Multimorbidity and Blood Pressure Control in Patients Attending Primary Care in Canada.

BACKGROUND: There has been conflicting evidence on the association between multimorbidity and blood pressure (BP) control. This study aimed to investigate this associations in people with hypertension attending primary care in Canada, and to assess whether individual long-term conditions are associated with BP control. METHODS: This was a cross-sectional study in people with hypertension attending primary care in Toronto between January 1, 2017 and December 31, 2019. Uncontrolled BP was defined as systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg. A list of 11 a priori selected chronic conditions was used to define multimorbidity. Multimorbidity was defined as having ≥1 long-term condition in addition to hypertension. Logistic regression models were used to estimate the association between multimorbidity (or individual long-term conditions) with uncontrolled BP. RESULTS: A total of 67 385 patients with hypertension were included. They had a mean age of 70, 53.1% were female, 80.6% had multimorbidity, and 35.7% had uncontrolled BP. Patients with multimorbidity had lower odds of uncontrolled BP than those without multimorbidity (adjusted OR = 0.72, 95% CI 0.68-0.76). Among the long-term conditions, diabetes (aOR = 0.73, 95%CI 0.70-0.77), heart failure (aOR = 0.81, 95%CI 0.73-0.91), ischemic heart disease (aOR = 0.74, 95%CI 0.69-0.79), schizophrenia (aOR = 0.79, 95%CI 0.65-0.97), depression/anxiety (aOR = 0.91, 95%CI 0.86-0.95), dementia (aOR = 0.87, 95%CI 0.80-0.95), and osteoarthritis (aOR = 0.89, 95%CI 0.85-0.93) were associated with a lower likelihood of uncontrolled BP. CONCLUSION: We found that multimorbidity was associated with better BP control. Several conditions were associated with better control, including diabetes, heart failure, ischemic heart disease, schizophrenia, depression/anxiety, dementia, and osteoarthritis.

Diabetes care among individuals with and without schizophrenia in three Canadian provinces: A retrospective cohort study.

OBJECTIVE: Diabetes is present in approximately 10% of people living with schizophrenia and substantially contributes to early mortality, but some aspects of diabetes care among those with schizophrenia have been inadequately investigated to date. We assessed diabetes care and comorbidity management among people with and without schizophrenia. METHODS: We conducted a cohort study with data obtained from primary care electronic medical records stored in the Diabetes Action Canada (DAC) National Repository from Alberta, Ontario, and Quebec, Canada. The population studied included patients with diabetes, with and without schizophrenia, who had at least 3 primary care visits in a 2 year period between July 2017 and June 2019. Outcomes included glycemia; diabetes complication screening and monitoring; antihyperglycemic and cardioprotective medication prescription; health service use. RESULTS: We identified 69,512 patients with diabetes; 911 (1.3%) of whom also had schizophrenia. Prevalence of high HbA1C (>8.5%) (9083/68601; 13.2% vs. 137/911; 15.0%) and high blood pressure (>130/80 mmHg) (4248/68601; 6.2% vs. 73/911; 8.0%) was similar between the two groups. Half (50.0%) of patients with schizophrenia (n = 455) had 11 or more primary care visits in the past year, compared with 27.8% of people without schizophrenia. (p < 0.0001). Patients with schizophrenia had lower odds of ever having blood pressure recorded (OR = 0.81, 95% CI 0.71-0.94) and fewer of those with chronic kidney disease (CKD) were prescribed renin-angiotensin aldosterone system inhibitors, compared to patients without schizophrenia (10.3% vs 15.8%, p = 0.0005). CONCLUSIONS: Patients with diabetes and schizophrenia achieved similar blood glucose and blood pressure levels to those without schizophrenia, and had more primary care visits. However, they had fewer blood pressure readings and lower prescription of recommended medications among those who also had CKD. These results are both encouraging and represent opportunities for improvement in care.

Patient visits and prescriptions for attention-deficit/hyperactivity disorder from 2017-2021: Impacts of COVID-19 pandemic in primary care.

