Mortality prediction is often the basis for risk adjustment, and is essential for evaluating medical effectiveness and quality of care, and for informing health policy decisions. Focusing on the rapidly growing elderly population with complex chronic illness which is increasingly recognized as important in internal medicine, we evaluated the effects of multiple co-morbidities and functional status on mortality in efforts to identify parsimonious predictors. Our 10-year mortality index has a 0.75 probability of correctly assigning a higher score to a randomly chosen patient who died than to a randomly chosen patient who did not die, and thus fills a needed gap in the literature regarding a lack of long-term mortality prediction tools for community-dwelling elderly populations.11
Our study adds the development of a long-term predictive mortality index, combined with parsimonious 1- and 5-year mortality indexes to the literature. Our 1-year mortality index applying only 4 predictors has a 0.72 probability of correctly assigning a higher score to a randomly chosen patient who died than to a randomly chosen patient who did not die. All three mortality predictive indexes show internal validity, and are simple to apply in community settings. Our 1-, 5-, and 10-year mortality predictive indexes can maximize the implementation of short-, intermediate- and long-term mortality estimation in community settings assisting clinical decision-making, and may serve as important screening tools for the impact of complex chronic illness on mortality. Thus, our mortality indexes have the potential to inform medical care decisions, identify high-risk persons for interventions, and provide a foundation for discussing care goals with community-dwelling elderly individuals. These rules can further provide surveillance measures to policy makers and epidemiologists when projecting the mortality of older populations.
IADL stage was one of the most important predictors for 1-, 5-, and 10-year mortality. The association between 1-year mortality and IADL stage was particularly strong and only age, sex, and coronary artery disease added explanatory power to the mortality index. Although functional status measured by various methods is known to be associated with mortality in elderly populations,7–10, 29–36
only a few studies applied this knowledge to develop short- or intermediate-term mortality indexes.7–10
Additionally, none expressed IADL as stages.7–10
Unlike individual IADLs,7, 8, 10
IADL stages summarize overall severity of disability across activities. Dissimilar to counts9
where patterns of limitation are obscured, IADL stages define thresholds of function that specify severity but are also transparent to the specific patterns of activities limited reflecting the known hierarchical structure of IADL items.18, 23
We derived IADL stages to capture the persons’ functional status because IADL performance demands higher degrees of integration across individuals’ cognitive and physical capacities, as compared to the self-care ADLs which evaluate personal bodily tasks18, 37–39
IADL limitations can result from physical or cognitive impairments and can be used as a screening tool for cognitive impairment in elderly community-dwellers.37–39
Thus, IADL stage is a strong predictor because it can serve as a proxy for multiple conditions simultaneously contributing to physical and cognitive impairments. The value of IADL stage to the internist is that it is easily determined by self-report and enables a more parsimonious subset of predictors simplifying mortality prediction and enhancing ease of implementation in community-dwelling elderly populations.
Coronary artery disease remained a significant predictor for 1-, 5-, and 10-year mortality, and other heart disease was significantly associated with 5- and 10-year mortality in the final models. Heart disease is known to be the leading cause of death in the US.40, 41
Other leading causes of death, including malignant neoplasms, cerebrovascular diseases, chronic lower respiratory diseases, and diabetes mellitus,42
were all significantly associated with 5- and/or 10-year mortality in the final adjusted models. Most of these conditions were also significantly associated with 1-year mortality in the unadjusted analyses. These factors likely did not enter the final model for 1-year mortality prediction because of their strong correlations with IADL stages as shown in online Table 1
. These leading causes of death have both acute and chronic impacts on the subjects’ health although their long-term impact may be more significant.42
An individual’s IADL stage reflects current functional status as resulting from the person’s active cognitive and physical conditions, but functional status as captured by IADL stage will likely change over time due to the progression or regression of various health conditions. Thus, the IADL stage’s association with long-term mortality became attenuated over time while the chronic impact of certain medical diagnoses gained in importance for long-term mortality prediction. Further studies with more detailed clinical disease information are needed to confirm the association between IADL stage and acute and chronic disease burden over time.
We further evaluated the predictive ability of SPs’ perceived health status and various impairments common in the elderly, including blindness, deafness, broken hip, and falls for 1-, 5-, and 10-year mortality. These factors were significantly associated with mortality in unadjusted analyses, but only SPs’ perceived health status remained significantly associated with 5- and 10-year mortality in the final models. Blindness, deafness, broken hip, and falls were highly correlated with IADL stage in our study, and have been shown to have major impacts on functional status in other studies.42–45
Thus, IADL stages likely capture the effects of these conditions on mortality. Other studies showed self-rated health was a strong predictor for long-term mortality, and the association was only partly explained by medical conditions or sociodemographics.46–49
In our study, though SPs’ perceived health status did not contribute much to 1-year mortality, it was a significant predictor for 5- and 10-year mortality. Thus, SPs’ perceived health status appears to be adding health risk information in addition to sociodemographics, medical conditions, and functional status to long-term rather than short-term mortality.
There are several limitations in our study. First, we used prospectively collected self-reported data from the LSOA II, a well-designed national survey. Although the use of self-reported information will reduce the healthcare resources needed for implementation, recall and non-response biases in self-reported data could cause misclassification of our predictors. However, the LSOA II has been standardized and extensively tested.50, 51
Self-reported functional status has been validated,37, 52
and self-reported co-morbidities are commonly used in national surveys17, 23
and have been shown predictive of healthcare resource use and various outcomes.53–55
Second, excluding missing data from our complete-case analysis may have introduced bias. However, it is reassuring that we found similar results when we did multiple imputation as a sensitivity analysis. Third, 19.0% of the original sample used proxy-reports, while 15% of data from our complete-case analyses were reported by proxy due to the high prevalence of missing data in proxy-reports. We included proxy use as a variable in our analyses to adjust for the differences. However, this variable was not significant and hence was not included in the final models. Fourth, there are likely unmeasured predictors (unavailable in the data) that could increase prediction, such as cognitive status which is associated with mortality in the elderly population. 53–55
Although IADL stages37–39
and stroke56, 57
may be capturing some cognitive status information, further studies with directly measured cognition are needed. Finally, our baseline data was from the 1994-1996 national survey with mortality follow-up through 2006. The results may only be generalizable to the US community-dwelling population or developed countries with similar population structure.
In conclusion, the 1-, 5-, and 10-year mortality indexes developed from the LSOA II are practical for use in the community setting and can estimate prognosis for short-, intermediate-, and long-term mortality to assist with decision-making of clinicians, researchers, and policy makers. The use of IADL stage, which captures the cognitive and physical disease burden of the elderly population, can simplify mortality prediction in community settings when specific diagnostic information is lacking. If further studies demonstrate external validity of these mortality indexes in various community-dwelling elderly populations, these three mortality predictive indexes could become widely used tools for providing prognostic information and guiding therapeutic interventions among elderly community-dwellers.