Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Biln, Norma Kor
Doctor of Philosophy - PhD
University of British Columbia
Background: There are two broad types of arthritis, and patient care differs by type. Those with inflammatory arthritis (IA) should be treated by a rheumatologist within 12 weeks of symptom onset, while those with non-IA, can benefit from multi-disciplinary care and lifestyle modifications. No standard referral tools are used to help primary care physicians (PCP) direct people with joint pain to the appropriate healthcare provider. Many patients wait needlessly, sometimes in the wrong queue, increasing their chances of disability. Objectives: First, to understand recent trends in patient referrals to rheumatologists in British Columbia (BC). Second, to identify and test the accuracy of a patient-completed questionnaire to classify and prioritize patients with IA for referral from primary care to rheumatologists and seek the perspectives of patients and rheumatologists for implementation considerations. Methods: Using administrative databases from Population Data BC, current rheumatology referrals trends were examined. A scoping review was performed to identify validated questionnaire(s) to evaluate in a BC clinical study: The Patient Self-Administered Inflammatory Arthritis Detection (SAID) study. A multi- and mixed methods approach was used to gain user perspectives of barriers and opportunities to implement a questionnaire in the referral pathway. Results: Rheumatology referral trends in BC showed long wait times for patients with IA and a high proportion of referral of patients with non-IA. Access improved between 2010-2020 but remained long, particularly for females and people living outside of metropolitan areas. The scoping review identified 53 studies of referral tools. Two tools were selected for The Patient SAID Study; the Early Inflammatory Arthritis Questionnaire (EIAQ) and the Case Finding Axial Spondyloarthritis (CaFaSpa) questionnaire. The reported EIAQ performance was not replicated, but a new, preliminary scoring algorithm, that added a back pain question, had promising performance (AUC=0.740, 95%CI 0.632-0.849) suggesting that EIAQ questions were informative. Patients and rheumatologists supported the use of the questionnaire for referrals and provided recommendations for clinical implementation. Conclusions: There is a need to improve the rheumatology referral process in BC and this thesis tested a patient-completed questionnaire that may support that. The findings suggest the potential value of the questionnaire to streamline referrals and access to care.
IMPROVING REFERRALS TO RHEUMATOLOGISTS FOR PATIENTS WITH INFLAMMATORY ARTHRITIS by Norma Kor Biln BSc., McGill University, 1993 MBA, University of Phoenix, 2001 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2024 © Norma Kor Biln, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Improving referrals to rheumatologists for patients with inflammatory arthritis submitted by Norma Kor Biln in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Population and Public Health Examining Committee: Mark Harrison, Associate Professor, Faculty of Pharmaceutical Sciences, UBC Co-supervisor Nick Bansback, Professor, Faculty of Medicine, School of Population and Public Health, UBC Co-supervisor Kam Shojania, Professor, Faculty of Medicine, Division of Rheumatology, UBC Supervisory Committee Member Mary De Vera, Associate Professor, Faculty of Pharmaceutical Sciences, UBC University Examiner Antonio Avina-Zubieta, Associate Professor, Faculty of Medicine, Division of Rheumatology, UBC University Examiner Additional Supervisory Committee Members: Charlyn Black, Professor, Faculty of Medicine, School of Population and Public Health, UBC Supervisory Committee Member iii Abstract Background: There are two broad types of arthritis, and patient care differs by type. Those with inflammatory arthritis (IA) should be treated by a rheumatologist within 12 weeks of symptom onset, while those with non-IA, can benefit from multi-disciplinary care and lifestyle modifications. No standard referral tools are used to help primary care physicians (PCP) direct people with joint pain to the appropriate healthcare provider. Many patients wait needlessly, sometimes in the wrong queue, increasing their chances of disability. Objectives: First, to understand recent trends in patient referrals to rheumatologists in British Columbia (BC). Second, to identify and test the accuracy of a patient-completed questionnaire to classify and prioritize patients with IA for referral from primary care to rheumatologists and seek the perspectives of patients and rheumatologists for implementation considerations. Methods: Using administrative databases from Population Data BC, current rheumatology referrals trends were examined. A scoping review was performed to identify validated questionnaire(s) to evaluate in a BC clinical study: The Patient Self-Administered Inflammatory Arthritis Detection (SAID) study. A multi- and mixed methods approach was used to gain user perspectives of barriers and opportunities to implement a questionnaire in the referral pathway. Results: Rheumatology referral trends in BC showed long wait times for patients with IA and a high proportion of referral of patients with non-IA. Access improved between 2010-2020 but remained long, particularly for females and people living outside of metropolitan areas. The scoping review identified 53 studies of referral tools. Two tools were selected for The Patient SAID Study; the Early Inflammatory Arthritis Questionnaire (EIAQ) and the Case Finding Axial Spondyloarthritis (CaFaSpa) questionnaire. The reported EIAQ performance was not replicated, but a new, preliminary scoring algorithm, that added a back pain question, had promising performance (AUC=0.740, 95%CI 0.632-0.849) suggesting that EIAQ questions were informative. Patients and rheumatologists supported the use of the questionnaire for referrals and provided recommendations for clinical implementation. Conclusions: There is a need to improve the rheumatology referral process in BC and this thesis tested a patient-completed questionnaire that may support that. The findings suggest the potential value of the questionnaire to streamline referrals and access to care. iv Lay Summary There are over 100 types of arthritis. People with the inflammatory types need early treatment by a rheumatologist while disease processes are most responsive, ideally within 12 weeks of symptom onset. Determining which patients need to see a rheumatologist most urgently is difficult, and the availability of rheumatologists is limited in many areas. Consequently, their waiting lists can be long, and include people who do not need to see rheumatologists, resulting in patients not getting the care they need in time. This research aimed to describe historical and current access of patients to rheumatologists, identify already-validated referral tools to streamline the referral process, and test them in a clinical study in British Columbia. The findings suggest that using a patient self-administered questionnaire is possible and could help to prioritize referrals. Patients’ and rheumatologists’ feedback indicated that a standardized referral tool may improve efficient and more equitable access to care. v Preface The identification and design of this thesis research was performed by Norma K Biln and began while she was taking SPPH 621 with close guidance from Dr. Charlyn Black who taught that course. Dr. Kam Shojania, as the rheumatologist lead on Ms. Biln’s supervisory committee, offered expert clinical viewpoints, introductions, and direction throughout this research. Dr. Nick Bansback closely oversaw all the stages of this thesis research and ensured proper decision-making, scope, and timely completion. Dr. Mark Harrison’s extensive input and feedback was critical each step of the way. Ms. Biln did the data collection, analysis, interpretation and writing for all chapters, except for data preparation of the Population Data BC. Ms. Daphne Guh prepared the data from administrative databases that formed the basis of the analyses in Chapter 3. With extensive consultation and guidance from Ms. Guh and Dr. Harrison, who have an in-depth understanding of the databases, Ms. Biln led the analysis in close collaboration with them. Dr. Bansback ensured alignment with and scope of Chapter 3 in the context of the thesis and Dr. Shojania provided clinical perspective to the methods, results and discussion. This Population Data BC study was approved by the UBC Clinical Research Ethics Review Board (H16-02934), and the project was supported by the Canadian Institute of Health Research (Competition 201603) and a Strategic Operating Grant from The Arthritis Society (SOG-16-369). The following article was published as a version of Chapter 3; Biln NK, Guh D, Bansback N, Shojania K, Harrison M. The association of rheumatologist supply and multidisciplinary care with timely patient access to rheumatologists: evidence from British Columbia, Canada. Arthritis Care Res., 76 (4), 2024. The Chapter 2 scoping review search strategy was aided by reference librarian Ms. Ursula Ellis, and close guidance and oversight was provided by Dr. Lorri Puil to ensure adherence to PRISMA guideline best practices. Dr. Harrison and Dr. Bansback were second reviewers at the title/ abstract and full text review stages of the scoping review. Dr. Shojania provided clinical input on disease nomenclature and categorization. Patient partner, Ms. Samantha Rogers’ feedback on the findings enhanced the discussion content and Ms. Mary Berger assisted with the administrative steps to prepare the journal submission. The following article was published as a version of Chapter 2; Biln NK, Bansback N, Shojania K, Puil L, Harrison M. A scoping review of triage approaches for the referral of patients with suspected inflammatory arthritis, from primary to rheumatology care. Rheumatol Int. 2024; Available from: https://doi.org/10.1007/s00296-024-05575-8. Interim data for both Chapters 2 and 3 were presented as posters at the UBC Centre for Health Services and Policy Research (CHSPR) Conference in Vancouver in 2022. The REDCap project set up in Chapter 4 for The Patient SAID Study was prepared for data collection by Mr. Victor Trot who assisted with technical troubleshooting throughout the project. vi Ms. Guh assisted with data preparation and statistical analysis guidance for this study. Dr. Shojania introduced Ms. Biln to participant rheumatologists and helped categorize arthritis conditions for analysis. Dr. Adam Easterbrook provided input on the phrasing of questions and on analysis approach for the qualitative data in Chapter 5. Dr. Black closely oversaw and offered direction on the multi-methods approach and data illustration of Chapter 5 and Ms. Rogers assisted with the interpretation of results and development of implementation recommendations based on the user perspectives. The studies of Chapter 4 and 5 underwent review by the UBC Clinical Research Ethics Review Board (H21-03719). Stacey Berquist, professional editor from Q Editing, collated the chapters of the thesis ensuring correct layout, formatting, and referencing. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ........................................................................................................................ vii List of Tables .............................................................................................................................. xiv List of Figures ............................................................................................................................. xvi List of Abbreviations ............................................................................................................... xviii Acknowledgements ......................................................................................................................xx Dedication ................................................................................................................................... xxi Chapter 1: Introduction ................................................................................................................1 1.1 Types and Prevalence of Arthritis ....................................................................................... 1 1.1.1 Burden of Disease ........................................................................................................ 1 1.1.2 Management of IA and Non-IA ................................................................................... 2 1.1.3 Current Management of IA .......................................................................................... 3 1.1.4 Rheumatology Referrals in BC .................................................................................... 4 1.2 Rationale for this Research ................................................................................................. 5 1.3 Hypothesis ........................................................................................................................... 5 1.4 Research Objectives ............................................................................................................ 6 1.5 Dissertation Outline ............................................................................................................ 7 Chapter 2: A Scoping Review of Triage Approaches for the Referral of Patients with Suspected Inflammatory Arthritis, from Primary to Rheumatology Care ............................11 2.1 Introduction ........................................................................................................................11 viii 2.2 Methods ............................................................................................................................. 13 2.2.1 Protocol and Registration ........................................................................................... 13 2.2.2 Eligibility Criteria ...................................................................................................... 13 2.2.3 Information Sources and Search ................................................................................ 14 2.2.4 Study Selection .......................................................................................................... 15 2.2.5 Data Charting Process ................................................................................................ 15 2.2.6 Synthesis of Results ................................................................................................... 16 2.3 Results ............................................................................................................................... 16 2.3.1 Study Selection .......................................................................................................... 16 2.3.2 Study Characteristics.................................................................................................. 19 2.3.3 Disease Nomenclature of Conditions and Triage Approaches .................................. 22 2.3.4 Results of Studies by Rheumatologic Condition ....................................................... 25 2.3.4.1 Inflammatory Arthritis and Related Terms ......................................................... 25 2.3.4.2 Axial Spondyloarthritis ....................................................................................... 26 2.3.4.3 Rheumatoid Arthritis ........................................................................................... 27 2.3.4.4 Psoriatic Arthritis ................................................................................................ 27 2.3.4.5 Systemic Lupus Erythematosus .......................................................................... 28 2.3.4.6 Rheumatoid Arthritis & Axial Spondyloarthritis ................................................ 28 2.3.4.7 Musculoskeletal Conditions ................................................................................ 29 2.3.5 Synthesis .................................................................................................................... 29 2.4 Discussion ......................................................................................................................... 30 2.5 Conclusion ........................................................................................................................ 34 ix Chapter 3: The Association of Rheumatologist Supply and Multidisciplinary Care with Timely Patient Access to Rheumatologists: Evidence from British Columbia, Canada .......36 3.1 Introduction ....................................................................................................................... 36 3.2 Materials and Methods ...................................................................................................... 38 3.2.1 Data Sources .............................................................................................................. 38 3.2.2 Definition of Timepoints and Lag Times ................................................................... 39 3.2.3 Patient Demographics and Variables ......................................................................... 40 3.2.4 Statistical Methods ..................................................................................................... 41 3.2.4.1 Changes in Case-Mix and Lag Time ................................................................... 41 3.2.4.2 Variables Associated with Lag Time Outcomes.................................................. 41 3.3 Results ............................................................................................................................... 42 3.3.1 Changes in Rheumatologist First Visit Case-Mix Over Time ................................... 44 3.3.2 Time from PCP Referral to First Rheumatologist Visit ............................................. 46 3.3.3 RA Cohort Characteristics and Changes in Lag Time ............................................... 46 3.3.4 Univariate and Multivariable Associations with RA Patient Lag Times ................... 50 3.4 Discussion ......................................................................................................................... 52 Chapter 4: The Patient SAID (Self-Administered Inflammatory Arthritis Detection) Study........................................................................................................................................................56 4.1 Background ....................................................................................................................... 56 4.2 Methods ............................................................................................................................. 58 4.2.1 Study Procedure ......................................................................................................... 58 4.2.2 Participant Characteristics.......................................................................................... 60 4.2.3 Symptom Questionnaire Scoring ............................................................................... 61 x 4.2.4 Statistical Methods ..................................................................................................... 62 4.2.4.1 Discriminative Performance of Validated Questionnaires .................................. 62 4.2.4.2 Exploratory New Regression Equation to Predict IA ......................................... 63 4.2.4.3 Bootstrap Internal Validation Method of Exploratory Models ........................... 64 4.2.4.4 Sample Size Calculation ..................................................................................... 64 4.2.5 Timepoint Analysis .................................................................................................... 64 4.3 Results ............................................................................................................................... 65 4.3.1 Participant Sample and Characteristics ...................................................................... 65 4.3.2 Rheumatologists’ Diagnostic Reference Standard Distribution ................................ 68 4.3.3 Performance of EIAQ for Time-Sensitive IA ............................................................ 69 4.3.4 Performance of Functional Difficulty (Q08) for Time-Sensitive IA ......................... 70 4.3.5 Performance of Exploratory Models of SAIDx ......................................................... 70 4.3.5.1 Predicting a Narrow IA Outcome ....................................................................... 70 4.3.5.2 Predicting a Broad IA Outcome .......................................................................... 72 4.3.6 Internal Validation of Model 3 and Step AIC for Narrow and Broad IA Definitions 73 4.4 Discussion ......................................................................................................................... 74 4.5 Conclusion ........................................................................................................................ 81 Chapter 5: Patient and Rheumatologist Perspectives of the Patient SAID Tool: Implementation Considerations, Potential Barriers, and Opportunities ...............................83 5.1 Background ....................................................................................................................... 83 5.2 Methods ............................................................................................................................. 85 5.2.1 Participants and Data Collection ................................................................................ 85 5.2.2 Theoretical Framework .............................................................................................. 86 xi 5.2.3 Patient Surveys and Rheumatologist Interview Questions ........................................ 86 5.2.4 Data Analysis and Illustration .................................................................................... 87 5.3 Results ............................................................................................................................... 89 5.3.1 Patient Participant Perspectives ................................................................................. 89 5.3.2 Rheumatologist Participant Perspectives ................................................................... 96 5.3.3 Case-Mix and Source of Referrals ............................................................................. 96 5.3.4 Window of Opportunity and Effectiveness of Current Referrals .............................. 99 5.3.5 Rheumatologist Capacity Varies by Geographical Area ......................................... 100 5.3.6 Views on the Patient-Self Administered Questionnaire and its Performance ......... 102 5.3.7 Potential for the Tool to Improve on the Current Referral Approach and its Implementation ................................................................................................................... 103 5.3.8 Patient Partner Suggestions on How to Improve the Questionnaire ........................ 106 5.4 Discussion ....................................................................................................................... 106 5.4.1 Recommended Improvements to the Questionnaire ................................................ 107 5.4.2 Questionnaire Performance and Perceived Utility Along the Continuum of the Patient Journey ................................................................................................................................ 107 5.4.3 Use of the Tool to Streamline Geographical Distribution of Rheumatology Care .. 108 5.4.4 Study Strengths and Limitations .............................................................................. 109 5.5 Conclusion .......................................................................................................................110 Chapter 6: Thesis Discussion ....................................................................................................111 6.1 Introduction ...................................................................................................................... 111 6.2 Key Findings .................................................................................................................... 111 6.3 Limitations .......................................................................................................................114 xii 6.3.1 Patient Participants and Recruitment ....................................................................... 114 6.3.2 The Heterogeneity Inherent to the Study Context ................................................... 115 6.3.3 Implementation Risk Considerations ....................................................................... 116 6.4 Strengths ...........................................................................................................................117 6.5 Implementation Opportunities .........................................................................................118 6.5.1 The Role for a Patient Self-Administered IA Referral Questionnaire ..................... 118 6.5.2 Use of Patient-Reported Outcome Measures (PROMs) in Rheumatology .............. 119 6.5.3 Optimize with Digital Health Connectivity ............................................................. 120 6.5.4 Healthcare Initiatives in BC that Uniquely Position the Province to Leverage the Patient SAID Approach ...................................................................................................... 120 6.5.5 Future Work to Enable Implementation of a Triage Approach ............................... 121 6.6 Conclusion ...................................................................................................................... 122 References ...................................................................................................................................123 Appendices ..................................................................................................................................150 Appendix A: Triage Approaches and Study Characteristics by Condition Referred to Rheumatologists ...................................................................................................................... 150 Appendix B: Search Strategy .................................................................................................. 162 Appendix C: Scoping Review List of Patient Questions for Self-Reported Measures........... 164 Appendix D: Population Data BC/Chapter 3 Study Supplemental Methods ......................... 169 Appendix E: Population Data BC Matching Rules to Decide on Sample Frame ................... 172 Appendix F: ICD-9 Codes and Diagnoses .............................................................................. 173 Appendix G: Univariate Associations of Variables with Lag Times ...................................... 174 Appendix H: Definition of Time Points and Associated Lag Times ....................................... 175 xiii Appendix I: Population Data BC Study Sample Derivation ................................................... 176 Appendix J: Plots with Distribution of Lag Times by Year and Condition ............................ 177 Appendix K: Plots of Distribution of Lag Times for the sample of patients with RA ........... 178 Appendix L: CaFaSpa Questions and Scoring Method .......................................................... 179 Appendix M: SAID Study Sample Size Calculation Grid ...................................................... 180 Appendix N: SAID Study Rheumatology Site Profiles .......................................................... 181 Appendix O: Performance of CaFaSpa................................................................................... 182 Appendix P: SAIDx Regression Model Outputs .................................................................... 183 Appendix Q: Outputs of Model Bootstrap Internal Validation ............................................... 185 Appendix R: EIAQ Questions Included in Various Studies ................................................... 186 Appendix S: Questions Asked to Rheumatologist Participants .............................................. 187 Appendix T: Illustration of SAIDx Results Presented to Rheumatologists ............................ 189 xiv List of Tables Table 2.1 Triage methods designed to be performed by primary care physician (PCP) for referral, or by rheumatologists to triage the referral ................................................................................... 23 Table 2.2 Number of studies using single or multiple triage components, and by disease type .. 24 Table 3.1 Patient sample characteristics at the first rheumatologist visit, for the overall cohort, and between those people who had their first rheumatologist visit matched to a date of referral or not ................................................................................................................................................. 43 Table 3.2 Characteristics of people who received a DMARD within two years of their first rheumatologist visit ....................................................................................................................... 48 Table 3.3 Multivariable models of total lag between referral and first DMARD for people with RA ................................................................................................................................................. 51 Table 4.1 EIAQ questions, algorithm and scoring method ........................................................... 59 Table 4.2 Patient characteristics, overall, by time-sensitive (TS) IA, other IA and non-IA diagnosis ....................................................................................................................................... 66 Table 4.3 Median lag times with IQR ........................................................................................... 67 Table 4.4 Differences in time from physician referral to first rheumatologist visit (lag 1) based on sex and geography where patients reside ...................................................................................... 67 Table 4.5 Diagnoses, their frequency and categorization as time-sensitive (TS) IA, other IA, and non-IA .......................................................................................................................................... 68 Table 4.6 Exploratory models of SAIDx for Narrow and Broad IA outcome definitions............ 71 Table 5.1 Patient participant feedback mixed methods integrated matrix. Results centered on the response to the survey question “Do you think that these questions could add to your primary care physician's assessment of your condition?” .......................................................................... 92 xv Table 5.2 Main themes from patient participants, by reference frequency. In response to the open-ended question, “Are there any comments you have about the questionnaire in general, or specifically like questions that you think it should include, that it doesn't?” ............................... 93 Table 5.3 Highlights of rheumatologist feedback by interview question ..................................... 97 xvi List of Figures Figure 2.1 Prisma 2020 flow diagram (for searches of databases, and other sources) ................. 18 Figure 2.2 Countries where participants in studies were recruited (including multinational studies) .......................................................................................................................................... 20 Figure 2.3 Cumulative number of reports of triage tools by disease category 2002–October 27, 2023............................................................................................................................................... 21 Figure 3.1 Change in the case-mix of first visits to rheumatologists between the years 2011/12–2019/20 ......................................................................................................................................... 45 Figure 3.2 Median days from primary care physician referral to a first rheumatologist visit (Lag 1) by disease type, 2011/12–2019/20............................................................................................ 47 Figure 3.3 Median days for people with RA from primary care physician (PCP) referral to first rheumatologist visit (Lag 1), to first DMARD (Lag 2) and from PCP referral to first DMARD (Total Lag) over the study period ................................................................................................. 49 Figure 4.1 Discriminative performance of EIAQ; ROC area under the curve and diagnostic test metrics ........................................................................................................................................... 69 Figure 4.2 Distribution of broad IA SAIDx scores and ROC curve for Model 3 step AIC ......... 73 Figure 5.1 Number and proportion of patient respondents to closed-ended questions with ordinal data. a) “How easy was it to login to answer this questionnaire?” and “How easy was it to complete the questionnaire?”, b) “To what extent did the questionnaire include the important aspects of your condition?” and “Do you think that these questions could add to your primary care physician's assessment of your condition?” .......................................................................... 91 Figure 5.2 Main themes and frequency by diagnostic categories, TS-IA, other IA, and non-IA . 94 xvii Figure 5.3 Integrated feedback of patients and rheumatologists of barriers and opportunities for implementation of the patient self-administered questionnaire .................................................. 105 xviii List of Abbreviations ACR/EULAR American College of Rheumatology/European League Against Rheumatism AIC Akaike Information Criterion ASAS The Assessment of SpondyloArthritis International Society AxSpa Axial Spondyloarthritis AUC Area Under the Curve BASDAI Bath Ankylosing Spondylitis Disease Activity Index BC British Columbia CaFaSpa Case Finding Axial Spondyloarthritis CARE Clinical Arthritis RulE CI Confidence Interval CLBP Chronic Low-Back Pain DAD Discharge Abstract Database DMARD Disease-Modifying Anti-Rheumatic Drug DOAJ Directory of Open Access Journals DxOR Diagnostic Odds Ratio EARP Early Arthritis for Psoriatic Patients EATS Electronic Arthritis Triage Strategy EIAQ Early Inflammatory Arthritis Questionnaire EMR Electronic Medical Record FN False Negative FP False Positive FY Fiscal Year GP General Practitioner HAQ Health Assessment Questionnaire IA Inflammatory Arthritis IBP Inflammatory Back Pain ICD International Classification of Diseases IJD Inflammatory Joint Disease IQR Interquartile Range IRD Inflammatory Rheumatic Diseases JBI Joanne Briggs Institute KPI Key Performance Indicators LHA Local Health Area LR Likelihood Ratios MCA Multidisciplinary Care Assessment MCP Metacarpophalangeal xix MSK Musculoskeletal MSP Medical Services Plan MTP Metatarsophalangeal NPV Negative Predictive Value OA Osteoarthritis OSF Open Science Framework PASE Psoriatic Arthritis Screening Evaluation PASQ Psoriasis and Arthritis Screening Questionnaire Patient SAID Self-Administered Inflammatory Arthritis Detection PCP Primary Care Physicians PEST Psoriasis Epidemiology Screening Tool PICO Population, Intervention, Comparison and Outcome PPV Positive Predictive Value PROMs Patient-Reported Outcome Measures PsA Psoriatic Arthritis PRISMA-ScR Preferred Reporting Items of Systematic Reviews and Meta-Analyses Extension for Scoping Reviews RA Rheumatoid Arthritis REDCap Research Electronic Data Capture RDCI Rheumatic Disease Comorbidity Index Rheum Rheumatologist ROC Receiver Operating Characteristics – also ‘Operating’ SD Standard Deviation SLE Systemic Lupus Erythematosus SN Sensitivity SP Specificity SpASSS SpA Screening questionnaire in Sub Saharan Africa SSC Specialist Services Committee T0 Date of Referral from PCP T2 Date of Initiation of a first DMARD T1 First Visit to Rheumatologist TN True Negative ToPAS Toronto Psoriatic Arthritis Screen TP True Positive TS-IA Time-Sensitive Inflammatory Arthritis UA Undifferentiated Arthritis xx Acknowledgements The preface of this document shows the extensive support and expertise that I had the privilege to draw on to complete my PhD thesis. Many of these individuals are employed by Advancing Health (formerly CHEOS) and I am grateful for access to these experts. The greatest thanks go to my supervisory committee, Drs. Nick Bansback, Mark Harrison, Kam Shojania, and Charlyn Black who offered the perfect amount of support, guidance, challenge, sensibility, and encouragement at every stage. The introductions they made, roadblocks that they removed when I was stuck, and the momentum that they helped me keep, was extraordinary. I owe initial thanks to Dr. Black who back in 2017 helped me prove that I could do a PhD that led to me being accepted to the program in 2018 and to bringing together the excellent supervisory committee. I want to thank the patient and rheumatologist participants of The Patient SAID Study, and patient partner Samantha Rogers, whose contributions to my learning and the recommendations of this thesis were paramount. I am also particularly thankful to the rheumatologists’ medical office assistants who enabled my recruitment of patients for the clinical study, in particular, Carly Roberts, Cyndi Rosenbaum, and Tamra Clarke. I’m also grateful to my fellow PhD student peers who helped me get through the comprehensive exams during the pandemic and who became family that summer. Thank you to those who nominated and voted for me to be the Graduate Student Representative on the search committees for the UBC Chancellor, Vice-Principal and Associate Vice-President Research and Innovation (Okanagan) and the SPPH Director. I have been very lucky to be part of this school community. I want to thank my family who watched me relish in every moment of this PhD journey, even at the hardest times. They wondered why I was doing it at this stage of my career, and I explained how it was a charging outlet for me to thrive through six otherwise very intense personal years. Thank you for being so interested and always asking how it was going. xxi Dedication For Serge, Dalal, and Teresina, who each experienced life-threatening events in a 2-month span during my PhD. Your triage experience inspired my research to listen to what The Patient SAID. 1 Chapter 1: Introduction 1.1 Types and Prevalence of Arthritis Arthritis is a term that covers over 100 different types of joint conditions but can be generally divided into the two broad categories of inflammatory arthritis (IA) and non-inflammatory arthritis (non-IA). They differ in their underlying mechanisms, symptoms, and treatment approaches (1). IA is characterized by inflammation of the joints and surrounding tissues, often involving an autoimmune component where the body’s immune system attacks its own tissues (2). Types of IA include predominantly rheumatoid arthritis (RA), psoriatic arthritis (PsA), ankylosing spondylitis or axial spondyloarthritis (AxSpa), gout, and systemic lupus erythematosus (SLE). RA has an annual incidence from 115 to 271 per 100,000 adults and estimated prevalence of 0.6% in the US and between 0.65-0.78% in Canada (2). Non-IA, often referred to as degenerative or mechanical arthritis, primarily involves the wear and tear of the joints. The main type of non-IA is osteoarthritis (OA), which is the most common form of arthritis overall (3). OA affects about 10% of men and 13% of women aged 60 years and older globally (4). Another form is traumatic arthritis, resulting from joint injuries, but its specific prevalence varies widely depending on the incidence of such injuries in different populations (5). 