Epilepsy is one of the most common chronic neurological disorders in childhood and is frequently associated with psychiatric and behavioural comorbidities. These comorbidities contribute significantly to impaired quality of life, academic difficulties, and caregiver burden. Baniya and Verma (2022) demonstrated that nearly 40% of individuals with epilepsy in western Rajasthan experienced depression, with quality-of-life indices markedly reduced compared to seizure-free individuals [1]. Such findings highlight that seizure control alone does not fully determine long-term outcomes in epilepsy. Among the range of psychiatric comorbidities, autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are of particular concern. Tiwari et al. (2021) emphasized that ASD represents a growing public health challenge in India, with prevalence estimates of 1–1.5% in the general population, but substantially higher in children with neurological disorders [2]. ADHD is also closely linked with epilepsy. In a retrospective case-record analysis, Pingali and Sunderajan (2014) reported that up to 30% of children with ADHD presented with additional psychiatric comorbidities, underscoring the tendency for behavioural conditions to cluster rather than occur in isolation [3]. Similarly, Srinivasaraghavan et al. (2013) found that comorbid psychiatric conditions significantly influenced the short-term clinical outcomes of children with ADHD, with poorer recovery at three-month follow-up in those with additional behavioural disorders [4]. The burden of untreated mental health conditions in India is further amplified by systemic gaps in care. Patel et al. (2016) estimated that the mental health treatment gap in India exceeds 70%, with children disproportionately underserved compared to adults [5]. Even where services exist, they are often limited to tertiary centres, leaving semi-urban and rural populations without adequate support. Innovative approaches, such as the parent-mediated intervention trialed in South India by Manohar et al. (2019), have demonstrated feasibility in improving behavioural outcomes in children with ASD, but these remain localized and not widely scaled [6]. Despite increasing recognition of the psychiatric burden in epilepsy [1], autism [2,6], and ADHD [3,4], most studies have been confined to urban tertiary care centres and have tended to focus on single disorders in isolation. Consequently, there is a lack of integrated data examining the full spectrum of behavioural comorbidities in pediatric epilepsy, particularly from semi-urban northern India. Addressing this evidence gap is critical for guiding resource allocation, designing integrated models of care, and improving the overall outcomes of children with epilepsy. The present study was therefore conducted in a pediatric neurology clinic in Shahjahanpur, Uttar Pradesh, to quantify the prevalence of behavioural disorders among children with epilepsy, identify associated demographic and clinical factors, and highlight independent predictors that can inform targeted interventions in similar settings. Objectives The present study was conducted with the following objectives: 1. To estimate the prevalence of behavioural disorders (ADHD, anxiety, depression, autism spectrum disorder, and oppositional defiant disorder) among children with epilepsy attending a pediatric neurology clinic in Shahjahanpur, Uttar Pradesh. 2. To examine associations between demographic and clinical variables (age, sex, socioeconomic status, seizure frequency, treatment regimen, EEG/MRI findings, perinatal history, and school attendance) and the presence of behavioural disorders. 3. To identify independent predictors of behavioural disorders through multivariable analysis.

