Compressive Population Health: Cost-Effective Profiling of Prevalence for Multiple Non-Communicable Diseases via Health Data Science

随着人口老龄化和生活方式的改变，心脏病、糖尿病和癌症等非传染性疾病在我们的社会中变得非常普遍，与其他发达国家相比，英国的情况更具挑战性。人口健康监测是公共卫生服务的基础，而人口规模的不同地区多发性非传染性疾病的流行情况(例如，构建空间细粒度的发病率地图)是最重要的任务之一。然而，传统的公共卫生数据收集和流行率概况分析方法，如基于诊所访问的数据集成和健康调查，往往非常昂贵和耗时。该项目提出了一种新的范式，称为压缩人口健康(简称CPH)，以最大限度地降低流行率概况期间的数据收集成本。CPH的基本思想是以传统方式智能地选择区域的子集进行数据收集和人口健康概况分析，同时利用固有的数据相关性来执行其余区域的数据推理。通过利用流行病学家发现的下列类型的内在数据相关性，CPH得到了促进。(A)病内空间相关性。也就是说，当地区相邻或具有某些共同的环境、社会经济和人口特征时，它们在某些疾病的患病率上更加相似。(B)疾病间的相关性。多发病，通常定义为两种或两种以上慢性疾病的共同存在，表明不同类型疾病的统计数据也可能相互关联。例如，肥胖率较高的地区更有可能有更高的心脏病和癌症发病率。为了实现这一思想，本项目开发了三个技术工作包，以实现以下技术目标：(1)调查和提取潜在数据关联，并进一步利用它们来构建目标地理网格上流行推断的学习模型。(2)设计智能算法，以成本、可靠性和延迟为多目标优化目标，为每种疾病选择传统感应区。(3)对患病率推断结果进行评价和解释，以确保该方法的可靠性和稳健性。拟议的CPH是一种新的解决方案，以应对数据科学和人工智能带来的公共卫生数据收集挑战。它为一种颠覆性的人口健康监测模式打开了大门，可能会大幅降低公共卫生当局的成本。通过与公共卫生部门的合作伙伴密切合作，包括英国国民健康保险制度和沃里克郡议会的公共卫生，我们将根据多个公共卫生数据集以及相同地区的相关人口/地理统计数据来评估这种方法的可行性。

With a growing ageing population and changes in lifestyles, non-communicable diseases (NCD), e.g. heart disease, diabetes, and cancer, have become extremely prevalent in our society, and the situation is more challenging in UK compared to other developed countries. Population health monitoring is fundamental block for public health services, and profiling population-scale prevalence of multiple NCD across different regions (e.g., building the spatially fine-grained morbidity rate map) is one of the most important tasks. However, traditional public health data collection and prevalence profiling approaches, such as clinic-visit-based data integration and health surveys, are often very costly and time-consuming. This project proposes a novel paradigm, called compressive population health (CPH for short), to reduce the data collection cost during the profiling of prevalence to the maximum extent. The basic idea CPH is that a subset of areas is intelligently selected for data collection and population health profiling in the traditional way, while leveraging inherent data correlations to perform data inference for the rest of the areas. CPH is facilitated by the exploitation of the following types of inherent data correlations found by epidemiologists. (a) Intra-Disease Spatial Correlations. That is, regions are more similar in the prevalence rate of some diseases when they are neighbouring, or share certain common environmental, socioeconomic, and demographical attributes. (b) Inter-Disease Correlations. Multimorbidity, commonly defined as the co-presence of two or more chronic conditions, demonstrates that statistics for different types of disease may also correlate with each other. For example, regions with higher obesity rate are more likely to have higher rates of heart disease and cancers. In order to realize this idea, this project develops three technical work packages to accomplish the following technical goals: (1) Investigate and extract latent data correlations and further utilize them to build learning models for prevalence inference on the target geographical grids. (2) Design intelligent algorithms for selecting traditional-sensed areas for each disease with multi-objective optimization goals including cost, reliability, and latency. (3) Evaluate and interpret the inference results of prevalence rate to ensure the reliability and robustness of the approach. The proposed CPH is a novel solution to a public health data collection challenge enabled by data science and artificial intelligence. It opens the door for a disruptive population health monitoring paradigm with potential significant cost reductions for public health authorities. By closely working with partners from public health sector, including NHS England and Public Health at Warwickshire County Council, we will evaluate the feasibility of this approach based on multiple public health datasets together with relevant demographic/geographic statistics in the same regions.