Facteurs influençant le choix du futur lieu d’exercice chez les résidents en rhumatologie

Background: There are regional disparities in the distribution of Canadian rheumatologists. The objective of this study was to identify factors impacting rheumatology residents’ postgraduate practice decisions to inform Canadian Rheumatology Association workforce recommendations. Methods: An online survey was developed, and invitations were sent to all current Canadian rheumatology residents in 2019 (n = 67). Differences between subgroups of respondents were examined using the Pearson χ2 test. Results: A total of 34 of 67 residents completed the survey. Seventy-three percent of residents planned to practice in the same province as their rheumatology training. The majority of residents (80%) ranked proximity to friends and family as the most important factor in planning. Half of participants had exposure to alternative modes of care delivery (e.g. telehealth) during their rheumatology training with fifteen completing a community rheumatology elective (44%). Conclusions: The majority of rheumatology residents report plans to practice in the same province as they trained, and close to home. Gaps in training include limited exposure to community electives in smaller centers, and training in telehealth and travelling clinics for underserviced populations. Our findings highlight the need for strategies to increase exposure of rheumatology trainees to underserved areas to help address the maldistribution of rheumatologists. Contexte : Au Canada, il existe des disparités régionales dans la répartition des rhumatologues. La présente étude recense les facteurs qui influencent les choix des résidents en rhumatologie concernant leur lieu d’exercice futur afin de guider les recommandations de Société canadienne de rhumatologie relatives aux effectifs. Méthodes : Après l’élaboration d’un sondage en ligne, une invitation a été envoyée à tous les résidents en rhumatologie au Canada en 2019 (n = 67). Les différences entre les groupes ont été examinées à l’aide du test Pearson χ2. Résultats : Trente-quatre des 67 résidents contactés ont répondu au sondage. Soixante-treize pour cent des répondants prévoyaient d’exercer dans la province où ils avaient fait leur formation en rhumatologie. La majorité des résidents (80 %) ont classé la proximité des amis et de la famille comme le facteur le plus important dans leur choix de lieu d’exercice. La moitié des participants s’étaient familiarisés avec d’autres modes de prestation de soins (par exemple, la télésanté) pendant leur formation en rhumatologie et 15 d’entre eux (44 %) avaient fait un stage en rhumatologie communautaire. Conclusions : La majorité des résidents en rhumatologie déclarent avoir l’intention d’exercer près de chez eux, dans la province où ils ont fait leurs études. Les lacunes dans la formation comportent l’exposition limitée à des stages dans les petits centres en milieu communautaire, en télésanté et dans les cliniques mobiles ciblant les populations mal desservies. Nos conclusions soulignent le besoin de stratégies visant à augmenter l’exposition des résidents en rhumatologie à des zones mal desservies afin de remédier à la mauvaise répartition géographique des rhumatologues

Using Biomedical Text as Data and Representation Learning for Identifying Patients with an Osteoarthritis Phenotype in the Electronic Medical Record

Introduction Electronic medical records (EMRs) are increasingly used in health services research. Accurate/efficient identification of a target population with a specific disease phenotype is a necessary precursor to studying the health of these individuals. Objectives and Approach We explored the use of biomedical text as inputs to supervised phenotype identification algorithms. We employed a two-stage classification approach to map the discrete, sparse high-dimensional biomedical text data to a dense low dimensional vector space using methods from unsupervised machine learning. Next we used these learned vectors as inputs to supervised machine learning algorithms for phenotype identification. We were able to demonstrate the applicability of the approach to identifying patients with an osteoarthritis (OA) phenotype using primary care data from the Electronic Medical Record Administrative data Linked Database (EMRALD) held at ICES. Results EMRALD contains approximately 20Gb of biomedical text data on approximately 500,000 patients. The unit of analysis for this study is the patient. We were interested in identifying OA patients using solely text data as features. Labelled outcome information wass available from a random sample of 7,500 patients. We divided patients into training (N=6000), validation (N=750) and test (N=750) cohorts. We learned low dimensional representations of the input text data on the entire EMRALD corpus (N=500,000). We used learned numeric vectors as inputs to supervised machine learning models for OA classification (N=6,000 training set patients). We compared models in terms of accuracy, sensitivity, specificity, PPV and NPV. The best learned models achieved approximately 90\% sensitivity and 80\% specificity. Classification accuracy varied as a function of learned inputs. Conclusion/Implications We developed an approach to phenotype identification using solely biomedical text as an input. Preliminary results suggest our two-stage ML approach has improved operating characteristics compared to existing clinically derived decision rules for OA classification. Future work will explore the generalizability of this methodology to other disease phenotypes

