Abstract
Background:
Previous SARS-CoV-2 infection and vaccination, coupled to rapid evolution of SARS-CoV-2 variants, have modified COVID-19 clinical manifestations. We characterized the clinical symptoms of COVID-19 individuals in omicron BA.2 and BA.5 Japanese pandemic periods to identify omicron and subvariant associations between symptoms, immune status, and clinical outcomes.
Methods:
Individuals registered in Sapporo’s web-based COVID-19 information system entered 12 pre-selected symptoms, days since symptom onset, vaccination history, SARS-CoV-2 infection history, and background. Symptom prevalence, variables associated with symptoms, and symptoms associated with progression to severe disease were analysed.
Findings:
For 157 861 omicron-infected symptomatic individuals, cough was the most common symptom (99 032/62.7%), followed by sore throat (95 838/60.7%), nasal discharge (69 968/44.3%), and fever (61 218/38.8%). Omicron BA.5 infection was associated with a higher systemic symptom prevalence than BA.2 in vaccinated and unvaccinated individuals (adjusted odds ratio for fever: 2.18 [95% CI 2.12 – 2.25]). Omicron breakthrough-infected individuals with ≥ 3 vaccinations or previous infection were less likely to exhibit systemic symptoms (fever: 0.50 [0.49–0.51]), but more likely to exhibit upper respiratory symptoms (sore throat: 1.33 [1.29–1.36], nasal discharge: 1.84 [1.80–1.89]]. Infected elderly individuals had lower odds for all symptoms. However, when symptoms were manifest, systemic symptoms were associated with an increased odds (dyspnea: 3.01 [1.84–4.91], fever: 2.93 [1.89–4.52]), whereas upper respiratory symptoms with a decreased odds (sore throat: 0.38 [0.24–0.63], nasal discharge: 0.48 [0.28–0.81]), for severe disease.
Interpretation:
Host immunological status, omicron subvariant, and age were associated with a spectrum of COVID-19 symptoms and outcomes. BA.5 produced a higher systemic symptom prevalence than BA.2. Vaccination and prior infection reduced systemic symptom prevalence and improved outcomes but increased upper respiratory tract symptom prevalence. Systemic, but not upper respiratory, symptoms in the elderly heralded severe disease. Our findings may serve as a practical guide to utilize COVID-19 symptoms to appropriately modify healthcare strategies and predict clinical outcomes for elderly patients with omicron infections.
Keywords: COVID-19, omicron subvariant, vaccination, upper respiratory symptoms, older age
Introduction
The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome 2 (SARS-CoV-2), has caused more than 759 million infections worldwide1. Since early in the pandemic, multifaceted host factors, e.g., age and underlying disease, have been reported to impact the clinical course of individuals infected with SARS-CoV-22–5. Associated with the widespread implementation of SARS-CoV-2 vaccines and previous infections, the severity and mortality rates of the COVID-19 syndrome have decreased5–7. The emergence of mutant strains likely has also modified the clinical manifestations of COVID-198,9. A challenge for healthcare providers/public health officials is appropriately advising individuals with breakthrough infections and implementing healthcare strategies in the current omicron pandemic period10–12.
Patient clinical symptoms are the most accessible information describing the status of individuals with infections in healthcare settings. However, data linking patient manifestations to viral, host factor, and clinical outcomes must be continuously updated to maximize their utility and guide appropriate healthcare strategies13,14. Studies designed to compare the symptoms of alpha vs. delta SARS-CoV-2 variants with those of the original Wuhan strain15, and the original omicron variant compared to the delta variant, helped guide healthcare strategies8. However, no large-scale epidemiological studies describing COVID-19 symptoms and outcomes as a function of host factors across newer omicron subvariants are available.
Sapporo, a Japanese metropolitan city with 2 million people, launched a registration system to automate the acquisition of self-entered personal information from individuals with COVID-19. This unique system, in which individuals enter their current status via the internet, enables the collection of timely data describing COVID-19 symptoms that can be linked to databases representing individuals’ backgrounds, vaccination and previous SARS-CoV-2 infection status, and severity outcomes. Our objective was to update the characterization of COVID-19 clinical symptoms during the omicron BA.2-BA.5 variant pandemic period and identify associations between clinical symptoms, host factors, immune status, and clinical outcomes relevant to the patient and healthcare communities.
Methods
Study population and data sources
This registry-based observational study was based on the Sapporo population. Data from treatment decision sites (TDS) and registration centres for test-positive patients (RCPP) were utilized for analyses (Fig. S1). Individuals residing in Sapporo were eligible to register for TDS and RCPP if they met the following criteria: 1) symptomatic individuals who tested positive for SARS-CoV-2 (polymerase chain reaction or antigen test); 2) individuals who were not tested for SARS-CoV-2 but developed new symptoms after a household member tested positive for SARS-CoV-2. Those diagnosed with COVID-19 were requested to immediately register clinical information with the TDS through a web device, including the presence/absence of 12 preselected specific symptoms at the time of registration and the date of symptom onset. Details of the two registration systems are given in Fig. S1 and Supplementary methods.
Individuals were excluded from the study if they: (1) entered the date of onset inappropriately, (2) registered > 5 days after symptom onset, or (3) registered as asymptomatic. Detailed inclusion and exclusion criteria and the treatment of missing values are described in Supplementary methods.
