Abstract
Background
Severe acute coronavirus disease 2019 (COVID-19) is uncommon in children; however, its development can lead to longer-term health problems. Understanding factors associated with clinical deterioration in paediatric patients is therefore of public health relevance. Early triage enables closer monitoring, tailored counselling, and timely escalation of care. International studies have associated obesity, chronic illness, and certain sociodemographic factors with worse outcomes. However, robust real-world datasets remain sparse, particularly in Germany, where statutory surveillance often fails to capture detailed clinical data. To address this gap, we analysed 731 polymerase-chain-reaction (PCR)-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in children aged ≤ 15 years reported to the Cologne and Augsburg health departments between January and December 2021.
Objectives
• Primary: Identify independent factors associated with a severe acute COVID-19 course in this population-based study with defined sampling framepaediatric cohort.
• Secondary (exploratory): Estimate the prevalence of long COVID–compatible symptoms (persisting >4 weeks) and explore how their frequency changes across acute COVID-19 severity strata.
Methods
This analysis is based on the CoCo-Fakt cross-sectional study, which captured 731 PCR-confirmed SARS-CoV-2 infections in children aged ≤15 years between January and December 2021. The parent questionnaire captured sociodemographic variables, body mass index (BMI), chronic illnesses, quarantine details, acute COVID-19 severity (asymptomatic to severe), and persisting symptoms (>4 weeks). We then used multivariable logistic regression to examine whether age, sex, socioeconomic status, migration background, BMI, and chronic illness were independently associated with a severe disease course. For exploratory comparisons between children with and without long COVID, we applied t-tests for continuous and Fisher’s exact or chi-square tests for categorical variables.
Results
Among the included participants, 67 (9.6%) experienced a severe disease course of COVID-19. In multivariable analysis, chronic illness emerged as the only independent factor associated with severe COVID-19, conferring an almost sixfold higher odds of severe COVID-19 (OR 5.90, 95% CI 2.98–11.68). BMI showed a positive trend but was not statistically significant. Exploratory analyses indicated associations with older age, chronic illness, and increasing acute disease severity. The most frequently reported symptoms were fatigue, sleep disturbances, and problems concentrating.
Conclusion
Chronic illness was consistently associated with a higher likelihood of severe acute COVID-19 in children. Given the cross-sectional design, exploratory, parent-reported data suggest older age and more severe acute disease courses may increase the likelihood of persisting symptoms, which warrants confirmation in prospective cohorts. Therefore, children with more severe acute disease or chronic illness may benefit from tailored follow-up to better understand potential long-term impairment, including potential progression to myalgic encephalomyelitis/chronic fatigue syndrome.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12887-026-06724-7.
Keywords: COVID-19 severity, Children and adolescents, Associated factors, Real-world data, Long COVID, Chronic illness, Obesity
Introduction
The coronavirus disease 2019 (COVID-19) pandemic, spanning March 2020 to May 2023, profoundly affected global health systems worldwide. It placed exceptional strain on healthcare delivery and public life [1], with more than 700 million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and over 7 million related deaths reported by early 2025 [2]. Although COVID-19 primarily affects the respiratory tract, paediatric patients may also present with respiratory symptoms such as cough and dyspnoea, as well as systemic manifestations including fever, headaches, and altered taste or smell [3, 4]. While most children and adolescents experience mild or asymptomatic courses [5], a small subset develop serious complications such as multisystem inflammatory syndrome in children (MIS-C), an acute hyper-inflammatory condition involving multiple organs [6–8]. Echocardiographic abnormalities occur in about 54% of MIS-C patients, with an overall mortality 1.7% [8]. In Germany, 926 MIS-C cases were recorded between January 2020 and April 2023, none fatal [9] Beyond these acute threats, myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), which is characterised by persistent fatigue, post-exertional malaise, and cognitive dysfunction [10], has been recognised as a possible long-term sequela of COVID-19 in children [11, 12].
As acute COVID-19 case numbers declined, scientific attention shifted to potential long-term outcomes. Early data that paediatric COVID-19 would rarely cause persistent symptoms [13–16] have been challenged by evidence that even mild or asymptomatic SARS-CoV-2 infections can trigger lasting manifestations [17, 18].
Various terms describe prolonged or delayed-onset symptoms not otherwise explained by another diagnosis, including ‘long COVID’, ‘post-acute health sequelae of COVID-19’, ‘post-COVID-19 syndrome’, and ‘post-COVID-19 condition’. Following guidelines of the UK National Institute for Health and Care Excellence (NICE) and the German Association of the Scientific Medical Societies, (Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften, AWMF), this study defines long COVID as symptoms persisting beyond 4 weeks or re-emerging after initial recovery and post-COVID-19 syndrome as symptoms lasting over 12 weeks with a clear impact on daily life [3, 15, 19, 20] In contrast, the World Health Organization (WHO) later proposed a more restrictive definition of post-COVID-19 condition, requiring symptom to persist for at least 12 weeks and a minimum duration of two months. This threshold was chosen to capture early post-acute symptom patterns rather than to establish a WHO-defined post-COVID-19 condition, and it reflects national guidance in place at the time of data collection in 2021.
