BACKGROUND OF THE STUDY:

The International Committee on Taxonomy of Viruses formally called Severe Acute Respiratory Syndrome Coronavirus 2 in February 2020, is an enveloped, single-stranded RNA virus that is part of the Coronaviridae family and is genetically related to SARS-CoV and MERS-CoV.¹ The World Health Organization (WHO) declared a Public Health Emergency of International Concern on January 30, 2020, and a pandemic on March 11, 2020, since this virus that causes COVID-19 spread so quickly over the world.² The global public health emergency ended in May 2023, but the pandemic continued through several waves caused by varieties including Alpha, Delta, and Omicron, which caused a lot of sickness and death around the world.³

As of February 15, 2025, there have been nearly 778.6 million verified cases of COVID-19 and more than 7.1 million deaths around the world. The death rate is about 892 per million people.^4,5 The first confirmed case in India was in January 2020 in Kerala.⁶ After that, there were waves of infection, with the second wave in 2021 putting a lot of stress on the hospital system because of the Delta variant.⁶ The Indian government took huge steps to stop the spread of the virus, such as statewide lockdowns, testing, contact tracing, and mass vaccination efforts.^7,8 By November 2025, India had reported over 45 million confirmed cases and 533,847 deaths, with WHO estimates indicating a significantly higher excess mortality burden.^9-11 Kerala initially exhibited effective containment through early detection, isolation, and community-based surveillance; however, the return of expatriates and interstate travel led to multiple subsequent waves of infection.¹² In May 2021, the state had its biggest spike, but the case fatality rate stayed low at about 1.05%^13,14. Thiruvananthapuram district was one of the most affected districts in the state, with more than 508,000 confirmed cases and 6,274 deaths during the pandemic.^15,16

This shows that the disease had a big impact and shows that we need to look at how COVID-19 affected vulnerable groups like pregnant women and children.

The epidemic has hurt the health of mothers and their newborns, which has led to greater research on how SARS-CoV-2 infection affects pregnant women. Retrospective cohorts, observational studies, and systematic reviews consistently show that maternal COVID-19 infection is linked to increased rates of negative obstetric and neonatal outcomes, even when direct transmission is not verified.^17-22 These encompass heightened risks of preterm birth, fetal distress, low birth weight, cesarean delivery, diminished Apgar scores, and hospitalization to the neonatal intensive care unit (NICU).^17-22 Research from several contexts indicates that symptomatic and severe maternal illness significantly increases the risk of intensive care unit (ICU) admission, maternal complications, and newborn morbidity or fatality.^17-22

Most pregnant women who get COVID-19 don't get very sick, but severe cases have been related to very bad health consequences for both the mother and the baby, showing how vulnerable this group is. The evidence together suggests that although direct transmission of this virus is infrequent, the indirect consequences of maternal infection and associated pregnancy problems present significant dangers to newborn health. Preterm birth and perinatal compromise—recognized risk factors for subsequent developmental delays—are frequently reported in infants born to infected moms.^17-22 These results underscore the significance of prompt detection of maternal infection, rigorous prenatal and intrapartum surveillance, and tailored obstetric care. Additionally, organized postnatal monitoring and extended neurodevelopmental follow-up of infants exposed to SARS-CoV-2 are essential to alleviate possible developmental repercussions and to guide evidence-based mother and child health policies.

NEED FOR THE STUDY:

Neuro-behavioural development is a sequential and continuous process that commences early in gestation, encompassing neuronal proliferation, migration, differentiation, synaptogenesis, and myelination, so establishing the foundation for postnatal sensory, motor, affective, and cognitive activities. Disruption of these carefully controlled processes during the intrauterine period can lead to enduring modifications in brain structure and function, presenting as developmental delay. Neuro-developmental delay has various causes, including genetic, environmental, nutritional, and perinatal variables. Prenatal factors such as maternal illnesses, hypoxia, inadequate nutrition, toxin exposure, and stress, are particularly significant.¹⁷ Historical epidemiological data from influenza and measles outbreaks indicate that maternal infections during gestation elevate the risk of negative neurodevelopmental outcomes, such as intellectual disability, cerebral palsy, autism, and schizophrenia, with severity contingent upon the specific pathogen and the timing of exposure.²³ While direct viral transmission seems infrequent, case reports have indicated the presence of the virus in amniotic fluid, cord blood, and newborn samples, prompting concerns over possible in-utero exposure^24-32. More crucially, maternal immune activation and inflammatory responses linked to COVID-19 have been associated with detrimental fetal brain development, as evidenced by previous ecological, cohort, and animal studies^33-37.

