Abstract
IntroductionThe ability to estimate risk of multimorbidity will provide valuable information to patients and primary care practitioners in their preventative efforts. Current methods for prognostic prediction modelling are insufficient for the estimation of risk for multiple outcomes, as they do not properly capture the dependence that exists between outcomes. ObjectivesWe developed a multivariate prognostic prediction model for the 5-year risk of diabetes, hypertension, and osteoarthritis that quantifies and accounts for the dependence between each disease using a copula-based model. MethodsWe used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from 2009 onwards, a collection of electronic medical records submitted by participating primary care practitioners across Canada. We identified patients 18 years and older without all three outcome diseases and observed any incident diabetes, osteoarthritis, or hypertension within 5-years, resulting in a large retrospective cohort for model development and internal validation (n=425228). First, we quantified the dependence between outcomes using unadjusted and adjusted ϕ coefficients. We then estimated a copula-based model to quantify the non-linear dependence between outcomes that can be used to derive risk estimates for each outcome, accounting for the observed dependence. Copula-based models are defined by univariate models for each outcome and a dependence function, specified by the parameter θ. Logistic regression was used for the univariate models and the Frank copula was selected as the dependence function. ResultsAll outcome pairs demonstrated statistically significant dependence that was reduced after adjusting for covariates. The copula-based model yielded statistically significant θ parameters in agreement with the adjusted and unadjusted ϕ coefficients. Our copula-based model can effectively be used to estimate trivariate probabilities. DiscussionQuantitative estimates of multimorbidity risk inform discussions between patients and their primary care practitioners around prevention in an effort to reduce the incidence of multimorbidity.
Highlights
The ability to estimate risk of multimorbidity will provide valuable information to patients and primary care practitioners in their preventative efforts
This is achieved in the context of Canadian primary health care, whereby we developed a prognostic prediction model that estimates the 5-year risk of diabetes, hypertension, and osteoarthritis
In 2013, Canadian Primary Care Sentinel Surveillance Network (CPCSSN) patients were older and more likely to be female compared to the overall Canadian population as reported in census data [20], which is typical of primary care [21,22,23]
Summary
The ability to estimate risk of multimorbidity will provide valuable information to patients and primary care practitioners in their preventative efforts. Prognostic prediction models can provide decision support through quantitative estimates of disease risk based on a patient’s individual predictors (e.g., age, sex, physical activity level) [3,4,5]. Understanding a patient’s risk of disease empowers prevention efforts, a hallmark of population health, by guiding decision-making processes and identifying patients at increased risk [6]. There is a gap between one of the most prominent clinical challenges faced by primary care practitioners and their patients and the development of prognostic prediction models far. Using a series of singledisease models in a clinical setting to estimate risk of multiple diseases is burdensome and may give inaccurate perceptions of risk
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