Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Bola, Rajan
Master of Science - MSc
University of British Columbia
Background: Many Coronavirus Disease 2019 (COVID-19) survivors report long-term sequelae. However, few studies have measured patient-reported outcomes and compared them to those of patients who tested negative for severe acute respiratory syndrome coronavirus-2 (SARS-COV-2). This study compares the long-term physical and mental health outcomes of patients presenting to emergency departments who tested positive for SARS-COV-2 with those who tested negative. Methods: This study enrolled consecutive eligible patients presenting to emergency departments participating in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) between March 1st 2020 and July 14th 2021. Patients tested for SARS-COV-2 were eligible. Consecutive SARS-COV-2 positive patients were consented for follow-up, and matched with test-negative controls from the same hospital and date. The outcome measures were the Veterans RAND physical health component score (PCS) and mental health component score (MCS). The PCS and MCS of propensity score matched patients were analyzed using linear mixed effects models. Risk factors for PCS and MCS were modelled using linear regression. Results: Our cohort included 1170 SARS-COV-2 positive patients and 3716 test-negative controls. Comparing the groups, the adjusted mean difference in PCS was 0.50 (95%CI: -0.36, 1.36) and -1.01 (95%CI: -1.91, -0.11) for MCS. A World Health Organization Ordinal Outcome Score of 6-7, representing severe SARS-COV-2 disease, was the strongest predictor of PCS (β=-7.4; 95%CI: -9.8, -5.1). Prior mental health illness was the strongest predictor of MCS (β=-5.4; 95%CI: -6.3, -4.5). Conclusion: The mean PCS was similar among SARS-COV-2 positive and negative participants tested under similar circumstances, while mean MCS was worse among SARS-COV-2 positive participants. The mental health sequelae of COVID-19 should be considered when developing long-term support programs for survivors.
INVESTIGATING PATIENT-REPORTED OUTCOMES AMONG COVID-19 SURVIVORS: A LONGITUDINAL COHORT STUDY by Rajan Bola B.Sc., The University of British Columbia, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2022 © Rajan Bola, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Investigating Patient-reported Outcomes Among COVID-19 Survivors: A Longitudinal Cohort Study submitted by Rajan Bola in partial fulfillment of the requirements for the degree of Master of Science in Population and Public Health Examining Committee: Dr. Corinne Hohl, Associate Professor, Department of Emergency Medicine, UBC Co-supervisor Dr. Jason Sutherland, Professor, School of Population and Public Health, UBC Co-supervisor Dr. Rachel Murphy, Assistant Professor, School of Population and Public Health, UBC Supervisory Committee Member Dr. Mieke Koehoorn, Professor, School of Population and Public Health, UBC Additional Examiner iii Abstract Background Many Coronavirus Disease 2019 (COVID-19) survivors report long-term sequelae. However, few studies have measured patient-reported outcomes and compared them to those of patients who tested negative for severe acute respiratory syndrome coronavirus-2 (SARS-COV-2). This study compares the long-term physical and mental health outcomes of patients presenting to emergency departments who tested positive for SARS-COV-2 with those who tested negative. Methods This study enrolled consecutive eligible patients presenting to emergency departments participating in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) between March 1st 2020 and July 14th 2021. Patients tested for SARS-COV-2 were eligible. Consecutive SARS-COV-2 positive patients were consented for follow-up, and matched with test-negative controls from the same hospital and date. The outcome measures were the Veterans RAND physical health component score (PCS) and mental health component score (MCS). The PCS and MCS of propensity score matched patients were analyzed using linear mixed effects models. Risk factors for PCS and MCS were modelled using linear regression. Results Our cohort included 1170 SARS-COV-2 positive patients and 3716 test-negative controls. Comparing the groups, the adjusted mean difference in PCS was 0.50 (95%CI: -0.36, 1.36) and -1.01 (95%CI: -1.91, -0.11) for MCS. A World Health Organization Ordinal Outcome Score of 6-7, representing severe SARS-COV-2 disease, was the strongest predictor of PCS (β=-7.4; 95%CI: -9.8, -5.1). Prior mental health illness was the strongest predictor of MCS (β=-5.4; 95%CI: -6.3, -4.5). iv Conclusion The mean PCS was similar among SARS-COV-2 positive and negative participants tested under similar circumstances, while mean MCS was worse among SARS-COV-2 positive participants. The mental health sequelae of COVID-19 should be considered when developing long-term support programs for survivors. v Lay Summary Many COVID-19 survivors report worse health after being infected with SARS-COV-2, the virus that causes COVID-19. Not much research has examined the physical and mental quality of life of COVID-19 survivors post-infection. This study found similar physical health outcomes, but worse mental health outcomes among COVID-19 patients who presented to Canadian emergency departments compared to patients who presented to the same emergency departments but tested negative for SARS-COV-2. These findings are representative of COVID-19 survivors who sought care in emergency departments and consented to participate in our study. We were unable to capture those too sick to participate or not presenting to emergency departments. Future research on physical and mental health symptoms should be conducted to better understand the long-term health consequences of COVID-19. vi Preface This thesis is an original product of the author, Rajan Bola. Rajan developed the research objectives, study design, and analysis of the research data with assistance from Dr. Corinne Hohl and Dr. Jason Sutherland. This thesis utilized data from the Canadian COVID-19 Emergency Department Rapid Response Network, a pan-Canadian research network dedicated to the understanding and rapid dissemination of COVID-19 research in Canada. Preliminary results from this thesis were presented at the 2022 University of British Columba Department of Emergency Medicine Research Day. Sections of this thesis will be submitted to peer-reviewed journals for publication. This project received approval from the University of British Columbia Research Ethics Board (H20-01015). vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xiii Chapter 1: Introduction ................................................................................................................1 1.1 Problem statement ........................................................................................................... 1 1.2 Background and rationale ............................................................................................... 1 Chapter 2: Literature review ........................................................................................................3 2.1 The COVID-19 pandemic ............................................................................................... 3 2.2 Lessons learned from the 2003 SARS pandemic ............................................................ 3 2.3 Quality of life and patient-reported outcomes ................................................................ 4 2.3.1 Purpose of patient-reported outcome measurement tools ....................................... 4 2.3.2 Patient-reported outcomes to enhance clinical practice .......................................... 5 2.3.3 Veterans RAND 12-item Health Survey................................................................. 7 2.4 Long-term outcomes of COVID-19 patients ................................................................ 10 2.4.1 Physical health outcomes ...................................................................................... 10 2.4.2 Mental health outcomes ........................................................................................ 13 2.4.3 Factors associated with worse COVID-19 outcomes ........................................... 13 viii 2.5 Research rationale and thesis objectives ....................................................................... 15 2.5.1 Test-negative controls for comparisons of patient-reported outcomes ................. 15 2.5.2 Quality of life risk factors for COVID-19 survivors ............................................ 15 Chapter 3: Methods .....................................................................................................................16 3.1 Data sources .................................................................................................................. 16 3.2 Study design and participants ....................................................................................... 23 3.3 Outcome variables and covariates ................................................................................ 25 3.4 Statistical analysis ......................................................................................................... 27 3.4.1 Descriptive statistics ............................................................................................. 27 3.4.2 Propensity score matching .................................................................................... 28 3.4.3 Principal component analysis ............................................................................... 30 3.4.4 Statistical power and sample size ......................................................................... 31 3.4.5 Statistical software ................................................................................................ 31 3.5 Ethical considerations and patient partner engagement ................................................ 31 Chapter 4: Results........................................................................................................................32 4.1 Baseline characteristics ................................................................................................. 32 4.2 Differences in PCS and MCS between test-positive and test-negative controls .......... 34 4.3 Risk factors for worse physical and mental health ....................................................... 42 Chapter 5: Discussion ..................................................................................................................53 5.1 Physical and mental health outcomes of COVID-19 patients ...................................... 53 5.1.1 Physical health outcomes ...................................................................................... 53 5.1.2 Mental health outcomes ........................................................................................ 54 5.2 Risk factors for worse physical and mental health outcomes ....................................... 55 ix 5.2.1 Physical health risk factors ................................................................................... 55 5.2.2 Mental health risk factors...................................................................................... 58 5.3 Strengths and limitations............................................................................................... 61 5.3.1 Limitations ............................................................................................................ 61 5.3.2 Strengths ............................................................................................................... 63 5.4 Conclusion .................................................................................................................... 63 References .....................................................................................................................................64 Appendices ....................................................................................................................................76 Appendix A MANOVA output ................................................................................................. 76 Appendix B Subgroup analysis excluding patients without respiratory-related symptoms ..... 77 x List of Tables Table 1. Commonly used patient-reported outcome measurement tools ........................................ 6 Table 2. The Veterans RAND 12-item Health Survey: summary components, scales, and items . 9 Table 3. Contextual, Social, and Cultural Questionnaire.............................................................. 18 Table 4. Veterans RAND-12 Item Health Survey ........................................................................ 21 Table 5. Participating CCEDRRN Sites ....................................................................................... 24 Table 6. Baseline variables of participants ................................................................................... 35 Table 7. Confounder variables that were matched using propensity score matching ................... 