Producing reliable estimates of health and demographic indicators at fine areal scales is crucial for examining heterogeneity and supporting localized health policy. However, many surveys release outcomes only at coarser administrative levels, thereby limiting their relevance for decision-making. We propose a fully Bayesian, single-stage spatial modeling framework for area-level disaggregation that generates fine-scale estimates of indicators directly from coarsely aggregated survey data. By defining a latent spatial process at the target resolution and linking it to observed outcomes through an aggregation step, the framework adopts small-area estimation techniques while incorporating covariates and delivering coherent uncertainty quantification. The proposed methods are implemented with inlabru to achieve computational efficiency. We evaluate performance through a simulation study of general fertility rates in Kenya to demonstrate the models' ability to recover fine-scale variation across diverse data-generating scenarios. We further apply the framework to two national surveys to produce district-level fertility estimates from the 2022 Kenya Demographic and Health Survey and, more importantly, district-level indicators for unpaid care and domestic work and mass media usage from the 2021 Kenya Time Use Survey.

翻译：在精细区域尺度上生成可靠的健康与人口指标估计，对于考察异质性和支持地方性卫生政策至关重要。然而，许多调查仅发布较粗行政层级的结果，这限制了其决策相关性。我们提出了一种完全贝叶斯、单阶段的空间建模框架，用于区域层级分解，该框架可直接从粗粒度汇总的调查数据中生成精细尺度的指标估计。该框架通过在目标分辨率上定义潜在空间过程，并通过聚合步骤将其与观测结果相连接，从而采用小区域估计技术，同时纳入协变量并提供一致的不确定性量化。所提出的方法通过inlabru实现，以获得计算效率。我们通过对肯尼亚一般生育率的模拟研究来评估性能，以证明模型在不同数据生成场景下恢复精细尺度变化的能力。我们进一步将该框架应用于两项全国性调查：基于2022年肯尼亚人口与健康调查生成区县级生育率估计，更重要的是，基于2021年肯尼亚时间利用调查生成区县级无酬照护与家务劳动指标以及大众媒体使用指标。