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O steoarthritis is a common chronic condition affecting many older Canadians and is a considerable cause of disability.1 It is the most common form of arthritis and is frequently diagnosed and managed in primary care.2 As the Canadian population ages, the burden of this condition on our health care system will increase, and we must look at trends in risk factors, diagnosis and management.International reports on the prevalence of osteoarthritis diagnoses show an increasing number of patients with the condition.3 This is predominantly due to an increase in the number of people older than 60 years, as well as to an increase in obesity, a leading risk factor for osteoarthritis. [4][5][6][7] Previous studies have provided information about the state of osteo arthritis in Canada. [7][8][9][10][11] In British Columbia, an overall prevalence of 10.8% was found using administrative data (i.e., physician billing and hospital admissions data); by age 70-74 years, 30% of men and 40% of women had osteoarthritis. In Ontario, linked survey and administrative data showed that quality of life was 10%-25% lower among people with osteoarthritis than in the general population, and health care costs were 2-3 times higher than in the nonosteoarthritis group.10 Therefore there is a high prevalence, reduction of quality of life and a large economic burden associated with osteoarthritis in Canada.

Background: Building and validating electronic algorithms to identify patients with specific disease profiles using health data is becoming increasingly important to disease surveillance and population health management. The aim of this study was to develop and validate an algorithm to find patients with ADHD diagnoses within primary care electronic medical records (EMR); and then use the algorithm to describe the epidemiology of ADHD from 2008 to 2015 in a Canadian Primary care sample. Methods: This was a cross sectional time series that used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), a repository of primary care EMR data. A sample of electronic patient charts from one local clinic were manually reviewed to determine the positive predictive value (PPV) and negative predictive value (NPV) of an ADHD case-finding algorithm. In each study year a practice population was determined, and the algorithm was used to measure an observed prevalence of ADHD. The observed prevalence was adjusted for misclassification, as measured by the validity indices, to obtain an estimate of the true prevalence. Estimates were calculated by age group (4-17 year olds, 18 to 34 year olds, and 35 to 64 year olds) and gender, and compared over time. Results: The EMR algorithm had a PPV of 98.0% (95% CI [92.5, 99.5]) and an NPV of 95.0% (95% CI [92.9, 98.6]). After adjusting for misclassification, it was determined that the prevalence of patients with a clinical diagnosis of ADHD has risen in all age groups between 2008 and 2015, most notably in children and young adults (6.92, 95% CI [5.62, 8.39] to 8.57, 95% CI [7.32, 10.00]; 5.73, 95% CI [4.40, 7.23] to 7.33, 95% CI [6.04, 8.78], respectively). The well-established gender gap persisted in all age groups across time but was considerably smaller in older adults compared to children and young adults. Conclusion: Overall, the ADHD case-finding algorithm was found to be a valid tool to assess the epidemiology of ADHD in Canadian primary care practice. The increased prevalence of ADHD between 2008 and 2015 may reflect an improvement in the recognition and treatment of this disorder within primary care.

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