Data collected
Data from the TDS and the RCPP are integrated and stored at the Sapporo City Public Health Center. The following data were extracted from these systems: 1) date of first symptom onset; 2) dietary intake; 3) presence of 12 predefined symptoms (fever, cough, sore throat, nasal discharge, sputum, headache, joint or muscle pain, severe fatigue, dyspnoea, diarrhoea, and taste or smell disorder) at registration; and 4) demographic information (age, sex, height, weight, and underlying disease). Data describing previous SARS-CoV-2 infection and progression to severe disease, i.e., requiring oxygenation or mechanical ventilation, or death, were collected from the Health Center Real-time Information-sharing System for COVID-19 operated by the Ministry of Health, Labor, and Welfare, as well as data linked to patient information in the TDS and RCPP. Vaccination history, including the timelines of vaccination, was accessed from the Vaccination Record System operated by the Japan Government Digital Agency (Fig. S1).
Characterization of SARS-CoV-2 omicron subvariants
Whole genome analyses were performed at the Sapporo City Institute of Public Health to identify SARS-CoV-2 variants prevalent in Sapporo. Randomly collected specimens with positive SARS-CoV-2 test results were analysed for approximately 50–80 cases per week and the proportions of omicron subvariants calculated. In this study, omicron subvariant epidemic periods were defined as when the percentage of the most predominant subvariants exceeded 80%.
Statistical analysis
Continuous data were presented as medians (interquartile range [IQR]) and categorical data were presented as numbers and percentages. A 95% confidence interval (CI) for symptom prevalence, odds ratio (OR) and hazard ratio (HR) was calculated by the score method and Wald test, respectively. Multivariable analyses were performed to identify factors associated with the prevalence of each symptom and to test the association between each symptom and progression to severe disease. All available variables from the registry system that have been reported as predictors of COVID-19 outcomes were incorporated3,8,16. Vaccination status was divided into two groups with more vs. less than three vaccinations as the boundary, a number clinically relevant to omicron variants17–19. Propensity score matching was performed using a 1:1 nearest available matching algorithm without replacement and a caliper of 0.05. The propensity score for each variable was calculated by logistic regression analysis with the variables. Absolute value of standardized difference greater than 0.1 was considered as a sign of imbalance. All statistical analyses were performed using JMP® Pro Version 16.2.0 and SAS® Version 9.4 (SAS Institute Inc., Cary, NC, USA). The detailed statistical methods for the results presented in the figures or tables are described in Supplementary methods.
Role of the funding source
The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Results
Study population
Data collected from the TDS and RCPP systems were analysed between April 25, 2022, and September 25, 2022. The epidemic period for the omicron subvariant BA.2 spanned April 25 to June 26, and that for BA.5 spanned July 18 to September 25 (Fig. 1). After individuals who met the exclusion criteria were excluded (< 5.6%), a total of 157 861 symptomatic COVID-19 cases (34 336 and 123 525 for the BA.2 and BA.5 groups, respectively) were analysed (Fig. 1).
Figure 1. Schematic representations of the study period and eligibility determination.
The bars indicate the number of cases with new COVID-19 infection per day in Sapporo, Japan. The stacked area graph shows proportions of mutant strains detected. The BA.2 or BA.5 pandemic period was defined as when the detection rate of omicron subvariant BA.2 or BA.5 exceeded 80%, respectively, during the study period.
The median age of all individuals included in the study was 33 years (IQR 17–47) (Table 1). The most prevalent age group was the 30-year-old group (27 665 individuals [17.6%]). Those aged ≥ 65 years accounted for 10 874 (6.9%) of the total. Forty-eight percent were men, and the median BMI was 21.1 kg/m2 (IQR 18.6–24.0). The proportion of individuals with an underlying disease was highest for hypertension (8781 [5.6%]), followed by chronic respiratory (5652 [3.6%]) and cardiovascular diseases (1620 [1.0%]). SARS-CoV-2 vaccination status of the cohort was: 60 033 (38.0%) unvaccinated, 1052 (0.7%) one dose, 38 304 (24.3%) two doses, 53 389 (33.8%) three doses, and 5083 (3.2%) four doses. The vast majority (99.3%) of vaccinations administered were mRNA vaccines (BNT162b2 or mRNA-1273) (Table S1). Additionally, 5767 individuals (3.7%) had a history of previous SARS-CoV-2 infection.
Table 1.
Clinical characteristics of the study population.
