Date of Publication :5th February 2024

Abstract: The prevalence of disability, a leading global public health challenge, is profoundly influenced by an interplay of socioeconomic factors, diabetes, obesity, and geographic dispariti es/zip codes. This research provides a quantitative analysis exploring these determinants, focusing particularly on diabetes and disabilities. Utilizing data from the PLACES dataset, our study emp loyed multiple regression to dissect the impact of socioeconomic status, diabetes, and obesity on disability outcomes across varied neighborhood Zip Codes. The novelty of our approach lies in the application of innovative data analytics techniques that enhance the accuracy and depth of our findings. For instance, machine learning algorithms were employed to identify factors that are related to neighborhoods with high-risk zones for diabetes-related disabilities, facilitating targeted interventions. Furthermore, Geographic Information Systems (GIS) technology further provides information on the impact of geographic disparities/zip codes on health outcomes, which showcases how zip codes of neighborhoods influence health. Findings from the study indicate that lower socioeconomic status, higher obesity rates, and higher diabetes rates significantly correlate with an increased incidence of disabilities. This correlation underscores the need for public health strategies that address economic factors. This research aligns with the WCMRI-2024 theme of "Transforming Ideas into Impact" by demonstrating how multidisciplinary approaches, particularly the integration of socioeconomic analysis with cutting-edge technology, can lead to sustainable health solutions. It advocates for the incorporation of these technological advancements into policy-making and health strategy development to effectively tackle the complex challenge of diabetes management. By proposing a regression model that integrates socioeconomic data and technological innovations to identify significant associations between disabilities and three key variables—diabetes, obesity, and socioeconomic status—across different neighborhoods highlighted.

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