It is important to incorporate assessment of comorbidity into studies involving QOL outcomes for persons with chronic medical conditions, as coexisting conditions may substantially affect outcomes of interest such as physical functioning, overall health status, depression and self-efficacy. In our study population, patients with multiple chronic medical conditions accurately reported a majority of common comorbid conditions relative to chart review. In addition, they were aware of most of their own diagnoses. Furthermore, self-reported disease burden correlated well with QOL outcomes, and correlated more strongly than did the two other measures of comorbidity that we used for comparison. This is consistent with our hypothesis that, for investigations using QOL outcomes, it is most appropriate to adjust for comorbidity using a subjective measure of comorbidity.
Previous investigations that have compared self-report with administrative data reported 59–79%, 72–73%, and 78–83% agreement on diagnoses of hypercholesterolemia, diabetes, and hypertension respectively; and 56% and 69% agreement on stroke and myocardial infarction [30
]. In our investigation we expanded the number of conditions for comparison to 23 and additionally assessed respondents' tendencies to accurately report all of their own conditions. Certain diagnoses were reported with high levels of sensitivity and specificity, while others were not.
A sensitivity greater than specificity may be due to either 'over-reporting' by participants or 'under-reporting' in the chart. Examples from our list included asthma, back pain, overweight and hard-of-hearing. We suspect that, for the first case, some participants reported COPD as asthma. For the remaining cases, we suspect that the conditions were under-reported in the chart – either because they had not been brought to medical attention or because they had not been assessed as isolated problems in the context of medical visits during the period covered by the chart review.
Sensitivity was substantially less than specificity for angina, nerve conditions, cancer and kidney disease. Although there may be a tendency to under-report chronic conditions, and respondents are more likely to report conditions with more severe symptoms [17
]; we re-reviewed charts of persons with these diagnoses to see if we could determine the cause of the discrepancies. From these repeat chart reviews, we concluded that these discrepancies were due to wording based more on symptoms than diagnosis (angina), under-reporting of conditions with stable or few symptoms (renal and neurological), and possible perceptions of cure or remission after acute treatment (cancer). In addition we analyzed the demographic and health characteristics (from Table ) of respondents for each of these four conditions to see if any demographic or disease characteristics were likely to predict a low agreement with chart review and found no patterns.
In our assessments of sensitivity and specificity, we assumed that the presence of a diagnosis in the chart was a 'gold standard' – an assumption that may not be entirely accurate. We suspect that diagnoses for which there are obvious medical treatments – especially medications – are more likely to be recorded in the chart. Chart diagnoses may be less accurate for conditions for which a person is less likely to seek (or for which a provider is less likely to offer) specifically biomedical solutions.
We found a high correlation between our measure of disease burden and our QOL outcomes of interest, as compared to lower correlations between two other comorbidity indices and these same outcomes. However, the correlations between the other comorbidity indices and health status and physical functioning were also significant and have been noted previously [36
]. The correlations between the Charlson and RxRisk scores and our secondary outcomes of interest (depression screen and self-efficacy) were not significant. Based on the pattern of these associations, we suggest that assessment of comorbidity is a function of the outcome of interest, the population studied, and the different (subjective versus objective) aspects of comorbidity measured by each instrument. The effect of comorbidities on QOL outcomes may be most accurately assessed when subjective measures are used to adjust for comorbidity. In contrast, for situations in which mortality, for example, is the outcome of interest, comorbidity should be assessed using instruments that have been developed for that purpose. These suggestions are consistent with the notion that 'complete' measurement of all health states requires both self-reported and objectively reported measures [37
It is certainly possible that one comorbidity measure may work for many situations. Other self-report instruments have been shown to predict mortality and hospitalization in addition to QOL [15
]. We are also aware of at least two investigations in which comorbidity measured by chart review correlated with QOL outcomes [5
]. The two instruments with which we compared our own instrument use different methodologies and were originally developed to assess comorbidity in studies investigating the objective outcomes of mortality and cost of care respectively [4
]. The Charlson index has been subsequently validated against length of stay, post operative complications, discharge to nursing home, disability, hospital readmission and hospital charges [6
]. The RxRisk score has subsequently been adapted and validated against administrative data on diagnoses and disease burden in certain populations [4
]. Our investigation adds to the growing body of knowledge on measuring comorbidity by highlighting the different results that may be obtained when using different methodologies to adjust for comorbidity in studies assessing QOL outcomes.
We did not incorporate additional measures of comorbidity, such as those that use administrative data into our analysis [8
]. Previous comparative studies suggest that chart-review-based measures may be slightly more accurate than administrative data-based comorbidity measures in predicting objective outcomes such as mortality and length of hospital stay [6
]. Further investigation is necessary to assess association of comorbidity measured by administrative data with QOL outcomes.
As with any initial validation effort, the generalizability of our conclusions is limited by the characteristics of the population studied – a relatively small HMO population aged 65 years or older. It is possible that this population is relatively 'well-educated' regarding the number and type of their medical conditions. If so, some of the sensitivities we report may be at the upper end of the spectrum that may be anticipated from self-report. In addition, we terminated the sampling process when we attained a sample size sufficient to test our primary hypothesis, without maximizing response rate. Thus, the findings in this sample may not represent the associations of a broader population. Although respondents did not differ significantly from non-respondents on RxRisk comorbidity score, more motivated or knowledgeable participants may have been more likely to respond promptly to our survey. Correlations and sensitivities could be lower when examined in a less motivated population or those with a lower knowledge base. Specifically, self-report may be less reliable in the geriatric sub-population that may suffer from cognitive impairment. Additional validation studies will be required in order to assess the usefulness of this instrument in other populations and for different QOL and other outcomes. We anticipate that these changes will strengthen our results for sensitivity in comparison to chart review and that they will not change the overall correlations with our outcomes of interest.
Disease burden (as we defined it) may in itself constitute a substantial portion of any patient's assessment of health status and physical functioning. Our incorporation of perceived limitation into a disease count may be similar to other investigations that have coupled a simple disease count with a health status measure such as the SF-36®
and found that doing so strengthened the relationship between comorbidity and utilization and mortality [16
]. However, models that attempt to explain the relationship between symptom burden, overall quality of life and physical functioning note that these outcomes are also affected by environmental characteristics, individual personality, expectations, values, and social and psychological supports [42
]. What we refer to as disease burden explains part, but not all, of our QOL outcomes as is illustrated by the values of our c-statistics. To the extent that investigations that use QOL outcomes concentrate on participants with one index condition and need to adjust for comorbidities, a subjective measure of disease burden using self-report may be an accurate way to account for the effect of other coexisting conditions with regard to that outcome.
Finally, depression is both an important potential comorbidity for anyone with chronic illness as well as an equally important component of the QOL outcome of emotional well being. We chose to treat it as the latter. As depression severity independently contributes to general QOL over and above other coexisting chronic illness, we suspect that including depression on our list of conditions would have increased the strength of correlations between self-reported disease burden and general health status [44