38 Table 8. Factor loadings of 8 clinical variables used to construct two principal component composite variables: Oxygen Requirements and Medically Complex Comorbidities ................. 43 Table 9. Linear regression analysis using two composite variables for SARS-COV-2 positive and test-negative control participants’ PCS and MCS ........................................................................ 47 Table 10. Linear regression analysis using two composite variables for SARS-COV-2 positive participants’ PCS and MCS .......................................................................................................... 50 xi List of Figures Figure 1. The 8-point World Health Organization Ordinal Outcome Scale based on the patient’s condition during a COVID-19-related hospital visit .................................................................... 12 Figure 2. Flow diagram of enrolled participants ........................................................................... 33 Figure 3. Violin plots of physical and mental component scores across matched SARS-COV-2 positive and test-negative control participants .............................................................................. 41 Figure 4. Quantile-quantile plot of residuals from the linear regression model with mental component score as the outcome .................................................................................................. 44 Figure 5. Quantile-quantile plot of linear regression model with physical component score as the outcome ......................................................................................................................................... 45 xii List of Abbreviations 95%CI – 95% confidence interval CCEDRRN – Canadian COVID-19 Emergency Department Rapid Response Network COVID-19 – Coronavirus disease 2019 ED – Emergency department IQR – Interquartile range MANOVA – Multivariate analysis of variance MCS – Mental component score PCA – Principal component analysis PCS – Physical component score PROMs – Patient-reported outcome measures PROs – Patient-reported outcomes SARS – Severe acute respiratory syndrome SARS-COV-2 – Severe acute respiratory syndrome coronavirus-2 SD – Standard deviation SE – Standard error VR-12 – Veterans RAND 12-item health survey WHO – World Health Organization xiii Acknowledgements I would like to acknowledge all the individuals who supported me throughout my degree. Firstly, to my co-supervisor Corinne Hohl, thank you for accepting me as one of your students, for your clinical insight, and mentorship. To my co-supervisor Jason Sutherland, thank you for your expertise and contributions to this thesis, and for always being available to answer the many questions I had about statistics. I am grateful to everyone from the Center for Clinical Epidemiology and Evaluation, for welcoming me and supporting me as I became a graduate student affiliate. For the CCEDRRN coordinating team members, thank you for your guidance. I also want to acknowledge the site investigators, research coordinators, and research assistants across the country without whom this data would not have been generated. I am thankful to the CCEDRRN patient engagement committee, whose perspectives, opinions, and feedback helped me critically interpret the results of this study. I would like to thank the School of Population and Public Health, the Faculty of Graduate and Postdoctoral Studies, and the Faculty of Medicine, whose awards and scholarships helped support this research. Lastly, to my family, thank you for your encouragement and support. 1 Chapter 1: Introduction 1.1 Problem statement Long-term sequalae among patients with coronavirus disease 2019 (COVID-19) have been reported months after resolution of the infection.(1) However, robust studies investigating health-related quality of life that have controlled for time trends, as well as demographic, sociocultural, and clinical characteristics remain lacking. This research hypothesizes that survivors of COVID-19 have worse physical and mental health outcomes compared with individuals who presented to emergency departments (EDs) with similar respiratory syndromes, but tested negative for SARS-COV-2. 1.2 Background and rationale The COVID-19 pandemic was declared in March 2020 due to global spread of a novel coronavirus in late 2019.(2) The majority of patients infected with severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), the virus that causes COVID-19, experienced minor to mild symptoms.(3) However, some patients have experienced severe symptoms necessitating hospitalization including shortness of breath, cardiovascular symptoms, and neurological complaints.(4–7) Some patients have reported persistent symptoms months after resolution of the acute infection.(8–10) Research is emerging describing longitudinal health sequelae and excess morbidity among COVID-19 patients,(11,12) with preliminary reports suggesting that the pandemic resulted in 20% increased all-cause mortality in the United States.(13) This may be directly attributed to COVID-19 or due to secondary causes exacerbated by the pandemic, such as delayed access to lifesaving therapies for other health problems.(13) Much less is known about long-term patient-reported physical and mental health outcomes among COVID-19 survivors. 2 Demographic, clinical, and sociocultural factors associated with worse self-reported health outcomes among survivors are not well established. Understanding factors associated with poor patient-reported health outcomes could inform the development and prioritization of health interventions to improve health outcomes. 3 Chapter 2: Literature review 2.1 The COVID-19 pandemic COVID-19 was first identified in December 2019 based on epidemiologically linked clusters of patients diagnosed with pneumonia of an unknown cause.(14) As of February 2022, there were approximately 400 million cases of COVID-19, and 6 million deaths directly attributed to the disease.(15) Canada has seen approximately 3.1 million cases of COVID-19 to date and 35,000 deaths.(16) While COVID-19 mortality has been tracked meticulously in many countries, other sequelae that will impact health systems for years to come, including long-term symptoms, remain poorly understood among COVID-19 survivors. 2.2 Lessons learned from the 2003 SARS pandemic In 2003, 251 Canadians were infected with SARS-COV, another coronavirus which caused a global pandemic.(17) Severe acute respiratory syndrome (SARS) cases were clustered in hospital-settings in Ontario and British Columbia,(18,19) and presented with similar symptoms as COVID-19: fever, cough, and shortness of breath. At that time, widespread quarantine and social distancing measures were associated with psychological and emotional distress,(20) not unlike what has been reported as a result of COVID-19 pandemic restrictions.(21) Similarities between the 2003 SARS and 2019 COVID-19 pandemic suggest that literature on SARS may contribute to hypothesis generation for COVID-19 survivors.(22) The SARS-COV and SARS-COV-2 viruses belong to the family of Coronaviridae and cause the respiratory diseases SARS and COVID-19.(23) Several studies examining the sequelae of hospitalized SARS survivors noted that patients reported complications affecting a range of body systems, including persistent lung function and radiological abnormalities in over 75% of survivors,(24) and depression and post-traumatic stress disorder in 7-23%.(25) At 2-years post- 4 discharge, some survivors continued to report reduced exercise capability, post-traumatic stress disorder, depression, and poor health-related quality of life.(24,25) Moldofsky et al. described a post-SARS syndrome comprising neuromuscular disabilities and psychosomatic disturbances including chronic fatigue, myalgia, weakness, depression, and non-restorative sleep 3 years after resolution of the acute infection.(26) Given the similarities between SARS and COVID-19, long-term physical complications and poor mental health outcomes were not unexpected among COVID-19 survivors.(27,28) 2.3 Quality of life and patient-reported outcomes Individuals’ health-related quality of life after an acute illness or medical intervention is an important outcome in clinical research, and can assist in developing and prioritizing preventative and therapeutic interventions.(29–31) Quality of life can be measured in various ways,(32) but is commonly assessed using patient-reported outcomes (PROs), which capture patients’ perspectives of their own health, quality of life, and function.(33) While objective outcome measurements like death or time to death are more commonly used in clinical outcomes research to evaluate health interventions, PROs add value by incorporating patients’ perspectives of their own health when they survive.(34) Patients’ perspectives add to outcomes ascertained by providers by illustrating the impact of their illness on their daily functioning and wellbeing.(35) Capturing PROs is increasingly recognized as an essential component of patient-oriented research.(35) 2.3.1 Purpose of patient-reported outcome measurement tools Patient-reported outcome measures (PROMs) are tools used to measure PROs.(36) PROMs are structured questionnaires or surveys that are completed by patients to measure their perception of health and wellbeing.(37) PROMs can either be specific to a health condition or 5 generalized, and will have ideally been validated prior to their use in the population and health condition of interest. An example of a generic PROM is the 12-Item Short Form Survey, which assesses broad areas of wellbeing, also known as health domains.(38) Commonly used generic instruments to measure PROs are listed in Table 1. Two examples of disease-specific PROMs include the Parkinson’s Disease Questionnaire, which is used to assess quality of life among Parkinson’s patients,(39) and the Visual Function Questionnaire, which questions patients with cataracts about their visual functioning.(40) To date, no generic or disease-specific PROMs have been validated for COVID-19 survivors. 2.3.2 Patient-reported outcomes to enhance clinical practice Patient-reported outcomes can support clinical practice in several ways. Their integration into routine practice can help clinicians identify patients’ functional and psychological limitations to improve their care outcomes.(41) By gaining an understanding of patients’ perspectives of long-term health effects of a disease, counseling and anticipatory interventions can be initiated to manage or prevent further sequelae.(42) PROs can subsequently be used to monitor the progression of complications or the effect of an intervention.(42) In addition, clinically relevant changes to an individual’s quality of life that may or may not be related to the disease, such as fatigue, cognitive functioning, and sexual health, can also be assessed and managed.(42) Overall, PROs can enhance patient-centered care by improving clinicians’ ability to measure the impacts of disease or interventions on quality of life.(43) 6 Table 1. Commonly used patient-reported outcome measurement tools Instrument name (acronym) Domains within instrument Number of questions The 15D instrument of health-related quality of life (15D) Mobility, hearing, breathing, sleeping, eating, speech, elimination, mental function, discomfort, depression, distress, sexual activity, usual activities, vision, and vitality 15 questions EQ-5D-5L Anxiety/depression, mobility, pain, self-care, usual activities 5 questions 36-Item Short Form Survey (SF-36) Mental health, pain, physical function, role limitations, social function, vitality 36 questions 12-Item Short Form Survey (SF-12) Mental health, pain, physical function, role limitations, social function, vitality 12 questions The Veterans RAND 36 item health survey (VR-36) General health perception, physical functioning, role limitations due to physical and emotional problems, bodily pain, energy-fatigue, social functioning, and mental health 36 questions The Veterans RAND 12 item health survey (VR-12) General health perception, physical functioning, role limitations due to physical and emotional problems, bodily pain, energy-fatigue, social functioning, and mental health 12 questions 7 2.3.3 Veterans RAND 12-item Health Survey The VR-12 is a validated health-related quality of life assessment that captures an individual’s perspective of their physical and mental health status.(44) The VR-12 was adapted from the Veterans RAND 36-item health survey. The Veterans RAND 36-item health survey was developed and modified from the original RAND version of the 36-item Health Survey Version 1.0. Twelve question-items were chosen to create the VR-12 to ascertain patients’ general health perception, physical functioning, role limitations due to physical and emotional problems, bodily pain, energy-fatigue, social functioning, and mental health (Table 2).(44) Two additional items in the VR-12 assess changes in physical and emotional health over time.(45) Administering the VR-12 survey longitudinally and over the phone have both been proven feasible.