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<p>This study investigates the spatially varying influence of diverse factors on diabetes prevalence across the contiguous United States, with a specific aim to identify the single most influential factor in each county. Utilizing county-level data on diabetes rates and 18 predictor variables for 3108 counties, we employ Multiscale Geographically Weighted Regression to construct local regression models and analyze county-specific drivers of diabetes. Our analysis reveals that inactivity is the most influential factor in the largest number of counties (67%), followed by smoking (25%). Other variables identified as the most influential in certain counties include insufficient sleep, mental distress, food insecurity, uninsured rates, housing burden, and traffic volumes. These geographically nuanced findings empower local governments to prioritize interventions targeting the most critical, local drivers of diabetes for more efficient and effective public health strategies.</p>

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Examining county-level factors of diabetes prevalence in the United States

  • 期刊论文

  • 发表日期：

    2025年6月22日

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  • Xiaomin Qiu,
  • Shuo-Sheng Wu

摘要

This study investigates the spatially varying influence of diverse factors on diabetes prevalence across the contiguous United States, with a specific aim to identify the single most influential factor in each county. Utilizing county-level data on diabetes rates and 18 predictor variables for 3108 counties, we employ Multiscale Geographically Weighted Regression to construct local regression models and analyze county-specific drivers of diabetes. Our analysis reveals that inactivity is the most influential factor in the largest number of counties (67%), followed by smoking (25%). Other variables identified as the most influential in certain counties include insufficient sleep, mental distress, food insecurity, uninsured rates, housing burden, and traffic volumes. These geographically nuanced findings empower local governments to prioritize interventions targeting the most critical, local drivers of diabetes for more efficient and effective public health strategies.