Prevalence and predictors of long COVID among non-hospitalised adolescents and young adults: a prospective controlled cohort study

doi:10.21203/rs.3.rs-2021203/v1

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Prevalence and predictors of long COVID among non-hospitalised adolescents and young adults: a prospective controlled cohort study

https://doi.org/10.21203/rs.3.rs-2021203/v1

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The prevalence and predictors of long COVID in young people remain unresolved. We aimed to determine the point prevalence of long COVID in non-hospitalised adolescents and young adults six months after the acute infection, to determine the risk of developing long COVID adjusted for possible confounders, and to explore a broad range of potential risk factors (prespecified outcomes). We conducted a prospective controlled cohort study of 404 SARS-CoV-2-positive and 105 SARS-CoV-2-negative non-hospitalised individuals aged 12–25 years (ClinicalTrial ID: NCT04686734). Data acquisition was completed February 2022. Assessments included pulmonary, cardiac and cognitive functional testing, biomarker analyses, and completion of a questionnaire, and were performed at inclusion (early convalescent stage) and six months follow-up. The WHO case definition of long COVID was applied. The point prevalence of long COVID at six months was 49% and 47% in the SARS-CoV-2-positive and negative group, respectively. SARS-CoV-2-positivity did not predict development of long COVID (relative risk 1.06, 95% CI 0.83 to 1.37). The main predictor was symptom severity at inclusion, which correlated strongly to personality traits. Low physical activity and loneliness were also predictive, while biological markers were not. In conlusion, our study aims were met, and the findings suggest that persistent symptoms were not driven by the infection, but were associated with psychosocial factors.

Long COVID

post-COVID-19 condition

post-acute sequelae of COVID-19

adolescents

SARS-CoV-2

post-infective fatigue syndrome

Long COVID is characterised by the persistence of symptoms such as fatigue, dyspnoea and “brain fog” three months or longer after infection with the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)¹. The condition is also referred to as post-COVID-19 condition and post-acute sequelae of COVID-19, and appears to occur following acute SARS-CoV-2 infection of any severity, may cause prolonged disability, and has become a major public health concern globally. The period prevalence of long COVID remains uncertain. For instance, a review of long COVID symptoms in children and adolescents reported fatigue rates between 3 and 87%, whereas a systematic review reported the confidence interval of fatigue prevalence after COVID-19 to be 27 to 46%^2,3. Generally, studies with a control group report a lower prevalence of long COVID than uncontrolled studies^3,4.

In patients with severe acute infection, long COVID may be partly explained by tissue damage caused by viral and immunological mechanisms, as well as complications related to intensive care^5–7. When sequelae arise after mild acute infection, most cases seem to fit within the label of post-infective fatigue states, in which bodily complaints accompany scarce findings on standard clinical examination⁸. Post-infective fatigue states may occur in the aftermath of a diverse array of infections^9–12. Across multiple prospective cohort studies, almost one half of the subjects report post-infective fatigue states six months after the infectious event, and 10–15% suffer from moderate to severe disability meeting diagnostic criteria for a post-infective fatigue syndrome (PIFS), in line with current studies of long COVID^9–11.

Whereas case definitions of long COVID diverge and lack validation, studies of PIFS have benefitted from an international case-definition¹³. This definition is centred around the symptom of fatigue, which should be persistent from onset of the acute infectious event, severely affect daily activities, and not be caused by any other condition; also, cases must experience at least four of eight additional symptoms (such as headache and concentration/memory problems). In contrast, the case definition of long COVID established by the World Health Organisation (WHO) encompasses any symptom, not only fatigue, occurring in the aftermath of acute COVID-19; does not require symptom persistence since the infectious event; does not stipulate exclusions of other explanatory conditions; and does not stipulate significant disability¹.

Most previous studies of long COVID have been conducted on hospitalised patient populations and without control groups. Prospective studies of non-hospitalised COVID-19 patients with contemporaneous (non-COVID) control subjects are scarce⁸. To the best of our knowledge, no previous reports have provided prevalence estimates for long COVID based upon a rigorous evaluation of caseness, including the assessment of alternative medical and psychiatric diagnoses. Furthermore, there appears to be very few studies investigating a broad array of possible risk factors for long COVID, including both “direct” disease specific factors, such as immunological activation markers, and “indirect” general stressors^14,15. Hence, the aims of the present prospective controlled cohort study of non-hospitalised adolescents and young adults were threefold (as prespecified in the Statistical Analysis Plan): a) to determine the point prevalence of long COVID according to the WHO definition and the international PIFS definition six months after acute COVID-19; b) to determine the risk of development of long COVID six months after acute COVID-19, adjusted for possible confounders; and c) to explore a broad range of potential predictors of long COVID.

A total of 151,110 RT-PCR-tests of SARS-CoV-2 were carried out on individuals 12–25 years old at the collaborating microbiological laboratories during the recruitment period (Fig. 1). A total of 5,912 individuals (3.9%) were SARS-CoV-2-positive, of whom 2,251 (50% males) were invited into the study. Of this group 404 (39% males, mean age 17.8 years) were enrolled (Table 1, Supplementary Table S1). Among the SARS-CoV-2-negative individuals, a total of 105 were enrolled (35% males, mean age 17.7 years), and their negative status was confirmed by the absence of anti-nucleocapsid antibodies at baseline. The invited sample had a distribution of sex and age similar to the background population; within the enrolled sample, however, the age group 18–25 years had a disproportionately higher number of females (Supplementary Table S2). The SARS-CoV-2-positive group had a higher proportion of non-European ethnicity than the SARS-CoV-2-negative group; otherwise, the two groups were comparable across a wide range of characteristics (Table 1).

