Primary Care Electronic Medical Record Research Articles

Integrating User Preferences for Asthma Tools and Clinical Guidelines Into Primary Care Electronic Medical Records: Mixed Methods Study.

Asthma is a chronic respiratory disease that poses a substantial burden on individuals and the health care system. Despite published national guidelines for the diagnosis and management of asthma, considerable care gaps exist. Suboptimal adherence to asthma diagnosis and management guidelines contributes to poor patient outcomes. The integration of electronic tools (eTools) into electronic medical records (EMRs) represents a knowledge translation opportunity to support best practices. The purpose of this study was to determine how best to integrate evidence-based asthma eTools into primary care EMRs across Ontario and Canada to improve adherence to guidelines as well as measure and monitor performance. In total, 2 focus groups comprising physicians and allied health professionals who were considered experts in primary care, asthma, and EMRs were convened. One focus group also included a patient participant. Focus groups used a semistructured discussion-based format to consider the optimal methods for integrating asthma eTools into EMRs. Discussions were held on the web via Microsoft Teams (Microsoft Corp). The first focus group discussed integrating asthma indicators into EMRs using eTools, and participants completed a questionnaire evaluating the clarity, relevance, and feasibility of collecting asthma performance indicator data at the point of care. The second focus group addressed how to incorporate eTools for asthma into a primary care setting and included a questionnaire evaluating the perceived utility of various eTools. Focus group discussions were recorded and analyzed using thematic qualitative analysis. The responses to focus group questionnaires were assessed using descriptive quantitative analysis. Qualitative analysis of the 2 focus group discussions revealed 7 key themes: designing outcome-oriented tools, gaining stakeholder trust, facilitating open lines of communication, prioritizing the end user, striving for efficiency, ensuring adaptability, and developing within existing workflows. In addition, 24 asthma indicators were rated according to clarity, relevance, feasibility, and overall usefulness. In total, 5 asthma performance indicators were identified as the most relevant. These included smoking cessation support, monitoring using objective measures, the number of emergency department visits and hospitalizations, assessment of asthma control, and presence of an asthma action plan. The eTool questionnaire responses revealed that the Asthma Action Plan Wizard and Electronic Asthma Quality of Life Questionnaire were perceived to be the most useful in primary care. Primary care physicians, allied health professionals, and patients consider that eTools for asthma care present a unique opportunity to improve adherence to best-practice guidelines in primary care and collect performance indicators. The strategies and themes identified in this study can be leveraged to overcome barriers associated with asthma eTool integration into primary care EMRs. The most beneficial indicators and eTools, along with the key themes identified, will guide future asthma eTool implementation.

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.

Can Patients with Dementia Be Identified in Primary Care Electronic Medical Records Using Natural Language Processing?

The online version contains supplementary material available at 10.1007/s41666-023-00125-6.

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.

Using ICD-9 diagnostic codes for external validation of topic models derived from primary care electronic medical record clinical text data.

Background/Objectives: Unsupervised topic models are often used to facilitate improved understanding of large unstructured clinical text datasets. In this study we investigated how ICD-9 diagnostic codes, collected alongside clinical text data, could be used to establish concurrent-, convergent- and discriminant-validity of learned topic models. Design/Setting: Retrospective open cohort design. Data were collected from primary care clinics located in Toronto, Canada between 01/01/2017 through 12/31/2020. Methods: We fit a non-negative matrix factorization topic model, with K = 50 latent topics/themes, to our input document term matrix (DTM). We estimated the magnitude of association between each Boolean-valued ICD-9 diagnostic code and each continuous latent topical vector. We identified ICD-9 diagnostic codes most strongly associated with each latent topical vector; and qualitatively interpreted how these codes could be used for external validation of the learned topic model. Results: The DTM consisted of 382,666 documents and 2210 words/tokens. We correlated concurrently assigned ICD-9 diagnostic codes with learned topical vectors, and observed semantic agreement for a subset of latent constructs (e.g. conditions of the breast, disorders of the female genital tract, respiratory disease, viral infection, eye/ear/nose/throat conditions, conditions of the urinary system, and dermatological conditions, etc.). Conclusions: When fitting topic models to clinical text corpora, researchers can leverage contemporaneously collected electronic medical record data to investigate the external validity of fitted latent variable models.

