Abstract
Objective
This study aimed to compare the long-term physical and mental health outcomes of matched severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive and SARS-CoV-2–negative patients controlling for seasonal effects.
Study design
This was a retrospective cohort study.
Methods
This study enrolled patients presenting to emergency departments participating in the Canadian COVID-19 Emergency Department Rapid Response Network. We enrolled consecutive eligible consenting patients who presented between March 1, 2020, and July 14, 2021, and were tested for SARS-CoV-2. Research assistants randomly selected four site and date-matched SARS-CoV-2–negative controls for every SARS-CoV-2–positive patient and interviewed them at least 30 days after discharge. We used propensity scores to match patients by baseline characteristics and used linear regression to compare Veterans RAND 12-item physical health component score (PCS) and mental health component scores (MCS), with higher scores indicating better self-reported health.
Results
We included 1170 SARS-CoV-2–positive patients and 3716 test-negative controls. The adjusted mean difference for PCS was 0.50 (95% confidence interval [CI]: -0.36, 1.36) and -1.01 (95% CI: -1.91, -0.11) for MCS. Severe disease was strongly associated with worse PCS (β = −7.4; 95% CI: -9.8, -5.1), whereas prior mental health illness was strongly associated with worse MCS (β = −5.4; 95% CI: -6.3, -4.5).
Conclusion
Physical health, assessed by PCS, was similar between matched SARS-CoV-2–positive and SARS-CoV-2–negative patients, whereas mental health, assessed by MCS, was worse during a time when the public experienced barriers to care. These results may inform the development and prioritization of support programs for patients.
Keywords: CCEDRRN, COVID-19, Emergency departments, Patient-reported outcomes, Propensity scores
Research in context
Evidence before this study
We searched for the terms “patient-reported outcomes,” “coronavirus disease,” “COVID-19,” “mental health,” and “physical health” in PubMed and Google Scholar to identify studies examining the physical or mental health outcomes of COVID-19 survivors. Most studies examining physical and mental health outcomes were from China, the United States, and Italy. Persistent fatigue, malaise, anxiety, and depression were commonly reported symptoms after the acute infection resolved. Few studies examined patient-reported outcomes of COVID-19 survivors or compared them with test-negative controls, and none incorporated demographic or sociocultural factors such as income, race, education, or employment status.
Added value of this study
We examined a cohort of consenting site and date-matched patients who presented to Canadian Emergency Departments and were tested for SARS-CoV-2. SARS-CoV-2–positive patients reported similar physical outcomes but significantly worse mental health outcomes compared with those who tested negative for SARS-CoV-2. The risk factors for worse physical outcomes included complex comorbidities (immune disorders, neurological disease, etc.), illicit substance use, female sex, arrival by ambulance, atrial fibrillation, congestive heart failure, and severe disease at presentation. The risk factors for worse mental health outcomes included illicit substance use, female sex, lower income, unstable housing, living in a correctional facility, prior mental illness, and severe disease.
Implications of all the available evidence
This is the first study that compared physical and mental health outcomes of COVID-19 patients presenting to emergency departments (EDs) with site and date-matched test-negative controls. We identified several clinical, social, and demographic variables that were associated with worse health outcomes during the study period. Future research including complications and sequelae in their examination of physical and mental health outcomes of COVID-19 patients should be conducted to guide evidence-based policies and interventions aimed at reducing the longitudinal effects of COVID-19.
Introduction
Sequelae of COVID-19 infection have been described up to 12 months postinfection in cohorts from China, the United States, and Italy.1, 2, 3 These studies found persistent physical and mental health symptoms, including fatigue, general weakness, depression, and anxiety among survivors.4, 5, 6 Disease severity was a predictor of physical and mental sequelae.5, 6, 7
Studies that examined patient-reported quality of life among COVID-19 survivors have not compared the quality of life measures with those of patients who tested negative for SARS-CoV-2. Patient-reported outcomes represent a patient's perspective of their own health and well-being and are central to their experience of illness and recovery.8 Likewise, current COVID-19 research often does not include patients who tested negative for SARS-CoV-2, which is important to control for health-seeking behaviors that may have changed during the pandemic as well as social factors that may have affected healthcare delivery and mental well-being for all individuals throughout the pandemic.9
Our primary objective was to measure the physical and mental quality of life outcomes in a cohort of patients who presented to EDs and were tested for SARS-CoV-2. The secondary objectives were to identify clinical, demographic, and sociocultural factors associated with the quality of life measures.
