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Association of perinatal factors with suspected developmental delay in urban children aged 1–36 months - a large-scale cross-sectional study in China

BMC Pediatrics volume 23, Article number: 11 (2023) Cite this article

Abstract

Background

Studies on perinatal risk factors and the developmental delay of children have been inconclusive and few studies have assessed the association between infants and toddlers’ body mass index (BMI) and developmental outcomes.

Methods

We conducted a cross-sectional study of children aged 1—36 months who had a routine physical examination in the child health departments of hospitals from March 2018 to November 2021 in 16 provinces, 4 autonomous regions and 2 municipalities directly under the central government by using the Infant Toddler Growth Development Screening Test (ITGDST). Normal children were defined as those with scores ≥ mean – 2 standard deviations (SD), while children with developmental delay were those with scores < mean—2SD in terms of overall development, gross motor, fine motor and language development. Binary logistic regression was used to analyze the risk factors of gross motor, fine motor, language and overall neurodevelopment.

Results

After removing some provinces with a small sample size and children with incomplete data, 178,235 children with 12 complete variables were included in the final analysis. The rate of overall developmental delay was 4.5%, while 12.5% of children had at least one developmental delay aspect. Boys, parity, advanced maternal age, multiple birth, cesarean section, neonatal injury, family heredity history, microcephaly, abnormal BMI at birth and at physical examination after controlling the confounding of other factors had a significant effect on development delay (overall neurodevelopment, gross motor, fine motor or language development). Per capita gross domestic product was a protective factor for the children’s neuropsychological development.

Conclusions

This study reveals significant associations of perinatal factors and BMI with developmental delay in the Chinese children aged 1–36 months, which may be crucial for early intervention.

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Background

Child development can be affected by a combination of socioeconomic, environmental and nutritional factors during pregnancy and the early stage of life [1, 2]. Several studies have demonstrated the impact of nutrition on children’s cognition [3,4,5]. One study has shown that malnutrition was associated with increasing developmental deficits including suboptimal cognition, communication, and motor function in children [6]. On the other hand, children with severe obesity are more likely to have poor non-verbal intelligence quotient [7]. Other studies have documented the effect of sociodemographic variables on neuropsychological development, including child gender [8], ethnicity [9], economic situation [7].

In China, the National Survey on Physical Growth and Development of Children (NSPGDC) was conducted every 10 years in nine cities among children under 7 years. Although there were rapid positive secular trends in height and weight in both urban and suburban children from 1975 to 2005 [10], a recent NSPGDC conducted in 2015 displayed a new trend of slowing growth in urban children [11]. Both under- and over-nutrition in children are major global public health challenges [12, 13]. Since 2000, China has made remarkable progress in reducing child mortality, child malnutrition, and child at risk of poor neurodevelopment [14,15,16]. However, the gaps of those health indicators between developed and underdeveloped areas in China did not narrow as fast as the reduction of their national prevalence [14, 15, 17].

Furthermore, studies on perinatal risk factors and the developmental delay of children have been inconclusive [18]. In addition, few studies have compared the association of body mass index (BMI) with different developmental delays, even though early childhood nutrition is the foundation of neurodevelopment. In the present study, we aimed to determine whether perinatal and other risk factors were associated with children’s developmental delay through a large-scale cross-sectional study in Chinese cities.

Methods

Study population

In order to analyze the current situation and perinatal risk factors of developmental delay among children aged 1—36 months, children aged 1 to 36-months from the general population had a routine physical examination in the child health departments of hospitals from March 2018 to November 2021 in 16 provinces (Anhui, Gansu, Guangdong, Guizhou, Hainan, Hebei, Henan, Heilongjiang, Hubei, Hunan, Jiangsu, Jiangxi, Liaoning, Shandong, Yunnan, Zhejiang), 4 autonomous regions (Guangxi Zhuang Autonomous Region, Inner Mongolia Autonomous Region, Tibet Autonomous Region, Xinjiang Uygur Autonomous Region) and 2 municipalities (Shanghai and Chongqing) directly under the central government (Fig. 1). These regions took the lead in adopting better screening instrument for growth and development, since the existing Denver developmental screening test has not been updated for many years. During the physical examination, most parents or caregivers only provided information for assessing the health status of children, and some of the information was due to their concern on privacy. After removing some provinces with a small sample size (less than 1000) and children with incomplete data, 178,235 children with 12 complete variables were included in the final analysis.

