Identification of Preventable Risk Factors for Developmental Delay in Children: A Pilot Study

Article information

Ann Child Neurol. 2023;31(2):97-102
Publication date (electronic) : 2023 March 22
doi :
1Kamineni Academy of Medical Sciences and Research Center, Hyderabad, India
2Department of Pediatric Neurology, Rainbow Children’s Hospital, Hyderabad, India
Corresponding author: Tejanjani Vathada, MBBS Kamineni Academy of Medical Sciences and Research Center (KAMSRC), LB Nagar, Hyderabad 500068, India Tel: +91-6303554091, Fax: +91-4044665555 E-mail:
Received 2022 November 20; Revised 2022 December 29; Accepted 2022 December 29.



Developmental delay (DD) is reported to be frequent in developing countries, such as India. Hence, this study was carried out to evaluate preventable risk factors that can predispose children to DD, through observations of pediatric neurology outpatients.


This was a prospective, observational, and descriptive cross-sectional single-center hospital-based study for a period of 30 days, split into two separate time periods due to the impact of the coronavirus disease 2019 pandemic. Children who newly presented to the pediatric neurology outpatient department were considered. There were a total of 151 boys and girls, from 6 months to 14 years of age. Detailed demographic information on prenatal, natal and postnatal risk factors relevant to the neurological diagnosis was collected. Antenatal education for mothers about breastfeeding and newborn care, place of delivery, the availability of round-the-clock pediatric care during the delivery, gestational age, maternal fever, encephalitis, seizures, meningitis, blood pressure, gestational diabetes mellitus, infections, history of consanguinity, and genetic disorders were all considered. The data were analyzed with odds ratios and multivariable logistic regression.


Forty-three of the 151 enrolled children had DD. Significant associations were found between consanguinity and DD (adjusted odds ratio [AOR], 6.50; 95% confidence interval [CI], 1.96 to 21.51; P<0.002) and between prematurity and DD (AOR, 2.34; 95% CI, 1.07 to 5.13; P<0.033).


This study shows that consanguineous marriages and prematurity predisposed children to DD when prenatal, natal, and postnatal risk factors were comprehensively considered.


Developmental disabilities are a diverse group of conditions due to physical, learning, or behavioral impairment. These conditions begin during the developmental period, may impact day-to-day functioning, and usually last throughout a person’s lifetime [1]. Developmental delay (DD), in contrast, occurs when a child exhibits a significant delay in the attainment of milestones or skills. The various domains of development include gross motor, fine motor, language and hearing, and social behavior; these domains are complex and interrelated [2]. Considerable progress has been made in understanding and categorizing these childhood DDs. However, different countries use distinct sets of risk factors for categorization [3]. In India, a delay in any motor domain (gross or fine), or a delay in speech, language, cognitive, social function, hearing, and vision as compared to other children is classified as DD [3]. Apart from issues solely related to categorization, DDs and developmental disabilities impair children’s physical well-being, learning, and behavioral functioning. Affected children have sensory impairments, such as hearing and vision loss, epilepsy, cerebral palsy, attention deficit-hyperactivity disorder (ADHD), autism spectrum disorder (ASD), intellectual disability, or other learning disorders.

The onset of these disorders can be multifactorial, due to genetic factors, early alterations of embryonic development, late pregnancy or perinatal conditions, acquired childhood conditions, and conditions of unknown etiology. Children with DD and developmental disabilities are at greater risk of suboptimal health, education attainment, and well-being than children without disabilities [4]. In 2016, Global Research on Developmental Disabilities Collaborations [5] estimated that globally, 52.9 million children younger than 5 years had developmental disabilities, about 95% of whom were from low-income and middle-income countries. India had the highest number of children affected by developmental disabilities, except for ADHD, for which the highest number was reported for China. Globally, India ranked highest for epilepsy, intellectual disability, hearing loss, vision loss, and ASD [5]. Other studies from India have identified genetic factors as the most common cause of DD (61.4%), followed by perinatal causes (20.4%). With the younger population showing substantial numbers of people with DD, urgent preventive measures are needed [6].