| Total (N=15 7861) | BA.2 (n=34 336) | BA.5 (n=123 525) | |
|---|---|---|---|
| Median age (IQR) –yr. | 33 (17–47) | 28 (13–42) | 34 (19–48) |
| Age * – no. (%) | |||
| < 10 | 23 219 (14.7) | 6427 (18.8) | 16 792 (13.6) |
| 10s | 21 165 (13.4) | 6012 (17.6) | 15 153 (12.3) |
| 20s | 26 259 (16.7) | 5667 (16.6) | 20 592 (16.7) |
| 30s | 27 665 (17.6) | 6093 (17.8) | 21 572 (17.5) |
| 40s | 26 536 (16.8) | 5465 (16.0) | 21 071 (17.1) |
| 50s | 16 629 (10.6) | 2434 (7.1) | 14 195 (11.5) |
| 60s | 8881 (5.6) | 1156 (3.4) | 7725 (6.3) |
| 70s | 4839 (3.1) | 647 (1.9) | 4192 (3.4) |
| ≥ 80 | 2359 (1.5) | 310 (0.9) | 2049 (1.7) |
| Elderly (Age ≥ 65) – no. (%) | 10 874 (6.9) | 1441 (4.2) | 9433 (7.6) |
| Sex ** – no. (%) | |||
| Male | 75 281 (47.7) | 16 292 (47.5) | 58 989 (47.8) |
| Female | 82 468 (52.3) | 18 012 (52.5) | 64 456 (52.2) |
| Median BMI *** (IQR) –kg/m2 | 21.1 (18.6 – 24.0) | 20.7 (18.1–23.5) | 21·2 (18.7–24.1) |
| Obesity (BMI ≥ 30) –no. (%) | 6729 (4.3) | 1241 (3.6) | 5488 (4.4) |
| Comorbidities – no. (%) | |||
| Malignancy | 1284 (0.8) | 188 (0.5) | 1096 (0.9) |
| Immunocompromised | 291 (0.2) | 44 (0.1) | 247 (0.2) |
| Chronic respiratory diseases | 5652 (3.6) | 1186 (3.5) | 4466 (3.6) |
| Chronic kidney diseases | 88 (0.1) | 15 (0.1) | 73 (0.0) |
| Cardiovascular diseases | 1620 (1.0) | 229 (0.7) | 1391 (1.1) |
| Cerebrovascular diseases | 102 (0.1) | 22 (0.1) | 80 (0.1) |
| Hypertension | 8781 (5.6) | 1270 (3.7) | 7511 (6.1) |
| Diabetes | 3740 (2.4) | 550 (1.6) | 3190 (2.6) |
| SARS-CoV-2 vaccination – no. (%) | |||
| Unvaccinated | 60 033 (38.0) | 15 339 (44.7) | 44 694 (36.2) |
| One dose | 1052 (0.7) | 341 (1.0) | 711 (0.6) |
| Two doses | 38 304 (24.3) | 11 232 (32.7) | 27 072 (21.9) |
| Three doses | 53 389 (33.8) | 7424 (21.6) | 45 965 (37.2) |
| Four doses | 5083 (3.2) | 0 (0.0) | 5083 (4.1) |
| Previous SARS-CoV-2 infection – no. (%) | 5767 (3.7) | 732 (2.1) | 5035 (4.1) |
| Progression to severe disease – no. (%) | 142 (0.1) | 19 (0.1) | 123 (0.1) |
| Among the elderly (Age ≥ 65 years) | 111 (1.01) | 12 (0.83) | 99 (1.04) |
| Among individuals aged < 65 years | 31 (0.02) | 7 (0.02) | 24 (0.02) |
| Death within 30 days – no. | 4 | 2 | 2 |
N=157 552,
N=157 749,
N=157 781.
BMI, body mass index (kg/m2); IQR, interquartile range; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.
Coughing was the most prevalent symptom per individual (99 032 [62.7%]), followed by sore throat (95 838 [60.7%]), nasal discharge (69 968 [44.3%]), headache (66 425 [42.1%]), and fever (61 218 [38.8%]) (Fig. 2A, Table S2). Mild positive correlations were observed amongst systemic or upper airway symptoms whereas no correlation was observed between systemic and upper airway symptoms (Fig. S2). A total of 142 (0.1%) individuals developed severe disease (31 [0.02%] for individuals younger than 65 years of age and 111 [1.01%] for those 65 years of age or older), including four individuals who died within 30 days after onset (Table 1).
Figure 2. Associations of COVID-19 symptoms with the omicron subvariants BA.2 and BA.5.
A. Prevalence of COVID-19 symptoms in the total study population and in individuals with BA.2 or BA.5 infections. Error bars indicate 95% confidence intervals (CIs). B. Associations between omicron subvariants and symptom odds. Multivariable analysis used each symptom as an outcome and mutant strain, age, body mass index, underlying disease, vaccination history, and history of previous infection as explanatory variables. The type of omicron subvariant (BA.5 or not) and the adjusted odds ratio (OR) for each symptom are arranged from highest to lowest. Points indicate ORs, and bars indicate 95% CIs. The detailed results of the multivariable analysis are presented in Table S4. C. Differences in symptom odds between omicron subvariants BA.2 and BA.5 identified by a 1:1 propensity score matching analysis. Individuals infected with omicron subvariant BA.2 or BA.5 were grouped and matched for background clinical conditions, vaccine status, a history of previous infections, and time after last vaccination (N = 4063 per each group). Only individuals, who were registered into the system on the same day as symptom onset, were included in the matched analysis. Odds ratios of symptoms in BA.5 to BA.2 groups are shown in order from highest to lowest. Points indicate ORs, and bars indicate 95% CIs.
Clinical manifestations of individuals infected with omicron BA.2 or BA.5
The clinical features of symptomatic individuals with BA.2 vs. BA.5 infections were compared (Table 1). The BA.2 group had a lower median age, BMI, history of cerebrovascular disease, and a higher proportion of unvaccinated and ≤ 2 dose-vaccinated individuals. The BA.5 group exhibited higher proportions of other comorbidities, e.g., malignancy, immunodeficiency, cardiovascular disease, hypertension, and diabetes, previous SARS-CoV-2 infection, and contained all individuals with a fourth vaccine dose.
All 12 predefined symptoms, except for nasal discharge and phlegm, were more prevalent in the BA.5 vs. BA.2 populations (Fig. 2A, Table S2), findings that were replicated in the unvaccinated BA.5 vs. BA.2 subgroups (Fig. S3, Table S3). Multivariable logistic regression analyses of the BA.5 and BA.2 population data identified associations that predicted an increased odds for BA.5-infected populations of fever (adjusted OR [95% CI]: 2.18 [2.12–2.25]), decreased food intake (1.80 [1.75–1.86]), severe fatigue (1.64 [1.59–1.69]), joint or muscle pain, smell or taste disorders, headache, dyspnoea, diarrhoea, and sore throat, juxtaposed to a decreased odds of nasal discharge and phlegm (Fig. 2B, Table S4). Logistic regression analysis demonstrated comparable odds for progression to severe disease between the two subvariant groups (Table S5).
