发表机构
Boston University School of Public Health（波士顿大学公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出贝叶斯双广义贝塔回归框架，利用空间和时间借力建模受限个体幸福感指数，提升稀疏数据区域估计精度，发现收入、教育和婚姻状况与幸福感最相关。
AI 中文摘要
健康和幸福感指数被广泛用于评估人群健康结果并为政策决策提供信息。个体层面的幸福感评估可用于开发衡量不同地理单元健康状况的社区级指数。虽然许多现有指数在县或州等粗粒度地理层面上运作，但更精细的空间分辨率可以提供更具可操作性的见解。我们提出了一种新颖的贝叶斯双广义贝塔回归框架，使用2021年至2023年在马萨诸塞州收集的年度调查数据来建模受限的个体层面幸福感指数（WBI）。尽管调查受访者可能因年份而异，但回答被地理标记到邮政编码制表区（ZCTAs），从而能够整合空间和时间信息。我们的框架通过图拉普拉斯矩阵纳入空间依赖性，该矩阵编码了基于驾驶时间的ZCTA邻域结构，并利用时间借力，通过使用前一年的后验空间效应估计来为下一年的先验提供信息。这种在贝叶斯双广义贝塔回归框架内的双重借力策略提高了估计精度，特别是在数据稀疏的区域，并改善了对较小地理单元的推断。我们通过一项逼真的模拟研究展示了我们方法的实用性，该研究强调了在借力空间和时间信息时估计的改进。在真实数据分析中，我们为马萨诸塞州居民建模个体层面的幸福感，并发现收入、教育状况和婚姻状况与WBI最相关。此外，我们观察到马萨诸塞州西部、科德角以及波士顿附近的ZCTAs表现最佳，具有最高的空间效应。
英文摘要
Health and well-being indices are widely used to assess population health outcomes and inform policy decisions. Individual-level assessment of well-being can be used to develop community-level indices that measure wellness for different geographical units. While many existing indices operate at coarse geographic levels such as counties or states, finer spatial resolution can offer more actionable insights. We present a novel Bayesian double generalized beta regression framework to model a bounded individual-level well-being index (WBI) using annual survey data collected from 2021 to 2023 in Massachusetts. Although survey respondents may differ across years, responses are geotagged to ZIP Code Tabulation Areas (ZCTAs), enabling the integration of both spatial and temporal information. Our framework incorporates spatial dependencies via a graph Laplacian matrix that encodes driving time-based ZCTA neighborhood structure, and leverages temporal borrowing by using posterior spatial effect estimates from one year to inform priors in the next. This dual-borrowing strategy within a Bayesian double generalized beta regression framework enhances estimation precision, particularly in areas with sparse data, and improves inference for smaller geographic units. We demonstrate the utility of our method through a realistic simulation study that highlights improved estimation when borrowing spatial and temporal information. In the real data analysis, we model individual-level well-being for residents in Massachusetts and find that income, education status, and marital status are most associated with WBI. Additionally, we observe that ZCTAs in Western Massachusetts, Cape Cod, and those near Boston perform best with the highest spatial effects.