Fig. 1
figure 1

Regional distribution map of study population

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Case identification and grouping

The Infant Toddler Growth Development Screening Test (ITGDST, Shanghai Mengbaobao Health Technology Co., Ltd) can be used to screen for abnormal growth and development in children aged 1–36 months. The mean scores minus two standard deviations (SD) were used for the cut-off scores in terms of overall development, gross motor, fine motor and language development. Children with a score less than the mean score minus 2 SD were regarded as a developmental delay, while other children (i.e., a score equal to or greater than the mean score minus 2 SD) were considered as normal.

Data collection

In this study, ITGDST was used for collecting children’s basic information and evaluating children’s physical and neuropsychological development. All testers (doctors or nurses) undertook unified on-site training and assessment. The test was conducted in a separate and quiet room with plenty of light. The room temperature was set at around 25 ℃. Children were awake and quiet. Parents and caregivers were asked to complete the neuropsychological evaluation item by item with the animation demonstration and the testers’ instruction. The ITGDST evaluation usually takes less than 10 min. The system also collected children’s information about the perinatal period, parents and environment and inheritance. Parents provided the following information about their children: date of birth, gender, birthweight, length, gestational weeks (< 37 weeks, 37–42 weeks, and ≥ 42 weeks), normal delivery (yes or no), maternal age ≥ 35 years (yes or no), neonatal injury (yes or no), multiple birth (yes or no), cesarean section (yes or no), family heredity history (yes or no). The per capita gross domestic product (GDP) of each region is included as a continuous variable.

Head circumference, height and length were measured by using the Full Function Physical Examination Instrument (Shanghai Beigao Medical Technology Co., Ltd.) during physical examination by trained testers. The instrument is automatically calibrated when it is turned on. The measurement usually takes less than 5 min. The physical development of infants and young children was evaluated by Z-score recommended by the World Health Organization (WHO). Normal head circumference (-2 ≤ Z-score ≤ 2), macrocephaly (Z-score > 2) and microcephaly (Z-score < -2) were defined according to head circumference for age Z-score by the standard of WHO. Body weight and length values were converted into BMI as weight per height squared (kg/m2). Normal children (-2 ≤ Z-score ≤ 2), children with malnutrition (Z-score < -2) and obesity (Z-score > 2) were determined according to BMI for age Z-score by the standard of WHO.

Statistical analysis

Student t-tests, chi-squared tests, and logistic regression models were used to assess the associations of perinatal and other risk factors with the children’s developmental delay. Student t-test was used to assess the difference of Per capita GDP. Chi-squared tests were used to assess the differences of qualitative variables. The normal distribution test on the observation values of the quantitative data was made by using histogram and Quantile–Quantile plot. We used complete data and there was no imputation of data to replace missing observations. Binary logistic regression model was used to investigate the effect of relevant factors on developmental status of children (developmental delay and normal development). Adjusted odds ratios (ORs) with their 95% confidence intervals (CIs) were generated. A p value < 0.05 was set as the significant level (two tailed). All analyses were conducted using IBM SPSS version 22.0 (IBM Corp., Armonk, NY, USA).

Results

Description of the study population

The sample consisted of 178,235 children aged 1–36 months with complete data. The median age of children was 6.60 (1.08–36.99) months. More than half of the study population comprised males (53.9%). Most of the subjects were 1–12 months old, accounting for 68.4%. About 20% of children were aged 12–24 months. Most children (91.2%) came from 11 provinces including Anhui, Gansu, Henan, Heilongjiang, Hubei, Hunan, Jiangsu, Jiangxi, Shandong, Yunnan and Zhejiang. Birth weight and birth length were 3.39 (± 0.45) kg and 50.19 (± 1.45) cm, respectively. The malnutrition rate of children at birth was higher than that at physical examination (4.3% vs. 1.0%, p < 0.0001), while the rate of obesity at physical examination was higher than that at birth (6.5% vs. 2.6%, p < 0.0001). Table 1 shows the sample characteristics.