1.1.1 Burden of Disease These socioeconomic burdens that both IA and non-IA impose on individuals and society are extensive and multifaceted. Chronic pain and disability are common in both IA and non-IA, severely impacting patients' quality of life. Patients with IA, such as those with RA and SLE, often report significantly lower quality of life scores due to chronic pain, fatigue, and broader health issues beyond joint pain (6). Patients with OA also experience a reduced quality of life, with 2 chronic pain, reduced mobility, and depression being common due to the persistent nature of the disease (7). Both IA and Non-IA impose substantial economic burdens. The direct medical costs for IA tend to be higher due to the cost of advanced treatments like biologics, which can range from $20,000 to $30,000 per year in the US (8). In contrast, non-IA costs are driven by surgical interventions, such as joint replacements costing between $12,000 per procedure in Canada (9), and long-term management, with annual direct medical costs for OA ranging from $700 to $15,600 per patient based on a US report (10). Both types of arthritis lead to significant work disability and lost productivity. In RA patients for example, within ten years of diagnosis, the cumulative work disability prevalence in the US is 44%, with individuals leaving the workforce earlier due to severe symptoms (11). Similarly, about 23-39% of patients with PsA experience work disability (12). On the other hand, OA is a leading cause of work disability, with costs estimated to range from $3.4 to $13.2 billion per year in the U.S. (13), with approximately 30% of patients with OA reporting some degree of disability affecting their work performance (14). 1.1.2 Management of IA and Non-IA IA, notably RA, requires the care of a rheumatologist and demands more urgent treatment than non-IA due to its rapid progression, potential for irreversible joint damage and recognition that early treatment with disease-modifying antirheumatic drugs (DMARDs) and biologics can slow or halt disease progression, preventing joint deformities and disability (15). Moreover, IA can lead to systemic complications beyond the joints (16), necessitating prompt management to mitigate serious health consequences and has a higher likelihood of causing work disability, underscoring the importance of early treatment to preserve employment and productivity. Timely 3 intervention in IA improves treatment response, disease outcomes, and long-term prognosis, emphasizing the critical need for prompt diagnosis and therapeutic intervention within 12 weeks of symptom onset (17). Prompt treatment of patients with IA under the care of a rheumatologist is crucial. However non-IA, such as OA should also be attended to promptly. While it may progress more slowly than IA, it still causes the individual significant pain and functional impairment. Early intervention helps patients manage their pain and improves quality of life, enabling individuals to maintain daily activities and mobility (18). Additionally, timely treatment can slow the progression of joint degeneration, preserving joint function and preventing disability. By addressing non-IA early, individuals can reduce the need for costly surgical interventions like joint replacement surgery and minimize healthcare utilization associated with uncontrolled pain and disability (19). Overall, prompt treatment of non-IA is essential for alleviating pain, maintaining independence, and reducing long-term healthcare costs, emphasizing the importance of early intervention in managing arthritis effectively. In both cases, the sooner the intervention is started, the better, and in the case of IA, appropriate disease modifying drugs should be started within 12 weeks of symptom onset for optimal outcomes (20). 1.1.3 Current Management of IA The proportion of patients with IA who do not receive treatment within the window of opportunity varies depending on factors such as access to healthcare, disease awareness, and healthcare provider expertise (21). However, studies suggest that a significant number of patients with IA experience delays in diagnosis and initiation of appropriate treatment. For example, in RA, delays in diagnosis and treatment initiation are common, with one study reporting that over 4 one-third (35.6%) of patients with incident RA were initiated on DMARDs >90 days after presentation, which are the cornerstone of RA treatment (22). These delays can result in missed opportunities to prevent joint damage and disability. Similarly, delays in the diagnosis and management of other forms of IA, such as PsA (23) and AS, can lead to prolonged disease activity and increased risk of irreversible joint damage and functional impairment (24). While patients with IA and non-IA require different care, in British Columbia (BC) both types are often referred to rheumatologists by primary care physicians (PCP),1 who typically do not communicate the nature and urgency of each referral to the rheumatologist, reducing the efficiency of care delivery for patients with IA or non-IA and likely impacting outcomes. 1.1.4 Rheumatology Referrals in BC Patients in BC must be referred by a primary healthcare provider to access specialist care and the referral expires after 365 days if it is not accepted during that time. There is no specific system in place to direct rheumatology referrals in BC and the approach to making and accepting them is highly variable. Adult rheumatologists are primarily in private practice and are paid fee for service on a patient visit or procedure basis. Most GP referrals go to a private practice, usually a local rheumatologist, when possible, in major centers. Wait lists can vary from a few weeks to two years and it is up to the patient and the GP to consider referring to rheumatologists with a shorter wait list, as there is no automatic re-directing. There are some small groups of two to five rheumatologists who organize their practices together and cover each other, share patients and may triage as a group, or not. Less than 5% of the ~100 rheumatologists travel out of their clinics, and they are salaried through the Mary Pack Arthritis Program. 1 Throughout this dissertation primary care physician (PCP) and general practitioner (GP) are use interchangeably. 5 1.2 Rationale for this Research Health systems worldwide, and inter-provincially within Canada, differ from each other substantially. Despite their differences, one priority that most share is to reduce the cost of healthcare delivery while achieving good clinical outcomes (25–27). This is sometimes referred to as ‘right care, right place, right provider and right time’ and has received increasing emphasis since the emergence of digital health tools that enable early intervention and chronic disease prevention (28,29). Upstream intervention to improve outcomes in IA has been a rheumatology clinical priority in the last decade because appropriate treatments and care pathways have been shown to change the trajectory of disease and the corresponding economic burden (20,30). Currently in BC, when a PCP sees a patient with joint pain who they suspect of having a rheumatologic condition that needs specialist care, they write a referral note that may briefly state ‘arthritis, please see’. Other referrals may include a lengthy elaboration or even a phone call from the PCP. With the former example, even if the patient has IA, they may be seen at the end of the rheumatologist’s waitlist and miss the 12-week window of opportunity for optimal outcomes. Streamlining the process at the referral stage with a referral tool could potentially reduce the heterogeneity of the information provided, make it possible for the PCP to make the right referral in the first place, and support the rheumatologist to properly prioritize visits. 1.3 Hypothesis The hypothesis of this thesis is that a validated referral tool that accurately differentiates IA from non-IA, with stakeholder feedback incorporated, may be integrated into the referral pathway for patients to complete, to expedite their access to the right care. 6 1.4 Research Objectives The aim of this thesis is to examine how the use of a triage tool may improve the referral of patients between PCPs and rheumatologists in a healthcare system such as in BC. The objectives within this overall aim were to evaluate the accuracy of a referral tool and make recommendations for its potential implementation in BC by; 1. Identifying a validated tool that may be suitable for PCP-rheumatology referrals in BC (Chapter 2) to be tested in a clinical study (Chapter 4). 2. Characterizing recent trends and the current state of PCP-rheumatologist patient referrals in BC (Chapter 3). 3. Evaluating the accuracy of a referral tool and understanding the timeline from symptom onset to initiation of treatment (Chapter 4). 4. Determining the barriers and opportunities for tool implementation, from the perspective of patients and clinicians (Chapter 5). 5. Recommending next steps to advance the implementation agenda, of this action-oriented research (Chapter 6). This thesis is from the secondary care/ specialist viewpoint. This choice was made because as a first step, characterization of the problem and its potential solutions could be best informed by those who provide the ongoing care to patients with IA. Also, from a pragmatic standpoint, this allows more confidence in the definition of the sample in administrative data (Chapter 3) and the timely and concentrated recruitment of patients referred to rheumatologists for the tool evaluation study (Chapter 4). 7 1.5 Dissertation Outline To identify a validated tool that may be suitable for PCP-rheumatology referrals in BC, a scoping review was performed, and is presented in Chapter 2. This was done by 1) systematically reviewing the literature for existing tools along the PCP-rheumatologist referral pathway, 2) mapping out the characteristics and range of methodologies for their clinical use and, 3) determining their level of validation for use in a publicly funded healthcare system. The outcome is defined as the proportion of IA versus non-IA referrals made using the tool, where the standard is the rheumatologist diagnosis. The review identified several validated referral tools including the Early Inflammatory Arthritis Questionnaire (EIAQ) and the Case Finding Axial Spondyloarthritis (CaFaSpa) questionnaires that were selected based on the minimal overlap in the questions and the level of validation for prospective evaluation in The Patient SAID Study (Chapter 4). To characterize the current state of PCP-rheumatologist patient referrals in BC, administrative data from Population Data BC was examined in Chapter 3, to identify the historical trends and recent state of 1) rheumatologist referral case mix of IA versus non-IA, musculoskeletal (MSK) and other conditions, 2) patient time from primary care to first rheumatology visit, 3) time to DMARD treatment for patients with IA and 4) demographic variables that are associated with lag times. We find that the recent state of rheumatology patient referrals appears to be improving significantly since system level changes were made to increase rheumatologist supply and add a billing code that allows rheumatologists to charge for nursing time. The proportion of patients with IA in the case-mix of rheumatologist first visits improved to a higher proportion of IA, and the time to see a rheumatologist from PCP referral decreased. However, it seemed that the improvement had plateaued by 2017/18, and the time from referral to first DMARD for people 8 with RA remained too long. Signals were also detected that indicated that females and people living outside of metropolitan areas had poorer access to specialist rheumatology services. In Chapter 4, The Patient Self-Administered Inflammatory Arthritis Detection Study or The Patient SAID Study, aimed to evaluate the performance of the EIAQ and CaFaSpa questionnaires in discriminating IA from non-IA in newly referred rheumatology patients in BC. The secondary aims are exploratory; 1) to assess whether the potential discriminative performance of these instruments could be improved by using different combinations of questions from EIAQ and CaFaSpa questionnaires, 2) to examine the referral case-mix and time points from symptom onset through to rheumatologist visit and DMARD initiation for patients referred through the Patient SAID Study where applicable, and 3) compare these with the estimates derived from the administrative data study in Chapter 3. The study found that the discriminative performance of the EIAQ reported by the original authors is not replicated in a post-pandemic patient population in BC. However, in an exploratory analysis, the 12-item EIAQ questionnaire, with one question added related to the presence of back pain and a new algorithm, does discriminate IA from non-IA. This thesis provides preliminary evidence of the potential discriminative validation (internal) of these new algorithms. However, the failure to replicate the performance of the EIAQ and CaFaSpa measures externally in The Patient SAID Study provides a cautionary warning that the performance of these newly proposed algorithms will need to be replicated in larger and/or external patient populations. The continued signal of unequal access based on sex and geography was detected in this study, consistent with the findings in Chapter 3. A recommendation to implement a standardized tool might enable streamlined, equal, and timely access to rheumatology care. To consider how the routine collection of the patient self-administered questionnaires used in The Patient SAID Study may be potentially integrated into the healthcare system, patient and 9 clinician participant perspectives were collected and presented in Chapter 5. The multi and mixed-methods study aimed to; 1) analyze the feedback that patient participants in the SAID study provided about the prototype referral questionnaire, and 2) gain the perspective of rheumatologists on their current referral practices as well as the barriers and opportunities related to the potential implementation of such a tool for referrals in clinical practice in BC. These participants indicated that the patient self-administered questionnaires used in The Patient SAID Study, with some recommended changes to the content of the tool, could help improve on current referral processes if the proper mechanism for its implementation is worked out. The questionnaire could play an important role in educating patients on joint pain symptoms that require them to seek care and help inform the decision of PCP to refer patients to rheumatologists. Another role could be to supplement or replace the referral letters that rheumatologists receive with referrals, which are known to often lack salient information (31). This could support a more streamlined triage process for prioritizing urgent patients with IA to be seen by the rheumatologist and redirecting patients with non-IA to the care they need. Chapter 6 outlines the thesis 1) key findings, 2) limitations and strengths, 3) implementation opportunities in the context of healthcare trends and why BC is uniquely positioned to leverage the Patient SAID approach, and 4) next steps to advance the implementation agenda of this action-oriented research. BC has already paved the way through models to assess and plan for such innovations and arthritis experts and the province already have momentum with proposals to integrate such tools. Trends in the movement towards shared decision making with patients, the increased adoption of e-health technologies and a constrained health system offer an opportunity to 10 accelerate the implementation of tools to streamline referrals such as those used in the Patient SAID Study. 11 Chapter 2: A Scoping Review of Triage Approaches for the Referral of Patients with Suspected Inflammatory Arthritis, from Primary to Rheumatology Care2 2.1 Introduction Timely and appropriate referrals between PCP and specialists are critical for improving outcomes for patients and healthcare systems (33–35). Appropriateness, in the context of rheumatology referrals from a conceptual standpoint, would be where patients who require ongoing rheumatology care are referred by the PCP and those who don’t are not. However, there will be first referrals who require clinical assessments to determine whether they need ongoing rheumatology care or not. Waiting to access care allows disease to progress and the disease burden to increase (34). In RA for example, the most common IA, a 12-week window between onset of symptoms and treatment is thought to dramatically improve outcomes (20), underscoring the importance of optimizing this part of the continuum of care (31,36,37). Reasons for missing this window of opportunity for treatment may be patient-related, for example, help-seeking behaviour, and/or system-related, such as inadequate access to PCPs, or failure to refer to a rheumatologist for treatment (38–41). The process of referring patients between PCPs and rheumatologists is complicated by the limited supply of rheumatologists, high demand for rheumatology services (42), and difficulty for PCPs in identifying the patients who will benefit from referral to rheumatologists (43–45). Patients 2 Biln NK, Bansback N, Shojania K, Puil L, Harrison M. A scoping review of triage approaches for the referral of patients with suspected inflammatory arthritis, from primary to rheumatology care. Rheumatol Int [Internet]. 2024 Mar 26 [cited 2024 Mar 30]; Available from: https://doi.org/10.1007/s00296-024-05575-8. (32) 12 with RA in the UK and Netherlands average four visits with their PCP before being referred to a rheumatologist (46–48), and estimates from multiple countries suggest about half of rheumatology referrals are inappropriate (43–45,49). Consequently fewer than half of appropriate specialist visits occur within 12 weeks of RA symptom onset (20,50–52). The main reasons for delays in referral by PCPs are a lack of knowledge and awareness of IA disease manifestations (53,54) and incentives and processes to streamline referrals (55). There is considerable interest in improving the referral process for IA, including RA, AxSpa, PsA, gout and connective tissue diseases. A systematic review of strategies to improve IA referrals and reduce delays found 47 studies describing interventions targeting three delays; 1) symptom onset to PCP, 2) PCP referral to rheumatology, and 3) rheumatology referral to assessment (55). Interventions to tackle these delays included priority referral scores (56), decision rules (57,58), structured referral forms (56,59), online symptom checkers (60,61), patient self-administered questionnaires (51,62), and referral triage performed by rheumatologists, other health professionals, and/or early arthritis clinics (31). Nineteen of the 47 studies (40%) were strategies focussing on the primary care to rheumatology referral phase to promote earlier access to care for people with IA. A decade on from the last review, this scoping review updates the evidence on interventions aimed at improving the primary care to rheumatologist referral process for people with IA. This review is also timely, given the opportunity to integrate these interventions afforded by the accelerated adoption of telehealth interventions during the COVID-19 pandemic (63). The specific aims are to 1) identify existing triage approaches for referral of patients with suspected IA from PCPs to rheumatologists, 2) describe their characteristics and approaches and 3) report their validation for use in a publicly funded healthcare system. This review is part of a larger research 13 program to select and test an existing triage approach for use in the publicly funded healthcare system of the province of BC, Canada. 2.2 Methods 2.2.1 Protocol and Registration A scoping review protocol was drafted in accordance with the Joanne Briggs Institute (JBI) Manual for Evidence Synthesis (64), and prospectively published on the Open Science Framework (OSF); final version published June 2, 2021, prior to data extraction (65). Protocol revisions are documented in OSF. The conduct and reporting of the review were also guided by the JBI Manual (64) and the Preferred Reporting Items of Systematic Reviews and Meta-analyses Extension for Scoping Reviews (PRISMA-ScR) (66). 2.2.2 Eligibility Criteria Primary studies that were published or available as unpublished full study reports were eligible for inclusion. The search strategy did not restrict by language, but only studies reported in English were included in the final review. Books, reviews, opinion papers and other grey literature sources were not included but were reviewed for eligible studies. Conference abstracts were excluded but abstracts of potentially eligible studies were forward searched for full reports which, if they met inclusion criteria, were included. Quantitative studies of any design were included. A PICO framework was used for eligibility criteria. The population was adults (aged 18 years or older) presenting to their PCP with symptoms suggestive of IA (the target condition). Interventions of interest were PCP-level approaches for identifying individuals with IA who required a rheumatology referral. The comparator was the eventual rheumatologist's diagnosis of an IA condition or a reference standard indicative of the appropriateness of the referral such as 14 usual PCP referral. The outcome was the proportion of referrals considered to be appropriate, based on the reference standard. The performance of triage approaches in identifying ‘appropriate referrals’ against a reference standard was used as construct validity. Measures of ‘performance’ included measures of diagnostic test accuracy such as the area under the receiver-operator characteristic curve (AUC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) and positive and negative likelihood ratios (LR), and measures of reliability including internal consistency, test-retest reliability, and inter-rater reliability. The reporting of these outcomes was considered to be the threshold for an approach or study being in the ‘validation phase’. The performance of approaches was assessed in terms of their technical characteristics, using the AUC where available, to see which approaches performed better than chance (i.e. ≥0.5), and then were subsequently classified as poor (0.5-0.7), acceptable (0.7-0.8), excellent (0.8-0.9), or outstanding (>0.9) (67). 2.2.3 Information Sources and Search Index terms and text words contained in the titles and abstracts of an initial limited search of Ovid® MEDLINE and Embase, were used to develop a MEDLINE (Ovid) search strategy. The search strategy was developed in collaboration with a trained librarian and tested to find relevant articles known to the research team. The MEDLINE search strategy (Appendix B) was adapted for each of the other databases, namely EBSCO CINAHL, EMB Review – Cochrane Database of Systematic Reviews and Scopus, as well as Google Scholar and the Directory of Open Access Journals (DOAJ). The Web of Science Core Collection was searched for grey literature. All databases were searched from inception to October 27, 2023. Reference lists of relevant systematic reviews were reviewed using CitationChaser (backward citation chaining) (68), to identify any 15 additional primary studies. Included studies were screened by forward as well as backward citation chaining for additional studies. 2.2.4 Study Selection All identified citations were collated and uploaded to Zotero® citation management system Version 5.0.94/2020 (Corporation for Digital Scholarship, Virginia, USA), then imported to Covidence systematic review software for screening (Veritas Health Innovation, Melbourne, Australia), and duplicates removed. Title/abstract and full text screening were conducted independently by two reviewers (NKB + MH or NB). Discrepancies were either resolved by a third vote from the non-voting screener (MH or NB) or through consensus by the two screeners. KS, a rheumatologist, and clinical content expert on the team, helped refine elements of the PICO and reviewed the final selection of studies. The search results and the study inclusion process, including reasons for exclusion, were summarized using the PRISMA 2020 flow diagram (66,69). A list of individual reasons for study exclusion at the full-text level is available upon request. 2.2.5 Data Charting Process A data extraction tool in Microsoft Excel® was developed by NKB in consultation with NB, MH, and LP, piloted independently by NKB and MH on three studies, and subsequently refined. Data were extracted by one reviewer NKB with verification by MH. Any discrepancies were resolved by discussion between NKB and MH. The data extraction spreadsheet included details of the study design, sample size, sex, mean age and other characteristics of the patient population, the type of triage approach (index test), target disease(s) and/or condition(s), person completing the tool, format used, outcomes reported, including data on validation, evidence of clinical adoption, and country where the study was 16 conducted. Study design and type of triage approach included key contextual data; 1) design for development and validation, such as consensus-derived or retrospective/prospective testing in the population of interest, 2) specific stage of referral process targeted (i.e. use at the referral stage by PCP or prior to first appointment by the rheumatologist), 3) the components of the intervention, such as patient-reported symptoms, clinical exams, lab tests and/or imaging, or combinations of these, 4) inclusion/exclusion criteria reflecting the representativeness of the sample of the primary care population. 2.2.6 Synthesis of Results The number of studies over time was summarized and compared between diseases/conditions and by geographical area. Triage approaches were categorized according to whether they support PCPs at the referral stage, or rheumatologists in screening and prioritizing referrals. Studies were categorized in consultation with KS into these two broad triage approaches and then by disease/condition defined as IA and related terms, specific IA subtypes (e.g. AxSpa, RA, PsA, SLE or combinations of these), or other MSK symptom conditions. Triage approach validation information reported by the authors of these studies was summarized using these classifications. The components of triage approaches, including patient questions, clinical exams, labs, imaging, or photographs, alone or in combination, were tabulated. 2.3 Results 2.3.1 Study Selection After duplicates were removed, 13992 records were screened at the title/abstract level; citations were primarily identified from the comprehensive search of electronic databases (n=13952), with 40 identified from other sources (primary study authors n=5, citation searching n=35). Of these, 425 records were potentially eligible and underwent full-text screening after 17 which, 55 reports were included, 369 were excluded (see Figure 2.1), and one could not be retrieved. Agreement between reviewers was high at the title and abstract screening stage (NKB and NB: 95.9% (k=0.281); NKB and MH: 94.7% (k=0.377)) and at full text review (NKB and MH: 85.3% (k=0.0.509)). 18 Figure 2.1 Prisma 2020 flow diagram (for searches of databases, and other sources) Records identified from Databases (n = 19840) Ovid Embase: 7522 Web of Science: 5144 Ovid Medline: 2432 EBSCO CINAHL: 2029 Google Scholar: 1630 Scopus: 1020 DOAJ: 44 CDSR: 19 Records screened (title & abstract) (n =13952) Records excluded (n = 13567) Reports sought for retrieval (n = 385) 53 studies in 55 records included in review (n = 55) Duplicate records removed before screening (n = 5888) Reports not retrieved (n = 0) Identification Reports assessed for eligibility (full text review) (n =385) Screening Included Reports excluded: (n=344) Abstract only (n=179) No triage intervention (n=74) Not at PCP - rheum referral part of care continuum (n=36) Professional screened between PCP & rheum visit (n=25) Duplicate (n=9) Review (n=9) Not this PICO outcome (n=8) Language other than English (n=3) Non-MSK disease (n=2) Records identified from: Primary study authors (n = 5) Citation searching (n = 35) Reports assessed for eligibility (n = 39) Reports excluded: (n=24) Not this PICO outcome (n=9) Abstract only (n=3) No triage intervention (n=5) Not at PCP - rheum referral part of care continuum (n=3) Duplicate (n=2) Review (n=2) Identification of studies via databases Identification of studies via other methods Reports sought for retrieval (n = 40) Reports not retrieved (n = 1) 19 2.3.2 Study Characteristics Most studies (n=33) were from Europe, primarily Germany (n=9), the Netherlands (n=8), the United Kingdom (n=10) (Figure 2.2) or North America, six from Canada and four from the United States (US). There were four multinational studies, which included 34 countries in Europe (n=22), North America (n=3), Asia (n=2), Middle Eastern Arab Countries (n=6) and South America (n=1) Most (91%) studies came from countries with primarily publicly funded healthcare systems, and five (9%) from the US studies, although one included patients from the US and Europe (70), one indicated that 57% of the patients in the study were covered by public funds (Medicare, Medicaid, Tricare) (71), and one specified that the approach used was suitable in public healthcare systems (72). Most included studies were published after 2012 and the number of studies of triage increased rapidly after 2012 (Figure 2.3). Approaches relying solely on patient-reported questions were first published in 2009 (73) with a further seven by 2013 (26,62,74–77). Notably, of the five studies published in 2023, three used machine learning methods to develop and validate triage models (78–80). 20 Figure 2.2 Countries where participants in studies were recruited (including multinational studies) 0 1 2 3 4 5 6 7 8 9 10 11 12GermanyUKNetherlandsCanadaUSAAustraliaItalyBelgiumDenmarkHungarySaudi ArabiaAustriaNorwaySwedenSwitzerlandFranceGreeceSpainPortugalTurkeyRomaniaRussiaSlovak RepublicSloveniaCzech RepublicIsraelKuwaitBahrainEgyptIraqLebabonQatarUnited Arab EmiratesSubsaharan AfricaTaiwanIndiaVenezuelaColombiaNumber of studiesCountry21 Figure 2.3 Cumulative number of reports of triage tools by disease category 2002–October 27, 2023 0246810121416182002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023Cumulative number of studiesYearIA AxSpa RA SLE PsA RA & AxSpa MSK22 2.3.3 Disease Nomenclature of Conditions and Triage Approaches There was substantial heterogeneity both in the conditions targeted by individual studies as well as the disease nomenclature used by study investigators. The 48 included reports are listed in Appendix A and grouped using the definitions of the conditions from the studies as follows: 18 (33%) classified as “Inflammatory Arthritis and Related Terms” group (inflammatory joint disease (IJD), undifferentiated arthritis (UA), autoimmune and inflammatory rheumatic diseases (IRD) and a combination of IA and IRD), 30 (54%) “Specific types of IA” (AxSpa, RA, PsA, and SLE); two studies (4%) that “Combined RA & AxSpa”, and five (9%) “MSK Symptom Conditions”, which included hand arthritis, joint pain and rheumatic diseases. The 55 studies comprised 49 (89%) PCP-level triage approaches designed to help PCPs make appropriate referrals and 6 (11%) designed to help rheumatologists triage referrals (Table 2.1). Of the 49 PCP triage approaches, 14 (29%) were for ‘IA and Related Terms’, 30 (61%) for specific types of IA, most frequently AxSpa (n=15) and RA (n=8), two (4%) were for the ‘Combined Specific IA Conditions’ of RA & AxSpa, and three (6%) for ‘Conditions with MSK Symptoms’. The six rheumatologist-level triage tools were split between IA (n=4) and ‘MSK Symptom Conditions’ (n=2). Most studies (n=45; 82%) were prospective, six (11%) were retrospective, and four (7%) were developed through case scenarios. The rheumatologist diagnosis was the reference standard for all studies, but 13 of the 55 reports compared existing approaches (i.e. two or more index tests) (70,74,80–89); five in AxSpa and three for IA and four for PsA. There were 57 unique triage approaches assessed in the 55 reports (Table 2.2) and 23 of these reports assessed 8 approaches; 1 in IA (26,62,75,90), 4 in AxSpa (77,80,83,84,88,91–94), 2 in RA (95,96), 1 in PsA (70,73,74,85,89,97) and 2 in MSK (98,99). 23 Table 2.1 Triage methods designed to be performed by primary care physician (PCP) for referral, or by rheumatologists to triage the referral PCP for referral (n=49) Rheumatologist to triage referrals (n=6) Development or Validation Approach Development or Validation Approach Prospective Retrospective Case Scenarios Prospective Retrospective Case Scenarios Studies (n) 49 41 4 4 6 4 2 0 IA & Related Terms 14 11 1 2 4 3 1 0 Inflammatory Arthritis (IA) 10 8 1 1 2 2 0 0 Undifferentiated Arthritis 1 1 0 0 0 0 0 0 Inflammatory Rheumatic Diseases (IRD) 3 2 0 1 0 0 0 0 Autoimmune and IRD 0 0 0 0 1 0 1 0 Inflammatory Joint Disease 0 0 0 0 1 1 0 0 Specific Conditions 30 26 3 1 0 0 0 0 Axial Spondyloarthritis (AxSpa) 15 15 0 0 0 0 0 0 Rheumatoid Arthritis (RA) 8 4 3 1 0 0 0 0 Psoriatic Arthritis (PsA) 6 6 0 0 0 0 0 0 Systemic Lupus Erythematosus (SLE) 1 1 0 0 0 0 0 0 Combination of Conditions 2 2 0 0 0 0 0 0 RA & AxSpa 2 2 0 0 0 0 0 0 Musculoskeletal (MSK) Conditions 3 2 0 1 2 1 1 0 Hand Arthritis 2 2 0 0 0 0 0 0 Joint Pain 1 0 0 1 0 0 0 0 Rheumatic Diseases 0 0 0 0 2 1 1 0 24 Table 2.2 Number of studies using single or multiple triage components, and by disease type Triage approaches (n) % IA AxSpa RA PsA SLE RA & AxSpa MSK Patient questions only 22 40% 9 6 6 1 Patient questions + clinical exam + labs + imaging 14 25% 5 7 1 1 Patient questions + clinical exam + labs 4 7% 1 2 1 Patient questions + clinical exam 4 7% 2 1 1 Patient questions + labs 4 7% 1 3 Patient questions + pictures 1 2% 1 Patient questions + clinical exam + picture 1 2% 1 Clinical exam + labs 3 5% 1 1 1 Labs only 2 4% 2 Total number of reports (%) 55 100% 18 15 8 6 1 2 5 Number of unique triage approaches 57 4 17 18 8 7 1 2 25 2.3.4 Results of Studies by Rheumatologic Condition 2.3.4.1 Inflammatory Arthritis and Related Terms There were 18 studies of IA and IA-related terms (Appendix A). Four evaluated interventions for rheumatologists receiving referrals with the objective to differentiate patients that needed to be seen from those that did not, or to prioritize among those needing to be seen (71,100–102). Some studies also evaluated the diagnostic accuracy of the triage approach for specific arthritic conditions (87,103,104). All approaches, except one (78) used patient-reported questions, and 9 of the 18 studies only used patient-reported questions (26,62,75,76,87,90,103–105). The remaining 9 approaches combined patient-reported questions with laboratory testing, clinical examination, and imaging (n=5), or clinical examination (n=2), with labs added (n=1) and one with clinical exam and labs. Seven approaches included laboratory tests (56,58,71,78,86,100,102), most commonly rheumatoid factor (RF) (n=5). Three studies compared the referral accuracy of existing approaches to each other in IA (86,87,104). The EIAQ was assessed for consistency between different settings and languages in four studies (26,62,75,90) Sixteen of the 18 reports used an outcome that met our criteria of being in the validation phase. Most commonly (n=11), SN and SP (58,71,76,86,87,90,100,102,104), the PPV and NPV (n=10) (26,58,62,71,76,86,87,90,102,104), AUC (n=8), comprising 11 tools (26,57,62,78,86,90,101,104). SN ranged from 5.5% (58) to 98% (71), and SP from 34% (71) to 100% (86). In studies using patient-reported questions alone, SN ranged from 42.6% (87) to 86% (104) and SP from 51.8% (87) to 92.7% (76). The PPV ranged from 29.6% (76) to 100% (86) and NPV from 27% (86) to 99.5% (76); in approaches using patient-reported questions alone the PPV ranged from 35.4% (87) to 92% (90) and NPV from 69.3% (87) to 99.5% (76). Eight reports comprising 11 approaches reported that AUCs ranged from 0.66 (poor) (104) to 0.915 26 (outstanding) (62); three studies reported AUC values that suggested excellent (26,86,90) and two outstanding (62,78) discriminative ability in IA. 2.3.4.2 Axial Spondyloarthritis Fifteen studies assessed 18 triage approaches (five in multiple studies) in AxSpa (Appendix A), with inclusion criteria that affect the pre-test probability of disease. Most required people to have had chronic low-back pain (CLBP) symptoms lasting from >2 months (77) to ≤2 years (84) and age of onset ≤45 years of age, but in four, a record of CLBP from a PCP was required (86,106–108). The sample frame for two studies was patients referred by primary care specialists, e.g. orthopedists (77,91), thereby likely representing an enriched study population with higher pre-test probability of disease. Seven studies compared the referral outcomes of existing approaches (80–84,88,109) All approaches utilized patient-reported questions (Table 2.2), and in six these were the only data collected (77,81,93,94,109,110). The remaining nine either combined patient-reported questions with clinical examination (n=1), laboratory tests (n=1), or clinical examination with laboratory tests and imaging (n=7). All seven approaches using laboratory tests included human leukocyte antigen B27 (HLA-B27) (77,80,83,84,88,91,106,110), and three also included c-reactive protein (CRP) (80,84,109). The 15 AxSpa citations came from 13 unique studies, and nine reported outcomes meeting the definition of the validation phase, most commonly SN (n=8), SP (n=8) or AUC (n=5) (77,80,84,91,93,94,108–110). Five reports covering 4 unique studies only reported the percentage diagnosed which suggested they were in the development phase (81–83,88,91). The AUC was reported for five AxSpa triage approaches and ranged from 0.70 (acceptable) to 0.967 (outstanding) (77,93,94,106,110). In the four that relied solely on patient-reported questions, the 27 AUC ranged from 0.70 (acceptable) to 0.967 (outstanding) (77,93,94,110). Two studies reported AUC values with outstanding discriminative ability (106,110); the other three were acceptable (77,93,94). For studies reporting SN and SP (77,80,84,92–94,106,109) the range was 67.1% (80) to 98% (84) and 11% (84) to 79.7% (80), respectively. For approaches that only used patient-reported questions, SN ranged from 75% (94) to 97.4% (80) and SP from 46.4% (77) to 96.4% (80). 2.3.4.3 Rheumatoid Arthritis There were eight studies of seven unique triage approaches for RA. Some study populations included people who had blood tests ordered or were positive for target conditions (72,95,96,111,112), thereby representing a study sample with a high pre-test probability of the disease. Five approaches included patient-reported questions, either with laboratory tests (n=3) or with laboratory tests and clinical examination (n=2). The other three studies used laboratory tests alone (n=2) or a combination of clinical examination and laboratory tests (n=1). Six of the eight studies using laboratory tests used RF, and two used RF alongside anti–cyclic citrullinated peptide (anti-CCP) antibody levels. Six of the eight studies in RA report outcomes meeting the definition of the validation phase (72,79,95,96,112,113). Four report the PPV or NPV (72,96,112,113), four with SN and SP (79,96,112,113). SN ranged from 57.8% (96) to 96.69% (79) and SP from 56% (113) to 97.60% (79), and where the PPV and/or NPV was reported (72,96,112,113), the PPV ranged from 21.4% (95) to 77% (72) and NPV from 68.4% (112)to 98.7% (96). 