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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: Research based in primary care suggests that hearing loss may be underreported as well as inconsistently recorded in patient histories. In this study, we aimed to develop and validate a case definition for hearing loss among older adults in primary care, using electronic medical records. Methods: We used data from adult patients aged 55 years and older from 13 practices in the Southern Alberta Primary Care Research Network database, part of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), from Dec. 1, 2014, to Dec. 31, 2016. We developed a hearing loss case definition that was translated into an electronic algorithm. A record review was undertaken as the reference standard, followed by application of the algorithm to the sample. Validation metrics included sensitivity, specificity, positive predictive value and negative predictive value, as well as prevalence. We assessed risk factors using the Fisher exact test and odds ratios. Results: The sample included 1000 patients; 496 (49.6%) were female and the mean age was 67.5 (standard deviation 9.6) years. Sensitivity of the case definition algorithm was determined to be 87.3% (95% confidence interval [CI] 76.5%–94.4%) with specificity valued at 94.8% (95% CI 93.1%–96.1%). Positive and negative predictive values were 52.9% (95% CI 42.8%–62.8%) and 99.1% (95% CI 98.2%–99.6%), respectively. The prevalence of hearing loss within the sample was 6.3% (95% CI 4.9%–7.9%). Older age was a significant risk factor for hearing loss (t = 4.98, 95% CI 3.76–8.65). Men had greater odds of hearing loss than women (odds ratio 1.65, 95% CI 0.98–2.79). Interpretation: The validated case definition for hearing loss in community-based older adults had high sensitivity and specificity. It may be applied to surveillance and future epidemiologic research within the CPCSSN database. [ABSTRACT FROM AUTHOR]

Background: To date, there has been no validated method to identify cases of pelvic floor disorders in primary care electronic medical record (EMR) data. We aimed to develop and validate symptom-based case definitions for urinary incontinence, fecal incontinence and pelvic organ prolapse in women, for use in primary care epidemiologic or clinical research. Methods: Our retrospective study used EMR data from the Southern Alberta Primary Care Research Network (SAPCReN) and the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in southern Alberta. Trained researchers remotely reviewed a random sample of EMR charts of women aged 18 years or older from 6 rural and urban clinics to validate case definitions for urinary incontinence, fecal incontinence and pelvic organ prolapse. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), and estimated SAPCReN prevalence as appropriate. Results: Charts of 900 women were included. Sensitivity was 81.9% (95% confidence interval [CI] 75.1–87.2) for urinary incontinence, 61.2% (95% CI 46.2–74.5) for fecal incontinence, and 51.8% (95% CI 40.6–62.8) for pelvic organ prolapse. Corresponding specificity values were 71.9% (95% CI 68.4–75.1), 99.2% (95% CI 98.2–99.6) and 98.8% (95% CI 97.7–99.4), PPVs 40.6% (95% CI 35.4–46.0), 81.1% (95% CI 64.3–91.4) and 81.1% (95% CI 67.6–90.1), and NPVs 94.4% (95% CI 92.1–96.1), 97.8% (95% CI 96.5–98.6) and 95.3% (95% CI 93.6–96.6). The SAPCReN-observed prevalence for urinary incontinence was 29.7% (95% CI 29.3–30.0), but the adjusted prevalence was 2.97%. Interpretation: The case definition for urinary incontinence met our standard for validity (sensitivity and specificity > 70%), and the case definitions for fecal incontinence and pelvic organ prolapse had PPVs greater than 80%. The urinary incontinence definition may be used in epidemiologic research, and those for fecal incontinence and pelvic organ prolapse may be used in quality-improvement studies or creation of disease registries. Our symptom-based case definitions could also be adapted for research in other EMR settings. [ABSTRACT FROM AUTHOR]

Background: Identifying cases of disease in primary care electronic medical records (EMRs) is important for surveillance, research, quality improvement and clinical care. We aimed to develop and validate a case definition for type 1 diabetes mellitus using EMRs. Methods: For this exploratory study, we used EMR data from the Southern Alberta Primary Care Network within the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), for the period 2008 to 2016. For patients identified as having diabetes mellitus according to the existing CPCSSN case definition, we asked family physicians to confirm the diabetes subtype, to create the reference standard. We used 3 decision-tree classification algorithms and least absolute shrinkage and selection operator logistic regression to identify variables that correctly distinguished between type 1 and type 2 diabetes cases. Results: We identified a total of 1309 people with type 1 or type 2 diabetes, 110 of whom were confirmed by their physicians as having type 1 diabetes. Two machine learning algorithms were useful in identifying these cases in the EMRs. The first algorithm used "type 1" text words or age less than 22 years at time of initial diabetes diagnosis; this algorithm had sensitivity 42.7% (95% confidence interval [CI] 33.5%–52.5%), specificity 99.3% (95% CI 98.6%–99.7%), positive predictive value 85.5% (95% CI 72.8%–93.1%) and negative predictive value 94.9% (95% CI 93.5%–96.1%). The second algorithm used a combination of free-text terms, insulin prescriptions and age; it had sensitivity 87.3% (95% CI 79.2%–92.6%), specificity 85.4% (95% CI 83.2%–87.3%), positive predictive value 35.6% (95% CI 29.9%–41.6%) and negative predictive value 98.6% (95% CI 97.7%–99.2%). Interpretation: We used machine learning to develop and validate 2 case definitions that achieve different goals in distinguishing between type 1 and type 2 diabetes in CPCSSN data. Further validation and testing with a larger and more diverse sample are recommended. [ABSTRACT FROM AUTHOR]

Background: The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) previously carried out a validation study of case definitions for 8 chronic diseases (diabetes mellitus, hypertension, osteoarthritis, depression, dementia, chronic obstructive pulmonary disease, parkinsonism and epilepsy) using direct review of "raw" electronic medical record data. Although effective, this method is time-consuming and can present methodological and organizational challenges. We aimed to determine whether the processed and standardized data contained with the CPCSSN database might function as a reference standard for case definition validation. Methods: Using a traditional validation study design, we compared the case identification results of the chart reviews for the 8 chronic diseases with the results of a manual review of the CPCSSN processed data for the same conditions in the same patient sample. Patients were randomly sampled from the June 30, 2012 CPCSSN database, with oversampling of patients with rare conditions. Results: We analyzed data for 1906 patients. Manual review of the CPCSSN records for case ascertainment yielded sensitivity ranging from 77.5% (95% confidence interval [CI] 73.3%-81.6%) for depression to 97.2% (95% CI 95.4%-99.0%) for diabetes. Specificity was high for all definitions (range 93.1% [95% CI 91.4%-94.7%] to 99.4% [95% CI 99.0%-99.8%]). Positive predictive values and negative predictive values also showed high accuracy of the manual CPCSSN record review relative to review of the raw chart data. Interpretation: The use of CPCSSN records as the reference standard to validate case definitions substantially reduces the burden on sentinel physicians and clinic managers as well as on researchers while offering a reference standard that is a reasonable substitution for chart review. [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]