Methods
Study design and setting
This multicenter pan-Canadian study recruited patients from 22 EDs across five provinces that participated in the CCEDRRN (pronounced “sedrin”) collaboration.10, 11, 12, 13 We recruited consecutive consenting patients between March 1, 2020, to July 14, 2021.10 The University of British Columbia Research Ethics Board reviewed and approved the study protocol (H20-01015) with an exemption to obtain informed consent for retrospective registry enrollment, with permission to contact patients to obtain their consent for follow-up phone interviews. CCEDRRN's Patient Engagement Committee reviewed and provided significant input into the research question and edited consent and data collection forms to ensure readability and acceptability across diverse social, cultural, and ethnic contexts. They provided invaluable input in interpreting the study results, helping the authors contextualize results, and in writing the article.
Participants
We included consenting patients aged ≥17 years who presented to participating EDs and were tested for SARS-CoV-2.14 We assigned patients to the exposure group if they had a laboratory-confirmed case of COVID-19, defined as one or more nucleic acid amplification tests positive for SARS-CoV-2 from specimens collected within the community in the 14 days before the ED visit, during the ED visit, or in the first five days after admission.15 We assigned patients to the test-negative control group if all of their recorded SARS-CoV-2 tests were negative. We contacted consecutive SARS-CoV-2–positive patients by phone and matched consenting SARS-CoV-2–positive patient with up to four randomly selected SARS-CoV-2–negative patients (test-negative controls) who presented to the same site and within the same week. This allowed us to control for COVID-19 prevalence in the region, healthcare seeking behavior, as well as time trends in public health measures, which affected all patients and varied over the course of the pandemic. We recruited up to four test-negative controls per case to enable better matching on baseline variables.
Data collection
Trained research assistants abstracted data from paper-based and hospital electronic medical records. Research assistants conducted follow-up interviews contacting patients up to five attempts. We aimed to follow up patients at 30 days, 60 days, 6 months, and 12 months after their ED visit. Owing to varying delays in institutional ethics and privacy approvals, research assistants were only able to follow up patients who presented in the early pandemic (March 2020 – December 2020) at 200–395 days after the date of their ED or hospital discharge. These patients were generally only followed up once. Research assistants contacted all other patients for the first and second follow-up interviews per the approved protocol.
At follow-up, research assistants classified COVID-19 severity using the World Health Organization Ordinal Outcome Scale, a validated disease severity scale for COVID-19 patients assigned retrospectively for the patient's hospital visit.16 Research assistants also ascertained sociocultural and demographic variables including sex, race, and education, COVID-19 vaccination status, as well as the quality of life using the Veterans RAND 12-item health survey, a health-related quality of life tool commonly used in American and Canadian patient-reported outcomes research.17
We converted the VR-12 survey responses to physical health component scores (PCS) and mental health component scores (MCS) based on patients’ general health perception, physical functioning, role limitations, bodily pain, energy fatigue, social functioning, and perceived mental health.17 PCS and MCS ranged between 0 and 100. The US population standards indicate mean scores of 50, with standard deviations (SDs) of 10.17 Higher scores represent better self-reported health status, and an absolute difference of ≥1 point in either scores is considered socially and clinically relevant.17 We collected data using REDCap (Vanderbilt University, Nashville, Tennessee, USA).