Table 1

Cohort characteristics at baseline and six months follow-up.
		At inclusion (baseline)		At six months follow-up
		SARS-CoV-2-positive group (n = 404)	SARS-CoV-2-negative group (n = 105)	SARS-CoV-2-positive group (n = 382)	SARS-CoV-2-negative group (n = 85)
Background
Sex – no. males (%)		157 (39)	37 (35)	152 (40)	31 (37)
Age group – no. (%)
	12 < 15	101 (25)	25 (24)	98 (26)	18 (21)
	15 < 18	107 (27)	35 (33)	104 (27)	31 (37)
	18 < 21	84 (21)	26 (25)	80 (21)	21 (48)
	21 < 25	112 (28)	19 (18)	100 (26)	15 (18)
BMI, z-score ^a - mean (SD)		0.45 (1.2)	0.49 (1.1)	0.52 (1.2)	0.51 (1.1)
95% CI of the mean		0.34 to 0.47	0.27 to 0.70	0.40 to 0.64	0.27 to 0.76
Ethnicity
	Non-European - no. (%)	98 (24)	4 (3.8)	88 (23)	2 (2.4)
Current comorbidity
	Any comorbidity - no. (%)	81 (21)	36 (35)	89 (24)	31 (37)
	ADHD - no. (%)	5 (1.3)	3 (2.9)	5 (1.4)	3 (3.6)
	Asthma - no. (%)	26 (6.7)	5 (4.8)	27 (7.3)	4 (4.8)
	Allergy and atopy - no. (%)	16 (4.1)	10 (9.6)	17 (4.6)	9 (11)
	Anxiety and depression - no. (%)	1 (0.3)	3 (2.9)	4 (1.1)	3 (3.6)
	Endocrinological - no. (%)	6 (1.5)	1 (1.0)	6 (1.6)	1 (1.2)
	Gastrointestinal - no. (%)	5 (1.3)	4 (3.8)	6 (1.6)	5 (6.0)
	Gynaecological - no. (%)	4 (1.0)	1 (1.2)	4 (1.0)	1 (1.2)
	Neurological including primary headache disorders - no. (%)	10 (2.6)	5 (4.8)	9 (2.5)	4 (2.8)
Socieconomic level
	Parents’ highest ISEI-08, score 10 to 90 - median (IQR)	63 (21)	65 (17)	64 (21)	62 (18)
	95% CI of the median	59 to 69	60 to 68	59 to 69	59 to 69
Smoking
	Daily - no. (%)	1 (0.3)	0 (0)	1 (0.3)	0 (0)
	Never - no. (%)	376 (97)	101 (97)	355 (97)	84 (99)
COVID-19 immunisation
	No doses - no. (%)	399 (99)	99 (94)	145 (38)	8 (9.4)
	One dose - no. (%)	5 (1.2)	4 (3.8)	232 (61)	29 (34)
	Two doses - no. (%)	0 (0)	2 (1.9)	5 (1.3)	47 (55)
	Three doses - no. (%)	0 (0)	0 (0)	0 (0)	1 (1.2)
Symptoms and functional impairment^b
Fatigue^c, score 0 to 33 - mean (SD)		16.2 (5.7)	13.2 (4.7)	14.5 (5.2)	13.3 (3.8)
95% CI of the mean		15.6 to 16.8	12.4 to 14.2	14.0 to 15.1	12.5 to 14.1
Post-exertional malaise^d, score 0 to 100 - median (IQR)		20 (40)	10 (24)	10 (35)	10 (23)
95% CI of the median		15 to 25	10 to 15	10 to 15	5 to 10
Cognitive symptoms^e, score 3 to 15 - median (IQR)		6 (6)	6 (5)	6 (6)	6 (4)
95% CI of the median		5 to 6	5 to 6	6 to 7	5 to 7
Respiratory symptoms^f, score 2 to 10 - median (IQR)		4 (3)	3 (2)	3 (3)	3 (1)
95% CI of the median		4 to 5	3 to 3	3 to 4	3 to 4
Symptoms of anxiety^g, score 0 to 21 - median (IQR)		5 (6)	7 (6)	6 (6)	5 (7)
95% CI of the median		5 to 6	6 to 8	5 to 6	5 to 7
Symptoms of depression^h, score 0 to 21 - median (IQR)		3 (5)	3 (4)	3 (5)	3 (6)
95% CI of the median		3 to 4	3 to 5	2 to 3	2 to 5
Quality of lifeⁱ, score 0 to 100 - median (IQR)		77 (25)	77 (20)	78 (22)	76 (19)
95% CI of the median		75 to 80	72 to 79	77 to 80	72 to 79
Clinical findings
Days since symptom onset/PCR test - median (IQR)		18 (6)	17 (7)	213 (17)	211 (14)
Range		8 to 28	2 to 27	170 to 341	195 to 243
Time span between baseline and follow-up, days - median (SD)		NA	NA	193 (20)	193 (14)
Range				164 to 326	183 to 252
Tympanic temperature, ^oC - mean (SD)		36.8 (0.4)	36.7 (0.4)	36.6 (0.4)	36.7 (0.4)
95% CI of the mean		36.7 to 36.8	36.6 to 36.7	36.6 to 36.7	36.6 to 36.7
SpO₂, % - mean (SD)		98.6 (1.1)	98.6 (1.2)	98.5 (1.1)	98.3 (1.3)
95% CI of the mean		98.5 to 98.8	98.3 to 98.8	98.4 to 98.7	98.0 to 98.6
FVC, % of predicted^j - mean (SD)		99.5 (10.0)	100.4 (10.3)	99.5 (10.3)	99.9 (9.9)
95% CI of the mean		98.4 to 100.6	98.3 to 102.5	98.4 to 100.6	97.7 to 102.2
Laboratory findings
Blood Haemoglobin, g/dL - mean (SD)		13.5 (1.2)	13.5 (1.1)	13.6 (1.2)	13.7 (1.0)
95% CI of the mean		13.4 to 13.6	13.3 to 13.8	13.5 to 13.7	13.4 to 13.9
Blood Leukocyte count, 10⁹ cells/L - mean (SD)		5.9 (1.5)	5.6 (1.3)	6.1 (1.8)	5.9 (1.5)
95% CI of the mean		5.8 to 6.1	5.4 to 5.9	5.9 to 6.3	5.5 to 6.2
Plasma hsCRP, mg/L - median (IQR)		0.8 (2.2)	1.3 (3.0)	1.2	1.8
95% CI of the median		0.7 to 1.1	0.7 to 1.7	1.0 to 1.6	1.0 to 3.2
Plasma D-dimer, mg/L - median (IQR)		0.2 (0.2)	0.2 (0.1)	0.1 (0.1)	0.2 (0.1)
95% CI of the median		0.2 to 0.2	0.2 to 0.2	0.1 to 0.1	0.1 to 0.2
Serum NT-proBNP, ng/L - median (IQR)		35 (36)	34 (34)	30 (30)	34 (35)
95% CI of the median		31 to 38	26 to 37	28 to 34	26 to 42
Serum Troponin T, ng/L - median (IQR)		4.0 (3.6)	4.0 (3.5)	2.1 (3.0)	2.1 (3.0)
95% CI of the median		4.0 to 4.0	2.6 to 4.0	1.9 to 2.3	1.4 to 2.8
Serum SARS-CoV-2 antibody titer^k - median (IQR)		4.0 (14)	0 (0)	23 (44)	0.1 (0.01)
95% CI of the median		3.0 to 5.6	0 to 0	20 to 26	0.1 to 0.1
SARS-CoV-2 = Severe acute respiratory syndrome coronavirus 2; SD = Standard deviation; IQR = interquartile range; CI = Confidence interval; PCR = Polymerase chain reaction; NA = Not Applicable; BMI = Body mass index; ISEI = International socio-economic index; FEV1 = Forced Expiratory Volume of 1st second; FVC = Forced Vital Capacity; SpO₂ = Peripheral oxygen saturation; hsCRP = high-sensitive assay of C-reactive protein; ALT = Alanine aminotransferase; NT-proBNP = N-terminal pro-Brain Natriuretic Peptide. ^aStandardised score calculated according to World Health Organisation 2006 Child Growth Standards. ^bWith the exception of ‘Quality of life’, higher values imply more symptoms. For ‘Quality of life’, higher values imply higher quality of life and less functional impairment. ^cFrom the Chalder Fatigue Questionnaire, cf. Supplementary paragraph 1.6. ^dFrom the DePaul Symptom Questionnaire, cf. Supplementary paragraph 1.6. ^eThe sum score across the three items “memory problems”, “concentration problems” and “decision making problems”, cf. Supplementary paragraph 1.6. ^fThe sum score across the two items “dyspnoea” and “coughing”, cf. Supplementary paragraph 1.6. ^gFrom the Hospital Anxiety and Depression Scale; anxiety subscale, cf. Supplementary paragraph 1.6. ^hFrom the Hospital Anxiety and Depression Scale; depression subscale, cf. Supplementary paragraph 1.6. ⁱFrom the Pediatric Quality of Life Inventory, cf. Supplementary paragraph 1.6. ^jThe Global Lung Function Initiative 2012 reference values were used to calculate predicted values, cf. Supplementary paragraph 1.4. ^kTotal anti-nucleocapsid IgG and IgM.

