Canadian Primary Care Sentinel Surveillance Network Research Articles

Development and validation of a rheumatoid arthritis case definition: a machine learning approach using data from primary care electronic medical records

BackgroundRheumatoid Arthritis (RA) is a chronic inflammatory disease that is primarily diagnosed and managed by rheumatologists; however, it is often primary care providers who first encounter RA-related symptoms. This study developed and validated a case definition for RA using national surveillance data in primary care settings.MethodsThis cross-sectional validation study used structured electronic medical record (EMR) data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Based on the reference set generated by EMR reviews by five experts, three machine learning steps: ‘bag-of-words’ approach to feature generation, feature reduction using a feature importance measure coupled with recursive feature elimination and clustering, and classification using tree-based methods (Decision Tree, Random Forest, and Extreme Gradient Boosting). The three tree-based algorithms were compared to identify the procedure that generated the optimal evaluation metrics. Nested cross-validation was used to allow evaluation and comparison and tuning of models simultaneously.ResultsOf 1.3 million patients from seven Canadian provinces, 5,600 people aged 19 + were randomly selected. The optimal algorithm for selecting RA cases was generated by the XGBoost classification method. Based on feature importance scores for features in the XGBoost output, a human-readable case definition was created, where RA cases are identified when there are at least 2 occurrences of text “rheumatoid” in any billing, encounter diagnosis, or health condition table of the patient chart. The final case definition had sensitivity of 81.6% (95% CI, 75.6–86.4), specificity of 98.0% (95% CI, 97.4–98.5), positive predicted value of 76.3% (95% CI, 70.1–81.5), and negative predicted value of 98.6% (95% CI, 98.0-98.6).ConclusionA case definition for RA in using primary care EMR data was developed based off the XGBoost algorithm. With high validity metrics, this case definition is expected to be a reliable tool for future epidemiological research and surveillance investigating the management of RA in CPCSSN dataset.

Balancing Privacy and Precision: Evaluating Meta-Analysis for National Health Data Integration in Canada

BackgroundIn Canada, legislative restrictions exist for the disclosure and transfer of patient data across provincial boundaries. Often, meta-analysis techniques are employed to pool province-specific results to enable national results while maintaining compliance with privacy legislation. However, the effectiveness of this technique remains unexamined using electronic data. To evaluate its performance, we compare results obtained through meta-analysis with those from a fully pooled model. MethodsUsing chronic kidney disease as a case study, this retrospective cohort study simulates the meta-analysis method to evaluate its performance using real data. We analyze data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), a nationwide database of electronic medical records. We meta-analyze provincial logistic regression models to predict nephropathy over a 5-year period at a national level. Estimates are compared to a fully pooled national model. ResultsAnalyses are currently ongoing. We expect the meta-analysis technique to produce similar estimates compared to the fully pooled model. However, we expect certain differences as meta-analysis techniques are insensitive to heterogeneity within provinces that impact the precision of estimates. Observed differences will help to inform future work where we will examine a potential new methodology to analyze health information without sharing patient data. ConclusionsBased on our results, we will reveal strengths and limitations of the meta-analysis technique for interprovincial analyses. Limitations of the meta-analysis technique may underscore the need for further exploration into alternative methodologies that can effectively analyze health information without compromising patient privacy to facilitate better healthcare decision-making and policy development in Canada.

Screening and diagnosis of chronic kidney disease among patients living with diabetes: A retrospective cohort study using the Canadian Primary Care Sentinel Surveillance Network

