J. Arora
University of Rochester
9 Papers
5 Citations
J. Arora is an academic researcher from University of Rochester. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 1, co-authored 2 publications.
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Papers
Social disadvantage during pregnancy: effects on gestational age and birthweight
Joan L. Luby,Sarah K. England,Deanna M. Barch,B. Warner,Cynthia E. Rogers,Christopher D. Smyser,Regina L. Triplett,J. Arora,Tara A. Smyser,George M. Slavich,Peinan Zhao,Molly J. Stout,Erik Herzog,J. Philip Miller +13 more
TL;DR: In this paper , the authors investigated the relationship of forms of adversity to each other and to infant gestational age, and birthweight, and found significant negative effects of social adversity on the developing fetus.
Nicu network neurobehavioral scale profiles in term infants: associations with maternal adversity,medical risk, and neonatal outcomes.
A. Parikh,Regina L. Triplett,Tiffany J Wu,J. Arora,Karen Lukas,Tara A. Smyser,Joan L. Luby,Cynthia E. Rogers,Deanna M. Barch,B. Warner,Christopher D. Smyser +10 more
TL;DR: In this article , the authors examined healthy, full-term neonatal behavior using the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS) in relation to measures of maternal adversity, maternal medical risk, and infant brain volumes.
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Power analysis for clustered non-continuous responses in multicenter trials
TL;DR: The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation, and is illustrated with both real and simulated data, with good performance of the method.
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The association between maternal cortisol and infant amygdala volume is moderated by socioeconomic status
Max P. Herzberg,Regina L. Triplett,Ronald McCarthy,S. Kaplan,Dimitrios Alexopoulos,Dominique Meyer,J. Arora,Tara A. Smyser,Erik D. Herzog,Sarah K. England,Peinan Zhao,Deanna M. Barch,Cynthia E. Rogers,B. Warner,Christopher D. Smyser,Joan L. Luby +15 more
Abstract: Background It has been well established that socioeconomic status is associated with mental and physical health as well as brain development, with emerging data suggesting that these relationships begin in utero. However, less is known about how prenatal socioeconomic environments interact with the gestational environment to affect neonatal brain volume. Methods Maternal cortisol output measured at each trimester of pregnancy and neonatal brain structure were assessed in 241 mother-infant dyads. We examined associations between the trajectory of maternal cortisol output across pregnancy and volumes of cortisol receptor–rich regions of the brain, including the amygdala, hippocampus, medial prefrontal cortex, and caudate. Given the known effects of poverty on infant brain structure, socioeconomic disadvantage was included as a moderating variable. Results Neonatal amygdala volume was predicted by an interaction between maternal cortisol output across pregnancy and socioeconomic disadvantage (standardized β = −0.31, p < .001), controlling for postmenstrual age at scan, infant sex, and total gray matter volume. Notably, amygdala volumes were positively associated with maternal cortisol for infants with maternal disadvantage scores 1 standard deviation below the mean (i.e., less disadvantage) (simple slope = 123.36, p < .01), while the association was negative in infants with maternal disadvantage 1 standard deviation above the mean (i.e., more disadvantage) (simple slope = −82.70, p = .02). Individuals with disadvantage scores at the mean showed no association, and there were no significant interactions in the other brain regions examined. Conclusions These data suggest that fetal development of the amygdala is differentially affected by maternal cortisol production at varying levels of socioeconomic advantage.
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Models for surveillance data under reporting delay: applications to US veteran first-time suicide attempters
Yinglin Xia,Naiji Lu,Ira R. Katz,Robert M. Bossarte,J. Arora,Hua He,J. X. Tu,Brady Stephens,A. Watts,Xin Tu +9 more
TL;DR: A new approach is proposed to overcome limitations of models for disease incidence by allowing for separate models for the incidence and the reporting delay in a distribution-free fashion, but with joint inference for both modeling components, based on functional response models.
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