OBJECTIVE: To determine whether more patients presented with Attention-deficit/hyperactivity disorder (ADHD)-related visits and/or sought care from family physicians more frequently during the COVID-19 pandemic. METHODS: Electronic medical records from the University of Toronto Practice-Based Research Network were used to characterize changes in family physician visits and prescriptions for ADHD medications. Annual patient prevalence and visit rates pre-pandemic (2017-2019) were used to calculate the expected rates in 2020 and 2021. The expected and observed rates were compared to identify any pandemic-related changes. RESULTS: The number of patients presenting for ADHD-related visits during the pandemic was consistent with pre-pandemic trends. However, observed ADHD-related visits in 2021 were 1.32 times higher than expected (95% CI: 1.05-1.75), suggesting that patients visited family physicians more frequently than before the pandemic. CONCLUSION: Demand for primary care services related to ADHD has continued to increase during the pandemic, with increased health service use among those accessing care.

Estimation of marginal structural models under irregular visits and unmeasured confounder: calibrated inverse probability weights.

Clinical information collected in electronic health records (EHRs) is becoming an essential source to emulate randomized experiments. Since patients do not interact with the healthcare system at random, the longitudinal information in large observational databases must account for irregular visits. Moreover, we need to also account for subject-specific unmeasured confounders which may act as a common cause for treatment assignment mechanism (e.g. glucose-lowering medications) while also influencing the outcome (e.g. Hemoglobin A1c). We used the calibration of longitudinal weights to improve the finite sample properties and to account for subject-specific unmeasured confounders. A Monte Carlo simulation study is conducted to evaluate the performance of calibrated inverse probability estimators using time-dependent treatment assignment and irregular visits with subject-specific unmeasured confounders. The simulation study showed that the longitudinal weights with calibrated restrictions improved the finite sample bias when compared to the stabilized weights. The application of the calibrated weights is demonstrated using the exposure of glucose lowering medications and the longitudinal outcome of Hemoglobin A1c. Our results support the effectiveness of glucose lowering medications in reducing Hemoglobin A1c among type II diabetes patients with elevated glycemic index ([Formula: see text]) using stabilized and calibrated weights.

Emulating a Target Trial Using Primary-Care Electronic Health Records: Sodium-Glucose Cotransporter 2 Inhibitor Medications and Hemoglobin A1c.

Substantial effort has been dedicated to conducting randomized controlled experiments to generate clinical evidence for diabetes treatment. Randomized controlled experiments are the gold standard for establishing cause and effect. However, due to their high cost and time commitment, large observational databases such as those comprised of electronic health record (EHR) data collected in routine primary care may provide an alternative source with which to address such causal objectives. We used a Canadian primary-care data repository housed at the University of Toronto (Toronto, Ontario, Canada) to emulate a randomized experiment. We estimated the effectiveness of sodium-glucose cotransporter 2 inhibitor (SGLT-2i) medications for patients with diabetes using hemoglobin A1c (HbA1c) as a primary outcome and marker for glycemic control from 2018 to 2021. We assumed an intention-to-treat analysis for prescribed treatment, with analyses based on the treatment assigned rather than the treatment eventually received. We defined the causal contrast of interest as the net change in HbA1c (percent) between the group receiving the standard of care versus the group receiving SGLT-2i medication. Using a counterfactual framework, marginal structural models demonstrated a reduction in mean HbA1c level with the initiation of SGLT-2i medications. These findings provided effect sizes similar to those from earlier clinical trials on assessing the effectiveness of SGLT-2i medications.

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.

Factors Associated With Initiation of Antidepressant Medication in Adults With Type 1 and Type 2 Diabetes: A Primary Care Retrospective Cohort Study in Ontario, Canada.