Outcome variables and covariates
Our two main outcome measures were PCS and MCS scores. We identified covariates from retrospective chart review and telephone follow-up and included demographic, clinical, and sociocultural variables (Supplemental Table 1).10
Statistical analysis
We summarized continuous covariates with median and interquartile range (IQR) statistics and categorical as percentages. We reported PCS and MCS using means and SD. We imputed missing data via multiple imputation using a fully conditional specification for five imputations.18 We used propensity score matching to create a cohort of SARS-CoV-2–positive and SARS-CoV-2–negative patients matched on patient characteristics and clinical variables captured at the time of the ED visit.19 We used logistic regression to model patients' propensity of testing positive for SARS-CoV-2 including the baseline covariates age, biological sex, province of residence, pandemic wave of presentation, respiratory distress on arrival, lowest oxygen saturation recorded in ED, oxygen requirements, comorbidities, ambulance arrival, in-hospital intubation, 7-day community incidence of COVID-19 in their area of residence, housing situation, tobacco and illicit substance use, race, immigrant status, employment status, education level, and income level. We matched SARS-CoV-2–positive patients with test-negative patients from the control group in a one-to-one ratio using a greedy neighbor approach without replacement and a caliper of 0.2 pooled SDs of the logit of the propensity score. The outcomes of patients that were not successfully matched were not analyzed.20 We assessed residual differences in the baseline variables between groups using Student's t-test for continuous predictors and Chi-squared tests for categorical predictors. In the primary analyses, we used linear mixed effects models to model PCS and MCS adjusting for age, sex, all comorbidities, measures of oxygen requirements (presence of respiratory distress, oxygen requirements, and intubation), effect modification by age and immunization status, as well as the number of days between the index ED visit and follow-up interview.
We conducted two sensitivity analyses. The first modeled PCS and MCS jointly using multivariate analysis of variance for repeated outcomes to reflect the correlation between PCS and MCS scores from the same patient. The second included only patients who presented with recorded viral symptoms in the ED, including cough, shortness of breath, fever, chills, headache, nausea/vomiting, diarrhea, hemoptysis, chest pain, fatigue/malaise, myalgia, or dysgeusia/anosmia.11
As a secondary analysis, we examined demographic, clinical, and sociocultural risk factors for worse PCS and MCS using the entire sample of SARS-CoV-2 positive and test-negative patients.21 We used principal component analysis to evaluate whether the large number of potentially collinear predictors could be reduced. We used multivariable linear regression to model the reduced number of predictors with PCS and MCS outcomes. We conducted a sensitivity analysis repeating the same modeling strategy for the SARS-CoV-2–positive patients only to identify differences in the type and strength of PCS and MCS risk factors between COVID-19 patients and the entire cohort.
We considered a P-value below 0.05 to be significant. We used Bonferroni's correction to adjust for multiple comparisons during analysis.22 We analyzed data using R 4.1 (Vienna, Austria) and SAS 9.4 (SAS Institute Inc., Cary, USA).
Results
Of 12,388 patients enrolled in the CCEDRRN registry during the study period, 2739 were COVID-19–positive, and 9649 were test-negative controls. A total of 4886 patients (39%) consented to participate: SARS-CoV-2–positive patients comprised 24% of the sample (n = 1170), with 3716 test-negative controls (Fig. 1 ). A total of 2545 patients completed one follow-up interview, and 2341 completed two. The median follow-up time for the first interview was 169 days (IQR 112–237) for SARS-CoV-2–positive patients and 187 days (IQR 125–277) for test-negative controls (Fig. 1). The median follow-up time for the second interview was 203 days after the index ED visit for both groups with an IQR of 188–350 and 187–360 days for SARS-CoV-2–positive and test-negative groups, respectively.
Fig. 1.
Flow diagram of enrolled patients.
For both SARS-CoV-2–positive and test-negative control groups, the distribution of baseline characteristics is presented in Table 1 . The mean PCS was 42.4 (SD = 10.9) among SARS-CoV-2–positive patients and 40.7 (SD = 12.5) among test-negative controls. The mean MCS was 48.5 (SD = 11.2) among SARS-CoV-2–positive patients and 48.5 (SD = 11.5) among test-negative controls.
Table 1.
Baseline variables of patients.