Fatigue, dyspnoea, and cognitive disruptions are commonly reported among children and adolescents with COVID-19 [19, 21, 22], aligning with findings from the largely adult-focused Global Burden of Disease Study [21]. In one analysis, 68% of children with Long COVID experienced fatigue, often accompanied by headaches, limb pain, and cough [23]. Older adolescents (> 16 years) showed higher frequencies of fatigue (44% vs. 19%), shortness of breath (25% vs. 16%), and cognitive impairments (36% vs. 16%) than younger children. Girls were more often affected than boys, particularly regarding fatigue (46% vs. 35%), dyspnoea (34% vs. 21%), and cognitive difficulties (34% vs. 24%) [24]. Additional factors associated with the developing Long COVID include a strong antibody response, severe acute COVID-19, and pre-existing obesity, diabetes mellitus, or MIS-C [24, 25]. Although the exact pathophysiology of long COVID remains unclear, immunological, genetic, and metabolic factors likely contribute [24, 25].
Prevalence estimates of long COVID in children vary widely due to heterogeneous study designs, ranging from up to 25% in uncontrolled studies to 1%–5% in controlled analyses [14, 26, 27]. However, large-scale real-world data from Germany are limited. Ehm et al. [28] linked statutory health insurance data of > 59,000 polymerase-chain-reaction (PCR)-confirmed paediatric cases to 170,940 controls, finding that 6% of infected children developed long COVID–compatible symptoms within 3 months, increasing to 8% among those aged > 12 years. Although many recovered by 12 months, up to 10% remained symptomatic, indicating a substantial burden on paediatric care and highlighting the potential value of longitudinal follow-up in affected children.
Given the paucity of real-world data on the prevalence and clinical significance of long COVID in children in Germany, the Cologne health authority, one of the largest in Germany, included this topic in the cross-sectional CoCo-Fakt study (Cologne Corona Beratung und Unterstützung Für Index- und KontAKt-Personen während der Quarantäne-ZeiT [Cologne Corona Advice and Support for Index and Contact Persons During Quarantine]). To broaden geographic coverage and to capture regional variation, data from the Cologne and Augsburg health departments were combined, including 731 PCR-confirmed SARS-CoV-2 infections in children aged ≤ 15 years reported between January and December 2021.
The primary aim was to identify independent sociodemographic and clinical factors associated with a severe acute course. As a secondary, exploratory aim, we estimated the prevalence of early post-acute, long COVID–compatible symptoms and examined how their frequency varied across acute disease severity strata. Our findingssuggest that early triage of children with a higher likelihood of severe disease may support more efficient allocation of acute care resources and inform decisions on structured follow-up to monitor potential longer-term sequelae such as ME/CFS-like fatigue syndromes [27].
Methods
Study design
This study analysed data collected by the Cologne Public Health Department (Gesundheitsamt Köln) through systematic case and contact management under Germany’s Infection Protection Act (Infektionsschutzgesetz, IfSG). The city of Cologne implemented a digital contact-tracing system (Digitales Kontaktmanagement [DiKoMa]) that catalogued all confirmed SARS-CoV-2 infections among its residents between March 2020 and 28 February 2023 [29]. Upon diagnosis, infected individuals were quarantined, informed of legal considerations, such as possible quarantine, and notified about possible research follow-up [30]. The CoCo-Fakt study aimed to assess infection trajectories (including variant and vaccination status for adults), challenges during isolation, coping strategies, lifestyle changes, and sources of psychosocial support. For the present analysis of children ≤ 15 years, vaccination status was not collected, as paediatric vaccination was only recommended later in 2021 and uptake in this age group was negligible. The study was conducted in three cross-sectional survey waves, each capturing individuals who tested positive for SARS-CoV-2 during specific periods. The third wave also included participants from the city of Augsburg and its surrounding area.
This analysis focused on the second and third survey waves (January–December 2021). Parents or guardians of children aged ≤ 15 years with valid email addresses were invited to complete an online questionnaire, unless the child was deceased, in institutional care, or lacked contact information. Thus, the children’s health and symptoms were reported indirectly via their parents or guardians. The extent to which the children themselves may have contributed to the completion of the questionnaire with their parents is unknown. The parents or guardians were sent reminders after 2 and 4 weeks. After providing informed consent, the respondents spent approximately 45 min completing the survey. Joisten et al. described the full study design in April 2021 [30].
Survey
The CoCo-Fakt survey was based on the COVID-19 Snapshot Monitoring questionnaire from the University of Erfurt [31] and the World Health Organization’s guidelines [32], modified to address this study’s aims. The modified questionnaire combined quantitative and qualitative survey methods and was implemented using the online survey software Unipark. In addition to German, the survey was provided in Turkish, Arabic, and Bulgarian - the languages spoken by Cologne’s largest migrant group - to help reduce language barriers; however, these versions were only used in the second wave.
Study population
Of the 142,908 individuals invited, 21,116 accessed the survey, and 11,800 completed it. Data from 731 children aged ≤ 15 years with PCR-confirmed SARS-CoV-2 infections were included in the analysis. Of the 731 participants, 698 provided information on acute disease severity. Due to data protection regulations, the survey invitations were sent to parents or guardians (not directly to children), who reported their child’s health symptoms. Asymptomatic SARS-CoV-2 infections were commonly identified through routine PCR testing of household, schools, and daycare contacts as part of mandatory public health quarantine management.