An analysis of sex differences and socioeconomic deprivation among Canadian children with high blood pressure: a retrospective, cross-sectional study.

Understanding which children are at highest risk for high blood pressure (HBP) can inform surveillance and treatment. This study evaluated sex differences in childhood HBP and its associations with socioeconomic status. This retrospective cross-sectional study assessed 74,233 children with data from a national primary care electronic medical record database. Differences between sex and material and social deprivation scores for children with and without HBP were examined. Covariates included age, BMI z-score, diabetes, hyperlipidemia, and depression. HBP was defined as > 90th percentile for < 13-year-olds, and ≥ 120/80 for age ≥ 13years on 2 separate occasions between 2010 and 2017. The prevalence of HBP was 10.2% in males and 7.6% in females (p < 0.0001). Children with HBP had higher BMI z-scores (0.66 vs. 0.18, p < 0.0001), and higher rates of diabetes (1.31 vs. 0.54%, p < 0.0001), depression (9.89 vs. 7.11%, p < 0.0001), and hyperlipidemia (2.82 vs. 0.86%, p < 0.0001). In univariate regression analyses, boys in the most materially deprived quintile had increased odds of HBP (OR 1.24 (95% CI 1.08-1.43)), whereas females did not (OR 1.11 (95% CI 0.95-1.29)). In multivariate regression, male sex was associated with HBP with adjusted OR of 1.39 (95% CI 1.24-1.55). After statistical adjustment, material deprivation was no longer significant (aOR 1.05, 95% CI 0.94-1.17). Male sex is associated with HBP in Canadian children. This study also suggests a possible association between material deprivation and HBP, particularly in boys. Further study is required to better understand this relationship. A higher resolution version of the Graphical abstract is available as Supplementary information.

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study

The availability of electronic medical record (EMR) free-text data for research varies. However, access to short diagnostic text fields is more widely available. This study assesses agreement between free-text and short diagnostic text data from primary care EMR for identification of posttraumatic stress disorder (PTSD). This retrospective cross-sectional study used EMR data from a pan-Canadian repository representing 1574 primary care providers at 265 clinics using 11 EMR vendors. Medical record review using free text and short diagnostic text fields of the EMR produced reference standards for PTSD. Agreement was assessed with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Our reference set contained 327 patients with free text and short diagnostic text. Among these patients, agreement between free text and short diagnostic text had an accuracy of 93.6% (CI 90.4%-96.0%). In a single Canadian province, case definitions 1 and 4 had a sensitivity of 82.6% (CI 74.4%-89.0%) and specificity of 99.5% (CI 97.4%-100%). However, when the reference set was expanded to a pan-Canada reference (n=12,104 patients), case definition 4 had the strongest agreement (sensitivity: 91.1%, CI 90.1%-91.9%; specificity: 99.1%, CI 98.9%-99.3%). Inclusion of free-text encounter notes during medical record review did not lead to improved capture of PTSD cases, nor did it lead to significant changes in case definition agreement. Within this pan-Canadian database, jurisdictional differences in diagnostic codes and EMR structure suggested the need to supplement diagnostic codes with natural language processing to capture PTSD. When unavailable, short diagnostic text can supplement free-text data for reference set creation and case validation. Application of the PTSD case definition can inform PTSD prevalence and characteristics.

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.

Using Primary Care Clinical Text Data and Natural Language Processing to Identify Indicators of COVID-19 in Toronto, Canada.

