Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis

Divya Valiyattil Ramachandran; Balan Senthilkumar; Mohini Bhat; Ashok Jacob Mathews

doi:doi:10.11648/j.ijsda.20261201.12

Research Article |

| Peer-Reviewed

Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis

Divya Valiyattil Ramachandran^*

, Balan Senthilkumar

, Mohini Bhat

, Ashok Jacob Mathews

Published in International Journal of Statistical Distributions and Applications (Volume 12, Issue 1)

Received: 29 December 2025 Accepted: 9 January 2026 Published: 26 January 2026

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Abstract

The purpose of this study is to examine the effects of the COVID-19 pandemic and the ensuing lockdown on children's mental health in four Kerala districts. During the second wave of the COVID-19 pandemic, from May 16 to June 4, 2021, 400 parents with at least one child between the ages of 5 and 15 participated in an online cross-sectional study using a non-probability sampling technique. Children were classified according to their mental health scores using discriminant analysis, and the association between parental behavior and children's mental health was identified using confirmatory factor analysis (CFA). The chi-square test was also used to evaluate these correlations. Four levels of mental health difficulties were identified in children: 43% had minimal difficulties (mean MDD-10: 2.8), 30.5% had emerging difficulties (mean MDD-10: 8.9), 19.3% had significant difficulties (mean MDD-10: 15.9), and 7.2% had high-risk or severe difficulties (mean MDD-10: 25.2). Children whose parents were required to continue working on-site, had relatives infected with COVID-19, had higher levels of education, or exhibited abnormal behaviors were found to have a higher percentage of mental health disorders. In contrast, children whose parents did not experience these difficulties. The results show that during the second wave of COVID-19, especially during the lockdown period between May and June 2021, a significant percentage of children in Kerala had mental health issues. Children's psychological well-being may be improved by putting psychological intervention techniques into practice, increasing parental literacy, strengthening job security, ensuring appropriate childcare, and improving household financial stability. Children's psychological wellbeing may improve as a result of increased security. The study's conclusions, according to the authors, will help Kerala make progress toward the Sustainable Development Goals (SDGs) pertaining to health.

Published in	International Journal of Statistical Distributions and Applications (Volume 12, Issue 1)
DOI	10.11648/j.ijsda.20261201.12
Page(s)	13-23
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

COVID-19, Psychological Impact, Confirmatory Factor Analysis, Statistical Models

1. Introduction

One of the biggest global health emergencies of the twenty-first century, the COVID-19 pandemic has had a profound impact on people's psychological and emotional well-being in addition to their physical health. While early research primarily centered on morbidity, mortality, and health-system challenges, the indirect consequences of the pandemic especially its impact on mental health have drawn increasing attention in recent years

[1]

. Children, in particular, represent a highly vulnerable group. Interruptions in schooling, restricted social interactions, insecurity about the future, and changes in family environments collectively contributed to heightened emotional distress among children during lockdown periods across the globe

[2]

. The pandemic struck India in several waves, the most devastating of which occurred in April and June of 2021. Kerala experienced severe lockdowns, a sharp increase in infections, and significant socioeconomic disruptions during this time, despite having a robust healthcare system

[3]

. Long-term school closures, home detention and increased exposure to household stressors were all experienced by children in Kerala. Children's behavioral and psychological development is known to be impacted by such circumstances, particularly those in the vulnerable 5–15 age range

[4]

. Children's mental health became a concern that needed systematic research as families dealt with unstable finances, health concerns, and abrupt lifestyle changes. Child mental health outcomes are significantly influenced by family dynamics and parental behavior. According to research, the emotional climate of the home can be greatly influenced by parents' stress levels, coping mechanisms, work status, and health experiences during pandemics

[10, 11]

. Parents with COVID-19-infected relatives or those who continued to work outside the home were particularly burdened psychologically, which may have had an indirect impact on their children's emotional health. The pandemic focused research has demonstrated a strong correlation between increased anxiety, irritability, and behavioral disturbances in children and parental distress and uncertainty

[7]

. Examining children's mental health during crises requires an understanding of such relational fluctuation. Although several studies have assessed the psychological impact of COVID-19 on adults and healthcare workers in India, research specifically focusing on children especially within the Kerala situation remains limited. The few available studies highlight rising levels of anxiety, sleep disturbances, academic stress, and behavioral issues among children during lockdowns

[9, 20]

. However, a systematic understanding of how demographic, financial, and parental factors interact to influence children’s mental health during the second wave of the pandemic is still lacking. This gap overview the need for empirical research targeting these specific variables and contexts.

Mental health assessment tools such as the Major Depressive Disorder scale (MDD-10) have been widely used to classify children into varying levels of emotional and behavioral difficulties

[6, 18, 19]

. Categorizing/ children based on severity scores helps in identifying high-risk groups and understanding the distribution of psychological issues across populations. Additionally, advanced statistical methods such as discriminant analysis and confirmatory factor analysis (CFA) provide strong structure for examining mental health patterns and validating the relationship between child outcomes and parental or environmental factors. Discriminant analysis assists in classifying individuals based on predictor variables, whereas CFA allows researchers to test theoretical models and assess how well observed data fit expected constructs

[5]

. The use of such methods strengthens the reliability and interpretability of mental health research findings. The second wave of COVID-19 in Kerala, from May to June 20219999/, represented a critical period during which families were exposed to never done the levels of uncertainty, financial strain, and mobility restrictions. For children, these experiences were compounded by prolonged school closures, increased dependency on digital learning, and reduced physical interaction with peers

[14, 15]

. Such multidimensional stressors may contribute to a range of emotional outcomes, including fear, sadness, irritability, attention difficulties, and depressive symptoms

[8, 16, 17]

. Furthermore, children whose parents faced additional challenges such as job insecurity, low financial stability, or stressful working conditions are more likely to experience adverse psychological effects.