As a sensitivity analysis, we performed propensity score matching on backgrounds between BA.2 and BA.5-infected individuals, including the duration from last vaccination date to symptom development. For this matched analysis, we focused on unvaccinated, two-dose, and three-dose vaccinated and included BA.2 or BA.5-infected individuals who registered COVID-19 symptoms in the system on the same day as symptom onset, i.e., day 0 after the first symptom onset. The BA.2 and BA.5 groups each were matched for 4063 individuals. This analysis reproduced data obtained from the unmatched symptom comparison between BA.2 vs. BA.5-infected individuals, including a higher proportion in the BA.5 vs. BA.2 groups of systemic (fever: 2606 [64.1%] vs. 2040 [50.2%], decreased food intake: 1533 [37.7%] vs. 1219 [30%]) vs. a lower proportion of upper airway (nasal discharge: 1372 [33.8%] vs. 1571 (38.7%), Phlegm: 905 [22.3%] vs. 1088 [26.8%]) symptoms (Table 2). Odds ratios for each symptom demonstrated specific associations between higher odds for systemic symptoms with BA.5 vs. BA.2 (Fig. 2C), consistent with data observed in the unmatched analysis. These findings were similarly observed in matched subgroup analyses of unvaccinated, two-dose, or three-dose vaccinated individuals with BA.2 vs. BA.5 infection (Fig. S4, Table S6).
Table 2.
Propensity score matching analysis of day-of-onset symptoms in unvaccinated, two- and three-dose populations.
| Before match | 1:1 matched | |||||
|---|---|---|---|---|---|---|
| BA.2 | BA.5 | Std diff | BA.2 | BA.5 | Std diff | |
| Total | 4692 | 16 995 | 4063 | 4063 | ||
| Age, mean (SD) | 26.7 (19.3) | 30.2 (20.3) | −0.173 | 25.4 (19.7) | 24.0 (19.0) | 0.073 |
| Age group | ||||||
| < 10 | 1191 (25.4) | 3643 (21.4) | 0.093 | 1191 (29.3) | 1229 (30.2) | −0.020 |
| 10s | 828 (17.6) | 2327 (13.7) | 0.109 | 720 (17.7) | 803 (19.8) | −0.052 |
| 20s | 703 (15.0) | 2544 (15.0) | 0.000 | 580 (14.3) | 569 (14.0) | 0.008 |
| 30s | 720 (15.3) | 2833 (16.7) | −0.036 | 565 (13.9) | 556 (13.7) | 0.006 |
| 40s | 639 (13.6) | 2631 (15.5) | −0.053 | 485 (11.9) | 464 (11.4) | 0.016 |
| 50s | 331 (7.1) | 1725 (10.2) | −0.111 | 285 (7.0) | 244 (6.0) | 0.041 |
| 60s | 160 (3.4) | 709 (4.2) | −0.040 | 129 (3.2) | 116 (2.9) | 0.019 |
| 70s | 80 (1.7) | 327 (1.9) | −0.016 | 69 (1.7) | 54 (1.3) | 0.030 |
| ≥ 80 | 40 (0.9) | 256 (1.5) | −0.061 | 39 (1.0) | 28 (0.7) | 0.030 |
| Male | 2337 (0.50) | 8679 (51.1) | −0.025 | 2056 (50.6) | 2014 (49.6) | 0.021 |
| Obesity (BMI ≥ 30) | 179 (3.8) | 702 (4.1) | −0.016 | 145 (3.6) | 119 (2.9) | 0.036 |
| Comorbidities | ||||||
| Malignancy | 24 (0.5) | 135 (0.8) | −0.035 | 22 (0.5) | 16 (0.4) | 0.022 |
| Immunocompromised | 5 (0.1) | 35 (0.2) | −0.025 | 4 (0.1) | 1 (0.0) | 0.030 |
| Chronic respiratory diseases | 176 (3.8) | 615 (3.6) | 0.007 | 141 (3.5) | 138 (3.4) | 0.004 |
| Chronic kidney diseases | 5 (0.1) | 5 (0.0) | 0.030 | 3 (0.1) | 3 (0.1) | 0.000 |
| Cardiovascular diseases | 30 (0.6) | 153 (0.9) | −0.030 | 22 (0.5) | 26 (0.6) | −0.013 |
| erebrovascular diseases | 4 (0.1) | 4 (0.0) | 0.026 | 3 (0.1) | 3 (0.1) | 0.000 |
| Hypertension | 176 (3.8) | 821 (4.8) | −0.053 | 141 (3.5) | 135 (3.3) | 0.008 |
| Diabetes | 73 (1.6) | 338 (2.0) | −0.033 | 61 (1.5) | 59 (1.5) | 0.004 |
| Unvaccinated | 2402 (51.2) | 7393 (43.5) | 0.155 | 2402 (59.1) | 2452 (60.3) | −0.025 |
| Days since last vaccination | ||||||
| Two doses | ||||||
| −60 | 42 (0.9) | 47 (0.3) | 0.081 | 41 (1.0) | 46 (1.1) | −0.012 |
| 61–120 | 41 (0.9) | 190 (1.1) | −0.025 | 41 (1.0) | 35 (0.9) | 0.015 |
| 121–180 | 210 (4.5) | 195 (1.1) | 0.202 | 198 (4.9) | 177 (4.4) | 0.025 |
| 180–240 | 759 (16.2) | 225 (1.3) | 0.545 | 221 (5.4) | 225 (5.5) | −0.004 |
| 241- | 200 (4.3) | 2770 (16.3) | −0.404 | 200 (4.9) | 197 (4.8) | 0.003 |
| Three doses | ||||||
| −60 | 347 (7.4) | 284 (1.7) | 0.278 | 271 (6.7) | 280 (6.9) | −0.009 |
| 61–120 | 455 (9.7) | 1504 (8.9) | 0.029 | 453 (11.1) | 410 (10.1) | 0.034 |
| 121–180 | 228 (4.9) | 2890 (17.0) | −0.397 | 228 (5.6) | 233 (5.7) | −0.005 |
| 181–240 | 8 (0.2) | 1381 (8.1) | −0.407 | 8 (0.2) | 8 (0.2) | 0.000 |
| 241- | 0 (0.0) | 116 (0.7) | −0.117 | 0 (0.0) | 0 (0.0) | 0.000 |
| Previous SARS-CoV-2 infection | 120 (2.6) | 804 (4.7) | −0.116 | 109 (2.7) | 107 (2.6) | 0.003 |
| Prevalence of symptoms | ||||||
| Fever | 2040 (50.2) | 2606 (64.1) | ||||
| Cough | 1990 (49.0) | 1888 (46.5) | ||||