(44) Participants’ responses to the VR-12 are scored and summarized into two distinct measures, a physical component score (PCS) and a mental component score (MCS). Each domain is standardized, ranging from 0–100, with a known population mean score of 50 and standard deviation of 10. Higher scores signify better reported physical and mental health in the PCS and MCS domains, respectively.(46) While the VR-12 has been validated in patients with rotator cuff injuries and patients who had elective surgery, like other PROMs, it lacks validation in COVID-19 survivors.(47,48) Nonetheless, when compared to the 12-Item Short Form Survey, high measures of convergent validity and responsiveness have been documented for the VR-12.(49) Convergent validity, measured by Pearson’s correlation coefficient, was found to be greater than 0.9, thus indicating strong validity in surgical patients.(48) Kazis et al. evaluated the reliability of the VR-12 using Cronbach’s alpha, and reported it to be 0.95, indicating excellent reliability in a representative sample of the United States population.(50) The acceptability of the VR-12, however, was 8 limited in healthcare settings, especially for older adults with complex functional and cognitive disorders.(45) Despite a lack of validation in COVID-19 patients, the VR-12 is generalizable, brief,(51) can be linked to other PROMs,(52) and is commonly used in Canada (45) and the United States.(44,46) The minimally important difference for the VR-12 may be subject to variability depending on the study population. For example, the minimally important difference for PCS was found to be 2.5 points among elective surgery patients,(53) whereas it was found to be 2-2.5 points among patients with rotator cuff injuries.(44) The developers of the VR-12 report that a difference of 1 point in the PCS or MCS is considered both clinically and socially significant for group-mean data.(54) No study has estimated a minimally important difference for VR-12 scores for COVID-19 patients. 9 Table 2. The Veterans RAND 12-item Health Survey: summary components, scales, and items Physical Health General Health - Your health Physical Functioning - Functioning with moderate activities - Climbing several stairs Role Limitations - Accomplished less than wanted - Limitations in the kind of activities Bodily Pain - Pain interference Mental Health Role Limitations - Accomplished less than wanted - Not carefully as usual Vitality - Energy Mental Health - Peacefulness - Downhearted Social Functioning - Interference in social activities Other Physical Change - Change in physical health Emotional Change - Change in emotional health 10 2.4 Long-term outcomes of COVID-19 patients Many COVID-19 patients show full recovery from the disease within several weeks.(55) Minor symptoms such as a cough or fever may linger, but most patients resume daily activities rapidly.(55) Others experience ongoing symptoms such as fatigue, dyspnea, and cognitive impairments.(56,57) Commonly used terms to describe persistent symptoms include “post-acute sequelae of COVID-19”, “post-acute COVID-19 syndrome”, “Long-COVID”, and “Post COVID-19 Condition”.(58) COVID-19 survivors with longitudinal symptoms or complications may identify themselves as “long-haulers”.(59) In 2021, the World Health Organization (WHO) defined the post-COVID condition as a condition that occurs in patients with a history of SARS-COV-2 infection with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis.(60) Studies in China,(4,61,62) the United States,(63–66) Italy,(67) and the Netherlands have examined short and long-term outcomes of COVID-19 patients.(68) Most studies focus on 6-month COVID-19 outcomes, emphasizing ongoing symptoms and/or complications, with only few examining patients’ quality of life.(62,63,65) An even smaller number of studies investigated outcomes beyond the 6-month timepoint, most not measuring quality of life.(64,67,68) 2.4.1 Physical health outcomes Physical health outcomes comprise much of the health outcomes literature for COVID-19 survivors. However, most studies restricted their cohorts to patients hospitalized for COVID-19. Leung et al. published a meta-analysis on the physical health outcomes of hospitalized COVID-19 survivors.(11) Their search strategy included literature from March 2020 to June 2020 and found that COVID-19 survivors experienced complications affecting the immune, respiratory, 11 cardiovascular, neurological, gastrointestinal, hepatic, and renal systems. Xiong et al. found that sequelae were common at 3-months post-discharge among hospitalized COVID-19 patients in China, reporting generalized malaise in 49.6% of patients, respiratory symptoms in 39%, and cardiovascular symptoms in 13%.(62) Shoucri et al. reported that cardiopulmonary complications were common at 3-month and 6-month follow-up periods.(63) Patients with severe COVID-19, as defined by the World Health Organization’s Ordinal Outcome Scale for coronavirus disease severity (Figure 1), were more likely to present with these complications. Wynberg et al. also found disease severity to be a predictor of long-term sequelae.(68) At 6-months after infection, Huang et al. found that the most common symptoms reported by COVID-19 survivors were fatigue and muscle weakness, but that quality of life for survivors with severe disease, as measured by the EQ-5D-5L, was not significantly different compared to patients with mild disease.(4) Boscolo-Rizzo et al. found that 12-months after hospital discharge, over 50% of COVID-19 survivors were suffering from persistent symptoms.(67) Despite a growing literature documenting persistence of symptoms in some COVID-19 patients, little is known about how their patient-reported outcomes compare to patients with similar disease presentations who tested negative for SARS-COV-2. 12 Figure 1. The 8-point World Health Organization Ordinal Outcome Scale based on the patient’s condition during a COVID-19-related hospital visit RRT – renal replacement therapy; ECMO – extracorporeal membrane oxygenation 13 2.4.2 Mental health outcomes Literature reporting mental health outcomes among COVID-19 survivors is more limited despite steadily increasing numbers of studies on physical health outcomes. Xiong et al. described the persistence of psychosocial disturbances in 23% of COVID-19 patients 3-months after hospital discharge.(62) These included depression, anxiety, and sleep disorders.(62) Shoucri et al. reported that 24% of patients had persistent neuropsychiatric symptoms 6-months after hospitalisation.(63) Huang et al. found that COVID-19 patients with severe disease were 1.8 times as likely to present with anxiety and depression at follow-up, when compared to patients with mild disease.(4) Frontera et al. compared COVID-19 patients who developed new neurological conditions during hospitalization to COVID-19 patients who were similarly hospitalized, but did not develop neurological complications.(65) They reported persistent abnormalities in function, increased levels of anxiety and depression, and sleep disorders in over 90% of patients 6-months after hospitalization for COVID-19. Taquet et al. found that 33% of patients had a neurological or psychiatric diagnosis 6 months following recovery, with 13% receiving their first such diagnosis.(66) Incidence and risk of new neuropsychiatric diagnoses were higher in patients with more severe presentations of COVID-19, thus presenting as a possible risk factor for these conditions in COVID-19 survivors. 2.4.3 Factors associated with worse COVID-19 outcomes A number of demographic, clinical, and sociocultural factors have been associated with disease severity and overall quality of life after COVID-19.(69,70) Similar factors may also present as risk factors for worse patient-reported outcomes. Demographic factors associated with COVID-19 disease severity and quality of life include age,(66,71) sex,(65,72) and housing context.(73–76) Clinical factors associated with poor health trajectories and influence patients’ 14 longitudinal quality of life include respiratory distress,(77,78) low oxygen saturation in the ED,(79–81) and mechanical ventilation.(82,83) Many comorbidities like diabetes, hypertension, immunodeficiency disorders,(84), psychiatric and mental health conditions,(85) asthma,(86) hypothyroidism,(87) atrial fibrillation,(88) and dyslipidemia(89) are associated with severe COVID-19 and poor quality of life. Sociocultural determinants are also important and include tobacco and illicit substance use.(64,90,91) Other social determinants of health such as race,(66,92,93) immigration status,(94) employment status,(95) education,(94,96) and income(97) may similarly be associated with worse outcomes among COVID-19 survivors. COVID-19 vaccines directly moderate the immune response to SARS-COV-2, and subsequently, the severity of the disease. Being fully immunized (i.e., 14-days post-vaccine) has been associated with lower odds of severe disease, and may also be a determinant of patient-reported outcomes.(98) As severe disease has been associated with a higher risk of medical complications and disability, these factors may similarly be associated with lower quality of life. Overall, the association between demographic, clinical, and sociocultural factors and post-COVID outcomes are not well established. My thesis aims to better understand the relationship between these factors and patient-reported outcomes among COVID-19 survivors. 15 2.5 Research rationale and thesis objectives 2.5.1 Test-negative controls for comparisons of patient-reported outcomes This thesis compares self-reported quality of life between COVID-19 survivors and propensity score matched test-negative patients. Incorporating test-negative controls will allow me to control for the remarkable time trends that occurred during the first two years of Canada’s COVID-19 pandemic, including the restrictive public health measures that may have influenced wellbeing. Controls from the same healthcare setting will also allow me to control for health-seeking behaviours that changed during the COVID-19 pandemic.(99) Research question one: Are the physical and mental quality of life outcomes of COVID-19 patients presenting to emergency treatments different from patients who test negative for COVID-19? 2.5.2 Quality of life risk factors for COVID-19 survivors Understanding factors that contribute to long-term adverse health effects of COVID-19 can assist in the development of policies and health interventions that support patients affected by or at risk of negative health consequences.(57) Examining the association between relevant clinical, demographic, and sociocultural factors on physical and mental health outcomes of COVID-19 survivors can address this knowledge gap. Research question two: What clinical, demographic, and sociocultural factors are associated with worse physical and mental quality of life in COVID-19 survivors? 16 Chapter 3: Methods 3.1 Data sources Data analyzed for this thesis were made available through the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN).(100) This pan-Canadian network collected data on suspected and confirmed COVID-19 patients presenting to participating EDs as of March 1st 2020.(100) CCEDRRN collected data on demographic, sociocultural and outcome variables using retrospective medical record reviews and follow-up phone interviews. Trained research assistants screened patients who presented to CCEDRRN EDs with respiratory symptoms or were tested for SARS-COV-2. Research assistants then conducted retrospective chart reviews for eligible patients, and phoned all eligible SARS-COV-2 positive patients to obtain consent for a follow-up questionnaire. Research assistants randomly sampled a subset of test-negative controls in a ratio of 1:4 (1 SARS-COV-2 positive participant to 4 test-negative controls) who presented to the same site within one week, and obtained consent for the follow-up questionnaire. Participants enrolled in this study were interviewed up to two times depending on the number of days that had lapsed from the date of their ED visit to study initiation. To include participants from the earliest stages of the pandemic, research assistants contacted participants for the first follow-up interview between 30 and 365 days after their ED or hospital discharge, and the second interview between 30 and 180 days after the first interview. Research assistants abstracted sociocultural and demographic variables using a contextual, social, and cultural questionnaire (Table 3), measured COVID-19 severity using the WHO Ordinal Outcome Scale (Figure 1), and quality of life with the Veterans RAND 12-item 17 health survey (Table 4). Research assistants obtained COVID-19 vaccination history through medical records or during the follow-up interview. Information pertaining to participants’ ED visits were collected, anonymized, and stored within REDCap software (Vanderbilt University, Nashville, Tennessee, USA). To maintain privacy, each participant was assigned a unique study identifier and imposed a small cell size restriction of less than 5. 