Effectiveness of Kinder Lebensqualität Fragebogen (KINDL) and Children’s Somatic Symptom Inventory-24 (CSSI-24) for measuring postacute sequelae of COVID-19 in children: a diagnostic validation study

Article information

Clin Exp Pediatr. 2025;68(11):944-951

Publication date (electronic) : 2025 September 12

doi : https://doi.org/10.3345/cep.2025.00983

¹Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung, Taiwan

²Research Center of Allergy, Immunology and Microbiome (A.I.M), China Medical University Hospital and Children's Hospital, China Medical University, Taichung, Taiwan

³Department of Microbiology & Immunology, College of Medicine, National Cheng Kung University, Tainan, Taiwan

⁴Department of Pediatric Psychiatry, China Medical University Children's Hospital, China Medical University, Taichung, Taiwan

⁵Department of Teaching and Research, China Medical University Children's Hospital, China Medical University, Taichung, Taiwan

⁶Department of Pediatric Infectious Diseases, China Medical University Children's Hospital, China Medical University, Taichung, Taiwan

⁷Department of Pediatric Cardiology, China Medical University Children's Hospital, China Medical University, Taichung, Taiwan

⁸Department of Pediatric Pulmonology and Critical Care Medicine, China Medical University Children's Hospital, China Medical University, Taichung, Taiwan

⁹Department of Pediatric Gastroenterology, China Medical University Children’s Hospital, China Medical University, Taichung, Taiwan

¹⁰Department of Pediatric Neurology, China Medical University Children's Hospital, China Medical University, Taichung, Taiwan

¹¹Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan

¹²Department of Medical Science, College of Life Sciences and Medicine, National Tsing-Hua University, Hsinchu, Taiwan

¹³Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan

¹⁴Biostatistics Consulting Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan

¹⁵School of Nursing, College of Nursing, Kaohsiung Medical University, Kaohsiung, Taiwan

¹⁶Department of Pediatric Allergy, Immunology and Rheumatology, China Medical University Children's Hospital, China Medical University, Taichung, Taiwan

Corresponding author: Chung-Ying Lin, PhD. Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, No. 1 University Road, Tainan, Taiwan Email: cylin36933@gs.ncku.edu.tw

Co-corresponding Author: Jiu-Yao Wang, PhD, Center of Allergy, Immunology, and Microbiome (A. I. M.), China Medical University Hospital and Children's Hospital, No. 2, Yude Road, North Dist., Taichung City 40447, Taiwan Email: a122@mail.ncku.edu.tw

^†

These authors contributed equally to this study as co-first authors.

Received 2025 April 29; Revised 2025 June 20; Accepted 2025 July 8.

Abstract

Background

The postacute sequelae of coronavirus disease 2019 (PASC), also known as pediatric long coronavirus disease (COVID), can significantly affect the quality of life (QoL) of affected children. Currently, there are no standardized screening tools to identify high-risk children. The Kinder Lebensqualität fragebogen (KINDL) is a psychometric method for measuring QoL in children.

Purpose

This study used the KINDL questionnaire and Children's Somatic Symptom Inventory-24 (CSSI-24) to evaluate symptom burden and establish a screening threshold for pediatric PASC.

Methods

We performed a cross-sectional study of children diagnosed with PASC defined as the presence of at least one persistent symptom lasting more than 4 weeks after a confirmed COVID-19 infection. Symptoms were assessed using a structured checklist developed by our team. The correlation between the KINDL and CSSI-24 scores was analyzed, and receiver operating characteristic (ROC) curve analysis was used to determine the optimal KINDL cutoff for identifying high-risk cases. QoL scores were also compared with those of historical cohorts to evaluate the impact of the PASC.

