Movers and stayers: how residential selection contributes to the association between female body mass index and neighborhood characteristics
International Journal of Obesity, 40, 1384, 2016
To examine how a woman’s current body mass index (BMI) is associated with nonrandom residential migration that is based on the average BMI of her origin and destination neighborhoods.
Subjects/Methods: Among women having at least two children, all birth certificates from Salt Lake county from 1989 to 2010 (n=34 010) were used to obtain prepregnancy weights before the first and second births, residential location and sociodemographic information. Census data were used for measures of walkability of neighborhoods.
Results: After adjustments for age, education, race/ethnicity and marital status, obese women living in the leanest neighborhoods are found to be three times more likely (odds ratio (OR)=3.03, 95% confidence interval (CI) 2.06–4.47) to move to the heaviest neighborhoods relative to women with healthy weight (BMI between 18 and 25 kg m−2). Conversely, obese women in the heaviest neighborhoods are 60% less likely (OR=0.39, 95% CI 0.22–0.69) to move to the leanest neighborhoods relative to healthy weight women. Indicators of relatively greater walkability (older housing, greater proportion of residents who walk to work) and higher median family income characterize leaner neighborhoods.
Conclusions: The findings are consistent with the hypothesis that nonrandom selection into and out of neighborhoods accounts for some of the association between BMI and neighborhood characteristics.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).