| Sore throat | 2039 (50.2) | 2018 (49.7) | ||||
| Nasal discharge | 1571 (38.7) | 1372 (33.8) | ||||
| Phlegm | 1088 (26.8) | 905 (22.3) | ||||
| Headache | 1656 (40.8) | 1855 (45.7) | ||||
| Joint or muscle pain | 1001 (24.6) | 1205 (29.7) | ||||
| Decreased food intake | 1219 (30.0) | 1533 (37.7) | ||||
| Severe fatigue | 1083 (26.7) | 1252 (30.8) | ||||
| Dyspnea | 407 (10.0) | 471 (11.6) | ||||
| Diarrhea | 239 (5.9) | 246 (6.1) | ||||
| Smell or taste disorder | 78 (1.9) | 97 (2.4) | ||||
| Number of symptoms, median (IQR) | 3 (2–5) | 3 (2–5) | ||||
Data are n (%) unless otherwise specified.
Absolute value of standardized difference (Std diff) greater than 0.1 is considered as a sign of imbalance.
BMI, body mass index (kg/m2); SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SD, standard deviation.
Associations between vaccine status and COVID-19 symptoms
Individuals were divided into two subgroups to determine the effects of the modification of host immune status by vaccination on the prevalence of COVID-19 symptoms. The group with ≥ 3 vaccinations exhibited breakthrough infections with a lower proportion of fever, decreased food intake, severe fatigue, joint or muscle pain, and diarrhoea than the group with ≤ 2 vaccinations (Fig. S5A, Table S2). In contrast, cough, sore throat, nasal discharge, and phlegm were more common with breakthrough infections in the ≥ 3 vaccination group. The relative frequencies of 12 preselected omicron symptoms were calculated on each day after symptom onset (Table S7). Cox regression analyses, using the presence of symptoms as the occurrence of an event, revealed that the appearance of coughing, sore throat, nasal discharge, and phlegm, but not systemic symptoms, was accelerated in the ≥ 3 vs. ≤ 2 vaccination groups (Fig. S5B, Table S8).
Logistic regression analyses also demonstrated that individuals with breakthrough infections after ≥ 3 vaccinations had a decreased odds of systemic symptoms, including fever (0.50 [0.49–0.51]), decreased food intake (0.39 [0.37–0.40]), severe fatigue (0.59 [0.58–0.61]), joint or muscle pain, headache, diarrhoea, smell or taste disorders, and dyspnoea, as compared to individuals with ≤ 2 vaccinations (Fig. 3A, Table S4). In contrast, the likelihood of upper airway symptoms, including nasal discharge (1.84 [1.80–1.89]), cough (1.49 [1.45–1.52]), sore throat (1.33 [1.29–1.36]), and phlegm, were higher in individuals who received ≥ 3 vaccinations than those who received ≤ 2 vaccinations. These findings were replicated in a 1:1 matched analysis of individuals who registered COVID-19 symptoms in the system on the same day as symptom onset (Figure 3B, Table S9).
Figure 3. Associations between vaccine status and COVID-19 symptoms.
A. Associations between vaccine status and symptom odds. Multivariable analysis used each symptom as an outcome and mutant strain, age, body mass index, underlying disease, vaccination history, and history of previous infection as explanatory variables. The detailed results of the multivariable analysis are presented in Table S4. Three or more vaccinations and the adjusted odds ratio (OR) for each symptom are arranged from highest to lowest. Points indicate ORs, and bars indicate 95% confidence intervals (CIs). B. Differences in symptom odds by vaccine status identified by a 1:1 propensity score matching analysis. Individuals, excluding those under 10 years of age, were grouped for individuals with ≥ 3 vaccinations or the others, and background matched between the two groups. Only individuals, who were registered into the system on the same day as symptom onset, were included in the matched analysis. The symptom odds ratios of individuals with 3 vaccinations to the others are shown in order from highest to lowest. Points indicate ORs, and bars indicate 95% CIs. The detailed results of matching analysis are shown in Table S9. C. Associations of vaccination dose, times since last vaccination, and symptom odds. Days since last vaccination were included as a variable as 60-days scales in the multivariable analysis. ORs of each symptom in individuals with two- or three-dose vaccinations were plotted over time as 60-days scales after last vaccination, referenced to unvaccinated individuals. Points indicate ORs, and bars indicate 95% confidence intervals (CIs). The detailed results of the multivariable analysis are presented in Table S10.
