Multilevel built environment features and individual odds of overweight and obesity in Utah

Highlights

• The BRFSS data in Utah is used to examine overweight and obesity risks by MLM.

• Built environment factors are defined at two levels (zip code and county).

• Distance to parks influences overweight and obesity at the zip code level.

• Fast food restaurant ratio is linked to overweight and obesity at the county level.

• Neighborhood size needs to capture residents' activity space.

Abstract

Based on the data from the Behavioral Risk Factor Surveillance System (BRFSS) in 2007, 2009 and 2011 in Utah, this research uses multilevel modeling (MLM) to examine the associations between neighborhood built environments and individual odds of overweight and obesity after controlling for individual risk factors. The BRFSS data include information on 21,961 individuals geocoded to zip code areas. Individual variables include BMI (body mass index) and socio-demographic attributes such as age, gender, race, marital status, education attainment, employment status, and whether an individual smokes. Neighborhood built environment factors measured at both zip code and county levels include street connectivity, walk score, distance to parks, and food environment. Two additional neighborhood variables, namely the poverty rate and urbanicity, are also included as control variables. MLM results show 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 stronger fast food presence linked to higher odds of overweight and obesity. These findings suggest that obesity risk factors lie in multiple neighborhood levels and built environment features need to be defined at a neighborhood size relevant to residents' activity space.

Introduction

Obesity is a lifestyle-based risk factor of a wide range of health problems, including heart disease, stroke, diabetes and some of the leading causes of preventable death, and has become a major public health concern in the United States in recent decades (Zhang, Lu, & Holt, 2011). It is now adding a shocking $190 billion to the annual national healthcare from obesity-related conditions; this amount constitutes almost 21% of the total healthcare costs (Begley, 2012). Although Utah is among the states with the lowest obesity rates in the U.S., the estimated prevalence of overweight and obesity is over 60% according to the BEE Well Utah (2014).

According to the energy balance theory, an individual's excessive body weight results from a positive balance where total energy intake such as food and drink cumulatively exceeds total energy expenditure including physical activity (Schoeller, 2009). The obesogenic environment thesis suggests that obesity-preventive factors include exposure to a healthy food environment that promotes healthier dietary choices and built environments that encourage physical activities (Hill & Peters 1998; Swinburn, Egger, & Raza 1999). Built environment is broadly defined as “human-formed, developed, or structured areas” (CDC, 2005), and includes walkable urban form, places to be physically active, and attractive and safe environment (Casey et al., 2008, Lovasi et al., 2009, Miles et al., 2008). In this paper, food environment is also considered part of the built environment.

Multilevel modeling is commonly used in research on obesity etiology by incorporating both individual-level risk factors and neighborhood characteristics (Wang et al., 2013, Wen and Maloney, 2011). Individual variables are often obtained directly from surveys while built environment factors are measured at some neighborhood level(s) from various data sources. One challenge is to determine what constitutes an appropriate neighborhood scale or size in defining the built environment. For example, in analyzing overweight risks, Gordon. Nelson, & Rage (2006) used an 8-km radius around one's residence as a reasonable range to define available physical activity facilities. Rutt and Coleman (2005) defined neighborhood as a 0.25-mile radius around each person's residence to examine the association between mixed land use and BMI. In examining the impact of urban sprawl index on obesity rate, Ewing, Schmid, Killingsworth, Zlot, and Raudenbush (2003) used the county level and Kelly-Schwartz, Stockard, Doyle, and Schlossberg (2004) chose primary metropolitan statistical areas (PMSA). Yamada et al. (2012) examined walkability in Salt Lake City in multiple geographic scales such as census tracts, block group and street network buffers. Other studies in this field also employed smaller area units such as census tracts (Wen & Maloney, 2011) and zip code areas (Wang, Guo, & McLafferty, 2012) to define neighborhoods, depending mainly on what geographic identifiers were available in the research data. The wide variability in neighborhood size without a fair justification of its choice may lead to questions of stability and reliability of research results, an issue related to the modifiable areal unit problem (MAUP) (Fotheringham & Wong, 1991).

More recently, several MLM-based studies examined the issue of appropriate area unit(s) for defining the neighborhood effect in public health. It is widely acknowledged that effective interventions on health behaviors and outcomes occur on multiple levels (Nader, Bradley, Houts, McRitchie, & O'Brien, 2008). Mobley, Kuo, and Andrews (2008) examined how contextual variables in four types of geographic areas (post code areas, primary care service areas, medical service study areas, and county) affected the use of mammography service, and found inconsistent results across the four levels. Another study offered some insights speculating that small local areas might reflect social support while a large area unit might reflect geo-political units and minorities' political influence (Kuo, Mobley, & Anselin, 2011). Wang et al. (2012) constructed a new level of geographic areas from zip code areas with comparable population size to examine the neighborhood effect when neighborhoods are defined in different sizes. Kwan (2012b) used a term “the uncertain geographic context problem (UGCoP)” to refer to unstable results derived from different delineations of contextual units, and went on to suggest that contextual units should be defined in a way that captures people's actual or potential activity spaces (Kwan, 2012a).

The current research continues this line of work to examine the neighborhood effects at both zip code and county levels on association of several built environment factors with individual odds of overweight and obesity. We seek to explore appropriate neighborhood units for a particular built environment factor in Utah.