In the second wave (January–June 2021), 37,532 individuals were invited, 6,965 accessed the questionnaire, and 5,970 provided complete responses (15.9% response rate). This yielded 900 child records, of which 678 included both their age (< 16 years) and sex. In the third wave (July–December 2021), 105,394 individuals were invited (50,008 from Cologne and 55,386 from Augsburg), 14,151 accessed the questionnaire, and 5,830 provided complete responses (5.5% response rate). Among these responses, 1,100 child records were available, of which 795 included both their age (< 16 years) and sex information. Thus, the response rates were higher in the second than in the third wave.
The study population selection process is illustrated in Fig. 1.
Fig. 1.
Flowchart of the selection of the study population from the CoCo-Fakt survey. The data were collected in the second (January–June 2021) and third (July–December 2021) waves of the COVID-19 pandemic
Demographic information
The parents and guardians reported each child’s age, sex, and type of school or childcare setting (e.g. primary, secondary, or preschool). Their socioeconomic status (SES) was classified as low, medium, or high following the German Health Update (GEDA) methodology [33] and considering parental education level and professional training.
Health characteristics
Body measurements and chronic illness
The parents and guardians indicated their child’s height, weight, and were asked whether their child had any chronic illnesses. If applicable, they selected one or more conditions from a predefined list included in the questionnaire, with an additional free-text option for other conditions; no clinical verification was performed. Due to small case numbers within individual disease categories and the heterogeneity of reported conditions, analyses were limited to the presence of any chronic illness rather than disease-specific categories. The full list of chronic diseases surveyed is provided in the Supplementary Table S1. Each child’s BMI was calculated and, following the German percentile charts of Kromeyer-Hauschild et al., [34] they were classified as underweight if their BMI was below the 10th percentile, normal weight if their BMI was between the 10th and 90th percentiles, overweight if their BMI was between the 90th and 97th percentiles, and obese if their BMI was above the 97th percentile. Higher BMI percentiles indicate increased adiposity in line with WHO growth standards. Whether the child’s weight changed during quarantine was also asked.
SARS-CoV-2 infection
SARS-CoV-2 variant
During 2021, Alpha and Delta were the predominant SARS-CoV-2 variants in Germany. Probable exposures were inferred from infection dates; however, no sequencing data were available in our dataset, and variant-specific analyses were not possible.
Quarantine duration
The parents and guardians were asked to indicate the duration of the child’s quarantine following a positive SARS-CoV-2 test, as well as whether multiple quarantine periods occurred.
Disease course
Acute
Based on the ‘Clinical classifications of SARS-CoV-2 infection by severity’ [35, 36], the parents and guardians were asked to rate their child’s acute COVID-19 disease courses as asymptomatic (completely asymptomatic), mild (mild symptoms), moderate (significant symptoms), or severe (parent/guardian-reported severe feeling of being ill, hospitalisation, or intensive care). They further reported whether the children underwent hospitalisation or intensive care (and for how many days), and whether long-term symptoms or disease-related anxiety were present. For symptomatic cases, typical COVID-19 symptoms [37] (fever, cough, loss of taste or smell, appetite loss, fatigue, muscle aches, diarrhoea, and shortness of breath) and each symptom’s severity were assessed, and final categories were derived according to the worst severity reported.
Long COVID symptoms
To assess long COVID-compatible symptoms, parents and guardians were asked whether their child experienced new or ongoing symptoms beyond the acute phase. A predefined symptom checklist, based on the available literature and surveillance experience at the time of questionnaire development, was provided. This checklist was primarily based on observations from adult populations and covered fatigue, dyspnoea, concentration and cognitive problems, sleep disturbances, headaches, loss of taste or smell, psychological symptoms, and musculoskeletal complaints. Symptom duration and severity were assessed, and additional symptoms could be reported using the free-text option.
Statistical analysis
The data were statistically analysed using SPSS (version 29.0, IBM), and a p value < 0.05 was considered to indicate significance. Categorical variables are described as the absolute or relative frequency, and continuous variables are described as the mean (M) ± standard deviation (SD) [1]. Differences in participants’ characteristics (age, sex, SES, migration background, BMI percentile, chronic illness, and weight development during quarantine) by acute disease course were examined using chi-square tests. When expected cell counts were < 5, Fisher’s Exact Test (for 2 × 2 tables) or the Fisher–Freeman–Halton test (for larger tables) was applied instead. Continuous variables were compared using independent-samples t tests. Effect sizes were calculated for significant results (Cramer’s V for chi-square tests; Cohen’s d for t tests). A logistic regression analysis was conducted to identify potential factors associated with acute disease severity, reported as odds ratios (ORs) with 95% confidence intervals (CIs). The considered covariates were age, sex, SES, migration background, BMI percentile, chronic illness, and quarantine duration. Potential limitations include non-response bias and the lack of SARS-CoV-2 variant sequencing data, which are further discussed below.