If prevention is more powerful than a cure, then it would be important to analyze the factors that may predispose children to DDs from an Indian perspective, which was the focus of this research. We further recognize that several healthcare providers are involved during the entire period of preconception to early childhood, underscoring the importance of identifying the healthcare providers who can best raise awareness of DD and play an important role in preventing DD by taking steps to address risk factors.

Materials and Methods

1. Subjects

This study was conducted at a pediatric neurology outpatient department of a single-center (specifically, a tertiary pediatric subspecialty hospital). This was a cross-sectional, observational, descriptive, prospective study with two separate time periods (December 14, 2020 to January 6, 2021; and April 12, 2021 to April 27, 2021). Due to circumstances related to coronavirus disease 2019, the study was conducted in two time frames. Approval of the research was obtained from Rainbow Children’s Medicare–Ethics Committee for Clinical Trials and Bioavailability and Bioequivalence Studies (RCM-EC CT) with application number (RCHBH/004/03-2021). Written informed consent was obtained from all patients. Only new patients who presented to the neurology clinic for the first time were considered. The researcher was physically present for all data collection.

In total, 151 boys and girls were enrolled in this study, ranging in age from 6 months to 14 years. Details were recording regarding events in the prenatal, natal, and postnatal periods. Subjects were evaluated by a team of a junior and a senior neurologist, and the investigator was present during this time. The assessment methods included the Bayley Scales of Infant and Toddler Development III from ages 6 months to 3 years 6 months. Prematurity was assessed using the corrected age. Other tests and reports as needed according to each patient’s diagnostic history included neurological examinations such as brain magnetic resonance imaging and electroencephalography, transcranial magnetic stimulation, biotinidase tests, genetic testing, muscle ultrasonography, and ophthalmic examinations. A detailed history was taken, with all the clinical findings, explained events, and investigations, and the etiology of the patient’s condition was ascertained. The researcher asked specific questions regarding other demographic information relevant to this study, such as the place of delivery (rural or urban, government or private hospital), mothers’ antenatal education on breastfeeding, the availability of round-the-clock pediatric care, monitoring for hypoglycemia, and high-risk conditions (maternal hypertension, maternal fever, encephalitis, seizures, meningitis, blood pressure, gestational diabetes, infections, gestational age, history of consanguinity [7], and genetic disorders).

2. Study data

The quantitative variables were age, gestational age, birth weight, neonatal intensive care unit stay, and time of hospital discharge. The qualitative variables were gender, demographic history and details, comorbidities, pathological conditions, and medical events. The scoring method for qualitative variables was a 2-point Likert scale [8] measure for agreement (1, yes/agree; 2, no/disagree). Quantitative and qualitative data were analyzed in terms of absolute values and percentages.

3. Power of the study

G*Power statistical software (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) [9] was used to calculate the actual power. Logistic regression analysis was selected, with an “a priori” type of analysis, a two-tailed binominal test, and α error probability of 0.05. The power (1-β error probability) of the study was 0.950.

The sample size estimation for logistic regression was based on the concept of the event per variable (EPV) [10]. As a rule of thumb, an EPV of 50 is recommended for validity. Considering that the final logistic model included two significant independent variables, the EPV of this study was 75, which is greater than the recommended EPV of 50.


1. Developmental status

From the total sample of 151 children, 28.47% (n=43) showed DD; 24.5% (n=37) had epilepsy; 21.19% (n=32) had autism; 14.56% (n=22) had other neurological complaints; 5.96% (n=9) had movement disorders; and 5.29% (n=8) had challenging behavior. The results are shown in Table 1. Of the total 43 children with DD, 33 children had perinatal insults and 10 children had a genetic predisposition. Among the 33 children with perinatal insults, 13 children had hypoxic ischemic encephalopathy, 16 children had hypoglycemia-induced brain injuries, and four children had periventricular leukomalacia. In 10 children with a genetic etiology, two children had aneuploidy, three children had trisomy 21, and five children had fragile X syndrome. Out of 37 children with seizures/epilepsy, 23 had congenital malformations, two had brain tumors, one had porphyria, one had uremia, five had aminoacidopathies, and three had neurocysticercosis. All 32 children with ASD had idiopathic causes. These details are described in Table 2.