2.3.4.4 Psoriatic Arthritis There were 6 reports evaluating triage approaches in PsA. In some, the sample frame was patients who were referred by their primary care specialists, e.g. dermatologists (70,74,97,114), thereby 28 representing an enriched study population with a potentially higher pre-test probability of disease. Some study populations included previously diagnosed patients (70,73,97), again representing a study sample with a higher pre-test probability of the disease to be referred. All approaches in PsA used patient-reported questions alone. Five of the 6 reports in PsA compared the referral accuracy of existing approaches (i.e. two or more index tests) to each other (70,73,74,85,89). All six studies in PsA report outcomes that indicate to us that they are in the validation phase; all report SN and SP, five report the AUC (73,74,85,89,97), and two report the PPV and NPV (70,97). Six approaches reported for PsA studies had AUCs between 0.56 (poor) and 0.91 (outstanding) (70,73,74,85,89,97); all relied on patient-reported outcomes alone. The reported SN, ranged based on score cut-off of the various approaches from 56% to 98% (97), although these came from a single study. Outside of this study, the sensitivities ranged within three other studies (70,73,89), also based on approach, from 0.64 to 0.92. SP was reported in all six PsA studies and for the various approaches ranged from 34% (89) to 85% (97). 2.3.4.5 Systemic Lupus Erythematosus The only SLE approach, used a combination of clinical examination and laboratory tests (115), and reported accuracy, AUC, SN and SP, and positive and negative LR meeting our criteria for being in the validation phase. The AUC reported was 0.98 (outstanding). 2.3.4.6 Rheumatoid Arthritis & Axial Spondyloarthritis Two studies evaluated triage approaches for RA and AxSpa together (59,116) using similar components as approaches for the conditions in isolation; patient-reported questions either along with clinical examination (n=1), or clinical examination, laboratory tests, and imaging (n=1). ESR 29 and CRP were used in both approaches, with one also including HLA-B27 (116). Both studies reported outcomes that meet our criteria for being in the validation phase. 2.3.4.7 Musculoskeletal Conditions Five studies evaluated triage approaches for broadly characterized MSK conditions such as hand arthritis, joint pain and rheumatic disease, and aimed to determine the correct diagnosis rather than the need for referral (44,98,99,117,118). All approaches used patient-reported questions either alone (n=1), with photographs (n=1), or with clinical examination and either laboratory tests (n=1), photographs (n=1), or laboratory tests and imaging (n=1). All studies met our criteria of being in the validation phase; four reported SN, SP and the PPV, and three the percentage with a “correct diagnosis”. SN and SP (98,99,117,118), ranged from 10.9% (117) to 99% (98) and 37.4% to 98.9% (117), respectively. PPV ranged from 16.4% (117) to 97.3% (98), and in the studies reporting accuracy (44,98,99), the range was 84.8% (44) to 95.2% (99). 2.3.5 Synthesis The studies reviewed evaluated triage approaches to support patient referral from PCPs to rheumatologists. Comparisons of the performance of triage approaches is beyond the scope of this review. However, the AUC is indicative of discriminatory performance and for the 21 of the 55 records that reported AUCs for 25 referral triage approaches, the performance ranged from 0.55 (poor) to 0.98 (outstanding). A small number of approaches in IA had reported AUC from multiple studies with consistent results, these were in the outstanding and excellent range for the EIAQ (0.915 (62), 0.829 (26), 0.88 (90)), acceptable for the CaFaSpa (0.75 (93), 0.70 (94)), and poor to outstanding for the Psoriasis Epidemiology Screening Tool (PEST) (0.610 (74), 0.652 (85), 0.70 and 0.76 (97), 0.71 (89) and 0.91 (73)), although the acceptable AUC values came from studies of populations with a high pre-test probability of disease. 30 The questions used in 19 of the 22 (40%) studies of approaches that relied only on patient-reported questions are shown in Appendix C; the remaining three used approaches with varying start points and decision trees (57,87) or a pain diagram (117). The patient-reported questions primarily assessed disease symptoms and functional ability parameters. There was considerable overlap in questions used by approaches within conditions but approaches for diagnosing AxSpa, and PsA included questions that were distinct from those in general IA approaches. Based on the number of studies evaluating a triage approach, with evidence of at least acceptable AUCs performance from a sample with pre-test probabilities reflective of a referral cohort, the most promising tools appeared to be the EIAQ and CaFaSpa. Since these both rely solely on patient-reported questions, with no overlap in the questions asked and were developed in publicly funded healthcare systems, these were selected to be put forward in a clinical study in BC, Canada. 2.4 Discussion This scoping review identified a range of referral and triage approaches covering the continuum of development, ranging from relatively newly developed approaches requiring further supportive evidence of their performance, to more established approaches with a growing, supportive evidence base. The approaches identified by this review primarily came from North America and Europe, which is consistent with the finding of the previous review (55). Studies focused on triage approaches also grew over the years, and the more recent studies have started to apply machine learning methods to triage approach development and validation. This trend is expected to continue in the advent of telemedicine, where electronic consultations with specialists now make up between 34-92% of visits (63), and increasingly so since the COVID-19 pandemic. Validated tools 31 could play an important role in ruling in IA and ruling out non-IA diagnosis for determining who may need a prompt in-person visit to a rheumatologist. A patient self-administered questionnaire can be done ahead of time to triage access to rheumatologists virtually or in-person, with uncertain cases being seen first by telemedicine (perhaps with a trained nurse), thereby contributing to the more efficient use of healthcare resources. Approaches with accumulating evidence in the validation phase of excellent and/or outstanding discriminative validity were found, often supported with evidence of the test characteristics of SN and SP, or predictive ability. This gathered evidence will help readers identify promising tools for their settings. The findings of these studies will likely have value for improving the care of patients in these regions, particularly in countries with publicly funded healthcare systems that rely on explicit forms of rationing to manage the supply and demand of resources within budget constraints. Rationing by selection places the PCP’s role as the gatekeeper to specialist services such as rheumatologists. The gatekeeper role is critical, requiring the physician to identify people with likely IA quickly and accurately and referring them to rheumatologists for timely treatment, which is essential for a good prognosis. However, the referral process can be long and complicated. MSK conditions account for up to one-third of primary care visits; symptoms of IA vary widely, and there has been a reported lack of knowledge and awareness of IA in primary care (46–48). Excitingly, in IA, some of the tools found with the best evidence of discriminative performance (i.e. EIAQ (26,62,75,90)) and Clinical Arthritis RulE (CARE) (104)) require only patient-reported questions meaning they could easily be incorporated into the referral process to support PCP decision-making in the short time they have available to consult with patients (119). These self-reported questions could be completed ahead of time or in the waiting room and then incorporated into the consult by the physician. It is important to consider the additional burden that this places on individuals, but some 32 studies have found that completing patient-reported questionnaires can support meaningful consultations and shared decision-making (120). One concerning finding was that few studies from Africa, South America and Asia were identified. Patients in these areas are likely experiencing similar (if not more significant) difficulties in accessing care, so there is a clear need for further development of tools for these settings and testing the transferability of the studies identified for other contexts. Whether the triage approaches identified in this review are useful will ultimately depend on their performance in improving clinical decision-making (121,122). The evidence in this review generally suggests that the triage approaches are performing well in terms of their technical characteristics, with most reporting at least acceptable discriminative ability based on the AUC (67). There was less evidence about the performance of the test in practice in terms of providing information that changed the likelihood that an individual requires a referral to a rheumatologist; a quarter of the studies identified reported predictive values, positive and negative. However, these values are influenced by the underlying prevalence of the disease in the study sample. In many studies identified in this review, the inclusion criteria used might have increased the background prevalence of disease in the study sample, which would lead to higher estimates of the PPV (and lower estimates of the NPV) than would be seen in a representative sample of a symptomatic primary care population. The critical metric of test performance is the degree to which they reduce diagnostic uncertainty by revising the probability that an individual does or does not have a condition that requires a specific intervention (122). While very few studies in this review provided this information, there appear to be enough quantitative reports for which a systematic review and meta-analysis could provide a more comprehensive analysis of performance. There is a need for further testing of the approaches identified by this review in prospective studies using 33 representative samples of patients seen in real life by PCPs and compared to usual referral practice. In addition, most approaches were for a general grouping of IA conditions, that might be sufficient for referral, but further research that explores supplementing general approaches with more specific questions, for example, those relating to AxSpa, might be valuable. Since the patient questions were similar within conditions but not across them, approaches at the PCP level that use patient self-administered questionnaires only would need to identify a broader range of conditions but be sensitive enough to detect specific ones as well. Heterogeneity in disease nomenclature, clinical settings, patient inclusion criteria and statistical outcome reporting limited the comparison of approach performance in this scoping review. Despite this, the preliminary view of the validity of these approaches is that they have merit in specific settings and could improve on the current referral process. Returning to the motivation for conducting this review, to identify potential triage approaches to test in a cross-sectional clinical study of a triage approach at the primary care referral stage in BC, Canada, the evidence collated led to the choice of the EIAQ (26,62,75,90) along with CaFaSpa (93,94) on the basis of the volume of evidence, the high performance (particularly discriminative), the representativeness of the populations in which these approaches were tested in reflecting a non-specialist primary care referral to rheumatology context, and the lack of overlap in patient-reported questions. There are some limitations of this scoping review that should be considered. In general, the nomenclature used in the included studies was heterogeneous, even for specific types of arthritis. This might mean that there is some misclassification and overlap in the categorization of studies. Secondly, as a scoping review, the aim was to identify and summarize the available literature on referral approaches and to indicate future research requirements. An assessment of the risk of bias in the individual studies was not undertaken, and a fuller assessment of the validity and 34 performance of the approaches within specific populations is needed. However, the findings of this review are valuable in describing the expanded array and development of approaches designed to support referral of patients from primary care to specialist services for IA. Another limitation is that there is great heterogeneity among healthcare systems and a major part of the world is not represented in these studies. The care pathways in developed versus developing countries may be quite different. Even countries like Australia that have resources spread out over thinly populated regions may behave differently or require alternate strategies. Also, although a comprehensive search was performed, there is a possibility that studies may have been missed. Regional databases were not searched, only studies written in English were included, and few studies in regions other than North America and Europe were identified. However, rigorous and transparent methods were used throughout the development, execution and reporting of the review, in alignment with published guidelines of best practice (123). This included developing an a priori protocol (65), consultations with a patient partner to inform the approach, and searching a broad range of databases, the grey literature, and the reference lists of any identified sources. Finally, qualitative studies that may have provided insight into barriers and facilitators of the referral process and issues related to the implementation of different triage approaches were not included. This information may be valuable for optimizing various tools or their uptake. 2.5 Conclusion This scoping review identifies the current evidence on approaches to streamline the referral of patients with IA from primary to rheumatology care. The existing triage approaches, including the EIAQ, CaFaSpa and PEST, have the potential to improve on current referral processes, and there may be a need to shift from ongoing development of additional tools to the testing and comparison of the performance of existing tools in real-life settings. As healthcare systems 35 recalibrate post-pandemic, having shown an ability to adapt and incorporate new ways of working, there is an opportunity to test triage approaches in the context of clinical practice changes, for their potential integration. 36 Chapter 3: The Association of Rheumatologist Supply and Multidisciplinary Care with Timely Patient Access to Rheumatologists: Evidence from British Columbia, Canada3 3.1 Introduction Across the world, healthcare systems are seeking to optimize care for patients whilst achieving efficiency through changes that include redesigning clinical services, balancing workloads and staffing levels, expanding the scope of practice, and enhancing team-based care. In BC, Canada, a 2010 survey forecasted a crisis in the provision of rheumatology services; the province had an insufficient number of rheumatologists, and concerningly, half of these were set to retire within a decade (125). In response, BC has expanded rheumatology capacity through recruitment of more newly-trained rheumatologists (125), and new billing codes for extended consultations and a ‘multidisciplinary care assessments’ to enable and incentivize collaborative care of patients with nurses (126). This reflects an evolution of rheumatology services over the past two decades and changes in rheumatology clinical guidelines and practice that have created an evolving opportunity to optimize care for better outcomes (127). Advances in the treatment of people with arthritis have allowed rheumatologists to improve the long-term prognosis of disease, reducing disability and improving quality of life, however, this requires patients to be seen in a timely and appropriate way. The various forms of arthritis require different treatments and management. IA, such as RA, is driven by inflammation and if untreated, often develops rapid joint damage. People with IA require DMARDs and ongoing care by a 3 Biln NK, Guh D, Bansback N, Shojania K, Harrison M. The association of rheumatologist supply and multidisciplinary care with timely patient access to rheumatologists: evidence from British Columbia, Canada. Arthritis Care Res. 2024;76(4):444–53. (124) 37 rheumatologist; in RA, these should be initiated within 12 weeks from symptom onset, the so-called ‘window of opportunity’ (20). In non-IA conditions such as OA, inflammation plays a less prominent role in joint damage (128); and people with these conditions do not require rheumatologist care but could benefit from different interventions and/or health behaviour changes that could be delivered by allied health professionals like physiotherapists and multi-disciplinary care (129,130). Regardless of the type of arthritis, early intervention generally leads to improved outcomes. To meet the ‘window of opportunity’, people with symptoms of arthritis need to be referred by their PCP to the correct health care provider who will be responsible for managing their condition, herein referred to as “appropriate referrals.” However, people with non-IA conditions are often initially referred to rheumatologists by PCPs. These inappropriate referrals for rheumatologists affect both people with IA and those with non-IA. People with non-IA wait for a rheumatologist appointment during a time when interventions like education, over-the-counter medication, exercise programs and lifestyle changes, with support from allied health professionals, can improve their prognosis (131). The referral of patients with non-IA simultaneously contribute to longer waits for first appointments, and consequently, people with IA often miss the ‘window of opportunity’ for early treatment, potentially accruing irreversible joint damage (20,132,133). Previous research has shown that the use of the “Multidisciplinary Care Assessment” code by rheumatologists was associated with an increase in the number of patients that could be seen and services provided by rheumatologists, that was maintained, but did not increase over time (134). The code enables rheumatologists to bill for nurse time to support the care of people with complex disorders of the MSK system, that require continuing management by a rheumatologist. However, it is unknown whether its introduction has impacted the pathway into rheumatology 38 services for people with symptoms of IA and their ability to receive treatment within the ‘window of opportunity’ for early treatment. Furthermore, the impact of the expansion of the number of rheumatologists on the referral process has not been evaluated. The objective of this study was to understand how the expansion of rheumatology supply and the introduction of multidisciplinary care was associated with access to rheumatology services. First, the change in access to rheumatology services for people in BC between 2011 and 2020 was described by examining the case-mix of first referrals and time between referral to first rheumatologist visit and treatment. Second, the variables associated with any changes in the times between referral and first rheumatologist visit, and to first treatment for people with RA were explored. 3.2 Materials and Methods 3.2.1 Data Sources Data from Population Data BC from April 1, 2006, to March 31, 2020 (government fiscal year (FY)) were used, which included: the Medical Services Plan (MSP) Payment Information File (135) that contains ICD-9-CA codes, dates and billing codes for services, and rheumatologist supply; the MSP Consolidation File (136), that includes basic demographic data including age, sex, disposable income, the Discharge Abstract Database (DAD) (137), that contains information on separations from hospitals and associated ICD-10-CA codes; and PharmaNet (138), that captures all prescriptions for drugs dispensed in BC, including DMARD dispensation dates. This study underwent review and was approved by the University of British Columbia Clinical Research Ethics Board (H16-02934). Access to data provided by the Data Stewards is subject to approval but can be requested for research projects through the Data Stewards or their designated service providers. The following data sets were used in this study: Consolidation File, Medical 39 Service Plan (MSP), Discharge Abstract Database (DAD), and PharmaNet. You can find further information regarding these data sets by visiting the PopData project webpage at: https://my.popdata.bc.ca/project_listings/17-073/collection_approval_dates. All inferences, opinions, and conclusions drawn in this publication are those of the author(s), and do not reflect the opinions or policies of the Data Steward(s). 3.2.2 Definition of Timepoints and Lag Times The key time points were defined as the date of the referral from a PCP to a rheumatologist (T0), the date of the first rheumatologist visit (T1), and for people with RA, the date of dispensation of their first DMARD (T2) (Appendix H). A rheumatologist visit was defined for an individual as their first visit (T1) if there were no other rheumatologist visits within a 5-year “wash out period” prior to that visit (see algorithm in Appendices D and I). Once a T1 was defined, the date of referral (T0) from a PCP was identified using the approach described in Appendices D.2 and E. The derivation of the RA cohort is shown in Appendix I and Appendix D.3 outlines the RA case definition, and T2 determination. Briefly, the two-year period following the first rheumatologist visit (T1) was used to identify a date of initiation of a first DMARD (T2). For those who had a DMARD initiated within two years after T1, a most probable diagnosis was assigned with the ICD-9-CA codes that were used by the rheumatologist at the most recent visit prior to DMARD initiation. The RA cohort study period was therefore restricted to 2011/12 to 2017/18 to allow for a two-year period following the first rheumatologist visit to identify the start of DMARD treatment. Lag times were defined as the difference between the identified time points in days. The first lag time (Lag 1) was the time in days between the date of referral from a PCP to a rheumatologist to the first rheumatologist visit. The second lag time (Lag 2) was calculated for 40 people with RA and was the time in days between the first rheumatologist visit and the date of the first DMARD dispensation. The total lag time, days between referral from a PCP to first DMARD dispensation (days between T0 and T2) was also calculated for people with RA. Since RA is the most common IA and has a recommended treatment target of receiving a DMARD within 12 weeks of symptom onset (20), this total lag for people with RA is interpreted here as a healthcare performance metric. 3.2.3 Patient Demographics and Variables Demographic variables were defined at T1 and included the age, sex, disposable income, and Local Health Area (LHA) (139) of people in the cohort. International Classification of Diseases (ICD) 9th (ICD-9-CA) and 10th (ICD-10-CA) revision Canada codes were used to calculate the Rheumatic Disease Comorbidity Index (RDCI) (140). The LHA of people in the cohort were classified based on healthcare services being “metropolitan”, “non-metropolitan (hub hospital)”, “non-metropolitan (small/no hospital)” and “remote” using a previously published algorithm (139). Rheumatologists were classified according to whether they had ever (or never) used the BC MSP Multidisciplinary Care Assessment (MCA) billing code (MSP billing code: 31060), which enables rheumatologists to bill for nursing time. Rheumatologist supply was measured as the number of rheumatologists with a billing record in each FY. The initial diagnosis a person received at T1 was based on the ICD-9-CA codes assigned by the rheumatologist at that visit (Appendix F) and categorized as IA, non-IA, or ‘other MSK’ (codes 711-739.9) (141) by the clinical principal investigator (KS). Any remaining codes were classified as ‘other’ conditions. 41 3.2.4 Statistical Methods 3.2.4.1 Changes in Case-Mix and Lag Time The proportion of IA, non-IA, ‘other MSK’ and ‘other’ conditions at T1 were calculated by financial year. The change in case-mix over the study period was graphed to illustrate trends. Lag times were summarized using the median days and interquartile range (IQR). Times for Lag 1 were presented for the entire cohort with a matched T1 and T0, and by each of the four disease categories calculated at T1. Times for Lag 2 and the overall Lag were calculated for people who received a most probable diagnosis of RA. To assess the generalizability of the results to the ability to match between different time points, the characteristics of these groups were compared. 1) People who had a T1 and could versus could not be matched to a T0. 2) People who had a T1, received a DMARD and who had a most probable diagnosis of RA versus not RA. 3) People with a probable RA diagnosis who received a DMARD and who could versus could not be matched with a T0. 3.2.4.2 Variables Associated with Lag Time Outcomes Quantile regression was used to assess associations between the available variables about the people in the cohort with RA (age, sex, disposable income, densely populated urban or more rural/remote area of residence, number of comorbidities) and the system (use of the MCA code by rheumatologists) and the median length of Lag 1, Lag 2 and the total Lag in days. Associations were first assessed univariately and then a multivariable model that started from a base model that first included the year of the T1 visit and the age, and sex of people in the cohort (Model 1), then additional characteristics of people (disposable income category, LHA residence, and the number of comorbidities - RDCI) (Model 2) and finally added the system variable of use of the MCA code (Model 3). The model Akaike information criterion (AIC) and R1 Koenker and Machado statistics 42 were compared to assess goodness of fit of the models (142). Percentile confidence intervals (CI) for quantile regression were estimated using the bootstrap method with 2000 bootstrap replications. All analyses were conducted using SAS 9.4 and RStudio (143). This study underwent review and was approved by the University of British Columbia Clinical Research Ethics Board (H16-02934). 3.3 Results Between April 1, 2006, and March 31, 2020, there were a total of 252,999 visits to a rheumatologist in BC. The study cohort (for the period April 1, 2011, to March 31, 2020) comprised 149,902 (59%) people with a first rheumatologist visit (T1) for whom a matched PCP referral date, T0, could be identified for 58,366 (39%). Rheumatologist supply increased by 44% from 61 practicing in 2010/11 to 88 in 2019/20. Over the same period respectively, the proportion of rheumatologist MCA code use for first visits increased from 67% (9,571 of 14316) to 83% (15,524 of 18,683). The characteristics of the overall cohort and the matched/unmatched cohorts were similar (Table 3.1). The mean age at the time of the first rheumatologist visit for the cohort was 57.0 years (SD 16.2), and 66% were female. Over the study period, the MCA billing code (G31060) was being used by 79% of the rheumatologists at the time of the first patient visit. 43 Table 3.1 Patient sample characteristics at the first rheumatologist visit, for the overall cohort, and between those people who had their first rheumatologist visit matched to a date of referral or not People with a First Rheumatologist Visit All Matched to Date of Referral Unmatched to Date of Referral Patient Characteristics 149,902 58,366 (38.9%) 91,536 (61.1%) Age, Mean (SD) 57.0 (16.2) 56.8 (15.7) 57.1 (16.5) Female Sex, n (%) 99,171 (66.2%) 39,300 (67.3%) 59,871 (65.4%) Diagnosis at T1, n (%) 1-IA 64,819 (43.2%) 25,134 (43.1%) 39,685 (43.4%) 2-Non-IA 45,853 (30.6%) 18,908 (32.4%) 26,945 (29.4%) 3-OtherMSK 25,032 (16.7%) 10,753 (18.4%) 14,279 (15.6%) 4-Other 14,198 (9.5%) 3,571 (6.1%) 10,627 (11.6%) Disposable Income Category, $CDN, n (%) <26,300 39,101 (26.0%) 14,074 (24.1%) 25,027 (27.3%) 26,300 to <31,600 32,465 (21.7%) 12,865 (22.0%) 19,600 (21.4%) 31,600 to <38,500 33,114 (22.1%) 13,212 (22.7%) 19,902 (21.7%) ≥38,500 36,561 (24.4%) 14,842 (25.4%) 21,719 (23.7%) Missing 8,661 (5.8%) 3,373 (5.8%) 5288 (5.7%) Geographical LHA cluster, n (%) Metropolitan 96,426 (64.3%) 37,211 (63.8%) 59,215 (64.7%) Non-metropolitan (hub hospital) 46,720 (31.2%) 18,586 (31.8%) 28,134 (30.7%) Non-metropolitan (small/no hospital) 6,384 (4.3%) 2,434 (4.2%) 3,950 (4.3%) Remote 230 (0.1%) 78 (0.1%) 152 (0.2%) Missing 142 (0.1%) 57 (0.1%) 85 (0.1%) Number of comorbidities (RDCI), n, (%) 0 66,110 (44.1%) 26,981 (46.2%) 39,129 (42.7%) 1 36,799 (24.5%) 14,631 (25.1%) 22,168 (24.2%) 2 26,399 (17.6%) 9,803 (16.8%) 16,596 (18.1%) 3 12,786 (8.5%) 4,473 (7.7%) 833 (9.2%) 4 or more 7,808 (5.3%) 2,478 (4.2%) 5,330 (5.8%) Rheumatologist ever billed MCA code: Yes, n (%) 118,666 (79.2%) 45,418 (77.8%) 73,248 (80.0%) T0 Matched and Unmatched mean that the referral to the rheumatologist could or could not be matched to a PCP-coded referral. Of the 149,902 people with an identified first visit (T1), 43% had IA, 31% had non-IA, 17% had an ‘other MSK’ condition, and 9% had an ‘other’ condition. The composition of diseases within these categories are shown in Appendix F. 44 3.3.1 Changes in Rheumatologist First Visit Case-Mix Over Time The total number of T1 visits per year in BC increased by 31% from 14,316 (2011/12) to 18,683 (2019/20). For the same respective years, first rheumatologist visits for IA increased by 139% from 4,022 to 9,611 and decreased for non-IA by 13% from 5,734 to 5,018. First rheumatologist visits decreased by 22% for ‘other MSK’ conditions from 3,069 to 2,381 and increased by 12% for ‘other’ conditions from 1,491 to 1,673, respectively. The proportion of first rheumatologist visits for IA increased from 28% to 51%, visits for ‘other’ conditions remained relatively constant at 10% to 9%, and visits for non-IA and ‘other MSK’ conditions decreased from 40% to 27%, and 21% to 13%, respectively (Figure 3.1). The first visit case-mix change toward IA occurred between 2011/12 and 2017/18 and plateaued thereafter. 45 Figure 3.1 Change in the case-mix of first visits to rheumatologists between the years 2011/12–2019/20 0%10%20%30%40%50%60%2011/12(n=14,316)2012/13(n=14,977)2013/14(n=14,600)2014/15(n=14,947)2015/16(n=16,781)2016/17(n=18,340)2017/18(n=18,095)2018/19(n=19,163)2019/20(n=18,683)Percentage of new visits to rheumatologistsIA Non-IA Other MSK Other 46 3.3.2 Time from PCP Referral to First Rheumatologist Visit The median time from referral to first rheumatologist visit for the matched cohort of 58,366 patients decreased by 22 days (35%) from 63 days (IQR 21-120) in 2011/12 to 41 days (IQR 15-84) in 2019/20 (Figure 3.2). The most significant reduction was for people with non-IA, who waited 32 (42%) fewer days, down from 77 days (IQR 31-134) to 45 days (IQR 18-92). People with ‘other MSK’ conditions waited 30 days fewer (47%), from 64 days (IQR 20-118) to 34 days (IQR 10-82) and time to see a rheumatologist for people with IA decreased by 8 days (17%) from 47 days (IQR 18-98) to 39 days (IQR: 15 - 81 days). The most significant reduction of time to first rheumatologist visit (Lag 1) for Patients with IA was seen by 2017/18. 3.3.3 RA Cohort Characteristics and Changes in Lag Time Characteristics of people with RA were similar for all subgroups (Table 3.2). The MCA code was used by 84% of the rheumatologists who saw people with RA at T1. In the 2,614 people with RA, the total median lag time from PCP referral to initiation of DMARD treatment decreased by 4 days (6%) over the study period, from 66 days (IQR 30-140) in 2011/12 to 62 days (IQR 26-151) in 2017/18. This reduction in total lag time was distributed between the time from PCP referral to first rheumatologist visit (Lag 1), that decreased by 12 days (32%) from 38 days (IQR 17-77) in 2011/12 to 26 days (IQR 13-61) in 2017/18, and the time from first rheumatologist visit to DMARD treatment (Lag 2), that increased by 3 days (-33%) from 9 days (IQR 1-47) in 2011/12 to 12 days (IQR 1-70) in 2017/18 (Figure 3.3). 47 Figure 3.2 Median days from primary care physician referral to a first rheumatologist visit (Lag 1) by disease type, 2011/12–2019/20 203040506070802011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18 2018/19 2019/20Mediandays (Lag 1)IA Non-IA Other MSK Other 48 Table 3.2 Characteristics of people who received a DMARD within two years of their first rheumatologist visit Received DMARD within two years of first rheumatologist visit Most probable diagnosis: RA Characteristics All Not RA All RA Matched to a date of referral Unmatched to date of referral n (%) 14,680 8,742 (59.5%) 5,938 (40.4%) 2,614 (44.0%) 3,324 (56.0%) Age, Mean (SD) 56.4 (15.4) 54.8 (15.4) 58.7 (15.1) 58.8 (15.1) 58.7 (15.0) Female Sex, n (%) 9,722 (66.2%) 5,694 (65.1%) 4,028 (67.8%) 1,769 (67.7%) 2,259 (68.0%) Disposable Income Category, $CDN n, (%) <26,300 3,591 (24.5%) 2,106 (24.1%) 1,485 (25.0%) 619 (23.7%) 866 (26.0%) 26,300 to <31,600 3,459 (23.6%) 2,039 (23.3%) 1,420 (23.9%) 630 (24.1%) 790 (23.8%) 31,600 to <38,500 3,461 (23.6%) 2,054 (23.5%) 1,407 (23.7%) 600 (23.0%) 807 (24.3%) ≥38,500 3,411 (23.2%) 2,112 (24.2%) 1,299 (21.9%) 613 (23.4%) 686 (20.6%) missing 758 (5.1%) 431 (4.9%) 327 (5.5%) 152 (5.8%) 175 (5.3%) Geographical LHA cluster, n (%) Metropolitan 8,061 (54.9%) 4,851 (55.5%) 3,210 (54.1%) 1,405 (53.7%) 1,805 (54.4%) Non-metropolitan (hub hospital) 5,854 (39.9%) 3,478 (39.8%) 2,376 (40.0%) 1,043 (39.9%) 1,333 (40.2%) Non-metropolitan (small/no hospital) 743 (5.0%) 398 (4.5%) 345 (5.8%) 166 (6.4%) 179 (5.4%) Remote 11 (0.1%) 8 (0.1%) <5 (<0.1%) 0 (0.0%) <5 (<0.1%) Missing 11 (0.1%) 7 (0.1% <5 (<0.1%) 0 (0.0%) <5 (<0.1%) Number of comorbidities (RDCI), n, (%) 0 6,822 (46.5%) 4,126 (47.2%) 2,696 (45.4%) 1,222 (46.7%) 1,474 (44.3%) 1 3,605 (24.6%) 2,100 (24.1%) 1,505 (25.3%) 647 (24.8%) 858 (25.8%) 2 2,446 (16.7%) 1,455 (16.6%) 991 (16.7%) 427 (16.3%) 564 (17.0%) 3 1,142 (7.8%) 658 (7.5%) 484 (8.2%) 206 (7.9%) 278 (8.4%) 4 or more 665 (4.4%) 403 (4.6%) 262 (4.4%) 112 (4.3%) 150 (4.5%) Rheumatologist ever billed MCA code: Yes, n (%) 11,773 (80.2%) 6,787 (77.6%) 4,986 (84.0%) 2,169 (83.0%) 2,817 (84.7%) T0 Matched: the referral to the rheumatologist could be matched to a PCP-coded referral 49 Figure 3.3 Median days for people with RA from primary care physician (PCP) referral to first rheumatologist visit (Lag 1), to first DMARD (Lag 2) and from PCP referral to first DMARD (Total Lag) over the study period 01020304050607080901002011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18Median daysYear Lag 1 Lag 2 Total Lag 50 3.3.4 Univariate and Multivariable Associations with RA Patient Lag Times Univariate associations between the median lag times from referral to the first rheumatologist visit and DMARD treatment are shown in Appendix G. Notably, there were positive univariate associations between female sex and a longer Lag 1 compared with males, and a longer Lag 1 and total lag for those living outside of a metropolitan area (compared with inside), despite a negative association for non-metropolitan areas with Lag 2. There was a negative association between year and Lag 1 in 2016/17 and 2017/18, and positive association with Lag 2 in 2013/15, but no univariate association for year with total lag (Appendix G). The year of first visit and rheumatologist supply, which increased from 61 to 88 practicing rheumatologists over the study period, were collinear and generated similar results. Use of the MCA code by rheumatologists had the most pronounced effect, and was negatively associated with the total lag time, primarily explained by a shorter median Lag 1. The multivariable model, with variables representing the characteristics of people with RA and the system, performed best based on AIC statistics (Table 3.3). Compared with 2011/12, the year 2013/14 was associated with a longer total lag time of 24.9 days (95% CI 2.6-47.3; p=0.016) and 2015/16 was associated with a longer total lag of 15.5 days (95% CI -3.0 to 30.5; p=0.049). In between these years, 2014/15 was associated with a longer median total lag time of 18.6 days (95% CI -1.9 to 35.5), but this was not statistically significant at the 5% level (p=0.073). Use of the MCA code by rheumatologists remained negatively and independently associated with total lag time of -24.1 days (95% CI -37.1 to -12.2; p<0.001) for people whose rheumatologists used the MCA code; the univariate association was not attenuated in the multivariable model. 51 Table 3.3 Multivariable models of total lag between referral and first DMARD for people with RA Model 1: Year + Age + Sex Model 2: Add demographics Model 3: Add MCA code Variables Coef. 95% CI p-value Coef. 95% CI p-value Coef. 95% CI p-value Year 2011/12 Ref Ref Ref 2012/13 1.9 (-10.7, 23.2) 0.819 0.4 (-13.5, 21.5) 0.963 8.4 (-8.6, 24.0) 0.299 2013/14 22.3 (3.7, 40.9) 0.009 18.1 (-1.6, 39.5) 0.094 24.9 (2.6, 47.3) 0.016 2014/15 8.8 (-6.0, 30.1) 0.354 7.1 (-6.6, 30.5) 0.496 18.6 (-1.9, 35.5) 0.073 2015/16 4.3 (-8.8, 22.0) 0.576 1.9 (-9.8, 21.8) 0.808 15.5 (-3.0, 30.5) 0.049 2016/17 -10.1 (-21.5, 2.9) 0.137 -12.3 (-23.3, 4.3) 0.071 0.7 (-17.6, 11.5) 0.910 2017/18 -4.6 (-17.5, 9.6) 0.495 -7.9 (-18.5, 12.7) 0.384 5.0 (-12.1, 20.2) 0.591 Age, years -0.1 (-0.4, 0.2) 0.489 -0.2 (-0.5, 0.1) 0.213 -0.2 (-0.5, 0.2) 0.289 Sex Male Ref Ref Ref Female 5.5 (-4.4, 13.3) 0.187 8.1 (-2.8, 16.2) 0.074 8.4 (-1.6, 16.2) 0.052 Disposable Income Category, $CDN <26,300 Ref Ref 26,300 to <31,600 1.4 (-10.2, 12.7) 0.812 1.0 (-10.4, 13.2) 0.852 31,600 to <38,500 -4.3 (-15.8, 9.2) 0.540 -4.5 (-15.5, 9.6) 0.501 ≥38,500 3.6 (-9.7, 16.2) 0.595 1.5 (-9.8, 14.6) 0.810 Geographical LHA cluster Metropolitan Ref Ref Non-Metropolitan/Remote 6.1 (-1.6, 17.5) 0.229 9.3 (1.0, 19.1) 0.049 Number of comorbidities (RDCI) 0 Ref Ref 1 -1.1 (-12.8, 11.0) 0.852 -2.3 (-13.3, 10.0) 0.697 2 5.7 (-8.1, 18.1) 0.415 3.7 (-9.4, 17.7) 0.589 3 9.0 (-7.8, 27.1) 0.332 7.0 (-8.0, 23.5) 0.377 4 or more 2.9 (-16.4, 27.0) 0.773 0.6 (-18.4, 25.8) 0.953 Rheumatologist ever billed MCA code No Ref Yes -24.1 (-37.1, -12.2) <0.001 Model n 2,614 2,462 2,462 AIC 32,867 30,937 30,919 R1 0.48% 6.94% 7.32% Coef: Coefficient R1: Koenker and Machado goodness of fit statistic 52 Living outside of a metropolitan area was independently associated with an increased total lag of 9.3 days (95% CI 1.0-19.1; p=0.049) compared with those living in metropolitan areas. Female sex was associated with a longer total lag of 8.4 days (95% CI -1.6, 16.2) than in univariate models, but this was not statistically significant at the 5% level (p=0.052). 