A total of 26 individuals (5.1%) were lost to follow-up (Fig. 1; Supplementary Table S3), which was completed by February 2022. Additionally, 16 individuals were excluded from the SARS-CoV-2-negative group due to acute COVID-19 in the observational period and/or the presence of anti-nucleocapsid antibodies at six months, leaving n = 382 in the SARS-CoV-2-positive group and n = 85 in the SARS-CoV-2-negative group for evaluation at six months (Fig. 1). The SARS-CoV-2-positive group had received fewer immunisation doses in the observational period; otherwise, the two groups remained comparable (Table 1). Missing data points were randomly distributed (Supplementary Table S4). A total of 10 (2.7%) individuals in the SARS-CoV-2 positive group and three (3.6%) individuals in the SARS-CoV-2 negative group had a serological pattern suggesting recent EBV infection prior to enrolment or during the observational period (Supplementary Tables S5 and S6).

At six months follow-up, 224 of 464 individuals were classified as having long COVID (Supplementary Figure S1). Correspondingly, 60 of 464 individuals were classified as having PIFS (Supplementary Figure S2). The point prevalence of long COVID was almost identical in the SARS-CoV-2-positive and the SARS-CoV-2-negative group, at 49% and 47%, respectively (Table 2). The prevalence of PIFS was 14% and 8.2% in the two groups, but with overlapping confidence intervals. Prevalence estimates were similar in sensitivity analyses (Supplementary Tables S7–S10). For the majority of individual symptoms, the confidence intervals of the prevalence overlapped between the SARS-CoV-2-positive and the SARS-CoV-2-negative groups; however, some dimensions of fatigue and ear-nose-throat symptoms were more common in the SARS-CoV-2-positive group (Supplementary Table S11).

