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Table of Contents
ORIGINAL ARTICLE
Year : 2022  |  Volume : 18  |  Issue : 3  |  Page : 80-85

Performance of neurodevelopment questionnaire among school children across optimal and high birth weight in a rural cohort of northern India


1 Department of Community Medicine, Dr. Rajendra Prasad Government Medical College, Kangra, Himachal Pradesh, India
2 Department of Pediatrics, Dr. Rajendra Prasad Government Medical College, Kangra, Himachal Pradesh, India

Date of Submission12-Aug-2022
Date of Acceptance05-Sep-2022
Date of Web Publication13-Dec-2022

Correspondence Address:
Dr. Dinesh Kumar
Department of Community Medicine, Dr. Rajendra Prasad Government Medical College, Kangra, Himachal Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/AMJM.AMJM_28_22

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  Abstract 

Background: Neurodevelopmental outcomes up to 6 years of age were observed to be negatively associated with birth weight. Limited evidence exists for its association with high birth weight in rural parts of India. Objective: The objective of the study was to assess the performance of Denver Development Screening Test II (DDST-II) questionnaire among children with high birth weight (>3500 g) and normal birth weight (2500–2999 g) in an established birth cohort in the rural area of Himachal Pradesh, India. Materials and Methods: A birth cohort study was carried out from April 2021 to March 2022 in an established birth cohort of children. Participants with birth weight from 2500 to 2999 g were considered as the not-exposed and more than 3500 as the exposed group. Neurodevelopmental assessment was done by DDST-II, and its overall score along with seven domain scores was compared. The association was measured by adjusted odds ratio (aOR) with 95% confidence interval (CI). Results: A total of 379 and 377 participants in the nonexposed and exposed groups were enrolled, respectively. Exposed group observed with significantly more mean age (9.0 years) as compared to the not-exposed (8.6 years) group. Multivariate analysis of variance and multivariate analysis of covariance for observation-based neurodevelopmental assessment found that except for mentioning the correct use of items (cup, chair, and pencil) and for their action in case of cold, tired, and hungry, all domains have discriminatory value for a statistical difference between the not-exposed and exposed group. Linear regression analysis observed a significant association between DDST-II score and exposure (aOR: 2.3; 95% CI: 0.8–3.4) after adjusting for gender, age, years of schooling, and body mass index. Conclusion: High birth weight (>3500 g) was observed with a better performance of DDST-II with a significant association.

Keywords: Birth weight, neurodevelopmental assessment, rural cohort


How to cite this article:
Kumar D, Sharma S, Raina SK. Performance of neurodevelopment questionnaire among school children across optimal and high birth weight in a rural cohort of northern India. Amrita J Med 2022;18:80-5

How to cite this URL:
Kumar D, Sharma S, Raina SK. Performance of neurodevelopment questionnaire among school children across optimal and high birth weight in a rural cohort of northern India. Amrita J Med [serial online] 2022 [cited 2023 Jan 28];18:80-5. Available from: https://ajmonline.org.in/text.asp?2022/18/3/80/363502




  Background Top


Neurodevelopmental outcomes up to 6 years of age were observed to be negatively associated with birth weight. Development is observed to be slow and low among children with very low birth weight.[1],[2],[3] Prematurity was observed to be significantly associated with adverse neurodevelopment. Adults with preterm birth and without disability were observed with lower achievement in education and earning less income.[4] A decreased intake of micronutrients is reported to be associated with suboptimal neurodevelopmental assessment in children with very low birth weight. High consumption of carbohydrates, lipids, and energy in antenatal period was reported to be positively associated with a better cognitive score in the later part of life. However, the supplementation of macronutrient/energy intakes was not observed with a significant improvement in the neurodevelopment.[5]