Prevalence of developmental status (n=151)

Categorization of diagnostic and etiological developmental status (n=151)

2. Sociodemographic characteristics and developmental status

A comparison of sociodemographic characteristics between children with DD and children with neurological complaints and no DD is presented in Table 3. Out of 151 children, 31.6% (n=30) of boys showed DD, compared to 19.6% of girls (n=11). Thirty-six children between the ages of 6 months to 5 years had DD and five children between the ages 5 to 14 years had DD; however, the percentages were similar in both age groups (27.1% and 27.8%, respectively). Nine children with third-degree consanguinity had DD, and consanguineous marriage showed a statistically significant association with DD (odds ratio [OR], 4.87; 95% confidence interval [CI], 1.61 to 14.73; P<0.005). Prematurity also showed a significant association (OR, 2.15; 95% CI, 1.02 to 4.53; P<0.044). On a positive note, although India is a developing country, medical assistance was reported to be present for 146 of 151 women at the time of delivery. The perinatal stay in the hospital was less than 1 week for 138 infants. Ninety-two deliveries were in urban hospitals. The birth weight exceeded 2 kg for 142 children, and blood glucose levels were checked at birth for all infants. All 151 women received information on breastfeeding guidelines. There were more cesarean section deliveries (n=109) than normal deliveries (n=42). No factors other than consanguinity and prematurity showed a statistically significant association with the risk of DD.

Sociodemographic characteristics and developmental status (n=151)

3. Multivariable logistic regression

The two variables which were significant at P<0.05 in simple logistic regression (OR) were considered for multivariable logistic regression analyses (Table 4). Both variables were significantly associated with DD, and consanguineous marriage (adjusted odds ratio [AOR], 6.50; 95% CI, 1.96 to 21.51; P<0.002) showed a stronger association than prematurity (AOR, 2.34; 95% CI, 1.07 to 5.13; P<0.033).

Multivariable logistic regression for predictors of developmental delay (n=151)


From the total sample of 151 children age ranging from 6 months to 14 years age, the most common diagnosis was DD (28.47%), followed by epilepsy (24.5%), ASD (21.2%), other neurological complaints (14.56%), movement disorders (5.96%), and challenging behavior (5.29%). Thus, DD and epilepsy accounted for 50% of the burden in Indian children presenting as outpatients with neurological complaints. As compared to the Western literature, the prevalence of ASD (21.19%) was closer to that of DD (28.47%) and epilepsy (24.5%), and ASD, DD, and epilepsy collectively comprised 74.16% of the subject population [5].

An analysis of ORs, including age, sex, and risk factors (prenatal, natal, and postnatal), identified consanguineous marriage and premature births as significant risk factors for DD. This is consistent with the findings of Gupta and Kabra [11]. Considering consanguineous marriage statistics from India, the National Family Health Survey (NFHS) 2019 to 2021 [12], 13% of married women were blood-related to their husbands before marriage, and 11% were consanguineous marriages or cousin marriages, of which 8% were between first cousins. According to a state report from Telangana [12], where the research study was conducted, almost one-fifth of women reported being in consanguineous marriages. Considering the number of consanguineous marriages in the state and the magnitude of the association between DD and consanguinity provides useful insights for educating men and women about the neurological risks involved for children from these marriages. Lower birthweight showed a slight association with risk (P<0.061); however, premature infants have low birth weight, so prematurity and low birth weight can overlap in risk predictions. Children born in rural areas or at government hospitals with baseline medical support did not show a statistically significant risk elevation for DD. It was, however, encouraging to find that DD could be a preventable risk factor, assuming that families and adults are counseled about non-consanguineous marriages, adequate nutritional well-being, and appropriate medical care for minimizing preterm deliveries.