Table 2

Point prevalence % (confidence intervals)a of long COVID and the post-infective fatigue syndrome at six months follow-up. Per protocol data.
	SARS-CoV-2-positive group (n = 379)^b	SARS-CoV-2-negative group (n = 85)	Risk difference (95% CI)
Long COVID^c (n = 224)	49 (44 to 54)	47 (37 to 58)	1.5 (-10 to 13)
Post-infective fatigue syndrome^d (n = 60)	14 (11 to 18)	8.2 (3.8 to 16)	5.7 (-2.0 to 12)
CI = Confidence interval. ^aAgresti-Coull and Agresti-Caffo confidence intervals were calculated, respectively, for prevalence and risk difference. ^bThree of the 382 individuals in the SARS-CoV-2-positive group that attended six months follow-up had missing values in questionnaire data precluding classification; hence, they were removed from prevalence analyses. ^cAccording to the WHO-definition of long COVID¹. ^dAccording to the international case definition of PIFS.¹³

SARS-CoV-2 status did not predict either long COVID or PIFS at six months (Table 3). The Principal Component Analysis of clinical symptoms and psychological traits extracted one main component from each of these two variable groups (Supplementary Tables S12–S14). These components, representing symptom severity and emotional maladjustment, were strongly associated with both long COVID and PIFS in bivariate regression analyses (Supplementary Table S15). Other notable risk factors for both conditions were female sex, low self-reported level of physical activity before infection, loneliness and negative life events during the preceding year. The majority of biological markers, including immunological markers and markers of CNS impairment, did not show any predictive value (Supplementary Table S15).

Table 3

Baseline independent predictors of long COVID and the post-infective fatigue syndrome at six months follow-up. Final multiple regression models of per protocol data (modified Poisson regression with log-link and robust error variances).
	Long COVID^a		Post-infective fatigue syndrome^b
	Relative risk (95% CI)^c	p-value^d	Relative risk (95% CI)^c	p-value^d
SARS-CoV-2 status
SARS-CoV-2-positive at baseline	1.06 (0.83 to 1.37)	0.663	1.16 (0.64 to 2.30)	0.636
Background and constitutional factors
Female sex	1.16 (0.94 to 1.44)	0.158	1.55 (0.90, 2.83)	0.117
Age, years	0.98 (0.95 to 1.00)	0.087	1.01 (0.95 to 1.08)	0.653
BMI, z-score^e	1.00 (0.92 to 1.08)	0.973	0.80 (0.66 to 0.96)	0.015
Ethnicity non-European	0.95 (0.75 to 1.20)	0.687	1.18 (0.72 to 1.87)	0.505
Any comorbidity	1.10 (0.89 to 1.36)	0.358	0.98 (0.61 to 1.53)	0.918
Observational period characteristics
Time span between baseline and follow-up, days	1.00 (1.00 to 1.01)	0.696	0.99 (0.98 to 1.00)	0.020
Immunisation against COVID-19^f	0.80 (0.32 to 1.67)	0.588	2.59 (0.68 to 7.23)	0.147
Remaining predictor variables
Symptom severity^g	1.41 (1.27 to 1.56)	< 0.001	3.29 (2.67 to 4.10)	< 0.001
Physical activity prior to infection^h, score 1 to 5	0.96 (0.92 to 1.00)	0.034
Lonelinessⁱ, score 20 to 80	1.01 (1.00 to 1.02)	0.013
Verbal learning^j, score 0 to 36			1.05 (1.00 to 1.11)	0.041
Negative life events prior to last year^k			0.89 (0.81 to 0.97)	0.007
LF-RRI^l, ms²			0.69 (0.56 to 0.86)	< 0.001
The analyses encompassed a total of n = 464 individuals. Three individuals belonging to the SARS-CoV-2-positive group at baseline had missing values in questionnaire data at six months precluding classification according to the case definitions; hence, they were removed from regression analyses. CI = Confidence interval; SARS-CoV-2 = Severe acute respiratory syndrome coronavirus 2; BMI = Body mass index; LF-RRI = Low-frequency power of heart rate variability. ^aAccording to the WHO-definition of long COVID¹. ^bAccording to the international case definition of PIFS¹³. ^c95% Profile likelihood-based confidence intervals. ^dLikelihood ratio p-values. ^eStandardised score calculated according to World Health Organisation 2006 Child Growth Standards. ^fOne or more doses of COVID-19 vaccine. ^gComponent extracted from Principal Component Analysis (PCA) of 10 clinical symptoms variables at baseline; higher value implies more severe symptoms. ^hFrom a single questionnaire item; higher scores imply more physically active. ⁱFrom the UCLA (University of California, Los Angeles) loneliness questionnaire; higher scores imply more loneliness, cf. Supplementary paragraph 1.6. ^jFrom the HVLT-R (Hopkins verbal learning test, revised) immediate recall score; higher scores imply better verbal learning, cf. Supplementary paragraph 1.4. ^kFrom the Life events checklist impact score; higher scores imply more subjective negative impact, cf. Supplementary paragraph 1.6. ^lLog-transformed variable was used for regression analysis

In the final multivariable model, the symptom severity component remained as the main predictor, both for long COVID (RR (95% CI) 1.41 (1.27 to 1.56)), and for PIFS (RR (95% CI) 3.29 (2.67 to 4.10)) (Table 3). Additionally, loneliness and low levels of physical activity predicted long COVID. The symptom severity component correlated with the emotional maladjustment component as well as with sex, explaining why the two latter variables did not show independent predictive value in the multivariable modelling (Supplementary Figure S3).

The sensitivity analyses yielded comparable results, both for bivariate regression analyses and multivariable regression modelling (Supplementary Tables S16–S20).

The main results from the present study of prolonged illness after acute COVID-19 in non-hospitalised adolescents and young adults were: a) the prevalence of long COVID was almost 50% six months after acute COVID-19, but was equally high in a comparable SARS-CoV-2-negative group; b) acute COVID-19 was not an independent risk factor for long COVID; c) the severity of clinical symptoms at baseline, irrespective of SARS-CoV-2 status, was the main predictor of persistent symptoms six months later.

The symptom prevalence data in the present study are in line with other controlled studies of young people after acute COVID-19 reporting a generally high symptom load, but either no or only subtle differences between SARS-CoV-2-positive and SARS-CoV-2-negative individuals^4,16−19. Also, a study of non-hospitalised older age groups showed similar results²⁰, and a large population-based study found no associations between persistent symptom attributed to COVID-19 and serological evidence of SARS-CoV-2 infection²¹.