IntroductionIn Canada, regional evaluations of screening practices for chronic kidney disease (CKD) among people with diabetes highlight areas for improvement; however, national estimates are notably absent. Estimates of CKD incidence often discount the expected decline in eGFR associated with age; age-adaptive thresholds may help account for this. We describe the frequency of screening and diagnosis of CKD among adults with diabetes among a nationally representative primary care cohort. MethodsIn this retrospective cohort study, we used electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). We followed adult patients (18+) with diabetes without CKD at baseline for 5 years starting in 2014. We determined the frequency of ACR and/or eGFR testing over time. We identified incident CKD diagnoses based on eGFR measurements using fixed-threshold and age-adaptive definitions and quantified the incidence proportion and rate. ResultsWe analysed records from 37,604 patients with diabetes. Only 13% of patients had yearly eGFR and ACR testing for CKD, though roughly 60% had non-yearly use of both tests in 5 years. eGFR testing was performed more frequently than ACR testing (94.1% vs. 76.6% having testing over follow-up). We found increased incidence proportions (14.6 % vs. 6.0%) and rates (33.1 vs. 13.4 diagnoses/1000 person-years) of CKD using the fixed-threshold compared to age-adaptive definitions. ConclusionsOur study presents the first national understanding of screening practices for CKD among people with diabetes in Canada. In particular, increased use of ACR testing should be encouraged for early detection of changes in kidney function.

Understanding the impact of COVID-19 on antibiotic use in Canadian primary care: a matched-cohort study using EMR data

BackgroundInappropriate or overuse of antibiotic prescribing in primary care highlights an opportunity for antimicrobial stewardship (AMS) programs aimed at reducing unnecessary use of antimicrobials through education, policies and practice audits that optimize antibiotic prescribing. Evidence from the early part of the pandemic indicates a high rate of prescribing of antibiotics for patients with COVID-19. It is crucial to surveil antibiotic prescribing by primary care providers from the start of the pandemic and into its endemic stage to understand the effects of the pandemic and better target effective AMS programs.MethodsThis was a matched pair population-based cohort study that used electronic medical record (EMR) data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Participants included all patients that visited their primary care provider and met the inclusion criteria for COVID-19, respiratory tract infection (RTI), or non-respiratory or influenza-like-illness (negative). Four outcomes were evaluated (a) receipt of an antibiotic prescription; (b) receipt of a non-antibiotic prescription; (c) a subsequent primary care visit (for any reason); and (d) a subsequent primary care visit with a bacterial infection diagnosis. Conditional logistic regression was used to evaluate the association between COVID-19 and each of the four outcomes. Each model was adjusted for location (rural or urban), material and social deprivation, smoking status, alcohol use, obesity, pregnancy, HIV, cancer and number of chronic conditions.ResultsThe odds of a COVID-19 patient receiving an antibiotic within 30 days of their visit is much lower than for patients visiting for RTI or for a non-respiratory or influenza-like-illnesses (AOR = 0.08, 95% CI[0.07, 0.09] compared to RTI, and AOR = 0.43, 95% CI[0.38, 0.48] compared to negatives). It was found that a patient visit for COVID-19 was much less likely to have a subsequent visit for a bacterial infection at all time points.ConclusionsEncouragingly, COVID-19 patients were much less likely to receive an antibiotic prescription than patients with an RTI. However, this highlights an opportunity to leverage the education and attitude change brought about by the public health messaging during the COVID-19 pandemic (that antibiotics cannot treat a viral infection), to reduce the prescribing of antibiotics for other viral RTIs and improve antibiotic stewardship.

Validation of adult asthma case definitions for primary care sentinel surveillance.

Most asthma diagnoses and patient care take place in primary care settings. Electronic medical records (EMRs) offer an opportunity to utilize technology to improve asthma diagnosis and care. The purpose of this study was to create and validate separate case definitions for suspected and confirmed asthma in primary care EMRs, to enable surveillance, benchmarking, and quality improvement in primary care settings. The objective of this study was to develop a case definition for suspected and confirmed asthma for use in a primary care sentinel surveillance system. A single chart abstractor conducted a manual audit of 776 randomly selected patient charts from an academic primary care practice EMR in Kingston, Ontario. Following the single chart abstractor classification, a consensus on chart classification as "not asthma", "suspected asthma", or "confirmed asthma" was achieved between the abstractor, a family physician, and a respirologist using Canadian Thoracic Society (CTS) criteria. Case definition algorithms based on billing codes, clinical data elements and medications were applied to the site's Canadian Primary Care Sentinel Surveillance Network (CPCSSN) data for the same charts and compared to abstractor classifications to determine each algorithm's measurement properties. The prevalence of suspected and confirmed asthma were 7.3% (n = 54) and 2.4% (n = 18), respectively. None of the proposed case definitions could differentiate between suspected and confirmed asthma. One algorithm consisting of billing, clinical, and medication elements had the highest Youden's Index for either suspected or confirmed asthma. The algorithm had a sensitivity of 81%, a specificity of 96%, positive predictive value of 71%, negative predictive value of 98%, and a Youden's Index of 0.77 for combined suspected or confirmed asthma cases. An EMR case definition for suspected or confirmed adult asthma has been validated for use in CPCSSN. Implementation of this case definition will enable the development of a surveillance electronic tool (eTool) for adult asthma that can foster quality improvement.