OBJECTIVES: Depression in patients with diabetes mellitus is common and associated with poorer outcomes. This study aims to identify demographic, socioeconomic and medical factors associated with the initiation of antidepressant medication after a diagnosis of diabetes in adult patients without a previous prescription for antidepressants. We also examined frequency of primary care visits in the year after antidepressant initiation compared with the year before treatment began. METHODS: This was a retrospective cohort study using routinely collected electronic medical record data spanning January 2011 to December 2019 from the University of Toronto Practice-based Research Network (UTOPIAN) Data Safe Haven. Our primary outcome was a first prescription for an antidepressant in patients with diabetes. We used a mixed-effects logistic regression model to identify sociodemographic and medical factors associated with this event. RESULTS: Among 22,750 patients with diabetes mellitus, 3,055 patients (13.4%) began taking an antidepressant medication. Increased odds of antidepressant initiation were observed in younger patients (odds ratio [OR], 1.77; 95% confidence interval [CI], 1.39 to 2.26), females (OR, 1.60; 95% CI, 1.46 to 1.7), those receiving insulin treatment (OR, 1.59; 95% CI, 1.43 to 1.78) and cases of polypharmacy (OR, 3.67; 95% CI, 3.29 to 4.11). There was an increase in the mean number of primary care visits from 4.6 to 5.9 per year after antidepressant initiation. CONCLUSIONS: In patients with diabetes, age, sex and medical characteristics were associated with the initiation of antidepressants. These patients accessed primary care more frequently. Screening and prevention of depression, particularly in these subgroups, could reduce its personal and systemic burdens.

Socioeconomic and immigration status and COVID-19 testing in Toronto, Ontario: retrospective cross-sectional study.

BACKGROUND: Preliminary evidence suggests that individuals living in lower income neighbourhoods are at higher risk of COVID-19 infection. The relationship between sociodemographic characteristics and COVID-19 risk warrants further study. METHODS: We explored the association between COVID-19 test positivity and patients' socio-demographic variables, using neighborhood sociodemographic data collected retrospectively from two COVID-19 Assessment Centres in Toronto, ON. RESULTS: Eighty-three thousand four hundred forty three COVID-19 tests completed between April 5-September 30, 2020, were analyzed. Individuals living in neighbourhoods with the lowest income or highest concentration of immigrants were 3.4 (95% CI: 2.7 to 4.9) and 2.5 (95% CI: 1.8 to 3.7) times more likely to test positive for COVID-19 than those in highest income or lowest immigrant neighbourhoods, respectively. Testing was higher among individuals from higher income neighbourhoods, at lowest COVID-19 risk, compared with those from low-income neighbourhoods. CONCLUSIONS: Targeted efforts are needed to improve testing availability in high-risk regions. These same strategies may also ensure equitable COVID-19 vaccine delivery.

Health system use among patients with mental health conditions in a community based sample in Toronto, Canada: A retrospective cohort study.

OBJECTIVE: To identify hospital and primary care health service use among people with mental health conditions or addictions in an integrated primary-secondary care database in Toronto, Ontario. METHOD: This was a retrospective cohort study of adults with mental health diagnoses using data from the Health Databank Collaborative (HDC), a primary care-hospital linked database in Toronto. Data were included up to March 31st 2019. Negative binomial and logistic regression were used to evaluate associations between health care utilization and various patient characteristics and mental health diagnoses. RESULTS: 28,482 patients age 18 or older were included. The adjusted odds of at least one mental health diagnosis were higher among younger patients (18-30 years vs. 81+years aOR = 1.87; 95% CI:1.68-2.08) and among female patients (aOR = 1.35; 95% CI: 1.27-1.42). Patients with one or more mental health diagnoses had higher adjusted rates of hospital visits compared to those without any mental health diagnosis including addiction (aRR = 1.74, 95% CI: 1.58-1.91) and anxiety (aRR = 1.28, 95% CI: 1.23-1.32). 14.5% of patients with a psychiatric diagnosis were referred to the hospital for specialized psychiatric services, and 38% of patients referred were eventually seen in consultation. The median wait time from the date of referral to the date of consultation was 133 days. CONCLUSIONS: In this community, individuals with mental health diagnoses accessed primary and hospital-based health care at greater rates than those without mental health diagnoses. Wait times for specialized psychiatric care were long and most patients who were referred did not have a consultation. Information about services for patients with mental health conditions can be used to plan and monitor more integrated care across sectors, and ultimately improve outcomes.

Marginal Structural Models Using Calibrated Weights With SuperLearner: Application to Type II Diabetes Cohort.