Given these considerations, the present study aims to examine how the COVID-19 pandemic and associated lockdown during the second wave affected the mental health of children in four districts of Kerala. By collecting data from 400 parents of children aged 5–15 years and examining socio-demographic, behavioral, and psychological dimensions, this research provides a comprehensive understanding of child mental health during a critical pandemic phase. The application of discriminant analysis, chi-square testing, and CFA enables a detailed exploration of how parental behavior, financial factors, and COVID-related experiences contribute to variations in children's mental health scores. The findings of this study are expected to have significant implications for child welfare, public health planning, and mental health policy in Kerala. Identifying groups at elevated risk can support the design of targeted interventions and preventive strategies. Moreover, the study underscores the importance of strengthening family-centered support systems, improving parental awareness, and enhancing economic and workplace security to promote better mental health outcomes for children. Such insights align with the broader framework of the Sustainable Development Goals (SDGs), particularly SDG-3, which emphasizes ensuring healthy lives and promoting well-being for all

[12, 13]

. Understanding the drivers of child mental health during pandemics is therefore essential not only for mitigating current challenges but also for improving resilience in future public health emergencies.

2. Methodology

2.1. Sample Size Determination

This study was conducted among parents residing in Kerala through an online survey carried out from 16 May to 4 June 2021, during the second wave of the COVID-19 pandemic. Data were collected after an extended period of restricted movement and lockdown measures enforced across the state. A non-probability (purposive) sampling technique was used to collect primary data. Parents who had at least one child aged between 5 and 15 years and were accessible through the researchers’ social networks were invited to participate by completing the online questionnaire.

The required sample size was determined using the standard formula for cross-sectional studies:

n = \frac{Z^{2} p (1 - p)}{d^{2}}

where:

n = required sample size

Z = standard normal value at 95% confidence level (1.96)

p = expected proportion of mental health disturbances among children (assumed to be 0.5 due to lack of prior regional data, ensuring maximum sample size)

d = allowable margin of error (0.05)

Substituting the values:

n = \frac{(1.96)^{2} \times 0.5 \times (1 - 0.5)}{(0.05)^{2}} = 384.16

Thus, the minimum required sample size was approximately 384. A total of 400 parents completed the survey, exceeding the required sample size and ensuring adequate statistical power for the analyses.

2.2. Data Collection Procedure

Primary data were collected through an online questionnaire, as face-to-face interviews were not feasible due to the lockdown restrictions in place during the second wave of COVID-19. Prior to the main study, the questionnaire was pilot-tested among 40 participants to ensure clarity and validity. The final survey, created using Google Forms, was disseminated to parents through social media platforms between 16 May and 4 June 2021. Parents were included if they had at least one child aged 5–15 years. The questionnaire comprised four major sections: (i) socio-demographic details (age, gender, educational status, place of residence, number of earning members, monthly household income, COVID-19 knowledge, and whether any family member, relative, or neighbor tested positive); (ii) financial and lifestyle information of parents; (iii) details regarding the child’s daily activities and parental attitudes toward the child; and (iv) information related to the child’s mental health. Participants received no financial incentives, and anonymity was strictly maintained to ensure data confidentiality. Informed consent was obtained at the beginning of the online survey, and participants were informed that they could withdraw at any point without providing a reason.

2.3. Child Mental Status Evaluation

Children’s mental health was evaluated using standardized tools that are well-validated for online survey administration. Emotional and behavioral functioning was assessed using the Strengths and Difficulties Questionnaire–Parent Version (SDQ-P), a widely used 25-item online screening tool that measures emotional symptoms, conduct problems, hyperactivity, peer relationship issues, and prosocial behavior. Depressive symptoms were evaluated through the Patient Health Questionnaire for Adolescents (PHQ-A), an online-compatible 9-item scale capturing core signs of depression such as persistent sadness, sleep disturbances, appetite changes, fatigue, impaired concentration, and feelings of worthlessness. Anxiety symptoms were measured using the Screen for Child Anxiety Related Disorders–Parent Version (SCARED-P), a parent-report questionnaire suitable for digital platforms that assesses generalized anxiety, separation anxiety, panic symptoms, and school avoidance. Broader psychosocial concerns were examined using the Pediatric Symptom Checklist (PSC), a 35-item online-friendly tool used to identify cognitive, emotional, and behavioral difficulties in children. Sleep-related issues were assessed using the short version of the Children’s Sleep Habits Questionnaire (CSHQ-Abridged), a digital-ready tool that evaluates bedtime behavior, sleep duration, nighttime waking, and daytime tiredness. All instruments used in the study are validated for remote administration and ensure reliable assessment in an online data-collection environment. The online assessment tools used in this study were scored using standardized digital rating formats. The Strengths and Difficulties Questionnaire–Parent Version (SDQ-P) items were rated on a 3-point scale (0 = Not true, 1 = Somewhat true, 2 = Certainly true), yielding subscale scores that reflect children’s emotional and behavioral functioning. Depressive symptoms measured through the Patient Health Questionnaire for Adolescents (PHQ-A) followed a 4-point response format (0 = Not at all, 1 = Several days, 2 = More than half the days, 3 = Nearly every day), producing a total score ranging from 0 to 27, with higher scores indicating greater severity. Anxiety symptoms assessed with the Screen for Child Anxiety Related Disorders–Parent Version (SCARED-P) used a 3-point scale (0 = Not true or hardly ever true, 1 = Somewhat true or sometimes true, 2 = Very true or often true), generating a total score indicative of anxiety severity. Sleep-related concerns evaluated through the Abridged Children’s Sleep Habits Questionnaire (CSHQ-A) also employed a 3-point rating system (0 = Rarely, 1 = Sometimes, 2 = Usually), producing a cumulative score reflective of the degree of sleep disruption. Higher scores across these instruments denoted greater levels of emotional distress, anxiety, behavioral difficulties, and sleep problems. Internal consistency analysis demonstrated strong reliability, with Cronbach’s alpha reported at 0.814, exceeding the acceptable threshold of 0.70.