Artificial intelligence with temporal features outperforms machine learning in predicting diabetes.

Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learning models to incorporate the time dimension of risk. The proposed research investigates a variety of deep learning models and features for predicting diabetes. Model performance was appraised and compared in relation to predominant features, risk factors, training data density and visit history. The framework was implemented on the longitudinal EMR records of over 19K patients extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Empirical findings demonstrate that deep learning models consistently outperform other state-of-the-art competitors with prediction accuracy of above 91%, without overfitting. Fasting blood sugar, hemoglobin A1c and body mass index are the key predictors of future onset of diabetes. Overweight, middle aged patients and patients with hypertension are more vulnerable to developing diabetes, consistent with what is already known. Model performance improves as training data density or the visit history of a patient increases. This study confirms the ability of the LSTM deep learning model to incorporate the time dimension of risk in its predictive capabilities.

Impact of Sex and Gender on Metabolic Syndrome in Adults: A Retrospective Cohort Study From the Canadian Primary Care Sentinel Surveillance Network

ObjectiveMetabolic syndrome (MetS), a cluster of 5 interconnected factors, is the main contributor to cardiovascular disease. Although sex- and gender-related elements have been linked to MetS and its components, this association has not been explored among Canadians with or without MetS. In this study, we aimed to identify sex and gender differences in characteristics of MetS in the Canadian population. MethodsThis retrospective cohort study used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database. The CPCSSN contains de-identified electronic health records of >1.5 million Canadians (2010–2019). Individuals 35 to 75 years of age who had a primary care encounter formed the study sample (N=37,813). Multiple logistic regression models were used to estimate adjusted odds ratios for sex and gender differences among Canadians with and without MetS, which was the primary outcome variable. ResultsThe estimated prevalence of MetS was 41.9%. The risk of developing MetS was significantly lower among females compared with males (odds ratio 0.73, 95% confidence interval 0.70 to 0.76). However, the risk was higher in females who used antidepressants (odds ratio 1.53, 95% confidence interval 1.42 to 1.65). An equal distribution of deprivation indexes was observed between males and females with MetS, with risk slightly higher for those with material deprivation. Females were found to be the most socially deprived. ConclusionsThis study provides important sex- and gender-specific differences in MetS among Canadians. Targeting sex- and gender-specific risk factors could assist in reversing the trend of adverse cardiovascular outcomes associated with MetS.

Effects of the COVID-19 Pandemic on Primary Health Care for Chronic Conditions in Canada: Protocol for a Retrospective Pre-Post Study Using National Practice-Based Research Network Data.

