Care Electronic Medical Record Data Research Articles

Prevalence and management of dyslipidemia in primary care practices in Canada.

To estimate the prevalence of dyslipidemia and to describe its management in Canadian primary care. Retrospective cohort study using primary care electronic medical record data. Canada. Adults aged 40 years or older who saw a Canadian Primary Care Sentinel Surveillance Network contributor between January 1, 2018, and December 31, 2019. Presence or absence of dyslipidemia as identified by a validated case definition and the treatment status of patients identified as having dyslipidemia based on having been prescribed a lipid-lowering agent (LLA). In total, 50.0% of the 773,081 patients 40 years of age or older who had had a primary care visit in 2018 or 2019 were identified as having dyslipidemia. Dyslipidemia was more prevalent in patients 65 or older (61.5%), in males (56.7%) versus females (44.7%), and in those living in urban areas (50.0%) versus rural areas (45.2%). In patients with documented dyslipidemia, 42.8% had evidence of treatment with an LLA. Stratifying patients by Framingham risk score revealed that those in the high-risk category were more likely to have been prescribed an LLA (65.0%) compared with those in the intermediate-risk group (48.7%) or the low-risk group (22.8%). The strongest determinants of receiving LLA treatment for dyslipidemia include sex, with males being 1.95 times more likely to have been treated compared with females (95% CI 1.91 to 1.98; P<.0001); and body mass index, with those with obesity having a significantly increased likelihood of being treated with an LLA (adjusted odds ratio of 1.36, 95% CI 1.32 to 1.41; P<.0001). This study provides an updated look at the prevalence and treatment of dyslipidemia among Canadians. Half of patients aged 40 years or older have dyslipidemia, with an even higher prevalence observed among adults aged 65 years or older, males, and those with obesity or other chronic conditions. There are still gaps in treatment among those with documented dyslipidemia, principally among those calculated to have high or intermediate Framingham risk scores. Particular attention should also be paid to those at higher risk for not receiving treatment, including female patients and those within normal body mass index ranges.

Identifying patients with diagnosed cirrhosis in administrative health databases: a validation study.

Case ascertainment algorithms were developed and validated to identify people living with cirrhosis in administrative health data in Manitoba, Canada using primary care electronic medical records (EMR) to define the reference standards. We linked provincial administrative health data to primary care EMR data. The validation cohort included 116,675 Manitobans aged >18 years with at least one primary care visit between April 1998 and March 2015. Hospital records, physician billing claims, vital statistics, and prescription drug data were used to develop and test 93 case-finding algorithms. A validated case definition for primary care EMR data was the reference standard. We estimated sensitivity, specificity, positive and negative predictive values (PPV, NPV), Youden's index, area under the receiver operative curve, and their 95% confidence intervals (CIs). A total of 116,675 people were in the validation cohort. The prevalence of cirrhosis was 1.4% (n = 1593). Algorithm sensitivity estimates ranged from 32.5% (95% CI 32.2-32.8) to 68.3% (95% CI 68.0-68.9) and PPV from 17.4% (95% CI 17.1-17.6) to 23.4% (95% CI 23.1-23.6). Specificity (95.5-98.2) and NPV (approximately 99%) were high for all algorithms. The algorithms had slightly higher sensitivity estimates among men compared with women, and individuals aged ≥45 years compared to those aged 18-44 years. Cirrhosis algorithms applied to administrative health data had moderate validity when a validated case definition for primary care EMRs was the reference standard. This study provides algorithms for identifying diagnosed cirrhosis cases for population-based research and surveillance studies.

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.

Treatment Patterns, Healthcare Utilization and Clinical Outcomes of Patients with Chronic Obstructive Pulmonary Disease Initiating Single-Inhaler Long-Acting β2-Agonist/Long-Acting Muscarinic Antagonist Dual Therapy in Primary Care in England.