Study design and setting This was a cross-sectional, hospital-based observational study conducted in the Pediatric Neurology Clinic of Varun Arjun Medical College and Rohilkhand Hospital, Banthra, Shahjahanpur, Uttar Pradesh, India. Data were collected over a three-month period from 19 June 2025 to 5 September 2025. Participants Consecutive children aged 3–16 years with a physician-confirmed diagnosis of epilepsy were invited to participate. Epilepsy was defined as per International League Against Epilepsy (ILAE) criteria, i.e., two or more unprovoked seizures occurring >24 hours apart or current treatment with antiseizure medication for a prior diagnosis. Inclusion criteria: • Children aged 3–16 years. • Confirmed diagnosis of epilepsy. • Caregiver willing to provide informed consent. Exclusion criteria: • Children with acute symptomatic seizures only. • Severe acute illness precluding behavioural assessment. • Lack of reliable informant for behavioural screening. Variables and measurements Demographic variables included age, sex, residence (rural/urban), and socioeconomic status (classified using the modified Kuppuswamy scale and grouped as lower, middle, and upper-middle). Clinical variables included age at seizure onset, epilepsy type (focal/generalized/mixed), seizure frequency over the prior 12 months (seizure-free ≥12 months; ≤1/month; 2–4/month; ≥1/week), treatment regimen (monotherapy or polytherapy), commonly prescribed antiseizure medications, abnormal EEG findings, MRI-detected structural lesions, family history of epilepsy, perinatal complications, and school attendance (regular vs irregular/dropout). Behavioural outcomes were assessed in two stages: 1. Screening with the Strengths and Difficulties Questionnaire (SDQ) (parent-report version). 2. Clinical confirmation by a pediatric neurologist/psychiatrist, classifying children into: • Attention deficit hyperactivity disorder (ADHD) • Anxiety disorder • Depressive disorder • Autism spectrum disorder (ASD) • Oppositional defiant disorder (ODD) Children were coded as having “any behavioural disorder” if one or more diagnoses were confirmed. Data collection Data were obtained during routine outpatient visits using a structured case record form. Clinical histories were corroborated with available medical records, and behavioural assessments were performed in consultation with caregivers. Statistical analysis Data were entered into IBM SPSS Statistics for Windows, Version 26.0 (Armonk, NY: IBM Corp.) and cross-validated in R version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables (e.g., age) were summarized as mean ± standard deviation (SD), while categorical variables were expressed as counts and percentages. Associations between categorical predictors and the presence of behavioural disorders were examined using the χ² test. To identify independent predictors, a binary logistic regression model was fitted with covariates selected a priori: seizure frequency (ordinal score), treatment regimen (monotherapy vs. polytherapy), EEG and MRI findings, perinatal complications, socioeconomic status, age, and sex. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were reported. A two-sided p < 0.05 was considered statistically significant. Ethical considerations The study protocol was reviewed and approved by the Institutional Ethics Committee of Varun Arjun Medical College and Rohilkhand Hospital, Shahjahanpur. Written informed consent was obtained from caregivers prior to participation. Confidentiality and anonymity of all patient information were strictly maintained throughout the study in accordance with the Declaration of Helsinki.

Cohort Characteristics The study included 100 children with epilepsy aged 3–16 years (mean 9.9 ± 3.8 years); 58% were male. A majority resided in rural areas, with almost half belonging to lower socioeconomic strata. Focal epilepsy was slightly more common than generalized epilepsy, and nearly one-third of patients had been seizure-free for at least 12 months. Polytherapy was required in over two-fifths of cases. Abnormal EEG findings were present in about two-thirds, while structural MRI lesions were detected in nearly half. Regular school attendance was reported by most children. Table 1. Baseline demographic and clinical characteristics of children with epilepsy (n=100) Variable Category n (%) Age (years) Mean ± SD 9.9 ± 3.8 Sex Male 58 (58%) Female 42 (42%) Residence Rural 62 (62%) Urban 38 (38%) Socioeconomic status Lower 43 (43%) Middle 39 (39%) Upper-middle 18 (18%) Epilepsy type Focal 48 (48%) Generalized 42 (42%) Mixed 10 (10%) Seizure frequency Seizure-free ≥12m 30 (30%) ≤1/month 28 (28%) 2–4/month 26 (26%) ≥1/week 16 (16%) Treatment Monotherapy 