Since the COVID-19 pandemic began, there have been concerns that interruptions to the health care system may have led to changes in primary care, especially for care of chronic conditions such as diabetes and heart failure. Such changes may have longer term implications for population health. This study aims to describe the impacts of the COVID-19 pandemic on indicators of primary care access, comprehensiveness, and appropriateness among adult patients, as well as on specific indictors of chronic conditions. Additionally, this study aims to determine whether any identified changes were associated with patient sociodemographic characteristics and multimorbidity. This is a retrospective, single-arm, pre-post study using Canadian Primary Care Sentinel Surveillance Network (CPCSSN) data. CPCSSN is a research network supported by a primary care electronic medical record database, comprising over 1500 physicians and nearly 2 million patients. We are examining changes in care (eg, frequency of contacts, laboratory tests and investigations, referrals, medications prescribed, etc) among adults. We will also examine indicators specific to evidence-based recommendations for care in patients with diabetes and those with heart failure. We will compare rates of outcomes during key periods of the pandemic between March 13, 2020, and December 31, 2022, with equal time periods before the pandemic. Differences will be examined among specific subgroups of adults, including by decade of age, number of comorbidities, and socioeconomic status. Regression models appropriate to outcome distributions will be used to estimate changes, adjusting for potential confounders. This analysis is part of a mixed-methods study with a qualitative component investigating how patients with diabetes with or without concurrent heart failure perceived the impact of the pandemic on access to primary care and health care-related decisions. This study was approved by the Hamilton Integrated Research Ethics Board (14782-C). The start date of this study was October 5, 2022, and the prospective end date is January 31, 2024. As of May 2023, the study cohort (n=875,934) is defined, data cleaning is complete, and exploratory analyses have begun. Extended analyses using 2022 data are planned once the new data becomes available. We will disseminate results through peer-reviewed publications and academic conference, as well as creating evidence briefs, infographics, and a video for policy maker and patient audiences. This study will investigate whether the COVID-19 pandemic has resulted in changes in the provision of primary care in Canada and whether these potential changes have led to gaps in care. This study will also identify patient-level characteristics associated with changes in care patterns across the COVID-19 pandemic. Indicators specific to chronic conditions, namely diabetes and heart failure, will also be explored to determine whether there were changes in care of these conditions. ClinicalTrials.gov NCT05813652; https://clinicaltrials.gov/ct2/show/NCT05813652. RR1-10.2196/49131.

#3667 REVEAL-CKD: PREVALENCE OF UNDIAGNOSED STAGE 3 CHRONIC KIDNEY DISEASE IN AUSTRALIA, BRAZIL, CANADA AND SPAIN

Abstract Background and Aims Chronic kidney disease (CKD) is a progressive condition that affects >850 million people globally. Global clinical guidelines from K-DIGO recommend early CKD identification and management to mitigate disease progression. The REVEAL-CKD study aims to assess the prevalence of, and factors associated with, undiagnosed stage 3 CKD across 11 countries. Here we report data from 4 countries in 4 continents. Method REVEAL-CKD is a multi-national, observational study, using secondary data from electronic medical records (EMR) and claims data. For this analysis, data were extracted from Australian General Practices through Pen CS (Australia), University of Sao Paulo (Brazil), the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) (Canada), and BIGPAC (Spain). The study cohort included patients aged ≥18 years with two consecutive estimated glomerular filtration rate (eGFR) values ≥30 and <60 mL/min/1.73 m2 recorded 91-730 days apart. The date of the second qualifying eGFR was the index date. Patients with no CKD diagnosis code before and up to 6 months after their index date were considered undiagnosed. The prevalence of undiagnosed CKD was calculated as the ratio of undiagnosed patients to all patients meeting the study inclusion criteria. Results The prevalence and baseline characteristics of undiagnosed Stage 3 CKD appear in Table 1. In all 4 countries, the undiagnosed prevalence was >80% and predominantly was more commonly observed in female patients, in patients aged > 65y, and remained high in patients with baseline comorbidities, particularly for those at highest risk of CKD (DM, CVD, HTN). Underreporting of CKD diagnosis in Brazil may be due to the inability to code for >1 condition in a patient. Conclusion These results indicate that a large proportion of patients across four world regions and who have biochemical evidence of early CKD lack a recorded diagnosis. An opportunity exists to identify, diagnose, and implement guideline-directed management for early CKD in order to delay its complications and improve clinical outcomes.

