用机器学习分析美国农村社区医疗保健的地理空间变化
Radion Svynarenko1, Hyun Kim2, Tracey Stansberry1
1College of Nursing, University of Tennessee, 1412 Circle Dr., Knoxville, TN 37996, USA.
Healthcare (Basel, Switzerland)
|July 12, 2025
概括
健康的社会驱动因素 (SDOH) 显著影响农村公共卫生. 这项研究使用空间统计方法分析农村医疗保健获取的地理空间变化,并确定了影响这些差异的关键因素.
科学领域:
- 公共卫生 公共卫生
- 农村健康差异 农村健康差异
- 空间统计的空间统计.
背景情况:
- 健康的社会驱动因素 (SDOH) 严重影响农村公共卫生,带来诸如人口老龄化,就业限制,收入较低,死亡率较高和医疗保健供不应求等挑战.
- 现有的农村和SDOH研究经常使用狭窄的定义,忽视了获得医疗保健的关键空间维度.
研究的目的:
- 通过使用县级数据,调查不同农村级别的医疗保健的地理空间变化.
- 确定影响农村医疗保健获取的健康和医疗保健特征的关键社会驱动因素.
主要方法:
- 利用来自2020年卫生保健研究和质量局 (SDOH) 数据库的县级数据.
- 采用先进的空间统计方法,包括梯度增强机 (GBM),沙普利增量解释 (SHAP) 和多尺度地理加权回归 (MGWR).
主要成果:
- 从对1976个县的262个变量的分析中,确定了影响农村医疗保健的20个关键变量.
- 关键变量分为医疗保险状况,获得护理和医疗保险支付量.
- 多尺度地理加权回归揭示了医疗保健特征的显著全球和地方影响,与社会地理因素密切相关.
结论:
- 政策制定者必须制定公平,创新的护理模式,以解决弱势农村社区的SDOH和获得护理问题的问题,特别是在没有医疗补助扩张的州.
- 需要采取紧急行动,以减轻潜在的公共卫生计划削减对农村人口的影响.
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