Hence, in contrast to what is commonly thought, relatively mild acute COVID-19 per se does not seem to be a driver of persistent symptoms. Rather, two other phenomena appear to have a major impact: firstly, symptoms associated with long COVID are common in the general population. For instance, Rimes et al. reported a point prevalence of fatigue at 34–38% among British adolescents²². Similar high rates have been reported for other symptoms, such as dyspnoea and memory problems²³. Secondly, several studies have documented a significant increase in mental distress in the general population during the pandemic²⁴, particularly affecting young people²⁵, which in turn may impact on physical symptoms¹⁵. Hence, non-specific stressors during the pandemic, affecting SARS-CoV-2-positive and SARS-CoV-2-negative individuals similarly, may be important for symptom persistence and associated disability. This effect should be considered when societal countermeasures against infection outbreaks such as lockdowns are implemented.

The observation that baseline SARS-CoV-2-status did not predict long COVID in the regression analyses corroborates the six month point prevalence results. The strong predictive value of baseline symptom severity for symptom persistence echoes previous findings from studies of both long COVID and PIFS^10,11,26,27, as well as general studies of clinical symptoms²². Of particular relevance, Wessely et al. reported that common, minor acute infections in general practice did not predict PIFS, whereas fatigue and psychosocial stress prior to presentation with a clinical infection had strong predictive value²⁸. In the present study, severity of baseline clinical symptoms were strongly associated with female sex and psychological traits of emotional maladjustment. These associations might be important for the understanding of persistent symptoms in general, and deserve attention in future studies.

In contrast to previous reports on long COVID as well as PIFS^11,14,29, no immune markers predicted symptom persistence in the present study. This may be seen as a logical consequence of the absence of the predictive value of COVID-status. Also, vaccination against COVID-19 did not appear to be a protective factor against prolonged illness, in contrast to previous reports⁷. Unaltered concentrations of CNS injury markers in blood in the long COVID group speak against ongoing neuronal injury and astrocytic activation.

When the international case definitions of PIFS was applied to the present data set, the prevalence was comparable to observations from studies of sequelae after other infections^{9–11, 30,31}, and also yielded a non-significant trend towards a higher prevalence in the SARS-CoV-2-positive group. Furthermore, certain dimensions of fatigue (e.g. post-exertional malaise) were more common in the SARS-CoV-2-positive group. These observations suggest that further analysis of the phenomenon of fatigue following COVID-19 might be of value. Also, the results from the present study question the utility of the WHO-definition of long COVID, and suggests that a modification of existing PIFS definitions may be more useful.

Taken together, the results from the present study suggest that factors often labelled ‘psychosocial’ are stronger predictors of persistent symptoms after COVID-19 than ‘biological’ factors. This finding, however, does not imply that long COVID is not ‘real’. Indeed, patients’ experience of disabling symptoms ought to be taken at face value, even though they may have an alternative aetiology than commonly perceived. The results do suggest, however, that non-pharmacological interventions may be beneficial and should be investigated in future studies, in line with experiences from PIFS following other infections³².

The strengths of the present study include rigorous case-definitions and evaluation tools, comprehensive predictor assessment, a well-defined control group, and a low dropout rate. Hence, the risk of biases are low. An important limitation is related to external validity: persistent symptoms seem to be more common and have different predictors in hospitalised patients^6,7,33, and it is unclear to what extent the results of the present study are applicable to those with more severe acute COVID-19. Also, the present study included only young people with the great majority infected with the alpha variant of SARS-CoV-2 hence the generalisability to older age groups and other viral variants is uncertain. Given the potential important impact of external factors influencing symptom persistence, studies across different cultural contexts might yield different results. Finally, while the present study showed no predictive value of acute COVID-19 for the diagnostic entities of long COVID and PIFS, COVID-19 may still be a risk factor for specific symptoms.

Another limitation is the low number of SARS-CoV-2-negative individuals as compared to the SARS-CoV-2-positive group, reducing the statistical power.

Study design

The Long-Term Effects of COVID-19 in Adolescents (LoTECA) project is a prospective cohort study of SARS-CoV-2 positive and negative non-hospitalised adolescents and young adults, with follow-up six and 12 months after inclusion (ClinicalTrials ID: NCT04686734) (Fig. 1; Supplementary paragraph 1.1). The six months follow-up data are the main focus of the present paper; selected baseline data have been reported elsewhere³⁴.

Participants

From 24 December 2020 until 18 May 2021, consecutive individuals 12–25 years old undergoing SARS-CoV-2 testing in two accredited microbiological laboratories in South-East Norway (Fürst Medical Laboratories and Dept. of Microbiology and Infection Control, Akershus University Hospital) were recruited. During the first weeks of the recruitment period, different genetic variants of the SARS-CoV-2 virus belonging to the B.1 lineage were present in the geographical area. From late-February 2021, the B.1.1.7 (alpha) variant became dominant for the remaining part of the recruitment period. Individuals with laboratory-confirmed SARS-CoV-2 infection (detected by upper respiratory tract swabs followed by reverse-transcription polymerase chain reaction (RT-PCR)) were eligible for enrolment after completing quarantine (10 days) (SARS-CoV-2-positive group) (Supplementary paragraph 1.2). Individuals having approximately the same distribution of sex and age as the SARS-CoV-2-infected cases, but with a negative SARS-CoV-2 test from the same microbiological laboratories during the same period were recruited as controls (SARS-CoV-2-negative group). Some individuals in the SARS-CoV-2-negative group had been tested because of acute infectious symptoms, other were asymptomatic close contacts of SARS-CoV-2-positive cases. Exclusion criteria were: a) greater than 28 days since onset of symptoms or SARS-CoV-2 test; b) hospitalisation due to COVID-19; c) pregnancy; d) serological evidence of previous COVID-19 (in the SARS-CoV-2-negative group).