REVEAL-CKD: PREVALENCE OF UNDIAGNOSED STAGE 3 CHRONIC KIDNEY DISEASE IN PATIENTS WITH DIAGNOSED HYPERTENSION IN CANADA

Objective: Hypertension is a risk factor for and complication of chronic kidney disease (CKD), a progressive condition affecting 11.1% of the global population. Early recognition and diagnosis of CKD is crucial for effective management to slow disease progression and improve patient outcomes. This REVEAL-CKD study assessed the prevalence of, and factors associated with, undiagnosed stage 3 CKD in patients with hypertension. Design and method: REVEAL-CKD is an observational study using secondary data from electronic medical records (EMR) and claims data. Data were extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database, that consists of primary care EMR data from approximately 1,500 general practice clinicians across eight provinces and one territory of Canada. The study cohort included patients aged at least 18 years with two consecutive estimated glomerular filtration rate (eGFR) values 30-59 mL/min/1.73m2 recorded 91-730 days apart between 2015-2021. The date of the second qualifying eGFR was the index date. Included patients were required to have hypertension diagnosis code before index. Patients with no CKD diagnosis code before and up to 6 months after index were considered undiagnosed. This study was granted approval by the CPCSSN data access committee (project number: 2022SRE147). Results: Of the 45,914 patients meeting the study inclusion criteria for stage 3 CKD, median age at index date was 77 years (interquartile range [IQR]: 69-84 years) and 19,597 (42.7%) were male. Of these patients, 32,192 (70.1%) also had an existing diagnosis of hypertension. Among patients meeting the criteria for stage 3 CKD and having a diagnosis of hypertension there were 30,523 patients, (94.8%) who did not have a recorded diagnosis of CKD up to 6 months after index. Of these patients, only one fifth was diagnosed with CKD after 4 years. Conclusions: A large proportion of patients in Canada with hypertension and stage 3 CKD do not have a recorded CKD diagnosis, and there is substantial diagnostic delay despite clinical evidence. This likely results in patients not receiving guideline-directed management to reduce the risk of disease progression and adverse clinical outcomes that could have been avoided by early detection of CKD.

HEALTH SURVEILLANCE OF PERSONS LIVING WITH DEMENTIA AND CAREGIVER DYADS

Abstract The relationship between individuals living with dementia and their caregivers is important and can impact their dementia journey. However, limited national longitudinal data exists about the caregiver-person living with dementia dyad. Availability of such data would provide important information about joint trajectories and help to better identify the needs of caregivers and persons living with dementia across the dementia journey. The objective of this study was to develop a linked national longitudinal database of persons living with dementia and their caregivers. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) extracts and de-identifies clinical data from electronic medical records (EMR) from approximately 2 million patients across Canada. CPCSSN data is used to identify persons living with dementia and caregivers willing to participate in the study. CPCSSN data from participating dyads are linked (e.g., chronic and mental health conditions, diagnoses, laboratory test results) and additional information about the experiences of persons living with dementia and their caregivers (e.g., ethnicity, amount and type of care provided, burden, availability of support) are collected yearly using surveys. The growing database contains linked, de-identified, comprehensive information about persons living with dementia and their caregivers that will become a rich source of data for researchers, clinicians, and policymakers. Specifics around how the database was developed, and lessons learned will be discussed as these findings can be used as a template to develop similar linked health surveillance databases.

Characterizing primary care patients with posttraumatic stress disorder using electronic medical records: a retrospective cross-sectional study.

Posttraumatic stress disorder (PTSD) has significant morbidity and economic costs. This study describes the prevalence and characteristics of patients with PTSD using primary care electronic medical record (EMR) data. This retrospective cross-sectional study used EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). This study included 1,574 primary care providers located in 7 Canadian provinces. There were 689,301 patients that visited a CPCSSN provider between 1 January 2017 and 31 December 2019. We describe associations between PTSD and patient characteristics using descriptive statistics, chi-square, and multiple logistic regression models. Among the 689,301 patients included, 8,817 (1.3%, 95% CI 1.2-1.3) had a diagnosis of PTSD. On multiple logistic regression analysis, patients with depression (OR 4.4, 95% CI 4.2-4.7, P < 0.001), alcohol abuse/dependence (OR 1.7, 95% CI 1.6-1.9, P < 0.001), and/or drug abuse/dependence (OR 2.6, 95% CI 2.5-2.8, P < 0.001) had significantly higher odds of PTSD compared with patients without those conditions. Patients residing in community areas considered the most material deprived (OR 2.1, 95% CI 1.5-2.1, P < 0.001) or the most socially deprived (OR 2.8, 95% CI 2.7-5.3, P < 0.001) had higher odds of being diagnosed with PTSD compared with patients in the least deprived areas. The prevalence of PTSD in Canadian primary care is 1.3% (95% CI 1.25-1.31). Using EMR records we confirmed the co-occurrence of PTSD with other mental health conditions within primary care settings suggesting benefit for improved screening and evidence-based resources to manage PTSD.

