Food Swamps, Obesity & Health Zoning Restrictions on Fast Food Restaurants

Protecting public health is the most legitimate use of zoning, and yet there is minimal progress in applying it to the obesity problem. Zoning could potentially be used to address both unhealthy and healthy food retailers, but lack of evidence regarding the impact of zoning and public opinion on zoning changes are barriers to implementing zoning restrictions on fast food on a larger scale. My dissertation addresses these gaps in our understanding of health zoning as a policy option for altering built, food environments.

Chapter 1 examines the relationship between food swamps and obesity and whether spatial mapping might be useful in identifying priority geographic areas for zoning interventions. I employ an instrumental variables (IV) strategy to correct for the endogeneity problems associated with food environments, namely that individuals may self-select into certain neighborhoods and may consider food availability in their decision process. I utilize highway exits as a source of exogenous variation .Using secondary data from the USDA Food Environment Atlas, ordinary least squares (OLS) and IV regression models were employed to analyze cross-sectional associations between local food environments and the prevalence of obesity. I find even after controlling for food desert effects, food swamps have a positive, statistically significant effect on adult obesity rates.

Chapter 2 applies theories of message framing and prospect theory to the emerging discussion around health zoning policies targeting food environments and to explore public opinion toward a list of potential zoning restrictions on fast-food restaurants (beyond moratoriums on new establishments). In order to explore causality, I employ an online survey experiment manipulating exposure to vignettes with different message frames about health zoning restrictions with two national samples of adult Americans age 18 and over (N1=2,768 and N2=3,236). The second sample oversamples Black Americans (N=1,000) and individuals with high school as their highest level of education. Respondents were randomly assigned to one of six conditions where they were primed with different message frames about the benefits of zoning restrictions on fast food retailers. Participants were then asked to indicate their support for six zoning policies on a Likert scale. Subjects also answered questions about their food store access, eating behaviors, health status and perceptions of food stores by type.

I find that a message frame about Nutrition and increasing Equity in the food system was particularly effective at increasing support for health zoning policies targeting fast food outlets across policy categories (Conditional, Youth-related, Performance and Incentive) and across racial groups. This finding is consistent with an influential environmental justice scholar’s description of “injustice frames” as effective in mobilizing supporters around environmental issues (Taylor 2000). I extend this rationale to food environment obesity prevention efforts and identify Nutrition combined with Equity frames as an arguably universal campaign strategy for bolstering public support of zoning restrictions on fast food retailers.

Bridging my findings from both Chapters 1 and 2, using food swamps as a spatial metaphor may work to identify priority areas for policy intervention, but only if there is an equitable distribution of resources and mobilization efforts to improve consumer food environments. If the structural forces which ration access to land-use planning persist (arguably including the media as gatekeepers to information and producers of message frames) disparities in obesity are likely to widen.

Methods for Imputing Missing Values and Synthesizing Confidential Values for Continuous and Magnitude Data

Abstract

Continuous variable is one of the major data types collected by the survey organizations. It can be incomplete such that the data collectors need to fill in the missingness. Or, it can contain sensitive information which needs protection from re-identification. One of the approaches to protect continuous microdata is to sum them up according to different cells of features. In this thesis, I represents novel methods of multiple imputation (MI) that can be applied to impute missing values and synthesize confidential values for continuous and magnitude data.

The first method is for limiting the disclosure risk of the continuous microdata whose marginal sums are fixed. The motivation for developing such a method comes from the magnitude tables of non-negative integer values in economic surveys. I present approaches based on a mixture of Poisson distributions to describe the multivariate distribution so that the marginals of the synthetic data are guaranteed to sum to the original totals. At the same time, I present methods for assessing disclosure risks in releasing such synthetic magnitude microdata. The illustration on a survey of manufacturing establishments shows that the disclosure risks are low while the information loss is acceptable.

The second method is for releasing synthetic continuous micro data by a nonstandard MI method. Traditionally, MI fits a model on the confidential values and then generates multiple synthetic datasets from this model. Its disclosure risk tends to be high, especially when the original data contain extreme values. I present a nonstandard MI approach conditioned on the protective intervals. Its basic idea is to estimate the model parameters from these intervals rather than the confidential values. The encouraging results of simple simulation studies suggest the potential of this new approach in limiting the posterior disclosure risk.

The third method is for imputing missing values in continuous and categorical variables. It is extended from a hierarchically coupled mixture model with local dependence. However, the new method separates the variables into non-focused (e.g., almost-fully-observed) and focused (e.g., missing-a-lot) ones. The sub-model structure of focused variables is more complex than that of non-focused ones. At the same time, their cluster indicators are linked together by tensor factorization and the focused continuous variables depend locally on non-focused values. The model properties suggest that moving the strongly associated non-focused variables to the side of focused ones can help to improve estimation accuracy, which is examined by several simulation studies. And this method is applied to data from the American Community Survey.