies did not look at the risk of mortality associated with chronic conditions. We found a similar pattern with respect to mortality in that as the number of chronic conditions increased, the 5-year mortality rates also increased for patients with three or four chronic conditions. It was also apparent that combining the conditions by simply using a count of the number of conditions results in substantial loss of information regarding the outcomes of patients with multiple chronic conditions. That is, not all clusters of two conditions were equivalent, and the risk of mortality was highly dependent on the conditions that comprised the clusters. Therefore, when examining persons with multiple coexisting conditions, it is important to understand the components of a cluster, and it may be misleading to combine groups based on the number of chronic conditions.
Unlike the previous work examining multiple chronic conditions,5,6
we focused on a limited number of chronic conditions. Where Yu et al.6
included 29 conditions, and Wolff et al.5
used 24 categories of diagnostic groups, we chose to focus on 11 major conditions in creating our clusters of diseases. The conditions we included are high prevalence and high cost conditions in the United States and as such represent high priority conditions for health care agencies. Expansion of our list from these 11 conditions would have resulted in a larger number of total clusters that patients could have fallen into (there were 1,348 unique disease groups with at least one patient in this analysis) and would have also reduced the sample size in each of the clusters. However, by focusing only on these conditions, we are essentially ignoring the impact of other conditions that the patient may have and that could affect both costs and the risk of mortality. For example, if a condition occurred more frequently with one of the conditions included in this analysis, we would be picking up the effect of the condition that we measured as well as the unmeasured condition.
There are other limitations to our analysis, one of which is the fact that the classification of individuals into chronic disease clusters was based on ICD-9 codes available at the inception of the cohort, and subsequent development of disease during the 5-year follow-up was not incorporated into the analysis. As noted above, we did not consider other conditions in creating our disease clusters nor did we use other diseases as risk adjusters. We confined our analysis to veterans between 55 and 64 years to limit the effects of age on outcomes and to use a population that was not yet eligible to receive Medicare services. Therefore, the results are most generalizable to males in this age range, and findings could be substantially different for females and other ages. Only diagnoses that were associated with a VA encounter or VA contracted services were used in the analysis, and thus, if veterans were receiving care outside of the system, these diagnoses were not captured. Finally, because we rely on administrative data from a single year to characterize our cohort, we are unable to accurately capture the duration and severity of disease and the validity of ICD-9 codes in identifying diseases likely varies. The risk of mortality within the selected age group may be modified by the duration of disease, which is not accounted for in this analysis. For example, the effect of diabetes on mortality in persons included in this analysis may be very different if the diabetes developed when the patient was 40 years old compared to when they were 60 years old. In addition, if severity of disease is associated with the presence of more conditions, the increase in risk of mortality would be because of a combination of disease severity and other conditions.
We found that patients with multiple chronic conditions in the VA health care system represent a significant proportion of VA patients and have an increased risk for mortality. Not surprisingly, more conditions were associated with higher rates of mortality; however, there was significant variation in clusters with the same number of diseases. Thus, when studying patients with multiple coexisting illnesses, it is important to understand not only that several conditions may be present but that the specific conditions can differentially impact the risk of mortality.