On the other hand, the direction of association between high birth weight and neurodevelopmental assessment is expected to be positive or stay negative. Amidst of a paucity of evidence, an assessment during an early part of life (1–6 months of age) observed that macrosomia (birth weight > 4000 g) was negatively associated with fine motor and adaptability, whereas positively associated with gross motor, language, and social behavior without any significant effect on the clinical diagnosis of neurodevelopmental delay.[6] The literature suggested about various study tools to measure the neurodevelopmental growth, but Denver Development Screening Test II (DDST-II) was observed to be with good criteria such as validity, reliability, and internal consistency.[7],[8] It is used widely in routing clinical settings for children up to 6 years of age. The current study was planned to assess the performance of DDST-II questionnaire among children with high birth weight (>3500 g) and normal birth weight (2500–2999 g). It was assessed in an established rural cohort of children with 7–10 years of age, and the application of DDST-II was considered with the assumption that children with completed age of 6 years were expected to perform better and complete neurodevelopmental assessment. DDST-II gives a reference to children already completed the trajectory of growth and development as all had achieved 6 years of age of their life.


  Materials and Methods Top


A birth cohort study was carried out from April 2021 to March 2022 in an established birth cohort of children. Children were recruited from the villages of district Una, Himachal Pradesh. As per the local census, studied health block had a population of 71,416 with 14,107 households (HHs). However, the state has a reported census population of about 6.9 million with 1.4 million HHs. For the study purpose, children with birth weights from 2500 to 2999 g were considered as not-exposed and more than 3500 as an exposed group. A sample size was calculated with the assumptions that the prevalence of childhood obesity was 11.0% in the not-exposed and 15.0% in the exposed group with a relative risk of 1.7. The sample size of 748 (374 in each group) was calculated at 5.0% level of significance and 80.0% study power. The criteria for childhood obesity were z-score of body mass index (BMI) of more than two standard deviation (SD) as per World Health Organization (WHO). The study was carried out in two phases. At first, a total of 399 (not exposed: 306; exposed: 93) children were surveyed in the year 2013–14. In order to meet the required sample size, the second survey was carried out from 2021 to 2022, and additional 349 (not-exposed: 68; exposed: 281) study participants were recruited. Birth weight for exposure classification was done from an immunization card as a means of verification. Participants with a known cause for pathological obesity were set as an exclusion criterion.

A trained field attendant collected data using a pretested questionnaire containing information about background characteristics such as name, gender, socioeconomic status, and schooling. The anthropometric assessment was done for body height (in meters) without shoes by Seca portable stadiometer (Seca, Corporation, Germany) with participant’s head in the Frankfurt plane, and the body weight (in kilograms) was measured by portable Tanita SC-240 body composition analyzer (TANITA Corporation, Japan) with a minimal light clothing and removal of heavy clothing, pocket items, and shoes. Both height and weight were measured twice, and a third time only if the first two values of height and weight were more than 0.5 cm and 0.5 kg apart. In this case, the final value will be an average of two closet values. Neurodevelopmental assessment was done in two ways: first, the ten-question questionnaire (TTQ) that was administered to the mothers. It assessed language, motor, visual-spatial, and learning/memory domains.[9] Second, an adapted version of DDST-II was administered by the project staff for a total of 29 direct areas of observation.[10] Children with abnormal responses to screening questionnaires were assessed clinically by the pediatrician for a pathological cause. Informed consent and assent were taken before proceeding to data collection. The study was approved by Institute Ethics Committee (IEC), Dr. RPGMC (HFW-HDRPGMC/Ethics/2019/209 dated 13/08/2019).