Selection of treatments for patients with chronic obstructive pulmonary disease (COPD) may impact clinical outcomes, healthcare resource use (HCRU) and direct healthcare costs. We aimed to characterize these outcomes along with treatment patterns, for patients with COPD following initiation of single-inhaler long-acting muscarinic antagonist/long-acting β2-agonist (LAMA/LABA) dual therapy in the primary care setting in England. This retrospective cohort study used linked primary care electronic medical record data (Clinical Practice Research Datalink-Aurum) and secondary care administrative data (Hospital Episode Statistics) in England to assess outcomes for patients with COPD who had a prescription for one of four single-inhaler LAMA/LABA dual therapies between 1st June 2015-31st December 2018 (indexing period). Outcomes were assessed during a 12-month follow-up period from the index date (date of earliest prescription of a single-inhaler LAMA/LABA within the indexing period). Incident users were those without previous LAMA/LABA dual therapy prescriptions prior to index; this manuscript focuses on a subset of incident users: non-triple therapy users (patients without concomitant inhaled corticosteroid use at index). Of 10,991 incident users included, 9888 (90.0%) were non-triple therapy users, indexed on umeclidinium/vilanterol (n=4805), aclidinium/formoterol (n=2109), indacaterol/glycopyrronium (n=1785) and tiotropium/olodaterol (n=1189). At 3 months post-index, 63.3% of non-triple therapy users remained on a single-inhaler LAMA/LABA, and 22.1% had discontinued inhaled therapy. Most patients (86.9%) required general practitioner consultations in the first 3 months post-index. Inpatient stays were the biggest contributor to healthcare costs. Acute exacerbations of COPD (AECOPDs), adherence, time-to-triple therapy, time-to-first on-treatment moderate-to-severe AECOPD, time-to-index treatment discontinuation, HCRU and healthcare costs were similar across indexed therapies. Patients initiating treatment with single-inhaler LAMA/LABA in primary care in England were unlikely to switch treatments in the first three months following initiation, but some may discontinue respiratory medication. Outcomes were similar across indexed treatments.

Older persons living with dementia and their use of acute care services over 2 years in Alberta

To characterize transitions to acute and residential care and identify variables associated with specific transitions among community-based persons living with dementia (PLWD). Retrospective cohort study using primary care electronic medical record data linked with health administrative data. Alberta. Adults aged 65 years or older living in the community who had been diagnosed with dementia and who saw a Canadian Primary Care Sentinel Surveillance Network contributor between January 1, 2013, and February 28, 2015. All emergency department visits, hospitalizations, residential care (supportive living and long-term care) admissions, and deaths within a 2-year follow-up period. In total, 576 PLWD were identified who had a mean (SD) age of 80.4 (7.7) years; 55% were female. In 2 years, 423 (73.4%) had at least 1 transition and, of these, 111 (26.2%) had 6 or more. Emergency department visits, including multiple visits, were common (71.4% had ≥1, 12.1% had ≥4). Of those hospitalized (43.8%), nearly all were admitted from the emergency department; the average (SD) length of stay was 23.6 (35.8) days, and 32.9% had at least 1 alternate level of care day. In total, 19.3% entered residential care, most admitted from hospital. Those admitted to hospital and those admitted to residential care were older and had greater historical health system use, including home care. One-quarter of the sample did not have any transitions (or die) during follow-up; they were typically younger and had limited historical health system use. Older PLWD experienced frequent, and frequently compound, transitions that have implications for them, their family members, and the health system. There was also a large proportion without transitions suggesting that appropriate supports enable PLWD to do well in their own communities. The identification of PLWD who are at risk of or who make frequent transitions may allow for more proactive implementation of community-based supports and smoother transitions to residential care.

Considerations for Creating a Restricted Data Environment with Complete Primary Care Electronic Medical Record Data.

