Single index of multimorbidity did not predict multiple outcomes

Abstract

Background and objectives: Measurement of multimorbidity and comorbidity is important in epidemiologic and health services research. The aim of this research was to derive a generic multimorbidity index based on patient self-report, incorporating severity, for predicting a range of outcomes.

Methods: The dataset was obtained from a trial including 1,541 Veterans and war widows aged 70 years and over. The survey included sociodemographics, hospital admissions, SF-36, and information on deaths was obtained. The methods of Charlson were used to derive Multimorbidity Indices.

Results: All indices predicted quality of life, with decreasing quality of life for each increase in multimorbidity category. Multimorbidity scores incorporating severity significantly contributed to the prediction of mortality, hospital admission, and follow-up quality of life, regardless of adjustment for baseline quality of life.

Conclusions: Our results indicate that a single index cannot predict a variety of relevant outcomes. Consequently, research undertaken to assess the impact of intervention or illness on health outcomes should use an index that is valid for predicting the specific outcome of interest.