As different scientific disciplines begin to converge on machine learning for causal inference, we demonstrate the application of machine learning algorithms in the context of longitudinal causal estimation using electronic health records. Our aim is to formulate a marginal structural model for estimating diabetes care provisions in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2i. The binary outcome of diabetes care provisions was defined using a composite measure of chronic disease prevention and screening elements [27] including (i) primary care visit, (ii) blood pressure, (iii) weight, (iv) hemoglobin A1c, (v) lipid, (vi) ACR, (vii) eGFR and (viii) statin medication. We used several statistical learning algorithms to describe causal relationships between the prescription of three common classes of diabetes medications and quality of diabetes care using the electronic health records contained in National Diabetes Repository. In particular, we generated an ensemble of statistical learning algorithms using the SuperLearner framework based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, and (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population generated from the marginalization of the time-dependent confounding process. Covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2i) may have improved diabetes care provisions in relation to treatment naïve (i.e. no treatment) cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with type II diabetes through the improvement of diabetes care provisions in primary care.

Non-negative matrix factorization temporal topic models and clinical text data identify COVID-19 pandemic effects on primary healthcare and community health in Toronto, Canada.

OBJECTIVE: To demonstrate how non-negative matrix factorization can be used to learn a temporal topic model over a large collection of primary care clinical notes, characterizing diverse COVID-19 pandemic effects on the physical/mental/social health of residents of Toronto, Canada. MATERIALS AND METHODS: The study employs a retrospective open cohort design, consisting of 382,666 primary care progress notes from 44,828 patients, 54 physicians, and 12 clinics collected 01/01/2017 through 31/12/2020. Non-negative matrix factorization uncovers a meaningful latent topical structure permeating the corpus of primary care notes. The learned latent topical basis is transformed into a multivariate time series data structure. Time series methods and plots showcase the evolution/dynamics of learned topics over the study period and allow the identification of COVID-19 pandemic effects. We perform several post-hoc checks of model robustness to increase trust that descriptive/unsupervised inferences are stable over hyper-parameter configurations and/or data perturbations. RESULTS: Temporal topic modelling uncovers a myriad of pandemic-related effects from the expressive clinical text data. In terms of direct effects on patient-health, topics encoding respiratory disease symptoms display altered dynamics during the pandemic year. Further, the pandemic was associated with a multitude of indirect patient-level effects on topical domains representing mental health, sleep, social and familial dynamics, measurement of vitals/labs, uptake of prevention/screening maneuvers, and referrals to medical specialists. Finally, topic models capture changes in primary care practice patterns resulting from the pandemic, including changes in EMR documentation strategies and the uptake of telemedicine. CONCLUSION: Temporal topic modelling applied to a large corpus of rich primary care clinical text data, can identify a meaningful topical/thematic summarization which can provide policymakers and public health stakeholders a passive, cost-effective, technology for understanding holistic impacts of the COVID-19 pandemic on the primary healthcare system and community/public-health.

Effects of COVID-19 pandemic on anxiety and depression in primary care: A retrospective cohort study.

BACKGROUND: Population-based surveys indicate that many people experienced increased psychological distress during the COVID-19 pandemic. We aimed to determine if there was a corresponding increase in patients receiving services for anxiety and depression from their family physicians. METHODS: Electronic medical records from the University of Toronto Practice Based-Research Network (UTOPIAN; N = 322,920 patients) were used to calculate incidence rates for anxiety/depression related visits and antidepressant prescriptions before the COVID-19 pandemic (January 2018-February 2020) and during the COVID-19 pandemic (March-December 2020). Data from the pre-pandemic period were used to predict expected rates during the pandemic period which was compared to the observed rate. RESULTS: The number of patients presenting with anxiety/depression symptoms in primary care varied across age groups, sex, and time since pandemic onset. Among the youngest patients (ages 10-18 years), there were fewer patients than pre-pandemic visiting for new episodes of anxiety/depression and being prescribed antidepressants in April 2020, but by the end of 2020 this trend had reversed such that incidence rates for anxiety/depression related visits were higher than pre-pandemic levels. Among older adults, incidence rates of anxiety/depression related visits increased in April 2020 with the onset of the pandemic, and remained higher than expected throughout 2020. LIMITATIONS: A convenience sample of 362 family physicians in Ontario was used. CONCLUSION: Demand for mental health services from family physicians varied by patient age and sex and changed with the onset of the COVID-19 pandemic. By the end of 2020, more patients were seeking treatment for anxiety/depression related concerns.