Table 1 Sociodemographic characteristics of the sample
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Assessment of potential risk factors for developmental delay

Table 2 examines the association of perinatal and other factors with the developmental delay. The results of the univariable analysis showed that sex, parity of more than three children, advanced maternal age, multiple birth, cesarean section, neonatal injury, family heredity history, microcephaly, abnormal BMI at birth and at physical examination were significantly associated with developmental delay.

Table 2 Comparison between normal and developmental delayed children with respect to perinatal and physical examination variables
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Table 3 reveals the adjusted ORs for factors associated with the developmental delay. Boys, parity, advanced maternal age, multiple birth, cesarean section, neonatal injury, family heredity history, microcephaly, abnormal BMI at birth and at physical examination had a significant effect on developmental delay after controlling the potential confounding factors. Per capita GDP was protective factors for the children’s neuropsychological development.

Table 3 Factors associated with developmental delay according to multivariable logistic regression analysis
Full size table

Discussion

Adverse birth outcomes such as low birth weight and preterm have been reported to be associated with suboptimal developmental outcomes [19,20,21,22]. However, few large, population-based studies have assessed the association of perinatal factors and other variables at physical examination with developmental outcomes in newborns, infants, toddlers [23]. We assessed the association of pregnancy and neonatal factors and BMI at physical examination with overall neurodevelopment, gross-motor, fine-motor and language development. Our results show that the rate of overall developmental delay was 4.5%, while 12.5% of children had at least one developmental delay aspect, which is consistent with a previous study reporting that 5–17% of children suffered from developmental disabilities [8]. Preterm infants were more likely to have developmental delay. In this study, prematurity was a significant risk factor for language development in the univariate analysis, but in the multivariable logistic regression model, after taking into account the effects of confounding factors, it was no longer statistically significant. Our result is consistent with the report of Gurka et al. [24].

In the present study, boys had a higher rate of overall developmental delay, fine motor and language delay, which is consistent with previous studies [8, 25, 26]. According to the study by Whitehouse et al., a high level of testosterone in the male umbilical cord was a risk factor for speech developmental delay at the age of 1, 2 and 3 years old [27]. Research also suggests the effect of epigenetic mechanisms on sex differences in the human brain [28, 29]. In the study of Martínez-Nadal et al., cesarean delivery was associated with the risk of developmental delay [30], especially in the gross motor area, which is consistent with our study. Mehreen et al. also found the differences remained for gross-motor skills at the 12-month assessment between infants born by caesarean section and vaginally born [31]. However, in the study by de. Moura et al., cesarean section did not have a significant relationship with the developmental delay of children [26]. In the study of Kerstjens et al., cesarean section had a significant correlation with developmental delay in a univariate analysis but the significance disappeared after adjustment for confounding factors in multivariable analysis [32]. The cause of cesarean section and experimental design may account for different conclusions [33].

From early childhood through adolescence, higher family income tends to be associated with higher scores on assessments of language, memory, self-regulation, and social-emotional processing [34,35,36,37]. Early childhood poverty has been associated with differences in brain structure and function. The causal impact of a poverty reduction intervention on brain activity in the first year of life has been reported [38]. Such changes reflect neuroplasticity and environmental adaptation and display a pattern that has been associated with the development of subsequent cognitive skills [38]. According to the previous studies and our result, it suggests that economical advantage may be linked with differences in brain structure among children for their neurodevelopment.

In the current study, we found a significant association of mothers’ parity of more than three children with gross motor, fine motor and overall developmental delay, which is consistent with a recent study [39]. The mother’s parity of more than three children may be linked with socioeconomic status, which may limit adequate child care and nurturing. The findings suggest that parity was an independent risk factor for the children’s neurodevelopment. The association between advanced maternal age and neonatal outcomes remains controversial. In one study, advanced maternal age did not affect any short-term outcomes. However, at 2 years of corrected age, advanced maternal age was associated with a higher incidence of severe speech delay, even after controlling other confounding factors [40]. The statistically significant association between advanced maternal age and developmental delay (overall development and fine motor) was observed in our study in the multivariate (adjusted) model. The mechanism might be due to alterations in DNA methylation and changes in the expression of miRNAs regulating neuronal plasticity [41].