Unpaired students’ “t” and chi-square test were used to look for statistical significance for continuous and categorical variables, respectively. For an analysis of adapted version of DDST-II, a total of 29 items of direct observation-based neurodevelopmental assessment were categorized into the following seven domains: drawing figures (03), mention of the correct use of items (cup, chair, and pencil) (03), correct identification of pictures (02), activities (talking, clothing, brushing, hopping, and balancing) (05), placing paper/object (front, behind, on, and under the chair) (04), correct identification of four colors and HH items (09), and action in case of cold, tiredness, and hunger (03). Each correct response was given a score of one, and a mean score was standardized with a multiplication factor of 100. The mean score was then compared across the not-exposed and exposed groups. Because there are a total of seven-domains scores, multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA) for age, years of schooling, and gender were used as a statistical technique to identify the domain(s) causing the difference. Children age and years of schooling were considered continuous, whereas gender as a categorical variable for analysis. Assumptions of independence, normality, and linearity were assessed before proceeding for analysis. Independence assumption was assessed using intraclass correlation coefficient (ICC) and covariance matrix. Indicative of a significant distribution of covariance matrix logarithmic transformation of the standardized score was done. Before proceeding for analysis, ICC and covariance matrix were assessed again and showed no violation of independence assumption. Data analysis was done using R studio version 3.3.1 using “car” and “psych” packages.[11]


  Results Top


The mean age of mothers was about 33 years and not significantly different between groups. The mean age of children in years was observed to be significantly high in the exposed (9.0) as compared to the not-exposed (8.6) group. Age category distribution was observed to be skewed as participants with lower age (7 and 8 years) were significantly high in the not-exposed, whereas children with 10 years of age were statistically more in the exposed group. Apart from age, the gender of children also showed significant differences between groups. Socioeconomic status was observed to be statistically similar, and all were going to school (mostly in public schools). Because of differential age categories, mean years of schooling are expected to be significantly high in the exposed group [Table 1]. TTQ observed statistically indifferent results across exposure groups [Table 2]. Thereafter, a neurological assessment with DDST-II was done, and a significant difference was observed for: ability to draw a circle correctly; self-dressing of clothes; identify and draw “+” sign; ability to place paper under, behind, and front of chair seated participant; correct identification of colors; correct mention of lake, desk, banana, curtain, and fence; correct drawing of a person; and balance on one leg (alternatively) for more than 8 s. They all were significantly high in the exposed group. All children were assessed clinically by the pediatrician, and no pathological anomaly was observed [Table 3].
Table 1: General characteristics of enrolled children in a rural cohort of Himachal Pradesh, 2021–22

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Table 2: Mother-reported neurodevelopmental assessment based on TTQ among enrolled children in a rural cohort of Himachal Pradesh, 2021–22

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Table 3: Distribution of mean scores of observation-based neurodevelopmental assessment of enrolled children in a rural cohort of Himachal Pradesh, 2021–22

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MANOVA and MANCOVA for observation-based neurodevelopmental assessment revealed that except for mentioning of correct use of items (cup, chair, and pencil) and for action in case of cold, tired, and hungry, all domains have discriminatory values for statistical difference between the not-exposed and exposed group. The raw discriminant coefficient found out that the domains of activities (talk, clothing, brushing, hopping, and balancing) and action in case of cold, tired, and hungry had caused a significant difference between groups. Drawing figures and placing paper as told have the least discrimination values [Table 4]. Linear regression analysis observed a significant association between DDST-II score and exposure (aOR: 2.3; 95% CI: 0.8–3.4) after adjusting for gender, age, years of schooling, and BMI. However, after adjusted for exposure, a significant association between DDST-II was observed for females (aOR: 2.3; 95% CI: 0.8–3.6), age (aOR: 2.9; 95% CI: 0.6–5.2), and BMI (aOR: 0.5; 95% CI: 0.2–0.8) [Table 5].
Table 4: One-way MANOVA and MANCOVA of observation-based neurodevelopmental assessment across seven domains among enrolled children in a rural cohort of Himachal Pradesh, 2021–22

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Table 5: Unadjusted and adjusted regression between observation-based neurodevelopmental assessment across and exposure in a rural cohort of Himachal Pradesh, 2021–22