Systematic reviews demonstrate that COVID-19 in pregnancy markedly elevates maternal morbidity, obstetric difficulties, and negative neonatal outcomes, such as preterm delivery, NICU hospitalization, and newborn mortality³⁸. Research investigating neurodevelopmental consequences subsequent to prenatal SARS-CoV-2 infection yields inconclusive results. Some cohorts indicate no substantial differences at 6–12 months, highlighting the influence of pandemic-related stressors over direct virus effects.^39,⁴⁴, others indicate heightened risks of motor, linguistic, socioemotional, or early neurodevelopmental diagnoses, with potential sex-specific susceptibility in male infants^40-43,45. In general, the evidence we have now is not very strong, mostly comes from Western populations, and is mostly from early infancy. Considering the biological feasibility of enduring effects due to maternal inflammation and psychosocial stress, coupled with the absence of data from India—especially regarding older children—there is a definitive necessity for longitudinal studies to assess the long-term neurobehavioral consequences of in-utero SARS-CoV-2 exposure in the Indian context to guide screening, early intervention, and public health strategies.

OBJECTIVES:

Primary Objective:

1. To determine whether SARS CoV 2 is an independent risk factor of neuro-behavioural development delay in children born between April 2020 to January 2023

Secondary Objectives:

1. To assess the neuro-behavioral development of children born between April 2020 to January 2023.

2. To identify the risk factors associated with neurobehavioral development delay of children born between April 2020 to January 2023

HYPOTHESIS:

The proportion of children having neuro-behavioural development delay is increased among those exposed to maternal SARS CoV 2 infection during pregnancy than those not exposed to maternal SARS CoV 2 infection during pregnancy.

MATERIALS AND METHODS:

Study Design:

A community-based pilot case–control study was conducted among children born between April 2020 and January 2023 and residing in Kottukunnam ward of Nellanad Panchayat, Thiruvananthapuram district, Kerala.

Definition of Cases and Controls:

Cases included children who screened positive for neurobehavioral developmental delay in at least one domain using TDSC, LEST, or VSMS, or children with a previously documented diagnosis of developmental delay verified through medical records. Controls were children who screened negative on all developmental assessment tools.

Sampling and Variables:

Purposive sampling was used. For each identified case, two consecutive screened-negative children were selected as controls. Maternal SARS-CoV-2 infection during pregnancy, assessed through maternal self-report, was considered the exposure variable. Other prenatal, intranatal, postnatal, and childhood factors were treated as extraneous variables.

Data Collection Procedure:

The study was conducted between August and September 2025 after obtaining permission from the District Medical Officer, Thiruvananthapuram. Children were identified with the assistance of Accredited Social Health Activist (ASHA) workers. Home visits were conducted after obtaining informed consent from parents.

Developmental screening was performed using standardized tools—TDSC, LEST, and VSMS. Sociodemographic information and data on potential risk factors were collected through a semi-structured interview with the mother.

Table 5: Association of maternal and child sociodemographic characteristics with neurobehavioral development of children

Variable	Category	Developmental Delay (Case group n=6)	No Delay (Control group n=12)	Total (n=18)	Chi-square	p-value	OR	95% CI
Mother’s Age at Conception	18–30 years	5 (83.3%)	11 (91.7%)	16 (88.9%)	0.281	0.596	0.455	0.023
Mother’s Age at Conception	31–45 years	1 (16.7%)	1 (8.3%)	2 (11.1%)	0.281	0.596	0.455	0.023
Socioeconomic Status of Mother	Upper Lower	3 (50.0%)	1 (8.3%)	4 (22.2%)	4.018	0.070	11.0	0.818
Socioeconomic Status of Mother	Upper Middle/ Lower Middle	3 (50.0%)	11 (91.7%)	14 (77.8%)	4.018	0.070	11.0	0.818
Marital Status	Married	6 (100.0%)	12 (100.0%)	18 (100.0%)	-	-	-	-
Place of Residence	Rural	6 (100.0%)	12 (100.0%)	18 (100.0%)	-	-	-	-
Age of Child (Months)	32–48	4 (66.7%)	8 (66.7%)	12 (66.7%)	0.281	0.596	0.455	0.023
Age of Child (Months)	49–61	2 (33.3%)	4 (33.3%)	6 (33.3%)	0.281	0.596	0.455	0.023
Gender of Child	Male	3 (50.0%)	4 (33.3%)	7 (38.9%)	0.468	0.497	0.5	0.068
Gender of Child	Female	3 (50.0%)	8 (66.7%)	11 (61.1%)	0.468	0.497	0.5	0.068
Birth Order	First	2 (33.3%)	6 (50.0%)	8 (44.4%)	0.450	0.502	0.5	0.065
Birth Order	Second	4 (66.7%)	6 (50.0%)	10 (55.6%)	0.450	0.502	0.5	0.065
Number of Family Members	<4 Members	3 (50.0%)	7 (58.3%)	10 (55.6%)	0.112	0.738	0.714	0.1
Number of Family Members	≥4 Members	3 (50.0%)	5 (41.7%)	8 (44.4%)	0.112	0.738	0.714	0.1

