Examining the Prevalence and Patterns of Multimorbidity in Canadian Primary Healthcare: A Methodologic Protocol using a National Electronic Medical Record Database

Abstract

In many developed countries, the burden of disease has shifted from acute to long-term or chronic diseases – producing new and broader challenges for patients, healthcare providers, and healthcare systems. Multimorbidity, the coexistence of two or more chronic diseases within an individual, is recognized as a significant public health and research priority. This protocol aims to examine the prevalence, characteristics, and changing burden of multimorbidity among adult primary healthcare (PHC) patients using electronic medical record (EMR) data. The objectives are two-fold: (1) to measure the point prevalence and clusters of multimorbidity among adult PHC patients; and (2) to examine the natural history and changing burden of multimorbidity over time among adult PHC patients. Data will be derived from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). The CPCSSN database contains longitudinal, point-of-care data from EMRs across Canada. To identify adult patients with multimorbidity, a list of 20 chronic disease categories (and corresponding ICD-9 codes) will be used. A computational cluster analysis will be conducted using a customized computer program written in JAVA. A Cox proportional hazards analysis will be used to model time-to-event data, while simultaneously adjusting for provider- and patient-level predictors. All analyses will be conducted using STATA SE 13.1. This research is the first of its kind using a pan-Canadian EMR database, which will provide an opportunity to contribute to the international evidence base. Future work should systematically compare international research using similar robust methodologies to determine international and geographical variations in the epidemiology of multimorbidity.

Comparing the self-perceived quality of life of multimorbid patients and the general population using the EQ-5D-3L

“…Multimorbidity is associated with increased rates of mortality and disability, reduced levels of function, increased polypharmacy, poor health-related quality of life (HRQoL), and a greater utilization of healthcare resources (costs, number of physician visits, length of hospital stay) [ 6 ]. Indeed, multimorbidity is recognized as a significant healthcare system cost and a major public health issue deserving more research [ 7 ]. As multimorbidity becomes more prevalent worldwide, it is becoming a more relevant, clinically important topic.…”

Medical diagnoses showed low relatedness in an explorative mutual information analysis of 190,837 inpatient cases

“…For instance, a rare genetic disease may drastically change the management of a multimorbid patient who otherwise only has diseases belonging to the cardiovascular and metabolic disease cluster [10e17]. Furthermore, multimorbidity patterns and clusters have been shown to often be heterogeneous across studies and to overlap with other disease clusters [4,6,16]. This may further complicate the comprehensive characterization of multimorbidity patterns and subsequent development of generalizable treatment strategies and guidelines for multimorbid patients.…”

“…Consequently, from an information theoretical point of view, one could explore the mutual (i.e., shared) information between medical diagnoses to characterize multimorbidity patterns and to quantify the information entropy, that is unrelatedness, of medical diagnoses. Previous studies investigating multimorbidity patterns focused largely on specific patient populations (e.g., elderly patients, individuals with mental diseases) and yielded heterogeneous multimorbidity patterns with a few relatively consistent and clinically well-established disease combinations (e.g., metabolic syndrome) [4,6].…”

Evaluating Prevalence and Patterns of Prescribing Medications for Depression for Patients With Obesity Using Large Primary Care Data (Canadian Primary Care Sentinel Surveillance Network)

“…To select patients with lifetime depression, a CPCSSN definition of depression and a validated case detection algorithm (28) were applied. The algorithm combines information from patients' problem list [Encounter Diagnosis Codes, used by some providers/sites to record the information on diagnosis (29)], prescription records, and billing [Billing Diagnosis Codes, used by other providers/sites to record the information on diagnosis (29)]. This algorithm detects lifetime depression, including an ongoing depression episode or a history of depression (28).…”