3.4 Discussion Over the past decade, BC has sought to optimize the care of people with rheumatologists by expanding supply and introducing billing codes which facilitate and incentivize multidisciplinary care. This study examined historical trends of case-mix and lag times for people seeking rheumatology care in BC between April 2011 and March 2020. Overall, there were improvements in the appropriateness of referrals, defined as 1) an increasing proportion of first rheumatologist visits for people with IA, and 2) access to specialist rheumatology care, defined as the time from referral to the first rheumatologist visit, and for people with RA, first DMARD treatment. However, these improvements are not yet sufficient for many patients with RA to meet the 12-week ‘window of opportunity’ which could have consequences for their prognosis. The substantial shift in case-mix over the study period, in which the proportion of first rheumatologist visits for IA increased from 28% to 51% over the study period, corresponded with an increase in the number of rheumatologists and the introduction of new billing codes to support the care of people requiring rheumatologist care. The number of rheumatologists in BC increased from 61 to 88 between 2011 and 2018. The improvement in the case-mix may represent changes in training and/or practice of new rheumatologists which has impacted the way referrals are managed and/or accepted. The time frame of this study also coincides with the first decade immediately following the publication of the 2010 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) RA classification criteria (127) that potentiated a lot 53 of research for earlier intervention for patients with RA. Additionally, both new billing codes introduced in 2011/12, one for extended consultations (PG31050) and one as a substitute for regular consultations to promote multidisciplinary care supported by nurses (PG31060), prioritized care for people with complex, chronic MSK disorders requiring ongoing care from rheumatologists. However, up to 49% of first rheumatologist visits in 2020, still represented people who may have needlessly waited in the wrong queue. While there appears to have been an improvement in the proportion of “appropriate” rheumatologist first visits, the ideal case-mix is unknown, and it is unclear whether people with non-IA, other MSK conditions, received “appropriate” care elsewhere in the system. Use of these codes could have encouraged rheumatologists to prioritize seeing new patients with IA, and de-prioritize the acceptance of referrals for people with likely non-inflammatory conditions. Use of the MCA billing code by a rheumatologist was independently associated with considerably shorter total lag times of 26 days (compared with people seeing rheumatologists who did not bill for this code) and appeared to explain much of the improvement over time. Use of the MCA code, which has been shown to increase access to rheumatology services, measured as the number of patients seen within a period or the number of services provided (134,144). The finding of a shorter lag time associated with the use of this MCA code supports the evidence describing its contribution to improved access to rheumatology services in BC. Wait times are a measure of healthcare system performance and a determinant of clinical outcomes in rheumatology. Despite the improvement in lag times observed, people with RA still waited a median 39 days in 2019/20 to see a rheumatologist. The amount who received DMARD treatment within the target ‘window of opportunity’ of 12 weeks from symptom onset (20) could not be quantified, since symptom onset is not observable in this data. However, assuming that people with RA in this study saw their 54 PCP within 28 days of symptom onset, the results suggest that approximately 50% received a DMARD within the target. Although this improved over the study period, there remains more than 50% of people with RA still likely missing the window. The plateauing of improvement in the case-mix and lag times by 2017/18 suggests that maximal benefit of increases in the rheumatologist supply and the addition of billing codes may have been realized. Further changes are likely needed to optimize the appropriateness of referrals and reduce wait times. These may include the implementation of standardized referral patient-completed questionnaires to support referral decisions (62,94), adoption of information technology (99) to electronically streamline care (145), and increased involvement of extended healthcare professionals (146–148). Of concern, it was found that female sex and living outside densely populated urban areas were associated with longer waits in the total lag between referral by a PCP to initiation of DMARD treatment for people with RA. This was driven primarily by delays from PCP referral to first rheumatologist visit. This finding adds to the mixed literature on differences in times to consultation and treatment by sex, where some studies have found delays/longer times from referral to consultation (149) and diagnosis (150) for females, whereas others have reported no difference between males and females (34) or shorter times to consultation for females (151). The findings here indicate potential inequities in referral and require further investigation. There are some limitations to this study. While the patient characteristics in the matched and overall cohorts were similar, only 39% of all first rheumatologist visits were able to be matched to a PCP referral, and this subsample was used as a proxy for lag time outcomes for all people accessing rheumatology services in BC. There is also potential imprecision in the definition of new rheumatology visits, however, it is believed that using a 5-year washout period to detect 55 prior rheumatology visits is a robust approach. Similarly, for people with RA, the first rheumatologist visit date does not necessarily represent the date at which a diagnosis of RA is made. Finally, the effects of the expansion in the number of rheumatologists and use of the MCA code were not able to be separated from the changes that were observed. However, the billing code was introduced as part of an exploratory suite of codes to change patient care approaches through the Specialist Services Committee (SSC) (144) and attract new and retain existing rheumatologists; therefore, it is appropriate to consider both together. The study also has several notable strengths. First, as a population-based study, all rheumatologists and people receiving rheumatology care in the BC health care system were eligible for inclusion in this study, and included and excluded groups were compared at each stage to help assess the generalizability of the results. The study time frame also preceded the COVID-19 pandemic and therefore the results are unaffected by its impact; however, the results likely reflect a different estimate of the “current state” of rheumatology services today. In conclusion, there were improvements in access to rheumatology care in BC between 2011/12 and 2019/20 but there remains considerable scope for improvement, especially in time to treatment for patients with RA. The findings may be considered by other jurisdictions seeking to expand access to rheumatology care. Changes in the supply of rheumatologists and the introduction of specific billing codes, aligned with consensus guidelines, appear to have been impactful in changing access to services for people in the province. However, there were concerning signals of unequal access to rheumatology services for females and people living outside of metropolitan areas. 56 Chapter 4: The Patient SAID (Self-Administered Inflammatory Arthritis Detection) Study 4.1 Background Outcomes for patients with IA have changed dramatically in the last two decades, primarily due to the effective use of DMARDs (50,152,153). The possibility of changing the course of these debilitating conditions has given rise to treat-to-target strategies including earlier treatment initiation during a window of opportunity within 12 weeks of symptom onset, where biological processes are more reversible (17,152). While barriers to receiving a diagnosis of early IA occur at several levels for patients along their journey (154), from a health system standpoint, one barrier that can be reduced is from the moment the patient seeks care to their first rheumatologist visit. In Chapter 3, the impact of BC’s response to this opportunity in an under-resourced environment was presented (124). Rheumatology capacity was expanded through the recruitment of more newly-trained rheumatologists (125), and by the introduction of new billing codes for extended consultations and ‘multidisciplinary care assessments’ to enable and incentivize collaborative care of patients with nurses (126). In this pre-pandemic period between 2011/12 and 2019/20, there were improvements in access to rheumatology care, in terms of case-mix and time to rheumatologist visit. The plateauing of improvement in the case-mix and lag times by 2017/18 suggested that the contribution of increases in the rheumatologist supply and the addition of billing codes may have been mostly realized. Thus, there remains considerable scope for improvement, especially in time to treatment from referral for patients with IA. This is particularly important for people with RA, a patient group where the urgency to initiate disease modifying therapy is high. It is proposed here that the use of standardized patient-completed questionnaires to support referral 57 decisions (62,94) and adoption of information technology (99) may streamline care (145) and reduce the time it takes for patients with IA to see a rheumatologist, for the first time. In the scoping review presented in Chapter 2, existing validated patient-completed questionnaires that have the potential to improve on current referral processes were identified (32). The motivation for conducting the review was to identify potential triage approaches to test in a cross-sectional clinical study at the primary care-rheumatologist referral stage in BC. The collated evidence led to the choice of the EIAQ (26,62,75,90), an approach validated in IA, along with CaFaSpa (93,94) specifically validated for AxSpa. The decision was based on the volume of evidence, the high performance (particularly discriminative validity), the representativeness of the populations in which these approaches were tested (i.e. reflecting a non-specialist referral), and the lack of overlap in patient-reported questions. The primary aim of this chapter, named The Patient SAID Study, was to evaluate the performance of the EIAQ and CaFaSpa questionnaires in discriminating IA from non-IA in newly referred rheumatology patients in BC. Previous studies of these screening questionnaires have not been conducted electronically, an approach that might enable simpler widescale implementation. Furthermore, neither of the previous studies of these questionnaires were conducted in recent years when treatment and referral practices have changed, partly due to changing clinical best practices, and in part due to the COVID-19 pandemic. The secondary aims of the Patient SAID Study are exploratory; to assess whether the potential discriminative performance of these instruments could be improved by using different combinations of questions from EIAQ and CaFaSpa questionnaires, and also to examine the referral case-mix and time points from symptom onset through to rheumatologist visit and 58 DMARD initiation for patients referred through the Patient SAID Study where applicable, and compare these with the estimates derived from the study using Population Data BC in Chapter 3. The primary hypothesis in this study is that the EIAQ and/or a different combination of its questions has acceptable discriminative performance for the prediction of IA for recommendation to help streamline rheumatology referrals. 4.2 Methods 4.2.1 Study Procedure From October 2022 to January 2024, patients were recruited from the clinical practices of 11 rheumatologists in BC on a convenience sampling basis. Patients who were newly referred to these rheumatology practices were made aware of the study during regular clinical communications by phone or email for appointment scheduling and reminders, as well as in-person when attending their appointment. Newly referred consecutive patients who consented, completed the questionnaires online through the Research Electronic Data Capture (REDCap) database Version 14.0.6 (155) or via a paper-version in the practice of one rheumatologist. Eligible participants were 19 years of age or older (adult in BC), who could understand and complete the questionnaire in English. The questionnaire included the EIAQ (See Table 4.1) and CaFaSpa instruments, as well as demographic and user experience data (Appendix L). 59 Table 4.1 EIAQ questions, algorithm and scoring method Early Inflammatory Arthritis Questionnaire (EIAQ) Tavares et al. J Rheumatol 2013; 40:4; doi:10.3899/jrheum.120096 Regression Equation = 4.62-0.083*Age + 0.79*Male + 0.78*Q04 + 1.29*Q05 +1.59*Q09 + 0.88*Q11 Positive Value = Inflammatory Arthritis Negative Value = Non-Inflammatory Arthritis EIAQ Questions Response Option Age What is your date of birth (to derive age in years) YYYY/MM Sex What was you Sex at Birth (male = 1, female = -1) male or female Inflammatory Arthritis Detection Tool Items (Yes = 1, No = -1) Response Option Q01 Do you have pain in your joint(s)? (joint pain general) yes or no Q02 Do you have pain in your wrists & hands? (hand/wrist pain) yes or no Q03 Are your hands or wrists swollen? (hand/wrist swelling) yes or no Q04 Do you have trouble making a fist? (trouble making a fist) yes or no Q05 Are your joints stiff in the morning? (morning stiffness general) yes or no Q06 From the time you wake up in the morning, does it take more than 60 minutes for your joints to move more freely? (morning stiffness >1 hr) yes or no Q07 Are the same joints involved on both sides of your body? (symmetry) yes or no Q08 Have important activities in your life been affected because of bone or joint problems, such as having difficulty with personal care or having to make a change regarding leisure or work activities? (functional difficulty) yes or no Q09 Have you ever been told that you have rheumatoid arthritis (RA)? (ever told you have RA) yes or no Q10 Does anyone in your family have RA? (RA family history) yes or no Q11 Have you been diagnosed with a rash called Psoriasis? (psoriasis diagnosis) yes or no Q12 Have you had these symptoms for more than 6 weeks and less than 1 year? (6 to 52 week symptom duration) yes or no Rheumatologists were blinded to the patient responses and in the case of online participants, rheumatologists were not aware of which patients completed questionnaires. This ensured that the study did not influence the usual clinical care in each practice. Patients were scheduled for their appointments and a diagnosis was assigned by the rheumatologist, which served as the reference standard. After a diagnosis was made, the medical office assistant entered information from the patients’ medical records directly into the REDCap database. This included 60 the diagnosis code and name, as well as the dates of physician referral, rheumatologist visit, and if prescribed, the DMARD name and prescription date. The referral case-mix was calculated as a proportion of the number of patients whose diagnoses were categorized as a time-sensitive IA (TS-IA) that optimally were treated within a narrow window of opportunity, versus ‘Other IA or non-IA’ diagnoses. These definitions were consistent with those defined in the study in Chapter 3 using Population Data BC (124). However, for this study, the patients with IA were further categorized into TS-IA to align with the definition used for the EIAQ (62) and the non-IA group included those defined as other MSK conditions in the Chapter 3 study. This study underwent review and was approved by the University of British Columbia Clinical Research Ethics Board (Study # H21-03719). 4.2.2 Participant Characteristics Participant demographics were collected including age, sex, household income range, and city/ postal code where they reside. Age was calculated by subtracting the year of their birth from the year of the rheumatologist visit date. The five annual household income range categories were <$42,000, $42,000-$68,000, $68,001-$102,000, $102,001-163,000, and >$163,000. The first three digits of the postal code where the participants reside was used to derive their LHA categorized into metropolitan, non-metropolitan with a hospital hub, non-metropolitan with no hospital, and remote (139). The sample data were examined to identify missing patient responses and the reasons provided by patients, for these missing data, in the open-text comment field in REDCap. If questions were unanswered without an explanation, patients were contacted by email and/or phone and asked for the information to maximize the number of patients for which a questionnaire score could be calculated. Other participant information such as the proportion who answered ‘no’ to 61 having joint or back pain, the type of practitioner they were referred by, and symptom duration were examined to see how closely they reflected the cohort characteristics of the studies in which the EIAQ and CaFaSpa questionnaires were developed and validated. This was used to help explain potential differences in discriminative performance between those cohorts and participants of the SAID study and to inform subset analysis feasibility for the validated questionnaires and new score development. 4.2.3 Symptom Questionnaire Scoring The EIAQ score was used to define the predicted outcome of TS-IA versus ‘Other IA or non-IA’ using a published algorithm (62). Similarly, a CaFaSpa score, to recommend a referral for likely AxSpa versus no AxSpa, was calculated using the published scoring method (94) for patients who indicated that they had back pain and answered the ensuing questions. In the exploratory analysis, one EIAQ question related to functional difficulty was tested as a stand-alone predictive question. This was decided because in one study identified in the Chapter 2 scoping review (101) found that inquiring about difficulties with dressing equalled the discriminative ability of the total Health Assessment Questionnaire (HAQ) score for prediction of IA. The HAQ is the most widely used measure of function in IA. The first question of the HAQ is “Are you able to dress yourself without assistance?” (156,157). The exact validated question was “Are you able to dress yourself, including shoelaces and buttons?” (101). In the EIAQ, Q08 with the following wording was used as a ‘functional difficulty’ proxy; “Have important activities in your life been affected because of bone or joint problems, such as having difficulty with personal care or having to make a change regarding leisure or work activities?” It is noteworthy, that this question is not used in the scoring algorithm of the EIAQ. 62 4.2.4 Statistical Methods 4.2.4.1 Discriminative Performance of Validated Questionnaires An EIAQ score was calculated for each patient using the published regression equation 4.62-0.083*Age + 0.79*Male + 0.78*Q04 + 1.29*Q05 +1.59*Q09 + 0.88*Q11 (62). The exact wording for each of these questions are shown in Table 4.1, but briefly Q04 asks about difficulty making a fist, Q05 about morning stiffness in the joints, Q09 whether you have ever been told you have RA, and Q11 whether you have a diagnosis of psoriasis. For the binary response ‘Male’ question, answering ‘Yes’ to being male is coded ‘1’, while answering ‘No’ answer is coded ‘-1’. A positive value resulting from the regression equation indicates TS-IA, whereas a negative result indicates Other IA or non-IA. This value, as a binary explanatory variable to predict IA, was compared to the rheumatologist diagnosis as the reference standard, and tested for discriminative performance, using the area under the receiver-operating characteristics curve (ROC AUC). Resampling was used across 2000 bootstrap samples and the corresponding 95% CI is reported. The single EIAQ functional difficulty question (Q08) was also tested as a binary explanatory variable for discriminating TS-IA from other IA and non-IA, and the CaFaSpa score for discriminating AxSpa from non-AxSpa outcomes, using the ROC AUC. The optimal threshold was selected by maximizing the Youden index, which is the difference between the true positive (TP) rate and false positive (FP) rate. This corresponds to the highest value of the index balancing the trade-off between SN and SP to achieve the best overall diagnostic performance. Using the optimal threshold, a confusion matrix is generated for the number of TP, true negatives (TN), FP and false negatives (FN), and the corresponding SN and SP calculated for the three tools in the whole study sample and across subsets, if appropriate. Since the EIAQ predictor and diagnostic outcome are binary classifiers of 0 or 1, the SN and SP metrics are reported at a cut-off of 0.5. The 63 PPV, NPV and LR as well as diagnostic odds ratio (DxOR) and 95% CI were derived from these statistics. 4.2.4.2 Exploratory New Regression Equation to Predict IA Exploratory associations of explanatory variables with outcomes were assessed using questions to differentiate ‘narrow’ and ‘broad’ classifications of IA. ‘Narrow IA’ was used to differentiate TS-IA from ‘Other IA and non-IA’ and ‘Broad IA’ was used to differentiate ‘TS-IA and Other IA’ from non-IA. The approach to exploring the combination of questions that could best predict the diagnostic category outcomes used three regression models. Model 1 tested the EIAQ base questions only (i.e. those used in the published algorithm, but without the published algorithm applied) (Table 4.1). Model 2 added the back pain question to the EIAQ base questions since it was answered by all participants (although only those who answered, ‘yes’ to “Do you have back pain?” proceeded to the CaFaSpa questionnaire). Model 3 further added demographic variables that were certain to be captured in routine practice, such as where participants lived, and type of referring physician. Additionally, for Model 3 (for both ‘narrow’ and ‘broad’ definitions of IA) an AUC and 95% CI was generated first using all variables. Then a stepwise selection procedure was used, applying the most explanatory variables that resulted from the model based on the lowest AIC, with both forward and backward selection. Predictors were selected or eliminated based on an AIC delta of 2. The best model was chosen including the most appropriate set of explanatory variables, a parsimonious approach that balances the model complexity against the best fit of the data. 64 4.2.4.3 Bootstrap Internal Validation Method of Exploratory Models Model 3 for both the narrow and broad IA definition and the corresponding AIC step models were internally validated using a 2000 bootstrap resampling method (158–160). The bootstrap predictive performance indices were examined accounting for ‘optimism’ as measured by the difference between the bootstrapped training and test datasets. The specific indices used were Somers’ D, a statistical measure of correlative rank of cases from highest to lowest probabilities of the outcome, the Nagelkerke R2, as a measure of the proportion of the outcomes that is explained by the model, and Briers score (B index) as a measure of model performance in predicting the binary outcome. Statistical analyses were carried out using RStudio Version 4.3.3 (143) and Microsoft Excel Version 2312 (161). 4.2.4.4 Sample Size Calculation The sample size required to detect a suspected difference from the null hypothesis of ‘chance’ (0.50) in ROC AUC with 80% power and 5% type I error based on a two-sided test was estimated (Appendix M). The ratio of negative (non-IA) to positive (IA) was expected to range from 2.0 to 3.0 for the narrow IA definition and 0.3 to 0.5 for the broad IA definition. This was derived using the observed ratio in the administrative data study in Chapter 3 (124) and arriving at a sample size estimate between 73 and 81. All calculations were performed by MedCalc Version 22.021 statistical software (162). 4.2.5 Timepoint Analysis For the timepoint analysis the median time and IQR was calculated from symptom onset to physician referral (lag 0). Since one of the study questions for patients was their symptom onset date, it was possible to approximate this lag 0. For all patients, time was measured from physician referral to rheumatologist visit (lag 1), from rheumatologist visit to DMARD (lag 2), and from 65 symptom onset to DMARD (total lag) for those who were prescribed one. To assess the signals of unequal access to rheumatology services for females and people living outside of metropolitan areas observed in the Chapter 3 study (124) for lag 1, group differences in lag 1 were examined based on patient sex and where they live. 4.3 Results 4.3.1 Participant Sample and Characteristics A total of 97 patient participants from 11 rheumatology practices were recruited over a 16-month period. Five patients who completed the questionnaire online were excluded because, as confirmed by clinic staff, they were not new referrals based on the dates of their referral and first rheumatologist visit. Therefore, 92 patient participants represent the sample population for this study; 73 of whom completed the SAID study questionnaire online and 19 on paper (Appendix N). The mean age (SD) of participants was 53.6 (15.6) years, and 62 (67.4%) were female, as shown in Table 4.2. One patient aged 18 years was included to maximize the sample despite being below the exclusion age of 19 years. The age and sex distribution were similar between participants classified as having TS-IA, Other IA and non-IA. There was a relatively even distribution of participants across income categories and most (79.4%) lived in metropolitan areas. Just over half (56.5%) had the shorter disease duration of joint pain for >6 weeks but < 1 year with a slightly higher proportion represented by patients with Other IA (60.0%) and non-IA (63.0%) compared to TS-IA (46.6%). Most participants (66.3%) were referred by a general practitioner (GP)/ PCP, with a higher proportion for patients with TS-IA (73.3%). Time to complete the questionnaire was similar 66 between the groups with a mean (SD) of 8.0 (6.3) minutes (Kruskal-Wallis chi-sq = 1.286, p = 0.526). Table 4.2 Patient characteristics, overall, by time-sensitive (TS) IA, other IA and non-IA diagnosis All participants (n=92) Time-sensitive IA (n=30) Other IA (n=35) Non-IA (n=27) Age Mean (SD) 53.6 (15.6) 53.4 (14.7) 52.9 (15.9) 54.7 (16.6) Median (IQR) 55.0 (40.8, 66.0) 53 (40.3-64.8) 55.0 (41.5-63.5) 58.0 (41.5-68.0) Sex, n (%) Female 62 (67.4%) 21 (70.0%) 21 (60.0%) 20 (74.1%) Male 30 (32.6%) 9 (30.0%) 14 (40.0%) 7 (25.9%) Household income range Less than $42,000 11 (12.0%) 2 (6.7%) 5 (14.3%) 4 (14.8%) $42,000 - $68,000 15 (16.2%) 7 (23.3%) 4 (11.4%) 4 (14.8%) $68,001 - $102,000 16 (17.4%) 4 (13.3%) 7 (20.0%) 5 (18.5%) $102,001 - $163,000 23 (25.0%) 8 (26.7%) 7 (20.0%) 8 (29.6%) > $163,000 16 (17.4%) 5 (16.7%) 8 (22.9%) 3 (11.1%) Missing 11 (12.0%) 4 (13.3%) 4 (11.4%) 3 (11.1%) Geographical Metropolitan 73 (79.4%) 24 (80.0%) 28 (80.0%) 21 (77.8%) Non-metropolitan (hospital hub) 7 (7.6%) 3 (10.0%) 3 (8.6%) 1 (3.7%) Non-metropolitan 4 (4.3%) 1 (3.3%) 2 (5.7%) 1 (3.7%) Remote 8 (8.7%) 2 (6.7%) 2 (5.7%) 4 (14.8%) Q12: Have you had joint pain for >6 weeks but < 1 year? Yes 52 (56.5%) 14 (46.6%) 21 (60.0%) 17 (63.0%) No 40 (43.5%) 16 (53.3%) 14 (40.0%) 10 (37.0%) Referring MD General Practitioner 61 (66.3%) 22 (73.3%) 21 (60.0%) 18 (66.7%) Specialist 26 (28.3%) 8 (26.7%) 11 (31.4%) 7 (25.9%) Nurse practitioner 1 (1.1%) 0 1 (2.9%) 0 (0.0%) Other 4 (4.3%) 0 2 (5.7%) 2 (7.4%) Time to Complete Questionnaire Minutes, mean (SD) 8.0 (6.3) 9.1 (6.5) 8.0 (7.4) 6.6 (2.9) The median time from symptom onset (available for 89 patients) to physician referral (lag 0) was 1025 days (IQR, 165-3807) (Table 4.3). It was shortest for patients with Other IA at 648 days (IQR, 120-1649), then for TS-IA at 936 days (IQR, 171-3230) and longest for non-IA at 3444 days (IQR, 1536-7437). The time from physician referral to first rheumatologist visit (Lag 1), was available for all 92 participants and was a median 61 days (IQR, 61-101). It was shortest for 67 patients with TS-IA at 44 days (IQR, 28-83), and similar for those with Other IA, 69 days (IQR, 40-102) and non-IA, 65 days (IQR, 34-99). A DMARD was prescribed for 20 patients in the study period: this comprised 11 of the 30 patients with TS-IA, eight of the 35 with Other IA, and one with non-IA. The time between first rheumatologist visit and a DMARD prescribed was a median of 0 days for all groups with the IQR for patients with TS-IA being 0-14 days, then Other IA at 0-3 days. Median total lag, from symptom onset to a DMARD being prescribed was 309 days (IQR, 119-1553). It was shortest for the eight patients with Other IA at 171 days (IQR, 82-350) and was 922 days (166-1614) for TS-IA. The patient with non-IA had a total lag of 12447 days. Table 4.3 Median lag times with IQR Days, median (IQR) All participants (n=92) Time-sensitive IA (n=30) Other IA (n=35) Non-IA (n=27) lag 0 Symptom Onset to PCP referral 89 1025 (165-3807) 30 946 (171-3230) 35 648 (120-1649) 24 3444 (1536-7437) lag 1 PCP Referral to Rheum Visit 92 61 (29-101) 30 44 (28-83) 35 69 (40-102) 27 65 (34-99) lag 2 Rheum visit to DMARD Rx 20 0 (0-13) 11 0 (0-14) 8 0 (0-3) 1 0 (0-0) total lag Symptom onset to DMARD Rx 20 309 (119-1553) 11 922 (166-1614) 8 171 (82-350) 1 12447 As shown in Table 4.4, median days between physician referral and rheumatologists visit, lag 1 was 9 days longer for women than men, 62 days (IQR, 31-95) versus 53 (IQR, 26-103). People who did not live in a metropolitan area had a median lag 1 of 16 days longer at 71 days (IQR, 44-102) versus those living in metropolitan areas, 55 days (26-98). Table 4.4 Differences in time from physician referral to first rheumatologist visit (lag 1) based on sex and geography where patients reside n (%) lag 1 (median days, IQR) Sex n, % female Female 62 (67.4%) 62 (31-95) Geography (LHA) Metropolitan 73 (79.3%) 55 (26-98) Non-Metropolitan – Hospital Hub 7 (7.6%) 66 (47-147) Non-metropolitan 4 (4.3%) 44 (38-59) Remote 8 (8.7%) 80 (67-97) All Non-Metropolitan 19 (20.7%) 71 (44-102) 68 4.3.2 Rheumatologists’ Diagnostic Reference Standard Distribution The rheumatologists’ reference diagnoses by category (Table 4.5) and the corresponding patient proportions in those, were approximately one-third each. For all IA (i.e. TS-IA and Other IA) versus non-IA it was 71% to 29%. Table 4.5 Diagnoses, their frequency and categorization as time-sensitive (TS) IA, other IA, and non-IA Reference Diagnosis n % Time-sensitive IA 30 33% Rheumatoid arthritis 12 40% Psoriatic arthritis 6 20% Unspecified inflammatory polyarthritis 5 17% Axial spondyloarthritis 5 17% Unspecified inflammatory spondyloarthropathy 2 7% Other IA 35 38% Unspecified diffuse connective tissue disease 5 14% Other and unspecified arthropathies 5 14% Connective tissue disease 5 14% Polymyalgia rheumatica 4 11% Giant cell arteritis 4 11% Arthralgia 3 9% Connective tissue disorder, NYD 3 9% Gout 3 9% Unspecified polyarthropathy 1 3% Systemic lupus erythematosus (SLE) 1 3% Granulomatosis with polyangiitis 1 3% Non-IA 27 29% Osteoarthritis 13 48% Carpal tunnel syndrome 3 11% Soft tissue disorder 2 7% Osteoporosis 1 4% Enthesopathy of hip region 1 4% Rotator cuff syndrome of shoulder and allied disorders 1 4% Flexor tendinitis 1 4% Back pain 1 4% HIV 1 4% Muscle cramps benign 1 4% Hypermobility syndrome 1 4% Lupus (not SLE) 1 4% Total 92 100% Narrow IA Broad IA 69 4.3.3 Performance of EIAQ for Time-Sensitive IA An EIAQ score could be calculated for all 92 participants because the published algorithm does not require the answer to the family history of RA question (Q10) for which there were 4 missing values. The EIAQ correctly classified 10 (11%) as true positives and 52 (57%) as true negatives, and incorrectly classified 10 (11%) as false positives and 20 (22%) as false negatives. This predictive performance corresponded to a ROC AUC of 0.586 (95% CI, 0.489-0.683) delivering a sensitivity of 33% and specificity of 84%. The corresponding predictive diagnostic metrics for the EIAQ were PPV=50%, NPV=72%, LR+ was 2.07, LR- was 0.79 and DxOR=2.60 (95% CI, 0.94-7.19). An analysis on the subset of participants (n=42) who answered ‘yes’ to the EIAQ question about symptom duration > 6 weeks but < 1 year (Q12) improved the ROC AUC to 0.622 (95% CI, 0.474-0.771) slightly but with a wider 95% CI. For the group of patients who answered ‘No’ to Q12 (n=50), indicating that they had a longer disease duration, the ROC AUC was slightly worse at 0.563 (95% CI, 0.434-0.691). This study sample size (Appendix M) was too small to statistically significantly measure an AUC ~0.55 based on the EIAQ published algorithm. Figure 4.1 Discriminative performance of EIAQ; ROC area under the curve and diagnostic test metrics AUC (95% CI) 0.586 (0.489-0.683) True Positive 10 True Negative 52 False Positive 10 False Negative 20 Sensitivity 33% Specificity 84% Positive Predictive Value 50% Negative Predictive Value 72% Likelihood Ratio Positive 2.07 Likelihood Ratio Negative 0.79 Diagnostic Odds Ratio (95% CI) 2.60 (0.94 - 7.19) 70 4.3.4 Performance of Functional Difficulty (Q08) for Time-Sensitive IA To estimate the discriminative performance of the functional difficulty question (Q08), this single binary explanatory variable, as a predictor of TS-IA versus ‘Other IA or non-IA’ was tested by ROC AUC. Of the 92 participants, Q08 correctly classified 24 (26%) as true positives and 20 (22%) as true negatives, and incorrectly classified 42 (46%) as false positives and six (7%) as false negatives. This corresponded to a ROC AUC of 0.561 (95% CI, 0.468-0.655) delivering a sensitivity of 80% and specificity of 33%. The corresponding predictive diagnostic metrics for Q08 were PPV=36%, NPV=77%, LR+ was 1.18, LR- was 0.62 and DxOR=1.91 (95% CI, 0.67–5.39). A subset analysis of Q08 performance by symptom duration > 6 weeks but < 1 year, as informed by a ‘yes’ response to Q12 (n=42), delivered a reduced ROC AUC of 0.462 (95% CI, 0.323-0.602). Conversely, for the group of patients who answered ‘No’ to Q12 (n=50), indicating that they had a longer disease duration, the ROC AUC significantly discriminated TS-IA from the other two group at 0.688 (95% CI, 0.555-0.820). 4.3.5 Performance of Exploratory Models of SAIDx 4.3.5.1 Predicting a Narrow IA Outcome The diagnostic performance metrics of the models are shown in Table 4.6. The narrow IA outcome definition aims to differentiate TS-IA from all others (Other IA and non-IA). Narrow IA Model 1 uses all the EIAQ questions (without the EIAQ algorithm applied); its score delivered an AUC of 0.728 (95% CI, 0.612-0.844). In Model 2 the back pain question was added and resulted in an improved AUC of 0.785 (95% CI, 0.674-0.896). Model 3 added the binary geographical (metropolitan versus all others) and referring MD (GP versus all others) variables and delivered a similar AUC to Model 2 at 0.785, 95% CI, 0.674-0.897. The household income variable was not included in Model 3, since 11 patients (12%) did not answer this question. 71 Table 4.6 Exploratory models of SAIDx for Narrow and Broad IA outcome definitions Narrow IA Outcome TS-IA versus all others Model 1 EIAQ questions only Model 2 Add back pain (QBP) Model 3 Add geo + referring MD All Variables AIC Step n 88 88 88 88 AIC 126.59 121.97 125.84 105.89 AUC (95% CI) 0.728 (0.612-0.844) 0.785 (0.674-0.896) 0.785 (0.674-0.897) 0.734 (0.614-0.855) Optimal Threshold - Youden 0.371 0.369 0.325 0.296 True Positive 18 21 22 21 True Negative 45 49 46 39 False Positive 15 11 14 21 False Negative 10 7 6 7 Sensitivity 64% 75% 79% 75% Specificity 75% 82% 77% 65% Positive Predictive Value 55% 66% 61% 50% Negative Predictive Value 82% 88% 88% 85% Likelihood Ratio Positive 2.57 4.09 3.37 2.14 Likelihood Ratio Negative 0.48 0.31 0.28 0.38 Diagnostic OR (95% CI) 5.40 (2.05 – 14.23) 13.36 (4.55 - 39.22) 12.04 (4.08 – 35.58) 5.57 (2.04 - 15.24) Broad IA Outcome All IA versus non-IA AIC 123.58 122.02 125.02 108.06 AUC (95% CI) 0.748 (0.629-0.868) 0.773 (0.658-0.888) 0.787 (0.676-0.898) 0.740 (0.632-0.849) Optimal Threshold - Youden 0.591 0.714 0.748 0.770 True Positive 56 44 40 36 True Negative 14 21 23 22 False Positive 12 5 3 4 False Negative 6 18 22 26 Sensitivity 90% 71% 65% 58% Specificity 54% 81% 88% 85% Positive Predictive Value 82% 90% 93% 90% Negative Predictive Value 70% 54% 51% 46% Likelihood Ratio Positive 1.96 3.69 5.59 3.77 Likelihood Ratio Negative 0.18 0.36 0.40 0.50 Diagnostic Odds Ratio 10.89 (3.38 -34.10) 10.27 (3.35 – 31.43) 13.94 (3.76 – 51.70) 7.62 (2.34 - 24.75) Note: 92 participants answered all questions except for Q10, where 4 participants did not know if they had a family history of RA, hence the sample of n=88 An AIC step was applied to Model 3 returning a less discriminative ROC AUC of 0.734 (95% CI, 0.614-0.855) but it was the best-fitting with the lowest AIC at 105.89. This was considered the best model to predict narrow IA with diagnostic sensitivity being 75%, specificity 65%, PPV 50%, NPV 85%, LR+ 2.14, LR- 0.38 and DxOR 5.57 (95% CI, 2.04 - 15.24). The Step AIC model variables included back pain (QBP), pain in wrists and hands (Q02), swollen wrists and hands (Q03), symptoms >6wks & <1yr (Q12). Only backpain (QBP) and symptoms >6wks & 72 <1yr (Q12) were statistically significant independent predictors of the narrow IA outcome in this model. Having back pain and symptom duration of more than one year, significantly decrease the log odds of receiving a TS-IA diagnosis. For model outputs see Appendix P. 4.3.5.2 Predicting a Broad IA Outcome For the broad IA outcome definition to differentiate all IA (TS-IA and Other IA) from non-IA, Model 1 delivered an AUC of 0.748 (95% CI, 0.629-0.868). Model 2 had a slightly higher performance with an AUC of 0.773 (95% CI, 0.658-0.888) while Model 3 delivered the best AUC at 0.797, 95% CI 0.676-0.898). Like the AIC step for Model 3 in narrow IA, here too the AUC of 0.740 (95% CI, 0.632-0.849) showed less discrimination but was more parsimonious with the lowest AIC of 108.06 (Figure 4.2). For AIC step Model 3, the corresponding diagnostic sensitivity was 58%, specificity 85%, PPV 90%, NPV 46%, LR+ 3.77, LR- 0.50 and DxOR 7.62 (95% CI, 2.34-24.75). This model’s variables included sex, back pain (QBP), pain in wrists and hands (Q02), swollen wrists and hands (Q03), morning stiffness general (Q05), symptoms >6wks & <1yr (Q12). Only pain in wrists and hands (Q02), swollen wrists and hands (Q03) and morning stiffness general (Q05) were statistically significant independent predictors of the broad IA outcome in this model. Having morning stiffness and swollen wrists and hands, but that were not painful, was positively associated with having IA (TS-IA or Other IA). 