Background: Historically, primary care databases have been limited to subsets of the full electronic medical record (EMR) data to maintain privacy. With the progression of artificial intelligence (AI) techniques (i.e., machine learning, natural language processing, and deep learning), practice-based research networks (PBRNs) have an opportunity to utilize previously difficult to access data to conduct essential primary care research and quality improvement. However, to ensure patient privacy and data security, novel infrastructure and processes are required. We describe the considerations related to accessing complete EMR data on a large-scale within a Canadian PBRN. Setting: Queen's Family Medicine Restricted Data EnviroNment (QFAMR), Department of Family Medicine (DFM), Queen's University, Canada Methods: QFAMR is a central holding repository hosted at the Centre for Advanced Computing at Queen's University. Complete, de-identified EMR records (e.g., full chart notes, PDFs, and free text) from approximately 18,000 patients from Queen's DFM can be accessed. An iterative process over 2021-2022 was used to develop QFAMR infrastructure in collaboration with Queen's DFM members and stakeholders. Results: In May 2021, the QFAMR standing research committee was established for review and approval of all potential projects. DFM members consulted with Queen's University computing, privacy, legal, and ethics experts to develop data access processes, policies and governance, agreements, and associated documents. Initial QFAMR projects involved applying and improving de-identification processes for DFM-specific full-chart notes. Five major elements were recurrent throughout the QFAMR development process: data and technology, privacy, legal documentation, decision-making frameworks, and ethics and consent. Conclusion: Overall, the development of the QFAMR has provided a secure platform to successfully access data-rich primary care EMR records without data ever leaving Queen's University. Although accessing complete primary care EMR records has certain technological, privacy, legal, and ethical considerations and challenges, QFAMR is a significant opportunity to conduct novel and innovative primary care research.

Disruptions in Primary Care among People with Schizophrenia in Ontario, Canada, During the COVID-19 Pandemic.

To investigate how primary care access, intensity and quality of care changed among patients living with schizophrenia before and after the onset of the COVID-19 pandemic in Ontario, Canada. This cohort study was performed using primary care electronic medical record data from the University of Toronto Practice-Based Research Network (UTOPIAN), a network of > 500 family physicians in Ontario, Canada. Data were collected during primary care visits from 2643 patients living with schizophrenia. Rates of primary care health service use (in-person and virtual visits with family physicians) and key preventive health indices indicated in antipsychotic monitoring (blood pressure readings, hemoglobin A1c, cholesterol and complete blood cell count [CBC] tests) were measured and compared in the 12 months before and after onset of the COVID-19 pandemic. Access to in-person care dropped with the onset of the COVID-19 pandemic. During the first year of the pandemic only 39.5% of patients with schizophrenia had at least one in-person visit compared to 81.0% the year prior. There was a corresponding increase in virtual visits such that 78.0% of patients had a primary care appointment virtually during the pandemic period. Patients prescribed injectable antipsychotics were more likely to continue having more frequent in-person appointments during the pandemic than patients prescribed only oral or no antipsychotic medications. The proportion of patients who did not have recommended tests increased from 41.0% to 72.4% for blood pressure readings, from 48.9% to 60.2% for hemoglobin A1c, from 57.0% to 67.8% for LDL cholesterol and 45.0% to 56.0% for CBC tests during the pandemic. There were substantial decreases in preventive care after the onset of the pandemic, although primary care access was largely maintained through virtual care. Addressing these deficiencies will be essential to promoting health equity and reducing the risk of poor health outcomes.

Timely access to Canadian primary care electronic medical record data for secondary use

The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 5.120 (2020 JCR, received in June 2021)The IJIC 20th Anniversary Issue was published in 2021.

Impact of the COVID-19 pandemic on routine immunization coverage in children under 2 years old in Ontario, Canada: A retrospective cohort study

BackgroundThe COVID-19 pandemic has caused a disruption in childhood immunization coverage around the world. This study aimed to determine the change in immunization coverage for children under 2 years old in Ontario, Canada, comparing time periods pre-pandemic to during the first year of the pandemic. MethodsObservational retrospective open cohort study, using primary care electronic medical record data from the University of Toronto Practice-Based Research Network (UTOPIAN) database, from January 2019 to December 2020. Children under 2 years old who had at least 2 visits recorded in UTOPIAN were included. We measured up-to-date (UTD) immunization coverage rates, overall and by type of vaccine (DTaP-IPV-Hib, PCV13, Rota, Men-C-C, MMR, Var), and on-time immunization coverage rates by age milestone (2, 4, 6, 12, 15, 18 months). We compared average coverage rates over 3 periods of time: January 2019-March 2020 (T1); March-July 2020 (T2); and August-December 2020 (T3). Results12,313 children were included. Overall UTD coverage for all children was 71.0% in T1, dropped by 5.7% (95% CI: −6.2, −5.1) in T2, slightly increased in T3 but remained lower than in T1. MMR vaccine UTD coverage slightly decreased in T2 and T3 by approximately 2%. The largest decreases were seen at ages 15-month and 18-month old, with drops in on-time coverage of 14.7% (95% CI: −18.7, −10.6) and 16.4% (95% CI: −20.0, −12.8) respectively during T2. When stratified by sociodemographic characteristics, no specific subgroup of children was found to have been differentially impacted by the pandemic. ConclusionChildhood immunization coverage rates for children under 2 years in Ontario decreased significantly during the early period of the COVID-19 pandemic and only partially recovered during the rest of 2020. Public health and educational interventions for providers and parents are needed to ensure adequate catch-up of delayed/missed immunizations to prevent potential outbreaks of vaccine-preventable diseases.