Although people can be educated to avoid consanguineous marriages, the premature birth rate must be taken into consideration. More studies have to be focused on reasons for premature deliveries, considering both maternal health and intrauterine growth to elucidate risk factors for preterm labor. Directions for future research could include maternal nutritional status, pre-eclampsia, antenatal infections, maternal blood pressure, stress during pregnancy, work exhaustion, anemia, intrauterine resistance of blood flow, periodic intrauterine fetal growth monitoring, and maternal age, as Indian woman’s median age at first birth is currently 21.2 years [12]. NFHS data showed that antenatal care for women aged 15 to 49 years has risen from 84% (NFHS-4, 2015 to 2016) to 94% (NFHS-5, 2019 to 2021) [12]. Eighty-five percent of women received antenatal care from a skilled provider, 70% had their first antenatal care visit in the first trimester, and 59% had four or more antenatal care visits [12]. Thus, although antenatal care support increased over time, the support weakened later in pregnancy, and regular monitoring in the later trimesters can help minimize high-risk premature births. Institutional deliveries have increased in India from 39% (NFHS-4, 2005 to 2006) to 89% (NFHS-5, 2019 to 2021), and 82% of newborns had postnatal checks within 2 days of birth [12]. This window of 2 to 3 days is very crucial for hypoglycemic insults, leading to neurological concerns. In the present study, 16 children with DD had hypoglycemic brain injuries. An improvement in blood glucose monitoring during the critical period of the first 3 days of newborn life can minimize the neurological risks of low blood glucose levels. With proper education on the risks of consanguineous marriages, adequate medical health support through the antenatal period and extending into the postpartum 1-week period will reduce the risk of DD in India. The limitations of this study include the fact that it was a pilot study with a relatively small sample size, which may have prevented a complete evaluation of the risk factor associations. However, within this limited sample, we preliminarily found that both consanguinity and premature births can predispose children to DD risk.


No potential conflict of interest relevant to this article was reported.

Author contribution

Conceptualization: LL. Data curation: TV. Formal analysis: TV and LL. Methodology: TV. Writing-original draft: TV. Writing-review & editing: TV and LL.


I would like to thank entire the Neurology Department and Rainbow Children’s Hospital for giving me immense support and help to conduct this study.


1. Rubin IL. Developmental disabilities: delivery of medical care for children and adults Philadelphia: Lea & Febiger; 1989.
2. Gupta S, Shrivastava P, Samsuzzaman M, Banerjee N, Das DK. Developmental delay among children under two years of age in slums of Burdwan Municipality: a cross-sectional study. J Family Med Prim Care 2021;10:1945–9.
3. Sharma N, Masood J, Singh SN, Ahmad N, Mishra P, Singh S, et al. Assessment of risk factors for developmental delays among children in a rural community of North India: a cross-sectional study. J Educ Health Promot 2019;8:112.
4. Ertem IO, ; World Health Organization. Developmental difficulties in early childhood: prevention, early identification, assessment and intervention in low- and middle-income countries: a review Geneva: World Health Organization; 2012. p. 2–4.
5. Global Research on Developmental Disabilities Collaborators. Developmental disabilities among children younger than 5 years in 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Glob Health 2018;6:e1100–21.
6. Kapoor D, Mukherjee SB. Global developmental delay and intellectual disability in Indian children: where do we stand? Indian Pediatr 2018;55:1083–5.
7. Anwar S, Taslem Mourosi J, Arafat Y, Hosen MJ. Genetic and reproductive consequences of consanguineous marriage in Bangladesh. PLoS One 2020;15e0241610.
8. USAID/JORDAN. Likert scale examples for surveys 2010 [Internet]. Washington: Iowa State University Extension; 2018. [cited 2023 Jan 17]. Available from:
9. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods 2009;41:1149–60.
10. Bujang MA, Sa’at N, Sidik TMITAB, Joo LC. Sample size guidelines for logistic regression from observational studies with large population: emphasis on the accuracy between statistics and parameters based on real life clinical data. Malays J Med Sci 2018;25:122–30.
11. Gupta N, Kabra M. Approach to the diagnosis of developmental delay: the changing scenario. Indian J Med Res 2014;139:4–6.
12. International Institute for Population Sciences. National Family Health Survey (NFHS-5), 2019-21 [Internet]. Mumbai: International Institute for Population Sciences; 2022. [cited 2023 Jan 17]. Available from:

Article information Continued

Table 1.

