Faculty Bibliography

OBJECTIVE:To determine whether prenatal social support was associated with infant adiposity in the first 18 months of life in a low-income, Hispanic sample, known to be at high risk of early child obesity. STUDY DESIGN/METHODS:We performed a longitudinal analysis of 262 low-income, Hispanic mother-infant pairs in the control group of the Starting Early child obesity prevention trial. Prenatal social support was measured using an item from the Maternal Social Support Index. We used multilevel modeling to predict weight-for-length z-score trajectories from birth to age 18 months and logistic regression to predict macrosomia and overweight status at ages 6, 12, and 18 months. RESULTS:High prenatal social support was independently associated with lower infant adiposity trajectories from birth to age 18 months (B = -0.40; 95% CI, -0.63 to -0.16), a lower odds of macrosomia (aOR = 0.35; 95% CI, 0.15-0.80), and a lower odds of overweight at ages 12 (aOR = 0.28; 95% CI, 0.10-0.74) and 18 months (aOR = 0.35; 95% CI, 0.14-0.89). Prenatal social support was not significantly associated with overweight status at age 6 months. CONCLUSIONS:Prenatal social support may protect against excessive infant adiposity and overweight in low-income, Hispanic families. Further research is needed to elucidate mechanisms underlying these associations and to inform preventive strategies beginning in pregnancy.

BACKGROUND:Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. The ability to predict obesity before age five could be a useful tool, allowing prevention strategies to focus on high risk children. The few existing prediction models for obesity in childhood have primarily employed data from longitudinal cohort studies, relying on difficult to collect data that are not readily available to all practitioners. Instead, we utilized real-world unaugmented electronic health record (EHR) data from the first two years of life to predict obesity status at age five, an approach not yet taken in pediatric obesity research. METHODS AND FINDINGS/RESULTS:We trained a variety of machine learning algorithms to perform both binary classification and regression. Following previous studies demonstrating different obesity determinants for boys and girls, we similarly developed separate models for both groups. In each of the separate models for boys and girls we found that weight for length z-score, BMI between 19 and 24 months, and the last BMI measure recorded before age two were the most important features for prediction. The best performing models were able to predict obesity with an Area Under the Receiver Operator Characteristic Curve (AUC) of 81.7% for girls and 76.1% for boys. CONCLUSIONS:We were able to predict obesity at age five using EHR data with an AUC comparable to cohort-based studies, reducing the need for investment in additional data collection. Our results suggest that machine learning approaches for predicting future childhood obesity using EHR data could improve the ability of clinicians and researchers to drive future policy, intervention design, and the decision-making process in a clinical setting.

[This corrects the article DOI: 10.1371/journal.pone.0215571.].

OBJECTIVES: Childhood obesity is rarely identified in hospitalized pediatric patients despite the high prevalence of obesity and potential for associated morbidity. The purpose of this study was to identify specific patient characteristics associated with the documentation of obesity and related weight management recommendations in the inpatient setting. METHODS: Retrospective chart review was conducted on all pediatric patients ages 2 to 18 years old and discharged between January 1, 2012, and December 31, 2014, to determine the following: (1) if obesity was noted in the clinical documentation of those with a BMI >/=95th percentile; and (2) if those with documented obesity had evidence of an obesity-specific management plan. Using chi(2) and multivariable logistic regression, we determined patient characteristics associated with the documentation of obesity and presence of a management plan. RESULTS: Only 26% (214 of 809) of inpatients with obesity had documentation of weight status. The odds of obesity documentation were higher in patients with comorbid cholelithiasis, severe obesity, and older age. Of those with obesity documentation, 23% (49 of 214) had an obesity management plan. Comorbid sleep apnea and admission to a surgical service with a pediatric hospital medicine consult were significantly associated with the presence of an obesity management plan. CONCLUSIONS: Increased efforts are necessary to improve obesity diagnosis and management in younger children who have not yet developed comorbidities. Additionally, the role of pediatric hospitalists as consultants for surgical patients should be further explored as a tool for addressing obesity during inpatient hospitalization.