57 (57%) Polytherapy 43 (43%) EEG Abnormal 62 (62%) Normal 38 (38%) MRI Structural lesion 48 (48%) Normal/Non-specific 52 (52%) School attendance Regular 72 (72%) Irregular/Dropout 28 (28%) Prevalence of Behavioural Disorders Nearly half of the children with epilepsy (48%) were identified with at least one behavioural disorder (Table 2). The most frequently observed conditions were ADHD (24%), anxiety (24%), and depression (22%), while smaller proportions had autism spectrum disorder (13%) or oppositional defiant disorder (14%). Overlap between disorders was common, with several children exhibiting more than one diagnosis. The relative burden of each condition is illustrated in Figure 1. Table 2. Prevalence of behavioural disorders among children with epilepsy (n=100) Behavioural disorder n % Any behavioural disorder 48 48% ADHD 24 24% Anxiety disorder 24 24% Depression 22 22% Autism spectrum disorder 13 13% Oppositional defiant disorder 14 14% Bivariate Associations The prevalence of behavioural disorders varied across clinical and demographic subgroups (Table 3). Children with more frequent seizures demonstrated a higher prevalence of behavioural disorders in descriptive terms (ranging from 39% in seizure-free children to 52% in those with 2–4 seizures per month), although this trend was not statistically significant (χ² = 0.961; p = 0.811). Polytherapy was associated with a somewhat higher prevalence compared to monotherapy (53% vs 44%), but the difference did not reach significance (χ² = 1.452; p = 0.228). Similarly, socioeconomic status showed a gradient, with behavioural disorders more common in lower SES groups (58%) than in middle (46%) or upper-middle (33%), approaching statistical significance (χ² = 5.103; p = 0.077). These relationships are summarized in Table 3, while the distribution of behavioural disorders across seizure frequency categories is illustrated in Figure 2. Table 3. Association between clinical/demographic factors and any behavioural disorder in children with epilepsy (n=100) Factor Category Any behavioural disorder (%) χ² p-value Seizure frequency Seizure-free ≥12m 39% 0.961 0.811 ≤1/month 46% 2–4/month 52% ≥1/week 50% Treatment regimen Monotherapy 44% 1.452 0.228 Polytherapy 53% Socioeconomic status Lower 58% 5.103 0.077 Middle 46% Upper-middle 33% Multivariable Analysis In the logistic regression model, several predictors were examined for their association with the presence of any behavioural disorder (Table 4). After adjustment for age, sex, and clinical covariates, lower socioeconomic status emerged as a significant independent predictor (adjusted OR 2.56; 95% CI 1.03–6.35; p = 0.043). Children with a history of perinatal complications also showed higher odds of behavioural disorders (OR 2.33; 95% CI 0.91–5.95), though this did not reach statistical significance. Other variables, including seizure frequency, polytherapy, EEG abnormality, and MRI structural lesions, showed directionally higher odds but were not statistically significant in this sample. These relationships are summarized in Table 4 and visually represented in Figure 3 as a forest plot of adjusted odds ratios. Table 4. Multivariable logistic regression for predictors of behavioural disorders in children with epilepsy (n=100) Predictor Adjusted OR 95% CI Lower socioeconomic status 2.56 1.03 – 6.35 Perinatal complications 2.33 0.91 – 5.95 Seizure frequency (per category worse) 1.29 0.84 – 2.00 Polytherapy 1.79 0.70 – 4.57 EEG abnormal 2.02 0.82 – 4.97 MRI structural lesion 1.62 0.69 – 3.81 Female sex 0.66 0.27 – 1.64 Age at onset (per year) 1.04 0.77 – 1.40 Current age (per year) 0.93 0.69 – 1.25 *Statistically significant at p < 0.05

In this cross-sectional study, nearly half (48%) of children with epilepsy were identified with at least one behavioural disorder, most commonly ADHD (24%), anxiety (24%), and depression (22%), followed by ASD (13%) and ODD (14%). This profile underscores the high psychiatric comorbidity burden in pediatric epilepsy populations in India. Our prevalence aligns with the broader literature on psychiatric comorbidity in chronic pediatric neurological disorders. For instance, Babu et al. (2025) demonstrated that even in conditions like spinal muscular atrophy, comorbidity rates approach 40–50% when assessed systematically [7]. While the underlying neurogenetic pathology differs, the commonality suggests that chronic neurological disease states frequently predispose children to significant behavioural morbidity. Autism spectrum disorder accounted for 13% of our sample. Taufiq and McKeithan (2024), in their review of autism diagnostic tools in the Indian subcontinent, highlighted the wide variability in reported prevalence, ranging