In individuals with two or three vaccinations, we also analysed for associations between time that passed since last vaccination and symptom prevalence, referenced to unvaccinated individuals. Consistent with the logistic regression analyses in the unmatched population groups with ≥ 3 vs. ≤ 2 vaccinations, individuals with three vaccinations had lower odds of systemic symptoms (fever, headache, severe fatigue, decreased food intake, and joint and muscle pain), but a higher odd of nasal discharge, as compared to individuals with two vaccinations throughout the period since last vaccination. For individuals with both two- and three-dose vaccinations, the ORs of systemic symptoms increased, whereas the ORs of sore throat and nasal discharge decreased, over time since last vaccination (Fig. 3C, Table S10). These data indicate that the effects of vaccine on reducing systemic, but increasing upper respiratory, symptoms waned over time after last vaccination in individuals with omicron breakthrough infection.
The study population was also divided into subgroups based on a history of previous SARS-CoV-2 infection. The individuals with previous SARS-CoV-2 infection exhibited associations similar to vaccination status, including a higher likelihood of upper respiratory symptoms and a lower likelihood of systemic symptoms (Fig. S6, Table S2, S4, and S7).
Associations between COVID-19 symptoms and clinical outcomes in elderly individuals
The proportion of each symptom by age group was calculated (Fig. 4A, Table S11). Fever and decreased food intake were most prevalent in individuals under 10 years of age and decreased with age. Cough prevalence exhibited bimodal peaks in the 20s and 70s age groups. Other symptoms were most prevalent in individuals in their 20s and 30s, with proportions decreasing with increasing age throughout the 70s. Consistent with these data, multivariable analyses identified advanced age (≥ 65 years) as an independent factor associated with a lower likelihood of development of any of the 12 preselected symptoms than younger ages (Fig. 4B, Table S12).
Figure 4. Associations of COVID-19 symptoms with age and progression to severe disease.
A. Prevalence of COVID-19 symptoms according to age. The shade colour of the cells is linked to the high and low percentage values. B. Associations between age and symptom odds. Multivariable analysis used each symptom as an outcome and mutant strain, age (elderly or non-elderly), body mass index, underlying disease, vaccination history, and history of spontaneous infection as explanatory variables. Elderly individuals (age ≥ 65 years) and the adjusted odds ratio (OR) for each symptom are arranged from highest to lowest. The detailed results of the multivariable analysis are presented in Table S12. C. Associations of COVID-19 symptoms with progression to severe disease in elderly subjects. Multivariable analysis was performed using progression to severe disease as an outcome and mutant strain, age, body mass index, underlying disease, vaccination history, history of previous infection, and all symptoms as explanatory variables. Symptoms are sorted in descending order of OR for progression to severe disease. Points indicate ORs, and bars indicate 95% confidence intervals. The detailed results of the multivariable analysis are presented in Table S5.
Multivariable analyses also identified specific COVID-19 symptoms associated with adverse clinical outcomes in elderly individuals. Dyspnea, fever, decreased food intake, and severe fatigue were associated with an increased odds of severe disease (3.01 [1.84–4.91], 2.91 [1.89–4.51], 2.41 [1.55–3.74], and 1.93 [1.22–3.07], respectively) (Fig. 4C, Table S5). Indeed, the combination of these four symptoms was associated with progressively increasing odds of severe disease (adjusted OR [95% CI] for the number of symptoms 1–4, with none as a reference: 2.98 [1.73–5.14], 7.46 [4.15–13.41], 14.38 [7.14–28.98], and 40.72 [14.72–112.68], respectively) (Table S13). In contrast, sore throat and nasal discharge were associated with a decreased odds of severe disease (0.39 [0.24–0.63] and 0.48 [0.28–0.82], respectively) (Fig. 4C, Table S5). The combination of these two upper airway symptoms was associated with a decreased odds of severe disease (adjusted OR [95% CI] with no upper respiratory symptom as a reference: 0.20 [0.09–0.46]) (Table S13).
Discussion
This registry-based self-entry COVID-19 symptom study was conducted over an interval when two omicron subvariants were prevalent. Collectively, individuals with omicron infections exhibited more commonly upper respiratory symptoms, e.g., cough, sore throat, and nasal discharge, than systemic symptoms (Fig. 2A). The clinical features of individuals with omicron breakthrough infections differed from those in the early Wuhan strain-dominated pandemic period, which was characterized by a higher prevalence of fever, cough, dyspnoea, and fatigue and a lower prevalence of upper airway symptoms20–22. The pattern of a higher incidence of upper respiratory symptoms and a lower incidence of systemic symptoms was replicated in unvaccinated individuals with omicron infections (Fig. S3), suggesting that viral strain is a variable causing differences in clinical manifestations between individuals with the Wuhan strain vs. omicron infections.
The omicron subvariants themselves also differed in symptom prevalence. Although emerging later in the pandemic, BA.5 was associated with a higher prevalence of systemic symptoms than BA.2 (Fig. 2B). The increased BA.5 systemic symptom prevalence may reflect greater escape from humoral immunity as compared to BA.223,24 and, as indicated by differences in unvaccinated individuals, intrinsic strain differences (Fig. S4, Table S6). Notably, the risk of progression to severe disease did not significantly differ between BA.2 vs. BA.5 (Table S5).