2.4. Statistical Analysis

First, descriptive statistics were used to summarize the socio-demographic characteristics of the participants. Next, Discriminant Analysis was applied to classify children into distinct mental health categories based on their depression, anxiety, behavioral, and sleep-related scores obtained through the online assessment tools. The chi-square test was then employed to examine the association between socio-demographic factors, parental behaviors, and the different dimensions of child mental health. Following this, the key components influencing child mental well-being were identified and incorporated into a structural model. Structural Equation Modelling (SEM) was subsequently performed to evaluate the relationships among these components and to test the overall model fit. All statistical hypotheses were evaluated at a significance level of p < 0.05. Data analysis was conducted using IBM SPSS Statistics (Version 31.0.1.0), IBM SPSS AMOS (Version 31.0.x), and Microsoft Excel.

3. Results and Discussions

In this study, the majority of respondents were female (74.7%), while male participants accounted for 24.5% of the sample. Most parents were aged between 36 and 45 years (35.2%), followed by those aged 26–35 years (30.7%) and 46–55 years (30.5%). Educational attainment was predominantly at the S.S.C/H.S.C level (58.3%), with 22.1% having completed graduation and 7.6% possessing a post-graduation qualification. Nearly half of the participants resided in urban areas (46.6%), with the remaining living in rural (45.1%) and semi-urban (8.3%) regions.

Regarding employment-related factors, 56.3% of respondents reported being engaged in a job at the time of the survey, and among them, 25.3% were required to attend their workplace physically. Additionally, a substantial proportion of participants (67.7%) expressed experiencing financial tension during the COVID-19 pandemic (Table 1).

Table 1. Socio-demographic characteristics of parents and children. Socio-demographic characteristics of parents and children. Socio-demographic characteristics of parents and children.

Group	Variable	Number	Percentage (%)
Sex	Female	287	74.7
Sex	Male	94	24.5
Sex	Other	3	0.8
Age	< 25 years	14	3.6
Age	26–35 years	118	30.7
Age	36–45 years	135	35.2
Age	46–55 years	117	30.5
Age	> 55 years	0	0.0
Education	S.S.C / H.S.C	224	58.3
Education	Graduation	85	22.1
Education	Primary	46	12.0
Education	Post-Graduation	29	7.6
Education	PhD	0	0.0
Place of Living	Urban	179	46.6
Place of Living	Rural	173	45.1
Place of Living	Semi-urban	32	8.3
Job Status	No	168	43.7
Job Status	Yes	216	56.3
Financial Tension	No	124	32.3
Financial Tension	Yes	260	67.7
Need to Go to Workplace	No	287	74.7
Need to Go to Workplace	Yes	97	25.3

The cluster analysis categorized children into three psychological disturbance levels based on emotional symptoms, hyperactivity, and peer-related difficulties. The majority of the children were placed in the Minimal Disturbance group (93.0%), indicating predominantly healthy psychological functioning. A smaller proportion fell under Mild Disturbance (5.7%), and only 1.3% of the children exhibited Severe Disturbance, reflecting more pronounced emotional and behavioral concerns. Across the clusters, there were significant differences in all three measured psychological dimensions. Children in the Severe Disturbance group demonstrated the highest mean emotional symptoms (Depression proxy), anxiety-related behaviors (Hyperactivity proxy), and sleeping-related issues (Peer-problem proxy). In contrast, children classified under Minimal Disturbance showed the lowest levels across all psychological indicators.

Specifically, mean emotional symptom scores increased progressively from Minimal (M = 2.41, SD = 1.49) to Mild (M = 4.55, SD = 2.26) to Severe Disturbance (M = 4.80, SD = 1.48). Similarly, anxiety-related behaviors showed a graded increase across clusters, with scores ranging from 2.87 in the Minimal group to 6.20 in the Severe group. Sleeping-related difficulties (measured through peer problem scores) followed the same trend, increasing from 1.35 in the Minimal group to 2.60 in the Severe group.

Statistical testing confirmed that the differences among clusters were highly significant (p < 0.001) for all three psychological dimensions. These findings suggest a clear gradient of psychological distress across the clusters, with children in the Severe Disturbance group showing notably elevated emotional, behavioral, and sleep-related difficulties. This pattern underscores the importance of early identification and targeted intervention for children who fall into higher-risk clusters. (Table 2)

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Figure 1. Bar diagram representing the average score of depression, anxiety, and sleeping disorder among different cluster. Bar diagram representing the average score of depression, anxiety, and sleeping disorder among different cluster.

Table 2. Cluster Analysis Grouping.Cluster Analysis Grouping.Cluster Analysis Grouping.