Results

We included 84 participants: 46 children (mean age, 8.74±1.77 years; 41.3% girls) and 38 adolescents (mean age, 14.50±1.56 years; 44.7% girls). KINDL and CSSI-24 scores showed a significant negative correlation (r=-0.44, P<0.001), suggesting that increased somatic symptoms were associated with a reduced QoL. The ROC analysis identified a KINDL cutoff score of 74.75 (area under the curve, 0.82) for detecting high-risk children. Compared to historical cohorts, children with PASC had QoL scores comparable to child-reported norms from 2010 but lower than parent-reported norms from the same year.

Conclusion

KINDL and CSSI-24 are practical tools for screening for pediatric PASC in outpatient settings. A KINDL cutoff of 74.75 may help clinicians identify children with PASC who require early intervention. Further studies in larger and more diverse populations are required to validate these findings.

Keywords: COVID-19; Postacute COVID-19 syndrome; Quality of life; ROC curve; Surveys and questionnaires

Key message

Question: Although children with postacute sequelae of coronavirus disease 2019 (PASC) may experience persistent symptoms that affect their quality of life (QoL), a screening tool for identifying high-risk children is lacking.

Finding: Kinder Lebensqualität fragebogen (KINDL) and Children's Somatic Symptom Inventory-24 (CSSI-24) were significantly correlated. An optimal KINDL cutoff score (74.75) detected those at high risk of a reduced QoL.

Meaning: Integrating KINDL and CSSI-24 into routine pediatric outpatient care may enable timely identification and interventions for children at risk of PASC-related impairments.

Introduction

The global impact of the coronavirus disease 2019 (COVID-19) pandemic, stemming from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has touched the lives of millions worldwide [1]. For most people, recovery from the initial infection happens within weeks, but some experience lingering or new symptoms that persist for months—a condition known as postacute sequelae of COVID-19 (PASC) infection, or long COVID [2]. It encompasses a spectrum of effects on various organ systems and functional domains, including respiratory, cardiovascular, neurological, psychological, and cognitive aspects [3].

Children have not been spared from these effects. During the pandemic, school closures and the shift to online learning disrupted education in many countries, potentially impacting children’s health even in the short term [4]. Beyond school learning, kids faced stress and anxiety from parents or caregivers, sensing that something serious was unfolding around them. This often left them confused and eager for clear, honest information [5]. The fear and uncertainty of the outbreak, combined with the isolating effects of lockdowns, have sparked growing concerns about children’s mental and emotional well-being during and after pandemic period [6-8]. Among these concerns, PASC in children has drawn increasing attention because of its lasting impact on their quality of life (QoL). While much research has focused on adults with long COVID, the effects on children remain less understood. Symptoms like tiredness, trouble thinking, and ongoing physical discomfort can disrupt their daily lives and overall health [9]. However, standardized screening tools are lacking to identify children at high risk for PASC-related QoL impairment in clinical practice.

QoL assessment is a method of evaluating an individual's subjective perception of satisfaction with their own health. It is particularly valuable for assessing the long-term impact of diseases on pediatric populations [10]. In Taiwan, this approach has been used in child psychiatry to explore how physical health issues affect mental and social wellbeing [11,12]. This study relies on the Kinder Lebensqualität fragebogen (KINDL) questionnaire, a trusted tool that assesses different aspects of children’s QoL. It has been adapted and validated for use in Taiwan, ensuring it fits the local culture and language [13,14]. To measure physical symptoms, we used the Children’s Somatic Symptom Inventory-24 (CSSI-24), a 24-question survey that asks children to rate symptoms over the past 2 weeks on a scale from 0 (not at all) to 4 (a lot). Higher scores reflect more severe physical complaints [15]. Widely applied to children with chronic conditions, the CSSI-24 helps gauge the intensity of their symptoms [16,17].

The purpose of this study is threefold: (1) to examine how KINDL (a QoL tool) and CSSI-24 (a symptom tool) relate to each other in Taiwanese children with PASC, shedding light on how symptoms affect their well-being [13,15]; (2) to use receiver operating characteristic (ROC) analysis to find a practical KINDL score cutoff that identifies children at high risk of PASC-related QoL issues early on; and (3) to offer pediatricians clear, actionable advice on using these tools in routine checkups to better support affected kids. This research seeks to fill the gap in pediatric PASC screening, providing a starting point for better care and understanding of long COVID’s impact on children.