In the present study, the history of neonatal injury has a significant relationship with developmental delay. Infants with neonatal injury can have conditions like periventricular leukomalacia. Severe germinal matrix-intraventricular hemorrhage, and post-hemorrhagic hydrocephalus, which may directly affect developmental outcomes [42]. Also, neonatal birth injury also brings the risk of neurodevelopmental delay due to increased hospital stay [18]. In this study, family heredity history was associated with the risk of developmental delay. Family background has long been known to play an important role in influencing patterns of phenotypic expression in genetic syndromes and common diseases. The inheritance is complex because it involves multiple pairs of genes, as well as other environmental factors [43]. Many studies have suggested that shared molecular pathways could account for the multiple clinical signs that characterize neurodevelopmental disorders [44, 45]. The severity and variability of neurodevelopmental features is contingent upon family history of neuropsychiatric disease [46].

Microcephaly is a clinical finding and a crude but trusted assessment of intracranial brain volume. Microcephaly may develop at birth for developmental processes reducing in utero neuron generation or after birth for predominant dendritic or white matter diseases [47]. Some families showing autosomal dominant microcephaly have normal intelligence, psychometric evaluation of microcephalic children [48]. Children born with microcephaly associated with congenital Zika virus have a significant neurodevelopmental delay [49]. In our study, microcephaly was associated with the risk of gross motor and overall developmental delay. For its heterogeneous etiology, the family history of microcephaly needs further inquiry and increasingly genomic tests are available that allow an exact diagnosis.

In terms of multiple birth, twins are considered to be at an increased risk for neurodevelopmental impairments [50]. Triplet or higher-order births are associated with an increased risk of neurodevelopmental impairment [51, 52]. The area most at risk of delay is language. Twins had cognitive and neuropsychological outcomes that were otherwise comparable with singletons, but they had a slightly lower verbal intelligence quotient [53], which is consistent with our results.

Few studies have assessed the association between infants and toddlers’ BMI and developmental outcomes. In this study, abnormal BMI at birth and at physical examination was significantly associated with impaired gross motor, fine motor, language and overall development. This finding is consistent with the results of other studies. Previous studies showed that low birth weight, small-for-gestational-age or stunting newborns are associated with an increased risk of developmental delay in motor and behavioral evaluation [32, 54, 55]. Many children born with low BMI may have had chronic nutritional needs and oxygen during the fetal period. These chronic defects may alter the formation of neuronal connections and structure of the brain [32, 56]. It is estimated that approximately 25–50% of infants with low birth weight have brain abnormalities associated with cognitive, behavioral, attentional, and socialization impairment [56, 57].

Childhood obesity has also become a global concern. Obesity begins early in life and has been associated with impaired cognition [58]. Nutritional and syndromic obesity due to chromosomal or monogenic defects has attendant co-morbidities, which may include neurodevelopmental delays. Possible mechanisms include altered brain structure, leptin/insulin regulation, oxidative stress, cerebrovascular function, blood–brain barrier, inflammation, and decreased motor performance associated with a degraded musculoskeletal system [59]. Individualized nutrigenomic managements of obesity should be highlighted.

This study has three major strengths. First, it is the first large-scale cross-sectional study of development screening in China. Second, a large sample size enforces the precision of the study. Finally, all neuropsychological evaluation was conducted item by item with the animation demonstration and the doctors’ instruction to ensure the screening accuracy. This study is, however, also limited in several ways. First, children who participated in this study were not randomly selected so the potential for selection bias cannot be ruled out. Second, there may be greater subjectivity of self-reported information to assess perinatal and other risk factors. Additionally, the data on home environment, parental characteristics and individual-level economic factors were unavailable in the study. Third, as a cross-sectional study, the reliance on associations at a single time point make it inadequate for evaluating the causality between exposure and response variables. A cohort study of multiple time points is required for the association of perinatal factors with developmental delay.

Conclusions

This study reveals significant associations of perinatal factors and BMI with developmental delay among children aged 1–36 months in China, which may be crucial for early intervention. The Infant Toddler Growth Development Screening Test appeared to be a useful instrument to screen for abnormal growth and development in young children.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Shonkoff JP, Boyce WT, McEwen BS. Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. JAMA. 2009;301(21):2252–9.