73 Figure 4.2 Distribution of broad IA SAIDx scores and ROC curve for Model 3 step AIC 4.3.6 Internal Validation of Model 3 and Step AIC for Narrow and Broad IA Definitions For the narrow and broad IA Model 3, there were instances where the bootstrapping process could not converge, or it produced extreme estimates suggesting the models may be too complex relative to the size of the data and will likely not generalize well. This was also indicated by the high optimism of the Somers’ D, R2, and Brier score indices. However, both the narrow and broad AIC step model bootstrap validation indicated some predictive accuracy and explanatory power and are presented here (Appendix Q). The narrow IA AIC step model had a corrected Somers' D of 0.3109 (post-bootstrapping), down from the original (pre-bootstrapping) estimate of 0.4198. This decrease suggests the performance of the model might be overestimated. The corrected R2 of 0.0830, was also reduced from the original 0.1915, meaning that after adjusting for optimism, the model has modest explanatory power explaining ~8.3% of the variance in predicting whether a patient has TS-IA or 74 not. The corrected Brier score increases slightly to 0.2022 from the original 0.1777, indicating that the error in the model’s predictions, is on average larger than the pre-bootstrapped sample. The broad IA AIC step model shows a corrected Somers' D of 0.3596 (down from the pre-bootstrapped 0.5145) suggesting moderate predictive discrimination, after adjusting for optimism. The corrected R2 value of 0.0901 indicates that only a modest amount of the variance in predicting broad IA (TS-IA or Other IA) is explained by the model. The corrected Brier Score is 0.2120, slightly worse than the original 0.1757, indicating higher error in the model’s prediction. These internal validation results indicate that the broad AIC step model may better predict the broad definition and explains slightly more of the variance in the outcome (relative to the narrow). 4.4 Discussion Between 2010 and 2020, by implementing new billing codes and increasing rheumatologist supply, BC achieved an improved, but still insufficient, rheumatologist case-mix of patient first-visits having IA, and shorter wait times that plateaued in 2018 (124). Intervening at the PCP-rheumatologist referral stage of the care continuum is another approach to consider to achieve arthritis patient access to the right care, at the right place, at the right time (33–35). One way that clinical investigators internationally have endeavored to address this is by developing and validating patient-completed questionnaires that seek to predict IA and may inform a triage approach for the decision to refer (32). This study specifically evaluated the performance of one such tool, the EIAQ score (62), using an online version of the patient self-administered questionnaire in a prospective, post-pandemic newly referred BC patient population. Overall, in the SAID study cohort, the discriminative performance of the algorithmic score that was reported by the authors of the EIAQ (62) was not reproducible. However, the exploratory analysis indicates that the questions that they selected through the Delphi consensus process (51) together with other 75 patient specific information, provide a good basis to optimize IA prediction models to potentially streamline arthritis patient care. There are some possible explanations why this study could not reproduce the performance of the EIAQ reported by the developers. The SAID (n=92) and EIAQ (n=143) cohorts were similar in that they both recruited consecutive new referrals on the waiting list of rheumatologists and the tool was self-administered by patients before a diagnosis was made, with rheumatologists blinded to the results of the questionnaire. Importantly this study was conducted post-pandemic while the EIAQ authors conducted their analyses in 2013. Both studies included 30 patients with TS-IA, representing 33% and 21% of the SAID and EIAQ sample, respectively. One difference that could have potentially affected the reproducibility of the EIAQ in the SAID cohort is the ratio of TS-IA to all other conditions ‘Other IA’ and ‘non-IA’, accounting for 33% to 67% of the sample in the SAID study, versus 21% to 79% of the EIAQ study sample. While the diagnosis received by the patients recruited to the EIAQ study were reported, 38.1% of the diagnoses were ‘other miscellaneous’, making it difficult to compare to diagnostic composition of the non-TS-IA group. Other differences between the cohorts were that EIAQ recruited from the practices of two rheumatologists in Ontario, while SAID recruited from 11 clinical practices in BC, potentially adding an element of heterogeneity of the sample populations and the recruiting clinical practices. Patients’ mean (SD) age was similar in the SAID study for TS-IA at 53.4 (14.7) versus 52.9 (15.9) for Other-IA and 54.7 (16.6) for non-IA (Table 4.2). However, in the EIAQ study, mean (SD) age of patients with IA was considerably younger at 49 (15) years of age compared to 63 (17) for those with non-IA. Similarly, there were differences in sex in the EIAQ study between groups: 30.0% of patients with IA were male, but only 19.6% were male in the 4-times-larger non-IA group. While patient demographics do clinically differ between diagnostic categories in practice, for the 76 development and validation of a discriminative tool, matching or standardizing for age and sex may affect performance results. This helps ensure that any observed differences in test outcomes are more likely attributable to the condition rather than age and gender differences. For example, it follows that answers to functional difficulty (Q08) and morning stiffness (Q05) would affect the EIAQ score development in unmatched age groups. The discriminative properties of the EIAQ were externally validated by the same authors in a parallel group study of patients with suspected IA (n = 127) (26). The design differed in that participants were purposively recruited into each group rather than randomly selected and the patient questionnaire did not include Q12, symptom duration of >6weeks and <1 year. Instead, this question, with a ‘yes’ response was used as an inclusion criterion for the other 11 questions to be answered. In this context, the same EIAQ score performed well with a ROC AUC (SE) of 0.829 (0.003), although as anticipated it had lower performance than in the derivation cohort of 0.915 (0.002). While the proportion of males across the groups were similar, the patients with early IA were a median 10 years younger than patients with non-IA. The development of the EIAQ was an extensive exercise in that a core set of dimensions and constructs for early IA detection was first systematically derived from the literature. These were formulated for lay users, and a three-round Delphi consensus panel of 181 early IA experts eventually arrived at an eleven-question instrument, narrowed down from an initial 35. As was found in this study and others described above, all the questions, except morning stiffness >1hr (Q06), symmetry (Q07) and RA family history (Q10) emerged in some combination to discriminate IA (Appendix R). This set of consensus-derived clinical questions is likely to inform the presence of IA in patients in different clinical settings and along different points in the continuum of disease, based on various algorithmically defined scores. Functional ability (Q08) 77 for example, was not discriminative across the whole SAID cohort but was in the subset of patients with symptom duration of >1 year. The exploratory analysis conducted in this cohort delivered models with good discrimination with ROC AUC of 0.734 and 0.740 using a parsimonious set of questions, internally validated on the narrow and broad IA outcome definitions, respectively, with different equations. As is the case in clinical practice, the studies that tested the set of EIAQ questions, including the exploratory analysis in this study, applied different recruitment methodologies, tested it in slightly different diagnostic cohorts, and arrived at different regression equations. Two other studies exist where the EIAQ has been translated into other languages and tested for performance. An Arabic version of the EIAQ was translated by investigators in Kuwait (75) who did not test the tool’s discriminative ability, but they conducted a cross-sectional study among 30 consecutive patients to evaluate the reliability and validity of the questions in the Arabic version of EIAQ. They examined the test-retest reliability for the overall and subscale scores (kappa=0.558), the internal consistency (KR-20=0.869), and construct validity (r = 0.727 – 0.896, all p<0.001) for disease activity variables. Each of these studies concluded that the EIAQ questions had favorable measurement properties for a reliable tool. In the second study conducted by investigators in Taiwan, the EIAQ was translated to Chinese and tested in a sample of 131 participants who were purposively recruited with established diagnoses (90). Sex and age variables were different between the groups and the investigators did not use disease duration (Q12) in the translated questionnaire. While in this study, the authors did not test the discriminative performance of the EIAQ published algorithm, they performed a multivariable logistic regression and arrived at an equation for a predictive score for IA that only shared two variables with EIAQ being sex, and ever told you have RA (Q09). Also, they coded using 0 for No and female and 1 78 for Yes and male (dummy coding) rather than -1 and 1 (effects coding). Using the latter implies that the outcomes can vary across a wider range of values leading to different predicted scores and thresholds for diagnostic decision-making. The ROC AUC, sensitivity, specificity, PPV and NPV were respectively, 0.88, 84%, 86%, 92% and 76%, indicating good discriminative performance. This study also reported test-retest reliability (kappa = 0.42 to 0.94) and internal consistency (KR-20 = 0.78), as was also done in the parallel cohort validation study. In contrast, in the SAID study, symptom duration (Q12) did emerge as an important variable to keep included. For real-life clinical applications, this question would be important to keep in since longer symptom duration (Q12) as well as the presence of back pain (QBP) both corresponded with a lower log odds of having IA. The IA new patient referral proportion of 71% in this study was much higher than the 51% in the case-mix in 2020 reported in Chapter 3. While the reason for this improvement cannot be ascertained from this SAID study, it might be that rheumatologists implemented an approach to prioritize first-visits for patients with IA, to address the backlog post-pandemic. The next chapter reports user perspectives of the SAID questionnaire, including those of participating rheumatologists and their insights to potentially inform this phenomenon in case-mix. For the whole SAID study cohort, the median time associated with Lag 1 was 61 days, while in 2020 from the analysis in Chapter 3 using administrative data it was reported as 41 days (124). It is possible that some of the improvement that was gained through billing codes and rheumatologist supply may have been lost post-pandemic, similar to the 2010 median lag 1 of 63 days. Even more concerning is that for patients with TS-IA, lag 1 was 44 days in this study, higher than the median 26 days achieved for RA patients in 2018. The improvement in lag 1 achieved from system level changes for RA patients by 12 days from the 2011 lag 1 of 38 days in Chapter 79 3 was not only not observed in this study but was 6 days worse. These results taken together suggest that using a standardized tool may help streamline care when healthcare capacity expands or contracts. In Chapter 3, concerning signals in lag 1 were reported wherein females waited 8.4 days longer than men to see a rheumatologist, which was also seen in the SAID study (Table 4.4) where females waited a median 62 days and men waited 53 days. A standardized triage questionnaire if self-administered online by patients, could potentially contribute to a reduction in these inequities of access to care based on sex and where patients live. The strengths of this study are that it prospectively recruited consecutive patients from multiple rheumatology practices in the province with about 27% of patient participants being from outside of metropolitan areas. This provides a heterogeneous sample that may increase the generalizability of the findings to other practices accepting new patients in BC. The sample size was adequate to generate internally validated predictive models of narrow and broad IA. The arthritic categories were also well-balanced in terms of age and sex and the questionnaire tested a new symptom variable for back pain that was not included in EIAQ, yet was an independent predictor of TS-IA and was included in the parsimonious broad IA model for predicting TS-IA and Other IA. Another strength is the consistency of the findings of this study with the observations made in the study using administrative data in Chapter 3 wherein access to care for females, and people living outside of metropolitan areas signalled potential inequities. This study was also the first in BC assessing an online referral tool, and the short time it took patients to complete it and its potential for predicting IA, provide important datapoints to consider for potential implementation. 80 A weakness of this study was that the sample size for the whole and subset groups were inadequate to conclude on the statistical significance of the discriminative performance of Q08. The sample size for AxSpa was also too small to assess the performance of CaFaSpa. Only five patients in the sample received a rheumatologist assigned diagnosis of AxSpa and two with unspecified inflammatory spondyloarthropathy (Appendix O). Other limitations include that patients were recruited from rheumatologist waitlist while the intended use of The Patient SAID tool is upstream, at the primary care level to assist with referrals. Related to this limitation is that the study population may have been enriched for patients with IA thereby increasing the pre-test probability as rheumatologists possibly aimed to identify patients with IA who have been waiting a long time during the pandemic. Also, patients completed the questionnaire online in advance of the visit or in the waiting room, and some patients completed a paper version of the questionnaire. While these different routes aimed to maximize recruitment and the study sample, this could have influenced the results based on modality used or proximity of time to the actual first rheumatologist visit. Regarding the timepoint analysis of lag 1 being the time from symptom onset to PCP referral, the lags were extremely long. Some patients commented that the date field in REDCap was ‘glitchy’ which could have contributed to the wrong date being captured but also how the question was phrased in asking when their joint pain started, which could have dated back too far from the current joint pain event. It was also uncertain why only 11 of 30 patients with time-sensitive IA started a DMARD based on the data input by the MOAs. Possible reasons for this are that the initial placeholder diagnosis may change later from a patient being thought to have RA that is later determined to have another IA not requiring a DMARD. So that patient would be classified as having RA but not be on a DMARD. Sometimes patients decline taking a DMARD or perhaps there was more work-up of the patient needed before the decision was made to prescribe a 81 DMARD by the time the MOA input the data. Another limitation is that despite the sample size being adequate to demonstrate that the models’ discriminative performance is greater than chance, it is still a small sample size, only internally validated without evidence of external generalizability. However, the sample size was sufficient for the objective of the study and had a balanced case-mix. Seeing its potential clinical value and low burden on patients, as a next step, using the set of SAID questions to routinely collect data on a larger more representative sample would allow for ongoing re-evaluation when linked to population data outcomes. Dependent on the patient and rheumatologist user perspectives discussed in the next chapter, feedback may by applied to optimize its role in rheumatology referral management. 4.5 Conclusion The Patient SAID Study, applying the published algorithm of the EIAQ did not reproduce its discriminative performance. However, the 12-item EIAQ questions, with one question added related to the presence of back pain, had discriminative performance for IA versus non-IA, albeit with a different combination of questions in the algorithm. Two different regression equations, one for patients with TS-IA with a narrow window of opportunity and one for all IA versus non-IA emerged as discriminative and were internally validated. The online questionnaire took under 10 minutes for patients to complete. It is likely that different combinations of questions can identify patients with IA in different settings along the continuum of care and at different stages of disease. Consistent with the Chapter 3 study findings, concerning signals for inequities in time to care were seen for females and people living outside of metropolitan areas, that could be mitigated by using a standardized, online, patient self-administered questionnaire. Implementing such a questionnaire may assist in decision-making, for PCP to refer, and rheumatologist to accept or prioritize referrals partly overcoming the challenge of the heterogeneity of current referral letters. Such a tool may 82 also be incorporated into population data collection, applying learning language models over time to improve patient outcomes and streamline their care. To optimize its potential integration into the healthcare system, patient and clinician perspectives should be incorporated to fine-tune the tool’s content and determine how best to administer it. 83 Chapter 5: Patient and Rheumatologist Perspectives of the Patient SAID Tool: Implementation Considerations, Potential Barriers, and Opportunities 5.1 Background The referral of patients with IA for rheumatologic care on a timely basis depends on correct primary care assessment. This is often a challenge, and leads to multiple healthcare provider visits for patients (163), contributing to high system costs and delays in the order of several weeks or months, before they see a rheumatologist (124,164). Several reasons have been reported for this delay including PCP confidence in detecting IA (165), heterogeneity in referral letters (31,166), and healthcare system characteristics impacting patient access to care (167). Several strategies to streamline referrals and reduce delay have been proposed, including the use of easily accessible standardized referral and triage tools (32,33,154). The Patient SAID Study (Chapter 4) tested the EIAQ validated tool (62) in a cross-sectional clinical study in BC and explored new predictive models of IA. While the EIAQ was not discriminative of IA versus non-IA in this cohort, the new predictive models using the EIAQ questions plus a back pain question (SAIDx) demonstrated discriminative performance when testing ‘Narrow’ and ‘Broad’ outcome definitions of IA. The ‘Narrow’ outcome definition focused on identifying TS-IA for a rheumatology referral versus ‘Other IA and Non-IA’ while the ‘Broad’ definition aimed to identify all IA for referral, being ‘TS-IA and Other IA’ versus Non-IA. The Patient SAID Study was exploratory, had a small sample size (n=92), and will need further validation. Similar to how the EIAQ algorithm by the original authors performed differently in this study, it is likely that the SAIDx algorithms generated in this BC cohort will too, in a bigger population and/or a different setting. Since it is possible to electronically collect patient-self- 84 administered questionnaire data if it is integrated into routine practice, then it will be possible in the future to revisit EIAQ and SAIDx algorithms with larger sample sizes in various settings. The SAID study also assessed the current rheumatologist case-mix of IA and non-IA, as well as differences in lag times from referral to rheumatologist visit based on sex and whether patients lived in or out of metropolitan areas. In the SAID study, compared to the administrative data study (Chapter 3), the case mix of rheumatologist first visits for IA was 71% compared to 51% respectively, suggesting that efforts at the PCP and/or rheumatologist level might have been made to prioritize first visits for patients with IA. Consistent with the findings from Chapter 3, females and people who lived outside of a metropolitan area waited longer from the PCP referral to see a rheumatologist. Taken together, the encouraging preliminary discriminative performance of the SAIDx scores, the indication from the case mix ratio that rheumatologists are prioritizing referrals for patients with IA, and the continued signal of unequal access based on sex and geography, suggest that a recommendation to implement a standardized tool might enable streamlined, more equal, and more timely access to rheumatology care. Stakeholder perspectives represent a critical consideration for the potential implementation of any tool to help address these clinical unmet needs. It is important to understand the perspectives of both patient and clinician end-users, system-level policymakers’ interests, and potential impact-driven incentives, to achieve adoption of a new triage approach and realize its intended benefit (168,169). In 2022, a qualitative study of patients and caregivers about the implementation of a digital rheumatology referral algorithm in the Netherlands found that these groups endorsed the need for early referral and recognized the perceived benefit of a digital algorithm (170). However, themes emerged underscoring important considerations for its successful implementation, including inclusivity, accessibility as well as content and outcome of the algorithm. Similarly, in 85 the evaluation of a centralized intake program for patients with RA and OA in Alberta in 2015, stakeholder input was used to develop system key performance indicators (KPIs) for quality improvement efforts (171). Twenty-eight KPIs were proposed to evaluate centralized intake, that were relevant to patients, practitioners, and health systems. With that in mind, and to understand tool implementation considerations, this multi-methods study aims to; 1) analyze the feedback that patient participants in the SAID study provided about the prototype referral questionnaire, and 2) gain the perspective of rheumatologists on their current referral practices as well the barriers and opportunities related to the potential implementation of such a tool for referrals in clinical practice in BC. The corresponding research question for this study is, “What are the SAID study patient and rheumatologist views of the referral questionnaire, and what are the perceived barriers and opportunities for its implementation?” The hypothesis is that with acceptable discriminative performance of a referral tool from The Patient SAID Study, patient and rheumatologist feedback may be incorporated to inform a recommendation for its potential adoption by the BC Ministry of Health. 5.2 Methods 5.2.1 Participants and Data Collection The 92 patients who completed The Patient SAID symptom questionnaire were eligible for this study and were, in a separate section of the same survey, asked questions related to their personal profile and participant experience with the questionnaire. This eligible study population included 73 participants that completed the symptom questionnaire before the rheumatologist visit online using the REDCap database Version 14.0.6 (155), and 19 participants who completed a paper version when seeing the rheumatologist in person. 86 For clinician perspectives, a representative sample of rheumatologists involved in the study was sought; of the eleven participating rheumatologists, six were invited to participate. Selection was based on how many patients were recruited from their practice, the mix of geographical regions of patients that they served, and included a mix of male and female rheumatologists, years in practice, and sites recruiting via paper and online questionnaires. To minimize bias, one of the eleven rheumatologists who was a co-investigator in the SAID study and his practice associate were not invited to participate. For rheumatologists who agreed to participate, interviews were conducted using Zoom (172) cloud-based, videoconference meetings, and were recorded and transcribed. 5.2.2 Theoretical Framework The epistemological stance in this multi-methods research study is based on the combination of a post-positivist and pragmatic researcher worldview. This perspective acknowledges the limits of objectivity recognizing that interpretation and context are important to understanding phenomena and that a flexible, pluralistic approach to research methods effectively addresses real world problems (173–175). The research question was addressed using an inductive multi methods approach at data collection, analysis, and interpretation for the patient survey and an inductive qualitative approach to rheumatologist interviews. There was a focus throughout on the practical implications of the findings and their relevance to the potential implementation of a rheumatology referral tool into clinical practice. 5.2.3 Patient Surveys and Rheumatologist Interview Questions The patient participant survey is shown in Table S1 in Appendix S and includes four questions with closed-ended ordinal responses, along with an open-text response field to the 87 question, “Are there any comments you have about the questionnaire in general, or specifically, like questions that you think it should include, that it doesn't?”. The rheumatologist interview questions are shown in Appendix S and were developed emergently after the SAID study was completed. The performance of the referral tool, in discriminating ‘Narrow’ or ‘Broad’ definitions of IA from Non-IA, was used for illustration, to elicit feedback from rheumatologists. The rheumatologist interview questions, patient self-administered questionnaire, and the referral tool’s discriminative performance, along with an interview consent form, were sent to rheumatologists in advance of the Zoom meeting and they were asked to consider their responses ahead of time. 5.2.4 Data Analysis and Illustration Patient participant data was first exported from the REDCap database to a file in Microsoft Excel Version 2312 (161). This included patient ordinal response data and free text comments that were then uploaded to NVivo. The proportion of patient responses within each ordinal response to the four questions was tabulated and graphed using Excel. The comments entered into the open-ended free-text question by patients were read and re-read several times noting down emergent themes, and codes were systematically developed for recurrently occurring topics. Summative semantic content analysis (176) was performed in NVivo by counting and comparing key phrases to understand their contextual use. Codes were grouped into themes, iteratively rearranged into a final structure, and then analyzed based on SAID study categorical variables of interest, such as disease type. Typology development (177,178), a process used to systematically organize qualitative data into meaningful types, was used to create substantive categories among TS-IA, Other IA and Non-IA participants. This was done based on whether they responded, ‘Not Really’, ‘Partly’ or ‘Very Much’ to the question “Do you think that 88 these questions could add to your primary care physician's assessment of your condition?”. This enabled analysis of homogeneity within and heterogeneity between the diagnostic groups based on their ordinal response. From there, using data consolidation (177,178) both quantitative and qualitative data types were comprehensively analyzed. An integrated results matrix (179) was used to jointly display the summaries of patient participant feedback. Analysis of rheumatologist interviews was based on the transcripts of the Zoom virtual interviews. When transcript text was unclear it was read and edited against the recordings. Text of participant feedback was categorized by question, including if it appeared later in the conversation, but related to an earlier question. Their responses were coded to correspond with the interview questions and data was consolidated from the interview participants to convey the range of responses and sentiments expressed. The breadth of feedback was analyzed specifically noting whether key sentiments were expressed by one rheumatologist only, or multiple. To maintain their anonymity, broad level identifiers were used while aiming to provide as much characterization as possible for the association of their perspectives. Patient and physician feedback were first analyzed separately and coded based on comments that communicate a barrier or opportunity sentiment. Patient ordinal response data was graphed to illustrate the proportion of responses related to questionnaire ease of login and completion and the extent to which participants felt that the questions reflect their condition and may assist their GP assessment. Three dimensions of responses were mixed with quantitative proportions and qualitative feedback. The first level was whether patients had time-sensitive IA, Other IA or Non-IA. Secondly within those, whether they felt that the questions could assist their GPs assessment of their conditions, being ‘very much’, ‘partly’ or ‘not really’, and thirdly within each of those, what comments patients offered as feedback in the free-text field of the 89 questionnaire. Exemplary quotes for each were provided, along with multiple level summaries and a meta-inference. The number of patient references made were quantitated by main emergent themes and presented for each of TS-IA, Other IA and Non-IA. Rheumatologist feedback was tabulated for each of the three participants alongside the interview question components, to characterize the various views of each. The feedback was described with a particular emphasis on implementation opportunities and barriers. Blending the rheumatologist perspectives with the summaries and meta-inferences from patient participants, an integrated feedback diagram was prepared. This was done by illustrating implementation opportunities and barriers for a patient self-administered questionnaire along the journey from having joint pain that they are seeking care for, to their first rheumatologist visit. These are presented based on patient routes to access care described by participants in terms of how and where the questionnaire could be implemented. These data were discussed with a patient partner who provided perspectives that informed the analysis and interpretation of the results, the discussion, and recommendations in this chapter. This study underwent review and was approved by the University of British Columbia Clinical Research Ethics Board (Study # H21-03719). An amendment for approval of the rheumatologist interview questions after The Patient SAID Study completion was submitted and approved under the same study number. 5.3 Results 5.3.1 Patient Participant Perspectives Responses to the ordinal closed-ended question regarding ease of logging in to the system were provided by all 73 patients who used the online questionnaire. All but one of the 92 patients responded to the ‘ease to complete’ question, all 92 responded to how much the ‘questions 90 reflected’ their condition, and 90 indicated the degree to which they think the questionnaire can help their GP’s assessment of their condition. Figure 5.1a illustrates the number and proportion of responses; 85% of patients indicated that the questionnaire was ‘easy’ or ‘very easy’ to log into and 78% found it ‘easy’ or ‘very easy’ to complete. Seventy-four patients (80%) indicated that the questionnaire ‘partly’ or ‘mostly’ reflected their condition and 68 (76%) felt that it could ‘partly’ or ‘very much’ add to their GP’s assessment of their condition. There was substantial overlap and frequency in participant responses to the two ordinal questions, “To what extent did the questionnaire include the important aspects of your condition?” and “Do you think that these questions could add to your primary care physician's assessment of your condition?”. Therefore, since the research question focuses on a tool that may assist with referrals, the analysis focused more specifically only on the distribution of patients with TS-IA, Other IA and non-IA in regard to how well they thought the questions could help their GP assessment (Table 5.1). The highest proportion of patients with TS-IA (80%) said the tool could ‘partly’ or ‘very much’ add to their GP assessment, followed by patients with Other IA at 74% and non-IA at 72%. 91 Figure 5.1 Number and proportion of patient respondents to closed-ended questions with ordinal data. a) “How easy was it to login to answer this questionnaire?” and “How easy was it to complete the questionnaire?”, b) “To what extent did the questionnaire include the important aspects of your condition?” and “Do you think that these questions could add to your primary care physician's assessment of your condition?” a) b) 1, 1%2, 2%14, 15%36, 40%38, 42%, 0%1, 1%10, 14%25, 34%37, 51%0% 10% 20% 30% 40% 50% 60%DifficultSomewhat DifficultOkayEasyVery EasyEase to Login (n=73) Ease to Complete (n=91)19.6%47.8%32.6%24.4%47.8%27.8%0.0%10.0%20.0%30.0%40.0%50.0%60.0%Not Enoughn=18Partlyn=44Mostlyn=30Not Reallyn=22Partlyn=43Very muchn=25Reflects my Condition (n=92) Add to GP Assessment (n=90)0 92 Table 5.1 Patient participant feedback mixed methods integrated matrix. Results centered on the response to the survey question “Do you think that these questions could add to your primary care physician's assessment of your condition?” Quantitative Qualitative Meta-Inference Participants Believe can assist PCP assessment Summary Exemplary Quotes Summary n % The majority >72% of each group indicated that the questionnaire could at least partly add to their GP assessment, with the highest belief among patients with TS-IA and the lowest among those with non-IA. All groups wanted more than a Yes/No answer and wanted more space to elaborate on their condition. 30 TS-IA 9 30% Very Much Patients with TS-IA had the highest proportion (80%) saying the tool could Partly or Very Much add Ask questions about fatigue and quality of life Want to say more about my condition, pain location and more than yes/ no answers 15 50% Partly I have more to say about thumbs, hands, wrists, knuckles 6 20% Not Really Selected NO even though the answer may be "sometimes" 35 Other IA 12 34% Very Much 74% of patients with Other IA also said it could partly or very much add with a higher Not Really than TS-IA It would be nice to rank the pain and stiffness Want to rank the pain, space for complex answers and description of types of pain 14 40% Partly There should be additional space to input related information. 9 26% Not Really I have different types of back pain; I want to discriminate… 25 Non-IA 4 16% Very Much Patients with non-IA had the smallest proportion of Very Much (16%) and the largest of Not Really at 28% Some of the answers are not yes or no as my conditions change Ask about other joints and more than a yes/ no answer 14 56% Partly Perhaps ask about other joints, not just back 7 28% Not Really I have arthritis in my hands, but the problem is with my knees 90 93 Fifty-eight (58%) of the 92 SAID study participants provided comments in the open text field to the question, “Are there any comments you have about the questionnaire in general, or specifically, like questions that you think it should include, that it doesn't?”. There were 85 participant references coded from the 53 participants, that were grouped into nine emergent themes. The themes are shown in Table 5.2 ordered from most to least frequent number of references per theme, among patients with TS-IA, Other IA and non-IA. Table 5.2 Main themes from patient participants, by reference frequency. In response to the open-ended question, “Are there any comments you have about the questionnaire in general, or specifically like questions that you think it should include, that it doesn't?” SAID Study Participants TS-IA (30) Other IA (35) Non-IA (27) Total (92) References made, by theme n % n % n % Want more than Yes or No answers 5 29% 6 35% 6 35% 17 Pain type, severity, and frequency should be captured 3 23% 7 54% 3 23% 13 Include more joints and their location 7 54% 2 15% 4 31% 13 Should be space for coexistent conditions 3 30% 4 40% 3 30% 10 Need space for more complex answers 0 0% 7 70% 3 30% 10 Consider adding these questions 5 56% 3 33% 1 11% 9 Positive comments 2 33% 3 50% 1 17% 6 Add longitudinal tracking to questionnaire 3 75% 1 25% 0 0% 4 Difficulties with questionnaire 2 67% 0 0% 1 33% 3 Total 85 references from 53 respondents 30 35% 33 39% 22 26% 85 Figure 5.2 graphically illustrates the frequency of references by theme. The most frequent reference at 17 of 85 (20%) was related to wanting to have the option of more than a Yes or No response to the questions in the questionnaire. The two next most frequent references at 13 of 85 (15%) each, were related to wanting to capture their type of pain, its severity and frequency (particularly for Other IA) as well as to include more joints and their location (versus only hands/wrists and back questions particularly for TS-IA). The fourth and fifth most frequent references at ten of 85 (12%) each, were recommendations to have space to describe coexistent conditions, and space for more complex answers (particularly for Other IA). The sixth most 94 common theme, with nine references, included proposed additional questions that could be asked in the questionnaire; these mostly came from patients with TS-IA. Six references were made to positive comments about the questionnaire being good, four references proposed that longitudinal symptom tracking would be a good feature for a patient questionnaire and three indicated the specific difficulties they had with certain questions in the questionnaire. Figure 5.2 Main themes and frequency by diagnostic categories, TS-IA, other IA, and non-IA TS-IA (30)Other IA (35)Non-IA (27)01234567 95 The integrated matrix in Table 5.1 illustrates the quantitative proportion, alongside exemplary quotes, of participant perspectives of the degree to which this questionnaire might assist their GP’s assessment of their condition. For example, a patient with TS-IA, who thought the questionnaire could ‘very much’ help the GP, suggested “Ask questions about fatigue and quality of life.” Those with TS-IA who thought it could ‘partly’ help said, “I have more to say about thumbs, hands, wrists, and knuckles”, and those who said it would ‘not really’ help said that they “Selected NO even though the answer may be ‘sometimes.’” Exemplary quotes from patients with Other IA, were; in the group that thought it could help ‘very much’; “It would be nice to rank the pain and stiffness”, in the group that thought it could ‘partly’ help “There should be additional space to input related information”, and in the group that thought it was ‘not really’ helpful “I have different types of back pain; I want to discriminate…”. In patients with non-IA who said that it could help ‘very much’, one patient said “Some of the answers are not yes or no as my conditions change”, an example quote from the group that thought it could help ‘partly’ was “Perhaps ask about other joints, not just back.”, and one from the group that said it was ‘not really’ helpful stated “I have arthritis in my hands, but the problem is in my knees”. The meta-inference made from these quantitative and qualitative mixed methods findings is that at least 72% of patients in each group feel that the questionnaire could at least ‘partly’ add to their GPs assessment of their condition, with the highest level of belief among the TS-IA group (80%) and the lowest among non-IA (72%) group. All groups wanted the opportunity to provide more than a Yes or No answer and each group indicated that they wanted more space for elaboration and that additional information was important to capture, beyond what was in the current questionnaire. 