The objective of this study was to investigate whether a rule-based natural language processing (NLP) system, applied to primary care clinical text data, could be used to monitor COVID-19 viral activity in Toronto, Canada. We employed a retrospective cohort design. We included primary care patients with a clinical encounter between January 1, 2020 and December 31, 2020 at one of 44 participating clinical sites. During the study timeframe, Toronto first experienced a COVID-19 outbreak between March-2020 and June-2020; followed by a second viral resurgence from October-2020 through December-2020. We used an expert derived dictionary, pattern matching tools and contextual analyzer to classify primary care documents as 1) COVID-19 positive, 2) COVID-19 negative, or 3) unknown COVID-19 status. We applied the COVID-19 biosurveillance system across three primary care electronic medical record text streams: 1) lab text, 2) health condition diagnosis text and 3) clinical notes. We enumerated COVID-19 entities in the clinical text and estimated the proportion of patients with a positive COVID-19 record. We constructed a primary care COVID-19 NLP-derived time series and investigated its correlation with independent/external public health series: 1) lab confirmed COVID-19 cases, 2) COVID-19 hospitalizations, 3) COVID-19 ICU admissions, and 4) COVID-19 intubations. A total of 196,440 unique patients were observed over the study timeframe, of which 4,580 (2.3%) had at least one positive COVID-19 document in their primary care electronic medical record. Our NLP-derived COVID-19 time series describing the temporal dynamics of COVID-19 positivity status over the study timeframe demonstrated a pattern/trend which strongly mirrored that of other external public health series under investigation. We conclude that primary care text data passively collected from electronic medical record systems represent a high quality, low-cost source of information for monitoring/surveilling COVID-19 impacts on community health.

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.

Transfer or tailor? Implementing a technology‐supported intervention for noncommunicable diseases across contexts

Transfer or tailor? Implementing a technology‐supported intervention for noncommunicable diseases across contexts

Developing a reference standard for pertussis by applying a stratified sampling strategy to electronic medical record data

Pertussis surveillance remains essential in Canada, but ascertainment bias limits the accuracy of surveillance data. Introducing other sources to improve detection has highlighted the importance of validation. However, challenges arise due to low prevalence, and oversampling suspected cases can introduce partial verification bias. The aim of this study was to build a reference standard for pertussis validation studies that provides adequate analytic precision and minimizes bias. We used a stratified strategy to sample the reference standard from a primary care electronic medical record cohort. We incorporated abstractor notes into definite, possible, ruled-out, and no mention of pertussis classifications which were based on surveillance case definitions. We abstracted eight hundred records from the cohort of 404,922. There were 208 (26%) definite and 261 (32.6%) possible prevalent pertussis cases. Classifications demonstrated a wide variety of case severities. Abstraction reliability was moderate to substantial based on Cohen's kappa and raw percent agreement. When conducting validation studies for pertussis and other low prevalence diseases, this stratified sampling strategy can be used to develop a reference standard using limited resources. This approach mitigates verification and spectrum bias while providing sufficient precision and incorporating a range of case severities.

Identification of Early Onset Dementia in Population-Based Health Administrative Data: A Validation Study Using Primary Care Electronic Medical Records.

Early onset dementia (EOD) occurs when symptoms of dementia begin between 45 to 64 years of age. We developed and validated health administrative data algorithms for EOD and compared demographic characteristics and presence of comorbid conditions amongst adults with EOD, late onset dementia (LOD) and adults with no dementia in Ontario, Canada. Patients aged 45 to 64 years identified as having EOD in their primary care electronic medical records had their records linked to provincial health administrative data. We compared several combinations of physician's claims, hospitalizations, emergency department visits and prescriptions. Age-standardized incidence and prevalence rates of EOD were estimated from 1996 to 2016. The prevalence of EOD for adults aged 45 to 64 years in our primary care reference cohort was 0.12%. An algorithm of ≥1 hospitalization or ≥3 physician claims at least 30 days apart in a two-year period or ≥1 dementia medication had a sensitivity of 72.9% (64.5-81.3), specificity of 99.7% (99.7-99.8), positive predictive value (PPV) of 23.7% (19.1-28.3), and negative predictive value of 100.0%. Multivariate logistic regression found adults with EOD had increased odds ratios for several health conditions compared to LOD and no dementia populations. From 1996 to 2016, the age-adjusted incidence rate increased slightly (0.055 to 0.061 per 100 population) and the age-adjusted prevalence rate increased three-fold (0.11 to 0.32 per 100 population). While we developed a health administrative data algorithm for EOD with a reasonable sensitivity, its low PPV limits its ability to be used for population surveillance.