Significant associations between vaccination status or previous COVID-19 infection and specific symptoms following omicron breakthrough infection were also identified. For example, a reduced risk of systemic symptoms, including fever, fatigue, and headache, was observed in individuals with omicron infections and ≥ 3 vaccinations or a history of previous infection. In contrast, a strong correlation was observed between a history of ≥ 3 vaccinations or previous infection and increased upper airway symptom prevalence (Fig. 3A/B, S5, and S6). Notably, it was shown that these vaccine effects on COVID-19 clinical symptoms waned over time after vaccination (Fig. 3C). These contrasting systemic vs. upper respiratory post-vaccination symptom manifestations may reflect: 1) vaccine-mediated reductions in viral load, reduced cytokine release into the systemic circulation, and, hence, reduced systemic symptoms25–27, and 2) vaccine-mediated amplification of local host antiviral responses to upper respiratory tract SARS-CoV-2 infection, the consequences being increased symptom prevalence with earlier symptom peaks (Fig. S5B)28,29. The increased prevalence of post-breakthrough upper respiratory symptoms after vaccinations likely accounts for the unexplained increase in total symptom burden, but reduced systemic symptoms (fever and chills), recently reported in a cohort of vaccinated United States Essential and Frontline workers after SARS-CoV-2 breakthrough infections25.
We observed a decreasing prevalence of COVID-19 symptoms with age (Fig. 4B). These findings are consistent with an earlier study showing that typical COVID-19 symptoms were less commonly reported in adult groups with advanced age30,31. Importantly, if systemic symptoms were present in the elderly (≥ 65-year-old group), strong associations with severe disease were observed (Fig. 4C), consistent with reports of pre-vaccinated United States veterans3. Unexpectedly, our data suggest that upper respiratory tract symptoms are associated with a reduced risk of severe disease in the elderly.
The structure of this study of single-queried symptomatic individuals with COVID-19 infection is associated with limitations. First, asymptomatic individuals with COVID-19 were not enrolled in the TDS or RCPP, and those who registered as asymptomatic were excluded from the study. Accordingly, we were unable to capture all COVID-19 cases during the study period in the overall Sapporo population. Our findings, therefore, are limited to symptomatic individuals with omicron infection and do not describe how distinct omicron subvariants, age, vaccination, or previous infection may have affected the prevalence of symptoms in the overall population. While practically challenging, future studies may consider prospective COVID-19 screening of large cohorts to identify all incident infections and characterize clinical symptoms. Second, there may have been differences in testing behaviours of individuals in specific subgroups. For example, given the temporal difference between the omicron subvariant BA.2 vs BA.5 pandemic periods, individual testing behaviours may have differed between the two periods, a potential confounding factor in comparing symptom prevalence between the two omicron subvariants. It is also possible that elderly individuals may have developed more frequent testing behaviours as compared to younger individuals, which might have contributed to our findings of lower symptom prevalence in elderly individuals. Third, while COVID-19 clinical symptoms generally track the kinetics of SARS-CoV-2 infections, symptom information was collected at a single time point upon registration in our study. Consequently, we were unable to provide longitudinal data describing the evolution of COVID-19 symptoms over a single infection.
Several other limitations exist more generally in our study design. First, since symptom data were entered directly by individuals without the assistance of healthcare providers, the data are subject to individual perception variance. However, these are the data provided in clinical encounters. Second, since symptom data were analysed for 12 predefined questions, COVID-19 symptoms other than the predefined questions, including neurological and psychological symptoms, were not evaluated. Third, the reporting of clinical symptom intensity was not collected, eliminating quantitative assessments of symptom intensity. Fourth, the small number of individuals with severe disease may have resulted in a statistically underpowered detection of factors potentially associated with severe COVID-19 outcomes. Nevertheless, the significance of the specific symptoms identified as independent factors associated with severe outcomes was statistically robust. Finally, our study may represent clinical pictures of omicron infection relatively unique to Sapporo, Japan. Future replication studies are needed to test whether our findings are generalized under different healthcare systems and population demographics.
In conclusion, our symptom-based description of the clinical manifestations of COVID-19 during the BA.2- and BA.5-dominated COVID-19 pandemic periods provides practical insights into clinical features of the current COVID-19 pandemic. First, BA.5 infections were associated with more prevalent systemic symptoms than BA.2 infections in both vaccinated and unvaccinated individuals. These data suggest that BA.5 emerged as a more troublesome variant than the BA.2 antecedent. Second, an increased prevalence of local upper respiratory tract symptoms, but reduced systemic symptom prevalence, was observed post-vaccination (or previous infection) following omicron breakthrough infections. Thus, it might be appropriate to counsel individuals contemplating vaccination that post-vaccination breakthrough COVID-19 infections may be associated with an increased likelihood of upper airway symptoms, with offsetting benefits being a shorter symptom interval and a reduced risk of severe outcomes. Third, individuals with advanced age experienced, on average, fewer omicron-induced symptoms, but when present, systemic but not upper respiratory symptoms heralded worsened outcomes. These observations may serve as a practical guide to utilize COVID-19 symptoms to predict clinical outcomes for elderly patients with omicron infections.
Supplementary Material
Research in context.