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.

Association between new-onset anosmia and positive SARS-CoV-2 tests among people accessing outpatient testing in Toronto, Ontario: a retrospective cross-sectional study.

BACKGROUND: Reports have suggested that anosmia is strongly associated with SARS-CoV-2 infection, but patients were often asked about this symptom after their diagnosis. This study assessed associations between prospectively reported anosmia and other symptoms related to SARS-CoV-2 infection, and SARS-CoV-2 positivity in community testing centres in Toronto, Ontario. METHODS: We conducted a retrospective cross-sectional study in which data were collected from 2 COVID-19 assessment centres affiliated with 2 hospitals in Toronto, Ontario, from Apr. 5 to Sept. 30, 2020. We included symptomatic profiles of all people who underwent a SARS-CoV-2 test at either clinic within the study period. We used generalized estimating equations to account for repeat visits and to assess associations between anosmia and other symptoms and SARS-CoV-2 positivity. RESULTS: A total of 83 443 SARS-CoV-2 tests were conducted across the 2 sites for 72 692 participants during the study period. Of all tests, 1640 (2.0%) were positive; 837 (51.0%) of people who tested positive were asymptomatic. The adjusted odds ratio for the association between anosmia and test positivity was 5.29 (95% confidence interval [CI] 4.50-6.22), with sensitivity of 0.138 (95% CI 0.121-0.154), specificity of 0.980 (95% CI 0.979-0.981), a positive predictive value of 0.120 (95% CI 0.106-0.135) and a negative predictive value of 0.983 (95% CI 0.982-0.984). INTERPRETATION: Anosmia had high specificity and a positive predictive value of 12% for SARS-CoV-2 infection in this community population with low prevalence of SARS-CoV-2 positivity. The presence of anosmia should increase clinical suspicion of SARS-CoV-2 infection, and our findings suggest that people presenting with this symptom should be tested.

Predicting hospital readmission risk: A prospective observational study to compare primary care providers' assessments with the LACE readmission risk index.

PURPOSE: This study aims to determine if the primary care provider (PCP) assessment of readmission risk is comparable to the validated LACE tool at predicting readmission to hospital. METHODS: A prospective observational study of recently discharged adult patients clustered by PCPs in the primary care setting. Physician readmission risk assessment was determined via a questionnaire after the PCP reviewed the hospital discharge summary. LACE scores were calculated using administrative data and the discharge summary. The sensitivity and specificity of the physician assessment and the LACE tool in predicting readmission risk, agreement between the 2 assessments and the area under receiver operating characteristic (AUROC) curves were calculated. RESULTS: 217 patient readmission encounters were included in this study from September 2017 till June 2018. The rate of readmission within 30 days was 14.7%, and 217 discharge summaries were used for analysis. The weighted kappa coefficient was 0.41 (95% CI: 0.30-0.51) demonstrating a moderate level of agreement. Sensitivity of physician assessment was 0.31 (95% CI: 0.22-0.40) and specificity was 0.80 (95% CI: 0.77-0.83). The sensitivity of the LACE assessment was 0.42 (95% CI: 0.25-0.59) and specificity was 0.79 (95% CI: 0.73-0.85). The AUROC for the LACE readmission risk was 0.65 (95% C.I. 0.55-0.76) demonstrating modest predictive power and was 0.57 (95% C.I. 0.46-0.68) for physician assessment, demonstrating low predictive power. CONCLUSION: The LACE index shows moderate discriminatory power in identifying high-risk patients for readmission when compared to the PCP's assessment. If this score can be provided to the PCP, it may help identify patients who requires more intensive follow-up after discharge.

Implementation of targeted screening for poverty in a large primary care team in Toronto, Canada: a feasibility study.

