充分利用现有健康大数据进行人群健康评价是健康管理决策、实现全民小康的重要研究课题。然而现有健康数据来源广泛且数据结构各异、数据质量参差不齐，单独使用这些数据进行人群健康评价会存在一定的偏倚，无法为健康管理决策提供可靠依据。因此，有效地对健康大数据进行整合、修匀，解决数据缺失、不规范、有偏等问题，并利用修匀后的数据进行全面的人群健康评价，具有重要的现实意义。本研究拟从公共卫生部门、医疗机构以及文献数据库中挖掘中国人群不同疾病的死亡率、患病率与发病率等数据信息，利用经过清洗后的数据，探索Meta回归、高斯过程回归（GPR）以及模型寿命表等统计学方法，通过这些方法进行数据整合、修匀，并在上述数据基础上计算伤残调整寿命年（DALY）和健康期望寿命（HALE）等人群健康评价指标，并开发可视化工具使不同地域、不同性别、不同时间的人群健康状况得以展示以及实时查询，为科学的健康管理决策提供可靠的数据支持。

It is an important research topic to make full use of the current big data for population health evaluation, which will contribute a lot to developing health management policies and achieving a well-off society. However, the existing health data are of wide sources, various structures, and uneven quality. Using these data directly for population health assessment will lead to some certain bias, thus unable to provide a reliable basis for health management decision-making. Therefore, it is of great practical significance to effectively integrate and smooth health big data, and properly deal well with missing, unstandardized and biased data, so as to conduct a comprehensive evaluation on population health using the data after smoothing. .In this program, we will first drastically collect and integrate the cause-specific mortality and morbidity data in Chinese population from various sources such as public health departments, medical institutions, and literature databases. Then, statistical methods of Meta regression, Gauss process regression (GPR) and model life table are explored and applied to integrate and smooth these extracted mortality and morbidity data. Moreover, based on the smoothed data, we are able to calculate and obtain the population health assessment indexes, such as disability adjusted life years (DALY) and health life expectancy (HALE). Finally, visualization tools can be further developed to clearly exhibit the health status of Chinese population for different genders and ages by regions and periods. Real time query of these health assessment results will thus provide more reliable data-based decision-making support for public health management.