96 5.3.2 Rheumatologist Participant Perspectives Six of the 11 rheumatologists were invited for interviews, three of whom agreed to participate. These included one female and two males, two who have been in practice for over 20 years and one under 10 years. Two were from the Metro Vancouver area and one from Victoria, and one had patients who completed paper-based SAID questionnaires. One of the rheumatologists currently travels extensively in BC to see patients; one sees mainly Vancouver-based patients but used to travel extensively in BC, and the rheumatologist from Victoria sees patients from throughout Vancouver Island and the Sunshine Coast. 5.3.3 Case-Mix and Source of Referrals The highlights of the rheumatologists’ feedback are summarized in Table 5.3. Each rheumatologist reported a different IA to non-IA case mix, driven by practice capacity and preference of types of patients to serve, based on demand. Rheumatologist 1, who reported a 50:50 IA:non-IA case mix indicated that they don’t have a need to be selective about which patient referrals they accept because they have capacity for ‘all comers’ and believe that they should meet the demand for care. Rheumatologist 2, with the 75:25 IA:non-IA case mix indicated that they have capacity and seem to be referred more complex cases by GPs, therefore they aim to fill that demand. Rheumatologist 3, with the 33:67 IA:non-IA case mix reported having very limited capacity and aims to fill the high demand of patients with non-IA who have no one who will see them. 97 Table 5.3 Highlights of rheumatologist feedback by interview question Interview question components Rheumatologist 1 Rheumatologist 2 Rheumatologist 3 Current case mix (IA : non-IA) 50:50 75:25 33:67 Has case mix changed over time No, except we have more capacity now Yes, I get more complex IA referrals Yes, I see more non-IA now What is the ideal case-mix Whatever the demand is, accept all More IA based on urgency and practice More non-IA to serve unmet need Sources of new referrals (MD and geography) Almost all from GPs, Metro Vancouver, Lower Mainland and I travel remote 80% from GPs, patients from Metro Vancouver Mostly GPs, Vancouver Island and Sunshine Coast Has the source of new referrals changed over time Yes, more rheums in Vancouver now Getting more referrals from the Interior due to rheum shortage Yes, new rheumatologists in Vancouver and other areas of BC now Yes, fewer GPs and most rheums on Vancouver Island not accepting new patients Approach to accepting new patients Take all comers to meet the need including non-IA who have nowhere else to go. Spend time digging for information Screen as much as possible for IA and urgency; best use of my time & patient’s Aim to meet the demand; non-IA have nowhere to go. Other rheums are seeing IA, non-IA need help Approach to prioritizing which new referral seen Review referrals same day, assign to be seen urgent (next week) or not urgent (within 1 month) Considerable effort to prioritize visits, A (3-4wks), B (1-2mos), C (≥3mos) Referral review once a week. Extensive triage for likely IA or non-IA and urgency. Estimate % of IA seen within window of opportunity Most are in the window in Vancouver, but not where under-resourced in BC Most are within a window that can be effectively treated Almost none are in the window. Many barriers causing delay Thoughts about effectiveness of current referrals in BC Referral letters are highly variable and often completely inadequate. There may be no GP, reduced capacity or little time and training A lot of heterogeneity of patient referral and acceptance. GPs are overworked and don’t have time It’s all over the map, the biggest problem being overburdened GPs, who don’t know what they are looking for or what to test for Ideas of how referrals could be improved Cautious consideration of centralized intake if GP-rheum relationship not compromised. A patient completed tool can help fill information gaps of referral letter. A central triage system like in Calgary. Can make GPs aware of available rheums. Patient questionnaire can fill in the otherwise-sparse referral letters. Cautiously…something like a central triage or area with all information in one place to assist my triage. But would need to be shown how effective it is before buying in. Thoughts on patient questionnaire It is short, user-friendly and the yes/no is good. Symmetry, swelling is good. The family history is not helpful, many patients don’t know or get it wrong. Straightforward, captures most of what we would be looking for. Symmetry less important and any rash is important as well as time reference. Wouldn’t capture AS. It’s as appropriate as can be. The yes/no is useful. Swollen joints is a problematic question for patients. History of psoriasis is very helpful. Thoughts on its performance and utility Concerned about the false negatives. Want to know more about them. Maybe they aren’t time sensitive except for the diagnosis. Psoriatic arthritis with one joint is not as time sensitive as an urgent RA, for example. Looks like the tool works with 75% true positive. Worry about the 25% false negatives. In rheumatology there is a lot of overlap. Patients with same disease show up different ways. It could confirm or help me rethink my clinical suspicion. The good true negative performance to identify true negatives as non-IA could create more space for other cases. 98 How it could improve on current BC referral approach It would help with prioritization, not exclusion. Everyone would still need to be seen, it just depends by who. It would be complementary, increase efficiency, reducing need to look closer before accepting a referral. Could help educate GPs on who to refer or give people access who don’t have a GP. It depends on who is going to complete it, how they use it and the timing that it gets to the rheum. GPs won’t have time and it’s not helpful if it gets to rheums after accepting the referral. How it might be implemented to streamline referrals It can’t be something that increases anyone’s workload. The patient is likely the most motivated person to complete it. If it’s something that must be completed in the system for a referral to be made, it can just be attached to the already sparse referral letter with no additional effort. It could live right in Plexia (EMR) where there are already many tools that can be sent to patients through closed secure communication. We already send out the HAQ and BASDAI tools to patients to complete before some visits. It will be dependent on timing. If it is filled by the patient before seeing the rheumatologist after the referral has already been accepted that isn’t helpful. It would have to happen at the GP referral level, and they don’t have time for this. What are the main opportunities for its implementation If a tool like this was used as part of the centralized intake, GPs would have patients complete it to help make a decision to refer and it would have to be included with the referral letter. They could refer to a rheumatologist that they prefer, first available one or geographically specific. This could help educate GPs and if it accompanies the referral letter it could expedite the process. This could be done by the Arthritis Society to help people with joint pain seek care and for those without a GP using telehealth it could improve quality referrals For it to be useful, you would have to integrate all the EMRs in the province. That would be the ideal but is unlikely to happen. What are the main barriers to its implementation The main barrier for sure is workload. No one wants that at any level unless it’s billable. You definitely have to work out the mechanism. The patient is the most motivated to complete this to get the referral. Some may think it is unnecessarily cumbersome, another thing that they have to do. Maybe they think they don’t need the tool they can make the decision on their own. The patient self-description is an important part but should not be detached from other parts of a complicated diagnostic process. If it becomes a referral requirement, it might actually create new barriers. The GP has to take extra steps to make it happen, and they have no time, so they may forgo the referral which is even worse. Anything that takes time and requires extra steps is never going to happen. Maybe this will change overtime with larger group practices and streamlined administrative systems. 99 The sources of new referrals are primarily from GPs and for the Metro Vancouver rheumatologists (1 and 2), most patients are from the lower mainland. An increasing number of referrals come from regions where there are fewer rheumatologists, such as Kelowna for the Metro Vancouver rheumatologists (Rheumatologists 1 and 2) and in the case of the Victoria rheumatologist (Rheumatologist 3), all over Vancouver Island and the Sunshine Coast where there are either no rheumatologists or none accepting new referrals. To varying degrees each rheumatologist interviewed spends time prioritizing new referrals to try to see the most urgent cases first, and they spend a considerable amount of time looking for information when triaging. In many cases, “we dig into our EMR [Electronic Medical Record] that link with CareConnect2 to find information on the acuity of the patient’s situation.” 4 All three rheumatologists indicated that they do this because referral letters are often inadequate to inform prioritization of visits. Rheumatologist 1 indicated that they spend a particularly long-time accessing information on patients in remote areas who need to be seen because “while all the information is there in the medical records, GPs just don’t have the time to put it into the referral letter. In some places, there isn’t even a GP, but rather a locum or a physician who works out of the emergency room.” 5.3.4 Window of Opportunity and Effectiveness of Current Referrals The two Metro Vancouver-based rheumatologists indicated that they think that most patients with TS-IA are seen within a window where treatment could be effective, while the Victoria-based rheumatologist indicated “almost none are in a 12-week window” because of so many barriers. All three rheumatologists indicated that the shortage of GPs is ‘crisis-like’; there are either no GPs in certain areas, or they are not accepting new patients, or they are overwhelmed, 4 CareConnect is “BC’s secure, view-only Electronic Health Record (EHR) that offers authorized care providers access to an integrated, provincial view of patient-centric information available 24/7 to support the delivery of patient care” (180). 100 not up to date on rheumatology and in some areas like the Fraser Valley, may not even know that there are rheumatologists who have capacity to accept referrals. “The biggest problem is overburdened GPs who don’t know what they are looking for, what to test for and when to refer.” Issues with referral letters were characterized in the following quotes: “referral letters are highly variable and often completely inadequate”, “there is a lot of heterogeneity of patient referral and acceptance, and GPs are overworked, and they don’t have time” and “it’s all over the map”. In addition to the concern for GPs being overburdened, which rheumatologists believed contributed to the effect of less thorough referral decision-making and less time to maintain knowledge about the care of patients with MSK disease, the upstream concern of patient access to care was highlighted. The use of the questionnaire was viewed as a potential aid to alleviate the general practice bottleneck or for patients who don’t have a GP, due to scarcity or geography. One idea was that patients could access the questionnaire if it was hosted on a public website for patients, to increase their urgency to access a care provider, or that could be provided to telemedicine doctors to help improve the quality of referrals. The tool was also viewed as a resource that could help educate GPs and support their decision to refer, as patients complete the questionnaire during the visit and the results could be appended to the referral letter. Again, the ‘how’ was a critical part of delivering this value without adding work to anyone along the continuum; except “…the patient would be the one to complete it and they are the most motivated person to do it. But it needs to be inclusive, connected within the system somehow, and attached to the referral.” 5.3.5 Rheumatologist Capacity Varies by Geographical Area The two rheumatologists from Metro Vancouver thought that the rheumatologists in this region have significant capacity to see patients and one said “I would be surprised if any Vancouver 101 rheumatology practices are full and can’t accept new patients.” However, the rheumatologist based in Victoria on Vancouver Island had a different perspective describing how “Most rheumatologists on Vancouver Island are not accepting new patients and those of us who are, can’t keep up.” When asked how referral practices in BC could be improved, one suggestion was to address the regional lack of supply of rheumatologists by having those with capacity to travel to other locations or have patients travel to see them. They said that some GPs may assume that the patient is not willing to travel when in fact they may be. Another suggestion was to use Pathways more actively to identify available rheumatologists.5 For example, “Someone might indicate on Pathways that they see patients with Lupus and you know you can refer the patient to them in Surrey.” Although the Victoria rheumatologist indicated that Pathways was not up to date. Another idea raised by all three rheumatologists was a central intake/ triage system that could help streamline referrals, such as the one in Alberta (171). However, two of the rheumatologists indicated that they would be reluctant to buy in to this kind of system until they saw how effective it was and that it allowed the preferential referral relationship to be maintained between referring GPs and rheumatologists rather than just on a regional or first available basis. All rheumatologists indicated that a patient-completed questionnaire could help to fill some gaps in referral letters to some extent to prioritize who they see. Rheumatologist 2 said, “If someone self-described as having bilateral joint pain and swelling, impact on work lasting more than 6 weeks, I wouldn’t have to review much more to see them promptly.” 5 Pathways “is an online resource that provides physicians and their office staff/teams quick access to current and accurate referral information, including wait times and areas of expertise of specialists and specialty clinics. Pathways can also provide access to hundreds of patient and clinician resources, as well community service and allied health information that is categorized and searchable” (181). 102 5.3.6 Views on the Patient-Self Administered Questionnaire and its Performance Rheumatologists indicated that the questionnaire captured most of the information that they would be looking for, pointing out questions they thought were most and least useful. There was some disagreement on the importance of affected joint symmetry in that one rheumatologist said that they could be bilateral, but not necessarily the same affected joints on each side, while another said they liked that symmetry was one of the questions. Two rheumatologists indicated that the accuracy of patient-reported swelling is often low and that many patients don’t know whether there is a family history of IA and if they say they do, often after probing, it appears that the family member likely had OA. One rheumatologist said, “It [the questionnaire] is as appropriate as it can be.” All rheumatologists liked the fact that the questionnaire had yes/no questions, was short, that it was “straight forward” and appeared “user-friendly”. In terms of the data describing the discriminative performance of the questionnaire, when presented with the results in Appendix T, the reactions of the rheumatologists included, “It looks like the tool works; capturing 75% of the TS-IA, although the 25% false negatives is concerning. But with the overlap in IA disease and heterogeneity of patient presentation, as long as the tool isn’t used to exclude anyone, it would be helpful.” One said, “It would confirm or have me rethink my clinical suspicion when reviewing information about the patient being referred.” Two of the rheumatologists especially thought that the performance of the ‘Broad’ IA algorithm in identifying non-IA (IA TN) was particularly helpful to free up the early referral spots for more time-sensitive cases. And another said, "Time sensitive in your model is based on diagnosis. These are not all equally time sensitive; for example, someone with PsA with one joint is not as time sensitive as an urgent RA.” 103 However, each rheumatologist emphasized that there is a lot of heterogeneity in patient presentation and even in rheumatologists’ diagnoses and confirmation over time. In light of this, perhaps the better way to consider the tool scoring is less binary and more on a continuum, and therefore should not be used to exclude anyone from being seen, but rather to determine who they should be seen by. One rheumatologist indicated that using patient questionnaires in rheumatology patient management is commonplace where tools like the HAQ are routinely used in RA (182). 5.3.7 Potential for the Tool to Improve on the Current Referral Approach and its Implementation The main ways that rheumatologists thought the questionnaire could improve on the current referral processes in BC covered the whole continuum of the patient journey to the first rheumatologist visit. For patients who are researching their joint pain, this could be a tool on, for example, the Arthritis Society website that patients could complete, and potentially urge them to seek care. For patients who don’t have a GP, whether they live in a metropolitan or remote area, it could accompany a telemedicine consult or another healthcare provider assessment to complete the patient profile for a referral. One rheumatologist said, “Virtual consults are a source of a lot of junk referrals”, and another said that this tool could help improve the quality of these referrals. It was also suggested these questionnaires could help educate GPs on the basic things to look for in IA as they consider whether to refer or not. For example, the tool could fill gaps in sparse referral letters, thereby providing more information for the rheumatologist to consider when triaging the referral. At the rheumatologist level, the triage process could be simplified with a tool like this by possibly reducing the amount of time spent on searching for the most pertinent information on the referred patient. One rheumatologist said that “an important datapoint is the patient perspective of their condition which is sometimes lost in the GPs interpretation” and that “this could add 104 important information to speed up their access to timely care. As imperfect as a tool may be, self-reporting is a welcome part to this multi-pronged triage process and can be very informative.” Whilst rheumatologists had ideas on how the use of the tool could be implemented, they each stressed that working out the mechanism of how the tool could be introduced into the workflow is critically important for it to be useful. The timing of when a questionnaire is completed to inform the GP referral or provide useful information prior to the rheumatologists’ triage is important. The question of who would complete the questionnaire (other than the patient) in circumstances where a patient was unable to complete it themselves because of language or digital fluency, was raised. Similarly, the issue of how the information would get into the system to influence timing was another consideration. Paramount for each interviewee was that the tool should not add any additional burden of time on anyone; “It actually needs to make more time available, not use up time, and certainly not on the part of the GP who will never adopt it otherwise. The patient just won’t get referred.”, said one rheumatologist. One approach suggested was that the questionnaire could be integrated into EMRs and when a referral is being considered by the GP, and that the patient completes the form online and it is uploaded with the referral letter that is sent to the rheumatologist. “It may be possible to do this in Plexia where there are many tools that patients can securely complete,” offered one rheumatologist.6 Since not all practices use the same EMRs, one rheumatologist said, “It would be really ideal [and would make the tool possible to work this way], if all the EMRs in the province were integrated, but that’s not going to happen any time soon.” Other than the main barriers related to working out the mechanism of the integration of a patient self-reported symptom questionnaire into the system, ensuring that it is a time-saver rather 6 Plexia is one of the three most common EMRs used by rheumatologists in BC (183). 105 than a time-consumer, was mentioned repeatedly by all three rheumatologists. “Unless it is billable, no one will be able to afford the time to do the extra work,” said one rheumatologist. Other reasons for resistance to the adoption of the questionnaire tool might be that clinicians think that they don’t need the tool and believe that the way they are currently making decision is sufficient. Finally, one rheumatologist said that if the tool was required to accompany the referral, the unintended consequence of the referral not being made at all is a possibility; “That would be terrible!” they said. Figure 5.3 illustrates the integrated feedback of user perspectives of the patient self-administered questionnaire implementation barriers and opportunities. Figure 5.3 Integrated feedback of patients and rheumatologists of barriers and opportunities for implementation of the patient self-administered questionnaire Opportunities Barriers Patient with Joint Pain No GP Access Educate and Inform Decision to Refer GP Access Patient Perceived Value Completion Process GP Workload Telehealth or Allied Health Referral Supplement Information in Referral Letter GP Referral Non-integrated Digital Healthcare Information Rheumatologist Triage Add Efficiency to Mobilize Care and Accept or Redirect Referrals Rheumatologist Triage Mechanism and Timing to Receive Questionnaire Patient First Rheumatologist Visit 106 There were two trends pointed out by two of the rheumatologists that may positively impact the questionnaire’s adoption into the future. One was that shared decision-making with patients is increasingly valued and understood to improve outcomes, and as larger clinical group practices are formed with streamlined administrative systems, this type of questionnaire could help improve the efficiency of care delivery. 5.3.8 Patient Partner Suggestions on How to Improve the Questionnaire To address patient feedback while maintaining the consensus-derived and validated yes/no questions, ideas were generated in discussion with the patient partner. These included: 1) to add a homunculus, a visual representation of the human body where patients can mark the location of their affected joints, 2) have simple drop-downs for pain type and frequency, and 3) add a scale for the degree of pain to complete where patients answer ‘yes’. Additionally, a check list of arthritis-relevant comorbidities could be made available, along with a free-text field with a limited character count for patients to elaborate on pain if they want. These peripheral data captured, while not initially used in the algorithm score constructed using the yes/no responses, would serve the purpose of increasing value to the patient and clinician at the referral stage, and would potentially be available to generate a new algorithm if these variables could be connected to outcomes. Refinement of questions and explanation bubbles were also recommended to better define some questions. 5.4 Discussion This study examined the feedback of patients and rheumatologists who participated in The Patient SAID Study (Chapter 4) to understand potential opportunities and barriers for the implementation of the patient self-administered inflammatory arthritis questionnaire. These user perspectives were generally favourable for its implementation to improve rheumatology referrals 107 in BC, and they expressed robust actionable recommendations. A patient partner was consulted to help inform this discussion, given the range of implementation considerations offered by participants related to the patient journey from symptom onset to seeing the rheumatologist for the first time. 5.4.1 Recommended Improvements to the Questionnaire A high proportion of newly referred patients (>80%) found the questionnaire easy to complete and over 75% indicated that they believe it can help their GP to assess their condition. The feedback from over half of the patients indicated that they wanted 1) more than a yes/ no answer, 2) to expand on their pain type, severity and frequency, 3) identify the location of affected joints, 4) mention coexistent conditions, and 5) to have space to provide more complex answers. In contrast, rheumatologists liked the yes/ no answers and for the most part said it captured the main information that they would initially consider important, although they indicated that some questions were more useful than others. 5.4.2 Questionnaire Performance and Perceived Utility Along the Continuum of the Patient Journey The discriminative power of the score derived from the questionnaire was considered particularly good by rheumatologists for the ‘narrow’ IA model to identify TS-IA, and in the ‘broad’ IA model where it performed particularly well to identify patients with non-IA as true negatives. The ability of the questionnaire to identify urgent cases to be seen, and those who may not need to see the rheumatologist, was considered a useful feature. When offering perspectives on how the tool could be implemented, there was a high degree of concern expressed by rheumatologists for GPs being overworked and a high willingness on the rheumatologist’s part to do “the digging” to triage referrals. They each indicated that if the 108 information from the questionnaire was available at the time of referral to supplement a typical sparse referral letter, then this could alleviate this burden and streamline patient care. As will be described later, their main concern was the logistics of how and where the questionnaire would be completed and how the information would get to them without adding work for the GP. As mentioned by one of the rheumatologist participants, tools that collect patient-reported outcome measures (PROMs) are commonly used in the management of patients with IA. However, upstream at identification or diagnosis of disease, there is an absence of validated tools in practice. Online symptom checkers are prevalent, as well as other tools such as those described in the Chapter 2 scoping review (32). Positive trends that may increase interest in such a tool’s adoption are, society’s digital readiness, the appeal of centralized intake with some caveats, shared decision-making, efficiencies enabled and demanded by group practices, increased healthcare IT system connectivity and machine learning systems. 5.4.3 Use of the Tool to Streamline Geographical Distribution of Rheumatology Care When discussing IA to non-IA ratio of new referral case-mix, the difference in rheumatologist capacity by geographical region was illuminated. Disparities in rheumatology patient access to care based on geography, especially in remote regions has been a known concern (124,167). However there are ongoing barriers in other metropolitan areas with teaching hospitals such as Victoria and Kelowna despite the deliberate increase in rheumatology trainees over the last 15 years. It was suggested that the patient questionnaire could assist with motivating patients to travel to access care and to help Vancouver rheumatologists to prioritize visits when they travel to support underserviced regions. The fragmented connectivity of healthcare information technology between the various EMRs and other platforms like CareConnect and Pathways was raised as a barrier to widespread 109 streamlined implementation of the questionnaire. However, there are some existing routes that could be used as proxies. For example, in the Plexia EMR, there is a list of tools that can be sent to patients and shared in a private secure environment. If patients are seeking care from a GP with mainly rheumatologic symptoms, the questionnaire could be sent through the EMR to the patient to be completed ahead of the visit, thereby possibly assisting the GP referral decision. As healthcare IT systems become increasingly connected, its rollout can be expanded across the province over time. 5.4.4 Study Strengths and Limitations The strengths of this study are the robust feedback received from two-thirds of the patients in The Patient SAID Study and the enriched perspectives from patient partner input. The multi-methods approach qualitatively informed improvement dimensions of the quantitative results of patient perceptions of the tool assisting their GP assessment. Getting both rheumatologist and patient perspectives was very valuable especially where they differed, to enable ways that the combined feedback could be used to satisfy the interests of both stakeholders. Another strength is that rheumatologists were blinded to patient responses and patients were made aware of this in the consent form. Therefore, this would have encouraged patient candor since their responses could not impact the timing or nature of the care they received. This study also had limitations; only three rheumatologist were interviewed despite extensive attempts to reach a total of six and due to research time constraints. Because of this small sample and to maintain rheumatologist anonymity, the results had to be more generalized, rather than providing deep descriptions of their profiles and corresponding comments. There was a contrast in their views that could be further informed by speaking with more rheumatologists across different geographic regions of the province to gain additional implementation 110 considerations. Also, patient feedback was focused on the questionnaire rather than their ideas for implementation, which would add richness. Another limitation is that the views of GPs and policymakers were not sought, which would surely illuminate several other potential barriers and opportunities. 5.5 Conclusion Patient and rheumatologist participants in The Patient SAID Study indicated that the patient self-administered questionnaire, with some recommended changes and mechanisms for implementation, could help improve on current referral processes. The questionnaire could play an important role in educating patients on joint pain symptoms that require them to seek care, overcome the constraints of GP access and help inform a GPs decision to refer. It could also supplement the normally sparse referral letters that rheumatologists receive, thereby supporting more streamlined triage to accept or redirect patients to the care they need. As healthcare IT systems become more digitally connected and shared decision-making continues to gain traction, patient symptom reporting can be centrally integrated to improve access to care and outcomes. 111 Chapter 6: Thesis Discussion 6.1 Introduction This thesis sought to evaluate the potential value of the introduction of patient self-administered IA detection tools to support patient referrals between PCP and rheumatologists, by identifying the people most urgently needing care. The findings of this thesis have shown that, while improvements appear to have been made over the past decade in the referral times and case-mix of referrals in BC, further changes are needed to streamline care of patients with these chronic and potentially debilitative conditions. A scoping review found that validated patient self-administered IA detection tools exist, and a prospective clinical study demonstrated that they can be integrated into the referral process. Information provided through a questionnaire that is completed by patients can inform PCP decisions to refer and rheumatologists’ triage for patient prioritization and redirection. However, barriers need to be overcome before this can be implemented into routine clinical practice. The opportunities and challenges are discussed in this chapter. 6.2 Key Findings In Chapter 2, a scoping review was conducted to summarize the current evidence on approaches to streamline the referral of patients with IA from primary care to rheumatologists. Based on the reported discriminative performance of referral and triage tools, the level of evidence suggests that existing approaches have the potential to improve on current referral processes, however there is a need to move attention from ongoing development of additional tools to the testing and comparison of these existing tools in real-life settings. From the tools identified in the scoping review, the EIAQ and CaFaSpa questionnaires were selected based on the level of supportive data available for prospective evaluation in The Patient SAID Study (Chapter 4). 112 In Chapter 3, the current state of rheumatology referrals in BC was described using administrative data that captured all patient contacts with rheumatologists covered by the MSP in BC through linked administrative datasets provided by Population Data BC. The findings of Chapter 3 demonstrated how access to rheumatology care had improved in BC through shorter wait times for first visits and treatment, from 2010 to 2020. The proportion of patients with IA in the case-mix of first-visits to rheumatologists had increased suggesting more urgent cases were being prioritized. These improvements coincided with system-level changes that included an expansion in the supply of rheumatologists and a new billing code that offered an incentive for rheumatologists to provide multidisciplinary care by employing a nurse. Whilst these changes appeared to have improved access and referral patterns, it seemed that the improvement had plateaued by 2017/18, and the time from referral to first DMARD for people with RA remained too long. In both the population data and SAID study, signals were detected that indicated that females and people living outside of metropolitan areas had poorer access to specialist rheumatology services. Others have also described inequities based on sex related to access to care (149,150). The challenge in BC for patients who live in non-metropolitan areas is that rheumatologist wait lists vary in length from a few weeks to up to two years, depending on the area. For patients in more remote locations there are a small number of traveling and quaternary clinics that are serviced by approximately five rheumatologists who are salaried through the Mary Pack Arthritis program. Wait lists and infrequent access to rheumatologists may be the two reasons for their longer wait times. In Chapter 4, the prospective Patient SAID Study, recruited patients being seen by a rheumatologist for the first time to complete the EIAQ and CaFaSpa questionnaires. The performance of the published algorithms of these measures in predicting IA was then compared 113 with the diagnosis given by the rheumatologist. The discriminative performance of the EIAQ reported in the studies in the Toronto area was not replicated in this Patient SAID Study population in BC. However, in an exploratory analysis, the 12-item EIAQ questionnaire, with one question added related to the presence of back pain and a new algorithm, does discriminate IA from non-IA. This exploratory set of questions and algorithm is referred to herein as SAIDx, and included regression equations for distinguishing patients with non-IA from those with TS-IA with a ‘narrow’ definition of IA and a ‘broad’ definition of all IA. This thesis provides preliminary evidence of the potential discriminative validation (internal) of these new algorithms. However, the failure to replicate the performance of the EIAQ and CaFaSpa measures externally in The Patient SAID Study provides a cautionary warning that the performance of these newly proposed algorithms will need to be replicated in larger and/or external patient populations. Nonetheless, these findings suggest that the combinations of questions from the EIAQ and CaFaSpa can be collected in routine practice and can provide useful information for physicians seeking to identify and prioritize the referral of patients with IA in different settings along the continuum of care and at different stages of disease. A final finding of the prospective Patient SAID Study was a consistent, concerning, signal of inequities in referral times to care for females and people living outside of metropolitan areas, with the findings from Chapter 3 using Population Data BC linked administrative datasets. This consistent signal, albeit from a small exploratory study, suggests the potential value of a standardized, online, patient self-administered questionnaire to help mitigate these issues. In Chapter 5, to consider how the routine collection of the patient self-administered questionnaires used in the Patient SAID Study might potentially be integrated into the healthcare system, patient and clinician participant perspectives were collected and presented in Chapter 5. 114 Both of these participant groups indicated that the patient self-administered questionnaires used in the Patient SAID Study, with some recommended changes to the content of the tool, could help improve on current referral processes if the proper mechanism for its implementation is worked out. The questionnaire could play an important role in educating patients on joint pain symptoms that require them to seek care and help inform the decision of PCP to refer patients to rheumatologists. Another role could be to supplement the referral letters that rheumatologists receive with referrals, which are known to often lack salient information (31), thereby supporting a more streamlined triage process for prioritizing urgent patients with IA to be seen by the rheumatologist and redirecting patients with non-IA to the care they need. 6.3 Limitations 6.3.1 Patient Participants and Recruitment The challenges with recruitment in the Patient SAID Study, which necessitated significant site and patient follow-up for engagement and questionnaire completion, indicate that several mechanisms to support and remind patients to complete questionnaires might be required if integrated into the healthcare system. The small sample size limited the development of more robust predictive models and their validation. While a larger target sample size was planned in the study protocol, it appears that the peri- to post-pandemic period was a challenging time in general, for other observational and interventional rheumatology research studies (184). Nonetheless, meaningful results and recommendations emerged from this research. Furthermore, whilst the prospective Patient SAID Study suggests that tools to collect data might be useful to assist with GP referral decisions, the clinical study actually recruited newly referred patients from rheumatology practices, whereas in practice the data would need to be collected in the primary care stage of the referral pathway. Importantly, while user perspectives of 115 the tool were gained, those of PCP and policymakers have not yet been captured. The feedback and implementation considerations of both of these groups would be critical for its success. Another recruitment limitation is that this study was only conducted in the Province of BC and 75% of patients lived in metropolitan areas. For generalizability and analyses of geographical subsets that may need tool access accommodations, it would be important to include more patients from non-metropolitan regions and provinces. The patient self-administered questionnaire was also only available in English and there could have been a sample bias to people who were digitally proficient. 