BACKGROUND: Poverty has a significant influence on health. Efforts to optimize income and reduce poverty could make a difference to the lives of patients and their families. Routine screening for poverty in primary care is an important first step but rarely occurs in Canada. We aimed to implement a targeted screening and referral process in a large, distributed primary care team in Toronto, Ontario, Canada. The main outcome was the proportion of targeted patients screened. METHODS: This implementation evaluation was conducted with a large community-based primary care team in north Toronto. The primary care team serves relatively wealthy neighborhoods with pockets of poverty. Physicians were invited to participate. We implemented targeted screening by combining census information on neighborhood-level deprivation with postal codes in patient records. For physicians agreeing to participate, we added prompts to screen for poverty to the charts of adult patients living in the most deprived areas. Standardized electronic medical record templates recommended a referral to a team case worker for income optimization, for those patients screening positive. We recorded the number and percentages of participants at each stage, from screening to receiving advice on income optimization. RESULTS: 128 targeted patients with at least one visit (25%) were screened. The primary care team included 86 physicians distributed across 19 clinical locations. Thirty-four physicians (39%) participated. Their practices provided care for 27,290 patients aged 18 or older; 852 patients (3%) were found to be living in the most deprived neighborhoods. 509 (60%) had at least one office visit over the 6 months of follow up. 25 patients (20%) screened positive for poverty, and 13 (52%) were referred. Eight patients (62% of those referred) were ultimately seen by a caseworker for income optimization. CONCLUSIONS: We implemented a targeted poverty screening program combined with resources to optimize income for patients in a large, distributed community-based primary care team. Screening was feasible; however, only a small number of patients were linked to the intervention Further efforts to scale and spread screening and mitigation of poverty are warranted; these should include broadening the targeted population beyond those living in the most deprived areas.

Changes in family medicine visits across sociodemographic groups after the onset of the COVID-19 pandemic in Ontario: a retrospective cohort study.

BACKGROUND: It has been suggested that the COVID-19 pandemic has worsened socioeconomic disparities in access to primary care. Given these concerns, we investigated whether the pandemic affected visits to family physicians differently across sociodemographic groups. METHODS: We conducted a retrospective cohort study using electronic medical records from family physician practices within the University of Toronto Practice-Based Research Network. We evaluated primary care visits for a fixed cohort of patients who were active within the database as of Jan. 1, 2019, to estimate the number of patients who visited their family physician (visitor rate) and the number of distinct visits (visit volume) between Jan. 1, 2019, to June 30, 2020. We compared trends in visitor rate and visit volume during the pandemic (Mar. 14 to June 30, 2020) with the same period in the previous year (Mar. 14 to June 30, 2019) across sociodemographic factors, including age, sex, neighbourhood income, material deprivation and ethnic concentration. RESULTS: We included 365 family physicians and 372 272 patients. Compared with the previous year, visitor rates during the pandemic period dropped by 34.5%, from 357 visitors per 1000 people to 292 visitors per 1000 people. Declines in visit volume during the pandemic were less pronounced (21.8% fewer visits), as the mean number of visits per patient increased during the pandemic (from 1.64 to 1.96). The declines in visitor rate and visit volume varied based on patient age and sex, but not socioeconomic status. INTERPRETATION: Although the number of visits to family physicians dropped substantially during the first few weeks of the COVID-19 pandemic in Ontario, patients from communities with low socioeconomic status did not appear to be disproportionately affected. In this primary care setting, the pandemic appears not to have worsened socioeconomic disparities in access to care.

Over-use of thyroid testing in Canadian and UK primary care in frequent attenders: A cross-sectional study.