Evidence before this study:
COVID-19 clinical symptoms and disease severity have evolved over the COVID-19 pandemic as host immunity has become more widespread through vaccination and natural infection and the transition to mutant strains. Data linking patient clinical presentations to viral, host factor, and disease outcomes must be continuously updated to guide healthcare responses to successive SARS-CoV-2 mutant strains. We searched PubMed up to November 2022, using terms (“COVID-19” or “SARS-CoV-2”) AND “symptom” AND (“vaccination” or “natural infection” or “age”) AND “omicron”. Multiple studies of the original Wuhan strain have described associations between COVID-19 symptoms and viral loads, vaccination status, and clinical outcomes. To the best of our knowledge, the most up-to-date report relating COVID-19 symptoms and outcomes with SARS-CoV-2 mutant strains focused on delta vs. the original dominant omicron strain BA.1. This United Kingdom self-reporting prospective observational study of 63 002 individuals infected with SARS-CoV-2 delta or omicron described a shorter symptom interval and a lower risk of hospitalization with the omicron vs. delta variant. Another prospective cohort study of 1119 United States essential and frontline workers with SARS-CoV-2 delta or omicron infections reported similar findings but also an unexplained increase in total symptom burden in individuals with breakthrough infections for the omicron variant after vaccination (≥ three doses). Notably, no large-scale epidemiological studies describing the relative COVID-19 symptoms and outcomes in the newer BA.2 and BA.5 omicron subvariants, nor relationships to host immune status, are available despite the widespread prevalence of these variants.
Added value of this study:
Using a highly integrated large-scale web-based healthcare registry system in Sapporo, Japan, we enrolled 157 861 individuals with symptomatic COVID-19 BA.2 and BA.5 omicron infections into this study. We report comprehensive and updated data describing BA.2 vs BA.5 clinical manifestations, linked to individual background, vaccination status, history of previous SARS-CoV-2 infection, and clinical outcomes. The large study population, the focus on COVID-19 clinical symptoms, and recent period (from April 25, 2022 to September 25, 2022) for data acquisition, allowed us to: 1) compare the relative symptom prevalence for BA.2 vs. BA.5 infections; and 2) identify novel associations between mutant omicron strains, specific symptoms, host immune status, as modified by vaccination or previous infection, and clinical outcomes.
Implications of all the available evidence:
We found that individuals infected with BA.5 exhibited a higher risk for systemic symptoms than BA.2, independent of host factors, including vaccination status. Individuals infected with BA.2 or BA.5, who were vaccinated with ≥ three doses or had a history of previous infection, were less likely to develop systemic symptoms, but more likely to develop upper respiratory symptoms, than unvaccinated, vaccinated with ≤ two doses, or previously uninfected individuals. Our study also found that older individuals were less likely to develop clinical symptoms with omicron infection. However, when symptoms were manifest, systemic symptoms were associated with an increased risk, whereas upper respiratory symptoms were associated with a decreased risk, of severe disease. In summary, our study provides the most up-to-date characterization of clinical manifestations of COVID-19 in the omicron BA.2 and BA.5 pandemic periods. Our findings may serve as a practical guide to utilize COVID-19 symptoms to broadly modify healthcare strategies and predict clinical outcomes for elderly patients with omicron infections. Continuous updates for our observations are needed as newer SARS-CoV-2 variants emerge.
Acknowledgment
The authors thank the Public Health Office, Health and Welfare Bureau, City of Sapporo, which has long been involved in the identification of all infected populations in Sapporo since the beginning of the COVID-19 epidemic and in the management and integration of the data. Additionally, we thank the Sapporo City Institute of Public Health for providing genomic information on Sapporo’s prevalent SARS-CoV-2 mutant strains. We also thank Eric C. Roe at University of North Carolina at Chapel Hill for editorial assistance.
Funding:
Japan Agency for Medical Research and Development.
This project was supported in part by AMED under Grant JP223fa627005 and JP21zf0127004, the National Institutes of Health, the National Institute of Diabetes and Digestive and Kidney Diseases (P30 DK065988 to RCB), the National Heart, Lung, and Blood Institute (R01HL163602 to KO), the Cystic Fibrosis Foundation (RDP BOUCHE15R0 to RCB; BOUCHE19XX0 to RCB and KO; OKUDA20G0 to KO), and a research grant from the Cystic Fibrosis Research Institute to KO. This project was also partially supported by the Rapidly Emerging Antiviral Drug Development Initiative at the University of North Carolina at Chapel Hill, funded by the North Carolina coronavirus state and local fiscal recovery funds program appropriated by the North Carolina General Assembly, and by the project of junior scientist promotion at Hokkaido University.
Declaration of interests
KO reports grants from the National Heart, Lung, and Blood Institute, the Cystic Fibrosis Foundation (CFF), and the Cystic Fibrosis Research Institute. IY reports grants from the Japan Agency for Medical Research and Development (AMED), the Health, Labour and Welfare Policy Research Grants and Medi-Physics, and speaker fees from Chugai Pharmaceutical Co. and AstraZeneca, outside the submitted work. RCB reports grants from the National Institutes of Health, the National Institute of Diabetes and Digestive and Kidney Diseases, and the CFF. SK reports grants from the AMED. The other authors declare that they have no conflict of interest.
Footnotes
Ethics Approval
The research protocol was approved by the Ethics Committee of Hokkaido University Hospital (Research No. 022–0225). Analysis was performed using database in Sapporo city, with no additional risks to the patients. Therefore, the requirement for informed consent from individual participants was waived by the ethics committee. All methods were performed in accordance with the relevant guidelines and regulations of the Ethics Committee of Hokkaido University Hospital. All patient data were anonymized.
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Data sharing statement
This study is an analysis of confidential data held by the City of Sapporo. The data will not be made publicly available at the request of the City of Sapporo.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
This study is an analysis of confidential data held by the City of Sapporo. The data will not be made publicly available at the request of the City of Sapporo.