Results
Study population
The analysis included 731 children and adolescents with PCR-confirmed SARS-CoV-2 infections (Table 1). Among the participants, the mean age was 8.3 ± 4.2 years, and the mean BMI was 17.5 ± 5.94 kg/m2; 354 (48.4%) were female, and 65 (8.9%) had a chronic illness. BMI distribution differed slightly by sex (p = 0.047, Cramer’s V = 0.109), though the effect size was negligible.
Table 1.
The participants’ demographic characteristics
| Total | Boys | Girls | p value | Effect size | |
|---|---|---|---|---|---|
| Total number of participants, n (%) | 731 (100) | 377 (51.6) | 354 (48.4) | ||
| Age (years) | |||||
| M ± SD | 8.3 ± 4.2 | 8.3 ± 4.1 | 8.3 ± 4.3 | ||
| Age group (childcare type), n (%) | 0.607b n.s. | ||||
| Too young for kindergarten/preschool | 60 (8.2) | 26 (6.9) | 34 (9.7) | ||
| Childcare facility | 188 (25.8) | 100 (26.5) | 88 (25.1) | ||
| Primary school | 206 (28.3) | 112 (29.7) | 94 (26.8) | ||
| Secondary school | 214 (29.4) | 110 (29.2) | 104 (29.6) | ||
| Other | 60 (8.2) | 29 (7.7) | 31 (8.8) | ||
| Height (cm) | |||||
| M ± SD | 133.7 ± 27.6 | 135.2 ± 27.9 | 132.02 ± 27.3 | 0.127a n.s. | |
| Weight (kg) | |||||
| M ± SD | 33.5 ± 18.2 | 34.1 ± 18.2 | 32.94 ± 18.2 | 0.417a n.s. | |
| BMI (kg/m2) | |||||
| M ± SD | 17.5 ± 5.94 | 17.2 ± 3.3 | 17.9 ± 7.9 | 0.149a n.s. | |
| BMI classification, n (%) | 0.047b | 0.109c | |||
| Underweight | 108 (16.1) | 58 (16.5) | 50 (15.6) | ||
| Normal weight | 478 (71.1) | 253 (71.9) | 225 (70.3) | ||
| Overweight | 48 (7.1) | 29 (8.2) | 19 (5.9) | ||
| Obese | 38 (5.7) | 12 (3.4) | 26 (8.1) | ||
| Migration background,n (%) | 0.470b n.s. | ||||
| Yes | 33 (4.6) | 15 (4.0) | 18 (5.2) | ||
| No | 686 (95.4) | 356 (96.0) | 330 (94.8) | ||
| Chronic illness, n(%) | 0.244b n.s. | ||||
| Yes | 65 (8.9) | 38 (10.1) | 27 (7.6) | ||
| No | 666 (91.1) | 339 (89.9) | 327 (92.4) | ||
| SES according to GEDA, n (%) | 0.463b n.s. | ||||
| Low/medium(< 4.6) | 238 (34.1) | 127 (35.4) | 111 (32.7) | ||
| High (> 4.6) | 460 (65.9) | 232 (64.6) | 228 (67.3) | ||
p < 0.05 was considered to indicate significance; p > 0.05 = not significant (n.s.)
a Unpaired t-test
b Chi-square test
c Cramer’s V
Disease course
Acute COVID-19
Information on acute disease course was available for 698 of 731 participants (4.5% missing). Among these, 573 (82.1%) experienced symptoms during the acute phase, including 333 (47.7% with mild, 173 (24.8%) with moderate, and 67 (9.6%) with severe courses, while 125 (17.9%) remained asymptomatic. In comparisons by disease severity (Table 2), chronic illness was significantly more common among children with severe courses (p < 0.001; Cramer’s V = 0.201). While BMI did not differ significantly (p = 0.086), numerically higher values were observed in the severe group.
Table 2.
Differences in demographic characteristics and health factors by acute COVID-19 disease course
| Total | Acute SARS-CoV-2-infection | |||||||
|---|---|---|---|---|---|---|---|---|
| Not severe symptoms(asymptomatic, mild or moderate) | Severe symptoms | p value | Effect size | |||||
| Age (years), M ± SD | 9.9 | 4.4 | 8.1 | 4.2 | 8.99 | 4.4 | 0.112a n.s. | |
| Age group, n (%) | 0.083b n.s. | |||||||
| Too young for kindergarten/preschool | 58 | 8.3 | 50 | 8.0 | 8 | 11.9 | ||
| Childcare facility | 183 | 26.2 | 173 | 27.5 | 10 | 14.9 | ||
| Primary school | 196 | 28.1 | 180 | 28.7 | 16 | 23.9 | ||
| Secondary school | 202 | 28.9 | 176 | 28.0 | 26 | 38.8 | ||
| Other | 56 | 8.0 | 49 | 7.8 | 7 | 10.4 | ||
| Sex, n (%) | 0.650b n.s. | |||||||
| Male | 360 | 51.6 | 330 | 52.3 | 30 | 44.8 | ||
| Female | 338 | 48.4 | 301 | 47.7 | 37 | 55.2 | ||
| SES according to GEDA,n (%) | 0.547b n.s. | |||||||
| Low/medium(<4.6) | 228 | 34.1 | 208 | 34.5 | 20 | 30.8 | ||
| High (>4.6) | 440 | 65.9 | 395 | 65.5 | 45 | 69.2 | ||
| Migrant background (yes), n (%) | 30 | 4.4 | 28 | 4.4 | 2 | 3.0 | 0.576b n.s. | |
| Chronic illness (yes),n (%) | 58 | 8.3 | 41 | 6.5 | 17 | 25.4 | <0.001b | 0.201c |
| BMI (kg/m2), M ± SD | 19.2 | 4.9 | 17.3 | 6.1 | 18.7 | 4.4 | 0.086an.s. | |
| BMI classification,n (%) | 0.638b n.s. | |||||||
| Underweight | 104 | 16.2 | 98 | 16.9 | 6 | 9.0 | ||
| Normal weight | 457 | 71.2 | 410 | 70.8 | 47 | 74.6 | ||
| Overweight | 46 | 7.2 | 40 | 6.9 | 6 | 9.5 | ||
| Obese | 35 | 5.5 | 31 | 5.4 | 4 | 6.3 | ||
p < 0.05 was considered to indicate significance; p > 0.05 = not significant (n.s.)
