The Association Between Prenatal Food Insecurity and Breastfeeding Initiation and Exclusive Breastfeeding Duration: A Longitudinal Study Using Oregon PRAMS and PRAMS-2, 2008-2015.

Background: In the United States, 11.1% of households experience food insecurity; however, pregnant women are disproportionately affected. Maternal food insecurity may affect infant feeding practices, for example, through being a source of chronic stress that may alter the decision to initiate and continue breastfeeding. Thus, we sought to determine whether prenatal food insecurity was associated with breastfeeding (versus not) and exclusive breastfeeding duration among Oregon women. Method: The Oregon Pregnancy Risk Assessment Monitoring System (PRAMS) data of live births from 2008 to 2015 and the Oregon PRAMS-2 follow-up survey were used (n = 3,624) in this study. Associations with breastfeeding initiation and duration were modeled with multivariable logistic regression and accelerated failure time (AFT), respectively. Models were adjusted for maternal sociodemographic and pre-pregnancy health characteristics. Results: Nearly 10% of women experienced prenatal food insecurity. For breastfeeding initiation, unadjusted models suggested non-significant decreased odds (odds ratio (OR) 0.88 [confidence intervals (CI): 0.39, 1.99]), whereas adjusted models revealed a non-significant increased odds (OR 1.41 [CI: 0.58, 3.47]). Unadjusted AFT models suggested that food-insecure mothers had a non-significant decrease in exclusive breastfeeding duration (OR 0.76 [CI: 0.50, 1.17]), but adjustment for covariates attenuated results (OR 0.89 [CI: 0.57, 1.39]). Conclusions: Findings suggest minimal differences in breastfeeding practices when exploring food security status in the prenatal period, though the persistence of food insecurity may affect exclusive breastfeeding duration. Lower breastfeeding initiation may be due to other explanatory factors correlated with food insecurity and breastfeeding, such as education and marital status.

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.

A simple and robust method for multivariate meta-analysis of diagnostic test accuracy.

Meta-analysis of diagnostic test accuracy often involves mixture of case-control and cohort studies. The existing bivariate random-effects models, which jointly model bivariate accuracy indices (e.g., sensitivity and specificity), do not differentiate cohort studies from case-control studies and thus do not utilize the prevalence information contained in the cohort studies. The recently proposed trivariate generalized linear mixed-effects models are only applicable to cohort studies, and more importantly, they assume a common correlation structure across studies and trivariate normality on disease prevalence, test sensitivity, and specificity after transformation by some pre-specified link functions. In practice, very few studies provide justifications of these assumptions, and sometimes these assumptions are violated. In this paper, we evaluate the performance of the commonly used random-effects model under violations of these assumptions and propose a simple and robust method to fully utilize the information contained in case-control and cohort studies. The proposed method avoids making the aforementioned assumptions and can provide valid joint inferences for any functions of overall summary measures of diagnostic accuracy. Through simulation studies, we find that the proposed method is more robust to model misspecifications than the existing methods. We apply the proposed method to a meta-analysis of diagnostic test accuracy for the detection of recurrent ovarian carcinoma. Copyright © 2016 John Wiley & Sons, Ltd.

Impact of rurality, broiler operations, and community socioeconomic factors on the risk of campylobacteriosis in Maryland.

OBJECTIVES: We evaluated the combined impact of community-level environmental and socioeconomic factors on the risk of campylobacteriosis. METHODS: We obtained Campylobacter case data (2002-2010; n = 3694) from the Maryland Foodborne Diseases Active Surveillance Network. We obtained community-level socioeconomic and environmental data from the 2000 US Census and the 2007 US Census of Agriculture. We linked data by zip code. We derived incidence rate ratios by Poisson regressions. We mapped a subset of zip code-level characteristics. RESULTS: In zip codes that were 100% rural, incidence rate ratios (IRRs) of campylobacteriosis were 6 times (IRR = 6.18; 95% confidence interval [CI] = 3.19, 11.97) greater than those in urban zip codes. In zip codes with broiler chicken operations, incidence rates were 1.45 times greater than those in zip codes without broilers (IRR = 1.45; 95% CI = 1.34, 1.58). We also observed higher rates in zip codes whose populations were predominantly White and had high median incomes. CONCLUSIONS: The community and environment in which one lives may significantly influence the risk of campylobacteriosis.