Prevalence of developmental status (n=151)

Condition Total number Age (yr)
Percentage (%)
Range Mean Median
DD 43 10 mo–9 yr 4 mo 2.6 1.3 28.47
Epilepsy 37 10 mo–9 yr 9 mo 3.6 2.5 24.5
ASD 32 7 mo–9 yr 4 mo 4.1 4 21.19
Other neurological complaints 22 7 mo–7 yr 6 mo 2.9 1.7 14.56
Movement disorders 9 5 mo–8 yr 9 mo 3 1.3 5.96
Challenging behavior 8 1 yr–6 yr 2 1.4 5.29

DD, developmental delay; ASD, autism spectrum disorder.

Table 2.

Categorization of diagnostic and etiological developmental status (n=151)

Variable Number Total number
 Perinatal insult 33 43
 Genetic 10
 Structural 25 37
 Metabolic 7
 Infections 5
 Other neurological complaints 32
  Voiding dysfunction 1 22
  Heliotropic rash (dermatomyositis) 1
  Vision problems 2
  Bilateral ptosis-congenital myasthenia 2
  Oculomotor apraxia 1
  Congenital night blindness 1
  Progressive myopia 1
  Optic neuritis 1
  Headache 1
  Cycling vomiting 1
  Hydrocephalous 1
  Microcephaly 1
  Cerebral ataxia 3
  Nerve paralysis 2
  Limb weakness 3
  Duchenne muscular dystrophy 1
 Movement disorders
  Tics 3 9
  Shuddering attacks 3
  Spastic paraplegia 3
 Challenging behavior
  Acute behavioral change 1 8
  Behavioral issues 3
  Stubbornness 3
  Aggressiveness 1

DD, developmental delay; ASD, autism spectrum disorder.

Table 3.

Sociodemographic characteristics and developmental status (n=151)

Variable No. DD NC-NDD OR (95% CI) P value
 Male 95 30 (31.6) 65 (68.4) 1.88 (0.85–4.15) 0.114
 Female 56 11 (19.6) 45 (80.4) Reference
 6 months–5 years 133 36 (27.1) 97 (72.9) 0.96 (0.32–2.89) 0.949
 5 years–14 years 18 5 (27.8) 13 (72.2) Reference
Consanguineous marriage
 Yes 15 9 (60) 6 (40) 4.87 (1.61–14.73) 0.005a
 No 136 32 (23.5) 104 (76.5) Reference
Gestational age
 Premature 79 27 (34.2) 52 (65.8) 2.15 (1.02–4.53) 0.044a
 Term 72 14 (19.4) 58 (80.6) Reference
Pediatrician care at the time of delivery
 No 5 3 (60) 2 (40) 4.26 (0.68–26.49) 0.119
 Yes 146 38 (26) 108 (74) Reference
Perinatal hospital stay (wk)
 >1 13 5 (38.5) 8 (61.5) 1.77 (0.544–5.76) 0.342
 <1 138 36 (26.1) 102 (73.9) Reference
Birth weight (kg)
 <2 9 5 (55.6) 4 (44.4) 3.68 (0.93–14.4) 0.061
 >2 142 36 (25.4) 106 (74.6) Reference
Infant blood glucose monitoring at the time of birth
 No 0 0 0 2.66 (0.05–136.39) 0.625
 Yes 151 41 (27.2) 110 (72.8) Reference

Values are presented as number (%).

DD, developmental delay; NC-NDD, neurological complaints without developmental delay; OR, odds ratio; CI, confidence interval.


Significant at P<0.05.

Table 4.

Multivariable logistic regression for predictors of developmental delay (n=151)

Variable AOR (95% CI) P value
 Yes 6.50 (1.96–21.5) 0.002a
 No Reference
Gestational age
 Premature 2.34 (1.07–5.13) 0.033a
 Term Reference

AOR, adjusted odds ratio; CI, confidence interval.


Significant at P<0.05.