POLITICAS SOBRE EL ACCESO DE MEDICAMENTOS Y SU USO RACIONAL

The sixth and final hypothesis states that areas that are more rural will have lower access to healthful facilities, which will be associated with a higher prevalence of diabetes. The results of the descriptive analysis are shown in Figure 7.14. The first map shows the locations of urban and rural counties in Georgia. As expected, all counties with one major city are considered

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Figure 7.14. Urban and Rural County Designations, Diabetes Prevalence in Georgia.

urban, along with the entire metro Atlanta Area, reaching out to Athens in the east and Columbus in the central west. Most of the coastal counties are considered urban, and a few counties along Interstate 75 are also designated as urban. Most of the rest of the central and southern part of Georgia is considered rural. Of the 159 counties in Georgia, 50 are urban and 109 are rural. This corresponds interestingly with the diabetes prevalence map. In general, urban counties have lower rates of diabetes prevalence than rural counties. Most rural counties follow the above- mentioned diabetes belt, located in the southwestern portion of the state, reaching toward the central-eastern part to Augusta.

Unlike most of the other hypotheses, the main independent variable of interest is dichotomous. Thus, it is inappropriate to conduct hot-spot analyses of urban or rural counties. Therefore, I am unable to discuss the results of the built environment in terms of a hot-spot analysis.

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150 The results of the descriptive maps of urban or rural status are overlaid by the built

environment outlets. In the first four variables, the clear majority of built environment outlets are in urban areas. This is clear in terms of the food environments, both positive and negative. While there is a scattering of positive food outlets in rural areas, they are by far more common in urban areas. This is also true for negative food outlets, although they are more common in rural areas than positive food outlets. Healthcare facilities and physical activity facilities are in

predominately urban areas as well. While healthcare facilities do exist in small clusters in each county, physical activity facilities often do not. There are very few physical activity facilities located in rural counties. Finally, public administration facilities are distributed evenly throughout the counties in Georgia, regardless of urban or rural designation.

7.3.2 GB2SFCA Method Results

The results of the GB2SFCA method results are shown in Figure 7.16. As a reminder, areas with no purple dots are areas where there is no accessibility to the relevant built

environment feature. For example, for physical activity facilities, either the county with no dot has no physical activity facilities at all, or the ones that exist are more than one mile away from the population center in urban counties or ten miles away from the population center in rural counties. This one-mile urban catchment limitation is why many of the counties with no dot at all are in urban areas. For public administration, the same idea exists. Every county has public administration facilities, but in areas with no dot, they are not located within one (or ten) miles of the population center of the county.

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152 For positive food outlets, it appears that there is no distinct difference in accessibility for urban versus rural counties. Many counties in rural areas have high accessibility, while some urban areas have no or little accessibility. Negative food environment outlets are a little different. There is higher accessibility to negative food environment outlets in rural areas compared to urban areas. Healthcare facilities are the opposite. In general, there is higher accessibility to healthcare facilities in urban areas compared to rural areas. The same is generally true for physical activity facilities. Except for a couple of counties in southwest Georgia, there is generally low accessibility to physical activity facilities in rural areas compared to urban areas. Finally, public administration facilities, while they number fewer in rural areas, are more accessible to the residents in those counties than for those living in urban areas. Almost exclusively, public administration shows higher accessibility rates in rural areas.

7.3.3 Linear Regression Results

The results of the linear regression analysis are shown in Table 7.1. As stated with the previous regressions, the results of the ANOVA f-test and r-squared are the same as those in hypothesis 2A. Additionally, the four built environment outlets that were significant in the previous hypotheses continue to be so in this model. Urban or rural residence has a significant effect on diabetes prevalence before the built environment variables are introduced. Rural

residence is associated with a 0.792 percentage point increase in diabetes prevalence, on average. However, this association disappears completely once every built environment outlet are

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7.3.4 Spatial Regression Results

Finally, the results of the spatial regression analyses are shown in Table 7.2. The results for the spatial regression analysis are very similar to the linear regression analysis. Urban or rural residence is only significantly associated with diabetes prevalence before the introduction of the built environment outlet variables. Rural residence is associated with a 0.710 percentage point increase in diabetes prevalence, on average. This significant association disappears once the built environment variables are introduced. In the built environment models, urban or rural residence is not significantly associated with diabetes prevalence. However, the built environment

variables themselves are significantly associated with diabetes prevalence, and are described in detail in section 7.1.4.

In the next section, I will discuss the results of the analyses. Further, I will conduct hypothesis testing to determine whether each hypothesis is substantiated by the data and the analyses. I will then discuss the limitations associated with the variables associated with the analyses as well as the analyses themselves. Next, I will discuss implications of the results in the sociological and public health fields. Finally, I will discuss how these results could be used in future research to reduce disparities in built environment access and diabetes prevalence.

In Chapter 6, I examined the association between sociodemographic factors and the built environment. The results from that chapter show strong support for an existing association between the two. In Chapter 7, I moved deeper into the analysis, by examining whether there was an association between sociodemographic factors, the built environment, and diabetes prevalence. The results in this chapter, for the most part, continue to show strong support for an association between race, income, the built environment facilities, and diabetes prevalence. This

154 outcome was predicted by the application of fundamental causes theory. Fundamental causes theory states that health disparities are persistently associated with social or physical factors despite dramatic changes in diseases, risk factors, and health interventions (Hatzenbuehler, Phelan, & Link, 2013). The results of these chapters show support for fundamental causes theory in that there are indeed racial and income disparities in which built environment facilities are available to residents of particular counties, which is further associated with diabetes prevalence. The results will be discussed in detail in the hypothesis testing section below.

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