Methods

1. Study participants and data collection

This research was a prospective cohort study conducted at China Medical University Children's Hospital, a major medical center in central Taiwan. We enrolled children from the DISCOVER (Diagnosis and Support for COVID Children to Enhance Recovery) cohort, a program designed to study and support kids recovering from severe acute respiratory syndrome coronavirus 2 infections confirmed by reverse transcription-polymerase chain reaction or rapid antigen tests. Participants were children with PASC, defined as having symptoms lasting more than 4 weeks after their initial COVID-19 recovery [18,19]. Recruitment took place from July 1, 2022, to July 31, 2023, during Taiwan’s Omicron variant outbreak, which began in April 2022. This study was approved by the Research Ethics Committee of China Medical University Hospital (CMUH111-REC2-113 (AR-1)). Written informed consent was obtained from all participants and/or their legal guardians before enrollment. All procedures were conducted in accordance with institutional guidelines and the Declaration of Helsinki. Patients and the public were not involved in the design, conduct, reporting, or dissemination of this research.

2. Data gathering

We collected information from children and their parents during outpatient visits for post-COVID-19 concerns. This included details on COVID-19 vaccination status, how their acute infection was managed, and symptoms persisting for at least 4 weeks postrecovery, categorized based on prior studies [2,20]. Laboratory tests were also conducted, measuring markers like complete blood count, erythrocyte sedimentation rate, hsCRP, liver enzymes (aspartate aminotransferase, alanine aminotransferase), creatine phosphokinase, lactic dehydrogenase, immunoglobulin E, and other relevant biomedical indicators.

3. Measuring physical symptoms

To assess physical symptom burden in PASC children, we used the CSSI-24, developed by Walker et al. [15] This 24-item questionnaire asks participants to rate symptoms over the past 2 weeks on a 5-point scale (0=not at all, 4=a lot), with scores ranging from 0 to 96. Higher scores indicate greater physical distress. We treated CSSI-24 scores as a continuous variable and used ROC analysis to pinpoint a threshold for significant symptom burden.

4. Assessing QoL

QoL was measured using the revised KINDL questionnaire (KINDL-R), which is a traditional Chinese version culturally adapted for use in Taiwan, and consists of 24 items rated on a 5-point scale (1=never, 5=all the time).21) Scores are converted to a 0–100 scale across 6 areas—physical, psychological, self-esteem, family, friends, and school—plus an overall QoL score, where higher values reflect better QoL. We used age-specific versions: the 7–11-year-old version for younger children (completed with parental help) and the 12–18-year-old version for adolescents (self-completed). Previous research confirms KINDL’s reliability and validity across these age groups and reporting methods [22-26].

5. Statistical analysis

We summarized participant characteristics (e.g., age, sex, clinical data) with descriptive statistics. Spearman correlation tested the relationship between KINDL scores (total and domain-specific) and CSSI-24 scores or lab results. ROC analysis, guided by Youden’s Index, identified an optimal KINDL cutoff for detecting high-risk children, prioritizing sensitivity due to the lack of a control group [27].

To explore PASC symptoms, we developed a structured questionnaire based on existing literature and clinical observations [2,15]. Although not validated, it captured common post-COVID-19 complaints in children. Principal component analysis (PCA) with parallel analysis (PA) condensed these symptoms into key categories, excluding those reported by fewer than 10 participants [28]. PA, using 1000 Monte Carlo simulations, determined the number of components, followed by PCA with Oblimin rotation to define symptom clusters. These clusters were then correlated with KINDL and CSSI-24 scores using Pearson correlations.

We built 7 multiple regression models to identify factors affecting QoL, with each model using a different QoL score (6 domains plus total) as the outcome. Predictors included age, sex (reference: male), vaccination status (reference: unvaccinated), symptom clusters, and CSSI-24 scores. Finally, we compared our participants’ QoL to prepandemic norms using independent t-tests: children (7–11 years) against a 2010 cohort [25], and adolescents (12–18 years) against a 2008 cohort [13].