a t-test
b Chi-square test
c Cramer’s V
Regression analysis
The logistic regression analysis identified chronic illness as the only independent factor associated with severe COVID-19 in children; however, this variable represents a heterogeneous group and should be interpreted as a broad vulnerability marker rather than a disease-specific risk factor. Having a chronic illness increased the odds by almost sixfold (OR 5.897, 95% CI 2.977–11.681; Table 3, Fig. 2). Although BMI showed a positive trend (OR 1.015, 95% CI 0.985–1.047), the association did not reach statistical significance in the adjusted model. Age, sex, quarantine duration, repeated quarantine episodes, migration background, and socioeconomic status likewise showed no effect. The model explained 10.2% of the variance in COVID-19 severity.
Table 3.
Factors associated with the severity of the acute COVID-19 course in children
| Variable | B | Std. error | p value | OR for exp (B) | 95% CI for exp (B) |
|---|---|---|---|---|---|
| Age (years) | 0.039 | 0.034 | 0.256 | 1.040 | 0.972–1.112 |
|
Sex (1 = female; 2 = male) |
-0.297 | 0.288 | 0.303 | 0.743 | 0.423–1.306 |
| BMI percentile | 0.015 | 0.016 | 0.330 | 1.015 | 0.985–1.047 |
|
Chronic illness (yes) (0 = no;1 = yes) |
1.774 | 0.349 | < 0.001 | 5.897 | 2.977–11.681 |
|
Repeated quarantine episodes (yes) (0 = no;1 = yes) |
0.567 | 0.397 | 0.153 | 1.763 | 0.809–3.843 |
| Quarantine duration (days) | −0.018 | 0.025 | 0.463 | 0.982 | 0.935–1.031 |
|
Migration background (0 = no;1 = yes) |
0.134 | 0.782 | 0.864 | 1.144 | 0.247–5.299 |
|
SES (education level grouped according to GEDA) (0 = low/medium; 1 = high) |
0.264 | 0.309 | 0.393 | 1.302 | 0.710–2.388 |
Binary logistic regression: dependent variable = severe course (yes/no), independent variables = chronic illness (parent/guardian-reported, yes/no), BMI percentile (continuous, German reference percentiles), age (years), sex (male/female), socioeconomic status (composite index per GEDA), migration background (≥1 parent born abroad, yes/no), quarantine duration (days), and repeated quarantine episodes (yes/no)
Covariates were coded as follows: age (years), sex (1 = female, 2 = male), SES (0 = low/medium, 1 = high), migration background (0 = no, 1 = yes), BMI (kg/m²), chronic illness (0 = no, 1 = yes), and quarantine duration (days), repeated quarantine episodes (0 = no, 1 = yes)
Fig. 2.
Forest plot of logistic regression analysis identifying factors associated with a severe acute course of COVID-19 in children. Odds ratios (OR) are shown on a logarithmic scale with 95% confidence intervals. Chronic illness was a significant predictor. BMI showed a positive trend but did not reach statistical significance. Covariates were coded as follows: age (years), sex (1 = female, 2 = male), SES (0 = low/medium, 1 = high), migration background (0 = no, 1 = yes), BMI (kg/m²), chronic illness (0 = no, 1 = yes), and quarantine duration (days), repeated quarantine episodes (0 = no, 1 = yes)
Long COVID
According to the parent and guardian responses, twelve participants (1.7%) met the criteria for long COVID. Children with long COVID were significantly older (p = 0.003; Cohen’s d = 0.86, large) and more often had chronic illness (p = 0.002; Cramer’s V = 0.158). A severity-dependent gradient across the acute disease course was observed (p < 0.001; Cramer’s V = 0.192), with proportions increasing from 0% after asymptomatic infection to 50% after severe courses. Long COVID was also associated with age group/type of childcare (p = 0.001; Cramer’s V = 0.203), with prevalence rising across older school levels. No significant associations were found for sex, SES, migration background, BMI, or repeated quarantine episodes.
