VPT2 Task 2 Sona Buchanan (final) PDF

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Running head: IDAHO RATES OF OBESITY PREVALENCE AND LOW ACCESS

Idaho Rates of Obesity Prevalence and Low Access to Healthy Food Sona Buchanan Western Governors University C207 VPT2 Task 2

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IDAHO RATES OF OBESITY PREVALENCE AND LOW ACCESS

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Idaho Rates of Obesity Prevalence and Low Access to Healthy Food A. Summary of the Situation In the United States, obesity is an epidemic that leads to numerous health conditions, such as diabetes, heart disease, and stroke. These conditions decrease the quality of life and increase both medical and financial burden on the individual and society as a whole (Centers for Disease Control, 2020). By gaining knowledge of influencing factors, healthcare and tax dollars can be appropriately allocated to fund programs and education that target reducing obesity prevalence in Idaho. This analysis will examine if there is a relationship between low access to healthy food and obesity. By determining if there is a correlation, changes in healthy food environments can be made that will influence a decrease in obesity and better health in Idaho. B1. Summary of Data The methodology of secondary data collection will be used to collect public data from the Rural Health Information Hub (Rural Health Information Hub [RHI], 2015). Two sets of occurrence rates gathered in 2015 from Idaho's 44 counties will be used: obesity prevalence and low access to healthy food. These data points will be entered into an Excel spreadsheet to conduct a linear regression analysis. Table 1 RHI Data: Idaho Counties Obesity Prevalence and Access to Healthy Food Rates Idaho County Ada County Bannock County Boise County Bonneville County Butte County Canyon County Franklin County Gem County Jefferson County Jerome County

Low access to healthy food 60.97% 73.12% 100.00% 67.19% 100.00% 55.29% 47.46% 67.09% 42.59% 58.71%

Obesity prevalence 24.70% 30.70% 26.10% 28.60% 27.41% 34.70% 35.70% 33.20% 32.40% 32.10%

IDAHO RATES OF OBESITY PREVALENCE AND LOW ACCESS Kootenai County Nez Perce County Owyhee County Power County Twin Falls County Adams County Bear Lake County Benewah County Bingham County Blaine County Bonner County Boundary County Camas County Caribou County Cassia County Clark County Clearwater County Custer County Elmore County Fremont County Gooding County Idaho County Latah County Lemhi County Lewis County Lincoln County Madison County Minidoka County Oneida County Payette County Shoshone County Teton County Valley County Washington County

49.26% 33.95% 0.00% 42.55% 100.00% 73.75% 22.82% 45.45% 51.47% 100.00% 58.75% 23.78% 100.00% 34.70% 100.00% 44.98% 72.75% 0.00% 100.00% 36.56% 89.75% 68.66% 54.80% 100.00% 45.09% 63.10% 0.00% 64.77% 21.12% 28.98% 100.00% 43.53% 46.56% 16.23%

3 25.20% 31.60% 31.40% 31.30% 31.30% 28.21% 25.30% 29.60% 30.40% 17.10% 25.20% 28.90% 26.11% 28.90% 35.10% 27.54% 31.60% 26.80% 31.50% 27.50% 32.30% 25.60% 22.20% 25.80% 27.60% 28.31% 26.10% 32.20% 26.70% 32.20% 29.30% 21.60% 24.80% 31.60%

IDAHO RATES OF OBESITY PREVALENCE AND LOW ACCESS

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B2. Graphical Display Figure 1. Scatter plot: Obesity Prevalence and Low Access to Healthy Food

Rate of Obesity Prevalence

Idaho Counties Obesity Prevalence and Low Access to Healthy Food 100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00%

Low access to healthy food Obesity prevalence Linear (Obesity prevalence)

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Rate of Low Access to Healthy Food

C1. Description of Analysis Technique Linear regression is the analysis technique selected to determine if low access to healthy food is a predictor of obesity prevalence. Linear regression "attempts to model the relationship between two variables by fitting a linear equation to observed data" (Yale, 1997-98, para. 1). When using this technique, the dependent target variable "X" and the independent predictor variable "Y" must be identified. In this analysis, the dependent "X" variable is obesity prevalence rates, and the independent "Y" variable is low access to healthy food rates, collected from Idaho's 44 counties. Linear regression measures the strength and statistical significance of the relationship between these two variables by calculating the correlation coefficient (R-square) and statistical significance (Significance F / P-Value). For a relationship to have a strong correlation of "goodness of fit," the R² value is closer to one, and inversely a weak or no correlation would have an R² closer to zero. The F-value is the same as the P-value and represents the level of significance. To be considered a statistically significant relationship, this value must be...