Data on Patient Record Trajectory for Linkage (DataPRinT Linkage).

The linkage of Electronic Medical Records, Administrative and other data sources is highly valuable for research and health system monitoring. Once linked, combined resources can be analyzed to provide the answers to a variety of health questions that otherwise could not be answered. However, legislative and administrative barriers, including lengthy processes for data sharing agreements, may preclude timely linkage which is a key requirement during pandemics. ObjectiveTo develop a method using a patient’s health trajectory to probabilistically link primary care Electronic Medical Record (EMR) data with administrative and other data, without the need to transfer large datasets or identifiable information. To determine the legislative feasibility, accuracy and validity of this linkage process. Study DesignIdentify data strings that do not directly identify patients and could be used as unique linkage variables. The data strings, which we are calling dataprints, are sufficiently similar over time in different databases. One example in Ontario, Canada, is the pattern of submitted health claims. For every patient seen by a family physician, there exists a unique pattern of dates/billing codes/diagnoses over time. These unique patterns are reasonably similar in EMR and administrative datasets. We will apply an algorithm which turns the string in the selected dataprints to an irreversibly hashed code for each person. The hashed code and no additional information will be provided by both data controllers to a trusted-third party who will determine which records match and send a mapping table to both. This enables analyses to be run in parallel, without divulging any direct person identifiers. DatasetIndividuals contained in the University of Toronto Practice Based Research Network (UTOPIAN). Outcome MeasuresLinkage quality will be assessed by the number of true matches and represented by sensitivity, specificity and positive and negative predictive values. ResultsThe method will be evaluated against a validated, deterministically linked reference standard at North York General Hospital using de-identified EMR and hospital data. Results will inform processes to enable analyses across datasets while adhering to privacy legislation.

Morbidity Patterns in Primary Care in Hong Kong: Protocol for a Practice-Based Morbidity Survey.

BackgroundUp-to-date and accurate information about the health problems encountered by primary care doctors is essential to understanding the morbidity pattern of the community to better inform health care policy and practice. Morbidity surveys of doctors allow documentation of actual consultations, reflecting the patient’s reason for seeking care as well as the doctor’s diagnostic interpretation of the illness and management approach. Such surveys are particularly critical in the absence of a centralized primary care electronic medical record database.ObjectiveWith the changing sociodemographic profile of the population and implementation of health care initiatives in the past 10 years, the aim of this study is to determine the morbidity and management patterns in Hong Kong primary care during a pandemic and compare the results with the last survey conducted in 2007-2008.MethodsThis will be a prospective, practice-based survey of Hong Kong primary care doctors. Participants will be recruited by convenience and targeted sampling from both public and private sectors. Participating doctors will record the health problems and corresponding management activities for consecutive patient encounters during one designated week in each season of the year. Coding of health problems will follow the International Classification of Primary Care, Second Edition. Descriptive statistics will be used to calculate the prevalence of health problems and diseases as well as the rates of management activities (referral, investigation, prescription, preventive care). Nonlinear mixed effects models will assess the differences between the private and public sectors as well as factors associated with morbidity and management patterns in primary care.ResultsThe data collection will last from March 1, 2021, to August 31, 2022. As of April 2022, 176 doctor-weeks of data have been collected.ConclusionsThe results will provide information about the health of the community and inform the planning and allocation of health care resources.Trial RegistrationClinicalTrials.gov NCT04736992; https://clinicaltrials.gov/ct2/show/NCT04736992International Registered Report Identifier (IRRID)DERR1-10.2196/37334

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.