High cardiovascular disease risk-associated with the incidence of Type 2 diabetes among prediabetics

BackgroundPrediabetes is a risk factor for developing Type 2 diabetes mellitus (T2D). We report on the first cohort study of the association between high cardiovascular diseases (CVD) risk with the incidence of T2D in prediabetics. First, estimate the direct effect of developing T2D on patients with prediabetes who have high CVDs risk; and 2) assess the potential increased risk of developing T2D mediated by statins. MethodsWe conducted a population-based cohort study using a subset of data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from 2000 to 2015. Cox proportional hazards (PH) regressions were conducted to estimate our primary outcome, which is the time to T2D among patients with prediabetes. ResultsFrom the 4995 filtered prediabetic participants identified between 2000 and 2015, 2800 participants were diagnosed with high CVDs risk scores as measured by the Framingham risk score. 2195 participants were non-high CVDs risk controls. The covariate-adjusted hazard ratio (HR) of 1.24 [95% confidence interval (CI), 1.10-1.31] for T2D by CVDs risk among prediabetics was observed. The total effect of CVDs risk on developing T2D was decomposed to a natural direct effect of high CVDs risk HR= 1.18 [95% CI, 1.01-1.48] and an indirect effect through statin therapy of HR= 1.06 [95% CI, 0.97-1.30]. ConclusionPatients with prediabetes and high CVDs risk had a 24% higher chance of developing T2D. The high CVDs risk effect was mediated by statin therapy. Regular monitoring and counselling of prediabetics using statins is likely warranted to prevent the incidence of T2D.

A cross-sectional study evaluating cardiovascular risk and statin prescribing in the Canadian Primary Care Sentinel Surveillance Network database

BackgroundCardiovascular disease (CVD) is a major cause of morbidity and mortality in Canada. Assessment and management of CVD risk is essential in reducing disease burden. This includes both clinical risk factors and socioeconomic factors, though few studies report on socioeconomic status in relation to CVD risk and treatment. The primary objective of this study was to estimate the cardiovascular risk of patients attending primary care practices across Canada; secondly, to evaluate concordance with care indicators suggested by current clinical practice guidelines for statin prescribing according to patients’ cardiovascular risk and socioeconomic status.MethodsThis cross-sectional observational study used the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database, which is comprised of clinical data from primary care electronic medical records. Patients aged 35-75y with at least one visit to their primary care provider between 2012 and 2016 were included. Patients were assigned to a CVD risk category (high, medium, low) and a deprivation quintile was calculated for those with full postal code available. Descriptive analyses were used to determine the proportion of patients in each risk category. Logistic regression was used to evaluate the consistency of statin prescribing according to national clinical guidelines by risk category and deprivation quintile.ResultsA total of 324,526 patients were included. Of those, 116,947 (36%) of patients were assigned to a high CVD risk category, primarily older adults, males, and those with co-morbidities. There were statistically significant differences between least (quintile 1) and most (quintile 5) deprived socioeconomic quintiles, with those at high CVD risk disproportionately in Q5 (odds ratio 1.4). Overall, 48% of high-risk patients had at least one statin prescription in their record. Patients in the lower socioeconomic groups had a higher risk of statin treatment which deviated from clinical guidelines.ConclusionsPrimary care patients who are at high CVD risk are more often male, older, have more co-morbidities and be assigned to more deprived SES quintiles, compared to those at low CVD risk. Additionally, patients who experience more challenging socioeconomic situations may be less likely to receive CVD treatment that is consistent with care guidelines.

Antibiotic prescribing for respiratory tract infection across a national primary care network in 2019.