from 8–18% depending on methodology and screening thresholds [8]. Our observed rate falls within this interval. Similarly, Naik (2015) reported that clinical series of children with epilepsy and neurodevelopmental disorders often identified ASD in approximately 10–15%, again consistent with our findings [9]. However, Perera et al. (2017) using a culturally adapted pictorial screening tool in Sri Lanka, reported a higher prevalence approaching 20% in high-risk clinical populations [10], suggesting regional differences and sensitivity of tools may partly account for variation. The prominence of anxiety (24%) and depression (22%) in our cohort is comparable to the psychiatric burden described in other chronic pediatric conditions. Patel et al. (2011), in their analysis of non-communicable disease comorbidity in India, estimated that between 20–30% of children with chronic neurological or metabolic disease exhibit anxiety or mood symptoms [11]. Similarly, Yoosuf et al. (2021) reported psychiatric comorbidity rates of 28% in adolescents with obesity [12], reinforcing that psychiatric burden is not limited to epilepsy but part of a broader chronic disease–psychiatry interface. Our observed ADHD prevalence of 24% corresponds to rates seen in addiction research populations, where behavioural dysregulation was noted in about 20–25% of adolescents with chronic conditions [13]. The overlap of ADHD and ODD in our cohort echoes Siddiqi et al. (2019), who found that dietary and behavioural dysregulation in Indian children with autism often coexisted with oppositional traits in 15–20% [14]. Socioeconomic status emerged as an independent predictor in our logistic regression model (OR 2.56, 95% CI 1.03–6.35). This finding is consistent with Yerramilli and Bipeta (2012), who emphasized that neglect of mental health in lower-income families magnifies behavioural morbidity, particularly through reduced access to care and increased stigma [15]. Furthermore, Manohar et al. (2025) noted that limitations of the Indian Scale for Assessment of Autism disproportionately affected children from disadvantaged backgrounds, suggesting systemic inequities in recognition and service provision [16]. Neurophysiological correlates may also contribute. Hamdan et al. (2022) showed that children with ASD exhibit abnormal visual evoked potentials, which correlated with greater severity of behavioural impairment [17]. In our cohort, EEG abnormalities were present in 62%, and although not statistically significant in regression analysis, the direction of effect (OR 2.02) aligns with the hypothesis that neurophysiological disruptions elevate behavioural risk. Our results also highlight methodological and regional variability. While some Indian and South Asian studies report psychiatric comorbidity in epilepsy at rates exceeding 55–60% [9,10], our estimate of 48% may reflect differences in screening tools (SDQ + clinician confirmation), clinic catchment (semi-urban), and cultural reporting patterns. Strengths and Limitations This study adds to the limited literature on behavioural comorbidities in pediatric epilepsy from semi-urban India, providing prevalence estimates anchored within the ranges reported in other chronic neurological conditions. Strengths include the use of standardized screening tools and multivariable adjustment. However, limitations include reliance on clinical screening rather than gold-standard psychiatric interviews, modest sample size, and the cross-sectional design, which restricts causal inference. Implications Our findings reinforce calls for integrated neuropsychiatric care in pediatric neurology clinics in India. Embedding brief screening tools, culturally adapted autism scales, and referral pathways could bridge current service gaps [8,10,16]. Prioritizing support for low-SES families may help mitigate the disproportionate burden identified here.

In this cross-sectional analysis of 100 children with epilepsy from a semi-urban Indian clinic, nearly half (48%) were found to have at least one behavioural disorder, most commonly ADHD, anxiety, and depression. Autism spectrum disorder and oppositional defiant disorder were also observed at clinically meaningful rates. Lower socioeconomic status emerged as an independent predictor of behavioural morbidity, highlighting the influence of social determinants beyond clinical severity. These findings emphasize that behavioural comorbidities are integral to the epilepsy phenotype in children and should not be overlooked in clinical care. Routine behavioural screening, family-centered psychosocial support, and equitable access to neuropsychiatric services are crucial to improving outcomes for this vulnerable population.