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  Discussion Top


In the current study, TTQ did not observe a statistical difference between exposure groups as reported by the mothers, whereas, beyond 6 years of age, performance of observation-based neurodevelopmental assessment was found with statistically low score among children with the not-exposed as compared to the exposed group. It was observed with a statistical difference in the mean score for various domains of adapted DDST-II. Activities such as the ability to draw a “circle” correctly, self-dressing, correct identification of colors, and correct mention of things (lake, desk, curtain, and fence) observed a low score in the not-exposed group. Specific activities such as the ability to take care of oneself and required action in a particular situation (cold, tiredness, and hunger) were observed with the highest raw discriminant score but later were statistically nonsignificant. MANCOVA observed that the ability to draw figures, self-care, placing of paper around oneself, and correct identification of colors and HH objects were statistically significant to observe a difference between groups adjusted for age, years of schooling, and gender.

A delay in the neurodevelopment growth among children is associated with intrauterine factors such as prematurity and intrauterine growth retardation.[12] Small for gestational age in addition to prematurity observed to be associated with delayed neurodevelopmental delay among children born prematurely.[13] Late preterm births (340–366 days of a period of gestation) contribute most of the preterm birth and are also observed to be associated with suboptimal performance in executive function, short-term memory, literacy skills, attention, and processing speed.[14] After birth, malnutrition (stunting and wasting) was observed to be associated with a lower psychomotor and mental development index.[15] Even term infants with severe neonatal morbidity are associated with a poor childhood neurodevelopment.[16],[17] In children with a low birth weight, the promotion of nutrition and prevention of diarrhea in the first 2–3 years of life were observed to be associated with an improved neurodevelopment.[18] Factors such as gut microbiota also associated with bodyweight and was also hypothesized to be associated with cognition and behavioral development of children.[19]

A plenty of evidence support the negative impact of prematurity, small for gestational age, and low birth weight on the neurodevelopment of children. A longitudinal birth cohort study observed that children with overweight and obesity are associated with motor and mental delay.[20] Not only with the neurodevelopment delay, childhood obesity has also found to be associated with psychosocial issues.[21] Limited evidence hints at the potential association of childhood obesity and neurodevelopment delay. An association of high birth weight with neurodevelopment is less studied. One study from China observed that both low (<2500 g) and high (>4000 g) birth weights associated with neurodevelopment at the age of 1–6 months. Generalized linear modeling observed a negative linear relationship between high birth weight and fine motor, and adaptability, whereas positive linear relationship for gross motor, language, and social behavior. The tool observed differences in questionnaire but the study did not find a significant correlation with a neurological diagnosis.[6] The current study also supports this evidence, as no clinical pathology was observed among children with suboptimal performance on DDST-II. Differential performance in some of the domains of DDST-II was observed with a low mean score in the not-exposed (2500–2999 g) as compared to the exposed (>3500 g) group.

The current study has strength where a large number of children were assessed. As birth weight was recorded before the neurodevelopmental assessment, we expect that retrospective cohort analysis gave valid estimates. Another strength is that it adds to the literature about the association of neurodevelopment growth with high birth weight (>3500 g). The inclusion of participants in rural areas holds another point for its strength as its sociocultural milieu is different from urban areas. With strengths, there are some limitations: first, the application of DDST-II after completion of 6 years of age with the inability to assess intelligent quotient as all participants were going to school. Although years of schooling were not observed to be significantly associated, it would have given a better insight into the quality of neurodevelopment. Second, it is a one-time assessment where no conclusion can be drawn for the pattern of development.

The present study was an effort to assess the differential performance of screening tool for the neurodevelopmental assessment after completing 6 years of age. The finding suggested the discriminatory value of some domains in the not-exposed and exposed groups. DDST-II performs well with its discriminatory value in rural areas. Simultaneous and future assessment for intelligence, marks obtained in class, and social participation will give a better insight. In addition, it will help identify and track the children with learning disabilities in the rural area.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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