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Infant and early childhood physical health assessments in the HEALthy Brain and Child Development (HBCD) Study.

ABSTRACT: The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social and emotional development beginning prenatally and planned through early childhood. Many prenatal and early childhood exposures impact both later physical health and development. Moreover, early deficits in physical health, such as growth and vision, are associated with differences in brain development, language and cognitive functioning. For these reasons, the HBCD Study includes measures of early childhood physical health, many of which have clinical relevance, and are applicable for use as both predictors and outcomes. Study measures assess a broad range of physical health domains and include both objective measurement of child growth and health and subjective caregiver report of behaviors and attitudes about constructs known to influence growth and physical development. Lastly, we obtain caregiver report of the child's routine medical care as well as acute and chronic medical issues. We anticipate that these data will contextualize the impact of child physical growth and health on child brain development and function. In this report we present the rationale for each domain and an overview of the physical health measures included in the current HBCD Study protocol.

SUBMITTER: Cioffredi LA

PROVIDER: S-EPMC11315127 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Infant and early childhood physical health assessments in the HEALthy Brain and Child Development (HBCD) Study.

Cioffredi Leigh-Anne LA Garner Bailey B Maxwell Jessie R JR Merhar Stephanie S Peralta-Carcelen Myriam M Scott Lisa S LS Sisodia Misha M DeMauro Sara B SB

Developmental cognitive neuroscience 20240714

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social and emotional development beginning prenatally and planned through early childhood. Many prenatal and early childhood exposures impact both later physical health and development. Moreover, early deficits in physical health, such as growth and vision, are associated with differences in brain development, language and cognitive functionin ...[more]

PMID: 39032415

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Project description:BackgroundA major challenge in prenatal drug exposure research concerns the balance of measurement quality with sample sizes necessary to address confounders. To inform the selection of optimal exposure measures for the HEALthy Brain and Child Development (HBCD) Study, we employed integrated analysis to determine how different methods used to characterize prenatal tobacco exposure influence the detection of exposure-related risk, as reflected in normal variations in birth weight.MethodsParticipants were N = 2323 mother-infant dyads derived from 7 independent developmental cohorts harmonized on measures of exposure, outcome (birthweight), and covariates. We compared estimates of PTE-related effects on birthweight derived from linear regression models when PTE was categorized dichotomously based on any fetal exposure (30% exposed; 69% not exposed); versus categorically, based on common patterns of maternal smoking during pregnancy (never smoked 69%; quit smoking 16%; smoked intermittently 2%; smoked persistently 13%). We secondarily explored sex differences in PTE-birthweight associations across these categorization methods.ResultsWhen PTE was categorized dichotomously, exposure was associated with a - 125-g difference in birthweight (95% C.I. -173.7 - -76.6, p < .0001). When PTE was characterized categorically based on maternal smoking patterns, however, exposure was associated with either no difference in birthweight if mothers quit smoking by the end of the first trimester (B = -30.6, 95% C.I. -88.7-27.4, p = .30); or a - 221.8 g difference in birthweight if mothers did not [95% C.I. (-161.7 to -282.0); p < .001]. Qualitative sex differences were also detected though PTE x sex interactions did not reach statistical significance. Maternal smoking cessation during pregnancy was associated with a 239.3 g increase in birthweight for male infants, and a 114.0 g increase in birthweight for females infants (p = .07).ConclusionsCategorization of PTE based on patterns of maternal smoking rather than the presence or absence of exposure alone revealed striking nuances in estimates of exposure-related risk. The described method that captures both between-individual and within-individual variability in prenatal drug exposure is optimal and recommended for future developmental investigations such as the HBCD Study.

Project description:BackgroundEarly identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. This study aims to disentangle the relative timing and importance of early markers of asthma.MethodsUsing the CHILD Cohort Study, 132 variables measured in 1754 multi-ethnic children were included in the analysis for asthma prediction. Data up to 4 years of age was used in multiple machine learning models to predict physician-diagnosed asthma at age 5 years. Both predictive performance and variable importance was assessed in these models.ResultsEarly-life data (≤1 year) has limited predictive ability for physician-diagnosed asthma at age 5 years (area under the precision-recall curve (AUPRC) < 0.35). The earliest reliable prediction of asthma is achieved at age 3 years, (area under the receiver-operator curve (AUROC) > 0.90) and (AUPRC > 0.80). Maternal asthma, antibiotic exposure, and lower respiratory tract infections remained highly predictive throughout childhood. Wheezing status and atopy are the most important predictors of early childhood asthma from among the factors included in this study.ConclusionsChildhood asthma is predictable from non-biological measurements from the age of 3 years, primarily using parental asthma and patient history of wheezing, atopy, antibiotic exposure, and lower respiratory tract infections.ImpactMachine learning models can predict physician-diagnosed asthma in early childhood (AUROC > 0.90 and AUPRC > 0.80) using ≥3 years of non-biological and non-genetic information, whereas prediction with the same patient information available before 1 year of age is challenging. Wheezing, atopy, antibiotic exposure, lower respiratory tract infections, and the child's mother having asthma were the strongest early markers of 5-year asthma diagnosis, suggesting an opportunity for earlier diagnosis and intervention and focused assessment of patients at risk for asthma, with an evolving risk stratification over time.

Project description:Data from the 2016-17 Multiple Indicator Cluster Survey from Nigeria are used to study the relationship between child stature, mother's years of education, and indicators of early childhood development (ECD). The relationships are contrasted between two empirical approaches: the conventional approach whereby control variables are selected in an ad-hoc manner, and the double machine-learning (DML) approach that employs data-driven methods to select controls from a much wider set of variables and thus reducing potential omitted variable bias. Overall, the analysis confirms that maternal education and the incidence of chronic malnutrition have a significant direct effect on measures of early childhood development. The point estimates based on the ad-hoc specification tend to be larger in absolute value than those based on the DML specification. Frequently, the point estimates based on the ad-hoc specification fall inside the confidence interval of the DML point estimates, suggesting that in these cases the omitted variable bias is not serious enough to prevent making causal inferences based on the ad-hoc specification. However, there are instances where the omitted variable bias is sufficiently large for the ad hoc specification to yield a statistically significant relationship when in fact the more robust DML specification suggests there is none. The DML approach also reveals a more complex picture that highlights the role of context. In rural areas, mother's education affects early childhood development both directly and indirectly through its impact on the nutritional status of both older and younger children. In contrast, in urban areas, where the average level of maternal education is much higher, increases in a mother's education have only a direct effect on child ECD measures but no indirect effect through child nutrition. Thus, DML provides a practical and feasible approach to reducing threats to internal validity for robust inferences and policy design based on observational data.

Dataset Information

Infant and early childhood physical health assessments in the HEALthy Brain and Child Development (HBCD) Study.

Publications

Infant and early childhood physical health assessments in the HEALthy Brain and Child Development (HBCD) Study.

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