Association Between Family Income and Positive Developmental Screening Using the Infant Toddler Checklist at the 18-Month Health Supervision Visit.

OBJECTIVE: To examine the associations between several potential predictors (child biologic, social, and family factors) and a positive screen for developmental delay using the Infant Toddler Checklist (ITC) at the 18-month health supervision visit in primary care. METHODS: This was a cross-sectional study of healthy children attending an 18-month health supervision visit in primary care. Parents completed a standardized questionnaire, addressing child, social, and family characteristics, and the ITC. Logistic regression analyses were used to assess the associations between predictors and a positive ITC. RESULTS: Among 2188 participants (45.5% female; mean age, 18.2 months), 285 (13%) had a positive ITC and 1903 (87%) had a negative ITC. The aOR for a positive ITC for male compared with female sex was 2.15 (95% CI, 1.63-2.83; P < .001). The aOR for birthweight was 0.65 per 1 kg increase (95% CI, 0.53-0.80; P < .001). The aOR for a family income of <$40,000 compared with ≥$150,000 was 3.50 (95% CI, 2.22-5.53; P < .001), and the aOR for family income between $40,000-$79,999 compared with ≥$150,000 was 1.88 (95% CI, 1.26-2.80; P = .002). CONCLUSIONS: Screening positive on the ITC may identify children at risk for the double jeopardy of developmental delay and social disadvantage and allow clinicians to intervene through monitoring, referral, and resource navigation for both child development and social needs. TRIAL REGISTRATION: Clinicaltrials.gov (NCT01869530).

A novel systematic pipeline for increased predictability and explainability of growth patterns in children using trajectory features.

OBJECTIVE: Longitudinal patterns of growth in early childhood are associated with health conditions throughout life. Knowledge of such patterns and the ability to predict them can lead to better prevention and improved health promotion in adulthood. However, growth analyses are characterized by significant variability, and pattern detection is affected by the method applied. Moreover, pattern labelling is typically performed based on ad hoc methods, such as visualizations or clinical experience. Here, we propose a novel pipeline using features extracted from growth trajectories using mathematical, statistical and machine-learning approaches to predict growth patterns and label them in a systematic and unequivocal manner. METHODS: We extracted mathematical and clinical features from 9577 children growth trajectories embedded with machine-learning predictions of the growth patterns. We experimented with two sets of features (CAnonical Time-series Characteristics and trajectory features specific to growth), developmental periods and six machine-learning classifiers. Clinical experts provided labels for the detected patterns and decision rules were created to associate the features with the labelled patterns. The predictive capacity of the extracted features was validated on two heterogenous populations (The Applied Research Group for Kids and the 2004 Pelotas Birth Cohort, based in Canada and Brazil, respectively). RESULTS: Features predictive ability measured by accuracy and F1 score was ≥ 80% and ≥ 0.76 respectively in both cohorts. A small number of features (n = 74) was sufficient to distinguish between growth patterns in both cohorts. Slope, intercept of the trajectory, age at peak value, start value and change of the growth measure were among the top identified features. CONCLUSION: Growth features can be reliably used as predictors of growth patterns and provide an unbiased understanding of growth patterns. They can be used as tool to reduce the effort to repeat analysis and variability concerning anthropometric measures, time points and analytical methods, in the context of the same or similar populations.

Timing of Introduction to Solid Food, Growth, and Nutrition Risk in Later Childhood.

OBJECTIVE: To evaluate the relationship between the timing of infant cereal introduction between 4 and 6 months of age and growth and dietary intake in later childhood. STUDY DESIGN: A longitudinal cohort study was conducted among healthy children 0-10 years of age participating in The Applied Research Group for Kids cohort study between June 2008 and August 2019 in Toronto, Canada. RESULTS: Of 8943 children included, the mean (SD) age of infant cereal introduction was 5.7 (2.1) months. In the primary analysis, children who were introduced to infant cereal at 4 vs 6 months had 0.17 greater body mass index z score (95% CI 0.06-0.28; P = .002) and greater odds of obesity (OR 1.82; 95% CI 1.18-2.80; P = .006) at 10 years of age. In the secondary analysis, children who were introduced to infant cereal at 4 vs 6 months had 0.09 greater height-for-age z score (95% CI 0.04-0.15; P = .002) at 1 year of age, an association that was not observed at 5 or 10 years of age. Children who were introduced to infant cereal at 4 vs 6 months had greater nutrition risk which was primarily determined by a less-favorable eating behavior score at 18 months to 5 years of age (0.18 units higher; 95% CI 0.07-0.29; P = .001). CONCLUSIONS: Introduction of infant cereal at 4 vs 6 months was associated with greater body mass index z score, greater odds of obesity, similar height-for-age z score, and less favorable eating behavior. These findings support recommendations for introducing solid food around 6 months of age.