    Article  Google Scholar 

  2. Walker SP, Wachs TD, Grantham-McGregor S, Black MM, Nelson CA, Huffman SL, et al. Inequality in early childhood: risk and protective factors for early child development. Lancet. 2011;378(9799):1325–38.

    Article  Google Scholar 

  3. Kamath SM, Venkatappa KG, Sparshadeep EM. Impact of Nutritional Status on Cognition in Institutionalized Orphans: A Pilot Study. J Clin Diagn Res. 2017;11(3):CC01-CC4.

    Google Scholar 

  4. Hein S, Reich J, Thuma PE, Grigorenko EL. Physical growth and nonverbal intelligence: associations in Zambia. J Pediatr. 2014;165(5):1017-23 e1.

    Article  Google Scholar 

  5. Ranabhat C, Kim CB, Park MB, Kim CS, Freidoony L. Determinants of Body Mass Index and Intelligence Quotient of Elementary School Children in Mountain Area of Nepal: An Explorative Study. Children (Basel). 2016;3(1):3.

    Google Scholar 

  6. Sudfeld CR, McCoy DC, Fink G, Muhihi A, Bellinger DC, Masanja H, et al. Malnutrition and Its Determinants Are Associated with Suboptimal Cognitive, Communication, and Motor Development in Tanzanian Children. J Nutr. 2015;145(12):2705–14.

    Article  Google Scholar 

  7. Poh BK, Lee ST, Yeo GS, Tang KC, Noor Afifah AR, SitiHanisa A, et al. Low socioeconomic status and severe obesity are linked to poor cognitive performance in Malaysian children. BMC Public Health. 2019;19(Suppl 4):541.

    Article  Google Scholar 

  8. Boyle CA, Boulet S, Schieve LA, Cohen RA, Blumberg SJ, Yeargin-Allsopp M, et al. Trends in the prevalence of developmental disabilities in US children, 1997–2008. Pediatrics. 2011;127(6):1034–42.

    Article  Google Scholar 

  9. Moody EJ, Reyes N, Ledbetter C, Wiggins L, DiGuiseppi C, Alexander A, et al. Screening for Autism with the SRS and SCQ: Variations across Demographic, Developmental and Behavioral Factors in Preschool Children. J Autism Dev Disord. 2017;47(11):3550–61.

    Article  Google Scholar 

  10. Zong XN, Li H, Zhu ZH. Secular trends in height and weight for healthy Han children aged 0–7 years in China, 1975–2005. Am J Hum Biol. 2011;23(2):209–15.

    Article  Google Scholar 

  11. Zhang YQ, Li H, Wu HH, Zong XN, Zhu ZH, Pan Y, et al. The 5th national survey on the physical growth and development of children in the nine cities of China: Anthropometric measurements of Chinese children under 7 years in 2015. Am J Phys Anthropol. 2017;163(3):497–509.

    Article  Google Scholar 

  12. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766–81.

    Article  Google Scholar 

  13. Huang X, Yang B, Liu Q, Zhang R, Tang S, Story M. Improving maternal and child nutrition in China: an analysis of nutrition policies and programs initiated during the 2000–2015 Millennium Development Goals era and implications for achieving the Sustainable Development Goals. J Health Popul Nutr. 2020;39(1):12.

    Article  Google Scholar 

  14. Wang Y, Li X, Zhou M, Luo S, Liang J, Liddell CA, et al. Under-5 mortality in 2851 Chinese counties, 1996–2012: a subnational assessment of achieving MDG 4 goals in China. Lancet. 2016;387(10015):273–83.

    Article  Google Scholar 

  15. Yu DM, Zhao LY, Yang ZY, Chang SY, Yu WT, Fang HY, et al. Comparison of Undernutrition Prevalence of Children under 5 Years in China between 2002 and 2013. Biomed Environ Sci. 2016;29(3):165–76.

    Google Scholar 

  16. Lu C, Black MM, Richter LM. Risk of poor development in young children in low-income and middle-income countries: an estimation and analysis at the global, regional, and country level. Lancet Glob Health. 2016;4(12):e916–22.