Learning Unsupervised Representations from Biomedical Text

Introduction Healthcare settings are becoming increasingly technological. Interactions/events involving healthcare providers and the patients they service are captured as digital text. Healthcare organizations are amassing increasingly large/complex collections of biomedical text data. Researchers and policy makers are beginning to explore these text data holdings for structure, patterns, and meaning. Objectives and Approach EMRALD is a primary care electronic medical record (EMR) database, comprised of over 40 family medicine clinics, nearly 400 primary care physicians and over 500,000 patients. EMRALD includes full-chart extractions, including all clinical narrative information/data in a variety of fields. The input data (raw text strings) are discrete, sparse and high dimensional. We assessed scalable statistical models for high dimensional discrete data, including fitting, assessing and exploring models from three broad statistical areas: i) matrix factorization/decomposition models ii) probabilistic topic models and iii) word-vector embedding models. Results EMRALD is comprised of 12 text data streams. EMRALD text data is structured into 84 million clinical notes (3.5 billion word/language tokens) and is approximately 18Gb in storage size. We employ a “text as data” pipeline, i) mapping raw strings to sequences of word/language tokens, ii) mapping token sequences to numeric arrays, and finally iii) using numeric arrays as inputs to statistical models. Fitted topic models yield useful thematic summaries of the EMRALD corpora. Topics discovered reflect core responsibilities of primary care physicians (e.g. women’s health, pain management, nutrition/diet, etc.). Fitted vector embedding models capture structure of discourse/syntax. Related words are mapped to similar locations of vector spaces. Analogical reasoning is possible in the embedding space. Conclusion/Implications “Text as data” requires an understanding of statistical models for discrete, sparse, high dimensional data. We fit a variety of unsupervised statistical models to biomedical text data. Preliminary results suggest that the learned low dimensional representations of the biomedical text data are effective at uncovering meaningful patterns/structure

When does the increased mortality risk appear in rheumatoid arthritis? A distributed data analysis comparing mortality in two Canadian provinces

Introduction Rheumatoid arthritis (RA) is chronic inflammatory arthritis. For decades studies showed that RA patients died earlier than their general population counterparts. Some inception cohorts have failed to detect an increased mortality risk, possibly due to limited follow-up or to improvement in mortality risk in cohorts of more recent onset. Objectives and Approach We evaluated mortality risk in RA patients and estimated when the increased risk appears. Using a common protocol, we conducted distributed analyses using administrative data, of incident RA patients in British Columbia (BC) and Ontario (ON) over 2000-2015. We identified all RA patients (using validated criteria), and identified non-RA comparators, matched 1:2 on age, sex and index years. Adjusted hazard ratios (HRs) were estimated using multivariable Cox regression, controlling for comorbidities and other factors. To estimate when the increased risk appeared we included an interaction with follow-up time, to detect if and how the HR varied by RA duration. Results Among 13834 RA patients in BC (27668 comparators), 66% were female with a mean age of 58 years at cohort entry. Among 27405 RA patients in ON (54810 comparators), 70% were female with a mean age of 56 years. The prevalence of individual comorbidities was comparable across RA cohorts. During follow-up, 23% of RA patients in each province died, with corresponding crude mortality rates of 2.3 deaths per 100 person-years in both provinces. Multivariable analyses detected an increased mortality risk in RA by 6 years of follow-up, with a linear relationship suggesting further increase over time. By 10 years, the adjusted HR was 1.14 (95% CI 1.07,1.22) in BC and 1.13 (95% CI 1.08,1.18) in ON. Conclusion/Implications In 2 large Canadian RA inception cohorts, a small increased mortality risk appeared after 6 years of RA duration and increased to a 14% (in BC) and 13% (in ON) increased mortality risk after 10 years, suggesting increased efforts to prevent disease progression and optimizing comorbidity management are needed

An administrative data validation study of the accuracy of algorithms for identifying rheumatoid arthritis: the influence of the reference standard on algorithm performance