Results

Our study included 84 participants: 46 children (average age, 8.74±1.77 years; 41.3% girls) and 38 adolescents (average age, 14.50±1.56 years; 44.7% girls). Most participants (n=65, 77.4%) had received COVID-19 vaccination prior to infection, while 19 (22.6%) were unvaccinated. The average CSSI-24 score across all participants was 16.45±11.94. Table 1 provides detailed breakdowns by age group.

Table 1.

Demographics and clinical characteristics of the present sample

KINDL and CSSI-24 scores showed a significant negative correlation (r=-0.44, P<0.001), meaning higher symptom severity was tied to lower QoL (Fig. 1A). ROC analysis set a KINDL cutoff at 74.75 (area under the curve [AUC], 0.82), separating 72 participants scoring ≤74 (average CSSI-24: 18.43±11.95) from 12 scoring >75 (average CSSI-24: 6.53±4.55) (Fig. 1B). A heatmap of lab results revealed KINDL scores positively linked to eosinophil and lymphocyte percentages, but negatively tied to hsCRP and neutrophil percentages (Fig. 1C).

Fig. 1.

Associations among KINDL scores, CSSI-24 scores, long coronavirus disease (COVID) symptom components, and biomedical markers in children with PASC. (A) Scatter plot showing the negative correlation between KINDL and CSSI-24 scores (Spearman correlation coefficient=-0.44, P<0.001), with regression line and 95% confidence intervals. (B) Receiver operating characteristic (ROC) curve of KINDL scores in predicting high CSSI-24 scores. The optimal cutoff yielded an area under the curve (AUC) of 0.82 (95% confidence interval, 0.73–0.92; P=0.002). (C) Correlations between KINDL scores and selected biomedical markers. *P<0.05. **P<0.01. (D) Parallel analysis of persistent COVID-19 symptoms using 1000 Monte Carlo simulations to determine the number of retained components for principal component analysis. KINDL, Kinder Lebensqualität fragebogen; CSSI-24, Children’s Somatic Symptoms Inventory-24; LDH, lactate dehydrogenase; CPK, creatine phosphokinase; hsCRP, high-sensitivity C-reactive protein; ALT, alanine ami notransferase; AST, aspartate aminotransferase; RBC, red blood cell; Hb, hemoglobin; Hct, hematocrit; RDW, red cell distribution width; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; WBC, white blood cell; IgE, immunoglobulin E; VitD, vitamin D.

PCA and PA grouped PASC symptoms into 3 clusters (Fig. 1D): cluster I, psychological distress and cognitive impairment (17.18% variance, eigenvalue=5.84), cluster 2, physical fitness and discomfort (13.61% variance, eigenvalue=4.63), and cluster 3, somatic symptoms (9.97% variance, eigenvalue=3.39). Factor loadings are in Supplementary Table 1. All 3 clusters correlated significantly with CSSI-24 scores (r=0.30 to 0.53, P=0.005 to <0.001). Cluster 1 affected physical (r=-0.25, P=0.02), psychological (r=-0.25, P=0.03), self-esteem (r=-0.25, P=0.02), friends (r=-0.31, P=0.004), school (r=-0.28, P=0.01), and total QoL (r=-0.37, P=0.001). Cluster 2 impacted physical (r=-0.51, P<0.001), psychological (r=-0.28, P=0.01), school (r=-0.28, P=0.01), and total QoL (r=-0.41, P<0.001). Cluster 3 only affected physical QoL (r=-0.32, P=0.003) (Table 2).

Table 2.