Variables	Minimal Disturbance (n = 357)	Mild Disturbance (n = 22)	Severe Disturbance (n = 5)	p-value
Number (Percentages)	357 (93.0%)	22 (5.7%)	5 (1.3%)	—
Depression M(SD) (Emotional symptoms)	2.41 (1.49)	4.55 (2.26)	4.80 (1.48)	< 0.001
Anxiety M(SD) (Hyperactivity)	2.87 (1.66)	5.14 (1.86)	6.20 (1.30)	< 0.001
Sleeping Disturbance M (SD) (Peer problems)	1.35 (1.14)	2.36 (1.33)	2.60 (1.14)	< 0.001

The bar diagram illustrates the differences in the average levels of depression, anxiety, and sleeping disturbance among children across the three psychological disturbance clusters. A clear gradient emerges, with symptom severity rising steadily from the Minimal Disturbance group to the Severe Disturbance group.

Children classified under Minimal Disturbance demonstrated the lowest mean scores for depression, anxiety, and sleeping disturbance, reflecting generally healthy psychological functioning. In contrast, children in the Mild Disturbance cluster showed noticeably higher averages across all three domains, indicating emerging emotional and behavioral concerns that may require monitoring. The Severe Disturbance group consistently displayed the highest mean scores for all three psychological indicators. Their depression and anxiety levels were significantly elevated, and sleeping-related difficulties were more pronounced compared with the other clusters. This progressive escalation across clusters reflects a strong association between cluster category and psychological symptom severity.

Overall, the bar chart visually reinforces the statistical findings from the cluster analysis: as the psychological disturbance level increases, children show proportionally higher emotional symptoms, anxiety-related behaviors, and sleep-related difficulties. This pattern highlights the importance of early identification and targeted mental-health support, particularly for children in the Mild and Severe Disturbance clusters. (Figure 1)

Across the psychological disturbance clusters, notable differences emerged in children’s emotional, behavioral, and environmental characteristics. The majority of children were classified into the Subthreshold group (n=165, 43%), followed by Mild (n=117, 30.5%), Moderate (n=74, 19.3%), and Severe (n=28, 7.2%). Although sociodemographic factors showed no significant statistical associations (p > 0.05), patterns were evident for example, the proportion of females decreased from 45.5% in the Subthreshold group to 28.6% in the Severe group, and parents aged 36–45 years represented the largest share across clusters (44.2%–53.6%). Educational differences were more pronounced, with Primary-educated parents rising from 6.1% in Subthreshold to 20.3% in Moderate disturbance (p = 0.019), and post-graduation dropping from 38.2% to 25.6% across increasing severity. Environmental stressors showed similar gradients: rural residence increased from 26.7% to 50% (p = 0.003), and having a COVID-positive relative or neighbor rose from 10.9% in Subthreshold to 32.1% in Severe cases (p = 0.022). Behavioral and lifestyle indicators displayed some of the strongest patterns—children watching cartoons more than 6 hours daily rose from 1.8% to 7.1%, and those gaming 4–6 hours increased dramatically from 4.8% in Subthreshold to 39.3% in Severe (p < 0.001). Parental stress and negative discipline practices followed the same upward trajectory: fighting among children increased from 30.3% to 71.4% (p < 0.001), lack of actions to keep children busy rose from 17.6% to 42.9% (p < 0.001), screaming at children escalated from 27.9% to 89.3% (p < 0.001), and hitting with objects surged from 5.5% in Subthreshold to 64.3% in Severe (p = 0.003). Collectively, these values show a consistent and substantial increase in family stress, harsh parenting, and problematic child behaviors with rising psychological disturbance levels, underscoring the strong interplay between environmental adversity and children’s mental health during the COVID-19 lockdown. (Table 3).

Table 3. Comparison of different characteristic among the different cluster. Comparison of different characteristic among the different cluster. Comparison of different characteristic among the different cluster.