Table 6: Association of Prenatal Risk Factors with Neurobehavioral Development of Children

Prenatal Risk Factor	Category	Developmental Delay (Case group n=6)	No Delay (Control group n=12)	Total (n=18)	Chi square	p-value	OR	95% CI
Regular PrenatalCheck-up	Yes	6 (100.0%)	12 (100.0%)	18 (100.0%)	-	-	-	-
Iron and Folic Acid Intake	Yes	6 (100.0%)	11 (91.7%)	17 (94.4%)	0.529	0.469	-	-
Iron and Folic Acid Intake	No	0 (0.0%)	1 (8.3%)	1 (5.6%)	0.529	0.469	-	-
Gestational Diabetes Mellitus (GDM)	Yes	1 (16.7%)	2 (16.7%)	3 (16.7%)	0.000	1.000	1.0	0.072
Gestational Diabetes Mellitus (GDM)	No	5 (83.3%)	10 (83.3%)	15 (83.3%)	0.000	1.000	1.0	0.072
Low Amniotic Fluid	Yes	2 (33.3%)	0 (0.0%)	2 (11.1%)	4.500	0.034	-	-
Low Amniotic Fluid	No	4 (66.7%)	12 (100.0%)	16 (88.9%)
Consanguinity	No	6 (100.0%)	12 (100.0%)	18 (100.0%)	-	-	-	-
Psychiatric Disorder	No	6 (100.0%)	12 (100.0%)	18 (100.0%)	-	-	-	-
Alcohol Intake	No	6 (100.0%)	12 (100.0%)	18 (100.0%)	-	-	-	-
Use of Illicit Drugs	No	6 (100.0%)	12 (100.0%)	18 (100.0%)	-	-	-	-
Covid 19 infection	Yes	2(33.3%)	5 (41.7%)	7 (38.9%)	0.117	0.732	0.7	0.09
Covid 19 infection	No	4 (66.7%)	7 (58.3%)	11 (61.1%)	0.117	0.732	0.7	0.09
Asthma	Yes	0 (0.0%)	1 (8.3%)	1 (5.6%)	0.529	0.467	-	-
Asthma	No	6 (100.0%)	11 (91.7%)	17 (94.4%)	0.529	0.467	-	-
Infertility Treatment	Yes	1 (16.7%)	1 (8.3%)	2 (11.1%)	0.281	0.596	2.2	0.113
Infertility Treatment	No	5 (83.3%)	11 (91.7%)	16 (88.9%)	0.281	0.596	2.2	0.113
Intake of Antibiotics	Yes	0 (0.0%)	3 (25.0%)	3 (16.7%)	1.8	0.18	-	-
Intake of Antibiotics	No	6 (100.0%)	9 (75.0%)	15 (83.3%)	1.8	0.18	-	-
Type of Gestation	Singleton	5 (83.3%)	12 (100.0%)	17 (94.4%)	2.118	0.146	-	-
Type of Gestation	Multiple	1 (16.7%)	0 (0.0%)	1 (5.6%)	2.118	0.146	-	-
Low lying placenta	Yes	0 (0.0%)	2 (16.7%)	2 (11.1%)	1.125	0.289	-	-
Low lying placenta	No	6 (100.0%)	10 (83.3%)	16 (88.9%)	1.125	0.289	-	-
Hypertension	Yes	2 (33.3%)	3 (25.0%)	5 (27.8%)	0.138	0.710	1.5	0.176
Hypertension	No	4 (66.7%)	9 (75.0%)	13 (72.2%)	0.138	0.710	1.5	0.176

Miscarriage Symptoms	Yes	0 (0.0%)	3 (25.0%)	3 (16.7%)	1.8	0.18	-	-
Miscarriage Symptoms	No	6(100%)	9 (75%)	15 (83.3%)	1.8	0.18	-	-

Table 7: Association of Intranatal Risk Factors with Neurobehavioral Development of Children