Specialist wait time reporting using family physicians\u2019 electronic medical record data: a mixed method study of feasibility and clinical utility

BackgroundMore than 50% of Canadian adult patients wait longer than four weeks to see a specialist after referral from primary care. Access to accurate wait time information may help primary care physicians choose the timeliest specialist to address a patient’s specific needs. We conducted a mixed-methods study to assess if primary to specialist care wait times can be extracted from electronic medical records (EMR), analyzed the wait time information, and used focus groups and interviews to assess the potential clinical utility of the wait time information.MethodsTwo family practices were recruited to examine primary care physician to specialist wait times between January 2016 and December 2017, using EMR data. The primary outcome was the median wait time from physician referral to specialist appointment for each specialty service. Secondary outcomes included the physician and patient characteristics associated with wait times as well as qualitative analyses of physician interviews about the resulting wait time reports.ResultsWait time data can be extracted from the primary care EMR and converted to a report format for family physicians and specialists to review. After data cleaning, there were 7141 referrals included from 4967 unique patients. The 5 most common specialties referred to were Dermatology, Gastroenterology, Ear Nose and Throat, Obstetrics and Gynecology and Urology. Half of the patients were seen by a specialist within 42 days, 75% seen within 80 days and all patients within 760 days. There were significant differences in wait times by specialty, for younger patients, and those with urgently labelled medical situations. Overall, wait time reports were perceived by clinicians to be important since they could help family physicians decide how to triage referrals and might lead to system improvements.ConclusionsWait time information from primary to specialist care can aid in decision-making around specialist referrals, identify bottlenecks, and help with system planning. This mixed method study is a starting point to review the importance of providing wait time data for both family physicians, specialists and local health systems. Future work can be directed towards developing wait time reporting functionality and evaluating if wait time information will help increase system efficiency and/or improve provider and patient satisfaction.

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.

Measurement of obesity in primary care practice: chronic conditions matter.

Lay Summary Primary care providers can deliver tailored advice and support to patients who are overweight or have obesity. The 2020 Canadian Adult Obesity Practice Guideline for primary care providers recommended that patients’ waist circumference (WC) be measured if their height and weight place them in the overweight or Class I obesity category. The guideline does not recommend how often providers should measure WC nor describe how often this is measured in current practice. We reviewed electronic medical records (EMRs) of 707,819 Canadian adult patients aged 40 and older. Among them, 48.7% had 1 or more body mass index (BMI) recorded; 11.5% had at least 1 waist measurement recorded. Of those with a BMI classified as overweight or having Class I obesity, 23.7% had at least 1 WC measurement recorded, which differed by chronic disease. WC was documented in more patients who had diabetes mellitus (36.8%) than hypertension (26.1%), or osteoarthritis (24.3%). This difference may be reflective of more specific advice in diabetes guidelines. To our knowledge, this is the first study to describe documentation of WC measurement for patients who are overweight or have Class I obesity in Canadian primary care EMRs across obesity-related conditions.

Diagnostic accuracy of the Patient Health Questionnaire 2 (PHQ-2) in Qatar's primary care settings.

This cross-sectional study was designed to establish diagnostic accuracy of the Patient Health Questionnaire 2 in Qatar's primary care population. The data required for the study were anonymously extracted from Qatar's primary care electronic medical record system. The sensitivity, specificity, predictive values, negative values and optimal cut-off points were calculated for the tool. A total of 6921 individuals met the study's inclusion criteria. The diagnostic accuracy of cut-off values was calculated for scores 1-6. Based on the Youden's index (0.58), a score of 2 was identified as the most optimal cut-off. It offers a sensitivity of 88.73% and specificity of 69.31%. Further studies should aim to confirm the results using alternative study designs and to report them in accordance to population characteristics both in Qatar and internationally.