Variable	Category	Normal (N,%)	Borderline (N,%)	Abnormal (N,%)	Total	p-value
Sex	Female	269 (93.7%)	14 (4.9%)	4 (1.4%)	287	0.7354
Sex	Male	85 (90.4%)	8 (8.5%)	1 (1.1%)	94	0.7354
Sex	Other	3 (100.0%)	0 (0.0%)	0 (0.0%)	3	0.7354
Age	<25	13 (92.9%)	1 (7.1%)	0 (0.0%)	14	0.7454
Age	26-35	111 (94.1%)	7 (5.9%)	0 (0.0%)	118	0.7454
Age	36-45	126 (93.3%)	6 (4.4%)	3 (2.2%)	135	0.7454
Age	46-55	107 (91.5%)	8 (6.8%)	2 (1.7%)	117	0.7454
Education Level	Graduation	77 (90.6%)	6 (7.1%)	2 (2.4%)	85	0.8794
Education Level	Post-Graduation	27 (93.1%)	2 (6.9%)	0 (0.0%)	29	0.8794
Education Level	Primary	42 (91.3%)	3 (6.5%)	1 (2.2%)	46	0.8794
Education Level	S.S.C/H.S.C	211 (94.2%)	11 (4.9%)	2 (0.9%)	224	0.8794
Place of Living	Rural	166 (96.0%)	5 (2.9%)	2 (1.2%)	173	0.2413
Place of Living	Semi-urban	29 (90.6%)	3 (9.4%)	0 (0.0%)	32	0.2413
Place of Living	Urban	162 (90.5%)	14 (7.8%)	3 (1.7%)	179	0.2413
Relatives/Neighbor infected	No	311 (93.4%)	18 (5.4%)	4 (1.2%)	333	0.7035
Relatives/Neighbor infected	Yes	46 (90.2%)	4 (7.8%)	1 (2.0%)	51	0.7035
Average Family Income	2–3 lakhs	72 (94.7%)	4 (5.3%)	0 (0.0%)	76	0.86
Average Family Income	3–4 lakhs	56 (93.3%)	3 (5.0%)	1 (1.7%)	60	0.86
Average Family Income	4–5 lakhs	55 (93.2%)	3 (5.1%)	1 (1.7%)	59	0.86
Average Family Income	5+ lakhs	121 (92.4%)	9 (6.9%)	1 (0.8%)	131	0.86
Average Family Income	< 2 lakhs	53 (91.4%)	3 (5.2%)	2 (3.4%)	58	0.86
Need to go Workplace	No	265 (92.3%)	18 (6.3%)	4 (1.4%)	287	0.7025
Need to go Workplace	Yes	92 (94.8%)	4 (4.1%)	1 (1.0%)	97	0.7025
Chance of losing Job	No	310 (93.4%)	17 (5.1%)	5 (1.5%)	332	0.2994
Chance of losing Job	Yes	47 (90.4%)	5 (9.6%)	0 (0.0%)	52	0.2994
Feeling bored at Home	No	52 (91.2%)	5 (8.8%)	0 (0.0%)	57	0.6468
Feeling bored at Home	Sometimes	156 (92.3%)	10 (5.9%)	3 (1.8%)	169	0.6468
Feeling bored at Home	Yes	149 (94.3%)	7 (4.4%)	2 (1.3%)	158	0.6468
Smoker	No	289 (92.0%)	20 (6.4%)	5 (1.6%)	314	0.2854
Smoker	Yes	68 (97.1%)	2 (2.9%)	0 (0.0%)	70	0.2854
Child Cartoon Hours	2–4 h	126 (92.6%)	8 (5.9%)	2 (1.5%)	136	0.9696
Child Cartoon Hours	4–6 h	40 (90.9%)	4 (9.1%)	0 (0.0%)	44	0.9696
Child Cartoon Hours	6–8 h	7 (100.0%)	0 (0.0%)	0 (0.0%)	7	0.9696
Child Cartoon Hours	< 2 h	183 (93.4%)	10 (5.1%)	3 (1.5%)	196	0.9696
Child Cartoon Hours	> 8 h	1 (100.0%)	0 (0.0%)	0 (0.0%)	1	0.9696
Child Gaming Hours	2–4 h	81 (91.0%)	6 (6.7%)	2 (2.2%)	89	0.9338
Child Gaming Hours	4–6 h	30 (93.8%)	1 (3.1%)	1 (3.1%)	32	0.9338
Child Gaming Hours	6–8 h	4 (100.0%)	0 (0.0%)	0 (0.0%)	4	0.9338
Child Gaming Hours	< 2 h	240 (93.4%)	15 (5.8%)	2 (0.8%)	257	0.9338
Child Gaming Hours	> 8 h	2 (100.0%)	0 (0.0%)	0 (0.0%)	2	0.9338
Child Fights	No	192 (92.3%)	12 (5.8%)	4 (1.9%)	208	0.5049
Child Fights	Yes	165 (93.8%)	10 (5.7%)	1 (0.6%)	176	0.5049
Keep Child Busy	No	87 (91.6%)	6 (6.3%)	2 (2.1%)	95	0.6951
Keep Child Busy	Yes	270 (93.4%)	16 (5.5%)	3 (1.0%)	289	0.6951
Child Acting Normal	No	61 (95.3%)	3 (4.7%)	0 (0.0%)	64	0.374
Child Acting Normal	Not noticeable	93 (89.4%)	8 (7.7%)	3 (2.9%)	104	0.374
Child Acting Normal	Yes	203 (94.0%)	11 (5.1%)	2 (0.9%)	216	0.374
Child Complains Parent Busy	No	238 (92.6%)	16 (6.2%)	3 (1.2%)	257	0.797
Child Complains Parent Busy	Yes	119 (93.7%)	6 (4.7%)	2 (1.6%)	127	0.797
Calling Child Bad Names	No	254 (92.7%)	15 (5.5%)	5 (1.8%)	274	0.3459
Calling Child Bad Names	Yes	103 (93.6%)	7 (6.4%)	0 (0.0%)	110	0.3459
Threatening Child	No	216 (91.5%)	16 (6.8%)	4 (1.7%)	236	0.3602
Threatening Child	Yes	141 (95.3%)	6 (4.1%)	1 (0.7%)	148	0.3602
Screaming at Child	No	207 (95.4%)	7 (3.2%)	3 (1.4%)	217	0.0551
Screaming at Child	Yes	150 (89.8%)	15 (9.0%)	2 (1.2%)	167	0.0551
Hitting Child	No	287 (92.6%)	19 (6.1%)	4 (1.3%)	310	0.7879
Hitting Child	Yes	70 (94.6%)	3 (4.1%)	1 (1.4%)	74	0.7879
Hitting with Object	No	297 (92.5%)	19 (5.9%)	5 (1.6%)	321	0.5638
Hitting with Object	Yes	60 (95.2%)	3 (4.8%)	0 (0.0%)	63	0.5638
Common Abuse Type	Emotional abuse	112 (91.8%)	8 (6.6%)	2 (1.6%)	122	0.4616
Common Abuse Type	Family violence	69 (90.8%)	6 (7.9%)	1 (1.3%)	76	0.4616
Common Abuse Type	Neglect abuse	52 (96.3%)	2 (3.7%)	0 (0.0%)	54	0.4616
Common Abuse Type	Physical abuse	95 (96.9%)	2 (2.0%)	1 (1.0%)	98	0.4616
Common Abuse Type	Sexual abuse	29 (85.3%)	4 (11.8%)	1 (2.9%)	34	0.4616

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Figure 2. A path diagram of Confirmatory Factor Analysis (CFA) representing the factors of the child mental health. A path diagram of Confirmatory Factor Analysis (CFA) representing the factors of the child mental health.