BACKGROUND: Thyroid-stimulating hormone (TSH) is a common test used to detect and monitor clinically significant hypo- and hyperthyroidism. Population-based screening of asymptomatic adults for thyroid disorders is not recommended. OBJECTIVE: The research objectives were to determine patterns of TSH testing in Canadian and English primary care practices, as well as patient and physician practice characteristics associated with testing TSH for primary care patients with no identifiable indication. METHODS: In this 2-year cross-sectional observational study, Canadian and English electronic medical record databases were used to identify patients and physician practices. Cohorts of patients aged 18 years or older, without identifiable indications for TSH testing, were generated from these databases. Analyses were performed using a random-effects logistic regression to determine patient and physician practice characteristics associated with increased testing. We determined the proportion of TSH tests performed concurrently with at least one common screening blood test (lipid profile or hemoglobin A1c). Standardised proportions of TSH test per family practice were used to examine the heterogeneity in the populations. RESULTS: At least one TSH test was performed in 35.97% (N = 489 663) of Canadian patients and 29.36% (N = 1 030 489) of English patients. Almost all TSH tests in Canada and England (95.69% and 99.23% respectively) were within the normal range (0.40-5.00 mU/L). A greater number of patient-physician encounters was the strongest predictor of TSH testing. It was determined that 51.40% of TSH tests in Canada and 76.55% in England were performed on the same day as at least one other screening blood test. There was no association between the practice size and proportion of asymptomatic patients tested. CONCLUSIONS: This comparative binational study found TSH patterns suggestive of over-testing and potentially thyroid disorder screening in both countries. There may be significant opportunities to improve the appropriateness of TSH ordering in Canada and England and therefore improve the allocation of limited system resources.

Trends in diabetes medication use in Australia, Canada, England, and Scotland: a repeated cross-sectional analysis in primary care.

BACKGROUND: Several new classes of glucose-lowering medications have been introduced in the past two decades. Some, such as sodium-glucose cotransporter 2 inhibitors (SGLT2s), have evidence of improved cardiovascular outcomes, while others, such as dipeptidyl peptidase-4 inhibitors (DPP4s), do not. It is therefore important to identify their uptake in order to find ways to support the use of more effective treatments. AIM: To analyse the uptake of these new classes among patients with type 2 diabetes. DESIGN AND SETTING: This was a retrospective repeated cross-sectional analysis in primary care. Rates of medication uptake in Australia, Canada, England, and Scotland were compared. METHOD: Primary care Electronic Medical Data on prescriptions (Canada, UK) and dispensing data (Australia) from 2012 to 2017 were used. Individuals aged ≥40 years on at least one glucose-lowering drug class in each year of interest were included, excluding those on insulin only. Proportions of patients in each nation, for each year, on each class of medication, and on combinations of classes were determined. RESULTS: Data from 238 619 patients were included in 2017. The proportion of patients on sulfonylureas (SUs) decreased in three out of four nations, while metformin decreased in Canada. Use of combinations of metformin and new drug classes increased in all nations, replacing combinations involving SUs. In 2017, more patients were on DPP4s (between 19.1% and 27.6%) than on SGLT2s (between 10.1% and 15.3%). CONCLUSION: New drugs are displacing SUs. However, despite evidence of better outcomes, the adoption of SGLT2s lagged behind DPP4s.

Emergency Room Visit Prevention for Older Patients with Parkinsonism in a Geriatric Clinic.

BACKGROUND: Older persons with parkinsonism (PWP) are at high risk for hospitalization and adverse outcomes. Few effective strategies exist to prevent Emergency Department (ED) visits and hospitalization. The interdisciplinary Geriatrics Clinic for Parkinson's ("our clinic") was founded to address the complexity of parkinsonism in older patients, supported by a pharmacist-led telephone intervention (TI) service. Our primary objective was to study whether TI could avert ED visits in older PWP. METHODS: Using a prospective, observational cohort, we collected data from all calls in 2016, including who initiated and reasons for the calls, patient demographics, number of comorbidities and medications, diagnoses, duration of disease, and intervention provided. Calls with intention to visit ED were classified as "crisis calls". Outcome of whether patients visited ED was collected within 1 week, and user satisfaction by anonymous survey within 3 weeks. RESULTS: We received 337 calls concerning 114 patients, of which 82 (24%) were "crisis calls". Eighty-one percent of calls were initiated by caregivers. Ninety-three percent of "crisis calls" resolved without ED visit after TI. The main reasons for "crisis calls" were non-motor symptoms (NMS) (39%), adverse drug effects (ADE) (29%), and motor symptoms (18%). Ninety-seven percent of callers were satisfied with the TI. CONCLUSION: Pharmacist-led TI in a Geriatrics Clinic for Parkinson's was effective in preventing ED visits in a population of older PWP, with high user satisfaction. Most calls were initiated by caregivers. Main reasons for crisis calls were NMS and ADE. These factors should be considered in care planning for older PWP.