BACKGROUND: We have previously validated administrative data algorithms to identify patients with rheumatoid arthritis (RA) using rheumatology clinic records as the reference standard. Here we reassessed the accuracy of the algorithms using primary care records as the reference standard. METHODS: We performed a retrospective chart abstraction study using a random sample of 7500 adult patients under the care of 83 family physicians contributing to the Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Using physician-reported diagnoses as the reference standard, we computed and compared the sensitivity, specificity, and predictive values for over 100 administrative data algorithms for RA case ascertainment. RESULTS: We identified 69 patients with RA for a lifetime RA prevalence of 0.9%. All algorithms had excellent specificity (>97%). However, sensitivity varied (75-90%) among physician billing algorithms. Despite the low prevalence of RA, most algorithms had adequate positive predictive value (PPV; 51-83%). The algorithm of “[1 hospitalization RA diagnosis code] or [3 physician RA diagnosis codes with ≥1 by a specialist over 2 years]” had a sensitivity of 78% (95% CI 69–88), specificity of 100% (95% CI 100–100), PPV of 78% (95% CI 69–88) and NPV of 100% (95% CI 100–100). CONCLUSIONS: Administrative data algorithms for detecting RA patients achieved a high degree of accuracy amongst the general population. However, results varied slightly from our previous report, which can be attributed to differences in the reference standards with respect to disease prevalence, spectrum of disease, and type of comparator group

How do we enhance linked administrative data based chronic disease surveillance in Canada? Results of an environmental scan.

Introduction The Canadian Chronic Disease Surveillance System (CCDSS) is a collaboration of provincial and territorial surveillance systems which generates estimates of chronic diseases using linked population-level administrative health databases and standard case definitions. We conducted an environmental scan of administrative data validation studies and identified opportunities for CCDSS case definition enhancement. Objectives and Approach The purpose of this project is to develop a methodology for and conduct an environmental scan, identifying opportunities for enhancing the CCDSS. This multifaceted approach consists of the following elements: 1) key informant interviews and stakeholder consultations to identify new and existing priority conditions for updating/validating within the CCDSS, and new areas of conceptual and methodological relevance for administrative data disease surveillance, 2) a systematic literature review of PubMed, Ovid and Embase from 2013-2017 using MeSH terms and a librarian peer-reviewed search strategy, and 3) a review of the grey literature. Results Key stakeholders identified the following priorities for validation work and/or case definition enhancement: diabetes, mood and anxiety disorders, schizophrenia, obesity, hypertension, chronic obstructive pulmonary disease, osteoarthritis, stroke, early-onset dementia, rheumatoid arthritis and gout. Scientific and grey literature reviews of validation work for these conditions examined the following concepts/methods: 1) evaluating validity of disease-specific case definitions over time, and in different ages, sub-populations and settings, 2) defining incidence versus prevalence using linked administrative data, 3) determining opportunities and constraints of using linked administrative data to conduct surveillance on diseases that are chronic versus episodic in nature and defining active versus lifetime prevalence, and 4) assessing the feasibility of using new sources of data for linkage to enhance case definition validity. Conclusion/Implications Utilization of linked administrative databases for chronic disease surveillance has expanded across many jurisdictions since the inception of the CCDSS. As disease estimates generated in this manner are increasingly being relied upon by policy makers working to enhance public health, the methodological opportunities and constraints identified here require consideration

Accuracy of Ontario Health Administrative Databases in Identifying Patients with Rheumatoid Arthritis (RA)

Rheumatoid arthritis (RA) is a chronic, destructive, inflammatory arthritis that places significant burden on the individual and society. This thesis represents the most comprehensive effort to date to determine the accuracy of administrative data for detecting RA patients; and describes the development and validation of an administrative data algorithm to establish a province-wide RA database. Beginning with a systematic review to guide the conduct of this research, two independent, multicentre, retrospective chart abstraction studies were performed amongst two random samples of patients from rheumatology and primary care family physician practices, respectively. While a diagnosis by a rheumatologist remains the gold standard for establishing a RA diagnosis, the high prevalence of RA in rheumatology clinics can falsely elevate positive predictive values. It was therefore important we also perform a validation study in a primary care setting where prevalence of RA would more closely approximate that observed in the general population. The algorithm of [1 hospitalization RA code] OR [3 physician RA diagnosis codes (claims) with ≥1 by a specialist in a 2 year period)] demonstrated a high degree of accuracy in terms of minimizing both the number of false positives (moderately good PPV; 78%) and true negatives (high specificity: 100%). Moreover, this algorithm has excellent sensitivity at capturing contemporary RA patients under active rheumatology care (>96%). Application of this algorithm to Ontario health administrative data to establish the Ontario RA administrative Database (ORAD) identified 97,499 Ontarians with RA as of 2010, yielding a cumulative prevalence of (0.9%). Age/sex-standardized RA prevalence has doubled from 473 per 100,000 in 1996 to 784 per 100,000 in 2010, with approximately 50 new cases of RA emerging per 100,000 Ontarians each year. Our findings will inform future population-based research and will serve to improve arthritis surveillance activities across Canada and abroad.Ph