Only nine participants (1.2%) reported symptoms persisting beyond 12 weeks. Due to the very small case numbers, no further analyses were feasible. Detailed associations with demographic and health characteristics are presented in Table 4, while Fig. 3 illustrates the distribution of reported long-COVID symptoms by acute disease severity. The most frequently reported symptoms were fatigue, sleep disturbances, and problems concentrating, with prevalence declining over time.
Table 4.
Comparison of the demographic characteristics and health status of participants with and without long COVID symptoms
| Long COVID symptoms | ||||
|---|---|---|---|---|
| No | Yes | p-value | Effect size | |
| Sex, n (%) | 0.199b n.s. | |||
| Male | 359 (52.0) | 4 (33.3) | ||
| Female | 331 (48.0) | 8 (66.7) | ||
| Age (years), M ± SD | 8.2 ± 4.2 | 11.8 ± 4.0 | 0.003a | -0.861e |
| Age group by type of childcare, n (%) | 0.001c | 0.203d | ||
| Too young for kindergarten/school | 58 (8.4) | 0 (0) | ||
| Childcare facility | 183 (26.6) | 1 (8.3) | ||
| Primary school | 195 (28.4) | 2 (16.7) | ||
| Secondary school | 199 (29.0) | 3 (25.0) | ||
| Repeated quarantine episodes (yes), n (%) | 240 (34.8) | 3 (25.0) | 0.558cn.s. | |
| Migration background (yes), n (%) | 28 (4.1) | 2 (16.7) | 0.092cn.s. | |
| Chronic disease (yes), n (%) | 54 (7.8) | 5 (41.7) | 0.002c | 0.158d |
| Acute COVID-19 course, n (%) | < 0.001 | 0.192d | ||
| Asymptomatic | 125 (18.2) | 0 (0) | ||
| Mild | 331 (48.3) | 2 (16.7) | ||
| Moderate to severe | 169 (24.6) | 4 (33.3) | ||
| Severe | 61 (8.9) | 6 (50.0) | ||
| SES according to GEDA, n (%) | 1.000cn.s. | |||
| Low/medium | 224 (33.9) | 4 (33.3) | ||
| High | 436 (66.1) | 8 (66.7) | ||
| BMI (kg/m2), M ± SD | 17.4 + 5.99 | 19.6 + 3.9 | 0.212a n.s. | |
| BMI classification,n (%) | 0.700cn.s. | |||
| Underweight | 102 (16.1) | 2 (16.7) | ||
| Normal weight | 451 (71.2) | 8 (66.7) | ||
| Overweight | 46 (7.3) | 1 (8.3) | ||
| Obese | 34 (5.4) | 1 (8.3) | ||
p < 0.05 was considered to indicate significance; p > 0.05 = not significant (n.s.)Symptoms included fatigue, headaches, loss of taste or smell, dyspnoea, concentration and cognitive impairment, muscular exhaustion, hair loss, and sleep disorders. Multiple responses were possible
a t-test
b Chi-square test
cFisher’s exact/ Fisher-Freeman-Halton test (used when expected cell counts < 5 or group sizes were small)
d Cramer’s V
e Cohen’s d
Fig. 3.
Proportion of reported long COVID symptoms, stratified by severity of the acute COVID-19 course (scaled to 100% of responses). Multiple responses were possible
Discussion
Based on the large CoCo-Fakt survey using a population-based study with defined sampling frame via public health departments, we quantified the acute and early post-acute outcomes of German children and adolescents with PCR-confirmed SARS-CoV-2 infections. Only 9.6% of the participants met our definition of a severe disease course (parent/guardian-reported severe feeling of being ill, hospitalisation or intensive care) [35, 36] The ‘severe’ category combined clinically objective indicators (hospitalisation or intensive care) with parent/guardian-reported subjective severity. It was therefore used exclusively for descriptive stratification and exploratory analyses, rather than for conditional or causal inference. Chronic illness was found to be the strongest independent factor with severe acute SARS-CoV-2 infection (OR 5.9, 95% CI 2.98–11.68), whereas long COVID, occurred in 1.7% of cases, predominantly among older children with more severe acute disease. In adjusted analyses, chronic illness remained the only statistically significant independent factor associated with severe COVID-19, conferring an almost sixfold higher odds (OR 5.897, 95% CI 2.977–11.681), while BMI showed a positive but statistically non-significant trend. As chronic conditions are a heterogeneous group, and treatment-specific information (e.g. immunosuppressive or corticosteroid therapy) was unavailable, the variable ‘chronic illness’ should be interpreted as a broad vulnerability marker rather than a disease-specific risk factor. Due to the cross-sectional study design, all observed associations should be interpreted as exploratory and hypothesis-generating rather than predictive.
