Weight gain in pregnancy and child weight status from birth to adulthood in the United States.

BACKGROUND: High weight gain in pregnancy has been associated with child adiposity, but few studies have assessed the relationship across childhood or in racially/ethnically diverse populations. OBJECTIVES: The objectives of the study are to test if weight gain in pregnancy is associated with high birthweight and overweight/obesity in early, middle and late childhood and whether these associations differ by maternal race/ethnicity. METHODS: Mother-child dyads (n = 7539) were included from the National Longitudinal Survey of Youth 1979, a nationally representative cohort study in the USA (1979-2012). Log-binomial regression models were used to analyse associations between weight gain and the outcomes: high birthweight (>4000 g) and overweight/obesity at ages 2-5, 6-11 and 12-19 years. RESULTS: Excessive weight gain was positively associated, and inadequate weight gain was negatively associated with high birthweight after confounder adjustment (P < 0.05). Only excessive weight gain was associated with overweight in early, middle and late childhood. These associations were not significant in Hispanics or Blacks although racial/ethnic interaction was only significant ages 12-19 years (P = 0.03). CONCLUSIONS: Helping pregnant women gain weight within national recommendations may aid in preventing overweight and obesity across childhood, particularly for non-Hispanic White mothers.

Misclassified group-tested current status data.

Group testing, introduced by Dorfman (1943), has been used to reduce costs when estimating the prevalence of a binary characteristic based on a screening test of [Formula: see text] groups that include [Formula: see text] independent individuals in total. If the unknown prevalence is low and the screening test suffers from misclassification, it is also possible to obtain more precise prevalence estimates than those obtained from testing all [Formula: see text] samples separately (Tu et al., 1994). In some applications, the individual binary response corresponds to whether an underlying time-to-event variable [Formula: see text] is less than an observed screening time [Formula: see text], a data structure known as current status data. Given sufficient variation in the observed [Formula: see text] values, it is possible to estimate the distribution function [Formula: see text] of [Formula: see text] nonparametrically, at least at some points in its support, using the pool-adjacent-violators algorithm (Ayer et al., 1955). Here, we consider nonparametric estimation of [Formula: see text] based on group-tested current status data for groups of size [Formula: see text] where the group tests positive if and only if any individual's unobserved [Formula: see text] is less than the corresponding observed [Formula: see text]. We investigate the performance of the group-based estimator as compared to the individual test nonparametric maximum likelihood estimator, and show that the former can be more precise in the presence of misclassification for low values of [Formula: see text]. Potential applications include testing for the presence of various diseases in pooled samples where interest focuses on the age-at-incidence distribution rather than overall prevalence. We apply this estimator to the age-at-incidence curve for hepatitis C infection in a sample of U.S. women who gave birth to a child in 2014, where group assignment is done at random and based on maternal age. We discuss connections to other work in the literature, as well as potential extensions.