Serious Infections in a Population-Based Cohort of 86,039 Seniors With Rheumatoid Arthritis

Objective. To assess risk and risk factors for serious infections in seniors with rheumatoid arthritis (RA) using a case-control study nested within an RA cohort. Methods. We assembled a retrospective RA cohort age >66 years from Ontario health administrative data across 1992-2010. Nested case-control analyses were done, comparing RA patients with a primary diagnosis of infection (based on hospital or emergency department records) to matched RA controls. We assessed independent effects of drugs, adjusting for demographics, comorbidity, and markers of RA severity. Results. A total of 86,039 seniors with RA experienced 20,575 infections, for a rate of 46.4 events/1,000 person-years. The most frequently occurring events included respiratory infections, herpes zoster, and skin/soft tissue infections. Factors associated with infection included higher comorbidity, rural residence, markers of disease severity, and history of previous infection. In addition, anti-tumor necrosis factor agents and disease-modifying antirheumatic drugs were associated with a several-fold increase in infections, with an adjusted odds ratio (OR) ranging from 1.2-3.5. The drug category with the greatest effect estimate was glucocorticoids, which exhibited a clear dose response with an OR ranging from 4.0 at low doses to 7.6 at high doses. Conclusion. Seniors with RA have significant morbidity related to serious infections, which exceeds previous reports among younger RA populations. Rural residence, higher comorbidity, markers of disease severity, and previous infection were associated with serious infections in seniors with RA. Our results emphasize that many RA drugs may increase the risk of infection, but glucocorticoids appear to confer a particular risk

Factors influencing rheumatology residents’ decision on future practice location

Background: There are regional disparities in the distribution of Canadian rheumatologists. The objective of this study was to identify factors impacting rheumatology residents’ postgraduate practice decisions to inform Canadian Rheumatology Association workforce recommendations.Methods: An online survey was developed, and invitations were sent to all current Canadian rheumatology residents in 2019 (n = 67). Differences between subgroups of respondents were examined using the Pearson χ2 test.Results: A total of 34 of 67 residents completed the survey. Seventy-three percent of residents planned to practice in the same province as their rheumatology training. The majority of residents (80%) ranked proximity to friends and family as the most important factor in planning. Half of participants had exposure to alternative modes of care delivery (e.g. telehealth) during their rheumatology training with fifteen completing a community rheumatology elective (44%).Conclusions: The majority of rheumatology residents report plans to practice in the same province as they trained, and close to home. Gaps in training include limited exposure to community electives in smaller centers, and training in telehealth and travelling clinics for underserviced populations. Our findings highlight the need for strategies to increase exposure of rheumatology trainees to underserved areas to help address the maldistribution of rheumatologists.Contexte : Au Canada, il existe des disparités régionales dans la répartition des rhumatologues. La présente étude recense les facteurs qui influencent les choix des résidents en rhumatologie concernant leur lieu d’exercice futur afin de guider les recommandations de Société canadienne de rhumatologie relatives aux effectifs.Méthodes : Après l’élaboration d’un sondage en ligne, une invitation a été envoyée à tous les résidents en rhumatologie au Canada en 2019 (n = 67). Les différences entre les groupes ont été examinées à l’aide du test Pearson χ2.Résultats : Trente-quatre des 67 résidents contactés ont répondu au sondage. Soixante-treize pour cent des répondants prévoyaient d’exercer dans la province où ils avaient fait leur formation en rhumatologie. La majorité des résidents (80 %) ont classé la proximité des amis et de la famille comme le facteur le plus important dans leur choix de lieu d’exercice. La moitié des participants s’étaient familiarisés avec d’autres modes de prestation de soins (par exemple, la télésanté) pendant leur formation en rhumatologie et 15 d’entre eux (44 %) avaient fait un stage en rhumatologie communautaire.Conclusions : La majorité des résidents en rhumatologie déclarent avoir l’intention d’exercer près de chez eux, dans la province où ils ont fait leurs études. Les lacunes dans la formation comportent l’exposition limitée à des stages dans les petits centres en milieu communautaire, en télésanté et dans les cliniques mobiles ciblant les populations mal desservies. Nos conclusions soulignent le besoin de stratégies visant à augmenter l’exposition des résidents en rhumatologie à des zones mal desservies afin de remédier à la mauvaise répartition géographique des rhumatologues