Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted Environmental Influences on Food Access And Their Impacts in Turn on Health Conditions in Guilford County, NC(2023-02-24) Almalki, Abrar; Gokaraju, BalakrishnaFood access is a major key component in food security, as it is every individual’s right to proper access of nutritious and affordable food supply. Low access to healthy food sources influences people's diet and activity habits. Guilford County in North Carolina has a high ranking in low food security, and a high rate of health issues, such as high blood pressure, high cholesterol, and obesity. Therefore, the primary objective of this study was to investigate the geospatial correlation between health issues and food access areas. The secondary objective was to quantitatively compare food access areas and heath issues’ descriptive statistics. The tertiary objective was to compare several machine learning techniques and find the best model that fits health issues against various food access variables with the highest performance accuracy. In this study, we adopted a food-access perspective to show that communities, where residents had equitable access to healthy food options, were typically less vulnerable to health-related disasters. We proposed a methodology to help policymakers toward lowering the amount of health issues in Guilford County by analyzing them via correlation with respect to food access. Specifically, we conducted a geographic information system mapping methodology to examine how access to healthy food options influenced health and mortality outcomes in one of the largest counties in the state of North Carolina. We created geospatial maps representing food deserts, i.e., areas with scarce access to nutritious food; food swamps, i.e., areas with more availability of unhealthy food options compared to healthy food options; and food oases, i.e., areas with relatively higher availability of healthy food options than unhealthy. Our results presented a positive correlation coefficient with R2= 0.819 among Obesity and independent variables, transportation access, income, and population. The correlation coefficient matrix2 analysis helped to identify a strong negative correlation between obesity and median income. Overall, this study offers valuable insights that can help health authorities develop preemptive preparedness for healthcare disasters. COVID-19, or SARS-CoV-2, is considered as one of the greatest pandemics in our mod-ern time. It affected people’s health, education, employment, the economy, tourism, and trans-portation systems. It will take a long time to recover from these effects and return people’s lives back to normal. The main objective of this study is to investigate the various factors in health and food access, and their spatial correlation and statistical association with COVID-19 spread. The minor aim is to explore regression models on examining COVID-19 spread with these variables. To address these objectives, we are studying the interrelation of various socio-economic factors that would help all humans to better prepare for the next pandemic. One of these critical factors is food access and food distribution as it could be high-risk population density places that are spreading the virus infections. More variables, such as income and people density, would influence the pandemic spread. In this study, we produced the spatial extent of COVID-19 cases with food outlets by using the spatial analysis method of geographic information systems. The methodology consisted of clustering techniques and overlaying the spatial extent mapping of the clusters of food outlets and the infected cases. Post-mapping, we analyzed these clusters’ proximity for any spatial variability, correlations between them, and their causal relationships. The quantitative analyses of the health issues and food access areas against COVID-19 infections and deaths were performed using machine learning regression techniques to understand the multi-variate factors. The results indicate a correlation between the dependent variables and independent variables with a Pearson correlation R2-score = 0.44% for COVID-19 cases and R23= 60% for COVID-19 deaths. The regression model with an R2-score of 0.60 would be useful to show the goodness of fit for COVID-19 deaths and the health issues and food access factors.24 0