The project was approved by the Regional Committee for Ethics in Medical Research (ref. #203645) and given a limited confidentiality waiver allowing us to identify and approach individuals eligible for recruitment by a simple text message. In compliance with the Norwegian Health Research Act (ACT 2008-06-20 no. 44), written informed consent was obtained from legal guardians for participants < 16 years, or from the participants themselves when age ≥ 16 years.

Assessment program and clinical examination

At inclusion and follow-up, participants attended an identical one-day investigational program at our study centre at Akershus University Hospital, Norway, encompassing a clinical interview and examination, respiratory, cardiac and cognitive function testing, blood sampling, and completion of questionnaires. The clinical examination included recording of vital signs (blood pressure, resting heart rate and respiratory rate, blood oxygen saturation (SpO₂), body temperature), body mass index (BMI), a complete physical examination carried out by a physician, and urine dipstick analysis (including pregnancy testing if indicated) (Supplementary paragraph 1.3).

Functional testing

The forced vital capacity (FVC) and the forced expiratory volume in one second (FEV₁) was measured by standard spirometry, and the FEV₁/FVC ratio was calculated (Supplementary paragraph 1.4).

A five-minute supine electrocardiogram (ECG) recording was used to calculate heart rate variability indices, including power in the high-frequency (HF-RRI; a marker of parasympathetic activity) and low-frequency (LF-RRI; a marker of combined sympathetic and parasympathetic activity) range (Supplementary paragraph 1.4).

Cognitive function tests included the digit span test of working memory, and tests of verbal learning, recall and recognition (Supplementary paragraph 1.4).

Blood sampling and laboratory assays

Blood samples were obtained by antecubital venous puncture. Immune markers assayed in plasma included: C-reactive protein (CRP), growth/differentiation factor 15 (GDF-15), interleukin 1 beta (IL1β), the terminal complement complex (TCC or sC5b-9), regulated upon activation T cell expressed and secreted (RANTES or CCL5), monocyte chemotactic protein (MCP-1 or CCL2), and interferon-inducible protein (IP-10 or CXCL10). CRP, GDF-15 and IL1β are considered proinflammatory markers, TCC is a marker of complement activation, and RANTES, MCP-1 and IP-10 are leukocyte chemoattractant and activation markers (Supplementary paragraph 1.5).

Antibodies (ABs) against SARS-CoV-2 (anti-nucleocapsid and anti-receptor binding domain), as well as Epstein-Barr virus (anti-viral capsid, anti-nuclear antigen and infectious mononucleosis heterophile antibodies) were assayed in serum to document recent or previous infection with these pathogens (Supplementary paragraph 1.5).

Neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAp) were assayed in serum (Supplementary paragraph 1.5), providing markers of brain axonal damage and astrocytic activation, respectively.

Routine blood analyses of haematology and biochemistry (including D-dimer, ferritin, vitamin B₁₂ and the cardiac markers troponin T and N-terminal prohormone of Brain Natriuretic Peptide (NT-proBNP)) were carried out by the accredited laboratory at Akershus University Hospital (Supplementary paragraph 1.5).

Questionnaires

A composite questionnaire charted the personal history of medical and psychiatric comorbidities, the family history of disease, current medication, smoking habits, substance abuse, physical activity, parents’ occupation, vaccination, and history of COVID-19 (Supplementary paragraph 1.6). Parents’ occupations were used as an indicator of socioeconomic status.

Clinical symptoms of fatigue, post-exertional malaise, sleep problems, pain, anxiety, depression, and negative affect were recorded using validated inventories (Supplementary paragraph 1.6). In addition, a symptom inventory specifically designed for PIFS research consisting of 30 single items and slightly modified to fit COVID-19 sufferers was incorporated; sum scores were calculated for cognitive, respiratory and autonomic symptoms, respectively.

The psychological traits of neuroticism, emotional awareness, worrying tendencies, and body vigilance were all charted by validated inventories, as were quality of life and the social variables loneliness and significant life events (Supplementary paragraph 1.6).

Register linkage

Vaccination against SARS-CoV-2 (dosages, dates of administration, and manufacturers) were obtained through linkage with the Norwegian Immunisation Register³⁵.

Caseness assessment

Application of the WHO-definition of long COVID and the international case definition for PIFS at six months was operationalised using questionnaire items relating to the designated symptoms and functional status (Supplementary paragraph 1.7). All participants were classified as cases or non-cases according to both definitions. A distinction was made between certain and uncertain classification based upon a detailed assessment of other conditions (e.g. medical or psychiatric comorbidity) that may explain symptoms. This assessment exploited questionnaire data as well as clinical and laboratory findings, and was performed independently by two researchers blinded to initial SARS-CoV-2 status.

Predictor hypotheses

The scientific literature on long COVID, as well as PIFS, was scrutinised to identify baseline and observational period variables that may predict caseness at six months follow-up (Supplementary paragraph 1.8). A total of 51 variables were identified, grouped as: SARS-CoV-2 status (positive vs. negative), background/constitutional factors (sex, age, BMI, ethnicity, chronic disorders), observational period characteristics (vaccinations, duration from baseline to follow-up), organ function tests/biomarkers, immunological markers, autonomic markers, cognitive function tests, clinical symptoms, psychological traits, and social/behavioural markers. SARS-CoV-2 status was hypothesised to be the main risk factor for long COVID, as well as PIFS, at six months. Background/constitutional factors and observational period characteristics were regarded potential confounders. The remaining variables were assumed to be either mediating variables related to COVID-19 pathophysiology, or independent predictor variables.