18 Table 3. Contextual, Social, and Cultural Questionnaire Question List of Responses 1) What sex were you assigned at birth? Male Female Other Prefer not to answer 2) Which of the following best describes your current gender identity? Male Female Two-spirit Something else Prefer not to answer 3) Including yourself, how many persons lived in your household in the month prior to your visit in the ED? Numeric response 4) Can you please describe the race you most identify with? Arab Black Chinese Filipino First Nations Inuk Japanese Korean Latin American Metis South Asian West Asian White Other Prefer not to answer 5) Which ethnic groups do you identify with? Canadian Chinese Dutch English First Nations French German Inuit Irish Italian Jewish Metis Norwegian Polish 19 Portuguese Scottish South Asian Swedish Ukrainian Welsh Do not know Other Prefer not to answer 6) Are you now, or have you ever been a landed immigrant in Canada? Yes No Prefer not to answer 7) What is the highest level of education you have completed? No certificate, diploma, or degree High school diploma or equivalent Trade certificate or diploma College University certificate below bachelor level University degree at bachelor level Prefer not to answer 8) Do you have children under 18 living at home with you, or whom you have a regular responsibility for? Yes No Prefer not to answer 9) Do you provide unpaid assistance or care to a family member because of a health condition or disability? Yes No Prefer not to answer 10) Which describes your living situation when you came to the ED? Home Long-term care/rehab Single room occupancy Shelter Other Prefer not to answer 11) If you were COVID-19 positive, were you able to self-quarantine? Yes No Prefer not to answer Not applicable 12) What sector of the economy were you working in at the time you went to the Emergency Department for possible COVID? Construction Education Farming/fishing Financial services 20 Health care or social assistance Manufacturing Professional, scientific, or technical services Public sector Raw material extraction Real estate Research and development Retail Retired Tourism and hospitality Transportation and warehousing Unemployed Utilities Other Prefer not to answer 13) What was your household’s annual income level before the COVID pandemic? <$22,440 $22,401-29,900 $29,901-36,200 $36,201-42,300 $42,301-48,400 $48,401-55,300 $55,301-63,200 $63,201-73,700 $73,701-91,100 $91,101+ 14) What is your current annual income level? <$22,440 $22,401-29,900 $29,901-36,200 $36,201-42,300 $42,301-48,400 $48,401-55,300 $55,301-63,200 $63,201-73,700 $73,701-91,100 $91,101+ 21 Table 4. Veterans RAND-12 Item Health Survey Question List of Responses 1) From 1 being in excellent health, and 5 being poor health, how good is your health in general 2) Does your health limit you when doing Moderate activities, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf? If so, how much? Excellent Very good Good Fair Poor Yes, limited a lot Yes, limited a little No, not limited at all 3) Does your health limit you when climbing several flights of stairs? If so, how much? Yes, limited a lot Yes, limited a little No, not limited at all 4) During the past 4 weeks, have you Accomplished less than you would like with your work or other regular daily activities as a result of your physical health? Yes, all of the time Yes, most of the time Yes, some of the time Yes, a little of the time No, none of the time 5) During the past 4 weeks, were you limited in the kind of work or other activities as a result of your physical health? Yes, all of the time Yes, most of the time Yes, some of the time Yes, a little of the time No, none of the time 6) During the past 4 weeks, have you Accomplished less than you would like with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)? Yes, all of the time Yes, most of the time Yes, some of the time Yes, a little of the time No, none of the time 7) During the past 4 weeks, did you not do your work or other activities as carefully as usual as a result of any emotional problems (such as feeling depressed or anxious)? Yes, all of the time Yes, most of the time Yes, some of the time Yes, a little of the time No, none of the time 8) During the past 4 weeks, how much did pain interfere with your normal work (including both work outside the home and housework)? Extremely Quite a bit Moderately A little bit Not at all 22 9) How much of the time during the past 4 weeks have you felt calm and peaceful? All of the time Most of the time A good bit of the time Some of the time A little of the time None of the time 10) How much of the time during the past 4 weeks did you have a lot of energy? All of the time Most of the time A good bit of the time Some of the time A little of the time None of the time 11) How much of the time during the past 4 weeks have you felt downhearted blue? All of the time Most of the time A good bit of the time Some of the time A little of the time None of the time 12) During the past 4 weeks, how much of the time has your physical health or emotional problems interfered with your social activities (like visiting with friends, relatives, etc.)? All of the time Most of the time Some of the time A little of the time None of the time 23 3.2 Study design and participants This prospective cohort study included consecutive eligible participants 17 years of age and older who presented to 22 participating Canadian EDs with suspected or confirmed COVID-19 between March 1st 2020 to July 14th 2021 and consented for phone follow-up (Table 5).(101) Participants were assigned to the exposure group if they had a laboratory confirmed case of COVID-19, which was defined as one or more nucleic acid amplification tests positive for SARS-COV-2 from specimens collected during the index ED visit, 14 days prior to the ED visit in the community, or within five days after admission to account for early false negative tests.(102) Participants were eligible for the control group if they tested negative for SARS-COV-2 within the same EDs. Research assistants randomly sampled test-negative controls by inputting the number of eligible SARS-COV-2 negative patients into a random number generator and contacting only those that were selected by the number generator while remaining blinded to the participant’s clinical data. This process was repeated until four control participants consented and were matched to one SARS-COV-2 positive participant by site and date, within 1 week of both patients’ index presentations, to control for regional health-services access and period effects. A 1:4 ratio was chosen for matching due to cost limitations and minimal gained statistical power beyond this ratio.(103) 24 Table 5. Participating CCEDRRN Sites Site (City, Province) Number of SARS-COV-2 Positive Participants Number of SARS-COV-2 Negative Participants British Columbia Abbotsford Regional Hospital (Abbotsford, BC) 26 98 Eagle Ridge Hospital (Maple Ridge, BC) <5 <5 Lions Gate Hospital (North Vancouver, BC) 14 17 Mount St. Joseph’s Hospital (Vancouver, BC) 5 14 Royal Columbian Hospital (New Westminster, BC) <5 <5 Saint Paul’s Hospital (Vancouver, BC) 148 507 Surrey Memorial Hospital (Surrey, BC) 31 123 Vancouver General Hospital (Vancouver, BC) 290 1036 Saskatchewan Royal University (Saskatoon, SK) 9 34 Saint Paul’s Hospital (Saskatoon, SK) <5 <5 Ontario Health Science North (Sudbury, ON) 30 138 Hotel Dieu Hospital (Kingston, ON) <5 <5 Kingston General Hospital (Kingston, ON) <5 <5 Sunnybrook (Toronto, ON) 23 55 Quebec Hôpital de l’Enfant Jésus (Québec City, QC) 302 1085 Hôpital du Sacré-Cœur de Montréal (Montreal, QC) 30 <5 IUCPQ (Québec City, QC) <5 <5 Jewish General Hospital (Montreal, QC) 227 573 Royal Victoria Hospital (Montreal, QC) 12 <5 MUHC (Montreal, QC) <5 <5 Nova Scotia Halifax Infirmary (Halifax, NS) <5 <5 Secondary Assessment, Dartmouth General and Halifax Infirmary (Halifax, NS) 11 23 25 3.3 Outcome variables and covariates The two primary outcome measures were participants’ PCS and MCS scores ascertained using a scoring algorithm provided by the Kazis Lab (Boston University, 2021). The PCS and MCS scores were chosen as outcome measures, rather than the individual items of the VR-12, as these scores are global assessments of physical and mental health that reflect participants’ responses to all question items. The scoring algorithm for PCS and MCS also corrects for the differential impact of each item on participants’ overall health.(104) Furthermore, minimally important differences that correspond to clinical and social significance are only reported for PCS and MCS in the literature, but not the individual question items. Measured variables that could represent potential confounders, predictors, or effect modifiers of SARS-COV-2 positivity, disease severity, or quality of life were controlled for. The final set of covariates included demographic variables: age, biological sex at birth, housing situation at the time of the ED visit, province of residence, and the pandemic wave in which the participant presented to the ED; clinical variables at the ED visit: presence of respiratory distress during the ED visit, the lowest oxygen saturation recorded in the medical record, supplemental oxygen requirements, comorbidities, ambulance arrival, in-hospital intubation, participants’ immunization status, WHO Ordinal Outcome Score, and the 7-day community incidence of COVID-19 in the participant’s forward sortation area of their primary residence; and sociocultural variables: tobacco and illicit substance use, race, immigrant status, employment status, education level, and annual income level. Several variables were categorized during analysis. Participants’ waves were defined by categorizing participants’ index ED visits using a standardized definition for pandemic waves: Wave 1 (March 1st 2020 to June 30th 2020), Wave 2 (July 1st 2020 to February 28th 2021), or 26 Wave 3 (March 1st 2021 to July 14th 2021).(105) Provinces were categorized by geography (Western/Eastern/Ontario) due to small sample sizes. Active malignant neoplasm, organ transplant recipient, and severe liver disease were grouped together, and referred to as secondary immunodeficiency due to small sample sizes.(106) Long-term care, rehabilitation facilities, and inter-hospital transfer participants were grouped into “Institutional Living”, as these encompass dependent living conditions through a healthcare-social framework where residents are monitored or cared for institutionally.(107,108) Missing and unknown values for whether the participant required oxygen in the ED were considered as no oxygen administered. Any participant who had missing information pertaining to tobacco and illicit substance use was assumed to be a never user. This was based on the fact that clinicians generally only documented smoking history or illicit substance use for patients who smoke or use substances. Participants who were partially vaccinated (1 dose of a 2-series regimen) and fully vaccinated (2 doses of a 2-series regimen; or 1 dose of a 1-series regimen) were combined due to small sample sizes. Participants whose index visits were before January 1st 2021 were considered unvaccinated if this information was not confirmed during vaccine data collection. Interaction between vaccination history and age for PCS and MCS was controlled for, given that age is a strong predictor of disease severity.(71) COVID-19 disease severity was measured using the WHO Ordinal Outcome Scale, and examined as a potential risk factor due to its known association with physical and mental health sequelae.(63,66,68) Scores of 6 and 7 were grouped together due to small sample sizes. Community COVID-19 incidence was calculated as a 7-day average incidence for the participant’s health region.(105) The participant’s linked health region was based on the date of 27 the index visit and forward sortation area of the postal code of their primary residence. Participants with no fixed address were allocated to the forward sortation area of the hospital they presented to.(109) Gender was not examined due to low cell-counts associated with non-binary categorizations which could have resulted in reidentification. 3.4 Statistical analysis 3.4.1 Descriptive statistics Continuous covariates were summarized using median and interquartile range (IQR) statistics. Categorical variables were presented as proportions or frequencies. PCS and MCS were described using mean and standard deviation (SD). The median days between first and second participant interview were presented separately in the univariate analyses. The results from the first and second interviews were averaged and presented together. There were some missing data. The variables with the highest levels of missingness were: lowest oxygen saturation (1.4%), race (4.9%), income (34.8%), immigration status (3.2%), immunization status (0.6%), disease severity (1.4%), educational attainment (3.9%), and employment (3.0%). Missing data were imputed with 5 multiple imputations under the assumption that the data were missing at random using a fully conditional specification.