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.

AI in predicting COPD in the Canadian population

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that produces non-reversible airflow limitations. Approximately 10% of Canadians aged 35 years or older are living with COPD. Primary care is often the first contact an individual will have with the healthcare system providing acute care, chronic disease management, and services aimed at health maintenance. This study used Electronic Medical Record (EMR) data from primary care clinics in seven provinces across Canada to develop predictive models to identify COPD in the Canadian population. The comprehensive nature of this primary care EMR data containing structured numeric, categorical, hybrid, and unstructured text data, enables the predictive models to capture symptoms of COPD and discriminate it from diseases with similar symptoms. We applied two supervised machine learning models, a Multilayer Neural Networks (MLNN) model and an Extreme Gradient Boosting (XGB) to identify COPD patients. The XGB model achieved an accuracy of 86% in the test dataset compared to 83% achieved by the MLNN. Utilizing feature importance, we identified a set of key symptoms from the EMR for diagnosing COPD, which included medications, health conditions, risk factors, and patient age. Application of this XGB model to primary care structured EMR data can identify patients with COPD from others having similar chronic conditions for disease surveillance, and improve evidence-based care delivery.

Correction: Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis.

Correction: Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis.

A data quality assessment to inform hypertension surveillance using primary care electronic medical record data from Alberta, Canada

BackgroundHypertension is a common chronic condition affecting nearly a quarter of Canadians. Hypertension surveillance in Canada typically relies on administrative data and/or national surveys. Routinely-captured data from primary care electronic medical records (EMRs) are a complementary source for chronic disease surveillance, with longitudinal patient-level details such as sociodemographics, blood pressure, weight, prescribed medications, and behavioural risk factors. As EMR data are generated from patient care and administrative tasks, assessing data quality is essential before using for secondary purposes. This study evaluated the quality of primary care EMR data from one province in Canada within the context of hypertension surveillance.MethodsWe conducted a cross-sectional, descriptive study using primary care EMR data collected by two practice-based research networks in Alberta, Canada. There were 48,377 adults identified with hypertension from 53 clinics as of June 2018. Summary statistics were used to examine the quality of data elements considered relevant for hypertension surveillance.ResultsPatient year of birth and sex were complete, but other sociodemographic information (ethnicity, occupation, education) was largely incomplete and highly variable. Height, weight, body mass index and blood pressure were complete for most patients (over 90%), but a small proportion of outlying values indicate data inaccuracies were present. Most patients had a relevant laboratory test present (e.g. blood glucose/glycated hemoglobin, lipid profile), though a very small proportion of values were outside a biologically plausible range. Details of prescribed antihypertensive medication, such as start date, strength, dose, frequency, were mostly complete. Nearly 80% of patients had a smoking status recorded, though only 66% had useful information (i.e. categorized as current, past, or never), and less than half had their alcohol use described; information related to amount, frequency or duration was not available.ConclusionsBlood pressure and prescribed medications in primary care EMR data demonstrated good completeness and plausibility, and contribute valuable information for hypertension epidemiology and surveillance. The use of other clinical, laboratory, and sociodemographic variables should be used carefully due to variable completeness and suspected data errors. Additional strategies to improve these data at the point of entry and after data extraction (e.g. statistical methods) are required.

Development of a prognostic prediction model to estimate the risk of multiple chronic diseases: constructing a copula-based model using Canadian primary care electronic medical record data.