Intranatal Risk Factor	Category	Developmental Delay (Case group n=6)	No Delay (Control group n=12)	Total (n=18)	Chi-square	p-value	OR	95% CI
Gestational Age at birth	Term	5 (83.3%)	12 (100.0%)	17 (94.4%)	2.118	0.46	-	-
Gestational Age at birth	Preterm	1 (16.7%)	0 (0.0%)	1 (5.6%)
Birth Cry	Immediate	5 (83.3%)	12 (100.0%)	17 (94.4%)	2.118	0.146	-	-
Birth Cry	Delayed	1 (16.7%)	0 (0.0%)	1 (5.6%)
Duration of Labour	Normal	6 (100.0%)	11 (91.7%)	17 (94.4%)	0.529	0.469	-	-
Duration of Labour	Not known	0 (0.0%)	1 (8.3%)	1 (5.6%)
Birth Weight (kg)	1.5 – 2.49	2 (33.3%)	1 (9.1%)	3 (17.6%)	1.5	0.21	5.0	0.348
Birth Weight (kg)	2.5 – 3.5	4 (66.7%)	10 (90.9%)	14 (82.4%)
Mode of Delivery	Normal	4 (66.7%)	5 (41.7%)	9 (50.0%)	1	0.317	2.8	0.361
Mode of Delivery	LSCS	2 (33.3%)	7 (58.3%)	9 (50.0%)
Place of Birth	Hospital	6 (100.0%)	12 (100.0%)	18 (100.0%)	-	-	-	-
Birth Injury	No	6 (100.0%)	12 (100.0%)	18 (100.0%)	-	-	-	-
Cord Around Neck	Absent	6 (100.0%)	12 (100.0%)	18 (100.0%)	-	-	-	-

Table 9: Association of Childhood Risk Factors with Neurobehavioral Development of Children

Childhood Risk Factor	Category	Developmental Delay (n=6)	No Delay (n=12)	Total (n=18)	Chi-square	p-value	OR	95% CI
Exclusive Breastfeeding	Yes	5 (83.3%)	8 (66.7%)	13 (72.2%)	0.554	0.457	2.5	0.214
Exclusive Breastfeeding	No	1 (16.7%)	4 (33.3%)	5 (27.8%)		0.457	2.5	0.214
Complementary Feeding from 6 months	Yes	3 (50.0%)	10 (83.3%)	13 (72.2%)	2.215	0.137	0.2	0.022
Complementary Feeding from 6 months	No	3 (50.0%)	2 (16.7%)	5 (27.8%)	2.215	0.137	0.2	0.022
Received Consistent Interaction with Caregivers	Yes	5 (83.3%)	12 (100.0%)	17 (94.4%)	2.118	0.146	-	-
Received Consistent Interaction with Caregivers	No	1 (16.7%)	0 (0.0%)	1 (5.6%)
Exposure to Early Education	Yes	5 (83.3%)	6 (50.0%)	11 (61.1%)	2.118	0.146	-	-
Exposure to Early Education	No	1 (16.7%)	6 (50.0%)	7 (38.9%)	2.118	0.146	-	-
History of Head Injury	Yes	2 (33.3%)	0 (0.0%)	2 (11.1%)	1.87	0.171	5.0	0.442
History of Head Injury	No	4 (66.7%)	12 (100.0%)	16 (88.9%)	1.87	0.171	5.0	0.442
Diagnosis of Nutritional Deficiency	Yes	1 (16.7%)	1 (8.3%)	2 (11.1%)	-	-	-	-
Diagnosis of Nutritional Deficiency	No	5 (83.3%)	11 (91.7%)	16 (88.9%)	-	-	-	-
Screen Time	Yes	5 (83.3%)	11 (91.7%)	16 (88.9%)	0.281	0.596	2.2	0.113
Screen Time	No	1 (16.7%)	1 (8.3%)	2 (11.1%)	0.281	0.596	2.2	0.113
Diagnosed Developmental Delay	Yes	1 (16.7%)	1 (8.3%)	2 (11.1%)	0.281	0.596	2.2	0.113
Diagnosed Developmental Delay	No	5 (83.3%)	11 (91.7%)	16 (88.9%)	0.281	0.596	2.2	0.113

Maternal SARS-CoV-2 infection during pregnancy was not significantly associated with neurobehavioral developmental delay (χ² = 0.117, p=0.732). Among prenatal factors, low amniotic fluid demonstrated a statistically significant association with developmental delay (p=0.034). Intranatal factors did not show significant associations, although low birth weight showed a trend toward increased risk.

Postnatal NICU admission emerged as a strong predictor of developmental delay (χ² = 6.785, p = 0.009, OR = 22.0). Childhood factors did not reach statistical significance, though delayed complementary feeding and reduced caregiver interaction were more common among children with developmental delay.

DISCUSSION:

This pilot study investigated prenatal, intranatal, postnatal, and childhood risk variables linked to developmental delay (DD) in children born during the SARS-CoV-2 pandemic and assessed whether mother COVID-19 infection independently influenced neurobehavioral developmental delay. The results underscore the significance of particular prenatal variables over direct virus exposure, corroborating findings from both Indian and international investigations.