Although our data primarily reflect the period before Omicron and later variants, as well as the vaccination of children and adolescents, our findings are consistent with those of previous large-scale studies. In a Norwegian cohort of approximately 59,000 children with COVID-19 and 170,000 controls, the risk difference for post-acute symptoms was 155 per 1,000 person-years (relative risk 1.34), particularly evident among those with obesity or comorbidities [24]. A meta-analysis of 46,262 likewise identified severe acute illness, obesity, and comorbidities as key risk factors for developing Long COVID (ORs: 1.5–2.0) [25]. German health insurance data reported post-acute sequelae in 6% of children and 8% of adolescents within 3 months after SARS-CoV-2 infections, with approximately 10% remaining symptomatic after 1 year. Although not stratified by acute COVID-19 severity, the aforementioned study highlights long-term morbidity and supports our findings regarding persistent symptoms, reporting elevated rates of COVID-19–specific symptoms (e.g. fatigue and smell or taste disturbances) and a 34% increase in nonspecific complaints [28]. Altogether, these concordant findings highlight the clinical utility of simple baseline characteristics for triaging children with COVID-19. Notably, in our cohort BMI did not independently predict acute severity after adjustment. This divergence from other studies may reflect measurement imprecision of parent-reported height and weight or limited power to detect small effects. By contrast, chronic illness remained robustly associated with severe COVID-19 (sixfold higher odds compared to non-chronically ill peers; exploratory).
Regarding long COVID, our exploratory analyses suggest an association between acute disease severity and persistent symptoms. Although the overall prevalence of long COVID was 1.7%, exploratory analyses indicated a severity-dependent gradient across the acute disease course (from 0% after asymptomatic infection to 50% among those with severe courses). Older age and chronic illness were significantly associated with long COVID in our sample, whereas sex, SES, migration background, and BMI showed no significant associations. This pattern contrasts with previous studies and should be interpreted with caution. Given the use of a symptom cutoff of at least four weeks, persistent symptoms following severe acute SARS-CoV-2 infection may partly reflect prolonged recovery rather than post-SARS-CoV-2 condition in the strict sense. Differences from prior reports may further reflect our small number of long COVID cases (n = 12), the use of a 4-week rather than 12-week symptom cutoff, and the underrepresentation of children from lower socioeconomic backgrounds in our cohort. Symptom persistence beyond 12 weeks was reported by the parents/guardians of only nine participants (1.2%), which aligns more closely with the current WHO definition of post-COVID-19 syndrome and precluded meaningful separate analyses of long COVID, as defined by the NICE and AWMF. In this context, the WHO’s clinical case definition, published in October 2021, is therefore used as a reference framework for interpretation rather than as a basis for developing the questionnaire. Due to the very small number of affected children, further differentiation of long COVID phenotypes or the inclusion of more children from lower socioeconomic backgrounds would be speculative and unlikely to provide additional insights.
Increasing evidence suggests an association between severe acute COVID-19 in children and ME/CFS-like sequelae, including persistent fatigue, post-exertional malaise and cognitive dysfunction, with potential downstream psychosocial impacts (truancy, social withdrawal, and academic decline) [11, 12] Although our cross-sectional design did not allow a formal diagnosis of ME/CFS or causal inference, the observed stepwise increase in the prevalence of persistent symptoms - from 0% among asymptomatic cases to 50% following severe COVID-19 - provides an exploratory signal. This finding suggests that children with more severe acute disease may benefit from structured, multidisciplinary follow-up to monitor recovery and any potential long-term impairment. However, causality cannot be inferred from cross-sectional data.
Strengths and limitations
A key strength of the CoCo-Fakt study is its population-based sampling frame via public health departments. This allowed inclusion of PCR-confirmed SARS-CoV-2 cases, regardless of symptom severity or healthcare utilisation. By partnering directly with the Cologne and Augsburg health departments, the researchers captured every PCR-confirmed SARS-CoV-2 infection in children over a full calendar year and across multiple waves of the COVID-19 pandemic. This approach ensured a broad spectrum of real-world SARS-CoV-2 infections, ranging from asymptomatic to severe, and enabled us to link granular clinical, sociodemographic, and psychosocial data under uniform public health protocols. These features enhance both the internal validity of our acute severity findings and the generalisability of our early long COVID observations while highlighting the value of large, prospective cohort studies that can apply standardised severity scales and agreed long COVID outcome sets (e.g. post-COVID-19 Core Outcome Set [PC-COS] Children).
However, this study has several limitations that should be acknowledged. First, its cross-sectional design precludes causal inference and does not allow temporal sequencing of exposures and outcomes. Second, all health information (symptoms, BMI, and chronic illness) was reported by parents/guardians without clinical verification, which may have resulted in misclassification and underreporting of mild or subtle neurocognitive or emotional symptoms. This limitation may be particularly relevant given the relatively young mean age of our cohort, as emerging evidence suggests that post-COVID symptom profiles differ by age. Younger children may exhibit less specific or distinct symptom patterns than older children and adolescents, making them more difficult to capture using standardised symptom checklists and parent-proxy reporting. This could lead to an underestimation of persistent symptoms [38]. Direct self-reporting by adolescents was not possible because the CoCo-Fakt survey was distributed through local health departments to their parents, who served as the primary point of contact during quarantine management. However, adolescent perspectives would have provided valuable complementary insights and enabled comparisons between different age groups (e.g. adolescents and young adults). Future studies would benefit from clinically measured or validated health data (e.g. anthropometry and symptom assessments) to improve the precision of outcome estimates. Third, the number of severe cases was limited (n = 67, 9.6%), which resulted in wide confidence intervals for some associated factors, such as chronic illness. This limitation needs to be considered when interpreting the precision of our estimates. Fourth, the response rate of around 8% (ranging from 5.5% to 15.9% across survey waves) suggests potential selection bias towards families with higher SES. Fifth, as only a few participants met the criteria for long COVID (n = 12), our analyses were exploratory. Sixth, our study lacked a PCR-negative control group, which would have enabled an estimation of the background prevalence of nonspecific symptoms and thereby improved attribution of complaints to SARS-CoV-2 infection. This limitation is particularly relevant given that many reported symptoms, such as fatigue or headaches, also occur frequently in the general paediatric population. A parallel survey of uninfected children, such as through paediatric practices, could have provided valuable baseline data. However, this approach was infeasible within the CoCo-Fakt framework.