Respiratory tract infection (RTI) is the leading reason for avoidable antimicrobial use in primary care, yet provider-level feedback on its use is only available in some provinces. The aim of this study was to validate case definitions for RTI across the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) and determine baseline provider-level variability in antimicrobial prescribing in 2019. The RTI case definitions were developed using demographic, diagnostic coding and keywords in electronic medical record. Manual chart abstraction was performed to identify cases of acute otitis media. Remaining RTI definitions were validated using a random sample of 5,164 patients with encounters in 2019. The proportion of patients with an RTI treated with antibiotics was determined by provider, per patient, per episode and per patient encounter. Negative predictive value, positive predictive value and prevalence were as follows: 1.00 (0.99-1.00), 0.99 (0.96-0.99) and 4.14% (4.10-4.19) for common cold; 1.00 (0.99-1.00), 0.94 (0.88-0.98) and 1.09% (1.07-1.12) for acute otitis media; 0.98 (0.96-1.00), 0.93 (0.87-0.97) and 1.2% (1.18-1.22) for acute pharyngitis; 0.99 (0.99-1.00), 0.88 (0.81-0.93) and 1.99% (1.96-2.02) for sinusitis; 0.99 (0.97-0.99), 0.95 (0.89-0.98) and 4.01% (3.97-4.05) for acute bronchitis/asthma. By provider, median (interquartile range [IQR]) proportion treated with antibiotics (per patient) was 6.72 (14.92) for common cold, 64.29 (40.00) for acute otitis media, 20.00 (38.89) for pharyngitis, 54.17 (38.09) for sinusitis, 8.33 (20.00) for acute bronchitis/asthma and 21.10 (20.56) for overall RTI. The CPCSSN can provide national surveillance of antimicrobial prescribing practices for RTI across primary care. Baseline variability underscores the need for provider feedback and quality improvement.

Characterizing posttraumatic stress disorder in primary care using electronic medical records: a retrospective cohort study.

Posttraumatic stress disorder (PTSD) is a chronic mental health disorder associated with significant morbidity and economic cost. Primary care providers are frequently involved in the ongoing management of patients experiencing PTSD, as well as related comorbid conditions. Despite recognized need to enhance PTSD management in primary care settings, knowledge regarding its prevalence in these settings is limited. To apply a validated case definition of PTSD to electronic medical records (EMRs) of family physicians and nurse practitioners participating in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Retrospective cross-sectional study. This study accessed de-identified EMR from 1,574 primary care providers participating in the CPCSSN. The study population included all patients with at least one visit to a primary care provider participating in the CPCSSN between January 1, 2017 and December 31, 2019 (N = 689,301). We identified patients with PTSD and described associations between PTSD and patient characteristics (including sex, age, geography, depression, anxiety, medical comorbidities, substance use and social and material deprivation) using multivariable logistic regression models. Among the 689,301 patients meeting inclusion criteria, 8,213 (1.2%) had a diagnosis of PTSD. Patients with PTSD were significantly more likely to reside in an urban location (84.9% vs. 80.4%; p-value <.0001) and have one or more comorbid conditions (90.8% vs. 70.2%; p-value <.0001). On multivariable logistic regression analysis, patients with depression (OR 4.8; 95%CI 4.6-5.1) and anxiety (OR 2.2; 95%CI 2.1-2.3) had increased odds of having PTSD compared to patients without depression or anxiety. Patients with alcohol (OR 1.8; 95%CI 1.6-1.9) and drug (OR 3.1; 95%CI 2.9-3.3) use disorders had significantly higher odds of PTSD compared to patients without these disorders. Patients in the most deprived neighborhoods based on census data had 4.2 times higher odds of have PTSD (95%CI 3.2-5.43) compared to patients in the least deprived areas. This is the first study to describe PTSD prevalence in a large Canadian sample of primary care patients using an EMR-based case definition. Characterizing patients with PTSD in primary care may improve disease surveillance and inform the interdisciplinary care required to manage PTSD symptoms.

Risk factors for incidence of dementia in primary care practice: a retrospective cohort study in older adults.