Prenatal Risk Factors:

Oligohydramnios, or low amniotic fluid, was the only prenatal condition in this analysis that had a statistically significant link to developmental delay (χ² = 4.5, p = 0.034). This finding aligns with prior obstetric outcome research indicating detrimental fetal and neonatal effects linked to oligohydramnios. A comparative study including 100 pregnant women indicated significantly lower birth weight (2750±300g vs. 3100±280g; p< 0.001) and diminished gestational age (37.0±1.5 weeks vs. 38.5±1.0 weeks; p = 0.002) in pregnancies affected by oligohydramnios. Furthermore, NICU admissions were markedly elevated in the oligohydramnios cohort (30% vs. 12%; p = 0.012), substantiating the biological plausibility of its correlation with subsequent developmental delay.⁴⁶

In this pilot study, other prenatal factors, including gestational diabetes mellitus, hypertension, infertility treatment, iron and folic acid intake, low-lying placenta, miscarriage symptoms, type of gestation, and maternal infections such as COVID-19, did not demonstrate statistically significant associations (p>0.05). Nonetheless, a greater incidence of developmental delay was noted in children subjected to maternal hypertension and a low-lying placenta, indicating a potential trend. Similar findings have been shown in case-control studies, indicating substantial relationships between maternal infections and chronic disorders during pregnancy and developmental delays (p<0.05).⁴⁷

The absence of statistical significance in the current study may be ascribed to the constrained sample size and diminished power characteristic of pilot studies.

Importantly, maternal SARS-CoV-2 infection was not recognized as a distinct risk factor for developmental delay.

This conclusion corresponds with increasing evidence from the pandemic era indicating that secondary maternal and neonatal problems, rather than direct virus infection, may have a more significant impact on neurodevelopmentaloutcomes.

Intranatal Risk Factors:

None of the intranatal variables evaluated—including gestational age at birth, duration of labor, style of delivery, birth cry, birth injury, cord around the neck, and place of birth—exhibited statistically significant correlations with developmental delay.

The present analysis, however, showed a substantial tendency toward a higher risk (OR = 5.0) for low birth weight.

A substantial immunization-clinic-based study involving 460 infants corroborates this observation, revealing that gestational age and birth weight were significantly linked to all areas of developmental delay (gross motor, fine motor, communication, and problem-solving; p< 0.05), with the exception of the personal-social domain.⁴⁸ In a neurology outpatient research, preterm was identified as an independent predictor of developmental delay (AOR = 2.34; 95% CI: 1.07–5.13; p = 0.033).

The absence of statistical significance in this study likely indicates a small sample size rather than a genuine link.

Postnatal Risk Factors:

NICU admission exhibited a robust and statistically significant correlation with developmental delay (χ² = 6.785, p = 0.009), with an odds ratio of 22.0, signifying that children admitted to NICU were 22 times more likely to encounter developmental delay. This result aligns with previous research indicating heightened neurodevelopmental risk in newborns necessitating extensive neonatal care.

Even while neonatal jaundice and hypoglycemia didn't reach statistical significance (p>0.05), they were both seen more often in kids with developmental delay, which suggests that they are clinically important. Similar results have been observed in hospital-based observational studies, indicating that newborn metabolic abnormalities contributed to developmental morbidity when evaluated in conjunction with other perinatal variables.⁴⁷

Childhood Risk Factors:

None of the children characteristics evaluated—exclusive breastfeeding, timely complementary feeding, caregiver engagement, exposure to early education, nutritional deficit, head injury, or screen time—demonstrated statistically significant correlations with developmental delay. However, delayed supplemental feeding, inadequate caregiver engagement, and a history of head injury were more common in children with developmental delay, suggesting possible modifiable risk factors.

These results are partially consistent with case-control studies conducted in rural Indian contexts, which indicated that the absence of breastfeeding, inability to cry at birth, and maternal illnesses were considerably more prevalent among children with developmental delay (p<0.05).⁵⁰ The disparity in statistical significance may be attributed to sample size constraints and variations in study design.

The current findings diverge earlier neurology outpatient studies, which recognized consanguinity (AOR = 6.50; 95% CI: 1.96–21.51; p<0.002) and prematurity as important independent predictors of developmental delay.⁴⁹ Although consanguinity was not a major factor in the current analysis, the focus on prenatal morbidity and NICU hospitalization indicates that newborn difficulties may have a predominant influence in pandemic-era cohorts.

CONCLUSION:

This pilot investigation indicates that SARS-CoV-2 infection alone may not constitute an independent risk factor for neurobehavioral developmental delay; instead, low amniotic fluid levels and NICU admission emerge as important predictors. The results highlight the significance of early antenatal identification of high-risk pregnancies and rigorous newborn monitoring to reduce long-term developmental impacts. Since this was only a pilot study, we need be careful about how we read these data. To confirm these connections and to clarify the direct and indirect impacts of the COVID-19 pandemic on infant neurodevelopment, larger, multicentric investigations employing multivariable regression analysis are necessary.