Seventh, we lacked detailed information about non-responders, which precluded an assessment of potential demographic or temporal differences between participants and non-participants. This may have introduced selection bias, which limits the generalisability of our findings.
Finally, our dataset did not include viral sequencing. As our data were collected exclusively in 2021, they represent the period before the emergence of the Omicron variant and the widespread COVID-19 vaccination of children. At this time, the standards of clinical management were evolving, and Alpha and Delta were the predominant SARS-CoV-2 variants circulating in Germany. These strains have been associated with greater COVID-19 severity in children compared with the later Omicron variant. These contextual differences may limit the direct applicability of our findings to current practice and should be considered when interpreting both acute COVID-19 severity and long COVID prevalence. This interpretation is supported by German paediatric data from Jank et al., who reported a markedly higher risk of hospitalisation and severe outcomes in children and adolescents infected with the Alpha and Delta SARS-CoV-2 variants than in those infected with the Omicron variant [39]. Similar patterns have been observed in the UK; Nyberg et al. found markedly lower risks of hospitalisation and severe disease after Omicron variant versus Delta variant infection across all age groups [40].
Conclusions
Severe COVID-19 and long COVID were generally infrequent among our participants. Severe acute courses clustered strongly in children with chronic illness, while long COVID was additionally associated with older age and with increasing acute disease severity in exploratory analyses. Targeted monitoring and follow-up of children who are more likely to experience severe or persistent outcomes may help to identify acute deterioration and long-term sequelae, such as ME/CFS-like fatigue, at an early stage. Multicentre, longitudinal studies are needed to confirm the severity–sequelae gradient and refine evidence-based prevention and care strategies. Such efforts will help refine prevalence estimates, clarify pathophysiology, and optimise targeted approaches to COVID-19 prevention and care.
Supplementary Information
Acknowledgements
We thank the participating families, the staff of the Cologne and Augsburg health departments, and all colleagues involved in data collection and management. Additionally, we would like to thank Martin Sailer, Landrat of Augsburg, for his continuous support of the study.
Abbreviations
- AWMF
Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften(Association of the Scientific Medical Societies in Germany)
- BMI
Body Mass Index
- CI
Confidence Interval
- CoCo-Fakt
Cologne Corona Beratung und Unterstützung Für Index- und KontAKt-Personen während der Quarantäne-ZeiT(Cologne Corona Advice and Support for Index and Contact Persons During Quarantine)
- COSMO
COVID-19 Snapshot Monitoring
- COVID-19
Coronavirus Disease 2019
- DiKoMa
Digitales Kontaktmanagement (Digital Contact Management System)
- GEDA
Gesundheit in Deutschland aktuell (German Health Update)
- ME/CFS
Myalgic Encephalomyelitis / Chronic Fatigue Syndrome
- MIS-C
Multisystem Inflammatory Syndrome in Children
- NICE
National Institute for Health and Care Excellence
- OR
Odds Ratio
- PIMS
Pediatric Inflammatory Multisystem Syndrome
- PCR
Polymerase Chain Reaction
- PC-COS Children
Post-COVID-19 Core Outcome Set for Children and Young People
- RKI
Robert Koch Institute
- SARS-CoV-2
Severe Acute Respiratory Syndrome Coronavirus 2
- SD
Standard Deviation
- SES
Socioeconomic Status
- SPSS
Statistical Package for the Social Sciences
- STIKO
Ständige Impfkommission (German Standing Committee on Vaccination)
- WHO
World Health Organization
Authors’ contributions
L.S. wrote the initial manuscript draft and prepared all tables and figures. N.S. and C.J. conducted the statistical analyses. C.J. also provided input on manuscript structure, contributed to writing and editing, and supervised the project as principal investigator. N.S. prepared Fig. 2, supported manuscript revisions, and assisted in structuring the tables. S.F., A.K., J.N., and B.G. contributed to study design and implementation as part of the core project group. All authors critically reviewed and approved the final manuscript.
Funding
This research did not receive funding.
Data availability
The datasets analysed during the current study are not publicly available due to data protection regulations. Relevant summary data are included in the tables of the manuscript. Additional data may be made available by the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study was approved by the Human Ethics Research Committee of RWTH Aachen University (reference number: 351/20). Written informed consent was obtained from all participants or their legal guardians. The study was conducted in accordance with the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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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
The datasets analysed during the current study are not publicly available due to data protection regulations. Relevant summary data are included in the tables of the manuscript. Additional data may be made available by the corresponding author upon reasonable request.