The dementias are long-term, chronic conditions caused by progressive neurological degeneration. Current literature suggests that cardiovascular disease risk factors may contribute to the onset of dementia; however, evidence of these associations is inconsistent. This study aimed to examine the impact of risk factors on dementia onset in older adults diagnosed and managed in Canadian primary care settings. A retrospective cohort study was employed utilizing electronic medical records data in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Patients aged 65+ years with no dementia diagnosis at baseline who were followed from 2009 to 2017 with a run-in year to exclude existing undiagnosed dementia cases. Multivariate Cox proportional hazard models were used to estimate risk. Age was associated with an increased incidence risk of dementia in both examined age groups: 65-79 years (13%) and 80+ years (5%). History of depression increased dementia risk by 38% and 34% in the age groups. There were significant associations with lower social deprivation area quintile, smoking history, osteoarthritis, and diabetes mellitus in patients aged 65-79 years but not in those aged 80+ years. Sex, hypertension, obesity, dyslipidemia, and the use of antihypertensive medications and statins were not associated with risk of incident dementia diagnosis. The association between chronic health conditions and dementia onset is complicated. Primary care electronic medical record data might be useful for research in this topic, though follow-up time is still relatively short to observe a clear causal relationship. Future studies with more complete data may provide evidence for dementia preventive strategies within primary care practice.

Using the Edmonton Obesity Staging System in the real world: a feasibility study based on cross-sectional data.

Background:The Edmonton Obesity Staging System (EOSS) combined with body mass index (BMI) enables improved functional and prognostic assessment for patients. To facilitate application of the EOSS in practice, we aimed to create tools for capturing comorbidity assessments in electronic medical records and for automating the calculation of a patient’s EOSS stage.Methods:In this feasibility study, we used cross-sectional data to create a clinical dashboard to calculate and display the relation between BMI and EOSS and the prevalence of related comorbidities. We obtained data from the Northern Alberta Primary Care Research Network and the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). We included patients at least 18 years of age with BMI between 30 and 60 who visited a network clinic between July 2016 and July 2019. We calculated descriptive statistics and used stepwise ordinary least squares regression to assess the contributions of age, sex and BMI to EOSS variation.Results:We created a clinical dashboard using the CPCSSN data presentation tool. Of the total 31 496 patients included in the study, 23 460 had a BMI of at least 30; BMI was unavailable for 8036 patients. Within each EOSS disease severity stage, there were similar proportions of patients from each BMI class (e.g., patients with EOSS stage 2 included 51.8% of those with BMI class I, 55.3% of those with BMI class II and 58.8% of those with BMI class III).Interpretation:Using data from primary care electronic medical records, it was feasible to create a clinical dashboard for obesity that highlighted the severity and stage of obesity. Making this information easily accessible for individual clinical care and practice-level quality improvement may advance obesity care.

Use of electronic medical records to describe the prevalence of allergic diseases in Canada

BackgroundLeveraging the data management resources of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is a viable approach for describing the prevalence of allergic disease documented in primary care settings.MethodsThe dataset used for this study was inclusive of data from EMR initiation up to Dec 31st 2018. The sample included 1235 primary care providers representing 1,556,472 patients across Canada.ResultsIn total, there were 536,005 patients with a documented allergy that fit into one of the 10 suggested categories. The allergy table includes 718,032 distinct entries representing 564,242 unique patients, which is 36.3% of the patients within the CPCSSN repository. The most common allergies recorded were drug allergy (39.0%), beta-lactam allergy (14.4%), environmental allergy (11.0%), and food allergy (8.0%). Anticipated upcoming studies include physician-documented drug allergy with a focus on beta-lactam allergy, as well as stinging insect allergy, among others. To our knowledge, these will also be the first such prevalence studies of primary care physician-documented allergic disease done in Canada.InterpretationThe CPCSSN dataset represents electronic medical records from 1.5 million patients across Canada including documentation of allergic diseases. This dataset provides a national representative population to describe and characterize Canadian patients with common allergic conditions. This robust dataset provides the opportunity for health surveillance, and in particular data to explore the impact of allergic disease on primary care practice.Trial registrationNot applicable.

Development of a case definition for hearing loss in community-based older adults: a cross-sectional validation study.

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.