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

REFERENCES:

1. Ludwig S, Zarbock A. Coronaviruses and SARS-CoV-2: A Brief Overview. Anesth Analg. 2020 July; 131(1):93–6.

2. Archived: WHO Timeline - COVID-19 [Internet]. [cited 2025 Nov 21]. Available from: https://www.who.int/news/item/27-04-2020-who-timeline---covid-19

3. Wise J. Covid-19: WHO declares end of global health emergency. BMJ. 2023 May 9; 381:1041.

4. COVID-19 Pandemic - Our World in Data [Internet]. [cited 2025 Nov 21]. Available from: https://ourworldindata.org/coronavirus

5. The Economist [Internet]. 2022 [cited 2025 Nov 21]. The pandemic’s true death toll. Available from: https://www.economist.com/graphic-detail/coronavirus-excess-deaths-estimates

6. Sarkar A et al Covid-19 Infection in India: A Comparative Analysis of the Second Wave with the First Wave. Pathogens. 2021 Sept 21; 10(9): 1222.

7. Safi M. India’s shocking surge in Covid cases follows baffling decline. The Guardian [Internet]. 2021 Apr 21 [cited 2025 Nov 21]; Available from: https://www.theguardian.com/world/2021/apr/21/india-shocking-surge-in-covid-cases-follows-baffling-decline

8. Coronavirus in India: Modi Orders Total Lockdown of 21 Days - The New York Times [Internet]. [cited 2025 Nov 21]. Available from: https://www.nytimes.com/2020/03/24/world/asia/india-coronavirus-lockdown.html

9. Haq DSU, Shakeel S. COVID-19 Epidemiology in India: A Review, Lessons Learned, and Future Policy Development [Internet]. 2023 [cited 2025 Nov 21]. Available from: https://scienceopen.com/hosted-document?doi=10.14293/PR2199.000492.v1

10. Mathieu E et al. COVID-19 Pandemic. Our World Data [Internet]. 2020 Mar 7 [cited 2025 Nov 21]; Available from: https://ourworldindata.org/coronavirus

11. Covid: World’s true pandemic death toll nearly 15 million, says WHO. 2022 May 5 [cited 2025 Nov 21]; Available from: https://www.bbc.com/news/health-61327778

12. Kerala’s COVID-19 response puts “so-called first world” to shame: British economist- The Week [Internet]. [cited 2025 Nov 21]. Available from: https://www.theweek.in/news/india/2020/04/13/kerala-covid-19-response-puts-so-called-first-world-to-shame-british-economist.html

13. Standard B. Kerala sees biggest single-day spike of 43,529 new coronavirus cases [Internet]. 2021 [cited 2025 Nov 21]. Available from: https://www.business-standard.com/article/current-affairs/kerala-sees-biggest-single-day-spike-of-43-529-new-coronavirus-cases-121051201053_1.html

14. GoK Dashboard | Official Kerala COVID-19 Statistics [Internet]. [cited 2025 Nov 21]. Available from: https://dashboard.kerala.gov.in/covid/daily.php

15. Bulletin-HFWD-English-January-25.pdf [Internet]. [cited 2025 Nov 21]. Available from: https://dhs.kerala.gov.in/wp-content/uploads/2022/01/Bulletin-HFWD-English-January-25.pdf

16. Jaya AM et al. Epidemiology and Response to the COVID-19 Pandemic in Kerala, India, 2020-2021: A Cross-Sectional Study. Trop Med Infect Dis. 2022 June 14;7(6):105.

17. Di Mascio Det al. Outcome of coronavirus spectrum infections (SARS, MERS, COVID-19) during pregnancy: a systematic review and meta-analysis. Am J Obstet Gynecol MFM. 2020 May;2(2):100107.

18. Yesodharan DK, Raghavan D, Jones T. Outcomes of Covid-19 during pregnancy: A systematic review. Malays J Nurs [Internet]. 2021 Apr 1 [cited 2026 Jan 10];12(4). Available from: https://ejournal.lucp.net/index.php/mjn/article/view/1255

19. Xu K et al. The impact of COVID-19 infections on pregnancy outcomes in women. BMC Pregnancy Childbirth [Internet]. 2024 Aug 29 [cited 2026 Jan 10];24(1):562. Available from: https://doi.org/10.1186/s12884-024-06767-7

20. Kashyap S.et al Maternal and neonatal outcome in COVID-19 pregnancy: an ongoing review of first wave in a tertiary care center in North India. Int J Reprod Contracept Obstet Gynecol [Internet]. 2021 Oct 27 [cited 2026 Jan 10];10(11):4212. Available from: https://www.ijrcog.org/index.php/ijrcog/article/view/10864

21. Hassan N et al. COVID-19 infection during pregnancy - maternal and perinatal outcomes: a tertiary care centre study. Int J Reprod Contracept Obstet Gynecol [Internet]. 2020 Aug 27 [cited 2026 Jan 10];9(9):3764–9. Available from: https://www.ijrcog.org/index.php/ijrcog/article/view/8777

22. Xu K et al. The impact of COVID-19 infections on pregnancy outcomes in women. BMC Pregnancy Childbirth [Internet]. 2024 Aug 29 [cited 2026 Jan 10];24(1):562. Available from: https://doi.org/10.1186/s12884-024-06767-7

23. Elgueta D et al. Consequences of Viral Infection and Cytokine Production During Pregnancy on Brain Development in Offspring. Front Immunol. 2022; 13:816619.

