Faculty Bibliography

OBJECTIVE:Feeding concerns are common in the first 2 years of life and typically reflect maternal perceptions occurring within the larger context of the parent-child relationship. We aimed to determine whether (1) maternal depressive systems predicted perceived picky eating, mediated by maternal negative perceptions; (2) receipt of the Video Interaction Project (VIP) parenting intervention impacted perceived picky eating through this pathway; and (3) perceived picky eating was associated with child growth or subsequent dietary patterns. METHODS:We performed a partial longitudinal analysis of 187 low-income, predominantly Hispanic mother-child dyads enrolled in a randomized controlled trial of the VIP. Mother-infant dyads were enrolled postpartum in an urban public hospital. Participants randomized to the VIP met with an interventionist on days of well-child visits; sessions were designed to facilitate interactions in play and shared reading through provision of learning materials and review of videotaped parent-child interaction; the curriculum did not contain feeding-specific elements. We used structural equation modeling to determine direct, indirect, and total effects of maternal depressive symptoms, maternal negative perceptions, and the VIP on perceived picky eating. We then tested associations between perceived picky eating and (1) child growth, using multivariable linear regression and multilevel modeling; and (2) subsequent child dietary consumption, using multivariable multinomial logistic regression. RESULTS:Maternal depressive symptoms had significant total effects on negative maternal perceptions (β = 0.32, p < 0.001) and perceived picky eating (β = 0.21, p < 0.01) after controlling for potential confounders. This effect was partially mediated by maternal negative perceptions (indirect effect: β = 0.06, p = 0.04). When used in the model as the predictor, the VIP had a significant total effect on perceived picky eating (β = -0.16, p = 0.02), which was partially mediated by maternal depressive symptoms and negative perceptions (indirect effect: β = -0.05, p = 0.02). Perceived picky eating was not associated with child diet at age 2 years or adiposity from 6 months to 3 years. CONCLUSION/CONCLUSIONS:Maternal concerns about picky eating may reflect deeper depressive symptoms and negative perceptions of her child's behavior. Interventions designed to facilitate positive parenting in general may lessen feeding-specific concerns, such as picky eating. Although reassurance about growth and nutritional outcomes for children perceived as picky eaters is appropriate, clinicians should also consider probing for underlying symptoms of depression that could lead to eating concerns.

The goal of this study was to assess biomarkers of exposure to glyphosate and assess potential associations with renal function in children. Glyphosate is used ubiquitously in agriculture worldwide. While previous studies have indicated that glyphosate may have nephrotoxic effects, few have examined potential effects on kidney function in children. We leveraged three cohorts across different phases of child development and measured urinary levels of glyphosate. We evaluated associations of glyphosate with three biomarkers of kidney injury: albuminuria (ACR), neutrophil gelatinase-associated lipocalin (NGAL), and kidney injury marker 1 (KIM-1). Multivariable regression analyses examined associations of glyphosate with kidney injury biomarkers controlling for covariates. We identified glyphosate in 11.1% of the total participants. The herbicide was detected more frequently in the neonate population (30%). Multivariable regression models failed to identify significant associations of log-transformed glyphosate with any of the kidney injury biomarkers, controlling for covariates age, sex, and maternal education. While we confirm detectability of glyphosate in children's urine at various ages and stages of life, there is no evidence in this study for renal injury in children exposed to low levels of glyphosate. Further studies of larger sample size are indicated to better understand putative deleterious effects of the herbicide after different levels of exposure.

INTRODUCTION/BACKGROUND:Material hardships, defined as difficulty meeting basic needs, are associated with adverse child health outcomes, including suboptimal health care utilization. Children with special health care needs (CSHCN) may be more vulnerable to the effects of hardships. Our objective was to determine associations between material hardships and health care utilization among CSHCN. METHODS:We conducted a cross-sectional study surveying caregivers of 2-12 year old CSHCN in a low-income, urban area. Independent variables were parent-reported material hardships: difficulty paying bills, food insecurity, housing insecurity, and health care hardship. Dependent variables were parent-reported number of emergency department (ED) visits, any hospital admission and any unmet health care need. We used negative binomial and logistic regression to assess for associations between each hardship and each outcome. RESULTS:205 caregivers were surveyed between July 2017 and May 2018, and data analyzed in 2018. After adjustment, difficulty paying bills (IRR 1.51, 95% CI 1.08, 2.12) and health care hardship (IRR 1.72, 95% CI 1.08, 2.75) were associated with higher rate of ED visits. There were no associations between hardships and hospital admission. Difficulty paying bills (AOR 2.13, 95% CI 1.14, 3.98), food insecurity (AOR 1.95, 95% CI 1.02, 3.71), and housing insecurity (AOR 2.71, 95% CI 1.36, 5.40) were associated with higher odds of unmet health care need. CONCLUSIONS:Material hardships were associated with higher rate of ED visits and greater unmet health care need among low-income CSHCN. Future examination of the mechanisms of these associations is needed to enhance support for families of CSHCN.