| Variable | SARS-CoV-2 positive (n = 1170) | SARS-CoV-2 negative (n = 3716) |
|---|---|---|
| Age, n (%) | ||
| 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%) |
| >80 years | 61 (5.2%) | 393 (10.6%) |
| Sex, n (%) | ||
| Male | 581 (49.7%) | 1752 (47.1%) |
| Female | 589 (50.3%) | 1964 (52.9%) |
| Arrival from, n (%) | ||
| Home | 1124 (96.1%) | 3471 (93.4%) |
| Institutional living (long-term care/rehabilitation facility/interhospital transfer) | 27 (2.3%) | 168 (4.5%) |
| Homeless/correctional facility/other | 19 (1.6%) | 77 (2.1%) |
| Wave of presentation, n (%) | ||
| Wave 1 (March 1, 2020 – June 30, 2020) | 263 (22.5%) | 682 (18.4%) |
| Wave 2 (July 1, 2020 – February 28, 2021) | 460 (39.3%) | 1619 (43.6%) |
| Wave 3 (March 1, 2021 – July 14, 2021) | 447 (38.2%) | 1415 (38.0%) |
| Province, n (%) | ||
| 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 before the ED visit (%) per 100,000 | ||
| 0–1.99 | 167 (14.3%) | 628 (16.9%) |
| 2–7.99 | 125 (10.7%) | 528 (14.2%) |
| >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) |
| Comorbid conditions, n (%) | ||
| Secondary immunodeficiency (active malignancy, organ transplant recipient, severe liver disease) | 50 (4.3%) | 350 (9.4%) |
| Asthma | 131 (11.2%) | 341 (9.2%) |
| Atrial fibrillation | 43 (3.7%) | 271 (7.3%) |
| Chronic kidney disease | 43 (3.7%) | 205 (5.5%) |
| Chronic lung disease | 56 (4.8%) | 339 (9.1%) |
| Chronic neurological disorder | 57 (4.9%) | 363 (9.8%) |
| Congestive heart failure | 29 (2.5%) | 150 (4.0%) |
| Coronary artery disease | 89 (7.7%) | 392 (10.5%) |
| Diabetes | 198 (16.9%) | 543 (14.6%) |
| Dyslipidemia | 249 (21.3%) | 815 (21.9%) |
| Hypertension | 379 (32.4%) | 1229 (33.1%) |
| Hypothyroidism | 71 (6.1%) | 338 (9.1%) |
| Obesity (clinical impression) | 36 (3.1%) | 105 (2.8%) |
| Rheumatologic disorder | 107 (9.1%) | 471 (12.7%) |
| Past malignant neoplasm (cancer) | 51 (4.4%) | 272 (7.3%) |
| Psychiatric condition/Mental health diagnosis | 119 (10.2%) | 699 (18.9%) |
| Arrived by ambulance, n (%) | ||
| Yes | 488 (41.7%) | 1208 (32.5%) |
| No | 682 (58.3%) | 2508 (67.5%) |
| Respiratory distress, n (%) | ||
| Yes | 247 (21.1%) | 333 (9.0%) |
| No | 923 (78.9%) | 3383 (91.0%) |
| Tobacco use, n (%) | ||
| 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 (%) | ||
| Yes | 45 (3.8%) | 348 (9.4%) |
| No | 1125 (96.2%) | 3368 (90.6%) |
| Oxygen required in ED, n (%) | ||
| Yes | 267 (22.8%) | 352 (9.5%) |
| No | 903 (77.2%) | 3364 (90.5%) |
| Intubation in hospital, n (%) | ||
| Yes | 63 (5.4%) | 97 (2.6%) |
| No | 1107 (94.6%) | 3619 (97.4%) |
| WHO ordinal scale assessment, n (%) | ||
| 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 (%) | ||
| Not vaccinated | 1131 (96.7%) | 3445 (92.7%) |
| Partially/fully vaccinated | 29 (2.5%) | 253 (6.8%) |
| Missing | 10 (0.8%) | 18 (0.5%) |
| Race, n (%) | ||
| 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 (%) | ||
| <$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 (%) | ||
| 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 (%) | ||
| 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 (%) | ||
| 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, Québec; SK, Saskatchewan.
For the propensity-matched analysis, 1042 SARS-CoV-2–positive patients (89%) were matched with 1042 test-negative controls (28%). Matching was adequate for baseline characteristics and clinical measures (Table 2 ). The distribution of baseline characteristics for the unmatched sample is presented in Supplemental Table 2. The unadjusted mean PCS of matched patients was 42.4 (SD = 11.0) among SARS-CoV-2–positive patients and 41.8 (SD = 12.1) among test-negative controls. The unadjusted mean MCS was 48.3 (SD = 11.4) among SARS-CoV-2–positive patients and 49.5 (SD = 11.0) among test-negative controls (Fig. 2 ).
Table 2.
