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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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BackgroundElectronic medical records (EMRs) used in primary care contain a breadth of data that can be used in public health research. Patient data from EMRs could be linked with other data sources, such as a postal code linkage with Census data, to obtain additional information on environmental determinants of health. While promising, successful linkages between primary care EMRs with geographic measures is limited due to ethics review board concerns. This study tested the feasibility of extracting full postal code from primary care EMRs and linking this with area-level measures of the environment to demonstrate how such a linkage could be used to examine the determinants of disease. The association between obesity and area-level deprivation was used as an example to illustrate inequalities of obesity in adults.MethodsThe analysis included EMRs of 7153 patients aged 20 years and older who visited a single, primary care site in 2011. Extracted patient information included demographics (date of birth, sex, postal code) and weight status (height, weight). Information extraction and management procedures were designed to mitigate the risk of individual re-identification when extracting full postal code from source EMRs. Based on patients’ postal codes, area-based deprivation indexes were created using the smallest area unit used in Canadian censuses. Descriptive statistics and socioeconomic disparity summary measures of linked census and adult patients were calculated.ResultsThe data extraction of full postal code met technological requirements for rendering health information extracted from local EMRs into anonymized data. The prevalence of obesity was 31.6 %. There was variation of obesity between deprivation quintiles; adults in the most deprived areas were 35 % more likely to be obese compared with adults in the least deprived areas (Chi-Square = 20.24(1), p < 0.0001). Maps depicting spatial representation of regional deprivation and obesity were created to highlight high risk areas.ConclusionsAn area based socio-economic measure was linked with EMR-derived objective measures of height and weight to show a positive association between area-level deprivation and obesity. The linked dataset demonstrates a promising model for assessing health disparities and ecological factors associated with the development of chronic diseases with far reaching implications for informing public health and primary health care interventions and services.

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OBJECTIVES: This research examines the feasibility of using electronic medical records within the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) for obesity surveillance in Canada by assessing obesity trends over time and comparing BMI distribution estimates from CPCSSN to those obtained from nationally representative surveys. RESULTS:The estimated prevalence of obesity increased from 17.9% in 2003 to 30.8% in 2012. Obesity class I, II and III prevalence estimates from CPCSSN in 7.4%, 95% CI: 7.3-7.6; 4.2%, 95% CI: 4.1-4.3 respectively) were greater than those from the most recent (2009)(2010)(2011) cycle of the CHMS (16.2%, 95% CI: 14-18.7; 6.3%, 95% CI: 4.6-8.5; 3.7%, 95% CI: 2.8-4.8 respectively), however these differences were not statistically significant. CONCLUSION:The data from CPCSSN present a unique opportunity for longitudinal obesity surveillance among primary care users in Canada, and offer prevalence estimates similar to those obtained from nationally representative survey data.KEY WORDS: BMI -body mass index; CPCSSN -Canadian Primary Care Sentinel Surveillance Network; EMR -Electronic Medical Record; obesity La traduction du résumé se trouve à la fin de l'article.