Data used to estimate the burden of diseases (BOD) are usually sparse, noisy, and heterogeneous. These data are collected from surveys, registries, and systematic reviews that have different areal units, are conducted at different times, and are reported for different age groups. In this study, we developed a Bayesian geo‐statistical model to combine aggregated sparse, noisy BOD data from different sources with misaligned areal units. Our model incorporates the correlation of space, time, and age to estimate health indicators for areas with no data or a small number of observations. The model also considers the heterogeneity of data sources and the measurement errors of input data in the final estimates and uncertainty intervals. We applied the model to combine data from nine different sources of body mass index in a national and sub‐national BOD study. The cross‐validation results confirmed a high out‐of‐sample predictive ability in sparse and noisy data. The proposed model can be used by other BOD studies especially at the sub‐national level when the areal units are subject to misalignment.

中文翻译：

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使用地统计学方法对疾病数据负担错位的时空分析

用于估计疾病负担（BOD）的数据通常稀疏，嘈杂且异构。这些数据是从具有不同区域单位，在不同时间进行的，针对不同年龄组进行报告的调查，注册管理机构和系统评价收集的。在这项研究中，我们开发了一种贝叶斯地统计学模型，将来自不同来源的聚集的稀疏，嘈杂的BOD数据与未对齐的面积单位相结合。我们的模型结合了空间，时间和年龄的相关性，以估算没有数据或观测值少的区域的健康指标。该模型还考虑了数据源的异质性以及最终估计数和不确定性区间中输入数据的测量误差。在国家和地方BOD研究中，我们应用该模型合并了来自九种不同体重指数来源的数据。交叉验证的结果证实了稀疏和嘈杂数据的高样本外预测能力。提议的模型可用于其他BOD研究，尤其是在区域单位易错位的地方以下地方。