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Background: The proportion of Canadians living with Alzheimer disease and related dementias is projected to rise, with an increased burden on the primary health care system in particular. Our objective was to describe the prevalence and management of dementia in a community-dwelling sample using electronic medical record (EMR) data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), which consists of validated, national, point-of-care data from primary care practices. Methods: We used CPCSSN data as of Dec. 31, 2012, for patients 65 years and older with at least 1 clinical encounter in the previous 2 years. A validated case definition for dementia was used to calculate the national and provincial prevalence rates, to examine variations in prevalence according to age, sex, body mass index, rural or urban residence, and select comorbid conditions, and to describe patterns in the pharmacologic management of dementia over time at the provincial level. Results: The age-standardized prevalence of dementia among community-dwelling patients 65 years and older was 7.3%. Prevalence estimates increased with age; they also varied between provinces, and upward trends were observed. Dementia was found to be associated with comorbid diabetes, depression, epilepsy and parkinsonism. Most of the patients with dementia did not have a prescription for a dementia-related medication recorded in their EMR between 2008 and 2012 inclusive. Those who had a prescription were most often prescribed donepezil by their primary care provider. Interpretation: Overall prevalence estimates for dementia based on EMR data in this sample managed in primary care were generally in line with previous estimates based on administrative data, survey results or clinical sources. [ABSTRACT FROM AUTHOR]

Background: The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) has established a national repository of primary care patient health data that is used for both surveillance and research. Our main objective was to determine how representative the data for patients and primary care practitioners in the CPCSSN are when compared with the Canadian population. Methods: In this cross-sectional study, we compared the 2013 CPCSSN patient sample with age and sex information from the 2011 census. The CPCSSN provider sample in 2013 was compared with the 2013 National Physician Survey. Results were stratified by 5 clinically relevant age categories and sex, and male-to-female ratios were calculated. Results: Patients who were 65 years of age and older represented 20.4% of the CPCSSN sample but only represented 14.8% of the Canadian population (2011 census). Among young adults (20-39 yr), 39.3% fewer men than women visited their primary care practitioner within 2 years. CPCSSN sample practitioners were more likely to be under 45 years of age, more likely to be female and more likely to be in an academic practice. Interpretation: It is important to consider adjusting for age and sex when using CPCSSN data. CPCSSN practitioners are likely not representative of family physicians; therefore, CPCSSN needs to recruit more nonacademic practices, community clinics and practices that have a larger proportion of male providers. [ABSTRACT FROM AUTHOR]

Background: Most antibiotics dispensed by community pharmacies in Canada are prescribed by family physicians, but using the prescribing information contained within primary care electronic medical records (EMRs) for secondary purposes can be challenging owing to variable data quality. We used antibiotic medications as an exemplar to validate a machine-learning approach for cleaning and coding medication data in a pan-Canadian primary care EMR database., Methods: The Canadian Primary Care Sentinel Surveillance Network database contained an estimated 42 million medication records, which we mapped to an Anatomic Therapeutic Chemical (ATC) code by applying a semisupervised classification model developed using reference standard labels derived from the Health Canada Drug Product Database. We validated the resulting ATC codes in a subset of antibiotic records (16 119 unique strings) to determine whether the algorithm correctly classified the medication according to manual review of the original medication record., Results: In the antibiotic subset, the algorithm showed high validity (sensitivity 99.5%, specificity 92.4%, positive predictive value 98.6%, negative predictive value 97.0%) in classifying whether the medication was an antibiotic., Interpretation: Our machine-learning algorithm classified unstructured antibiotic medication data from primary care with a high degree of accuracy. Access to cleaned EMR data can support important secondary uses, including community-based antibiotic prescribing surveillance and practice improvement., Competing Interests: Competing interests: None declared., (© 2023 CMA Impact Inc. or its licensors.)