24. Dong L et al. Possible Vertical Transmission of SARS-CoV-2 From an Infected Mother to Her Newborn. JAMA. 2020 May 12;323(18):1846–8.

25. Pulinx B et al. Vertical transmission of SARS-CoV-2 infection and preterm birth. Eur J Clin Microbiol Infect Dis Off Publ Eur Soc Clin Microbiol. 2020 Dec;39(12):2441–5.

26. Hosier H et al. SARS-CoV-2 infection of the placenta. J Clin Invest. 2020 Sept 1;130(9):4947–53.

27. Dong L et al. Possible Vertical Transmission of SARS-CoV-2 From an Infected Mother to Her Newborn. JAMA. 2020 May 12;323(18):1846–8.

28. Patanè L et al. Vertical transmission of coronavirus disease 2019: severe acute respiratory syndrome coronavirus 2 RNA on the fetal side of the placenta in pregnancies with coronavirus disease 2019-positive mothers and neonates at birth. Am J Obstet Gynecol MFM. 2020 Aug;2(3):100145.

29. Tolu LB et al. Vertical transmission of Severe Acute Respiratory Syndrome Coronavirus 2: A scoping review. PloS One. 2021;16(4):e0250196.

30. Alzamora MC et al. Severe COVID-19 during Pregnancy and Possible Vertical Transmission. Am J Perinatol. 2020 June;37(8):861–5.

31. Kumar R et al. SARS-CoV-2 infection during pregnancy and pregnancy-related conditions: Concerns, challenges, management and mitigation strategies-a narrative review. J Infect Public Health. 2021 July;14(7):863–75.

32. Zeng H et al. Antibodies in Infants Born to Mothers With COVID-19 Pneumonia. JAMA. 2020 May 12;323(18):1848–9.

33. Meyer U, Feldon J. Epidemiology-driven neurodevelopmental animal models of schizophrenia. Prog Neurobiol. 2010 Mar;90(3):285–326.

34. Patterson PH. Immune involvement in schizophrenia and autism: etiology, pathology and animal models. Behav Brain Res. 2009 Dec 7;204(2):313–21.

35. Atladóttir HO et al. Maternal infection requiring hospitalization during pregnancy and autism spectrum disorders. J Autism Dev Disord. 2010 Dec;40(12):1423–30.

36. Zerbo O et al. Maternal Infection During Pregnancy and Autism Spectrum Disorders. J Autism Dev Disord. 2015 Dec;45(12):4015–25.

37. Mednick SA et al. Adult schizophrenia following prenatal exposure to an influenza epidemic. Arch Gen Psychiatry. 1988 Feb;45(2):189–92.

38. Greer O et al. COVID-19 in pregnancy. Obstet Gynaecol [Internet]. 2025 [cited 2026 Jan 11];27(1):43–56. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/tog.12960

39. Shuffrey LC et al. Association of Birth During the COVID-19 Pandemic with Neurodevelopmental Status at 6 Months in Infants with and Without in Utero Exposure to Maternal SARS-CoV-2 Infection. JAMA Pediatr [Internet]. 2022 June 6 [cited 2025 Nov 21];176(6): e215563. Available from: https://jamanetwork.com/journals/jamapediatrics/fullarticle/2787479

40. Edlow AG et al. Sex-Specific Neurodevelopmental Outcomes Among Offspring of Mothers with SARS-CoV-2 Infection During Pregnancy. JAMA Netw Open. 2023 Mar 1;6(3): e234415.

41. Frontiers | Impact of SARS-CoV-2 Infection During Pregnancy on Infant Neurobehavioral Development: A Case-Control Study [Internet]. [cited 2026 Jan 11]. Available from: https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2021.762684/full

42. Silva PYF et al. Risk of Global Developmental Delay in Infants Born from Mothers with COVID-19: A Cross-Sectional Study. Int J Womens Health. 2023; 15:467–74.

43. Wang Y et al. Impact of Covid-19 in pregnancy on mother’s psychological status and infant’s neurobehavioral development: a longitudinal cohort study in China. BMC Med. 2020 Nov 4;18(1):347.