Statistical analyses

Long COVID and PIFS at six months follow-up were defined as primary and secondary outcomes, respectively. Participants in the SARS-CoV-2-negative group showing serological evidence of COVID-19 during the observational period were excluded from the analyses. The study had a power of approximately 80% to detect a relative risk (RR) of 1.5 (Supplementary paragraph 1.9).

Prevalence data are reported separately in the SARS-CoV-2-positive and SARS-CoV-2-negative groups, and the risk difference calculated. For prediction analyses, bivariate analyses between the two outcome variables and each hypothesised predictor variable were performed by generalised linear modelling using a modified Poisson approach (log-link and robust error variances). Collinearity between variables were assessed by correlation analyses, and were demonstrated for all clinical symptoms and all psychological traits. Dimensionality reduction was performed by principal component analyses. SARS-CoV-2 status, background/constitutional factors, observational period characteristics and all remaining variables with an unadjusted p-value < 0.25 were included in a multivariable model; variables were then removed and eventually reinserted one by one dependent on their influence on overall goodness-of-fit, in order to find the most parsimonious model (Supplementary paragraph 1.9).

As sensitivity analyses, identical analytical procedures were performed on two different data sets: one with imputation of missing data points with mean or median values, and one with exclusion of participants with: a) uncertain caseness classification; b) vaccination prior to enrolment or less than five days prior to follow-up appointment; or c) evidence of recent EBV infection at enrolment or during the observational period (Supplementary paragraph 1.9). As an extra quality control, an additional sensitivity analysis of the final multivariable regression model was performed where individuals in the SARS-CoV-2-negative group with baseline symptoms suggesting an acute infection were removed alongside the exclusions listed above.

A p-value < 0.05 was considered statistically significant (two-sided tests). All statistical analyses were carried out in SPSS version 28.0 (SPSS Inc., Chicago, IL).

Statements and acknowledgements

Acknowledgements

We thank Helene Mathiesen, Jeanette Dulsrud, Ann Cathrin Pettersen, Siri H. Sletner, Lene Andersen, Birute Stankuniene, Line Sletner, David Hui and the Unit for Research Support at the Dept. of Internal Medicine, Akershus University Hospital, for invaluable practical assistance; Judith Anita Ludviksen and Sigve Fossum Grande for laboratory analyses; Kirsten Haugland, Helge Røsjø and Torbjørn Omland for institutional support; and finally, all the study participants.

Data sharing

Anonymised individual participant data as well as data dictionary will be made available with publication for scientific purposes upon reasonable request. The Services for Sensitive Data at the University of Oslo will be used as the data sharing platform (https://www.uio.no/english/services/it/research/sensitive-data/index.html); access to the designated server area will be granted by the corresponding author (email: [email protected]). The study protocol (encompassing the informed consent form) as well as the statistical analysis plan is freely available from the designated ClinicalTrial registry website (https://www.clinicaltrials.gov/ct2/show/NCT04686734).

Author contributions

Conceived of the study: VBBW

Study design: VBBW, CXS, ARL

Primary data acquisition: JS, LBH, EB, MD, LL-B, TS-S

Functional testing and laboratory analyses: LLB, TS-S, GE, TML, TT, AEM, TEM, FL-J, TH, HZ, KB, TO

Statistical analyses: JS, LBH, EC, VBBW

Drafting of manuscript: JS, VBBW

Revising manuscript for intellectual content: All authors.

Conflicts of interests

HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Alector, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Pinteon Therapeutics, Red Abbey Labs, reMYND, Passage Bio, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. KB has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, BioArctic, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Ono Pharma, Pharmatrophix, Prothena, Roche Diagnostics, and Siemens Healthineers, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. The other authors do not have any conflicts of interests related to the present study.

Funding

The Norwegian Research Council [grant #302079], and the DAM foundation [grant #2022/F0387180], as well as institutional support from Dept. of Paediatrics and Adolescent Medicine, Akershus University Hospital, and Institute of Clinical Medicine, University of Oslo.

HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#681712 and #101053962), Swedish State Support for Clinical Research (#ALFGBG-71320), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), the AD Strategic Fund and the Alzheimer's Association (#ADSF-21-831376-C, #ADSF-21-831381-C and #ADSF-21-831377-C), the Olav Thon Foundation, the Erling-Persson Family Foundation, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2019-0228), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), the European Union Joint Programme – Neurodegenerative Disease Research (JPND2021-00694), and the UK Dementia Research Institute at UCL (UKDRI-1003). KB is supported by the Swedish Research Council (#2017-00915), the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615), the Swedish Alzheimer Foundation (#AF-930351, #AF-939721 and #AF-968270), Hjärnfonden, Sweden (#FO2017-0243 and #ALZ2022-0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986 and #ALFGBG-965240), the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236), the National Institute of Health (NIH), USA, (grant #1R01AG068398-01), and the Alzheimer’s Association 2021 Zenith Award (ZEN-21-848495).