(110) For all linear regression models, normality was examined using quantile-quantile plots. For all analyses, a p-value below 0.05 was considered significant. Bonferroni’s Correction was used to adjust for multiple comparisons during analysis.(111) 28 3.4.2 Propensity score matching Propensity score matching was used to create matched test-positive and test-negative groups.(112) Propensity score matching is a quasi-experimental method that is commonly used in population health research.(113) The underlying premise is to create counterfactual matches based on a pre-specified set of predictors for the exposure variable. The exposure variable is chosen to generate propensity scores, rather than the outcome variable, as this would achieve similar levels of confounder balance gained through randomized exposure enrolment. A key limitation of propensity score techniques is that only the observed confounders can be used to generate propensity scores. Therefore, statistical inferences may be subject to bias from unmeasured or unobserved confounders.(114) Only complete datasets can be used with propensity score matching. Plots of variance for different numbers of imputations were examined to determine how many imputations were required for convergence. Characteristic signs of convergence were observed with 5 imputations; thus, one of these 5 datasets was randomly selected using a random number generator and examined in subsequent analysis. The propensity of SARS-COV-2 positivity was modelled using logistic regression modelling with all baseline covariates as predictors, with the exception of the WHO Ordinal Disease Severity Score which was not examined in this analysis, and immunization status which was adjusted for after matching. The selection of matching variables was based on previous COVID-19 outcomes research utilizing propensity score matching,(65,115,116) as well as known clinical, demographic, and sociocultural variables associated with COVID-19 positivity, disease severity, or health-related quality of life. 29 With the logistic regression results, the SARS-COV-2 positive participants were matched with test-negative controls in a one-to-one ratio using a greedy neighbour approach without replacement with a caliper of 0.2 pooled standard deviations of the logit of the propensity score. The remainder of the sample was not used to minimize bias.(117) Residual baseline differences after matching were examined through Student’s t-test for continuous predictors and Chi-squared test for categorical predictors. The mean PCS and MCS of matched groups were analyzed using linear mixed effects models adjusting for a priori confounders; age, sex, all comorbidities, measures of oxygen requirements (presence of respiratory distress, supplemental oxygen administration, and intubation), effect modification by immunization status and age, as well as the number of days between index ED visit and random component associated with follow-up interview(s) for participants with multiple interviews.(118) Age, sex, all comorbidities, measures of oxygen requirements, immunization status, and the number of days between index ED visit and follow-up interview were considered fixed effects. After the primary analyses, two sensitivity analyses were conducted: 1) to correct for correlation between PCS and MCS, and 2) to control for lack of SARS-COV-2 testing indication in our sample. First, PCS and MCS are commonly modelled independently in studies that utilize the VR-12, despite representing outcome measures that could be correlated due to their derivation from the same participant. During data cleaning the correlation between PCS and MCS was examined to determine whether to model these outcomes jointly or independently. The Spearman’s correlation coefficient was 0.03 (p=0.03) which represents a weak, but statistically significant correlation between these measures. Thus, the first sensitivity analysis involved modelling PCS and MCS jointly using multivariate analysis of variance (MANOVA) for 30 repeated outcomes.(119) Second, given that the study could not directly control for testing indication, the second sensitivity analysis included only participants with symptoms indicative of a viral illness: chest pain, chills, cough, diarrhea, dysgeusia/anosmia, fatigue/malaise, fever, headache, hemoptysis, myalgia, nausea/vomiting, or shortness of breath. 3.4.3 Principal component analysis This study also applied principal component analysis (PCA) using the entire sample of SARS-COV-2 positive participants and test-negative controls. This step reduced this study’s large number of potentially correlated variables to a possible smaller number of composite variables known as principal components to avoid model overfitting.(120) As a secondary analysis, clinical, demographic, and sociocultural factors were examined for their association with PCS and MCS including the composite variables using the entire sample of SARS-COV-2 positive participants and test-negative controls. PCA was not used when previously comparing PCS and MCS between groups due to the loss of interpretability for reduced variables, as well as the fact that adequate covariate balance had already been achieved through the matching procedure. Based on factor loadings greater than 0.3, numeric and dichotomous predictors were mapped to principal components with eigenvalues greater than 1 that reflected clinical concepts within the data. The unmapped variables, including categorical and numeric variables that were not considered in the derivation of principal components, were examined with the composite principal component variables as predictors using multivariable linear regression. To identify differences in the type and strength of PCS and MCS risk factors between COVID-19 participants and the entire cohort, a sensitivity analysis repeating the same modelling strategy for the SARS-COV-2 positive participants only was conducted. 31 3.4.4 Statistical power and sample size Ex-poste power was calculated based on the study objectives and known literature on PCS and MCS values. The necessary sample size to test the difference in mean PCS and MCS scores with α = 0.05, a power of 80%, and a mean difference in scores of 1 point, assuming that the population mean and standard deviation are 50 and 10 units, respectively, and that participants will be matched in a 1:1 ratio was 3140 participants: 1570 SARS-COV-2 positive and 1570 test-negative participants. 3.4.5 Statistical software Data cleaning and analysis were carried out in SAS 9.4 (SAS Institute Inc., Cary, USA) and R 4.1 (Vienna, Austria). 3.5 Ethical considerations and patient partner engagement Research Ethics Board approval to collect COVID-19 data on emergency department patients has been given to the CCEDRRN and affiliated research (UBC REB: H20-01015). Informed consent was obtained from all participants. Patient partners of the CCEDRRN Patient Engagement Committee reviewed and provided input into the research question, the study protocol, and the interpretation of results. 32 Chapter 4: Results 4.1 Baseline characteristics Of 17112 eligible participants who visited participating Canadian EDs between March 1st 2020 to July 14th 2021 who were contacted for consent, 4886 (29%) consented to participate. SARS-COV-2 positive participants comprised 24% of this sample (n=1170), with 3716 control participants who tested negative. A total of 2545 participants completed one follow-up interview, and 2341 completed two. The median follow-up time for the first interview was 169 days [IQR 112-237] after the index ED visit for the SARS-COV-2 positive participants, and 187 days [IQR 125-277] in test-negative controls (Figure 2). The median follow-up time for the second interview was 203 days after the index ED visit for both groups. The interquartile ranges were 188-350 and 187-360 days for the SARS-COV-2 positive and test-negative groups, respectively. For both SARS-COV-2 positive and test-negative groups, most participants were between 40-64 years of age, female, white, and previously living in a private residence (Table 6). Several notable differences were observed for tobacco and illicit substance use, immigration history, vaccination rates, employment, and comorbid conditions. Select clinical characteristics also differed between these groups; most notably being the higher rates of intubation, oxygen requirements, respiratory distress, and ambulance arrival in the SARS-COV-2 positive group. The distribution of participants across pandemic waves, provinces, and community incidence was similar between groups, with majority of participants presenting during Wave 2 of the pandemic (July 1st 2020 – Feb 28th 2021) in local health regions with an incidence of 8 or more cases of COVID-19 per 100,000 population. 33 Figure 2. Flow diagram of enrolled participants 34 The raw mean PCS was 42.4 (SD=10.9) among SARS-COV-2 positive participants, and 40.7 (SD=12.5) among test-negative controls. The raw mean MCS was 48.5 (SD=11.2) among SARS-COV-2 positive participants, and 48.5 (SD=11.5) among test-negative controls. 4.2 Differences in PCS and MCS between test-positive and test-negative controls After propensity score matching, 2084 participants were matched: 1042 each in the SARS-COV-2 positive and test-negative groups. There were 128 SARS-COV-2 positive participants who were not matched due to the caliper used in matching. Adequate matching was achieved on all baseline characteristics and clinical measures (Table 7). Of the 2084 matched participants, 982 completed two follow-up interviews; 534 belonged to the SARS-COV-2 positive group, and 448 belonged to the test-negative group. Unadjusted estimates of the mean PCS and MCS between matched groups were examined. The unadjusted mean PCS was 42.4 (standard error (SE)=0.28; SD=11.0) among SARS-COV-2 positive participants, and 41.8 (SE=0.31; SD=12.1) among test-negative controls. The unadjusted mean MCS was 48.3 (SE=0.29; SD=11.4) among SARS-COV-2 positive participants, and 49.5 (SE=0.28; SD=11.0) among test-negative controls. Violin plots of the unadjusted distributions of PCS and MCS are illustrated in Figure 3. The results of the linear model found that the adjusted effect of SARS-COV-2 infection on PCS was +0.50, with a SE of 0.44 (adjusted p-value=0.51; 95%CI: -0.36, 1.36). The adjusted effect of SARS-COV-2 infection on MCS was -1.01, with a SE of 0.45 (adjusted p-value=0.04; 95%CI: -1.91, -0.11). The MANOVA results are shown in Appendix A. Due to the lack of control for testing indication in our sample, a sensitivity analysis was conducted excluding all participants without viral symptoms (n=936). The results of this analysis did not differ from the analysis including these participants (Appendix B). 35 Table 6. Baseline variables of participants SARS-COV-2 Positive (n=1170) SARS-COV-2 Negative (n=3716) P-value Age, n (%) 0.15 17-24 years 36 (3.1%) 213 (5.7%) 25-39 years 213 (18.2%) 771 (20.8%) 40-64 years 559 (47.8%) 1350 (36.3%) 65-79 years 301 (25.7%) 989 (26.6%) Over 80 years 61 (5.2%) 393 (10.6%) Sex, n (%) 0.14 Male 581 (49.7%) 1752 (47.1%) Female 589 (50.3%) 1964 (52.9%) Arrival From, n (%) 0.002 Home 1124 (96.1%) 3471 (93.4%) Institutional Living (long-term care/Rehabilitation facility/inter-hospital transfer) 27 (2.3%) 168 (4.5%) Homeless/Correctional Facility/Other 19 (1.6%) 77 (2.1%) Wave of Presentation, n (%) 0.003 Wave 1 (March 1st 2020 – June 30th 2020) 263 (22.5%) 682 (18.4%) Wave 2 (July 1st 2020 – February 28th 2021) 460 (39.3%) 1619 (43.6%) Wave 3 (March 1st 2021 – July 14th 2021) 447 (38.2%) 1415 (38.0%) Province, n (%) 0.02 Western Canada (BC and SK) 529 (45.2%) 1835 (49.4%) Ontario 55 (4.7%) 197 (5.3%) Eastern Canada (QC and NS) 586 (50.1%) 1684 (45.3%) 7-day average COVID-19 incident cases prior the ED visit (%), per 100,000 <0.001 0-1.99 167 (14.3%) 628 (16.9%) 2-7.99 125 (10.7%) 528 (14.2%) Over 8 878 (75.0%) 2560 (68.9%) Lowest Oxygen Saturation in Emergency Department, median [IQR] 95 [5] Missing (n=9) 97 [3] Missing (n=59) <0.001 Comorbid Conditions, n (%) Secondary Immunodeficiency (active malignancy, organ transplant recipient, severe liver disease) 50 (4.3%) 350 (9.4%) <0.001 Asthma 131 (11.2%) 341 (9.2%) 0.05 Atrial fibrillation 43 (3.7%) 271 (7.3%) <0.001 Chronic kidney disease 43 (3.7%) 205 (5.5%) 0.01 Chronic lung disease 56 (4.8%) 339 (9.1%) <0.001 36 Chronic neurological disorder 57 (4.9%) 363 (9.8%) <0.001 Congestive heart failure 29 (2.5%) 150 (4.0%) 0.02 Coronary artery disease 89 (7.7%) 392 (10.5%) 0.004 Diabetes 198 (16.9%) 543 (14.6%) 0.06 Dyslipidemia 249 (21.3%) 815 (21.9%) 0.67 Hypertension 379 (32.4%) 1229 (33.1%) 0.69 Hypothyroidism 71 (6.1%) 338 (9.1%) 0.001 Obesity (clinical impression) 36 (3.1%) 105 (2.8%) 0.73 Rheumatologic disorder 107 (9.1%) 471 (12.7%) 0.001 Past malignant neoplasm (cancer) 51 (4.4%) 272 (7.3%) <0.001 Psychiatric condition/Mental health diagnosis 119 (10.2%) 699 (18.9%) <0.001 Arrived By Ambulance, n (%) <0.001 Yes 488 (41.7%) 1208 (32.5%) No 682 (58.3%) 2508 (67.5%) Respiratory Distress, n (%) <0.001 Yes 247 (21.1%) 333 (9.0%) No 923 (78.9%) 3383 (91.0%) Tobacco Use, n (%) <0.001 Current 30 (2.5%) 391 (10.5%) Past 77 (6.6%) 365 (9.8%) Never 1063 (90.9%) 2960 (79.7%) Illicit Substance Use, n (%) <0.001 Yes 45 (3.8%) 348 (9.4%) No 1125 (96.2%) 3368 (90.6%) Oxygen Required in ED, n (%) <0.001 Yes 267 (22.8%) 352 (9.5%) No 903 (77.2%) 3364 (90.5%) Intubation In Hospital, n (%) <0.001 Yes 63 (5.4%) 97 (2.6%) No 1107 (94.6%) 3619 (97.4%) WHO Ordinal Scale Assessment, n (%) <0.001 Score 1 394 (33.7%) 1817 (48.9%) Score 2 514 (43.9%) 1163 (31.3%) Score 3 59 (5.0%) 402 (10.8%) Score 4 117 (10.0%) 215 (5.8%) Score 5 31 (2.6%) 17 (0.5%) Score 6-7 44 (3.8%) 46 (1.2%) Missing 11 (1.0%) 56 (1.5%) Vaccination Status, n (%) <0.001 Not Vaccinated 1131 (96.7%) 3445 (92.7%) Partially/Fully Vaccinated 29 (2.5%) 253 (6.8%) 37 Missing 10 (0.8%) 18 (0.5%) Race, n (%) <0.001 Arab/Middle East 64 (5.5%) 158 (4.2%) Black 45 (3.9%) 104 (2.8%) East Asian/Southeast Asian 155 (13.2%) 315 (8.5%) Indigenous 26 (2.2%) 105 (2.8%) Latin American 49 (4.2%) 70 (1.9%) South Asian 129 (11.0%) 186 (5.0%) White 650 (55.6%) 2590 (69.7%) Unknown/Prefer not to answer 52 (4.4%) 188 (5.1%) Current Income Bracket, n (%) 0.05 <$22,440-29,900 168 (14.4%) 700 (18.8%) $29,901-42,300 108 (9.2%) 377 (10.2%) $42,301-55,300 72 (6.2%) 238 (6.4%) $55,301-73,700 97 (8.3%) 295 (7.9%) $73,701+ 294 (25.1%) 839 (22.6%) Prefer not to answer 431 (36.8%) 1267 (34.1%) Immigrated to Canada, n (%) <0.001 Yes 498 (42.6%) 1085 (29.2%) No 627 (53.6%) 2521 (67.8%) Prefer not to answer 45 (3.8%) 110 (3.0%) Highest Level of Education Achieved, n (%) 0.04 No high school 85 (7.3%) 336 (9.0%) High school 254 (21.7%) 879 (23.7%) Trade certificate or diploma 92 (7.9%) 303 (8.2%) College 148 (12.6%) 352 (9.5%) University certificate or diploma 105 (9.0%) 387 (10.4%) University degree 444 (37.9%) 1309 (35.2%) Prefer not to answer 42 (3.6%) 150 (4.0%) Employment, n (%) <0.001 Employed 733 (62.7%) 1812 (48.8%) Unemployed 114 (9.7%) 506 (13.6%) Prefer not to Answer 39 (3.3%) 108 (2.9%) Retired 284 (24.3%) 1290 (34.7%) BC – British Columbia; ED – emergency department; IQR – interquartile range; NS – Nova Scotia; QC – Quebec; SK – Saskatchewan 38 Table 7. Confounder variables that were matched using propensity score matching SARS-COV-2 Positive (n=1042) SARS-COV-2 Negative (n=1042) P-value Age, n (%) 0.62 17-24 years 35 (3.3%) 46 (4.4%) 25-39 years 202 (19.4%) 234 (22.5%) 40-64 years 499 (47.9%) 424 (40.7%) 65-79 years 253 (24.3%) 244 (23.4%) Over 80 years 53 (5.1%) 94 (9.0%) Sex, n (%) 0.43 Male 505 (48.5%) 524 (50.3%) Female 537 (51.5%) 518 (49.7%) Arrival From, n (%) 0.90 Home 998 (95.8%) 1002 (96.2%) Institutional Living (long-term care/Rehabilitation facility/inter-hospital transfer) 25 (2.4%) 23 (2.2%) Homeless/Correctional Facility/Other 19 (1.8%) 17 (1.6%) Lowest Oxygen Saturation in Emergency Department, median [IQR] 96 [4] 96 [4] 0.99 Wave of Presentation, n (%) 0.96 Wave 1 (March 1st 2020 – June 30th 2020) 233 (22.4%) 236 (22.6%) Wave 2 (July 1st 2020 – February 28th 2021) 419 (40.2%) 422 (40.5%) Wave 3 (March 1st 2021 – July 14th 2021) 390 (37.4%) 384 (36.9%) Province, n (%) 0.96 Western Canada (BC and SK) 470 (45.1%) 474 (45.5%) Ontario 54 (5.2%) 56 (5.4%) Eastern Canada (QC and NS) 518 (49.7%) 512 (49.1%) 7-day Incidence, n (%), per 100,000 0.94 0-1.99 150 (14.4%) 204 (19.6%) 2-7.99 116 (11.1%) 121 (11.6%) Over 8 776 (74.5%) 717 (68.8%) Comorbid Conditions, n (%) Secondary Immunodeficiency (active malignant neoplasm, organ transplant recipient, severe liver disease) 49 (4.7%) 67 (6.4%) 0.10 Asthma 112 (10.7%) 103 (9.9%) 0.56 Atrial fibrillation 39 (3.7%) 54 (5.2%) 0.14 Chronic kidney disease 43 (4.1%) 40 (3.8%) 0.59 Chronic lung disease 52 (5.0%) 60 (5.8%) 0.50 39 Chronic neurological disorder 55 (5.3%) 55 (5.3%) 1.00 Congestive heart failure 27 (2.6%) 33 (3.2%) 0.51 Coronary artery disease 77 (7.4%) 94 (9.0%) 0.20 Diabetes 176 (16.9%) 173 (16.6%) 0.91 Dyslipidemia 212 (20.3%) 222 (21.3%) 0.63 Hypertension 321 (30.8%) 340 (32.6%) 0.40 Hypothyroidism 66 (6.3%) 64 (6.1%) 0.93 Obesity (clinical impression) 33 (3.2%) 31 (3.0%) 0.90 Rheumatologic disorder 93 (8.9%) 106 (10.2%) 0.37 Past malignant neoplasm (cancer) 49 (4.7%) 42 (4.0%) 0.52 Psychiatric condition/Mental health diagnosis 111 (10.7%) 109 (10.5%) 0.94 Arrived By Ambulance, n (%) 0.89 Yes 401 (38.5%) 397 (38.1%) No 641 (61.5%) 645 (61.9%) Respiratory Distress, n (%) 1.00 Yes 166 (15.9%) 166 (15.9%) No 876 (84.1%) 876 (84.1%) Tobacco Use, n (%) 0.59 Current 30 (2.9%) 37 (3.6%) Past 69 (6.6%) 75 (7.2%) Never 943 (90.5%) 930 (89.2%) Illicit Substance Use, n (%) 1.00 Yes 44 (4.2%) 45 (4.3%) No 998 (95.8%) 997 (95.7%) Oxygen Required in ED, n (%) 0.82 Yes 174 (16.7%) 179 (17.2%) No 868 (83.3%) 863 (82.8%) Intubation In Hospital, n (%) 0.74 Yes 40 (3.8%) 44 (4.2%) No 1002 (96.2%) 998 (95.8%) Race, n (%) 0.99 Arab/Middle East 66 (6.3%) 70 (6.7%) Black 45 (4.3%) 50 (4.8%) East Asian/Southeast Asian 125 (12.0%) 108 (10.4%) Indigenous 24 (2.3%) 26 (2.5%) Latin American 45 (4.3%) 40 (3.8%) South Asian 112 (10.8%) 114 (10.9%) White 625 (60.0%) 634 (60.9%) Current Income Bracket, n (%) 0.99 <$22,440-29,900 246 (23.6%) 265 (25.4%) $29,901-42,300 160 (15.3%) 147 (14.1%) 40 $42,301-55,300 101 (9.7%) 103 (9.9%) $55,301-73,700 134 (12.9%) 133 (12.8%) $73,701+ 401 (38.5%) 394 (37.8%) Immigrated to Canada, n (%) 0.96 Yes 439 (42.1%) 441 (42.3%) No 603 (57.9%) 601 (57.7%) Highest Level of Education Achieved, n (%) 0.93 No high school 76 (7.3%) 86 (8.2%) High school 237 (22.7%) 239 (22.9%) Trade certificate or diploma 83 (8.0%) 81 (7.8%) College 131 (12.6%) 124 (11.9%) University certificate or diploma 95 (9.1%) 86 (8.3%) University degree 420 (40.3%) 426 (40.9%) Employment, n (%) 0.83 Employed 670 (64.3%) 675 (64.7%) Unemployed 111 (10.7%) 110 (10.6%) Retired 261 (25.0%) 257 (24.7%) BC – British Columbia; ED – emergency department; IQR – interquartile range; NS – Nova Scotia; QC – Quebec; SK – Saskatchewan 41 Figure 3. Violin plots of physical and mental component scores across matched SARS-COV-2 positive and test-negative control participants 42 4.3 Risk factors for worse physical and mental health The results from the PCA found that among the 36 predictor variables, eight were mapped to two principal components with eigenvalues greater than 1. The cumulative proportion of variance for the principal components was 39.6%. The two principal components were termed Oxygen Requirements (comprised of lowest oxygen saturation in the ED, presence of respiratory distress, intubation, and supplemental oxygen administered) and Medically Complex Comorbidities (comprised of secondary immunodeficiency, chronic neurological disorder, rheumatological disease, and past malignant neoplasm). The factor loadings for the principal components are illustrated in Table 8. Linear models were used to measure associations between PCS and MCS with the unmapped categorical and numeric variables and the two composite variables representing Oxygen Requirements and Medically Complex Comorbidities. Quantile-quantile plots of the residuals of the linear regression models were used to examine the normality assumption of the linear models (Figures 4 and 5). The outputs of the risk analysis for PCS and MCS are shown in Table 9 for all participants, and Table 10 for SARS-COV-2 positive participants only. 43 Table 8. Factor loadings of 8 clinical variables used to construct two principal component composite variables: Oxygen Requirements and Medically Complex Comorbidities Variable Principal Component 1 (Oxygen Requirements) Principal Component 2 (Medically Complex Comorbidities) Lowest oxygen saturation in the emergency department -0.57 — Presence of respiratory distress 0.43 — Supplemental oxygen required 0.58 — Intubated 0.40 — Secondary immunodeficiency (active malignant neoplasm, organ transplant recipient, severe liver disease) — 0.43 Chronic neurological disorder — 0.48 Rheumatological disorder — 0.56 Past malignant neoplasm — 0.40 44 Figure 4. Quantile-quantile plot of residuals from the linear regression model with mental component score as the outcome 45 Figure 5. Quantile-quantile plot of linear regression model with physical component score as the outcome 46 For all participants, worse physical health scores were significantly associated with demographic, clinical, and sociocultural variables including WHO Ordinal Scores of 2 (β=-6.7; p<0.001), 3 (β=-4.4; p<0.001), 4 (β=-5.5; p<0.001), 5 (β=-3.3; p=0.04), and 6-7 (β=-7.4; p<0.001), female sex (β=-1.6; p<0.001), tobacco use (β=-1.6; p=0.008), Medically Complex Comorbidities (β=-1.5; p<0.001), Oxygen Requirements (β=-0.31; p=0.03), and higher age (β= -0.15; p<0.001). Several factors were associated with improved physical health scores, including the number of days between follow-up interview and index visit (β=0.01; p=0.04), SARS-COV-2 infection (β=1.9; p<0.001), immigration (β=1.3; p=0.002), and living in Western Canada (β=0.87; p=0.04). For all participants, worse mental health scores were significantly associated with a history of psychiatric or mental health diagnoses (β=-5.4; p<0.001), WHO Ordinal Scores of 2 (β=-2.8; p<0.001), 3 (β=-1.4; p=0.02), and 4 (β=-1.8; p=0.01), illicit substance use (β=-2.4; p=0.001), and female sex (β=-2.3; p<0.001). Concordant risk factors observed between all participants and the SARS-COV-2 positive group for PCS were Medically Complex Comorbidities, female sex, arriving to the hospital by ambulance, congestive heart failure, and WHO Ordinal Scores of 2, 3, 4, and 6-7. Concordant risk factors observed between all participants and the SARS-COV-2 positive group for MCS were illicit substance use, female sex, income between $42,301-$48,400 or less than $22,440, history of a psychiatric or mental health diagnosis, and WHO Ordinal Scores of 2 and 4. 47 Table 9. Linear regression analysis using two composite variables for SARS-COV-2 positive and test-negative control participants’ PCS and MCS Principal Component (PC)/Variable Mental Component Score Physical Component Score Beta 95% confidence interval P-value Beta 95% confidence interval P-value Oxygen Requirements (PC1) 0.08 -0.37, 0.21 0.58 -0.31 -0.59, -0.03 0.03 Medically Complex Comorbidities (PC2) 0.25 -0.57, 0.07 0.13 -1.5 -1.8, -1.2 <0.001 Time between emergency department visit and interview (days) 0 -0.01, 0.00 0.9 0.01 0.00, 0.01 0.037 SARS-COV-2 Test Negative — — — — Positive -0.11 -0.91, 0.68 0.8 1.9 1.2, 2.7 <0.001 Smoking Never — — — — Current -2.5 -3.7, -1.3 <0.001 -1.6 -2.7, -0.40 0.008 Past 0.47 -0.65, 1.6 0.4 0.5 -0.58, 1.6 0.4 Illicit Substance Use Never — — — — Current -2.4 -3.6, -1.1 <0.001 -0.18 -1.4, 1.0 0.8 Age (years) 0.07 -0.01, 0.14 0.08 -0.15 -0.23, -0.08 <0.001 Sex Male — — — — Female -2.3 -2.9, -1.6 <0.001 -1.6 -2.2, -1.0 <0.001 Race White — — — — Arab/Middle East -1.3 -2.9, 0.29 0.11 -1.2 -2.7, 0.39 0.14 Black 1 -0.88, 2.9 0.3 0.29 -1.5, 2.1 0.8 East Asian/Southeast Asian -0.34 -1.6, 0.89 0.6 0.34 -0.85, 1.5 0.6 Indigenous -0.1 -2.0, 1.8 >0.9 -0.73 -2.6, 1.1 0.4 Latin American -0.68 -2.8, 1.4 0.5 -1 -3.0, 1.0 0.3 48 South Asian 1 -0.44, 2.4 0.2 0.18 -1.2, 1.5 0.8 Income <$22,440 -1.6 -2.7, -0.59 0.002 -1.6 -2.7, -0.64 0.001 $22,401-29,900 -1.3 -2.5, 0.00 0.05 -1.4 -2.6, -0.17 0.025 $29,901-36,200 -0.26 -1.5, 1.0 0.7 -1 -2.2, 0.24 0.11 $36,201-42,300 -0.76 -2.1, 0.56 0.3 -0.66 -1.9, 0.61 0.3 $42,301-48,400 -1.8 -3.4, -0.22 0.026 -1.2 -2.8, 0.33 0.12 $48,401-55,300 -0.78 -2.3, 0.73 0.3 -1.6 -3.0, -0.10 0.036 $55,301-63,200 -0.07 -1.5, 1.4 >0.9 -0.83 -2.2, 0.55 0.2 $63,201-73,700 1.3 -0.08, 2.7 0.065 -0.9 -2.2, 0.44 0.2 $73,701-91,100 0.71 -0.47, 1.9 0.2 0.06 -1.1, 1.2 >0.9 $91,100+ — — — — Immigrated to Canada No — — — — Yes 0.17 -0.72, 1.1 0.7 1.3 0.47, 2.2 0.002 Education No high school 0.39 -1.0, 1.8 0.6 0.31 -1.1, 1.7 0.7 High school 0.85 -0.28, 2.0 0.14 0.64 -0.45, 1.7 0.2 Trade certificate or diploma -0.19 -1.6, 1.2 0.8 0.04 -1.3, 1.4 >0.9 College 0.14 -1.2, 1.5 0.8 1.4 0.14, 2.7 0.03 University certificate or diploma 0.68 -0.40, 1.8 0.2 1.6 0.55, 2.6 0.003 University degree — — — — Wave of presentation Wave 1 — — — — Wave 2 0.18 -1.0, 1.4 0.8 2.6 1.5, 3.8 <0.001 Wave 3 -0.25 -1.6, 1.1 0.7 2.2 0.88, 3.5 <0.001 Province Eastern Canada — — — — Ontario -0.01 -1.6, 1.6 >0.9 -0.51 -2.0, 1.0 0.5 Western Canada -0.29 -1.2, 0.57 0.5 0.87 0.04, 1.7 0.039 7-day community incidence per 100,000 0.03 0.00, 0.06 0.05 -0.01 -0.04, 0.02 0.5 Arrival From Home — — — — 49 Institutional Living 1.2 -0.46, 2.8 0.2 -0.72 -2.3, 0.85 0.4 Homeless/Correctional Facility/Other -2.3 -4.6, 0.06 0.056 -0.62 -2.8, 1.6 0.6 Arrived by ambulance No — — — — Yes 0.17 -0.55, 0.88 0.6 -1.8 -2.5, -1.1 <0.001 Comorbidities Asthma -0.78 -1.8, 0.27 0.14 -1.7 -2.7, -0.65 0.001 Atrial fibrillation 0.08 -1.3, 1.4 >0.9 -0.92 -2.2, 0.39 0.2 Chronic kidney disease 0.23 -1.3, 1.7 0.8 -1.8 -3.2, -0.34 0.015 Chronic lung disease -0.22 -1.5, 1.0 0.7 -3.8 -5.0, -2.6 <0.001 Congestive heart failure 0.48 -1.3, 2.2 0.6 -4.5 -6.2, -2.8 <0.001 Coronary artery disease -0.69 -1.8, 0.47 0.2 -0.78 -1.9, 0.32 0.2 Diabetes -1 -1.9, -0.01 0.048 -1.1 -2.0, -0.13 0.025 Dyslipidemia 0.33 -0.58, 1.2 0.5 -0.01 -0.88, 0.86 >0.9 Hypertension 0.02 -0.82, 0.86 >0.9 -0.19 -1.0, 0.61 0.6 Hypothyroidism -0.32 -1.5, 