发表机构
Duke University; George Mason University（杜克大学; 乔治梅森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出用地理加权ML框架作为代理模型，解决传统小区域慢性病估计滞后问题，可快速生成美国县级10种慢性病的可比估计值以支持决策。
AI 中文摘要
小区域估计（SAE）可帮助研究人员和政策制定者识别健康结果的空间差异，但基于调查的SAE产品存在固有滞后。CDC PLACES等金标准估计值在基础调查数据收集后约两年才发布，限制了其在时间敏感决策中的应用。本研究评估机器学习（ML）作为代理的潜力，学习频繁更新的区域级预测因子与现有SAE输出的关系，以在调查SAE不可用或延迟的年份生成及时、可比的估计值。我们评估了几种全局和地理加权ML模型，用于美国县级10种慢性病的SAE：慢性阻塞性肺疾病（COPD）、哮喘、心脏病、关节炎、癌症、抑郁症、糖尿病、高血压、高胆固醇血症和中风。研究结果表明，地理加权随机森林、地理加权回归等地理加权ML框架可作为可扩展的开放数据代理，用于快速生成SAE并支持数据驱动决策。
英文摘要
Small-area estimation (SAE) enables researchers and policymakers to identify spatial disparities in health outcomes, but survey-based SAE products carry an inherent lag. Gold-standard estimates such as CDC PLACES are released roughly two years after the underlying survey data are collected, limiting their use for time-sensitive decision-making. This study evaluates the potential for machine learning (ML) to serve as a surrogate, learning the relationship between frequently updated area-level predictors and existing SAE outputs to generate timely, comparable estimates in years when SAE from surveys are unavailable or delayed. We evaluate several global and geographically weighted ML models for county-level SAE of ten chronic conditions across the US: COPD, asthma, heart disease, arthritis, cancer, depression, diabetes, high blood pressure, high cholesterol, and stroke. Our findings suggest that geographically weighted ML frameworks like geographically weighted random forest and geographically weighted regression offer scalable and open data surrogates for rapidly generating SAE and supporting data driven decision making.