    Article  Google Scholar 

  17. Zhang Y, Kang L, Zhao J, Song PY, Jiang PF, Lu C. Assessing the Inequality of Early Child Development in China - A Population-Based Study. Lancet Reg Health West Pac. 2021;14:100221.

    Article  Google Scholar 

  18. Kaviani M, Ranjbaran Z, Janghorban R. Relationship between perinatal period problems and developmental delay in children aged 4–24 months. Acta Facultatis Medicae Naissensis. 2020;37:337–48.

    Article  Google Scholar 

  19. Oudgenoeg-Paz O, Mulder H, Jongmans MJ, van der Ham IJM, Van der Stigchel S. The link between motor and cognitive development in children born preterm and/or with low birth weight: A review of current evidence. Neurosci Biobehav Rev. 2017;80:382–93.

    Article  Google Scholar 

  20. Hack M, Klein NK, Taylor HG. Long-term developmental outcomes of low birth weight infants. Future Child. 1995;5(1):176–96.

    Article  Google Scholar 

  21. Halpern R, Barros AJ, Matijasevich A, Santos IS, Victora CG, Barros FC. Developmental status at age 12 months according to birth weight and family income: a comparison of two Brazilian birth cohorts. Cad Saude Publica. 2008;24(Suppl 3):S444–50.

    Article  Google Scholar 

  22. Larroque B, Ancel PY, Marret S, Marchand L, Andre M, Arnaud C, et al. Neurodevelopmental disabilities and special care of 5-year-old children born before 33 weeks of gestation (the EPIPAGE study): a longitudinal cohort study. Lancet. 2008;371(9615):813–20.

    Article  Google Scholar 

  23. Vaivada T, Gaffey MF, Bhutta ZA. Promoting Early Child Development With Interventions in Health and Nutrition: A Systematic Review. Pediatrics. 2017;140(2):e20164308.

  24. Gurka MJ, LoCasale-Crouch J, Blackman JA. Long-term cognition, achievement, socioemotional, and behavioral development of healthy late-preterm infants. Arch Pediatr Adolesc Med. 2010;164(6):525–32.

    Article  Google Scholar 

  25. Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2010. Morb Mortal Wkly Rep. 2014;63(2):1–21. https://www.cdc.gov/mmwr/preview/mmwrhtml/ss6302a1.htm.

  26. de Moura DR, Costa JC, Santos IS, Barros AJD, Matijasevich A, Halpern R, et al. Risk factors for suspected developmental delay at age 2 years in a Brazilian birth cohort. Paediatr Perinat Epidemiol. 2010;24(3):211–21.

    Article  Google Scholar 

  27. Whitehouse AJ, Mattes E, Maybery MT, Sawyer MG, Jacoby P, Keelan JA, et al. Sex-specific associations between umbilical cord blood testosterone levels and language delay in early childhood. J Child Psychol Psychiatry. 2012;53(7):726–34.

    Article  Google Scholar 

  28. Chung WC, Auger AP. Gender differences in neurodevelopment and epigenetics. Pflugers Arch. 2013;465(5):573–84.

    Article  Google Scholar 

  29. Turano A, Osborne BF, Schwarz JM. Sexual Differentiation and Sex Differences in Neural Development. Curr Top Behav Neurosci. 2019;43:69–110.

    Article  Google Scholar 

  30. Martínez-Nadal S, Demestre X, Schonhaut L, Muñoz SR, Sala P. Impact of neonatal morbidity on the risk of developmental delay in late preterm infants. Early Hum Dev. 2018;116:40–6.

    Article  Google Scholar 

  31. Zaigham M, Hellström-Westas L, Domellöf M, Andersson O. Prelabour caesarean section and neurodevelopmental outcome at 4 and 12 months of age: an observational study. BMC Pregnancy Childbirth. 2020;20(1):564.

    Article  Google Scholar 

  32. Kerstjens JM, de Winter AF, Sollie KM, Bocca-Tjeertes IF, Potijk MR, Reijneveld SA, et al. Maternal and pregnancy-related factors associated with developmental delay in moderately preterm-born children. Obstet Gynecol. 2013;121(4):727–33.

