ABSTRACT
Current common comorbidity measures have poor to moderate predictive validity of mortality of community-dwelling older adults. Hence, our aim is to develop a simpler resource-efficient self-reported comorbidity index in the prediction of survival. 113 older adults in Greater Manchester, United Kingdom attended a routine medical examination whereby information gathered was used to construct Charlson Comorbidity Index (CCI). They completed the Cornell Medical Index (CMI) questionnaire and reported the number of medication prescribed to them. We compared the ability of CCI, CMI, number of medication, age and sex to predict mortality of the sample over 7-year period using Cox-regression and Kaplan-Meier plot and rank test. None of the variables individually was significant when tested using either Cox-regression via ENTER method or Kaplan-Meier test. Remarkably, by means of forward step-wise Cox-regression, two variables emerged significant: (i) number of medicine (beta coefficient=0.229, SE=0.090 and p=0.011) and (ii) age (beta coefficient=0.106, SE=0.051 and p=0.037). We demonstrated that simple count of medication predicted mortality of community-dwelling older adults over the next 7 years more accurately than CMI or CCI. Further works involving a larger scale of subjects is needed for use in epidemiological study of survival where cost and resources are concerned.
