Built Environment and Risk of Obesity in the United States: A Multilevel Modeling Approach

This dissertation provides a series of exploratory analyses of the relationship between built environment and obesity by using multiple data sets and employing the state-of-art Geographic Information Systems methods. Several built environment factors including street connectivity, walkability and food environment, are for the first time measured across 48 contiguous states of the U.S., built from a fine geographic scale such as the census tract level. Based on the nationwide BRFSS data, the first study used the Geographically Weighted Regression (GWR) model to analyze the obesity rates at the county level. The model results reveal that overall obesity rates are negatively related to walk score and street connectivity, but positively related to poverty rates and metro classification, while the effect of fast-food-to-full-service restaurant ratio is not evident. The strength of each variable’s effect also varies significantly across the country. To mitigate the ecological fallacy, the second study used a multi-level modeling (MLM) approach by accounting for individual attributes such as demographic, socioeconomic and behavior variables. Furthermore, models for areas of different urbanicity levels were tested. The national study found that obesity risk initially increases with the urbanicity level and then drops, resembling an inverted-V shape. The results lend support to the role of built environment in influencing people’s health behavior and outcome, and promote public policies that need to be sensitive to the diversity of demographic groups and geographically adaptable. Defining neighborhoods at the county level may be problematic in the previous MLM study since people’s activity space is seldom countywide. The third study added another level (zip code area) to the MLM analysis of the BRFSS data in Utah. The results showed that at the zip code level, poverty rate and distance to parks are significant and negative covariates of the odds of overweight and obesity; and at the county level, food environment is the sole significant factor with a stronger fast food presence linked to higher odds of overweight and obesity. These findings suggested that obesity risk factors lie in multiple neighborhood levels and built environment need to be defined at a neighborhood size relevant to residents’ activity space.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Social and Behavioral Sciences

Grantor

Geography and Anthropology

Year dc:date.available

2014

Author dc:creator

• Xu, Yanqing

• BRFSS
• Urbanicity
• Multilevel modeling
• Built environment
• Obesity

Statement dc:rights

• restricted
• Student has submitted appropriate documentation to restrict access to LSU for 365 days after which the document will be released for worldwide access.

Identifier

etd-08272014-210813
https://repository.lsu.edu/gradschool_dissertations/523

OAI identifier oai:identifier

oai:repository.lsu.edu:gradschool_dissertations-1522