    Article  Google Scholar 

  33. Bear LM. Early identification of infants at risk for developmental disabilities. Pediatr Clin North Am. 2004;51(3):685–701.

    Article  Google Scholar 

  34. Noble KG, Norman MF, Farah MJ. Neurocognitive correlates of socioeconomic status in kindergarten children. Dev Sci. 2005;8(1):74–87.

    Article  Google Scholar 

  35. Noble KG, McCandliss BD, Farah MJ. Socioeconomic gradients predict individual differences in neurocognitive abilities. Dev Sci. 2007;10(4):464–80.

    Article  Google Scholar 

  36. Noble KG, Engelhardt LE, Brito NH, Mack LJ, Nail EJ, Angal J, et al. Socioeconomic disparities in neurocognitive development in the first two years of life. Dev Psychobiol. 2015;57(5):535–51.

    Article  Google Scholar 

  37. Farah MJ, Shera DM, Savage JH, Betancourt L, Giannetta JM, Brodsky NL, et al. Childhood poverty: specific associations with neurocognitive development. Brain Res. 2006;1110(1):166–74.

    Article  Google Scholar 

  38. Troller-Renfree SV, Costanzo MA, Duncan GJ, Magnuson K, Gennetian LA, Yoshikawa H, et al. The impact of a poverty reduction intervention on infant brain activity. Proc Natl Acad Sci U S A. 2022;119(5):e2115649119.

  39. Namazzi G, Hildenwall H, Mubiri P, Hanson C, Nalwadda C, Nampijja M, et al. Prevalence and associated factors of neurodevelopmental disability among infants in eastern Uganda: a population based study. BMC Pediatr. 2019;19(1):379.

    Article  Google Scholar 

  40. Tseng KT, Peng CC, Chang JH, Hsu CH, Lin CY, Jim WT, et al. The impact of advanced maternal age on the outcomes of very low birth weight preterm infants. Medicine (Baltimore). 2019;98(5):e14336.

    Article  Google Scholar 

  41. Krug A, Wöhr M, Seffer D, Rippberger H, Sungur A, Dietsche B, et al. Advanced paternal age as a risk factor for neurodevelopmental disorders: a translational study. Mol Autism. 2020;11(1):54.

    Article  Google Scholar 

  42. Volpe JJ. The encephalopathy of prematurity–brain injury and impaired brain development inextricably intertwined. Semin Pediatr Neurol. 2009;16(4):167–78.

    Article  Google Scholar 

  43. Dauncey MJ, Bicknell RJ. Nutrition and neurodevelopment: mechanisms of developmental dysfunction and disease in later life. Nutr Res Rev. 1999;12(2):231–53.

    Article  Google Scholar 

  44. Cristino AS, Williams SM, Hawi Z, An JY, Bellgrove MA, Schwartz CE, et al. Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system. Mol Psychiatry. 2014;19(3):294–301.

    Article  Google Scholar 

  45. Hormozdiari F, Penn O, Borenstein E, Eichler EE. The discovery of integrated gene networks for autism and related disorders. Genome Res. 2015;25(1):142–54.

    Article  Google Scholar 

  46. Polyak A, Rosenfeld J, Girirajan S. An assessment of sex bias in neurodevelopmental disorders. Genome Med. 2015;7:94.

  47. Woods CG, Parker A. Investigating microcephaly. Arch Dis Child. 2013;98(9):707–13.

    Article  Google Scholar 

  48. Rossi LN, Candini G, Scarlatti G, Rossi G, Prina E, Alberti S. Autosomal dominant microcephaly without mental retardation. Am J Dis Child. 1987;141(6):655–9.

    Google Scholar 

  49. Alves LV, Paredes CE, Silva GC, Mello JG, Alves JG. Neurodevelopment of 24 children born in Brazil with congenital Zika syndrome in 2015: a case series study. BMJ Open. 2018;8(7):e021304.

    Article  Google Scholar 

  50. Lorenz JM. Neurodevelopmental outcomes of twins. Semin Perinatol. 2012;36(3):201–12.

    Article  Google Scholar 

  51. Wadhawan R, Oh W, Vohr BR, Wrage L, Das A, Bell EF, et al. Neurodevelopmental outcomes of triplets or higher-order extremely low birth weight infants. Pediatrics. 2011;127(3):e654–60.

