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Tuesday, November 3, 2009: 10:15 AM
Learning Objective 1: identify at least three major methodological approaches on which primary care patients’ composite comorbidity levels have been measured.
Learning Objective 2: discuss at least three analytic challenges posed for nurse researchers studying the influences of comorbidity to predict primary care patient health outcomes.
Research Objective:
Although there have been a growing number of methods proposed to measure patient’s composite comorbidity in hospital environments, the specific influences of comorbidity levels on primary care patients’ health outcomes has received much less attention. The use of measurement and weighting methods for individual health conditions has predominated despite demonstrated additive or synergistic effects on patient functional, clinical or quality of life outcomes. During this presentation, a combined outcomes data set from three funded studies will be used to illustrate how the manner in which patients’ comorbid conditions are measured, calculated and entered into predictive models can affect the significance of composite comorbidity on primary care patient health outcomes.
Study Design:
A series of stepwise multivariate and logistic regression predictive models were run with blocks of comorbid condition, socio-demographic, and other patient characteristic data to measure their relative influence on psychosocial, functional, and service use outcomes. Predictive models were run with, and without, different patient variable sets to determine how the predictive influence from comorbidity was affected. The qualitative remarks from comorbid patients in the sample were thematically analyzed to investigate the complex dimensions of patient’s perceived comorbidity burden.
Population Studied:
A heterogeneous sample of 265 primary care patients in the Midwestern United States.
Principal Findings:
The overall statistical significance of composite comorbidity levels on many patient outcomes was consistently demonstrated. Both multicollinear and likely confounding relationships between many patient characteristics with composite comorbidity were demonstrated for numerous health outcomes. Representative patient comments regarding these perceived relationships on their subsequent health outcomes demonstrated the inherent challenges imposed on nurse researchers working in typical primary care environments.
Conclusions:
The construct of comorbidity is a key element of the “sicker and quicker” phenomenon now seen in most contemporary healthcare settings. As an inherently multifaceted factor, comorbidity levels must be thoughtfully measured and evaluated in primary care patient outcome analyses.
Implications for Policy, Delivery, or Practice:
A series of research implications for health services researchers measuring patient comorbidity measurement methods for future studies will be discussed. A methodological framework to address the fundamental measurement, weighting, and multifactoral complexity of comorbidity for future primary care research will be presented.
Partially Funded by: 1. Blue Cross Blue Shield ofMichigan Foundation. Investigator Initiated Research Proposal Grant 649.11 “Feasibility Testing of a “Shared Decision Making/Self-Management” Diabetes Office Visit Program.” (W. Corser, PI) 2. Agency for Health Care Research and Quality Grant # 1 R03 HS0792-01, “An Investigation of Patient Outcomes related to Interdisciplinary Hospital Discharge Planning.” (W. Corser, PI). 3. Michigan State University 2008 College of Nursing Summer Scholar Fund. "An Investigation of Heavily Comorbid Patients' Self-Management Experiences with Primary Care Providers." (W. Corser, PI).
Although there have been a growing number of methods proposed to measure patient’s composite comorbidity in hospital environments, the specific influences of comorbidity levels on primary care patients’ health outcomes has received much less attention. The use of measurement and weighting methods for individual health conditions has predominated despite demonstrated additive or synergistic effects on patient functional, clinical or quality of life outcomes. During this presentation, a combined outcomes data set from three funded studies will be used to illustrate how the manner in which patients’ comorbid conditions are measured, calculated and entered into predictive models can affect the significance of composite comorbidity on primary care patient health outcomes.
Study Design:
A series of stepwise multivariate and logistic regression predictive models were run with blocks of comorbid condition, socio-demographic, and other patient characteristic data to measure their relative influence on psychosocial, functional, and service use outcomes. Predictive models were run with, and without, different patient variable sets to determine how the predictive influence from comorbidity was affected. The qualitative remarks from comorbid patients in the sample were thematically analyzed to investigate the complex dimensions of patient’s perceived comorbidity burden.
Population Studied:
A heterogeneous sample of 265 primary care patients in the Midwestern United States.
Principal Findings:
The overall statistical significance of composite comorbidity levels on many patient outcomes was consistently demonstrated. Both multicollinear and likely confounding relationships between many patient characteristics with composite comorbidity were demonstrated for numerous health outcomes. Representative patient comments regarding these perceived relationships on their subsequent health outcomes demonstrated the inherent challenges imposed on nurse researchers working in typical primary care environments.
Conclusions:
The construct of comorbidity is a key element of the “sicker and quicker” phenomenon now seen in most contemporary healthcare settings. As an inherently multifaceted factor, comorbidity levels must be thoughtfully measured and evaluated in primary care patient outcome analyses.
Implications for Policy, Delivery, or Practice:
A series of research implications for health services researchers measuring patient comorbidity measurement methods for future studies will be discussed. A methodological framework to address the fundamental measurement, weighting, and multifactoral complexity of comorbidity for future primary care research will be presented.
Partially Funded by: 1. Blue Cross Blue Shield of