6.3.2 The Heterogeneity Inherent to the Study Context One of the key challenges common to every chapter in this thesis is the heterogeneity across several aspects of IA. These areas of heterogeneity include: 1) the varied disease nomenclature for IA that affects the definition of cohorts in which tools were tested, results reported, and in seeking to compare and contrast these different studies, 2) the initial presentation of patients and their pre-test probability of IA, which reinforces the difficulties that primary care providers face in making referrals, 3) the coding of IA conditions and coding conventions of PCP and rheumatologists in health records which complicates the classifications of patients in the analyses which used administrative population-level data. To try to mitigate the impact of this limitation, a rheumatologist co-author on the study team assisted with defining these case classifications, and previously validated algorithms were applied, 4) heterogeneity in the content and comprehensiveness of referral letters (31), 5) regional differences in primary care and rheumatologist capacity, and their triage approaches, 6) the reported low agreement between PCP and rheumatology assessments (185,186) as well as variability in diagnostic agreement between rheumatologists (187,188) adding complexity to assigning a definitive outcome. This last point 116 underscores the inherent challenge with the reference standard being the rheumatologist diagnosis. The level of inaccuracy of a tool when there is ambiguity in the binary outcome needs to be considered in the context of what the tool will be used to inform. If it is to reprioritize a referral for patients with time sensitive IA ahead of those with non-IA, but all patients will be seen, perhaps a likelihood of IA as a function of sensitivity and specificity is a better result to report. In addition to this heterogeneity, evolution of clinical practice patterns varies over time, in part with fluctuations in human resources, and/ or circumstances such as the COVID-19 pandemic, that impact upon clinician time available. This would affect the ability to focus on continuing education of PCP in the management and referral of patients with symptoms of MSK disease and the thoroughness of investigations for timely referral. 6.3.3 Implementation Risk Considerations Although the scoping review identified several validated tools, there was no published evidence that any have been adopted for routine use in clinical practice. Therefore while, EIAQ and CaFaSpa were selected on the basis that they had the best available evidence, they would still be considered to be in the validation phase; the implications of this were realized in the Patient SAID Study in Chapter 4, where the promising early performance of these measures was not replicated in this study. The finding of inequities of access to care according to sex and geography from the study of administrative data in Chapter 3, and that seemed to follow in The Patient SAID Study in Chapter 4 are concerning but may also be indicative of further yet unidentified inequities. Sex and patient area of residence were among a limited set of variables available in these datasets; if more comprehensive data were available on other equity stratifiers (189), further inequities may have been documented. In The Patient SAID Study, age, sex, gender, race, household income, and 117 geographic location variables were collected, although education was not. It is possible that the small sample size didn’t allow other inequity variables to emerge as significant. Whilst standardized self-administered detection tools for patients with IA might help streamline care and potentially reduce inequities to access to rheumatology care, provisions would need to be made for inclusivity of individuals to address language barriers, literacy, and digital fluency to avoid unintentionally exacerbating existing or introducing new inequalities. 6.4 Strengths One of the foundational strengths of this research is the attempt to introduce rigour at each step of the research. The tools with the best evidence were identified through a systematic PRISMA-guided scoping review, which provided confidence that the tools selected for The Patient SAID clinical study represented the best available questions. Although the EIAQ authors’ results were ultimately not reproduced here using their algorithm, the rigorous process they used to develop the questions were of value, enabling development of preliminary, new, ‘narrow’ and ‘broad’ IA definition algorithms (when combined with the initial back pain question that facilitates the completion of the CaFaSpa tool). In this small study, evidence of an ability to discriminate between IA and non-IA was demonstrated. In future studies using these questions, the relative performance of the EIAQ algorithm and the newly proposed SAIDx algorithms can be compared, which may represent a legacy of benefit from the work conducted in this thesis. The characterization of the magnitude of the problem of the long referral times and case-mix of referrals in the study setting of BC, as well as trends in these rheumatology referrals, were established using a population-level dataset wherein all patients in BC were included. This substantiated the clear need for The Patient SAID Study as well as a ‘baseline’ for comparison prior to initiating the prospective clinical study in Chapter 4. This data supported critical steps in 118 the planning of The Patient SAID Study, for example for the sample size calculation and a comparison against which study findings could be assessed. The Patient SAID Study then recruited from 11 rheumatology practices covering several geographies in BC, to reflect the heterogeneity of the province, with 25% of patient participants being from non-metropolitan areas. Importantly, the same SAID study patient and rheumatology participants provided the sampling frame for obtaining user perspectives in a multi and mixed-methods design. This meaningfully informed opportunities for improvement of the data collected in the Patient SAID study, which will be invaluable for future iterations of this programme of work. Another notable strength of this research is that the planning and execution were overseen by a senior clinical rheumatology professor thesis committee member. Consultation with a patient partner from the scoping review stage through to helping shape implementation considerations of findings in Chapter 5 was a critical strength to enable recommendations in this thesis. 6.5 Implementation Opportunities 6.5.1 The Role for a Patient Self-Administered IA Referral Questionnaire One of the most important barriers for patients with IA and non-IA to receive the care they need is the absence, scarcity, and limited capacity of PCPs, leading to under or mis-referrals, with sparse information provided to rheumatologists to prioritize urgent cases. This was where the greatest value of the patient self-administered questionnaires and algorithms was expected, whereby they could assist the GP with a decision to refer, and if appended to the referral letter, could provide valuable information to rheumatologists allowing efficient triage. There are also potential ancillary benefits in providing patients with education about symptoms that should lead them to seek care and be directed to the right practitioner, as well as a standardized approach to guide PCP in their decision-making. 119 While care would need to be taken for inclusivity, the use of standardized, validated patient self-administered questionnaires (and the algorithms used to score them) could help to overcome the barriers to referral identified between patients from different geographies and sex. This could work by homogenizing information for decisions to refer and to prioritise first rheumatologist visits, if all patients could complete the questionnaire and if it could be administered with little disruption to normal practice or burden on PCP, rheumatologists, or their staff. The main challenges will be establishing this mechanism for patient completion and ensuring digital connectivity and communication through EMRs for ease of access by GPs and rheumatologists, lessening and in no way adding to workload. This thesis provides proof of concept of the way in which the collection of information from triage tools could work, supported by initial feedback from early users of this prototype approach. 6.5.2 Use of Patient-Reported Outcome Measures (PROMs) in Rheumatology PROMs are commonly used in rheumatology to assess disease activity, functional status, pain, and overall health-related quality of life. Their applications are typically downstream of a diagnosis alongside clinical assessments and include tools such as the HAQ (190), Patient Global Assessment (PGA) (191), Pain Visual Analog Scale (VAS) (192) and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) (193). However, there are no established PROMs that are routinely collected upstream of the referral to help identify and diagnose patients with rheumatologic conditions. This highlights the gap that could be filled by collection of validated tools and algorithms identified in Chapter 2 (and proposed in Chapter 4) such as PEST for PsA and CaFaSpa for AxSpa. 120 6.5.3 Optimize with Digital Health Connectivity The administrative data study in Chapter 3 showed that benefits are realized by system changes. Incorporation of technology into these systems for secure visibility of patient reported information can enable agile decision-making for early and streamlined patient referral and triage. Embraced by patients, being the most motivated stakeholder to achieve a positive outcome, by incorporating their symptoms and linking these with outcomes in Population Data BC, the combination of important variables can be machine learned over time. The technology industry has been employing this for decades where they launch a minimum viable product (MVP) and allow it to optimize iteratively as new inputs and outcomes are generated. As the patient research partner pointed out, “Validation in healthcare is a barrier in and of itself. By the time data is collected, analyzed, and undergoes several levels of validation, time has moved on making it obsolete or limited in its application.” With the GP crisis and growing constraints in the healthcare system, applying machine learning to a set of validated questions connected to outcomes, layering on other variables iteratively, is important to get started on. While models are optimized to direct care, in the short-term at a minimum, a homogenous set of patient-completed questions could be made available to inform clinical care. This would be an important step towards addressing system constraints while preventing disease progression and patient debilitation. 6.5.4 Healthcare Initiatives in BC that Uniquely Position the Province to Leverage the Patient SAID Approach BC is well-positioned to leverage innovative tools such as those included in The Patient SAID Study. Rheumatologist feedback in Chapter 5 included several mentions of health system technology in BC, that while still fragmented, is becoming increasing connected. This is a critical development underway for people of the Province of BC to receive the benefits of a multitude of 121 health technologies. The evidence in Chapter 3, also shows that health system changes made by the province in rheumatology, had a positive impact pre-pandemic, serving as important proof-of-concept to adopt new ways of working. Since then, the adoption and reimbursement of telemedicine has enabled BC patients’ efficient access to care that, taken together with patient and rheumatologist feedback in Chapter 5 indicates a high level of readiness to adopt a triage tool. BC also boasts one of the best administrative databases in the country, whereto outcome data can be linked with variables such as those collected in The Patient SAID Study to allow more sophisticated and granular analysis and be used to generate algorithms to better streamline care, in rheumatology and other disease areas. 6.5.5 Future Work to Enable Implementation of a Triage Approach User perspectives in Chapter 5 were synthesized into potential ways that the questions collected in The Patient SAID Study could be implemented to assist with rheumatology referrals. Translating these to practice may be achieved by applying the BC Health Quality Matrix which was first published in 2009 and updated in 2020 (194). The Matrix is intended to guide “planning, assessing, improving, and teaching at the practice, program, site and system levels.” Interestingly, this matrix was adapted using a yes/ no checklist for the introduction of virtual health in 2021 when physician visits pivoted to virtual consults during the pandemic (195). There are seven dimensions of quality that include five from the individual perspective (i.e. respect, safety, accessibility, appropriateness, and effectiveness) and two from the system perspective (i.e. equity and efficiency). The quality dimensions span across the full continuum of care distinguishing between five interconnected areas of care. From the findings of this thesis, the approach taken in the Patient SAID study may significantly impact individual experiences and improve the performance of systems that support and deliver care along all dimensions and more specifically 122 along two of the areas of care being 1) strengthening (preventing disease progression), and 2) returning (improving functional ability) to health & wellness. Other work that is already underway in BC that could be leveraged to support the routine collection of patient-reported data to streamline referrals relates to an Electronic Arthritis Triage Strategy (EATS). Clinicians at the Mary Pack Arthritis Program in partnership with the Provincial Health Services Authority (PHSA) completed a feasibility assessment in 2018 aimed to improve access to appropriate care for patients with arthritis. In this project titled ‘A population-based approach to improve appropriate and early access to treatment for people living with arthritis’, a sample care pathway and technology pathway were proposed. With refinement of the EATS model and application of the BC Quality Matrix, the approach taken in The Patient SAID Study could be included within EATS, forming the basis for an innovative and progressive system change. 6.6 Conclusion This dissertation asserts that a patient self-administered symptom questionnaire may help address the continued unmet need in BC to streamline rheumatology referrals and that there is a readiness on the part of patients and clinicians to see it implemented. Trends in the movement towards shared decision making with patients, the increased adoption of e-health technologies and a constrained health system offer an opportunity to accelerate the implementation of tools to support triage of referrals such as those used in the Patient SAID Study. 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Triage approaches and study characteristics by condition referred to rheumatologists Study Reference Country Triage Stage Study Design Triage Components Patient Questions (PQ), Clinical Exam (CE), Labs (L), Imaging (I), Photograph (P) Sample (n) Female (%) Mean age (yrs) Study Objective Population Outcomes Reported Inflammatory Arthritis and Related Terms (n=18) Inflammatory Arthritis (IA) Fitzgerald 2011 (56) Canada At PCP Referral Expert Consensus & Clinical Scenarios PQ, CE, L, I Deliberative process 5 rheumatologists 4 family physicians 1 nurse practitioner To improve referral from primary care to rheumatology by formulating and testing a Priority Referral Score (PRS) Clinical case studies of patients with IA requiring a rheumatology referral ICC test-retest average (range) PRS: Rheums 0.83 (0.57-0.97) PCPs 0.82 (0.76-0.88) Rank: Rheums 0.94 (0.74-0.97) PCPs 0.73 (0.57-0.97) Al-Jarallah 2013 (75) Kuwait At PCP Referral Prospective Cohort PQ n=30 70% female 32 years To evaluate the reliability and validity of the Arabic version of the EIA Questionnaire (EIAQ) 30 consecutive patients with or without MSK symptoms attending internal medicine clinic External construct validity - Spearman’s rank correlation coefficient between EIAQ-A score and 5 measures of disease activity. Kappa test-retest reliability: k=0.558 Kudar-Richardson- 20 Internal consistency: 0.869 Construct validity: r=0.727-0.896 (all p<0.001) Other: No floor or ceiling effect; 2.5min complete Bruschi 2013 (76) Italy At PCP Referral Prospective Cohort PQ n=484 75% female 57 years To evaluate whether preliminary triage by a healthcare worker without medical training could effectively identify patients with IA. Survey conducted by telephone and repeated in same patients at ambulatory visit. Patients referred by their PCP for any MSK SN: Telephone 94.1% Ambulatory 94.1% SP: Telephone 83.1% Ambulatory 92.7% PPV: Telephone 29.6% Ambulatory 49.2% NPV: Telephone 99.5% Ambulatory 99.5% Tavares 2013 (62) Canada At PCP Referral Prospective Cohort PQ n=143 IA n=30 70% female 49 years Non-IA n=113 80% female 63 years To develop and validate a scoring algorithm for the self-administered IA detection tool (EIAQ) to optimize its discriminative properties in the rheumatology triage clinical setting Consecutive newly referred adults At optimal cut-off: AUC: 0.915 (SE 0.002) SN: 0.855 (0.846–0.865) SP: 0.873 (0.869–0.878) LR+: 11.036 (10.981–11.092) LR-: 0.161 (0.068–0.253) PPV: 0.668 (0.657–0.679) NPV: 0.960 (0.957–0.963) 151 Tavares 2013 (26) Canada At PCP Referral Prospective Cohort PQ n=107 Early IA n=39 76% female 48 years Established IA n=43 74% female 54 years Non-IA n=50 76% female 58 years Non-MSK n=38 73% female 42 years To determine the comprehensibility, internal consistency, re-test reliability & discriminative properties of an early IA detection tool (EIAQ). 4 groups; early IA, established IA, non-IA MSK and non-MSK. Analyzed as single group. Internal consistency and test-retest : EIAQ self-administered on two occasions two weeks apart: test and retest. Retest restricted to 107 participants who reported no change in symptoms. Discriminative properties and validation: correlates of EIAQ with clinical assessment data from the test phase. Internal consistency: KR 0.985 (p<0.001) Test-re-test reliability: 0.81 (SD 0,02) ICC: 0.97 (0.96-0.98) AUC: 0.829 (SE 0.003) SN: 0.721 (SE 0.005) SP: 0.75 0.748 (SE 0.003) PPV: 0.583 (SE 0.005) NPV: 0.848 (SE 0.003) LR+: 3.36 (SE 0.02) LR-: 0.36 (0.03) Thompson 2014 (100) Canada For Rheum Triage Prospective Cohort PQ, CE, L, I n=696 Age by group; 44.5% aged 41-60 years 68% female To describe a referral tool and formally evaluate its sensitivity for urgent and early inflammatory arthritis referrals All referrals received on a standardized referral tool 30.2% of referrals categorized as urgent Referral tool: SN 80.5% SP 79.4% EIA occurred in 13.6%; correct classification as urgent SN 90.5% SP 69.6%. Lu 2015 (90) Taiwan At PCP Referral Prospective Cohort PQ n=131 61.8% female 51.6 years Potential IA n=120 Controls n=20 To translate the English version of EIA detection tool (EIAQ) to Chinese and determine its clinimetric properties Patients with IA and those with non-IA MSKs Internal consistency: KR-20: 0.78 Reliability: kappa 0.42* (0.24–0.60) to 0.94 (0.82–1.00) AUC 0.88 SN: 0.84 SP: 0.86 PPV: 0.92 NPV: 0.76 *0.43 in text Powley 2016 (103) UK At PCP Referral Prospective Cohort PQ n=34 RA n=13 77% female 52 years PsA n=4 75% female 47 years Unclassified arthritis n=4 25% female 69 years Inflammatory arthralgia n=13 69% female 42 years To evaluate how patients use the internet & to assess online symptom-checker diagnoses in relation to the patient's actual diagnosis Patients with IA and inflammatory arthralgia 4 out of 21 patients with IA were given a first diagnosis of RA or PsA 152 Almoallim 2017 (58) Saudi Arabia At PCP Referral Prospective Cohort PQ, CE, L n=203 81% female 63.4% aged 40 years or older To provide a standardized and validated set of criteria for use by PCPs to identify patients for referral ≥18 years presenting to PCP with small joint pain present for >6 weeks. 9 variables highly associated with diagnosis, based on variables that had: SP: range 89.47% (80.6−94.6) to 98.7% (92.7−99.8) SN range: 5.5% (2.7−10.9) to 28.9% (21.8−37.3) PPV range: to 75.8% (59.0−87.2) to 93.3% (78.7−98.2) NPV range: 38.0% (31.4−44.9) to 43.1% (35.7−50.9) Diagnostic accuracy range: 40.1% (33.6−47.0) to 52.5% (45.6−59.3) LR+ range: undefined to 8.5 (2.5−27.9) LR- range: 0.8 (0.7−0.8) to 0.90 (0.88−0.92) ten Brinck 2018 (57) Netherlands At PCP Referral Prospective Cohort PQ, CE n=1288 Derivation sample n=644 70% female 52 years Validation sample n=644 69% female 51 years To develop a tool that can be used by PCPs and other MDs to identify IA. Consecutive patients to a screening clinic in whom GPs suspected but were unsure of IA. Split-sample validation Model 2: AUC derivation: 0.75 (0.70-0.79) AUC validation: 0.72 (0.68- 0.77) Seven items prioritized for the simplified rule: AUC derivation 0.74 (0.70-0.78 AUC validation 0.71 (0.67-0.75) van Dijk 2020 (101) Netherlands For Rheum Triage Prospective Cohort PQ, CE n=2060 Derivation cohort 1 n=997 71% female 51 years Validation cohort 1 n=506 65% female 52 years Validation cohort 2 n=557 67% female 54 years To evaluate if inquiring about functional impairments aids to identify early IA. Patients coming to easy access rheumatology clinics intermediary between primary and secondary care in whom GPs suspect but are unsure about IA presence. Derivation cohort: AUC: Clinical variables: 0.72 (95% CI 0.69 to 0.76) Clinical variables plus ‘difficulties with dressing’ item: 0.73 (95% CI 0.70 to 0.77) Single item ‘difficulty with dressing’ PPV across cohorts: 42%–60% NPV across cohorts: 57%–74% Wang 2023 (78) UK At PCP Referral Retrospective Cohort CE (referral letters), L n=1264 na na To propose a heterogeneous data-driven hybrid machine learning model including Natural Language Processing (NLP) to improve efficiency at the point of triage using multiple probabilistic fusion methods. Consecutive referred rheumatology patients Best performing probabilistic fusion methods was weighted AUC (WAUC): AUC: 0.90 Accuracy: 0.82 Precision: 0.83 Recall: 0.82 F1-Score: 0.83 153 Inflammatory Joint Disease (IJD) Barbour 2003 (102) UK For Rheum Triage Prospective Cohort PQ, CE, L, I n=100 76% female 55 years (female) 50 years (male) A simple 8-point questionnaire was devised to separate patients with and without IJD. Consecutive patients whose GP suspected IJD and referred to a consultant for early assessment over a 10-month period. For a score of 3 or more: SN 97% SP 55% PPV 49% NPV 97% Undifferentiated Arthritis (UA) Alves 2011 (86) Netherlands At PCP Referral Prospective Cohort PQ, CE, L, I n=744 Undifferentiated arthritis (UA) n=231 68% female 53 years All patients incl. without synovitis n=513 73% female 50 years To evaluate diagnostic performance of algorithms/criteria of ACR/EULAR 2010 (AE2010), Van der Helm (VDH) & Visser UA and all patients including those without synovitis All patients: AUC: AE2010 0.74 (0.70-0.78) VDH 0.83 (0.79-0.87) Visser 0.74 (0.70-0.79) SN: AE2010 0.69 (0.61-0.76) VDH 0.08 (0.05-0.14) Visser 0.47 (0.39-0.55) SP: AE2010 0.72 (0.59-0.83) VDH 1.0 (0.93-1.0) Visser 0.93 (0.84-0.98) PPV: AE2010 0.87 (0.80-0.92) VDH 1.0 (0.75-1.0) Visser 0.95 (0.87-0.99) NPV: AE2010 0.46 (0.35-0.56) VDH 0.27 (0.21-0.34) Visser 0.40 (0.31-0.48) Autoimmune and Inflammatory Rheumatic Diseases (IRD) West 2019 (71) US For Rheum Triage Retrospective Cohort PQ, CE, L, I n=961 NR NR To evaluate the effectiveness of pre-appointment, consult screening to identify patients with autoimmune and inflammatory RD. Retrospectively analyzed all new outpatients who referred for rheumatology consults SN: 98% SP: 34% PPV: 60% NPV: 95% IA & IRD van Delft 2022 (104) Netherlands At PCP Referral Prospective Cohort PQ n=250 77% female 51 years To determine the diagnostic accuracy and clinical utility of REACH & CARE referral rules Independent population of unselected patients from primary care. IA AUC: REACH 0.72 (0.64-0.80) CARE 0.82 (0.75-0.88) SN: REACH 0.83 CARE 0.86 SP: REACH 0.53 CARE 0.70 IRD AUC: REACH 0.66 (0.58-0.74) CARE 0.76 (0.69-0.83) SN: REACH 0.75 CARE 0.75 SP: REACH 0.53 CARE 0.70 Composite referral rule AUC: 0.82 (0.75-0.88) SN: 0.69 SP:0.84 154 Knitza 2021 (87) Germany At PCP Referral Prospective Cohort PQ n=164 68.9% female 51 years (median) To assess the diagnostic accuracy, patient-perceived usability, and acceptance of two symptom checkers: (1) Ada (Ada’s top 1 (D1) and top 5 disease suggestions (D5)) and (2) Rheport. New patients to rheumatology clinics with MSK symptoms and unknown diagnosis SN Rheport: 53.7% (39.7-67.2) Ada D1 42.6% (29.5-56.7) Ada D5 53.7% (39.7-67.2) SP Rheport: 51.8% (42.1-61.4) Ada D1 63.6% (53.9-72.4) Ada D5 54.5% (44.8-64.0) PPV Rheport: 35.4% (25.3-46.8) Ada D1 36.5% (25.0-49.6) Ada D5 36.7% (26.4-48.4) NPV Rheport: 69.5% (58.2-78.9) Ada D1 69.3% (59.2-77.9) Ada D5 70.6% (59.6-79.7) Correct diagnosis: Ada D1 16.7% Ada D5 25.9% Krusche 2023 (105) Germany At PCP Referral Clinical Cases PQ n=132 46% female 39 years To assess the diagnostic accuracy of ChatGPT-4 in comparison to a previous analysis including physicians and symptom checkers regarding rheumatic and musculoskeletal diseases (RMDs) Clinical data set of Gräf 2022 (196) vignettes of actual patients newly presenting to three rheumatology outpatient clinics in Germany. Percentage correctly classified as IRD (ChatGPT-4 vs Physician) Top diagnosis 58% vs 56% (p=0.52) Top 3 diagnoses 36% vs 52% (p=0.01) Specific Disease Condition Referred Axial Spondyloarthritis (AxSpa, or AS) (n=15) Brandt 2007 (91) Germany At PCP Referral Prospective Cohort PQ, CE, L, I n=350 59% female 36 years To evaluate easy screening parameters in primary care for earlier AS referral/diagnosis. Ortho and PCP referred patients with chronic low back pain (CLBP) with onset < age 45 % diagnosed: 45.4% definite AxSpa 9.1% possible SpA 45.4% non-SpA Weisman 2010 (106) US At PCP Referral Prospective Cohort PQ, CE n=451;( n=316 development; n=135 validation) 38% female 48.6 years To test a set of questions derived from a comprehensive literature review & advisory board in a case-control study to distinguish AS from Chronic Back Pain (CBP) Patients with AS among patients with CBP AUC: 0.937 (development) SN: 69.9 (development) 67.4% (validation) SP: 99.1% (development) 94.6% (validation) Poddubnyy 2011 (82) Germany At PCP Referral Prospective Cohort PQ, CE, L, I n=560 46% female 38 years To evaluate 2 referral strategies for AxSpa in primary care patients with CLBP. Symptom duration > 3 months, age at onset < 45 yrs, and no diagnosis of AxSpa Between patients design: 318 assigned to strategy 1, 242 to strategy 2 Strategy 1: ≥1 of 3 (IBP, HLA-B27 positivity, sacroiliitis detected by imaging) Strategy 2: ≥2 of five( those in strategy 1, or positive family history of AS; good response to NSAID) % diagnosed Strategy 1: 41.8% (36.5- 47.3) Strategy 2: 36.8% (31.0-43.0) 155 Braun 2011 (77) Germany At PCP Referral Prospective Cohort PQ n=322 50% female 36 years To identify predictive clinical parameters for diagnosis of AxSpa in patients with CBP in primary care. Patients aged <45 yrs with back pain for >2 mo who presented to orthopaedic surgeons >= 3 items useful AUC: 75.7 SN: 78.8% SP: 46.4% LR+: 4.0 LR-: 0.5 Braun 2013 (92) Germany At PCP Referral Prospective Cohort PQ, L n=298 52% female 36 years To reanalyze data from the 2011 study adding HLA-B27 as a variable to the 3 criteria. SN: 80.4% SP: 75.4% LR+: 3.27 LR-: 0.26 Juanola 2013 (88) Spain At PCP Referral Prospective Cohort PQ, CE, L, I n=88 (n=60, n=28) 55.7% female 36.8 years To compare two referral strategies for AxSpa with rheumatologist diagnosis as the standard Patients with CBP (>3 months) with onset < age 45 from primary care Between patients design: 60 assigned to strategy 1, 28 to strategy 2 Strategy 1: ≥1 of 3 criteria (IBP, positive HLA-B27 or sacroiliitis detected by imaging). Strategy 2: ≥2 of 6 criteria (strategy 1, family history of Ax SpA, good response to NSAIDs, known extra-articular manifestations (EAMs)). % diagnosed Strategy 1: 25.4% (n=15) Strategy 2: 28.6% (n=8) P=NS Sieper 2013 (83) Multinational (12 in Europe, Canada, India, Israel and Venezuela At PCP Referral Prospective Cohort PQ, CE, L, I n=1072 51.4% female 40 years To determine which of 2 primary care AxSpa referral strategies to rheumatologists is superior. Patients with CBP Between patients design: 504 assigned to strategy 1, 568 to strategy 2 Strategy 1: ≥1 of 3 criteria (IBP, positive HLA-B27 or sacroiliitis detected by imaging). Strategy 2: ≥2 of 6 criteria (strategy 1, family history of Ax SpA, good response to NSAIDs, known extra-articular manifestations (EAMs)). Both equivalent % diagnosed = 35% 35.6% vs. 39.8% between-group difference 4.40% (−7.09-15.89), p=0.447 van Hoeven 2014 (93) Netherlands At PCP Referral Prospective Cohort PQ n=364 59% female 36.3 years To identify AxSpa in primary care and design a simple referral model. Primary care patients with CLBP (ages 20–45 yrs) were identified from GP records AUC: (C-statistic): 0.75 (SE 0.03)) SN: 83% SP: 59% van Hoeven 2015 (94) Netherlands At PCP Referral Prospective Cohort PQ n=579 59% female 36 years To externally validate and optimize the referral rule to identify primary care patients with CLBP suspected of AxSpa. Primary care patients 18-45 years with CLBP for ≥ 3 months & symptom onset < 45 yrs AUC (c-statistic): 0.70, 95% CI (0.64-0.75) SN: 75% SP: 58% PPV: 30.2% Abawi 2017 (84) Multinational (5 European Centres) At PCP Referral Prospective Cohort PQ, CE, L, I n=261 67% female 31 years To evaluate in a single cohort, 13 referral models for patients with CBP referred to a rheumatologist. Patients with back pain (≥3 months, ≤2 years, onset <45 yrs) Highest SN (Brandt1) SN: 98% SP: 11% LR+: 1.10 Highest SP&LR+ (MASTER) SN: 64% SP:76% LR+2.68 156 Baraliakos 2020 (109) Germany At PCP Referral Prospective Cohort PQ, CE, L, I n=326 49.4% female 36 years To evaluate a 2-step referral system for primary care early recognition of AxSpa & compare with other combination of symptoms. and AxSpa-related items Patients with CBP, ≤ 45 yrs Best performing model identified as either: 2 out of 3 of: Good response to NSAIDs, morning stiffness >30min and elevated C-reactive protein; or 1 of those criteria plus HLA-B27 Best performing model: SN: 0.91 (42/46) SP: 0.67 (188/280) PPV: 0.31 (42/134) NPV: 0.98 (188/192) LR+: 2.78 LR-: 0.13 Proft 2020 (81) Germany At PCP Referral Prospective Cohort PQ n=361 50.7% female 37 years To evaluate the online referral tool & compare it to established physician referral tool. Patients with low back pain (LBP) had to fulfill requirements of either tool. Between-patients comparison,181 assigned to physician-based Berlin tool, 180 to self-referral % diagnosed 19.4% Online self-referral 39.2% Physician-based Shridharmurthy 2022 (108) US At PCP Referral Prospective Cohort PQ n=134 72% female 45 years To examine the psychometric properties of a potential screening tool for AxSpa patients. ≥18 yrs old, CBP ≥3 months, no diagnosis of mechanical or inflammatory back pain Each question test-retest reliability in range of 0.6-0.76. Internal consistency, Cronbach α: 0.81 Lebughe Litite 2023 (110) Sub-Saharan Africa At PCP Referral Prospective Cohort PQ n=400 44% female 51.8 years To develop and validate a screening tool (SPondyloArthritis Screening in Sub-Saharan Africa (SpASSS) questionnaire) to identify patients with a high likelihood for AxSpa 50 patients with confirmed diagnosis of AxSpa, 150 non-Spa rheumatic controls and 200 non-rheumatic controls randomly chosen from general population. AxSpa vs 2 Control Groups AUC: 0.967 (0.939–0.995) SN: 97.4% SP: 96.4% PPV: 74.0 NPV: 99.7 Validation of components of SpASSS (C1 and C2 question sets) C1: Convergent: 0.883 Discriminative: 0.895 C2: Convergent: 0.833 Discriminative: 0.881 Ziade 2023 (80) Various Middle East At PCP Referral Prospective Cohort PQ, CE, L, I N=515 53.9% female 34.8 years To assess the value of referral strategies for AxSpa in patients with suspicious chronic inflammatory low back pain (LBP) Patients were included prospectively through a network of referring physicians and excluded if they had other inflammatory diseases, a known diagnosis of AxSpa. MASTER had highest positive likelihood ratio: SN: 67.1% SP:79.7% PPV: 78.8 NPV: 68.3 LR+: 3.3 157 Rheumatoid Arthritis (RA) (n=8) Wong 2004 (72) US At PCP Referral Prospective Cohort PQ, L n=265 67% female 57 years To describe the development and evaluation of an RA patient self-report case-finding method not dependent on direct contact treating MD Main sample n=175; validation subsample 90 RF+ patients. This method development eliminates RA patients for whom RF tests were not ordered or who were negative. The best strategy from an ROC curve which maximized SN with minimum SP 35% was: SN: 80% SP: 67% Accuracy: 74% PPV: 77% Steuer 2008 (111) UK At PCP Referral Prospective Cohort L n=112 NR NR To examine a new serological approach for the diagnosis of RA in primary care. New referrals to rheumatology with joint pain who previously had RF testing by PCP % diagnosed with: IA: 27.6% RA: 11.6% Miller 2013 (95) UK At PCP Referral Retrospective Cohort CE, L n=235 70% female 50 years To compare RF with recorded clinical ACR features as a diagnostic test of RA and compare testing behaviour in primary care, testing costs and results for age groups Patients in general practice with an RF test AUC: RF: 0.82 Clinical AUC (n of ACR criteria): 0.98 Miller 2015 (96) UK At PCP Referral Retrospective Cohort L n=1753 68.1% female 51.4 years for RF+ 57.5 years for RF- To investigate the diagnostic accuracy of RF as a test for RA in primary care and its impact on referral times Patients with 1st RF test recorded in an admin database & RA diagnosed within 2 yrs SN: 57.8% (55.4-60.1) SP: 93.9% (93.7- 94.1) LR+: 9.5 (9.0-10.0) LR-: 0.5 (0.4-0.5) PPV: 21.4% (20.3-22.6) NPV: 98.7% (98.6-98.8) Cummins 2015 (113) Australia At PCP Referral Prospective Cohort PQ, CE, L n=143 Group 1 n=63 70% female 56 years Group 2 n=49 73% female 49 years To determine if using the ACR EULAR criteria to primary care referrals improved triage decisions, reduced wait times and to determine SN and SP of this strategy Consecutive new rheumatology referrals separated into: Group 1 (met triage score) Group 2 (did not meet triage scores) SN: 96%, SP: 56% PPV: 40% NPV: 90% Martin 2015 (112) Canada At PCP Referral Retrospective Cohort PQ, CE, L n=315 75.5% female 36.8 years To evaluate the clinical accuracy of ACPA for an RA diagnosis in a real-time clinical setting Cases had to meet 3 criteria: (1) referred over 3 years (2) referral reason was a +ACPA test, and (3) seen by a rheumatologist. 