"Assessment of Socioeconomic and Environmental Factors for Diabetes Mel" by Godswill Melford Arugu

Date of Award

2024

Degree Type

Restricted to Claremont Colleges Dissertation

Degree Name

Public Health, DPH

Program

School of Community and Global Health

Advisor/Supervisor/Committee Chair

Bin Xie

Dissertation or Thesis Committee Member

Jay Orr

Dissertation or Thesis Committee Member

Jason T. Siegel

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Rights Information

© 2024 Godswill Arugu

Keywords

Diabetes Mellitus, Environmental Determinants, Public Health Interventions, San Bernardino County, Socioeconomic Factors, Spatial Epidemiology

Subject Categories

Epidemiology | Public Health

Abstract

Diabetes Mellitus (DM) is a major public health burden in San Bernardino County, California. Its crude prevalence is 11.4% higher than both state (10.6%) and national (9.6%) averages. This retrospective observational cross-sectional ecological study used spatial epidemiology and secondary data analysis to assess the socio-economic and environmental factors influencing DM prevalence. The study aims to identify the key socioeconomic and environmental determinants and spatial distribution patterns of DM to inform targeted public health interventions.

A purposive sampling method was used to collect data (n = 549 census tracts) from multiple secondary sources such as the American Community Survey (ACS) 5-year estimates (2018 – 2022), the California Healthy Places Index (2015 – 2019), the San Bernardino County Department of Public Health (SBCDPH) 2022 data, the 2022 California Health Interview Survey (CHIS) data, the 2020 SDOH data from Agency for Healthcare Research and Quality, and the CDC PLACES 2021 data. The variables analyzed included diabetes prevalence, socioeconomic factors (e.g., poverty, unemployment, lack of health insurance, educational attainment), and environmental exposures (e.g., particulate matter and access to parks and supermarket access, food insecurity, SVI, walkability, HPI scores, CalEnvironScr4.0, etc.). Spatial mapping and Hotspot analysis were conducted using ArcGIS Pro to identify the spatial distribution of diabetes. Multivariate linear and binary logistic regression analyses were deployed to determine the socioeconomic and environmental determinants of DM.

Although the prevalence of diabetes mellitus across census tracts in San Bernardino County was 12.61% (SD 2.18), with females having a marginally higher mean prevalence at 6.36% (SD 1.20), compared to 6.26% (SD 1.20) in males, spatial analyses conducted using ArcGIS Pro identified diabetes hotspots primarily in central and western regions of San Bernardino County, with prevalence rates between 15.01% and 16.65%. These hotspots matched with areas experiencing significant socioeconomic drawbacks, where poverty prevalence was as high as 28.95%, prevalence of unemployment reached 57.89%, and up to 92.19% of the population had no high school diploma. Environmental assessments also highlighted significant exposure to pollutants like PM2.5 and diesel particulate matter.

Multivariate linear regression results showed that key predictors of DM prevalence were poverty (β = -0.036, SE = 0.010, p < 0.001), unemployment (β = 0.041, SE = 0.010, p < 0.001), low educational attainment (β = 0.038, SE = 0.011, p < 0.001), Food insecurity (β = -0.165 SE = 0.061, p = 0.007), Walkability score (β = 0.065, SE = 0.030, p = 0.033), and the CalEnviroScreen4.0 score (β = -0.026, SE = 0.009, p = 0.005). Binary logistic regression underscored significant associations of DM odds with poverty (OR = 0.761, 95% CI [0.615, 0.943], p = 0.013), lack of health insurance (OR = 0.627, 95% CI [0.447, 0.880], p = 0.007), PM2.5 exposure (OR = 3.182, 95% CI [1.131, 8.951], p = 0.028), Food insecurity (OR = 0.122, 95% CI [0.124, 0.636], and walkability (OR = 2.662, 95% CI [1.098, 6.476], p = 0.030).

This study underlines the crucial role of socioeconomic and environmental determinants in influencing the prevalence of Diabetes Mellitus (DM) in San Bernardino County. Fundamental predictors identified include poverty, unemployment, educational attainment, walkability, food insecurity, and air quality. The findings uncover that even though parks and walkable areas are accessible, their underuse, primarily due to socioeconomic constraints and neighborhood security concerns, may have caused the high prevalence of diabetes in these areas. This suggests that simply improving walkability infrastructure is inadequate without concurrently addressing these underlying barriers. This research highlights the need to take a holistic approach that integrates both socioeconomic and environmental interventions for the prevention and control of diabetes mellitus. This comprehensive strategy is essential to effectively address local disparities and reduce the incidence of DM in the community.

ISBN

9798308121787

Share

COinS