Biopsychosocial predictors of rapid weight gain from birth to 6 months.

Background: Childhood obesity remains a public health crisis and identification of unique prenatal and early infancy predictors of obesity risk are critically needed.

Objectives: We test a comprehensive biopsychosocial model of the predictors of rapid weight gain (RWG) in the first 6 months of life.

Methods: Two hundred and ninety nine pregnant women and their infants participated.

A causal machine-learning framework for studying policy impact on air pollution: A case-study in COVID-19 lockdowns.

When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies and opening or closing an industrial facility, careful statistical analysis is needed to separate causal changes from other confounding factors. Using COVID-19 lockdowns as a case-study, we present a comprehensive framework for estimating and validating causal changes from such perturbations. We propose using flexible machine learning-based comparative interrupted time series (CITS) models for estimating such a causal effect.

Interactions between poor sleep and negative social cognition pose risk for lower maternal sensitivity.

The extent to which mother and infant sleep predict maternal sensitivity as (a) main effects and (b) moderate the association between social cognition about infant crying (i.e., cry processing) and maternal sensitivity was examined in a sample of 299 mother-infant dyads (43% of mothers non-White; 50.

Evaluation of Calibration Approaches for Indoor Deployments of PurpleAir Monitors.

Low-cost air quality monitors are growing in popularity among both researchers and community members to understand variability in pollutant concentrations. Several studies have produced calibration approaches for these sensors for ambient air. These calibrations have been shown to depend primarily on relative humidity, particle size distribution, and particle composition, which may be different in indoor environments.

Racial Disparities in Neighborhood and Household Socioeconomic Disadvantage Predict Postpartum Weight Retention.

Introduction: Structural racism leads to neighborhood-level socioeconomic disadvantage, which determines adverse birth outcomes. Individual socioeconomic disadvantage is associated with compromised healthy pregnancy outcomes. This study aimed to investigate the pathways by which race, neighborhood socioeconomic disadvantage, and household socioeconomic disadvantage predict subsequent maternal postpartum weight retention.