Correlations between principal components of PASC symptoms, quality of life (QoL), and CSSI-24

Regression models (Table 3) showed that, after adjusting for age and sex, vaccination (β=0.194, P=0.037), cluster 1 (β=-0.251, P=0.012), cluster 2 (β=-0.237, P=0.018), cluster 3 (β=-0.209, P=0.049), and CSSI-24 scores (β=-0.487, P<0.001) significantly predicted total QoL (R²=0.46, adjusted R²=0.41). Cluster 2 and CSSI-24 drove physical QoL; all clusters plus CSSI-24 influenced psychological QoL; CSSI-24 alone affected family and school QoL; and cluster 1 impacted friends QoL. In brief, Component 2 and CSSI-24 score were significant explaining factors for physical QoL; all 3 components and CSSI-24 score were significant explaining factors for psychological QoL; CSSI-24 score was the only significant explaining factor for both family and school QoL; Component 1 was the only significant explaining factor for friend QoL.

Table 3.

Regression models using principal components of PASC symptoms and CSSI to explain quality of life (QoL)

In the quality-of-life section (Table 4), the average KINDL score of the children in this study was 67.60±11.41. There was no significant difference (P=0.962) compared to the self-assessment scores of children from the 2010 generation (67.71±15.21). However, the KINDL scores of the children in this study were significantly lower than the parental evaluation scores of the 2010 child generation (75.42±11.27, P<0.001). The average KINDL score of adolescents in this study was 61.43±15.48. Compared with the self-assessment scores of children from the adolescent generation in 2008 (57.68±11.25), the scores were significantly higher (P=0.044). Table 4 also reports a detailed comparison of each KINDL domain. The important findings are summarized as follows: for the children group, there was no significant difference (all P>0.05) between the KINDL scores reported by this sample and the children in the 2010 cohort. However, compared to the scores reported by parents in the 2010 child cohort, current children have significantly lower scores in terms of physical QoL (P<0.001), psychological QoL (P=0.018), self-esteem QoL (P<0.001), and friend QoL (P=0.004). For the adolescent population, compared with the scores reported by adolescents in the 2008 adolescent cohort, the quality of family life (P<0.001) and school life (P=0.049) in this sample were significantly higher.

Table 4.

Quality of life (QoL) comparison between the present sample and prior Taiwan children

Discussion

This study provides valuable insights into the interplay between QoL and somatic symptom burden in children with PASC, or pediatric long COVID. By leveraging the KINDL questionnaire and the CSSI-24, the research reveals a significant negative correlation (r=-0.44, P<0.001) between these 2 measures. This finding indicates that children experiencing more severe physical symptoms, such as fatigue or pain, tend to report lower QoL, affecting their daily functioning and well-being. Additionally, PCA categorized PASC symptoms into 3 distinct clusters—psychological distress and cognitive impairment, physical fitness and discomfort, and somatic symptoms—each linked to specific QoL domains. A KINDL cutoff score of 74.75, derived from ROC analysis (AUC, 0.82), was proposed as a potential screening threshold to identify high-risk children in clinical settings. These results carry important implications for understanding PASC’s impact and improving pediatric care.

1. Comparison of QoL scores with historical cohorts

Our results showed that the overall QoL of PASC children was not significantly different from the 2010 child-reported KINDL scores (P=0.962), suggesting that the perceived QoL of PASC children was comparable to that of children before the pandemic. However, compared with the 2010 parent-reported KINDL scores, our sample showed significantly lower QoL (P<0.001). This also shows that the disruptions in education, social interactions, and physical health of PASC children due to the long-term impact of the COVID-19 pandemic have indeed affected their QoL.

Furthermore, adolescents in our study reported significantly higher overall QoL than the 2008 adolescent cohort (P=0.044), a finding that warrants further exploration. Due to lifestyle changes during the epidemic, such as distance education that reduced the pressure of commuting to study for Taiwanese teenagers, and the increased interaction and involvement of parents with teenagers in their lives, some of the negative psychological effects of long-term infection with COVID-19 among teenagers may have been alleviated. Future studies should investigate whether these trends persist across different pediatric populations.

