Abstract
Background/Objectives: 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.
| Original language | English |
|---|---|
| Pages (from-to) | 1384-1391 |
| Number of pages | 8 |
| Journal | International Journal of Obesity |
| Volume | 40 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 1 2016 |
| Externally published | Yes |
Funding
This research was supported by the NIH NIDDK Grant Number R21 DK080406. The funding agency had no involvement in study design, data analysis, interpretation of the results or decision to submit this article for publication. The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the National Institutes of Health. We thank Alison Fraser, Cindy Brown, Diana Lane Reed and Jennifer West for their assistance with data and project management as well as the Pedigree and Population Resource of the Huntsman Cancer Institute, University of Utah (funded by the Huntsman Cancer Foundation), for its role in the ongoing collection, maintenance and support of the Utah Population Database (UPDB). We appreciate the comments of two anonymous reviewers and the editor for suggestions on an earlier draft.
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