1. Baniya, G. C., & Verma, K. (2022). Prevalence of depression, risk factors, and quality of life in patients with epilepsy in a remote area of western Rajasthan. Epilepsy & Behavior, 127, 108488. 2. Tiwari, R., Purkayastha, K., & Gulati, S. (2021). Public health dimensions of autism spectrum disorder in India: An overview. Journal of Comprehensive Health, 9(2), 57-62. 3. Pingali, S., & Sunderajan, J. (2014). A study of comorbidities in attention deficit hyperactivity disorder: a retrospective analysis of case records. Archives of Mental Health, 15(2), 206-210. 4. Srinivasaraghavan, R., Mahadevan, S., & Kattimani, S. (2013). Impact of comorbidity on three month follow-up outcome of children with ADHD in a child guidance clinic: preliminary report. Indian Journal of Psychological Medicine, 35(4), 346-351. 5. Patel, V., Xiao, S., Chen, H., Hanna, F., Jotheeswaran, A. T., Luo, D., ... & Saxena, S. (2016). The magnitude of and health system responses to the mental health treatment gap in adults in India and China. The Lancet, 388(10063), 3074-3084. 6. Manohar, H., Kandasamy, P., Chandrasekaran, V., & Rajkumar, R. P. (2019). Brief parent-mediated intervention for children with autism spectrum disorder: a feasibility study from South India. Journal of autism and developmental disorders, 49(8), 3146-3158. 7. Babu, R. R., Maganthi, M., Datta, D., Ng, J., Krishna, G., Mathew, A. A., ... & Jagadeesh, S. (2025). GENOTYPE–PHENOTYPE CORRELATION OF SPINAL MUSCULAR ATROPHY IN THE ERA OF DISEASE MODIFYING THERAPIES: A TERTIARY INDIAN EXPERIENCE. European Journal of Paediatric Neurology. 8. Taufiq, N., & McKeithan, G. K. (2024). Screening and diagnostic tools for autism spectrum disorder utilized in the Indian Subcontinent: a scoping review. International Journal of Developmental Disabilities, 1-12. 9. Naik, U. S. (2015). Autism spectrum disorder: 70 Years on and the Plot Thickens. In Developments in Psychiatry in India: Clinical, Research and Policy Perspectives (pp. 275-311). New Delhi: Springer India. 10. Perera, H., Jeewandara, K. C., Seneviratne, S., & Guruge, C. (2017). Culturally adapted pictorial screening tool for autism spectrum disorder: A new approach. World journal of clinical pediatrics, 6(1), 45. 11. Patel, V., Chatterji, S., Chisholm, D., Ebrahim, S., Gopalakrishna, G., Mathers, C., ... & Reddy, K. S. (2011). Chronic diseases and injuries in India. The Lancet, 377(9763), 413-428. 12. Yoosuf, S., Gupta, G., Bhargava, R., Kumar, N., Ranjan, P., Pandey, R. M., ... & Vikram, N. K. (2021). Magnitude of psychiatric comorbidity in patients with obesity in Northern Indian population. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 15(5), 102270. 13. Basu, D., Ghosh, A., Patra, B., & Subodh, B. N. (2015). Addiction research in India. In Developments in Psychiatry in India: Clinical, Research and Policy Perspectives (pp. 367-403). New Delhi: Springer India. 14. Siddiqi, S., Urooj, A., & D’Souza, M. J. (2019). Dietary patterns and anthropometric measures of Indian children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 49(4), 1586-1598. 15. Yerramilli, S. S., & Bipeta, R. (2012). Economics of mental health: Part I-Economic consequences of neglecting mental health-an Indian perspective. Archives of Mental Health, 13(2), 80-86. 16. Manohar, H., Kishore, T., & Jacob, P. (2025). Indian Scale for Assessment of Autism (ISAA): Issues with the Current Assessment Scale and Recommendations for Disability Assessment. Indian Journal of Psychological Medicine, 47(1), 76-79. 17. Hamdan, F. B., Shareef, H. R., & Jasim, H. S. (2022). Visual evoked potential findings and correlation between visual evoked potential and clinical severity in children with autism spectrum disorder. Medical Journal of Babylon, 19(2), 133-141.