2. Clinical impact of KINDL and CSSI-24

The negative correlation between KINDL and CSSI-24 highlights the substantial impact of somatic symptoms on pediatric QoL. This finding supports the importance of using the assessment tools in pediatric clinic. It’s beneficial and meaningful for patients who need early intervention for symptom burden. The integration of QoL assessments in routine practice could help physicians recognize children who may require additional medical evaluation, psychological support, or rehabilitative care.

In the view of the influence of long COVID symptom on children with PASC, 3 components of the symptom cluster identified by using PCA analysis. And understand how PASC affect QoL form multiple aspect. Psychological distress and cognitive impairment were associated with both lower psychological and social functioning scores, whereas physical discomfort had the strongest impact on physical QoL. These patterns suggest that comprehensive assessments of both physical and psychological health are necessary for effective clinical management.

3. The clinical utility of the KINDL cutoff score

Our ROC curve analysis revealed a score of 74.75 might be a useful threshold for identifying children at risk of lower QoL due to persistent somatic symptoms. In a clinical setting, this cutoff score could help pediatrician quickly screen and identify children who need more attention or early intervention. However, it is essential to interpret this threshold with caution. While sensitivity and specificity values indicate its discriminative potential, additional validation in larger and more diverse populations is necessary to refine its applicability.

A key point in applying this cutoff is balancing sensitivity and specificity. If the threshold is too high, a large proportion of children may be classified as high-risk cluster, potentially leading to unnecessary interventions. On the contrary, if the threshold is too low, some children with significant impairment or needs may be missed. Future studies should examine how this cutoff performs in real-world clinical settings to optimize its practical use.

Interestingly, we observed that higher KINDL scores were associated with increased lymphocyte and eosinophil counts, while lower QoL correlated with elevated neutrophil counts and systemic inflammation markers such as hsCRP. These results suggest that immune system dysregulation may contribute to symptom persistence in PASC children. Elevated hsCRP has been implicated in chronic inflammatory conditions and may serve as a potential biomarker for QoL impairments in long COVID.

Although KINDL and CSSI-24 provide valuable patient-reported assessments, further studies should explore whether integrating hematologic markers into clinical screening could improve early detection of high-risk children.

4. Study limitations

This study has several limitations need to consider. First, the relatively small sample quantity limited the the generalizability of the findings.

Second, use of a nonstandardized questionnaire for PASC symptom collection (clinical chief complain, and capture commonly reported post-COVID-19 symptoms), self-reported measures rather than objective clinical assessments, which may introduce variability. However, this approach was necessary due to the evolving nature of long COVID symptoms, and the lack of a universally accepted pediatric-specific assessment tool at the time of data collection.

Third, this study utilized a cross-sectional design, did not established causal relationship between somatic symptoms and QoL outcomes. Longitudinal studies are needed to determine how these relationships evolve over time.

5. Future directions

Further research should focus on validating the KINDL cutoff in larger cohorts, ensuring its robustness in different pediatric populations. Additionally, future studies should explore whether interventions targeting specific symptom clusters can improve QoL outcomes in children with PASC. By improving screening criteria and enhancing symptom management strategies, healthcare providers can better support the long-term health of pediatric patients affected by PASC.

Future research should also investigate the potential role of immune markers in pediatric long COVID screening. The positive correlation between QoL and lymphocyte/eosinophil counts may indicate an adaptive immune response in children with better health status, and higher neutrophil and hsCRP levels in those with lower QoL suggest an ongoing inflammatory state. Longitudinal studies are needed to determine whether these hematologic markers can complement existing patient-reported outcomes and enhance clinical decision-making.

In conclusion, this study demonstrated evidence that KINDL and CSSI-24 are valuable tools for assessing the impact of PASC on QoL. The proposed KINDL cutoff score of 74.75 offers a potential benchmark for identifying high-risk children, but further validation is necessary. Integrating QoL assessments into routine clinical practice could enhance early detection and intervention, improving health outcomes for children experiencing persistent post-COVID symptoms.

Supplementary materials

Supplementary Table 1 is available at https://doi.org/10.3345/cep.2025.00983.