The Confirmatory Factor Analysis (CFA) model in Image 2 illustrates four interrelated latent constructs Parental Mental Health, Child Information, Parents’ Attitude Toward Child, and Child Mental Health each measured through multiple observed indicators. Parental Mental Health is represented by six indicators, including educational level, place of living, family income, COVID-19 exposure among relatives/neighbors, financial tension, and boredom during lockdown. These variables reflect the socioeconomic and emotional environment of parents during the pandemic. Child Information is measured using the number of children aged 5–15 and their schooling status, capturing structural family characteristics that may influence child well-being. The construct Parents’ Attitude Toward Child includes negative parenting behaviors such as children fighting each other, threats, screaming, and slapping, indicating disciplinary practices and emotional climate in the household. Finally, Child Mental Health is captured through major depressive disorder, generalized anxiety disorder, and sleeping disorder indicators, representing the emotional and psychological outcomes of children.

The model also displays bidirectional correlations among the latent variables, suggesting that parental mental health, parenting behaviors, and child mental health are strongly interconnected. In particular, the diagram indicates that poorer parental mental health and harsher parental attitudes are likely associated with higher child mental health problems. Similarly, child-related demographic factors (e.g., number of children, schooling status) may influence or be influenced by parental stress and parenting behavior. Overall, the CFA diagram highlights a coherent structural relationship in which socioeconomic stressors and parental emotional strain contribute to negative parenting behaviors, which in turn are associated with adverse child mental health outcomes. This visual model underscores the complex, multi-layered dynamics affecting children’s psychological well-being during the pandemic.

Table 4. Inter-correlations and covariance are among Latent Variables in the Structural Model. Inter-correlations and covariance are among Latent Variables in the Structural Model. Inter-correlations and covariance are among Latent Variables in the Structural Model.

Latent Variables	Parental MH	Child Info	Parent Attitude	Child MH
Parental MH	1.000	0.005	–0.022	0.100
Child Info	0.005	1.000	0.099	0.043
Parent Attitude	–0.022	0.099	1.000	–0.014
Child MH	0.100	0.043	–0.014	1.000

The inter-correlation and covariance matrix among the latent variables demonstrates only modest associations between the core constructs of the model. Parental Mental Health showed a weak positive correlation with Child Mental Health (r = 0.10; Cov = 0.407), suggesting that poorer parental emotional or socioeconomic well-being is slightly related to increased psychological problems in children. Child Information (represented primarily by child age) exhibited very small correlations with all other constructs (r values between 0.005 and 0.099), indicating that demographic child factors contribute minimally to variations in parental mental health, parenting attitudes, or child outcomes within this sample. Parents’ Attitude Toward the Child, representing negative or harsh parenting behaviors, showed near-zero correlations with both Parental Mental Health (r = –0.022) and Child Mental Health (r = –0.014), suggesting that within this dataset harsh parenting behaviors do not strongly covary with parental emotional strain or child mental health problems at the latent level, despite their descriptive trends. The strongest relations are the variances within constructs themselves (shown on the diagonal), confirming that each latent variable is internally coherent. Overall, the covariance structure indicates that although there are observable behavioral trends in the raw data, the latent constructs in the structural model exhibit only weak interdependencies, implying that parental stress, child demographics, harsh parenting, and child mental health function as relatively distinct dimensions in the CFA framework.

4. Conclusions

Findings emerging from this research indicate that a large number of children in Kerala were exposed to varying levels of mental disturbances and disturbances associated with the second wave of COVID-19 and subsequent lockdowns. While it is evident that most children were exposed to minimal disturbances, it is also seen that there are a large number of children exposed to mild and severe levels of mental disturbances and disturbances related to sleep. Parents' non-abandoning work responsibilities, parents with higher educational qualifications, financial problems, and family members and neighbors being infected with COVID-19 have considerably worsened mental disturbances among children. Parents' mental problems due to increased boredom, stress, and engaging with poorer coping methods such as screaming, threats, and beating have added to making homes stressful, which considerably affects mental disturbances among children. All these have been supported by SEM and CFA analysis. Children from families facing financial insecurity, disrupted routines, excessive screen time, increased sibling conflict, and harsher parental discipline appeared to be especially susceptible groups. Similarly, children whose parents reported higher levels of psychological distress or exposure to COVID-related stressors seemed more likely to exhibit anomalous emotional or behavioral markers. These trends underscore how the socioeconomic and psychological burdens of the pandemic trickle into family systems and affect children's mental health in nuanced and multilevel ways. In the backdrop of these findings, multidimensional intervention strategies are thus imperative to strengthen children's psychological well-being in large-scale public health emergencies. Improvement in family financial security, enhancement of parental mental-health literacy, easy accessibility to psychological support services, and facilitating practices of positive parenting may act as critical protective factors. In addition, job flexibility policies that reduce occupational stress among parents and ensure safe childcare arrangements may provide a cushioning effect to mitigate the emotional burden on children. Investments in community-based resources for mental health and family-centered support systems will be crucial for mitigating long-term psychological consequences. The authors believe the insights from this study will meaningfully contribute toward furthering Kerala's achievements in terms of health-related Sustainable Development Goals, specifically those pertaining to child well-being and building resilience to future public health adversities.

Abbreviations

CFA	Confirmatory Factor Analysis
MDD	Major Depressive Disorder
SDGs	Sustainable Development Goals
SDQ-P	Strengths and Difficulties Questionnaire–Parent Version
PHQ-A	Patient Health Questionnaire for Adolescents
SCARED-P	Screen for Child Anxiety Related Disorders–Parent Version
PSC	Pediatric Symptom Checklist
CSHQ-A	Abridged Children’s Sleep Habits Questionnaire
SEM	Structural Equation Modelling
MH	Mental Health

Conflicts of Interest

The authors declare no conflict of interest.

