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Author: Heggeseth, Brianna
Resulting in 2 citations.
1. Abrams, Barbara
Heggeseth, Brianna
Rehkopf, David
Davis, Esa M.
Parity and Body Mass Index in U.S. Women: A Prospective 25-year Study
Obesity, V.21, No. 8 (August 2013): 1514–1518.
Also: http://onlinelibrary.wiley.com/doi/10.1002/oby.20503/abstract
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Births, Repeat / Spacing; Body Mass Index (BMI); Childbearing; Life Course; Obesity; Racial Differences; Weight

Permission to reprint the abstract has not been received from the publisher.

Objective: To investigate long-term body mass index (BMI) changes with childbearing.

Design and Methods: Adjusted mean BMI changes were estimated by race-ethnicity, baseline BMI and parity using longitudinal regression models in 3943 young females over 10 and 25 year follow-up from the ongoing 1979 National Longitudinal Survey of Youth cohort.

Results: Estimated BMI increases varied by group, ranging from a low of 2.1 BMI units for white, non-overweight nulliparas over the first 10 years to a high of 10.1 BMI units for black, overweight multiparas over the full 25-year follow-up. Impacts of parity were strongest among overweight multiparas and primaparas at ten years, ranges 1.4-1.7 and 0.8-1.3 BMI units, respectively. Among non-overweight women at 10 years, parity-related gain varied by number of births among black and whites but was unassociated in Hispanic women. After 25 years, childbearing significantly increased BMI only among overweight multiparous black women.

Conclusion: Childbearing is associated with permanent weight gain in some women, but the relationship differs by maternal BMI in young adulthood, number of births, race-ethnicity and length of follow-up. Given that overweight black women may be at special risk for accumulation of permanent, long-term weight after childbearing, effective interventions for this group are particularly needed.

Bibliography Citation
Abrams, Barbara, Brianna Heggeseth, David Rehkopf and Esa M. Davis. "Parity and Body Mass Index in U.S. Women: A Prospective 25-year Study." Obesity, V.21, No. 8 (August 2013): 1514–1518. A.
2. Heggeseth, Brianna
Jewell, Nicholas P.
The Impact of Covariance Misspecification in Multivariate Gaussian Mixtures on Estimation and Inference: An Application to Longitudinal Modeling
Statistics in Medicine 32,16 (20 July 2013): 2790-2803.
Also: http://onlinelibrary.wiley.com/doi/10.1002/sim.5729/abstract
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Body Mass Index (BMI); Modeling; Statistical Analysis

Permission to reprint the abstract has not been received from the publisher.

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence—that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.
Bibliography Citation
Heggeseth, Brianna and Nicholas P. Jewell. "The Impact of Covariance Misspecification in Multivariate Gaussian Mixtures on Estimation and Inference: An Application to Longitudinal Modeling." Statistics in Medicine 32,16 (20 July 2013): 2790-2803.