Supplementary Table 1.

Factor loadings of 3 components for long coronavirus disease symptoms

cep-2025-00983-Supplementary-Table-1.pdf

Notes

Conflicts of interest

JYW, a member of the Editorial Board of Clinical and Experimental Pediatrics, is the co-corresponding author of this article. However, he played no role whatsoever in the editorial evaluation of this article or the decision to publish it. Except for that, no potential conflict of interest relevant to this article was reported.

Funding

This study was supported by following grants: NSTC 113-2314-B-039-057-from the National Science and Technology Council, Taiwan; a research grant (1JA8) from the Center for Allergy, Immunology, and Microbiome (A.I.M.), China Medical University Hospital, Taichung, Taiwan; DMR-113-114, DMR-114-024, and DMR-114-109 from the China Medical University Hospital, Taichung, Taiwan. The funder did not influence the results/outcomes of the study.

Author contribution

Conceptualization: LSHW, PCC, CYL, JYW; Data curation: XLL, STL, CHW, YLH, KSH, HCL, CYL, PCC, ACC, ICC, WJS; Formal analysis: CYL; Funding acquisition: JYW; Methodology: LSHW, PCC, HJT, CYL, JYW; Project administration: PCC, XLL; Writing - original draft: LSHW, PCC, XLL, STL, CYL; Writing - review & editing: LSHW, HJT, CYL, JYW

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Variable	Children^a) (N=46, 54.8%)	Adolescents^a) (N=38, 45.2%)	All (N=84)
Sex of the child
Male	27 (58,7)	21 (55.3)	48 (57.1)
Female	19 (41.3)	17 (44.7)	36 (42.9)
Age of the child (yr)	8.74±1.77	14.50±1.56	11.35±3.33
Vaccinated, yes	34 (73.9)	31 (81.6)	65 (77.4)
Rapid test positive, yes	45 (97.8)	35 (92.1)	80 (95.2)
PCR positive, yes	10 (21.7)	14 (36.8)	37 (44.0)
CSSI	14.70±11.64	18.58±12.10	16.45±11.94

	C1	C2	C3	PhyQoL	PsyQoL	SEQoL	FamQoL	FriQoL	SchQoL	TotalQoL	CSSI
C1	-
C2	0.20 (0.053)	-
C3	0.20 (0.06)	0.24 (0.02)	-
PhyQoL	-0.25 (0.02)	-0.51 (<0.001)	-0.32 (0.003)	-
PsyQoL	-0.25 (0.03)	-0.28 (0.01)	-0.02 (0.86)	0.58 (<0.001)	-
SEQoL	-0.25 (0.02)	-0.13 (0.25)	-0.07 (0.54)	0.09 (0.35)	-0.05 (0.61)	-
FamQoL	-0.02 (0.85)	-0.10 (0.37)	-0.03 (0.79)	0.24 (0.01)	0.48 (<0.001)	0.08 (0.44)	-
FriQoL	-0.31 (0.004)	-0.19 (0.08)	-0.10 (0.39)	0.26 (0.01)	0.46 (<0.001)	0.29 (0.002)	0.21 (0.03)	-
SchQoL	-0.28 (0.01)	-0.28 (0.01)	-0.11 (0.32)	0.27 (0.01)	0.11 (0.28)	0.27 (0.006)	0.02 (0.82)	0.26 (0.01)	-
TotalQoL	-0.37 (0.001)	-0.41 (<0.001)	-0.18 (0.10)	0.69 (<0.001)	0.70 (<0.001)	0.50 (<0.001)	0.52 (<0.001)	0.68 (<0.001)	0.54 (<0.001)	-
CSSI-24	0.30 (0.005)	0.43 (<0.001)	0.53 (<0.001)	-0.52 (<0.001)	-0.31 (0.001)	-0.04 (0.65)	-0.13 (0.20)	-0.22 (0.03)	-0.25 (0.01)	-0.41 (<0.001)	-