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Ramachandran, D. V., Senthilkumar, B., Bhat, M., Mathews, A. J. (2026). Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis. International Journal of Statistical Distributions and Applications, 12(1), 13-23. https://doi.org/10.11648/j.ijsda.20261201.12

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Ramachandran, D. V.; Senthilkumar, B.; Bhat, M.; Mathews, A. J. Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis. Int. J. Stat. Distrib. Appl. 2026, 12(1), 13-23. doi: 10.11648/j.ijsda.20261201.12

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AMA Style

Ramachandran DV, Senthilkumar B, Bhat M, Mathews AJ. Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis. Int J Stat Distrib Appl. 2026;12(1):13-23. doi: 10.11648/j.ijsda.20261201.12

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@article{10.11648/j.ijsda.20261201.12,
  author = {Divya Valiyattil Ramachandran and Balan Senthilkumar and Mohini Bhat and Ashok Jacob Mathews},
  title = {Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {12},
  number = {1},
  pages = {13-23},
  doi = {10.11648/j.ijsda.20261201.12},
  url = {https://doi.org/10.11648/j.ijsda.20261201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsda.20261201.12},
  abstract = {The purpose of this study is to examine the effects of the COVID-19 pandemic and the ensuing lockdown on children's mental health in four Kerala districts. During the second wave of the COVID-19 pandemic, from May 16 to June 4, 2021, 400 parents with at least one child between the ages of 5 and 15 participated in an online cross-sectional study using a non-probability sampling technique. Children were classified according to their mental health scores using discriminant analysis, and the association between parental behavior and children's mental health was identified using confirmatory factor analysis (CFA). The chi-square test was also used to evaluate these correlations. Four levels of mental health difficulties were identified in children: 43% had minimal difficulties (mean MDD-10: 2.8), 30.5% had emerging difficulties (mean MDD-10: 8.9), 19.3% had significant difficulties (mean MDD-10: 15.9), and 7.2% had high-risk or severe difficulties (mean MDD-10: 25.2). Children whose parents were required to continue working on-site, had relatives infected with COVID-19, had higher levels of education, or exhibited abnormal behaviors were found to have a higher percentage of mental health disorders. In contrast, children whose parents did not experience these difficulties. The results show that during the second wave of COVID-19, especially during the lockdown period between May and June 2021, a significant percentage of children in Kerala had mental health issues. Children's psychological well-being may be improved by putting psychological intervention techniques into practice, increasing parental literacy, strengthening job security, ensuring appropriate childcare, and improving household financial stability. Children's psychological wellbeing may improve as a result of increased security. The study's conclusions, according to the authors, will help Kerala make progress toward the Sustainable Development Goals (SDGs) pertaining to health.},
 year = {2026}
}

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TY  - JOUR
T1  - Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis
AU  - Divya Valiyattil Ramachandran
AU  - Balan Senthilkumar
AU  - Mohini Bhat
AU  - Ashok Jacob Mathews
Y1  - 2026/01/26
PY  - 2026
N1  - https://doi.org/10.11648/j.ijsda.20261201.12
DO  - 10.11648/j.ijsda.20261201.12
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 13
EP  - 23
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsda.20261201.12
AB  - The purpose of this study is to examine the effects of the COVID-19 pandemic and the ensuing lockdown on children's mental health in four Kerala districts. During the second wave of the COVID-19 pandemic, from May 16 to June 4, 2021, 400 parents with at least one child between the ages of 5 and 15 participated in an online cross-sectional study using a non-probability sampling technique. Children were classified according to their mental health scores using discriminant analysis, and the association between parental behavior and children's mental health was identified using confirmatory factor analysis (CFA). The chi-square test was also used to evaluate these correlations. Four levels of mental health difficulties were identified in children: 43% had minimal difficulties (mean MDD-10: 2.8), 30.5% had emerging difficulties (mean MDD-10: 8.9), 19.3% had significant difficulties (mean MDD-10: 15.9), and 7.2% had high-risk or severe difficulties (mean MDD-10: 25.2). Children whose parents were required to continue working on-site, had relatives infected with COVID-19, had higher levels of education, or exhibited abnormal behaviors were found to have a higher percentage of mental health disorders. In contrast, children whose parents did not experience these difficulties. The results show that during the second wave of COVID-19, especially during the lockdown period between May and June 2021, a significant percentage of children in Kerala had mental health issues. Children's psychological well-being may be improved by putting psychological intervention techniques into practice, increasing parental literacy, strengthening job security, ensuring appropriate childcare, and improving household financial stability. Children's psychological wellbeing may improve as a result of increased security. The study's conclusions, according to the authors, will help Kerala make progress toward the Sustainable Development Goals (SDGs) pertaining to health.
VL  - 12
IS  - 1
ER  -

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Author Information

Divya Valiyattil Ramachandran

Department of Statistics, Bharathidasan University, Tiruchirappalli, India

Contact Email

http://orcid.org/0000-0001-7801-0776
Balan Senthilkumar

Department of Statistics, Thanthai Periyar Government Arts & Science College (Autonomous), Tiruchirappalli, India

http://orcid.org/0009-0002-2489-4582
Mohini Bhat

Department of Management, Christ Academy Institute for Advanced Studies, Bengaluru, India

http://orcid.org/0000-0003-0655-6555
Ashok Jacob Mathews

Department of Political Science, St. Claret College (Autonomous), Bengaluru, India

http://orcid.org/0009-0006-7082-0102

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Figure 1

Figure 1. Bar diagram representing the average score of depression, anxiety, and sleeping disorder among different cluster.
Figure 2

Figure 2. A path diagram of Confirmatory Factor Analysis (CFA) representing the factors of the child mental health.