Confounder variables that were matched using propensity score matching.
| Variable | SARS-CoV-2 positive (n = 1042) | SARS-CoV-2 negative (n = 1042) | P-value |
|---|---|---|---|
| Age, n (%) | 0.62 | ||
| 17–24 years | 36 (3.5%) | 40 (3.8%) | |
| 25–39 years | 199 (19.1%) | 234 (22.5%) | |
| 40–64 years | 502 (48.2%) | 445 (42.7%) | |
| 65–79 years | 249 (23.9%) | 236 (22.6%) | |
| >80 years | 56 (5.4%) | 87 (8.3%) | |
| Sex, n (%) | 0.90 | ||
| Male | 511 (49.0%) | 515 (49.4%) | |
| Female | 531 (51.0%) | 527 (50.6%) | |
| Arrival from, n (%) | 0.64 | ||
| Home | 999 (95.9%) | 990 (95.0%) | |
| Institutional living (long-term care/rehabilitation facility/interhospital transfer) | 26 (2.5%) | 32 (3.1%) | |
| Homeless/correctional facility/other | 17 (1.6%) | 20 (1.9%) | |
| Lowest oxygen saturation in emergency department, median [IQR] | 96 [4] | 96 [4] | 0.99 |
| Wave of presentation, n (%) | 0.82 | ||
| Wave 1 (March 1, 2020 – June 30, 2020) | 237 (22.7%) | 249 (23.9%) | |
| Wave 2 (July 1, 2020 – February 28, 2021) | 413 (39.6%) | 405 (38.9%) | |
| Wave 3 (March 1, 2021 – July 14, 2021) | 392 (37.6%) | 388 (37.2%) | |
| Province, n (%) | 0.98 | ||
| Western Canada (BC and SK) | 468 (44.9%) | 464 (44.5%) | |
| Ontario | 51 (4.9%) | 52 (5.0%) | |
| Eastern Canada (QC and NS) | 523 (50.2%) | 526 (50.5%) | |
| 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%) | |
| >8 | 776 (74.5%) | 717 (68.8%) | |
| Comorbid conditions, n (%) | |||
| Secondary immunodeficiency (active malignant neoplasm, organ transplant recipient, severe liver disease) | 48 (4.6%) | 50 (4.8%) | 0.92 |
| Asthma | 109 (10.5%) | 97 (9.3%) | 0.42 |
| Atrial fibrillation | 40 (3.8%) | 44 (4.2%) | 0.74 |
| Chronic kidney disease | 41 (3.9%) | 42 (4.0%) | 1.00 |
| Chronic lung disease | 53 (5.1%) | 60 (5.8%) | 0.56 |
| Chronic neurological disorder | 55 (5.3%) | 61 (5.9%) | 0.63 |
| Congestive heart failure | 27 (2.6%) | 31 (3.0%) | 0.69 |
| Coronary artery disease | 82 (7.9%) | 90 (8.6%) | 0.58 |
| Diabetes | 172 (16.5%) | 169 (16.2%) | 0.91 |
| Dyslipidemia | 216 (20.7%) | 215 (20.6%) | 0.65 |
| Hypertension | 324 (31.1%) | 314 (30.1%) | 0.67 |
| Hypothyroidism | 69 (6.6%) | 63 (6.0%) | 1.00 |
| Obesity (clinical impression) | 31 (3.0%) | 31 (3.0%) | 1.00 |
| Rheumatologic disorder | 92 (8.8%) | 97 (9.3%) | 0.76 |
| Past malignant neoplasm (cancer) | 48 (4.6%) | 45 (4.3%) | 0.83 |
| Psychiatric condition/mental health diagnosis | 110 (10.6%) | 123 (11.8%) | 0.40 |
| Arrived by ambulance, n (%) | 0.79 | ||
| Yes | 403 (38.7%) | 396 (38.0%) | |