BACKGROUND:Low-income Hispanic women are at-risk of poor prenatal diet quality. Correlates associated with prenatal diet quality in this group of women are understudied. OBJECTIVE:The objective of this study was to examine the associations between financial, cultural, psychosocial, and lifestyle correlates and prenatal diet quality in low-income Hispanic women. DESIGN/METHODS:This cross-sectional analysis used data from pregnant women enrolled in the Starting Early Trial, a randomized-controlled trial of a primary-care based child obesity prevention program beginning in pregnancy. The trial enrolled women from clinics affiliated with a large urban medical center in New York City from 2012 to 2014. Financial, cultural, psychosocial, and lifestyle variables were collected using a comprehensive baseline questionnaire. Usual dietary intakes over the past year were assessed using the Block Food Frequency Questionnaire 2005 bilingual version. PARTICIPANTS/METHODS:The study enrolled low-income Hispanic women between 28 and 32 gestational weeks (N=519). MAIN OUTCOME MEASURES/METHODS:Prenatal diet quality was measured by the Healthy Eating Index 2015. STATISTICAL ANALYSES PERFORMED/METHODS:Unadjusted and adjusted multivariable linear regression analyses were performed to determine independent associations between financial, cultural, psychosocial, and lifestyle correlates and Healthy Eating Index 2015 total score. RESULTS:Overall prenatal diet quality was poor (mean Healthy Eating Index 2015 total score=69.0±9.4). Most women did not meet the maximum score for total vegetables (65.3%), whole grains (97.1%), dairy (74.8%), fatty acids (84.4%), refined grains (79.8%), sodium (97.5%), saturated fats (92.9%), and added sugars (66.5%). Women who reported screen time ≤2 hours/day, physical activity before and/or during pregnancy, and being born outside the United States had higher mean Healthy Eating Index 2015 total score than women with screen time >2 hours/day, no physical activity, and those born in the United States. CONCLUSIONS:Prenatal diet quality of low-income pregnant Hispanic women was suboptimal. This cross-sectional study revealed associations between cultural and lifestyle factors and prenatal diet quality in low-income Hispanic women. Longitudinal studies are needed to determine long-term influences and specific behaviors to target for effective intervention studies.

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 AND OBJECTIVES/OBJECTIVE:Pregnancy, infancy, and toddlerhood are sensitive times in which families are particularly vulnerable to household food insecurity and when disparities in child obesity emerge. Understanding obesity-promoting infant-feeding beliefs, styles, and practices in the context of food insecurity could better inform both food insecurity and child obesity prevention interventions and policy guidelines. METHODS:= 100) with infants in the first 2 years of life, all of whom were participants in a randomized controlled trial of an early child obesity prevention intervention called the Starting Early Program. Bilingual English-Spanish interviewers conducted semistructured qualitative interviews, which were audio recorded, transcribed, and translated. By using the constant comparative method, transcripts were coded through an iterative process of textual analysis until thematic saturation was reached. RESULTS:Three key themes emerged: (1) contributors to financial strain included difficulty meeting basic needs, job instability, and high vulnerability specific to pregnancy, infancy, and immigration status; (2) effects on infant feeding included decreased breastfeeding due to perceived poor maternal diet, high stress, and limiting of healthy foods; and (3) coping strategies included both home- and community-level strategies. CONCLUSIONS:Stakeholders in programs and policies to prevent poverty-related disparities in child obesity should consider and address the broader context by which food insecurity is associated with contributing beliefs, styles, and practices. Potential strategies include addressing misconceptions about maternal diet and breast milk adequacy, stress management, building social support networks, and connecting to supplemental nutrition assistance programs.

Adolescent depression causes morbidity and is underdiagnosed. It is unclear how mental health screening and integrated mental health practitioners change adolescent depression identification. We conducted a retrospective primary care network natural cohort study where 10 out of 19 practices implemented mental health screening, followed by the remaining 9 practices implementing mental health screening with less coaching and support. Afterward, a different subset of 8 practices implemented integrated mental health practitioners. Percentages of depression-coded adolescent visits were compared between practices (1) with and without mental health screening and (2) with and without integrated mental health practitioners, using difference-in-differences analyses. The incidence of depression-coded visits increased more in practices that performed mental health screening (ratio of odds ratios = 1.22; 95% confidence interval =1.00-1.49) and more in practices with integrated mental health practitioners (ratio of odds ratios = 1.58; 95% confidence interval = 1.30-1.93). Adolescent mental health screening and integrated mental health practitioners increase depression-coded visits in primary care.

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.].