    Article  Google Scholar 

  52. Mogford-Bevan K. Developmental language impairments with complex origins: learning from twins and multiple birth children. Folia Phoniatr Logop. 2000;52(1–3):74–82.

    Article  Google Scholar 

  53. Ylijoki M, Haataja L, Lind A, Ekholm E, Lehtonen L. Neurodevelopmental outcome of preterm twins at 5 years of age. Pediatr Res. 2020;87(6):1072–80.

    Article  Google Scholar 

  54. Silveira MF, Victora CG, Horta BL, da Silva BGC, Matijasevich A, Barros FC, et al. Low birthweight and preterm birth: trends and inequalities in four population-based birth cohorts in Pelotas, Brazil, 1982–2015. Int J Epidemiol. 2019;48(Suppl 1):i46–53.

    Article  Google Scholar 

  55. Caravale B, Mirante N, Vagnoni C, Vicari S. Change in cognitive abilities over time during preschool age in low risk preterm children. Early Hum Dev. 2012;88(6):363–7.

    Article  Google Scholar 

  56. Sripada K, Bjuland KJ, Sølsnes AE, Håberg AK, Grunewaldt KH, Løhaugen GC, et al. Trajectories of brain development in school-age children born preterm with very low birth weight. Sci Rep. 2018;8(1):15553.

    Article  Google Scholar 

  57. Bayless S, Stevenson J. Executive functions in school-age children born very prematurely. Early Hum Dev. 2007;83(4):247–54.

    Article  Google Scholar 

  58. John CC, Black MM, Nelson CA 3rd. Neurodevelopment: The Impact of Nutrition and Inflammation During Early to Middle Childhood in Low-Resource Settings. Pediatrics. 2017;139(Suppl 1):S59–71.

    Article  Google Scholar 

  59. Wang C, Chan JS, Ren L, Yan JH. Obesity Reduces Cognitive and Motor Functions across the Lifespan. Neural Plast. 2016;2016:2473081.

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to all the parents for their assistance and cooperation in this study.

Funding

The authors gratefully acknowledge the Project of Shanghai Children’s Health Service Capacity Construction (GDEK201708). The sponsor or funding organizations had no role in the design or conduct of this research.

Author information

Authors and Affiliations

  1. Department of Developmental and Behavioral Pediatrics, Shanghai Children’s Medical Center, Shanghai Jiaotong University School of Medicine, 1678 Dongfang Road, Shanghai, 200127, People’s Republic of China

    You Yang & Xingming Jin

  2. Department of Pediatrics, Shanghai Fengxian District Hospital of Traditional Chinese Medicine, Shanghai, People’s Republic of China

    Lei Shi

  3. Department of Clinical Epidemiology and Biostatistics, Shanghai Children’s Medical Center, Shanghai Jiaotong University School of Medicine, 1678 Dongfang Road, Shanghai, 200127, People’s Republic of China

    Shilu Tong

  4. School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, People’s Republic of China

    Shilu Tong

  5. Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China

    Shilu Tong

  6. School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia

    Shilu Tong

Authors
  1. You Yang
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  2. Lei Shi
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  3. Xingming Jin
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  4. Shilu Tong
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Contributions

X-MJ developed the project. YY analyzed the data and wrote the manuscript. LS collected the data. S-LT revised the manuscript and interpreted the data. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xingming Jin or Shilu Tong.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Ethics Committee of Shanghai Children’s Medical Center. The objectives and protocol of the study were explained to the students and their parents. A waiver of written informed consent was granted by the Ethics Committee of Shanghai Children’s Medical Center for this study. No unique identifier for the children was collected. Confidentiality of data collected was maintained throughout the study period.

Consent for publication

Not applicable.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Yang, Y., Shi, L., Jin, X. et al. Association of perinatal factors with suspected developmental delay in urban children aged 1–36 months - a large-scale cross-sectional study in China. BMC Pediatr 23, 11 (2023). https://doi.org/10.1186/s12887-022-03819-9

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  • DOI: https://doi.org/10.1186/s12887-022-03819-9

Keywords

BMC Pediatrics

ISSN: 1471-2431

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