| No | 639 (61.3%) | 646 (62.0%) | |
| Respiratory distress, n (%) | 0.95 | ||
| Yes | 170 (16.3%) | 168 (16.1%) | |
| No | 872 (83.7%) | 874 (83.9%) | |
| Tobacco use, n (%) | 0.98 | ||
| Current | 30 (2.9%) | 31 (3.0%) | |
| Past | 71 (6.8%) | 73 (7.0%) | |
| Never | 941 (90.3%) | 938 (90.0%) | |
| Illicit substance use, n (%) | 0.22 | ||
| Yes | 44 (4.2%) | 57 (5.5%) | |
| No | 998 (95.8%) | 985 (94.5%) | |
| Oxygen required in ED, n (%) | 0.91 | ||
| Yes | 170 (16.3%) | 167 (16.0%) | |
| No | 872 (83.7%) | 875 (84.0%) | |
| Intubation in hospital, n (%) | 1.00 | ||
| Yes | 40 (3.8%) | 39 (3.7%) | |
| No | 1002 (96.2%) | 1003 (96.3%) | |
| Race, n (%) | 0.91 | ||
| 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 (10.7%) | 114 (10.9%) | |
| White | 625 (60.0%) | 634 (60.8%) | |
| Current income bracket, n (%) | 0.74 | ||
| <$22,440–29,900 | 253 (24.3%) | 234 (22.5%) | |
| $29,901–42,300 | 162 (15.5%) | 153 (14.7%) | |
| $42,301–55,300 | 102 (9.8%) | 104 (10.0%) | |
| $55,301–73,700 | 130 (12.5%) | 145 (13.9%) | |
| $73,701+ | 395 (37.9%) | 406 (39.0%) | |
| Immigrated to Canada, n (%) | 0.93 | ||
| Yes | 439 (42.1%) | 436 (41.8%) | |
| No | 603 (57.9%) | 606 (58.2%) | |
| Highest level of education achieved, n (%) | 0.96 | ||
| No high school | 79 (7.6%) | 74 (7.1%) | |
| High school | 236 (22.6%) | 223 (21.4%) | |
| Trade certificate or diploma | 86 (8.3%) | 83 (8.0%) | |
| College | 127 (12.2%) | 137 (13.1%) | |
| University certificate or diploma | 97 (9.3%) | 99 (9.5%) | |
| University degree | 417 (40.0%) | 426 (40.9%) | |
| Employment, n (%) | 0.97 | ||
| Employed | 670 (64.3%) | 675 (64.8%) | |
| 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, Québec; SK, Saskatchewan.
Fig. 2.
Violin plots of physical and mental component scores across COVID-19 cases and test-negative controls after propensity score matching.
The effect of SARS-CoV-2 infection on PCS, adjusted for the time between the ED visit and interview, effect modification, and a priori confounders, was +0.50 (adjusted P-value = 0.51; 95% confidence interval [CI]: -0.36, 1.36). The adjusted effect of SARS-CoV-2 infection on MCS was -1.01 (adjusted P-value = 0.042; 95% CI: -1.91, -0.11). We reported similar findings for the adjusted joint analysis of PCS and MCS (Supplemental Table 3).
We completed a sensitivity analysis with patients who presented with documented SARS-CoV-2 symptoms (n = 936). The significant difference in MCS was maintained through our sensitivity analysis (Table 3 ).
Table 3.
Subgroup analysis excluding 936 patients without respiratory-related symptoms.