57.6% of patients with +ACPA were diagnosed as RA. PPV: 74.3% NPV: 68.4% Garcia-Montoya 2022 (197) UK At PCP Referral Prospective Cohort PQ, L n=6780 62% female 52 years To determine, in anti-CCP+ (high and low level) and anti-CCP negative (anti-CCP−) individuals presenting to primary care with a new non-specific musculoskeletal (MSK) complaint, the demographic features and patient-reported symptoms, which were associated with progression to IA. Progression to IA, OR: High anti-CCP+: 9.42 (3.13–28.30) Hand pain: 2.74 (1.03–7.27) Foot pain: 4.10 (1.59–10.54) NPV: In absence of hand and foot pain: 85.4% (72.1–92.9) Low anti-CCP+, no pain in hands or feet: 95.8% (78.6-99.3 158 Tarakci 2023 (79) Turkey At PCP Referral Clinical Cases PQ, L n=286 68.2% female na This study aims to develop a model for diagnosing RA using both expert systems and machine learning methods. The data set consists of nine characteristics and sample data various patients who applied to the Rheumatology clinic. Model 2 based on the LR machine learning algorithm for had best success rate Model 1 (only blood count data) AUC: 91.95 Error: 8.05 SN: 88.09 SP: 91.01 Recall: 93.27 F1-Score: 90.61 Model 2 (adding patient’s history) AUC: 97.90 Error: 2.10 SN: 96.69 SP: 97.60 Recall: 98.3 F1-Score: 97.50 Psoriatic Arthritis (PsA) (n=6) Ibrahim 2009 (73) UK At PCP Referral Prospective cohort PQ N=168 For 12 patients with PsA 50% female 54.9 years To evaluate an existing tool (the Swedish modification of the Psoriasis Assessment Questionnaire) and to develop a new instrument to screen for PsA in people with psoriasis. Community-based people with psoriasis and known cases of psoriatic arthritis were added AUC (95% CI): Alenius: 0.76 (0.69 – 0.85) PEST 0.91 (0.86 – 0.97) SN Alenius: 0.63 PEST: 0.92 SP Alenius: 0.72 PEST: 0.78 Coates 2013 (74) UK At PCP Referral Prospective Cohort PQ n=195 51% female 47 years To validate the PEST screening questionnaire in secondary care compared to PASE & ToPAS using CASPAR as the gold standard. Patients with diagnosis of psoriasis, not previously diagnosed PsA and positive on any one of the questionnaires AUC: PASE 0.594 (0.505–0.683) PEST 0.610 (0.516-0.704) ToPAS 0.555 (0.455-0.653) SN: PASE 0.745 PEST 0.766 ToPAS 0.766 SP: PASE 0.385 PEST 0.372 ToPAS 0.397 Coates 2016 (85) UK At PCP Referral Prospective Cohort PQ n=169 50.9% female 61 years To test the proposed CONTEST questionnaire developed to identify patients with psoriasis who have undiagnosed PsA and compare to the validated PEST questionnaire in a primary care setting. AUC: CONTEST 0.694 PEST 0.652 SN: CONTEST 0.816 PEST 0.816 SP: CONTEST 0.353 PEST 0.235 Mease 2014 (70) Multinational (7 European, 2 North American countries) At PCP Referral Prospective Cohort PQ n=949 40% female 50 years To compare the performance of three PsA screening tools in patients with psoriasis Consecutive unselected patients with psoriasis, some with previous PsA diagnosis SN: PASQ 0.67 (0.57-0.77) PEST 0.84 (0.75-0.90) ToPAS 0.77 (0.67-0.85) SP: PASQ 0.64 (0.57-0.70) PEST 0.75 (0.69-0.81) ToPAS 0.72 (0.65-0.78) PPV: PASQ 0.43 (0.35-0.52) PEST 0.60 (0.51-0.68) 159 ToPAS 0.54 (0.45-0.63) NPV: PASQ 0.83 (0.76-0.88) PEST 0.91 (0.86-0.95) ToPAS 0.88 (082-0.92) Karreman 2017 (89) Netherlands At PCP Referral Prospective cohort PQ n=473 49% female 55.7 years To compare screening performance of PEST, PASE, and EARP for detecting PsA among psoriasis patients in primary care. Primary care psoriasis patients at risk for PsA AUC: PEST: 0.71 PASE: 0.64 EARP: 0.68 SN: PEST: 0.68 (0.54, 0.80) PASE cut-off 47: 0.59 (0.44, 0.72) PASE cut-off 44: 0.66 (0.52, 0.79) EARP: 0.87 (0.75, 0.95) SP: PEST 0.71 (0.67, 0.76) PASE cut-off 47: 0.66 (0.61, 0.71) PASE cut-off 44: 0.57 (0.52, 0.62) EARP: 0.34 (0.30, 0.39) Leijten 2017 (97) Netherlands At PCP Referral Prospective Cohort PQ n=134 33% female 48 years To examine the optimal cut-off for the PEST questionnaire for PsA diagnosis Included all patients with psoriasis, some with previous PsA diagnosis AUC: PEST ≥3: 0.70 (0.60–0.80) PEST ≥2: 0.76 (0.66–0.85) SN: PEST ≥3: 56% PEST ≥2: 89% SP: PEST ≥3: 85% PEST ≥2: 63% PPV: PEST ≥3: 50% PEST ≥2: 39% NPV: PEST ≥3: 88% PEST ≥2: 96% Systemic Lupus Erythematosus (SLE) Adamichou 2021 (115) Greece At PCP Referral Machine Learning & Prospective Cohort CE, L Discovery cohort n=802 Validation cohort n=655 NR NR To apply machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis. Randomly selected adults with SLE or control rheumatologic diseases Validation cohort: 512 patients with SLE and 143 controls SLE Accuracy: 0.948 (0.928-0.964) AUC: 0.981 (0.970-0.993) SN: 0.951 (0.928-0.968) SP: 0.937 (0.880- 0.969) LR+: 15.11 (8.03-28.45) LR-: 0.052 (0.036-0.076) SN Subgroups: Early SLE 0.938 (0.903- 0.964) Nephritis 0.979 (0.891-0.996) Neuropsychiatric 0.918 (0.822-0.965) Haematological 0.986 (0.927-0.998) Severe 0.964 (0.910-0.986) 160 A combination of disease conditions referred (n=2) RA & AxSpa Arndt 2007 (59) Germany At PCP Referral Prospective Cohort PQ, CE, L n=345 NR NR To evaluate a combined physician patient questionnaire designed for identifying early RA and SpA. Patients referred to an early arthritis clinic. LR+=199.4, p<0.001 RA overestimated (EAC 25; rheumatologist 22) Suspected RA overestimated (EAC 87; rheumatologist 12) AxSpa underestimated. (EAC 6; rheumatologist 14) Fonseca 2018 (116) Portugal At PCP Referral Prospective Cohort PQ, CE, L, I RA n=340 Referral support /control n=144/n=196 92%/83% female 49.8/47.0 years AxSpA Referral support /control n=231/n=108 81%/68% female 43.8/40.3 years To evaluate the effectiveness of a referral program to identify RA and AxSpa Patients aged 18-65 years who presented: i) peripheral joint symptoms > 4 weeks or ii) LBP lasting ≥3 months onset < age 45 GP & Rheumatologist Agreement for RA Y/N (Kappa p Y/N): GP plus 1+ Swollen joint 23.1% (p=0.002/0.961) GP plus +ve Squeeze MCP 36.5% (p=0.087/0.091) GP plus +ve Squeeze MTP 73.9% (p=0.430/0.036) GP plus ESR> 20mm/1st h 78.0% (p=0.566/<0.001) GP plus CRP> 0.6 mg/dL 81.3% (p=0.632/<0.001) Musculoskeletal Symptom Conditions (n=5) Hand Arthritis Reed 2022 (98) Australia At PCP Referral Prospective Cohort PQ, P n=280 70% female 59 years To develop and test a screening tool to determine the most likely diagnosis in a patient presenting with hand arthritis. Consecutive new patients presenting to a rheumatology practice with hand arthritis Patients with IA Accuracy: 0.971 PPV: 0.973 SN: 0.991 SP: 0.905 Patients with OA Accuracy: 0.789 PPV: 0.928 SN: 0.542 SP: 0.969 Reed 2022 (99) Australia At PCP Referral Prospective Cohort PQ, CE, P n=248 66.9% female 60.1 years To assess the validity of the screening tool among different Rheumatologists. Consecutive new patients presenting to 7 Rheumatology practices across Australia Patients with OA (n=134) Accuracy: 77.4% PPV: 78.3% SN: 80.6% SP: 73.6% Patients with RA (n=109) Accuracy: 85.1% PPV: 80.0% SN: 88.1% SP: 82.7% Patients with PsA (n=33) Accuracy: 95.2% PPV: 76.9% SN: 90.9% SP: 95.8% 161 Joint pain Fernandez-Avila 2020 (44) Colombia At PCP Referral Clinical Cases PQ, CE, L 224 primary care physicians 112 control 112 intervention Effectiveness of an algorithm for PCPs to diagnose patients with cases of joint pain A series of clinical cases, presented to PCPs through a website on their cell phones. Intervention = algorithm to reduce misdiagnosis Correct diagnosis: Overall Control 47.5% (42.9–52.2) Int. 84.8% (81.5-88.1) Difference +37.3, p<0.001 Control vs Intervention RA: 61.6% (52.6-70.6) vs 91.9% (86.9-96.9) p<0.001 OA: 63.4% (54.5-72.3) vs 89.3% (83.5-95.1) p<0.001 SLE: 47.3% (38.1-56.5) vs 79.5% (71.9-86.9) p<0.001 SpA: 17.8% (10.8-24.9) vs 78.6% (70.1-86.1) p<0.001 Rheumatic Diseases (RD) Caines 2012 (117) Canada For Rheum Triage Prospective Cohort PQ n=1101 NR NR To determine patterns on pain diagrams, corresponding diagnoses, SN, SP, PPV & NPV New referrals from 2 rheumatologists over 6 years they were not previously diagnosed with a rheumatic disease. RA SN: 87.6 SP: 44.7% PPV: 16.4% NPV: 96.7% IA SN: 82.3 SP: 37.4% PPV: 50.4% NPV: 73.2% AS SN: 10.9% SP: 98.9% PPV: 41.7% NPV: 96.1% ICC across all patterns ≥0.777 Alder 2020 (118) Switzerland For Rheum Triage Retrospective Cohort PQ, CE, L, I n=160 72.7% female 50.2 years To evaluate the performance of RheumaTool in detecting the correct diagnosis in referrals to a rheumatology clinic. Patients with MSK complaints and an uncertain diagnosis referred to rheumatology Time point A end of first consultation (based on medical history, physical exam and previous laboratory or imaging results from referring physician). Time point B first medical report (first consultation, additional diagnostic work-up and follow-up consultations” Correct diagnosis Time point A 38.8% (31.3-46.8) Time point B 40% (32.4-48.1) Reference listed in either the first or second probability category of tool Time point B 58.8% (50.7-66.4) Time point B 63.8% (55.7-71.1) 162 Appendix B: Search Strategy Ovid MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Daily and Versions(R) <1946 to October 27, 2023> # Query Results 1 (arthritis or rheuma*).mp. 335,935 2 triage.mp. or triage/ or pre-referral.mp. or clinical decision rule/ or algorithm/ or clinical rule*.mp. or pre-appointment.mp. 337,373 3 (secondary care/ or special*.mp. or primary health care/ or rheum*.mp.) and "referral and consultation"/ 17,894 4 1 and (2 or 3) 2,432 Ovid Embase <1974 to October 27, 2023> # Query Results 1 (arthritis or rheuma*).mp. 543,099 2 (triage or pre-referral).mp. or clinical decision rule/ or algorithm/ or clinical rule*.mp. or pre-appointment.mp. 405,417 3 ("secondary care".mp. or secondary health care/ or ("primary care".mp. or primary medical care/) or medical specialist/ or rheum*.mp.) and ("referral and consultation".mp. or patient referral/) 31,250 4 1 and (2 or 3) 7,522 EBSCO host CINAHL Complete <1982 to October 27, 2023> # Query Results S1 ("secondary care" or "secondary health care" or ("primary care" or "primary medical care") or "medical specialist" or rheum*) and ("referral and consultation" or "patient referral") 5,265 S2 (triage or pre-referral) or clinical decision rule/ or algorithm/ or clinical rule* or pre-appointment 92,095 S3 arthritis or rheuma* 100,290 S4 S1 or S2 97,089 S5 S4 and S3 2,029 Web of Science Core Collection 1900 – October 27, 2023 (Sciences and Social Sciences) # Query Results 1 TI=(arthritis or rheuma*) or AB=(arthritis or rheuma*) 302,646 2 TI=((triage or pre-referral) or "clinical decision rule" or algorithm or" clinical rule" or pre-appointment) or AB=((triage or pre-referral) or "clinical decision rule" or algorithm or" clinical rule" or pre-appointment) 2,392,597 3 TI=(("secondary care" or special* or "primary health care" or rheum*) and (referral or consultation)) or AB=(("secondary care" or special* or "primary health care" or rheum*) and (referral or consultation)) 31,736 4 #3 OR #2 2,432,078 5 #4 AND #1 5,144 163 Database: EBM Reviews - Cochrane Database of Systematic Reviews <2005 to October 27, 2023> # Query Results 1 (arthritis or rheuma*).mp. [mp=title, abstract, full text, keywords, caption text] 1,125 2 (triage or pre-referral).mp. [mp=title, short title, abstract, full text, keywords, caption text] 192 3 1 AND 2 19 Scopus - <1788 to October 27, 2023> # Query Results 1 “arthritis” or “rheuma*” 485,666 2 "triag*" OR "pre-referral" OR "clinical decision rule" OR "pre-appointment" 44,374 3 "primary care" OR "secondary care" AND "referral*" OR "consultation" 30,347 4 #1 AND #2 OR #3 1,020 Google Scholar to October 27, 2023 Search terms: "inflammatory arthritis" and triage and referral n=1630 DOAJ to October 27, 2023 Article search terms: rheum* triag* n = 44 164 Appendix C: Scoping Review List of Patient Questions for Self-Reported Measures Table C1. List of patient questions from studies for inflammatory arthritis self-reported measures Patient Questions Bruschi (76) (yes to at least one question) CARE (57,104) (0.5 to 2.5 points) Van Dijk (101) EIAQ (26,62,75,90) (yes or no) *Best fit model Sex X X* Age X X* Symptom duration (in weeks) X X Acute onset of symptoms X Do you have pain in your joints X Number of painful joints X Do you have pain in your wrists and hands X Are your hands and wrists swollen X Number of patient-reported swollen joints X Are your joints stiff in the morning X Morning stiffness for >60 minutes X X* Are the same joints involved on both sides of your body X Difficulty with making a fist X X* Are you able to dress yourself, including shoelaces and buttons? X Have important activities in your life been affected because of bone or joint problems such as having difficulty with personal care or having to make a change regarding leisure or work activities X Have you ever been told that you have rheumatoid arthritis (RA) X* Does anyone in your family have RA X Have you been diagnosed with a rash call psoriasis X* Do you have any difficulty in making a fist when just awake, for at least 30 minutes? If yes, did this difficulty start less than 6 months ago? X Do you have swollen hands when just awake, for at least 30 minutes? If yes, did this symptom start less than 6 months ago? X Do you feel pain when someone shakes or squeezes your hands? If yes, did this symptom start less than 6 months ago? X 165 Table C2. List of patient questions for axial spondyloarthritis self-reported measures Patient Questions Studies that Assessed the Questions Optimal Set of Questions in Study Chronic back pain (>3 months) (ASAS) and; (77,81,93,94,108) (77,81,93,94) Age at onset ≤35 (77) Age at onset <40 (ASAS) (93,94) (93,94) Age at onset ≤45 years (77,81) (81) Inflammatory back pain parameters and ≥ 1 (81) Do you have back pain? (110) Insidious onset (ASAS) (81,93,94) (93,94) Improvement with exercise (ASAS) (77,81,93,94) (93,94) No improvement with rest (ASAS) (77,81,93,94) (77,93,94) Pain at night (with improvement upon getting up) (ASAS) (77,81,93,94) (77,93,94) Pain awakening you in the 2nd half of the night (110) Low back pain >5 years (93,94) (93,94) Family history for SpA positive (81,93,94,110) (93,94) Improvement with NSAIDS within 48hrs (77,81,93,94,110) (77,93,94) Other spondylarthritis parameters and ≤ 1 (81) Morning stiffness >30min (77,81,110) Alternating buttock pain (77,81,110) (77) History of enthesitis (heel pain) (77,81,93,94,110) History of arthritis (77,93,94) History of psoriasis (77,81,93,94,110) History of a positive HLA-B27 test (77,81) History of dactylitis (93,94,110) History of positive inflammation markers like CRP and ESR (81) History of uveitis or iritis (77,81,93,94) History of Inflammatory Bowel Disease (77,81,93,94) Over the last 3 months how often… have you suffered from back pain without a known cause? (108) did your back feel the same or worse? (108) did your back feel stiff during the first two hours after waking? (108) (108) have you experienced a decreased range of motion? (108) did your back pain get better with movement? (108) (108) did your back pain get better with movement after waking? (108) 166 did your back pain get better with movement within two hours after waking? (108) have you been unable to sit still for more than two hours because of your back pain? (108) (108) did your back pain make it uncomfortable to sit for more than 2hours? (108) are you aware of your back pain when sitting for two hours or more? (108) how often do you avoid social activities because of your back pain? (108) (108) did your back pain ease with rest? (108) did your back pain make it difficult to sleep? (108) (108) did your back pain wake you up from sleep? (108) (108) did you have pain in your hip, neck, heel, or elbow? (108) have you experienced alternating pain in your hips or buttocks? (108) have you experienced improvement in your back pain after taking non-steroidal anti-inflammatory medications (like naproxen, ibuprofen, etc.)? (108) Has a doctor ever told you that you have an autoimmune disease like iritis, Crohn’s disease, or psoriasis? (108) Has anyone in your family had an autoimmune disease? (108) Does one of your family members have psoriasis, uveitis, chronic diarrhea? (110) Do you have chronic diarrhea? (110) Do you have joint swelling? (110) Do you have joint pain? (110) Do you have joint swelling of more than 3 joints? (110) Are your legs affected? (110) Do you have red eyes/ now or in the past? (110) Do you have nodules? (110) Do you have anterior chest pain? (110) Assessment of SpondyloArthritis International Society IBP Classification Criteria are fulfilled if at least 4 out of 5 parameters in brackets above are present, in people with chronic back pain duration >3months. 167 Table C3. List of patient questions from studies for psoriatic arthritis self-reported measures Patient Questions/ Tool Name PEST (70,73,74,85,89,97) Yes/No ToPAS (70,85) Yes/No PASE (85,89) Never = 1 Rarely = 2 Sometimes = 3 Often = 4 Always = 5 PASQ (70) Yes/No EARP (89) Yes/No Alenius (73) Yes/No Have you ever thought you have arthritis? X Are your joints stiff when you wake in the morning? X If yes, how long does the stiffness last? X Have you ever had a swollen joint X X X X X Has a doctor ever told you that you have arthritis X X Do your fingernails or toenails have holes or “pits” X X Do your fingernails come loose from the nailbed? X Are your nails abnormally thick? X Have you had pain in your heel X X X Have you had a finger or toe that was completely swollen and painful for no apparent reason X X X X In the drawing [below], please tick the joints that have caused you discomfort (i.e., stiff, swollen, painful joints) X Have you taken anti-inflammatory more than twice a week for joint pain in the last 3 months? X Do you wake up at night because of low back pain? X Do your wrists and fingers hurt? X Do your wrists and fingers swell? X Does one finger hurt and swell for more than 3 days? X Do your feet or ankles hurt? X Do your elbow and hips hurt? X Have you ever had: pain in your joints X X X stiffness in your joints lasting more than 30 minutes after waking up? X X X 168 pain where the tendons or ligaments attach to the bone (such as at the back of the heel or bottom of the foot)? X X pitting, ridging, or lifting of your fingernails or toenails? X X X Does anyone if your family have arthritis? X Do you have a family member with psoriasis? X X Do you have a family member with psoriatic arthritis? X X Do you have psoriasis? X Have you ever had: neck pain lasting ≥3 months that was not injury related X pain in your lower back? X X Back stiffness in the morning? X If yes, how long does it last? X pain or swelling at the back of your heel? X a rash with your joint pain? X nail problems with your joint pain? X How often do you have difficulty: moving after sitting for a while? X getting up from a chair? X bending over to pick something up off the floor? X reaching above your head? X opening a jar or turning a doorknob? X climbing stairs? X walking for a block or two? X dressing yourself? X 169 Appendix D: Population Data BC/Chapter 3 Study Supplemental Methods D.1 Methods 1: Identifying the First Rheumatologist Visit (T1) First visits (incident cases) to a rheumatologist were distinguished from a visit for continuing care (prevalent case) using the following process: 1. Any individual who saw a rheumatologist in the study period was identified through Medical Service Plan (MSP) billing codes in a dataset provided by Population Data BC covering April 1, 2006, to March 31, 2020. Rheumatologist visits were identified if the practice number associated with a recorded patient visit matched the practice number of a list of all BC rheumatologist practice numbers; this matching was performed using an anonymous pseudo-identifier provided by Population Data BC based on a list of MSP billing numbers of practicing rheumatologists we had submitted. 2. Of all the people who had seen a rheumatologist, those with MSP billing codes for rheumatology consultation (31010), extended rheumatology consultation (31050) or internal medicine consultation (310), were identified as candidates for a first rheumatologist visit. These billing codes represent a full consultation as opposed to repeat or limited consultation. Since these codes can only be billed once per patient in a 6-month period, these are most likely to be used for a first consultation. 3. To identify and exclude prevalent cases who may have had previous rheumatologist visits, a retrospective washout period of five years prior was applied to any potential first visit (the index visit) identified through MSP billing codes (31010; 31050; 310). The index visit was classified as a first visit if no other rheumatologist visits were identified in this 5-year washout period. 4. Finally, the following criteria were applied to any individuals identified as having a potential first rheumatologist visits for inclusion in the study cohort: a. Registered with MSP for at least 75% of the washout period (to limit the effect of arriving/leaving the province) b. Aged 19 years or older at the time of rheumatologist first visit c. No DMARD dispensation during the 5-year washout period d. No rheumatologist referral greater than 365 days before the index visit, since PCP referrals to specialists in BC expire after one year. 170 To enable a washout period prior to any first rheumatologist visit to be assessed, the study timeframe was restricted to April 1, 2011, to March 31, 2020 (FY 2011/12 to 2019/20). D2. Methods 2: Determining the Time of Primary Care Referral (T0) Once a first rheumatologist visit date had been identified, the next step was to match this visit to the PCP referral, T0, that led to that first consult. The “gold standard” matching approach would be to use the ‘no charge referral’ (MSP code (03333)). The ‘no charge referral’ code is a double entry system which requires a referring PCP to input the MSP practitioner number of the physician (rheumatologist) to whom they are referring the patient to; when that physician (rheumatologist) then sees the patient, they enter the practitioner number of the PCP who referred the patient to them. However, this double-entry system is incomplete and not mandatory for payment in MSP. Using this matching algorithm, we were able to match the PCP and first rheumatologist visit for 24% (n=36,414) of the 149,902 patients with a first rheumatologist visit (Appendix E); 7,953 with a history of an IA diagnosis from their PCP (Appendix E, column 1) and 28,461 without a history of an IA code (Appendix E, column 3). To attempt to match the remaining 76% (n=113,488), we tried various definitions of matching using the ‘no charge referral’ (MSP code (03333)). The rule which maximized the number of matches was a “pragmatic match” where a T1 rheumatologist visit (any rheumatologist) was matched to the most recent PCP T0 visit in which the “refer to” field was inputted with the practitioner number of a rheumatologist. This matching rule (i.e., pragmatic match) allowed an additional 21,952 (15%) people with a first rheumatologist visit to be matched to a referral date; an additional 4,403 with a history of an IA diagnosis from a PCP and 17,549 without a history of an IA diagnosis from a PCP. Using a combination of these matching rules 58,366 (39%) of the people with a first rheumatologist visit could be matched to a referral date. A comparison of the median lag times between referral and first rheumatologist visit (T1) indicated that these times were comparable between matching rules (Appendix E), and therefore a combination of the “gold standard” and “pragmatic” matching rules was used to maximize the sample size. Comparison of the characteristics of people whose first visits could or could not be matched to a referral date did not indicate any significant differences between groups. 171 D3. Methods 3: Defining the Most Probable Diagnosis of RA and the Date of First DMARD Dispensation (T2) The derivation of the sample of people with a most probable diagnosis of RA is shown in Appendix J. People were defined as having a most probable diagnosis of RA if they had a DMARD initiated within two years after their T1, and their most recent rheumatologist visit prior to DMARD initiation contained an ICD-9 code for RA. The time frame for the sample of people with a most probable diagnosis of RA was therefore restricted to April 1, 2011, to March 31, 2018 (FY 2011-2017) to provide a two-year period in which DMARD initiation following a T1 could be identified. A two-year period was assessed for all people irrespective of the year in which their T1 occurred. There were 107,899 people with a T1 who had 2 years of follow-up available to determine whether or not they had a DMARD initiated. Of these, 14,680 (13.6%) had a DMARD initiated within two years. Of the people who received a DMARD, 5,938 (40.4%) had an ICD-9 code for RA at the rheumatologist visit preceding the start of DMARD treatment; 2,614 (44%) of these people with a most probable diagnosis of RA could be matched to a PCP referral. 172 Appendix E: Population Data BC Matching Rules to Decide on Sample Frame Table E1. Difference in Lag 1 based on ‘pragmatic’ or ‘gold standard’ matching rules First visits for IA First visits for non-IA N=58,366 / Group 1 2 3 4 1. History of IA Diagnosis (at PCP referral) ✔ ✔ X X 2. Gold standard match: Exact match using “no charge referral” ✔ X ✔ X 3. Pragmatic match: PCP referral to rheumatologist practitioner number for 1st visit by any rheumatologist, using “no charge referral” ✔ ✔ ✔ ✔ Sample size, n 7,953 4,403 28,461 17,549 Median Days for Lag 1 (IQR) 42.0 (18.0-89.0) 42.0 (16.0-90.0) 51.0 (22.0-104.0) 49.0 (16.0-106.0) Percentage difference in Lag 1 between Pragmatic and Gold standard matching approaches 0.0% -3.9% 173 Appendix F: ICD-9 Codes and Diagnoses Table F1. ICD-9 codes and frequency of conditions by disease category, defined at PCP referral ICD-9 All patients (n=149,902) IA (n=64,819) Non-IA (n=45,853) Other MSK (n=25,032) Other (n=14,198) 714 Rheumatoid arthritis 36.8% 710.x Connective tissue diseases 23.2% 720 Ankylosing spondylitis 10.0% 725 Polymyalgia rheumatica 7.3% 274 Gout 6.9% 696 Psoriatic arthritis 6.3% 446 Granulomatosis 3.0% 710.0 Systemic lupus erythematosus 2.9% 712 Crystal arthropathy 2.4% 443 Peripheral vascular disease 1.2% 715 Osteoarthritis 49.9% 724 Back pain 15.1% 729 Soft tissue disorders 13.9% 719 Other joint disorders 13.8% 733 Osteoporosis 7.3% 716 Other and unspecified arthropathies 42.4% 727 Other disorders of synovium, tendon, bursa 18.4% 728 Disorders of muscle, ligament and fascia 9.3% 726 Peripheral enthesopathies and allied syndromes 9.2% 780 General symptoms 5.6% 781 Symptoms of nervous and MSK systems 3.4% 723 Disorders of the cervical region 2.6% 15 other codes 9.1% 696.1 Psoriasis and similar disorders 13.9% 338 Pain 9.0% 401 Hypertension 3.2% 848 Ill-defined sprains and strains 2.2% 364 Acute and sub-acute iridocyclitis 2.1% 038 Septicemia 1.9% 795 Abnormal histological & immunological findings 1.9% 427 Supraventricular tachycardia 1.8% 782 Symptoms involving skin 1.8% 695 Red discoloration of skin 1.7% 428 Heart failure 1.6% 786 Symptoms of the respiratory system 1.6% 572 Other codes 57.3% 174 Appendix G: Univariate Associations of Variables with Lag Times Table G1. Univariate associations between individual and system variables and lag times for people with RA Lag 1 (referral to 1st visit) Lag 2 (1st visit to DMARD) Total Lag (referral to DMARD) Coef. 95% CI p-value Coef. 95% CI p-value Coef. 95% CI p-value Year 2011/12 Ref Ref Ref 2012/13 -3.0 (-11.0, 5.0) 0.505 4.0 (-4.0, 12.0) 0.322 3.0 (-10.0, 21.0) 0.700 2013/14 -1.0 (-8.0, 7.0) 0.782 12.0 (2.0, 19.0) 0.005 20.0 (6.0, 40.0) 0.010 2014/15 3.0 (-5.0, 14.0) 0.524 6.0 (-5.0, 15.0) 0.225 9.0 (-4.0, 28.0) 0.276 2015/16 -3.0 (-10.0, 4.0) 0.435 0.0 (-8.0, 11.0) 1.000 6.0 (-8.0, 21.0) 0.408 2016/17 -9.0 (-15.0, -1.0) 0.008 -2.0 (-9.0, 5.0) 0.604 -10.0 (-19.0, 2.0) 0.106 2017/18 -12.0 (-18.0, -4.0) 0.000 3.0 (-6.0, 9.0) 0.469 -4.0 (-15.0, 9.0) 0.526 Age, years -0.1 (-0.23, 0.04) 0.159 0.0 (-0.2, 0.1) 1.000 -0.1 (-0.4, 0.2) 0.481 Sex Male Ref Ref Ref Female 8.0 (4.0, 12.0) 0.000 -2.0 (-7.0, 3.0) 0.452 5.0 (-3.0, 14.0) 0.231 Disposable Income Category, $CDN <26,300 Ref Ref Ref 26,300 to <31,600 2.0 (-3.0, 9.0) 0.519 4.0 (-3.0, 10.0) 0.275 3.0 (-12.0, 15.0) 0.642 31,600 to <38,500 -1.0 (-7.0, 5.0) 0.740 1.0 (-5.0, 7.0) 0.748 -3.0 (-18.0, 6.0) 0.646 ≥38,500 -5.0 (-9.0, 2.0) 0.065 7.0 (1.0, 14.0) 0.041 4.0 (-11.0, 15.0) 0.527 Geographical LHA cluster Metropolitan Ref Ref Ref Non-Metropolitan/Remote 10.0 (6.0, 13.0) 0.000 -6.0 (-10.0, -1.0) 0.007 10.0 (0.0, 18.0) 0.026 Number of comorbidities (RDCI) 0 Ref Ref Ref 1 -3.0 (-8.0, 1.0) 0.262 -2.0 (-8.0, 4.0) 0.498 -5.0 (-14.0, 5.0) 0.285 2 -6.0 (-10.0, 0.0) 0.016 5.0 (-4.0, 12.0) 0.208 -1.0 (-12.0, 15.0) 0.890 3 -4.0 (-10.0, 7.0) 0.408 11.0 (0.0, 20.0) 0.030 8.0 (-11.0, 23.0) 0.381 4 or more -5.0 (-14.0, 12.0) 0.440 4.0 (-7.0, 9.0) 0.319 1.0 (-19.0, 18.0) 0.912 Rheumatologist ever billed MCA code No Ref Ref Ref Yes -18.0 (-26.0, -11.0) 0.000 7.0 (-5.0, 10.0) 0.106 -26.0 (-40.0, -13.0) 0.000 Coef: Coefficient 175 Appendix H: Definition of Time Points and Associated Lag Times Figure H1. Illustration of the definition of time points and associated lag times T0 is the date of PCP referral to a rheumatologist T1 is the date of first rheumatologist visit T2 is the date that a disease modifying anti-rheumatic drug (DMARD) is started for patients with RA Lag 1 is the number of days between T0 and T1 Lag 2 is the number of days between T1 and T2 for patients with RA Total Lag is the number of days between T0 and T2 for patients with RA PCP Referral Date T0 Rheumatologist Visit Date T1 RA DMARD Start Date T2 Lag 1 Lag 2 Total Lag 176 Appendix I: Population Data BC Study Sample Derivation Figure I1. Sample derivation of (a) first rheumatologist visit and (b) sample of RA patients 1st Rheum Visit Sample Derivation Excluded Patients with RA Sample Derivation Excluded April 1, 2011 to March 31, 2020 Patients with ≥ 1 rheumatologist visit n=252,999 Study Sample n=149,902 Patients with ≥ 1 visit of fee item: 31010, 31050, 310 n=216,325 (85.5%) n=36,674 (14.5%) Patients with 2 yrs follow up from T1 Period of DMARD Initiation n=107,899 (72.0%) n=42,003 (28.0%) Patients with no rheumatologist visit within 5-years (washout period) n=173,429 (80.2%) n=42,896 (19.8%) Patients who started a DMARD n=14,680 (13.6%) n=93,219 (86.4%) Patients registered under MSP for ≥ 75% of the time n=159,157 (91.8%) n=14,272 (8.2%) Patients who had an RA Diagnosis n=5,938 (40.4%) n=8,742 (59.6%) Patients aged ≥ 19 years at first rheumatology visit n=157,789 (99.1%) n=1,368 (0.9%) Patients with a PCP matched Referral Sample of RA Patients n=2,614 (44.0%) or 1.7% of Study Sample n=3,324 (56.0%) Patients with no DMARD dispensation during the washout period n=152,206 (96.5%) n=5,583 (3.5%) Patients who had their PCP referral ≤ 365 days of the first rheumatologist visit Study Sample n=149,902 (98.5%) or 59.3% of patients with ≥ 1 rheumatologist visit n=2,304 (1.5%) (a) (b) 177 Appendix J: Plots with Distribution of Lag Times by Year and Condition Figure J1. Median days from primary care physician referral to rheumatologist first visit (Lag 1) by disease type, 2011/12–2019/20 178 Appendix K: Plots of Distribution of Lag Times for the sample of patients with RA Figure K1. Median days for people with a most probable diagnosis of RA, from primary care physician referral to rheumatologist first visit (Lag 1), to DMARD start (Lag 2) and from PCP referral to DMARD start (Total Lag), 2011/12–2017/18 179 Appendix L: CaFaSpa Questions and Scoring Method Table L1. CaFaSpa questions and scoring method CaFaSpa Questionnaire (Score ≥ 1.5, Refer to Rheumatologist) QBP: Do you have back pain? Answer must be ‘yes’ to continue 1BP: Do you have back pain that has lasted more than 3 months? 1BP and 2BP must both be ‘yes’, to continue to calculate a CaFaSpa Score 2BP: Did your back pain start when you were aged 45 or under? Around what date did your joint or back pain start? If you are not sure, think back to an occasion when you noticed the pain and select a date on the calendar around then. It does not need to be exact. Calculate to determine if age at onset was < 40 years old 3BP: Did your back pain develop gradually? Positive ASAS IBP questionnaire (Table 2 below) requires that 4 of these 5 questions in blue be a ‘yes’ which would give a score of 1 toward CaFaSpa total 4BP: Does your back pain improve with exercise? 5BP: There is no improvement in your back pain when you rest. 6BP: Do you suffer from back pain at night which improves upon getting up? 7BP: Have you had back pain for at least 5 years? 8BP: As best as you can remember, do you get back pain relief when you take anti-inflammatory medicines like ibuprofen (NSAIDs)? 9BP: As far as you are aware, has anyone in your family had a similar back pain condition? Sieper et al. Ann Rheum Dis 2009;68:784–788. doi:10.1136/ard.2008.101501 Citation: van Hoeven L, Vergouwe Y, de Buck PDM, Luime JJ, Hazes JMW, Weel AEAM (2015) External Validation of a Referral Rule for Axial Spondyloarthritis in Primary Care Patients with Chronic Low Back Pain. PLoS ONE 10(7): e0131963 180 Appendix M: SAID Study Sample Size Calculation Grid Table M1. Sample size required for detecting a suspected difference from 0.50 in ROC AUCa Narrow IA Definition Broad IA Definition Expected AUCs Ratio negative/ positive sample Total sample required Positive sample required Negative sample required Ratio negative/ positive sample Total sample required Positive sample required Negative sample required 0.55 2.00 1182 394 788 0.30 1454 1118 336 0.55 2.50 1292 369 923 0.40 1267 905 362 0.55 3.00 1408 352 1056 0.50 1166 777 389 0.60 2.00 294 98 196 0.30 357 274 83 0.60 2.50 322 92 230 0.40 311 222 89 0.60 3.00 352 88 264 0.50 287 191 96 0.65 2.00 129 43 86 0.30 154 118 36 0.65 2.50 144 41 103 0.40 135 96 39 0.65 3.00 156 39 117 0.50 125 83 42 0.70 2.00 72 24 48 0.30 84 64 20 0.70 2.50 81 23 58 0.40 73 52 21 0.70 3.00 88 22 66 0.50 68 45 23 0.75 2.00 45 15 30 0.30 51 39 12 0.75 2.50 49 14 35 0.40 45 32 13 0.75 3.00 56 14 42 0.50 42 28 14 0.80 2.00 30 10 20 0.30 34 26 8 0.80 2.50 35 10 25 0.40 30 21 9 0.80 3.00 36 9 27 0.50 29 19 10 2.14 88 28 60 0.42 88 62 26 a 80% power, 5% type I error, two-tailed test. 88 patients: 4 missing family history of RA - Q10 181 Appendix N: SAID Study Rheumatology Site Profiles Table N1. Participating rheumatology practice characteristics and patient geographic location Patient geographic location Rheumatologist Sex Years in practice Sole or group practice Main practice location Metropolitan Non-metropolitan (hospital hub) Non-metropolitan (no hospital hub) Remote Total 1 Male >20 G Vancouver 17 1 18 2 Male >20 S Vancouver 15 2 1 18 3 Female >20 S Vancouver 14 1 3 18 4 Male <10 G Vancouver 10 1 11 5 Male <10 G Vancouver 10 1 11 6 Female >20 S Kamloops 1 1 2 4 7 Female >20 S Cranbrook 3 1 4 8 Male <10 G Victoria 1 2 3 9 Female <10 G Vancouver 2 2 10 Female 10-20 G Victoria 2 2 11 Male <10 G Vancouver 1 1 Total 73 7 4 8 92 182 Appendix O: Performance of CaFaSpa The AxSpa sample size was inadequate to run statistics. Five patients in the sample received a rheumatologist assigned diagnosis of AxSpa and 2 with unspecified inflammatory spondyloarthropathy. Of these 7 patients (with a condition that the questionnaire was designed to predict/identify), 5 scored positive for CaFaSpa at ≥1.5 indicating that they should have been referred (TP). In the two other patients, 1 of the 8 patients who scored negative for CaFaSpa had AxSpa (FN), and one answered ‘No’ to the question, “Do you have back pain?” and did not complete the CaFaSpa questionnaire (Table P1). However, there were 31 participants who answered, ‘yes’ to the question, “Do you have back pain?” and therefore accessed the CaFaSpa questionnaire. A positive CaFaSpa score for referral also resulted for 18 patients who did not have AxSpa (FP), and a negative score resulted correctly for 7 of 8 patients (TN). Table O1. CaFaSpa score confusion matrix for referral of AxSpa AxSpa Diagnosis + - CaFaSpa Score + 5 18 23 - 1 7 8 6 25 31 * One patient with AxSpa of the 7 did not have back pain and did not answer CaFaSpa 183 Appendix P: SAIDx Regression Model Outputs Table P1. Outputs for the three SAIDx exploratory models and step AIC for Model 3 for narrow and broad IA outcomes Narrow IA Outcome Model 1 Model 2 Model 3 Model 3 Step AIC 184 Broad IA Outcome Model 1 Model 2 Model 3 Model 3 Step AIC 185 Appendix Q: Outputs of Model Bootstrap Internal Validation Table Q1. Bootstrap internal validation for Model 3 and its Step AIC for narrow and broad IA definitions Bootstrap Model 3 – Narrow IA Definition Divergence or singularity in 34 samples > print(validation) index.orig training test optimism index.corrected n Somers’ Dxy 0.5702 0.7587 0.4108 0.3479 0.2223 1966 Nagelkerke R2 0.2880 0.5166 0.1321 0.3845 -0.0966 1966 Brier Score, B-index 0.1655 0.1191 0.2108 -0.0916 0.2572 1966 > calibration <- calibrate(Narrow_Model_3, B=2000, method='boot') Divergence or singularity in 42 samples > plot(calibration) n=88 Mean absolute error=0.061 Mean squared error=0.00676 0.9 Quantile of absolute error=0.148 Bootstrap Step AIC Model 3 – Narrow IA Definition > print(validation) index.orig training test optimism index.corrected n Somers’ Dxy 0.4198 0.4900 0.3811 0.1089 0.3109 2000 Nagelkerke R2 0.1915 0.2555 0.1470 0.1086 0.0830 2000 Brier Score, B-index 0.1777 0.1656 0.1901 -0.0246 0.2020 2000 > calibration <- calibrate(Step_AIC_Narrow, B = 2000, method = 'boot') > plot(calibration) n=88 Mean absolute error=0.073 Mean squared error=0.00653 0.9 Quantile of absolute error=0.098 Bootstrap Model 3 – Broad IA Definition Divergence or singularity in 39 samples > print(validation) index.orig training test optimism index.corrected n Somers’ Dxy 0.5744 0.7513 0.3921 0.3591 0.2153 1961 Nagelkerke R2 0.2605 0.4999 0.1144 0.3855 -0.1250 1961 Brier Score, B-index 0.1655 0.1176 0.2090 -0.0914 0.2568 1961 > calibration <- calibrate(Broad_Model_3, B=2000, method='boot') Divergence or singularity in 37 samples > plot(calibration) n=88 Mean absolute error=0.062 Mean squared error=0.00709 0.9 Quantile of absolute error=0.13 Bootstrap Step AIC Model 3 – Broad IA Definition > print(validation) index.orig training test optimism index.corrected n Somers’ Dxy 0.5145 0.5849 0.4300 0.1549 0.3596 2000 Nagelkerke R2 0.2352 0.3176 0.1725 0.1451 0.0901 2000 Brier Score, B-index 0.1757 0.1579 0.1941 -0.0363 0.2120 2000 > calibration <- calibrate(Step_AIC_Broad, B=2000, method='boot') > plot(calibration) n=88 Mean absolute error=0.063 Mean squared error=0.0064 0.9 Quantile of absolute error=0.129 186 Appendix R: EIAQ Questions Included in Various Studies Table R1. Variables included in regression equations of the EIAQ questions in different studies Variable EIAQ (26,62) Chinese Version (90) SAIDx Narrow SAIDx Broad Age X Sex X X Q01 (joint pain general) X Q02 (hand/wrist pain) X X Q03 (hand/wrist swelling) X X X Q04 (trouble making a fist) X Q05 (morning stiffness general) X X Q06 (morning stiffness >1 hr) Q07 (symmetry) Q08 (functional difficulty) Q09 (ever told you have RA) X X Q10 (RA family history) Q11 (psoriasis diagnosis) X Q12 (6 to 52 week symptom duration) X X QBP (back pain) X X 187 Appendix S: Questions Asked to Rheumatologist Participants 1. Please describe your rheumatology new referral case-mix in terms of your estimated ratio of patients with inflammatory arthritis (IA) versus non-IA and other referrals. Has this changed over time? What would be the ideal (IA vs non-IA) case mix in your practice, and why? 2. What are the sources of your new referrals (e.g. Specific practitioners, geographic, etc.) Has this changed over time? 3. Describe your approach to accepting new patients. Do you have a specific approach or system for accepting and/or prioritizing newly referred patients? 4. Do you have a sense of the percentage of patients with time-sensitive IA in your practice who may not be referred to you within the ‘window of opportunity’ (i.e. within 12 weeks of symptom onset)? 5. What are your thoughts about the effectiveness of current rheumatology referral practices/ approaches in BC? Do you have ideas of how these referral practices may be improved? 6. The rheumatologists’ diagnoses of patients in this study are shown on Page 2 and categorized as time-sensitive IA, other IA and non-IA. The SAID study aimed to assess a referral tool (a set of patient self-administered questions shown on Page 3) to differentiate IA from non-IA to help streamline referrals. Based on a statistically derived algorithm of the predictive questions, a score was generated and tested with the rheumatologists’ diagnoses being the reference standard. a) What are your general thoughts about the patient self-administered questionnaire (page 3)? b) …and your thoughts about its performance and potential utility as a referral tool (page 4)? c) In what ways do you believe the use of this tool could improve over the current BC rheumatology referral approach? d) Do you have thoughts about how it might be implemented to help streamline rheumatology referrals? e) What do you think may be the main opportunities and barriers to its implementation? 188 Table S1. Questions asked to patient participants in the SAID study questionnaire (excluding symptoms) Participant Personal Profile Questions Age What is your year of birth? Sex What sex were you assigned at birth? Male Female Gender What gender do you identify as? Man Woman Non-Binary Person Racial Ethnicity What is your ethnic origin? Other (Please specify in the space below): North American European Caribbean Latin, Central & South American African Asian Oceanian (i.e., around Australia) P-Q3 What city do you live in? P-Q4 What are the first three digits of your postal code? P-Q5 In what range is your household income? Income range is sometimes associated with outcomes or access to health-related resources, either alone or when combined with other variables. As with all other questions in this survey, you don't need to answer this one. If you prefer, you can leave it blank and continue. Less than $42,000 $42,000 - $68,000 $68,001 - $102,000 $102,001 - $163,000 Greater than $163,000 Patient Experience with the Questionnaire Exp-Q1 How easy was it to login to answer this questionnaire? 1-difficult 2-somewhat difficult 3-okay 4-easy 5-very easy Exp-Q2 How easy was it to complete the questionnaire? 1-difficult 2-somewhat difficult 3-okay 4-easy 5-very easy Exp-Q3 To what extent did the questionnaire include the important aspects of your condition? 1-not enough 2-partly 3-mostly Exp-Q4 Do you think that these questions could add to your primary care physician's assessment of your condition? 1-not really 2-partly 3-very much Exp-Q5 “Are there any comments you have about the questionnaire in general, or specifically, like questions that you think it should include, that it doesn't?” Note. For symptom EIAQ questions see Table 4.1 189 Appendix T: Illustration of SAIDx Results Presented to Rheumatologists Figure T1. Illustration presented to rheumatologists in advance of the interviews. SAID algorithm score results compared to rheumatologist reference diagnoses *Note: 92 participants answered all questions except for Q10, where 4 participants did not know if they had a family history of RA, hence the sample n=88. The diagnoses of these four patients with missing scores were RA, enthesopathy of hip region, unspecified inflammatory spondyloarthropathy, and arthralgia. 21 75.0% 10 29.4% 11 42.3% 7 25.0% 24 70.6% 15 57.7% Narrow IA Algorithm Score (from patient responses) 88 patients* 26 Non-IA 34 Other types of IA 28 Time-sensitive IA Rheumatologist (n=11) Diagnosis 17 60.7% 19 55.9% 4 15.4% 11 39.3% 15 44.1% 22 84.6% Broad IA Algorithm Score (from patient responses) True Positive True Positive True Negative False Negative False Positive False Negative
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Harrison, Mark (Assistant professor)
Bansback, Nick
Improving referrals to rheumatologists for patients with inflammatory arthritis
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