44. Favre G et al. Neurodevelopmental outcomes of infants after in utero exposure to SARS-CoV-2 or mRNA-COVID-19 vaccine compared with unexposed infants: a COVI-PREG prospective cohort study. Clin Microbiol Infect [Internet]. 2025 Feb [cited 2025 Nov 21];31(2):266–73. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1198743X24005019

45. Edlow AG, Castro VM, Shook LL, Kaimal AJ, Perlis RH. Neurodevelopmental Outcomes at 1 Year in Infants of Mothers Who Tested Positive for SARS-CoV-2 During Pregnancy. JAMA Netw Open [Internet]. 2022 June 9 [cited 2025 Nov 21];5(6): e2215787. Available from: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2793178

46. Kumari A, Kumari N. Oligohydramnios Impact on Fetal Growth and Development: A Prospective Cohort Study. J Chem Health Risks [Internet]. 2020 [cited 2026 Jan 21];10(4):406–11. Available from: https://jchr.org/index.php/JCHR/article/view/5409

47. Chigladze M. The Predictive Value of the Mother’s Risk Factors in Formation of Fetal Developmental Delay. Glob Pediatr Health [Internet]. 2021 Feb 27 [cited 2026 Jan 21]; 8:2333794X21999149. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7923979/

48. Kaur S, Sidhu NS, Randhawa RK. Biological Risk Factors and Early Developmental Delay Assessment in Infants Using Ages and Stages Questionnaire, Version 3 (ASQ-3). Int J Res Rev [Internet]. 2021 July 22 [cited 2026 Jan 21];8(7):272–7. Available from: https://www.ijrrjournal.com/IJRR_Vol.8_Issue.7_July2021/IJRR038.pdf

49. (PDF) Identification of Preventable Risk Factors for Developmental Delay in Children: A Pilot Study. ResearchGate [Internet]. 2025 Dec 19 [cited 2026 Jan 21]; Available from: https://www.researchgate.net/publication/369703240_Identification_of_Preventable_Risk_Factors_for_Developmental_Delay_in_Children_A_Pilot_Study

50. Gaikwad S et al. Risk factors of developmental delay in children from the age group of 6 months to 6 years. Sri Lanka J Child Health [Internet]. 2024 June 5 [cited 2026 Jan 21];53(2):146–51. Available from: https://account.sljch.sljol.info/index.php/sljo-j-sljch/article/view/10803

Received on 02.02.2026 Revised on 28.02.2026

Accepted on 24.03.2026 Published on 30.04.2026

Available online from May 02, 2026

Asian J. Nursing Education and Research. 2026;16(2):73-80.

DOI: 10.52711/2349-2996.2026.00016

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Type of Delay	Frequency (n = 6)	Percentage (%)
Cognitive	4	66.6%
Speech and Language	6	100 %
Motor	1	16.6%
Social–Emotional	0	0
Adaptive	0	0

Variables	Categories	Developmental Delay (Case group n=6)	No Delay (Control group n=12)	Total (n=18)
Mother’s Age (years)	18–30	5 (83.3%)	11 (91.7%)	16 (88.9%)
Mother’s Age (years)	31–45	1 (16.7%)	1 (8.3%)	2 (11.1%)
Socioeconomic Status	Upper Lower	3 (50.0%)	1 (8.3%)	4 (22.2%)
Socioeconomic Status	Upper/ Lower Middle	3 (50.0%)	11 (91.7%)	14 (77.8%)
Marital Status	Married	6 (100.0%)	12 (100.0%)	18 (100.0%)
Place of Residence	Rural	6 (100.0%)	12 (100.0%)	18 (100.0%)

Maternal SARS CoV 2 infection Status	Developmental Delay (Case, n=6)	No Delay (Control, n=12)	Total (n=18)
Yes	2 (33.3%)	5(41.7%0	7 (38.9%)
No	4 (66.6%)	7 (58.3%)	11 (61.1%)
Total	6 (100%)	12 (100%)	18 (100%)

Postnatal Risk Factor	Category	Developmental Delay (Case group n=6)	No Delay (Control group n=12)	Total (n=18)	Chi-square	p-value	OR	95% CI
NICU admission	Yes	4 (66.7%)	1 (8.3%)	5 (27.8%)	6.785	0.009	22.0	1.54
	No	2 (33.3%)	11 (91.7%)	13 (72.2%)
Neonatal Jaundice	Yes	2 (33.3%)	1 (8.3%)	3 (16.7%)	1.8	0.18	5.5	0.385
	No	4 (66.7%)	11 (91.7%)	15 (83.3%)
Breast fed without delay	Yes	6 (100.0%)	11 (91.7%)	17 (94.4%)	0.529	0.467	-	-
	No	0 (0.0%)	1 (8.3%)	1 (5.6%)
Hypoglycemia	Yes	0 (0.0%)	3 (25.0%)	3 (16.7%)	1.8	0.18	-	-
	No	6 (100.0%)	9 (75.0%)	15 (83.3%)

Table 4: Frequency and percentage wise distribution of children with and without developmental delay according to maternal SARS CoV 2 infection status