| Variable |
Physical component score |
Mental component score |
||
|---|---|---|---|---|
| Beta | P-value | Beta | P-value | |
| COVID-19 positive | 0.30 | 0.49 | −1.07 | 0.01 |
| Time between emergency department visit and interview (days) | 0.002 | 0.33 | 0.002 | 0.26 |
| Age (years) | −0.12 | <0.001 | 0.08 | <0.001 |
| Female sex | −2.09 | <0.001 | −2.62 | <0.001 |
| Smoking | ||||
| Never | 2.13 | 0.10 | 1.56 | 0.24 |
| Past | 2.51 | 0.10 | 1.66 | 0.28 |
| Intubated | −0.96 | 0.44 | −1.30 | 0.30 |
| Respiratory distress | −0.59 | 0.35 | 0.14 | 0.83 |
| Supplemental oxygen required in emergency department | −1.41 | 0.03 | −0.29 | 0.67 |
| Comorbidities | ||||
| Asthma | −1.62 | 0.02 | 0.08 | 0.91 |
| Atrial fibrillation | −4.39 | <0.001 | 0.30 | 0.80 |
| Chronic kidney disease | −2.87 | 0.02 | −0.29 | 0.81 |
| Chronic lung disease | −3.79 | <0.001 | −1.45 | 0.15 |
| Chronic neurological disorder | −0.73 | 0.45 | −0.68 | 0.48 |
| Congestive heart failure | −4.12 | 0.004 | −1.41 | 0.33 |
| Coronary artery disease | −1.43 | 0.11 | −1.30 | 0.15 |
| Diabetes | −2.34 | <0.001 | −1.19 | 0.07 |
| Dyslipidemia | −0.26 | 0.68 | −0.12 | 0.85 |
| Hypertension | 0.61 | 0.31 | 0.32 | 0.60 |
| Hypothyroidism | −0.62 | 0.49 | 0.72 | 0.43 |
| Obesity | −0.77 | 0.57 | 1.04 | 0.45 |
| Past malignant neoplasm | 0.09 | 0.92 | 2.82 | 0.006 |
| Psychiatric condition/mental health diagnosis | −1.26 | 0.08 | −6.42 | <0.001 |
| Rheumatological disorder | −3.57 | <0.001 | −0.46 | 0.55 |
| Secondary immunodeficiency | −3.00 | 0.004 | 1.79 | 0.09 |
| Immunized to SARS-CoV-2 | 4.23 | 0.31 | −4.47 | 0.29 |
| Immunized to SARS-CoV-2 × age (interaction) | −0.08 | 0.21 | −0.05 | 0.46 |
We summarized the risk factors for PCS and MCS in Table 4 for all 4886 patients enrolled in this study. The principal component analyses identified two principal components representing oxygen requirements (comprised lowest oxygen saturation in the ED, presence of respiratory distress, intubation, and supplemental oxygen requirements) and medically complex comorbidities (comprised secondary immunodeficiency, chronic neurological disorder, rheumatological disease, and history of past malignancy). The two principal components were analyzed as predictors for PCS and MCS in Table 4. The results of the sensitivity analysis for SARS-CoV-2–positive patients only are summarized in Supplemental Table 4.
Table 4.
Principal component regression analysis for SARS-CoV-2–positive and test-negative control patients.
| 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 | ||||||
| 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 |
| 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 | – | – | – | – | ||
| 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 |
| World Health Organization 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 |
Discussion
Using a propensity score–matched design, we found that COVID-19 patients who presented to EDs reported lower mental health status during the follow-up period. Interestingly, the physical health outcomes of COVID-19 patients were similar to those of other patients who presented to the ED at the same time during the pandemic.
Although studies that examined physical health outcomes of COVID-19 survivors to known population norms reported significantly worse outcomes, those studies did not control for important confounders such as the occurrence of complex comorbidities, age distributions, or health services access variables between populations.23 , 24 However, studies examining the physical health of COVID-19 patients that controlled for such factors reported similar outcomes as this study; Huang et al. observed no significant difference in the physical quality of life of hospitalized COVID-19 survivors with severe disease compared with ambulatory COVID-19 survivors with mild disease.25 Our findings may be explained by considering access barriers to care that occurred in the early waves of the pandemic. Non-COVID patients were reluctant to come to EDs due to fear of contracting the virus and may have suffered from complications and challenges related to the delay in seeking care, impacting self-reported physical health.26 Our results indicate that the physical health outcomes reported 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.
This study found significantly lower MCS among COVID-19 survivors compared with matched test-negative controls and is consistent with case–control studies investigating the direct effects of COVID-19 on patients who reported suffering from post-traumatic stress, anxiety, and depression postinfection.27 , 28 Because of the poor self-reported mental health outcomes observed among COVID-19 patients in this study, there is a need to investigate and understand the specific factors associated with worse mental health outcomes for this population. This information may be useful to inform targeted mental health services among COVID-19 survivors; recommending outpatient mental health services may be one strategy to address the suboptimal mental health status of post-COVID patients. However, in the Canadian context, this is complicated by the fact that many subacute mental health care services are not publicly funded and not universally available.
Several clinical, demographic, and sociocultural characteristics influenced patient's self-reported physical and mental quality of life. Not surprisingly, factors such as severe disease, oxygen requirements, smoking, and age were associated with worse physical health outcomes. Factors such as lower annual income, having no fixed address, illicit substance use, and having a history of a psychiatric or mental health diagnosis, were associated with worse mental health outcomes. Interventions and policies that aim to support COVID-19 survivors' health should reflect the differential impacts of clinical, demographic, and sociocultural factors on physical and mental health outcomes.
