健康状况测量中人口水平趋势的贝叶斯估计
Bayesian Estimation of Population-Level Trends in Measures of Health Status
摘要：
要改善全世界的健康状况，就需要对人口健康状况的趋势进行严格的量化。然而，全球一级的调查是不可用的，迫使研究人员依靠质量参差不齐的零碎的国别数据。我们提出了一个贝叶斯模型，系统地结合不同的数据，使国家，区域和全球一级的时间趋势估计的重要健康指标。该模型考虑到时间和年龄的非线性，它借用了时间、年龄、协变量以及区域国家集群内部和跨区域国家集群的力量，以便在数据稀疏的情况下进行估计。贝叶斯方法允许我们从缺失以及采样和参数不确定性的各个方面来考虑不确定性。MCMC采样允许在高维的、受约束的参数空间中进行推断，同时提供允许对各种感兴趣的泛函进行直接推断的后验绘制。在这里，我们使用血压作为健康指标的示例。高血压是心血管疾病的主要危险因素，心血管疾病是全球死亡的主要原因。研究结果强调了一个风险转变，高收入地区的血压水平下降，而许多低收入地区的血压水平上升。
英文摘要：
Improving health worldwide will require rigorous quantification of population-level trends in health status. However, global-level surveys are not available, forcing researchers to rely on fragmentary country-specific data of varying quality. We present a Bayesian model that systematically combines disparate data to make country-, region- and global-level estimates of time trends in important health indicators. The model allows for time and age nonlinearity, and it borrows strength in time, age, covariates, and within and across regional country clusters to make estimates where data are sparse. The Bayesian approach allows us to account for uncertainty from the various aspects of missingness as well as sampling and parameter uncertainty. MCMC sampling allows for inference in a high-dimensional, constrained parameter space, while providing posterior draws that allow straightforward inference on the wide variety of functionals of interest. Here we use blood pressure as an example health metric. High blood pressure is the leading risk factor for cardiovascular disease, the leading cause of death worldwide. The results highlight a risk transition, with decreasing blood pressure in high-income regions and increasing levels in many lower-income regions.
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