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Learning Objective 1: identify at least three major ways in which increasing primary care patient comorbidity will likely influence their subsequent health outcomes.
Learning Objective 2: analyze at least three ways in which the methodological measurement of patient comorbidity can be treated by nurse researchers for health outcomes studies.
Methods: A series of multivariate and logit predictive models were first run with blocks of comorbidity, socio-demographic, and other patient data on several psychosocial, functional, and healthcare service use outcomes. Predictive models were then run with, and without, varied groups of patient variables on the same set of health outcomes to determine how the measured predictive influence of comorbidity changed. The qualitative remarks from a subset of highly comorbid sample patients were then thematically analyzed to investigate the complex elements of patient’s perceived comorbidity burden.
Results: The statistical significance of composite patient comorbidity on numerous health outcomes was consistently demonstrated. Both multicollinear and likely confounded relationships of patient characteristics in conjunction with initially significant comorbidity levels were demonstrated for some, but not other, patient health outcomes.
Conclusion: The manner in which nurse researchers methodologically treat their study patients' composite comorbidity when studying their health outcomes must be carefully considered. A series of methodological implications for nurse researchers across the world striving to measure the influence of primary care patients’ increasing comorbidity will be discussed. A methodological framework depicting the fundamental measurement, weighting, and analytic complexity of comorbidity measures for future primary care outcomes research designs will be presented.