Soriano, J. B., Murthy, S., Marshall, J. C., Relan, P. & Diaz, J. V. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect. Dis. 22, e102–e107 (2022).
Fainardi, V. et al. Long COVID in Children and Adolescents. Life 12, 285 (2022).
Alkodaymi, M. S. et al. Prevalence of post-acute COVID-19 syndrome symptoms at different follow-up periods: a systematic review and meta-analysis. Clin. Microbiol. Infect. 28, 657–666 (2022).
Behnood, S. A. et al. Persistent symptoms following SARS-CoV-2 infection amongst children and young people: A meta-analysis of controlled and uncontrolled studies. J. Infect. 84, 158–170 (2022).
Nalbandian, A. et al. Post-acute COVID-19 syndrome. Nat. Med. 27, 601–615 (2021).
Huang, C. et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. The Lancet 397, 220–232 (2021).
Ayoubkhani, D. et al. Trajectory of long covid symptoms after covid-19 vaccination: community based cohort study. BMJ 377, e069676 (2022).
Sandler, C. X. et al. Long COVID and Post-infective Fatigue Syndrome: A Review. Open Forum Infect. Dis. 8, ofab440 (2021).
Hanevik, K. et al. Irritable bowel syndrome and chronic fatigue 6 years after giardia infection: a controlled prospective cohort study. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. 59, 1394–1400 (2014).
Hickie, I. et al. Post-infective and chronic fatigue syndromes precipitated by viral and non-viral pathogens: prospective cohort study. BMJ 333, 575 (2006).
Pedersen, M. et al. Predictors of chronic fatigue in adolescents six months after acute Epstein-Barr virus infection: A prospective cohort study. Brain. Behav. Immun. 75, 94–100 (2019).
Lam, M. H.-B. et al. Mental morbidities and chronic fatigue in severe acute respiratory syndrome survivors: long-term follow-up. Arch. Intern. Med. 169, 2142–2147 (2009).
Fukuda, K. et al. The chronic fatigue syndrome: a comprehensive approach to its definition and study. International Chronic Fatigue Syndrome Study Group. Ann. Intern. Med. 121, 953–959 (1994).
Cervia, C. et al. Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome. Nat. Commun. 13, 446 (2022).
Raker, E. J., Zacher, M. & Lowe, S. R. Lessons from Hurricane Katrina for predicting the indirect health consequences of the COVID-19 pandemic. Proc. Natl. Acad. Sci. U. S. A. <bvertical-align:super;>117</bvertical-align:super;>, 12595–12597 (2020).
Stephenson, T. et al. Physical and mental health 3 months after SARS-CoV-2 infection (long COVID) among adolescents in England (CLoCk): a national matched cohort study. Lancet Child Adolesc. Health 6, 230–239 (2022).
Berg, S. K. et al. Long COVID symptoms in SARS-CoV-2-positive adolescents and matched controls (LongCOVIDKidsDK): a national, cross-sectional study. Lancet Child Adolesc. Health 6, 240–248 (2022).
Blankenburg, J. et al. Comparison of mental health outcomes in seropositive and seronegative adolescents during the COVID19 pandemic. Sci. Rep. 12, 2246 (2022).
Zimmermann, P., Pittet, L. F. & Curtis, N. The Challenge of Studying Long COVID: An Updated Review. Pediatr. Infect. Dis. J. 41, 424–426 (2022).
Søraas, A. et al. Self-reported Memory Problems 8 Months After COVID-19 Infection. JAMA Netw. Open 4, e2118717 (2021).
Matta, J. et al. Association of Self-reported COVID-19 Infection and SARS-CoV-2 Serology Test Results With Persistent Physical Symptoms Among French Adults During the COVID-19 Pandemic. JAMA Intern. Med. 182, 19–25 (2022).
Rimes, K. A. et al. Incidence, prognosis, and risk factors for fatigue and chronic fatigue syndrome in adolescents: a prospective community study. Pediatrics 119, e603-609 (2007).
Krogstad, H. et al. Symptoms in the general Norwegian adult population - prevalence and associated factors. BMC Public Health 20, 988 (2020).
Pierce, M. et al. Mental health before and during the COVID-19 pandemic: a longitudinal probability sample survey of the UK population. Lancet Psychiatry 7, 883–892 (2020).
Newlove-Delgado, T. et al. Child mental health in England before and during the COVID-19 lockdown. Lancet Psychiatry 8, 353–354 (2021).
Sudre, C. H. et al. Attributes and predictors of long COVID. Nat. Med. 27, 626–631 (2021).
Peghin, M. et al. Post-COVID-19 symptoms 6 months after acute infection among hospitalized and non-hospitalized patients. Clin. Microbiol. Infect. Off. Publ. Eur. Soc. Clin. Microbiol. Infect. Dis. 27, 1507–1513 (2021).
Wessely, S. et al. Postinfectious fatigue: prospective cohort study in primary care. Lancet Lond. Engl. 345, 1333–1338 (1995).
Blomberg, B. et al. Long COVID in a prospective cohort of home-isolated patients. Nat. Med. 27, 1607–1613 (2021).
Buchwald, D. S., Rea, T. D., Katon, W. J., Russo, J. E. & Ashley, R. L. Acute infectious mononucleosis: characteristics of patients who report failure to recover. Am. J. Med. 109, 531–537 (2000).
Seet, R. C. S., Quek, A. M. L. & Lim, E. C. H. Post-infectious fatigue syndrome in dengue infection. J. Clin. Virol. Off. Publ. Pan Am. Soc. Clin. Virol. 38, 1–6 (2007).
Keijmel, S. P. et al. Effectiveness of Long-term Doxycycline Treatment and Cognitive-Behavioral Therapy on Fatigue Severity in Patients with Q Fever Fatigue Syndrome (Qure Study): A Randomized Controlled Trial. Clin. Infect. Dis. Off. Publ. Infect. Dis. Soc. Am. 64, 998–1005 (2017).
Evans, R. A. et al. Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study. Lancet Respir. Med. 9, 1275–1287 (2021).
Lund Berven, L. et al. Inflammatory Markers, Pulmonary Function, and Clinical Symptoms in Acute COVID-19 Among Non-Hospitalized Adolescents and Young Adults. Front. Immunol. 13, 837288 (2022).
Trogstad, L. et al. The Norwegian immunisation register – SYSVAK. Eurosurveillance 17, 20147 (2012).

Yes there is potential Competing Interest. HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Alector, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Pinteon Therapeutics, Red Abbey Labs, reMYND, Passage Bio, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. KB has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, BioArctic, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Ono Pharma, Pharmatrophix, Prothena, Roche Diagnostics, and Siemens Healthineers, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. The other authors do not have any conflicts of interests related to the present study.

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Prevalence and predictors of long COVID among non-hospitalised adolescents and young adults: a prospective controlled cohort study

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