Variable	Standardized coefficient (P value)
Variable	PhyQoL	PsyQoL	SEQoL	FamQoL	FriQoL	SchQoL	TotalQoL
Sex (ref: male)	-0.029 (0.74)	0.079 (0.40)	0.133 (0.24)	0.018 (0.87)	0.170 (0.11)	0.100 (0.30)	0.123 (0.17)
Age	0.007 (0.94)	0.336 (0.001)	-0.253 (0.043)	0.334 (0.006)	0.119 (0.30)	-0.415 (<0.001)	0.014 (0.88)
Vaccinated (ref: no)	0.165 (0.068)	0.142 (0.14)	0.059 (0.61)	-0.002 (0.99)	0.198 (0.07)	0.144 (0.15)	0.194 (0.037)
C1	-0.082 (0.39)	-0.289 (0.006)	-0.142 (0.25)	-0.06 (0.61)	-0.349 (0.003)	-0.004 (0.97)	-0.251 (0.012)
C2	-0.346 (0.001)	-0.211 (0.045)	-0.037 (0.77)	-0.08 (0.49)	-0.086 (0.46)	-0.083 (0.44)	-0.237 (0.018)
C3	0.041 (0.69)	-0.294 (0.009)	0.019 (0.88)	0.224 (0.08)	0.062 (0.62)	0.199 (0.08)	-0.209 (0.049)
CSSI-24	-0.412 (0.001)	-0.366 (0.004)	-0.10 (0.52)	-0.315 (0.031)	-0.182 (0.19)	-0.487 (<0.001)	-0.487 (<0.001)
Model fit
F-value (P value)	9.448 (<0.001)	6.973 (<0.001)	1.558 (0.16)	2.585 (0.019)	3.304 (0.004)	5.923 (<0.001)	8.901 (<0.001)
VIF	1.066–1.852	1.065–1.853	1.065–1.853	1.065–1.853	1.065–1.853	1.066–1.852	1.065–1.853
R² (adj. R²)	0.47 (0.42)	0.40 (0.34)	0.13 (0.05)	0.20 (0.12)	0.24 (0.17)	0.36 (0.30)	0.46 (0.41)

Variable	Present sample	Prior child-report	Prior parent-report	t (P value) vs. child-report	t (P value) vs. parent-report
Children group	N=46^a)	N=443^b)	N=236^c)
PhyQoL	68.21±22.20	73.86±19.05	79.08±16.34	-1.88 (0.060)	-3.873 (<0.001)
PsyQoL	75.82±14.17	78.06±19.34	80.99±13.40	-0.764 (0.445)	-2.371 (0.018)
SEQoL	52.58±25.23	53.96±27.85	70.47±21.52	-0.323 (0.747)	-5.009 (<0.001)
FamQoL	71.88±17.47	73.26±19.14	76.21±14.99	-0.469 (0.639)	-1.743 (0.082)
FriQoL	68.75±19.28	65.03±20.56	76.66±16.15	1.175 (0.241)	-2.940 (0.004)
SchQoL	68.34±17.65	62.11±22.20	69.12±16.79	1.843 (0.066)	-0.286 (0.775)
TotalQoL	67.60±11.41	67.71±15.21	75.42±11.27	-0.048 (0.962)	-4.297 (<0.001)
Adolescents group	N=38	N=1,657^d)
PhyQoL	58.88±25.07	62.80±17.27	-	-1.367 (0.172)	-
PsyQoL	66.10±21.12	65.76±19.02	-	0.109 (0.913)	-
SEQoL	48.52±24.42	46.70 ± 22.09	-	0.501 (0.616)	-
FamQoL	78.45±16.61	63.31±18.79	-	4.923 (<0.001)	-
FriQoL	63.65±21.49	59.17±15.87	-	1.705 (0.088)	-
SchQoL	52.47±19.58	48.43±12.32	-	1.966 (0.049)	-
TotalQoL	61.43±15.48	57.68±11.25	-	2.012 (0.044)	-