Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, The University of Tennessee, Knoxville, TN, USA.
Int J Health Geogr. 2024 Jan 6;23(1):1. doi: 10.1186/s12942-023-00360-5.
Early diagnosis, control of blood glucose levels and cardiovascular risk factors, and regular screening are essential to prevent or delay complications of diabetes. However, most adults with diabetes do not meet recommended targets, and some populations have disproportionately high rates of potentially preventable diabetes-related hospitalizations. Understanding the factors that contribute to geographic disparities can guide resource allocation and help ensure that future interventions are designed to meet the specific needs of these communities. Therefore, the objectives of this study were (1) to identify determinants of diabetes-related hospitalization rates at the ZIP code tabulation area (ZCTA) level in Florida, and (2) assess if the strengths of these relationships vary by geographic location and at different spatial scales.
Diabetes-related hospitalization (DRH) rates were computed at the ZCTA level using data from 2016 to 2019. A global ordinary least squares regression model was fit to identify socioeconomic, demographic, healthcare-related, and built environment characteristics associated with log-transformed DRH rates. A multiscale geographically weighted regression (MGWR) model was then fit to investigate and describe spatial heterogeneity of regression coefficients.
Populations of ZCTAs with high rates of diabetes-related hospitalizations tended to have higher proportions of older adults (p < 0.0001) and non-Hispanic Black residents (p = 0.003). In addition, DRH rates were associated with higher levels of unemployment (p = 0.001), uninsurance (p < 0.0001), and lack of access to a vehicle (p = 0.002). Population density and median household income had significant (p < 0.0001) negative associations with DRH rates. Non-stationary variables exhibited spatial heterogeneity at local (percent non-Hispanic Black, educational attainment), regional (age composition, unemployment, health insurance coverage), and statewide scales (population density, income, vehicle access).
The findings of this study underscore the importance of socioeconomic resources and rurality in shaping population health. Understanding the spatial context of the observed relationships provides valuable insights to guide needs-based, locally-focused health planning to reduce disparities in the burden of potentially avoidable hospitalizations.
早期诊断、控制血糖水平和心血管危险因素以及定期筛查对于预防或延缓糖尿病并发症至关重要。然而,大多数糖尿病患者并未达到推荐目标,某些人群的糖尿病相关住院率高得不成比例,这些住院本可预防。了解导致地域差异的因素有助于分配资源,并确保未来的干预措施能够满足这些社区的具体需求。因此,本研究的目的是:(1) 确定佛罗里达州邮政编码区 (ZCTA) 层面与糖尿病相关住院率相关的决定因素;(2) 评估这些关系的强度是否因地理位置和不同的空间尺度而异。
使用 2016 年至 2019 年的数据,在 ZCTA 层面计算与糖尿病相关的住院率 (DRH)。采用全局普通最小二乘回归模型,确定与对数变换后的 DRH 率相关的社会经济、人口统计学、医疗保健和建成环境特征。然后拟合多尺度地理加权回归 (MGWR) 模型,以调查和描述回归系数的空间异质性。
糖尿病相关住院率较高的 ZCTA 人群往往有更高比例的老年人 (p<0.0001) 和非西班牙裔黑人居民 (p=0.003)。此外,DRH 率与较高水平的失业 (p=0.001)、无保险 (p<0.0001) 和缺乏交通工具 (p=0.002) 相关。人口密度和家庭中位数收入与 DRH 率呈显著负相关 (p<0.0001)。非平稳变量在局部 (非西班牙裔黑人百分比、教育程度)、区域 (年龄构成、失业、医疗保险覆盖范围) 和全州尺度 (人口密度、收入、交通工具获得情况) 上表现出空间异质性。
本研究结果强调了社会经济资源和农村地区在塑造人口健康方面的重要性。了解观察到的关系的空间背景为基于需求、以地方为重点的健康规划提供了有价值的见解,以减少可避免的住院负担的差异。