IntroductionThe ability to estimate risk of multimorbidity will provide valuable information to patients and primary care practitioners in their preventative efforts. Current methods for prognostic prediction modelling are insufficient for the estimation of risk for multiple outcomes, as they do not properly capture the dependence that exists between outcomes.ObjectivesWe developed a multivariate prognostic prediction model for the 5-year risk of diabetes, hypertension, and osteoarthritis that quantifies and accounts for the dependence between each disease using a copula-based model.MethodsWe used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from 2009 onwards, a collection of electronic medical records submitted by participating primary care practitioners across Canada. We identified patients 18 years and older without all three outcome diseases and observed any incident diabetes, osteoarthritis, or hypertension within 5-years, resulting in a large retrospective cohort for model development and internal validation (n=425,228). First, we quantified the dependence between outcomes using unadjusted and adjusted Ø coefficients. We then estimated a copula-based model to quantify the non-linear dependence between outcomes that can be used to derive risk estimates for each outcome, accounting for the observed dependence. Copula-based models are defined by univariate models for each outcome and a dependence function, specified by the parameter θ. Logistic regression was used for the univariate models and the Frank copula was selected as the dependence function.ResultsAll outcome pairs demonstrated statistically significant dependence that was reduced after adjusting for covariates. The copula-based model yielded statistically significant θ parameters in agreement with the adjusted and unadjusted Ø coefficients. Our copula-based model can effectively be used to estimate trivariate probabilities.DiscussionQuantitative estimates of multimorbidity risk inform discussions between patients and their primary care practitioners around prevention in an effort to reduce the incidence of multimorbidity.

Feasibility of identifying and describing the burden of early-onset metabolic syndrome in primary care electronic medical record data: a cross-sectional analysis.

The prevalence of metabolic syndrome is growing worldwide, yet remains underinvestigated in Canadian young adults. We sought to explore the use of a harmonized case definition specific to early-onset metabolic syndrome and determine its feasibility in assessing the prevalence of metabolic syndrome among electronic medical record (EMR) data of young adults in Northern Alberta. We conducted a cross-sectional study using a sample of EMR data from young adult patients aged 18-40 years and residing in Northern Alberta, who had an encounter with a participating primary care clinic between June 29, 2015, and June 29, 2018. Physical examination, laboratory investigation and disease diagnosis data were collected. A case definition and algorithm were developed to assess the feasibility of identifying metabolic syndrome, including measures for body mass index (BMI), blood pressure (BP), dysglycemia, hypertriglyceridemia, high-density lipoprotein cholesterol, diabetes and hypertension. Among 15 766 young adults, the case definition suggested the prevalence of metabolic syndrome was 4.4%, 95% confidence interval (CI) 4.1%-4.7%. The most frequent 3-factor combination (41.6%, 95% CI 37.9%-45.3%) of metabolic syndrome criteria consisted of being overweight or obese, having elevated BP and hypertriglyceridemia. Half of metabolic syndrome cases (51.3%, 95% CI 47.6%-55.0%) were missing measures for fasting blood glucose, and one-fifth were missing a hemoglobin A1c (HbA1c) level. Notably, most young adults with a BMI of 25 or greater were missing HbA1c (68.7%, 95% CI 67.6%-69.8%), fasting blood glucose (84.0%, 95% CI 83.2%-84.8%) and triglyceride testing (79.0%, 95% CI 78.1%-79.9%). We have shown that our case definition is feasible in identifying early-onset metabolic syndrome using EMR data; however, the degree of missing data limits the feasibility in assessing prevalence. Further investigation is required to validate this case definition for metabolic syndrome in the EMR data, which may involve comparing this definition to other validated metabolic syndrome case definitions.

Validation of an EMR algorithm to measure the prevalence of ADHD in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN)

BackgroundBuilding 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.MethodsThis 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.ResultsThe 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.ConclusionOverall, 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.

Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology

BackgroundWe developed and evaluated a prognostic prediction model that estimates osteoarthritis risk for use by patients and practitioners that is designed to be appropriate for integration into primary care health information technology systems. Osteoarthritis, a joint disorder characterized by pain and stiffness, causes significant morbidity among older Canadians. Because our prognostic prediction model for osteoarthritis risk uses data that are readily available in primary care settings, it supports targeting of interventions delivered as part of clinical practice that are aimed at risk reduction. MethodsWe used the CPCSSN (Canadian Primary Sentinel Surveillance Network) database, which contains aggregated electronic health information from a cohort of primary care practices, to develop and evaluate a prognostic prediction model to estimate 5-year osteoarthritis risk, addressing contextual challenges of data availability and missingness. We constructed a retrospective cohort of 383,117 eligible primary care patients who were included in the cohort if they had an encounter with their primary care practitioner between 1 January 2009 and 31 December 2010. Patients were excluded if they had a diagnosis of osteoarthritis prior to their first visit in this time period. Incident cases of osteoarthritis were observed. The model was constructed to predict incident osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Evaluation of the model used internal 10-fold cross-validation; we argue that internal validation is particularly appropriate for a model that is to be integrated into the same context from which the data were derived. ResultsThe resulting prediction model for 5-year risk of osteoarthritis diagnosis demonstrated state-of-the-art discrimination (estimated AUROC 0.84) and good calibration (assessed visually.) The model relies only on information that is readily available in Canadian primary care settings, and hence is appropriate for integration into Canadian primary care health information technology. ConclusionsIf the contextual challenges arising when using primary care electronic medical record data are appropriately addressed, highly discriminative models for osteoarthritis risk may be constructed using only data commonly available in primary care. Because the models are constructed from data in the same setting where the model is to be applied, internal validation provides strong evidence that the resulting model will perform well in its intended application.

Diabetes prevalence and demographics in 25 First Nations communities in northwest Ontario (2014-2017).

First Nations communities are known to have high rates of diabetes. The rural First Nations communities in northwest (NW) Ontario are particularly affected. Regional studies in 1985 and 1994 found a high prevalence of diabetes. More recently, they are estimated to have the highest prevalence in Ontario at 19%, double the provincial norm. The purpose of this study is to examine the epidemiology and prevalence of diabetes in the total population and cardiovascular comorbidities in the adult population of 25 First Nations communities in NW Ontario. This retrospective diabetes prevalence study used primary care electronic medical record data for a 3-year period, 1 August 2014-31 July 2017. Diabetes prevalence was calculated for both the total and the adult (18+) populations and comorbid hypertension and dyslipidaemia were identified in adults. The age-adjusted diabetes prevalence for the total population was 15.1% versus a Canadian prevalence of 8.8%. The age-adjusted adult prevalence was 14.1%, double Canada's average of 7.1%. The average age of adults with diabetes was 52 years (±14.9); 57% were female. Comorbid hypertension (58%) and dyslipidaemia (73%) were common. Metformin was the most commonly used medication (58%), followed by insulin/analogues (23%) and sulphonylureas (13%). The diabetes prevalence in the First Nations population of NW Ontario is double Canada's norm. Addressing it will require addressing relevant social determinants of health, including poverty and food security.

Predicting dementia with routine care EMR data.

Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia. Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis, prescriptions, and medical notes. Each of these three data sets is used to construct an individual model along with a combined model which is derived by using all three data sets. Human-interpretable data processing and ML techniques are selected in order to facilitate adoption of the proposed model by health care providers from multiple institutions. The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data. Moreover, the analysis of the models identified important predictors for dementia. Some of these predictors (e.g., age and hypertensive disorders) are already confirmed by the literature while others, especially the ones derived from the unstructured medical notes, require further clinical analysis.

Clinical risk groups and patient complexity: a case study with a primary care clinic in Alberta

Complexity and risk adjustment are two strategies employed to understand chronic disease and healthcare cost within patient populations. There is a general assumption that the data applied to risk adjustment models, such as the clinical risk groups (CRG) is sufficient to infer patient complexity. Our aim in this study was to compare the calculated complexity of a patient population using the 3 M™ CRG software to complexity data extracted from community-based primary care electronic medical records (EMR). We found that the distribution of the 3 M™ CRG health status was significantly different from the primary care EMR health status distribution, and that the number and type of chronic conditions identified differed between the two methods. We calculated a new variable that combined the information from the 3 M™ CRG software with the primary care EMR data. The distribution of the Combined-CRG distribution was significantly different from the 3 M™ CRG software; specifically, we saw many patients originally classified as being healthy or having minor chronic condition(s) re-categorized into having significant chronic condition(s). The CRG health statuses alone may be sufficient to predict future health expenditures, but caution is warranted if CRGs are to be used to infer complexity of the patient population.