0.83 0.6 -0.53 -1.6, 0.58 0.3 Obesity 0.69 -1.2, 2.6 0.5 -1.9 -3.7, -0.10 0.039 Psychiatric condition/Mental health diagnosis -5.4 -6.3, -4.5 <0.001 -0.09 -0.93, 0.75 0.8 Immunized to SARS-COV-2 Yes — — — — No 0.2 -4.7, 5.1 >0.9 -2.1 -6.8, 2.6 0.4 Immunized to SARS-Cov-2*age (interaction) 0.01 -0.07, 0.08 0.8 0.03 -0.04, 0.10 0.4 WHO Ordinal Outcome Score Score 1 — — — — Score 2 -2.8 -3.5, -2.0 <0.001 -6.7 -7.4, -6.0 <0.001 Score 3 -1.4 -2.5, -0.21 0.021 -4.4 -5.5, -3.3 <0.001 Score 4 -1.8 -3.2, -0.41 0.011 -5.5 -6.8, -4.1 <0.001 Score 5 -2.6 -5.8, 0.61 0.11 -3.3 -6.4, -0.20 0.037 Score 6-7 -0.08 -2.5, 2.4 >0.9 -7.4 -9.8, -5.1 <0.001 50 Table 10. Linear regression analysis using two composite variables for SARS-COV-2 positive participants’ PCS and MCS Principal Component (PC)/Variable Mental Component Score Physical Component Score Beta 95% confidence interval P-value Beta 95% confidence interval P-value Oxygen Requirements (PC1) 0.14 -0.33, 0.62 0.6 0.24 -0.22, 0.70 0.3 Medically Complex Comorbidities (PC2) 0.29 -0.51, 1.1 0.5 -1 -1.8, -0.23 0.011 Time between emergency department visit and interview (days) 0.01 0.00, 0.02 0.041 0.01 0.00, 0.02 0.2 Smoking Never — — — — Current 0.28 -3.8, 4.4 0.9 0.19 -3.8, 4.2 >0.9 Past 0.72 -2.0, 3.4 0.6 1.5 -1.1, 4.2 0.3 Illicit Substance Use Never — — — — Current -5.8 -9.3, -2.3 0.001 -3.8 -7.2, -0.37 0.03 Age (years) -0.17 -0.57, 0.22 0.4 -0.04 -0.42, 0.34 0.8 Sex Male — — — — Female -3.6 -4.9, -2.3 <0.001 -1.4 -2.7, -0.18 0.025 Race White — — — — Arab/Middle East 1.4 -1.7, 4.5 0.4 -1.3 -4.3, 1.7 0.4 Black 2.3 -1.2, 5.7 0.2 1.7 -1.6, 5.0 0.3 East Asian/Southeast Asian 0.49 -1.8, 2.7 0.7 0.77 -1.4, 3.0 0.5 Indigenous 4.2 -0.07, 8.4 0.054 1.4 -2.7, 5.5 0.5 Latin American -0.56 -4.0, 2.9 0.7 1.4 -1.9, 4.7 0.4 South Asian 2.9 0.54, 5.3 0.017 1.2 -1.1, 3.6 0.3 Income <$22,440 -2.2 -4.3, -0.04 0.046 -1.8 -3.9, 0.26 0.087 $22,401-29,900 -1.9 -4.4, 0.64 0.14 -1.3 -3.8, 1.1 0.3 51 $29,901-36,200 0.21 -2.4, 2.8 0.9 -2.3 -4.8, 0.21 0.072 $36,201-42,300 -0.47 -3.1, 2.1 0.7 -0.32 -2.9, 2.2 0.8 $42,301-48,400 -3.3 -6.7, -0.04 0.047 -1.6 -4.9, 1.6 0.3 $48,401-55,300 0.89 -2.1, 3.9 0.6 -0.7 -3.6, 2.2 0.6 $55,301-63,200 1.3 -1.6, 4.1 0.4 0.01 -2.8, 2.8 >0.9 $63,201-73,700 1.2 -1.3, 3.7 0.3 0.03 -2.4, 2.5 >0.9 $73,701-91,100 1.6 -0.67, 3.9 0.2 0.59 -1.7, 2.8 0.6 $91,100+ — — — — Immigrated to Canada No — — — — Yes -0.91 -2.7, 0.86 0.3 0.78 -0.94, 2.5 0.4 Education No high school 2.5 -0.49, 5.4 0.1 0.65 -2.2, 3.5 0.7 High school 1.7 -0.45, 3.9 0.12 1 -1.1, 3.1 0.4 Trade certificate or diploma 1.2 -1.5, 4.0 0.4 -0.67 -3.4, 2.0 0.6 College 0.07 -2.6, 2.7 >0.9 0.87 -1.7, 3.4 0.5 University certificate or diploma 0.6 -1.4, 2.6 0.6 1.3 -0.69, 3.3 0.2 University degree — — — — Wave of presentation Wave 1 — — — — Wave 2 1.8 -0.76, 4.3 0.2 2.7 0.20, 5.2 0.034 Wave 3 0.9 -2.0, 3.8 0.5 2.2 -0.62, 5.0 0.13 Province Eastern Canada — — — — Ontario 0.93 -2.4, 4.3 0.6 -1.3 -4.6, 2.0 0.4 Western Canada -0.82 -2.6, 1.0 0.4 0.7 -1.1, 2.5 0.4 7-day community incidence per 100,000 0.05 -0.01, 0.10 0.1 0.02 -0.03, 0.08 0.4 Arrival From Home — — — — Institutional Living -3.2 -7.4, 1.0 0.13 -0.31 -4.4, 3.8 0.9 Homeless/Correctional Facility/Other -8.4 -13, -3.3 0.001 2.8 -2.2, 7.7 0.3 Arrived by ambulance 52 No — — — — Yes 0.01 -1.4, 1.4 >0.9 -1.7 -3.1, -0.37 0.013 Comorbidities Asthma 0.26 -1.7, 2.2 0.8 -1.3 -3.2, 0.65 0.2 Atrial fibrillation 0.78 -2.7, 4.3 0.7 -3.8 -7.2, -0.45 0.027 Chronic kidney disease 1.4 -2.1, 5.0 0.4 -1 -4.5, 2.5 0.6 Chronic lung disease -1.8 -4.9, 1.3 0.3 -2 -5.0, 1.0 0.2 Congestive heart failure -0.35 -4.9, 4.2 0.9 -4.5 -8.9, -0.11 0.045 Coronary artery disease 0.07 -2.6, 2.7 >0.9 -1.6 -4.2, 0.93 0.2 Diabetes -1 -2.9, 0.90 0.3 -1.4 -3.2, 0.46 0.14 Dyslipidemia -0.09 -1.9, 1.8 >0.9 -0.62 -2.4, 1.2 0.5 Hypertension -0.01 -1.7, 1.6 >0.9 0.56 -1.1, 2.2 0.5 Hypothyroidism -1 -3.7, 1.6 0.5 -0.33 -2.9, 2.3 0.8 Obesity 0.94 -2.7, 4.6 0.6 -1.4 -5.0, 2.1 0.4 Psychiatric condition/Mental health diagnosis -5.9 -8.0, -3.8 <0.001 1.1 -1.0, 3.1 0.3 Immunized to SARS-COV-2 Yes — — — — No -21 -48, 5.6 0.12 2.7 -23, 29 0.8 Immunized to SARS-Cov-2*age (interaction) 0.24 -0.16, 0.63 0.2 -0.03 -0.41, 0.36 0.9 WHO Ordinal Outcome Score Score 1 — — — — Score 2 -3.3 -4.7, -1.8 <0.001 -5.5 -6.9, -4.0 <0.001 Score 3 -2.7 -5.7, 0.26 0.073 -5.6 -8.5, -2.6 <0.001 Score 4 -2.6 -5.2, -0.03 0.047 -6.1 -8.6, -3.6 <0.001 Score 5 -0.8 -4.9, 3.3 0.7 -3.3 -7.4, 0.68 0.1 Score 6-7 1.1 -3.0, 5.1 0.6 -9.7 -14, -5.7 <0.001 53 Chapter 5: Discussion In this study, the physical and mental health outcomes of COVID-19 patients presenting to EDs across Canada were examined. Risk factors for worse physical and mental health outcomes were also examined. 5.1 Physical and mental health outcomes of COVID-19 patients This study found that COVID-19 survivors frequently reported suffering from poor mental health outcomes up to 14 months after infection. There was no evidence to support a similar finding for physical health in this cohort of consenting patients. 5.1.1 Physical health outcomes The difference in PCS between COVID-19 participants and matched controls was 0.5. This result was not statistically significant, and did not meet the clinically important difference for VR-12 scores of 1 point. While studies that examined physical health outcomes of COVID-19 survivors to known population norms have reported significantly worse outcomes in comparison, these studies did not use a control group to control for time trends, or adjust for important confounders like the occurrence of complex comorbidities, age distributions, or health-services access variables between populations.(121,122) While medical complications and sequelae among survivors have been reported in the literature, one study that examined the physical health of COVID-19 patients that controlled for time trends and confounding factors reported similar outcomes as this study; Huang et al. observed no significant difference in the quality of life of hospitalized COVID-19 survivors with severe disease 6-months after discharge compared to COVID-19 survivors with mild disease.(4) 54 My results do not imply that COVID-19 patients never experience poor physical health outcomes, as I did not compare participants’ reported health to their own baselines. The control group was made up of other participants presenting to EDs with non-COVID-19 respiratory infections and other disease entities with the potential for complications and disability. During the early waves of the pandemic, non-COVID patients were reluctant to come to emergency departments due to fear of contracting the virus and may have suffered from complications and challenges related to the delay in care-seeking.(123) My results indicate that the physical health outcomes experienced by COVID-19 survivors are comparable to those experienced by non-COVID patients presenting to EDs with acute health-related challenges at similar time points during the pandemic. 5.1.2 Mental health outcomes The difference in MCS between COVID-19 participants and matched controls was -1.01 (95%CI -1.91, -0.11), representing worse mental health outcomes of COVID-19 participants. I could not find observational literature on the mental health of COVID-19 survivors that incorporated test-negative controls to support or refute these findings. However, these results are consistent with case-control studies investigating the direct effects of COVID-19 among patients who reported suffering from post-traumatic stress, anxiety, and depression post-infection.(124,125) It was proposed that these conditions may have manifested due to traumatic experiences from having the disease, stigma associated with the disease, or the fear of contracting the disease again.(124,125) Since the MCS is a composite score derived from several questions, this study is unable to examine the precise symptoms or concerns that participants suffering from poor mental health outcomes have. Thus, understanding which domains of mental health are most impacted among 55 COVID-19 participants could enhance our understanding of how to support the mental health of those affected by the disease. Due to the poor self-reported mental health outcomes observed among COVID-19 participants in this study, there is a need to develop and evaluate mental health services for this population. In the Canadian context, this is complicated by the fact that sub-acute mental health care services are not publicly funded and not universally available. One strategy to address the suboptimal mental health status of post-COVID patients would be to recommend further study of mental health outcomes to understand what services or interventions would be most appropriate. My study is not sufficiently specific to describe the type or intensity of services most beneficial to improving mental health outcomes among COVID-19 survivors. 5.2 Risk factors for worse physical and mental health outcomes 5.2.1 Physical health risk factors Both composite variables constructed through principal component analysis were associated with worse physical health, consistent with the understanding of how complex comorbid conditions and oxygenation interventions can adversely impact one’s quality of life. The number of days between ED visit and follow-up interview were positively associated with physical health. This is representative of a positive recovery trajectory: as time passes, participants begin to feel better physically. Physical health scores were higher among COVID-19 participants, contrary to the result reported through propensity score matching in this study. Given that this secondary analysis did not directly control for confounding, this finding cannot be used to claim that physical health was 56 better among COVID-19 survivors. However, it does allow for comparisons of PCS between COVID-19 participants and test-negative controls. Smoking was associated with worse physical health for all participants, but not COVID-19 participants only. This may be due to the limited number of SARS-COV-2 positive participants who also smoked. Illicit substance use was not associated with worse physical health for all participants, but was for COVID-19 participants. This discordance may be due to more severe disease presentations among substance users; Allen et al. report substance users having higher odds of severe COVID-19 compared to non-users.(126) Age was a relevant predictor of self-reported physical health, which supports prior knowledge on the relationship between age and physical health outcomes, however this association was not observed for the COVID-19 group, possibly due to the limited number of older participants included. Contrary to previous studies on the relationship between COVID-19 and sex, females had worse physical health outcomes. When generalized quality of life measurements are used to examine gender-differences in health-related quality of life, women tend to report poorer quality of life than men, which may in part explain this finding.(127) Race was not a significant predictor of poor physical health, when using white racial groups as reference. Although SARS-COV-2 infections were more prevalent in most non-white racial groups examined in this study, the effect of COVID-19 on physical health outcomes was likely not as disparate across races. Participants with lower levels of income often had worse physical health outcomes. Although this trend was not consistent throughout all income levels, it was progressively 57 stronger for lower levels of income. Albeit this trend was not demonstrated for the SARS-COV-2 positive group. Immigration was a positive predictor of physical health for all participants. This contrasts with the assumption that immigrants would have worse health outcomes from COVID-19. However, this finding may be influenced by the healthy immigrant effect, which proposes that immigrants tend to have higher overall quality of life immediately after immigrating that may wane over time.(128) Time since immigration was not examined, thus should be controlled for in future research. Educational attainment at the university diploma or college level was associated with higher physical health scores, compared to participants with a level of education equivalent to a university degree. There was no consistent effect for lower levels of educational attainment, thereby suggesting that this may have been a spurious association. Participants from subsequent waves had better reported physical health, compared to those from Wave 1. This may be due to temporal trends in waves: subsequent waves occurred when there was more evidence about COVID-19 to safely ease restrictions, encourage the resumption of daily activities, and better treat those with severe disease.(129) Participants may have benefited from this by being able to partake in physical and recreational activities, as well as see healthcare practitioners more regularly, as compared to the first wave of the pandemic. Similarly, restrictions were eased differently depending on regional case counts which may have mediated a positive effect of physical heath for participants living in Western Canada. Participants living in institutional settings, correctional facilities, or were homeless had lower mean physical health scores, although this was not a statistically significant finding. 