This study was not without limitations. We only interviewed patients by phone because of restrictions with in-person research at hospital sites. This necessarily excluded individuals without phone access and those unable to use phones. This may have led us to underestimate the impact of COVID-19 on the health of disadvantaged or vulnerable populations. Furthermore, we focused solely on two composite outcome measures of physical and mental health. Single numerical scores are effective for identifying trends in aggregate but cannot provide details about different dimensions of physical and mental health at an individual level.29 This study was initiated before the World Health Organization defined the post-COVID condition, which prevented us from determining whether patients meeting the diagnostic criteria for the post-COVID condition had worse physical or mental health outcomes compared with patients who did not meet the diagnostic criteria for the post-COVID condition. Finally, our results are only representative of consenting patients who sought care in an ED. Because a large proportion of COVID-19 patients did not seek evaluation or medical treatment in EDs, we cannot assume these findings apply more generally to patients with COVID-19 who did not access ED care.
The strengths of this study include incorporation of data from the largest consecutive cohort of COVID-19 patients presenting to academic, non-academic, urban, and remote EDs, which were included in the CCEDRRN registry. Given that most of the available literature on COVID-19 outcomes are from studies that neither control for social or demographic characteristics of patients, nor include test-negative comparison groups, this study strengthens the knowledge about patient-reported outcomes among COVID-19 survivors by reporting results that control for health-seeking behaviors and time trends in addition to clinical, demographic, and sociocultural variables.
With increasing case counts of COVID-19 throughout the world, it is essential for policy makers to develop comprehensive strategies to mitigate the long-term outcomes of COVID-19 patients. These findings may be useful for identifying individuals at greater risk for worse mental health outcomes post-COVID-19 and may guide the development of interventions and policies to better support COVID-19 survivors.
Author statements
Acknowledgments
The authors thank the University of British Columbia clinical coordinating center, legal, ethics, privacy, and contract staff. The authors thank the Kazis Lab of Boston University for providing access to the Veterans RAND 12-item health survey and scoring algorithm. The authors would like to thank the CCEDRRN patient engagement committee, whose perspectives, opinions, and feedback helped them critically interpret the results of this thesis. Finally, the authors want to acknowledge their colleagues in medicine, nursing, and the allied health professions who have been on the front lines of this pandemic from day 1 staffing the ambulances, emergency departments, intensive care units, and hospitals, bravely facing the risks of COVID-19 to look after their fellow citizens and after one another.
Fundings
The Canadian Institutes of Health Research (447,679, 464,947, and 466,880), Ontario Ministry of Colleges and Universities (C-655-2129), Saskatchewan Health Research Foundation (5357), Genome BC (COV024 and VAC007), Fondation du CHU de Québec (Octroi No. 4007), and Sero-Surveillance and Research (COVID-19 Immunity Task Force Initiative) provided peer-reviewed funding. The BC Academic Health Science Network and BioTalent Canada provided non–peer-reviewed funding. These organizations are not-for-profit and had no role in study conduct, analysis, or manuscript prepar-ation.
Competing interests
None.
Author contributions
This article was derived from R.B.'s master's thesis in population and public health supervised by C.H., J.S., and R.M. R.B., C.H., and J.S. conceptualized the study design. R.B. analyzed the data and wrote the original draft. The writing proposal was reviewed by the CCEDRRN Publication and Protocol Review Committee and Data Access and Management Committee before release of data. P.A., C.H., L.G., M.L., and J.H. contributed to data collection at participating CCEDRRN sites. T.R. and L.G. provided patient-partner input in the interpretation of the study results. All authors contributed to data interpretation and to the review and editing of the article. All authors had final responsibility for the decision to submit for publication.
Data sharing
For investigators who wish to access data from the Canadian COVID-19 Emergency Department Rapid Response Network, proposals may be submitted to the network for review and approval by the network's peer-review publication committee, the data access and management committee, and the executive committee, as per the network's governance. Information regarding submitting proposals and accessing data may be found at https://ccedrrn.com/.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.puhe.2022.11.016.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
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