Table 1

Table 1. Socio-demographic characteristics of parents and children. Socio-demographic characteristics of parents and children.
Table 2

Table 2. Cluster Analysis Grouping.Cluster Analysis Grouping.
Table 3

Table 3. Comparison of different characteristic among the different cluster. Comparison of different characteristic among the different cluster.
Table 4

Table 4. Inter-correlations and covariance are among Latent Variables in the Structural Model. Inter-correlations and covariance are among Latent Variables in the Structural Model.

Plain Text BibTeX RIS

APA Style

Ramachandran, D. V., Senthilkumar, B., Bhat, M., Mathews, A. J. (2026). Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis. International Journal of Statistical Distributions and Applications, 12(1), 13-23. https://doi.org/10.11648/j.ijsda.20261201.12

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ACS Style

Ramachandran, D. V.; Senthilkumar, B.; Bhat, M.; Mathews, A. J. Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis. Int. J. Stat. Distrib. Appl. 2026, 12(1), 13-23. doi: 10.11648/j.ijsda.20261201.12

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AMA Style

Ramachandran DV, Senthilkumar B, Bhat M, Mathews AJ. Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis. Int J Stat Distrib Appl. 2026;12(1):13-23. doi: 10.11648/j.ijsda.20261201.12

Copy | Download

@article{10.11648/j.ijsda.20261201.12,
  author = {Divya Valiyattil Ramachandran and Balan Senthilkumar and Mohini Bhat and Ashok Jacob Mathews},
  title = {Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {12},
  number = {1},
  pages = {13-23},
  doi = {10.11648/j.ijsda.20261201.12},
  url = {https://doi.org/10.11648/j.ijsda.20261201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsda.20261201.12},
  abstract = {The purpose of this study is to examine the effects of the COVID-19 pandemic and the ensuing lockdown on children's mental health in four Kerala districts. During the second wave of the COVID-19 pandemic, from May 16 to June 4, 2021, 400 parents with at least one child between the ages of 5 and 15 participated in an online cross-sectional study using a non-probability sampling technique. Children were classified according to their mental health scores using discriminant analysis, and the association between parental behavior and children's mental health was identified using confirmatory factor analysis (CFA). The chi-square test was also used to evaluate these correlations. Four levels of mental health difficulties were identified in children: 43% had minimal difficulties (mean MDD-10: 2.8), 30.5% had emerging difficulties (mean MDD-10: 8.9), 19.3% had significant difficulties (mean MDD-10: 15.9), and 7.2% had high-risk or severe difficulties (mean MDD-10: 25.2). Children whose parents were required to continue working on-site, had relatives infected with COVID-19, had higher levels of education, or exhibited abnormal behaviors were found to have a higher percentage of mental health disorders. In contrast, children whose parents did not experience these difficulties. The results show that during the second wave of COVID-19, especially during the lockdown period between May and June 2021, a significant percentage of children in Kerala had mental health issues. Children's psychological well-being may be improved by putting psychological intervention techniques into practice, increasing parental literacy, strengthening job security, ensuring appropriate childcare, and improving household financial stability. Children's psychological wellbeing may improve as a result of increased security. The study's conclusions, according to the authors, will help Kerala make progress toward the Sustainable Development Goals (SDGs) pertaining to health.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Psychological Impact of the COVID-19 Pandemic on Children in Kerala: A Cross-sectional Analysis
AU  - Divya Valiyattil Ramachandran
AU  - Balan Senthilkumar
AU  - Mohini Bhat
AU  - Ashok Jacob Mathews
Y1  - 2026/01/26
PY  - 2026
N1  - https://doi.org/10.11648/j.ijsda.20261201.12
DO  - 10.11648/j.ijsda.20261201.12
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 13
EP  - 23
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsda.20261201.12
AB  - The purpose of this study is to examine the effects of the COVID-19 pandemic and the ensuing lockdown on children's mental health in four Kerala districts. During the second wave of the COVID-19 pandemic, from May 16 to June 4, 2021, 400 parents with at least one child between the ages of 5 and 15 participated in an online cross-sectional study using a non-probability sampling technique. Children were classified according to their mental health scores using discriminant analysis, and the association between parental behavior and children's mental health was identified using confirmatory factor analysis (CFA). The chi-square test was also used to evaluate these correlations. Four levels of mental health difficulties were identified in children: 43% had minimal difficulties (mean MDD-10: 2.8), 30.5% had emerging difficulties (mean MDD-10: 8.9), 19.3% had significant difficulties (mean MDD-10: 15.9), and 7.2% had high-risk or severe difficulties (mean MDD-10: 25.2). Children whose parents were required to continue working on-site, had relatives infected with COVID-19, had higher levels of education, or exhibited abnormal behaviors were found to have a higher percentage of mental health disorders. In contrast, children whose parents did not experience these difficulties. The results show that during the second wave of COVID-19, especially during the lockdown period between May and June 2021, a significant percentage of children in Kerala had mental health issues. Children's psychological well-being may be improved by putting psychological intervention techniques into practice, increasing parental literacy, strengthening job security, ensuring appropriate childcare, and improving household financial stability. Children's psychological wellbeing may improve as a result of increased security. The study's conclusions, according to the authors, will help Kerala make progress toward the Sustainable Development Goals (SDGs) pertaining to health.
VL  - 12
IS  - 1
ER  -

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