58 Ambulance arrival and comorbidities like asthma, chronic kidney disease, chronic lung disease, congestive heart failure, diabetes, and obesity were associated with worse physical health. Being immunized to SARS-COV-2 was not associated with PCS, but was necessary to control for due to its influence on disease severity, and subsequently, physical health outcomes. Lastly, all scales of disease severity were associated with worse physical health, consistent with the current state of knowledge on the effect of disease severity on the sequalae of COVID-19 participants.(63,66,68) A WHO Ordinal Score of 5 was not significantly associated with worse physical health outcomes among the SARS-COV-2 positive group, however this may be due to the limited number of participants who presented within this category, thereby influencing imprecision in the effect estimate. 5.2.2 Mental health risk factors None of the principal component composite variables were significantly associated with MCS. This may be due to the weak relationship these factors have with mental health outcomes. Unlike with physical health, there was no relationship between MCS and time since infection for the entire cohort. However, I found this effect for the SARS-COV-2 positive group, indicating that the mental health of COVID-19 participants may improve over time. Smoking and illicit substance use were significantly associated with worse mental health outcomes. Illicit substance use behaviors may be indicative of psychosocial pathologies like substance use disorders, thus relating these behaviors to participants’ mental health. This finding may also be supported by the fact that the COVID-19 pandemic has exacerbated the challenges experienced by people who use drugs in Canada, such as precarious housing and lack of safe testing and consumption facilities.(130) 59 Age was not associated with mental health scores. This may be due to heterogeneity in how mental health concerns present across age groups, thereby biasing the results to null. For example, older adults experience and report anxiety differently than younger adults, making ascertainment and comparison of this condition multifaceted.(131) Women had significantly worse mental health outcomes compared to men. This finding may be explained in part by higher rates of incident mental health diagnoses in this group.(132) Race was not a significant predictor of poor mental health. However, South Asian racial groups had higher MCS compared to white racial groups. This is in contrast to high rates of mental health disorders often reported among this racial group,(133) thus may have been a spurious finding. Lower levels of income were significant predictors of worse mental health. Although this association was not consistently demonstrated for all levels of income, it was strongly apparent for the lowest income levels. Immigration was not associated with worse mental health outcomes. This finding is contrary to the state of literature on the mental health of immigrants but may be explained by considering how diverse aspects of immigration can intersect with typical mental health presentations, thus biasing the outcome measurement. For example, level of English language fluency, cultural stigma, and comprehension of survey semantics can vastly differ across immigrant populations, and therefore may bias the association towards a null result.(134) Despite a reported inverse effect of education and mental health concerns in the literature,(135) there was no association between level of education and mental health scores. However, some Canadian literature reports an alternate effect of education and mental health.(136) This heterogeneity may explain why no association was found. 60 Wave of presentation was not associated with mental health outcomes, despite it being associated with physical health outcomes. This difference may be due to the effectiveness of accommodations made at the onset of the pandemic to support mental health services. For example, mental health services could be transitioned to an online space, and participants could still see friends and family virtually to support their emotional health, whereas physical activities were more restricted during the same period. This rationale would also support the lack of an association between mental health outcomes and provinces in Canada; although self-reported physical health was higher in Western Canada, this was not seen for mental health for the same reasons. Where participants were living prior to the ED visit was found to be an important predictor of mental health outcomes for the COVID-19 participants who were homeless, living in shelters, correctional facilities, or temporary living arrangements. Having a history of a mental health or a psychiatric diagnosis was significantly associated with worse MCS. Diabetes was found to be a relevant predictor of MCS for the entire cohort, which may be supported by the fact that compared to the general population, psychiatric diagnoses are almost twice as prevalent among diabetic patients.(137) Being immunized against SARS-COV-2 was not a predictor of mental health scores. Lastly, disease severity was associated with worse mental health for all participants with WHO Ordinal Scores of 2, 3, and 4, and COVID-19 participants with Scores of 2 and 4. Since no clear dose-response relationship was observed, this may have been a spurious relationship. 61 5.3 Strengths and limitations 5.3.1 Limitations This study was not without limitations. As this was an observational study, causality could not be directly assessed. Strong quasi-experimental research methods were incorporated to address this. Furthermore, due to COVID-induced barriers to in-person research at hospital sites, participants could only be interviewed over the phone. Telephone follow-up excluded individuals with inability to use or communicate over a telephone, thus impacting study generalizability. It excluded individuals who were unable to access or who did not own a telephone; frequently the homeless, prisoners, and those living in long-term care facilities. Finally, it did not account for the health-related quality of life of individuals who died. The VR-12 survey was susceptible to biases such as response bias, social desirability bias, interviewer bias, and recall bias.(138) The definition of wellness proposed by the VR-12 may have differed from participants’ own cultures and values, thus limiting the ability of the VR-12 to assess wellness across cultural contexts. Several assumptions were made throughout the course of this thesis work which may have biased the findings. Participants were analyzed by their biological sex, rather than the gender they identified with. The WHO Ordinal Outcome Scale had not been validated for non-COVID-19 patients, yet these criteria were applied similarly for both groups. Lastly, missing responses for tobacco and illicit substance use, as well as oxygen administration in the ED, were assumed to be equivalent to never use or no oxygen administered, respectively, and that participants who presented to EDs between March 1st 2020 to December 31st 2020 were not vaccinated, unless otherwise reported during follow-up interviews. It is possible that a small 62 proportion of these individuals were current users of substances, administered oxygen, or vaccinated, but this was not documented. The choice of covariates that were included in the propensity score modelling may have influenced the outcome measure.(139) As such, sensitivity analyses should be conducted for different subsets of matching variables to ascertain differences in the physical and mental health outcomes of matched participants. These results do not represent a comparison with each participants’ baseline health, as the comparator group was participants presenting to EDs with other health concerns. Thus, these results should not be misinterpreted as invalidating the experiences of participants who have developed declining health relative to their pre-COVID-19 baseline health status. This study focused solely on the physical and mental health of participants using aggregate PCS and MCS scores from a PROM that has not been validated among COVID-19 patients.(53) As such, the individual items of the VR-12 that were associated with adverse physical and mental health status were not reported. Thus, future research should be conducted to investigate the association between SARS-COV-2 infection and the individual VR-12 items. Additionally, these results may have been underpowered to detect multiple significant differences in the group-means of PCS and MCS. The necessary sample size to detect a 1-point mean difference was estimated to be 1570 participants for both SARS-COV-2 positive and test-negative groups. However, this study analyzed 1042 matched participants in each group. Finally, these results are only representative of a consenting population who sought care at an ED. Since many COVID-19 patients do not seek evaluation or medical treatment in an ED, these findings cannot be assumed to apply generally to all COVID-19 patients. 63 5.3.2 Strengths Strengths of this study include incorporation of data from a large consecutively-presenting cohort of COVID-19 patients seeking medical care at academic, non-academic, urban, and remote EDs, that were included in the CCEDRRN registry. It is the largest multi-centre study examining COVID-19 patient-reported experiences using test-negative patients and quasi-experimental methods to control for concurrent time trends and known confounders. 5.4 Conclusion This study found evidence of worse mental health outcomes of COVID-19 survivors in Canada compared with participants presenting at the same EDs who tested negative for the disease. There was no significant difference in the physical health outcomes between these groups, despite extensive reports of physical health sequelae among survivors. These results are representative of consenting ED COVID-19 survivors but are not necessarily reflective of all COVID-19 patients and are not a comparison to patients’ baseline health status before infection. Most studies examining the health outcomes of COVID-19 patients were based on the clinical attributes of COVID-19 patients discharged from acute care. 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Am J Epidemiol. 2006 Jun 15;163(12):1149–56. 76 Appendices Appendix A MANOVA output Variable F-Statistic P-value COVID-19 Positive 5.61 p<0.05 Time 0.98 p>0.05 Age 162.61 p<0.05 Smoker 4.09 p<0.05 Female Sex 37.04 p<0.05 Intubated 5.16 p<0.05 Required oxygen in the emergency department 14.87 p<0.05 Respiratory distress 2.02 p>0.05 Asthma 4.37 p<0.05 Atrial Fibrillation 5.19 p<0.05 Secondary Immunodeficiency 4.02 p<0.05 Chronic Kidney Disease 9.23 p<0.05 Chronic Lung Disease 12.24 p<0.05 Chronic Neurological Disease 10.93 p<0.05 Congestive Heart Failure 12.96 p<0.05 Coronary Artery Disease 2.28 p>0.05 Diabetes 4.34 p<0.05 Dyslipidemia 1.63 p>0.05 Hypertension 0.02 p>0.05 Hypothyroidism 2.04 p>0.05 Obesity 0.96 p>0.05 Past Malignant Neoplasm 4.51 p<0.05 Mental Health Diagnosis 36.95 p<0.05 Rheumatological Disease 10.86 p<0.05 Immunized to COVID-19 0.14 p>0.05 Age*immunized (interaction) 0.25 p>0.05 77 Appendix B Subgroup analysis excluding patients without respiratory-related symptoms Variable Physical Component Score Mental Component Score Beta P-value Beta P-value COVID-19 Positive 0.30 p>0.05 -1.07 p<0.05 Time between emergency department visit and interview (days) 0.002 p>0.05 0.002 p>0.05 Age (years) -0.12 p<0.05 0.08 p<0.05 Female Sex -2.09 p<0.05 -2.62 p<0.05 Smoking Never 2.13 p>0.05 1.56 p>0.05 Past 2.51 p>0.05 1.66 p>0.05 Intubated -0.96 p>0.05 -1.30 p>0.05 Respiratory distress -0.59 p>0.05 0.14 p>0.05 Supplemental oxygen required in emergency department -1.41 p<0.05 -0.29 p>0.05 Comorbidities Asthma -1.62 p<0.05 0.08 p>0.05 Atrial fibrillation -4.39 p<0.05 0.30 p>0.05 Chronic kidney disease -2.87 p<0.05 -0.29 p>0.05 Chronic lung disease -3.79 p<0.05 -1.45 p>0.05 Chronic neurological disorder -0.73 p>0.05 -0.68 p>0.05 Congestive heart failure -4.12 p<0.05 -1.41 p>0.05 Coronary artery disease -1.43 p>0.05 -1.30 p>0.05 Diabetes -2.34 p<0.05 -1.19 p>0.05 Dyslipidemia -0.26 p>0.05 -0.12 p>0.05 Hypertension 0.61 p>0.05 0.32 p>0.05 Hypothyroidism -0.62 p>0.05 0.72 p>0.05 Obesity -0.77 p>0.05 1.04 p>0.05 Past malignant neoplasm 0.09 p>0.05 2.82 p<0.05 Psychiatric condition/Mental health diagnosis -1.26 p>0.05 -6.42 p<0.05 Rheumatological disorder -3.57 p<0.05 -0.46 p>0.05 Secondary immunodeficiency -3.00 p<0.05 1.79 p>0.05 Immunized to SARS-COV-2 4.23 p>0.05 -4.47 p>0.05 Immunized to SARS-COV-2*age (interaction) -0.08 p>0.05 -0.05 p>0.05
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hohl, Corinne M
Sutherland, Jason M.
Investigating patient-reported outcomes among COVID-19 survivors : a longitudinal cohort study
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