Sex-specific effects of exercise ancestry on metabolic, morphological and gene expression phenotypes in multiple generations of mouse offspring.

Early life and preconception environmental stimuli can affect adult health-related phenotypes. Exercise training is an environmental stimulus affecting many systems throughout the body and appears to alter offspring phenotypes. The aim of this study was to examine the influence of parental exercise training, or 'exercise ancestry', on morphological and metabolic phenotypes in two generations of mouse offspring. The F0 C57BL/6 mice were exposed to voluntary exercise (EX) or sedentary lifestyle (SED) and bred with like-exposed mates to produce an F1 generation. The F1 mice of both ancestries were sedentary and killed at 8 weeks or bred with littermates to produce an F2 generation, which was also sedentary and killed at 8 weeks. Small but broad generation- and sex-specific effects of exercise ancestry were observed for body mass, fat and muscle mass, serum insulin, glucose tolerance and muscle gene expression. The F1 EX females were lighter than F1 SED females and had lower absolute tibialis anterior and omental fat masses. Serum insulin was higher in F1 EX females compared with F1 SED females. The F2 EX females had impaired glucose tolerance compared with F2 SED females. Analysis of skeletal muscle mRNA levels revealed several generation- and sex-specific differences in mRNA levels for multiple genes, especially those related to metabolic genes (e.g. F1 EX males had lower mRNA levels of Hk2, Ppard, Ppargc1a, Adipoq and Scd1 than F1 SED males). These results provide preliminary evidence that parental exercise training can influence health-related phenotypes in mouse offspring.

Estimating time to full uterine cervical dilation using genetic algorithm.

The objectives of this study were to provide new parameters to better understand labor curves, and to provide a model to predict the time to full cervical dilation (CD). We studied labor curves using the retrospective records of 594 nulliparas, including at term, spontaneous labor onset, and singleton vertex deliveries of normal birth weight infants. We redefined the parameters of Friedman's labor curve, and applied a three-parameter model to the labor curve with a logistic model using the genetic algorithm and the Newton-Raphson method to predict the time necessary to reach full CD. The genetic algorithm is more effective than the Newton-Raphson method for modeling labor progress, as demonstrated by its higher accuracy in predicting the time to reach full CD. In addition, we predicted the time (11.4 hours) to reach full CD using the logistic labor curve using the mean parameters (the power of CD = 0.97 cm/hours, a midpoint of the active phase = 7.60 hours, and the initial CD = 2.11 cm). Our new parameters and model can predict the time to reach full CD, which can aid in the forecasting of prolonged labor and the timing of interventions, with the end goal being normal vaginal birth.

Bayesian random-effects threshold regression with application to survival data with nonproportional hazards.

In epidemiological and clinical studies, time-to-event data often violate the assumptions of Cox regression due to the presence of time-dependent covariate effects and unmeasured risk factors. An alternative approach, which does not require proportional hazards, is to use a first hitting time model which treats a subject's health status as a latent stochastic process that fails when it reaches a threshold value. Although more flexible than Cox regression, existing methods do not account for unmeasured covariates in both the initial state and the rate of the process. To address this issue, we propose a Bayesian methodology that models an individual's health status as a Wiener process with subject-specific initial state and drift. Posterior inference proceeds via a Markov chain Monte Carlo methodology with data augmentation steps to sample the final health status of censored observations. We apply our method to data from melanoma patients with nonproportional hazards and find interesting differences from a similar model without random effects. In a simulation study, we show that failure to account for unmeasured covariates can lead to inaccurate estimates of survival probabilities.