Spatial differentiation and key determinants of health-related well-being in Tameside and Glossop: geo-visualisation and multi-level spatial modelling

这项拟议的研究旨在通过从人-环境的角度系统地检查城市社区与健康相关的不同福祉的关键决定因素及其相互关系，来弥合上述知识差距。这项研究将极大地受益于获得合作伙伴提供的不同地理参考健康数据集，并促进规划学者和公共卫生从业者之间的跨学科研究。拟议的研究将解决两个主要问题：我们如何理解不同社区与健康相关的幸福感的空间变化模式；以及不同维度的决定因素如何相互关联并影响不同社区居民的与健康相关的幸福感？主要研究目标有五个：(1)通过采用注重人-环境视角的有益健康的方法，建立一个概念框架，指导我们对关键决定因素(在不同维度下)及其与健康相关幸福感的相互关系的理论理解和经验建模。(2)通过整理、验证和整合来自不同地理参考数据集和人口/患者调查的相关数据源和指标，实现关键健康相关幸福感决定因素的多变量测量。(3)通过地理信息系统制图和空间聚类分析，建立不同社区不同慢性病和健康结果的不同地理模式和时间变化，并了解它们与不同健康相关决定因素的空间相关性。(4)应用多层次空间模型框架，建立不同空间层次上不同维度决定因素之间的相互关系，并确定它们在解释不同社区居民与健康相关的不同福祉方面的相对重要性。(5)提取证据基础，为解决健康不平等问题的政策和实践发展提供信息，并促进地方当局和联合当局的联合工作，以减少案例研究区域内外的健康不平等。虽然案例研究区域由具有不同社会空间背景的社区组成，但许多社区的居民患有预期寿命较低和其他慢性病，生活在该国10%最贫穷的社区

This proposed study aims to bridge the above identified knowledge gaps by systematically examining the key determinants, and their interrelations, over differential health-related well-being in urban neighbourhoods from a human-environment perspective. The study will greatly benefit from gaining access to different geo-referenced health datasets provided by the collaborative partner and fostering inter-disciplinary research between planning academics and public health practitioners. The proposed study will address two main questions: how can we understand the spatially variated patterns of health related well-being across different neighbourhoods; and how different dimensions of determinants are interrelated and impact on health-related well-being of residents in different neighbourhoods?There are five key research objectives:(1) To develop a conceptual framework that guides our theoretical understanding and empirical modelling of key determinants (under different dimensions) and their interrelations on health-related well-being, both physical and emotional, by adopting a salutogenic approach that focuses on the human-environment perspective. (2) To operationalise multi-variate measures of key health-related well-being determinants by collating, validating and integrating relevant data sources and indicators from different geo-referenced datasets and population/patient surveys.(3) To establish the variegated geographic patterns and temporal change of different chronic diseases and health outcomes across different neighbourhoods and to understand their spatial correlations with the different health-related determinants via GIS mapping and spatial clustering analysis. (4) To apply multi-level spatial modelling framework to establish the interrelations among different dimensions of determinants over different spatial levels and to establish their relative importance in explaining differential health-related well-being of residents in different neighbourhoods.(5) To draw out the evidence-base to inform policy and practice development over the tackling of health inequalities issues and to foster joint working in local authorities and combined authorities to reduce health inequalities in and beyond the case study area.Tameside and Glossop, where our collaborative partner operates, is the case study area. While the case study area consists of neighbourhoods with varied socio-spatial contexts, residents in many neighbourhoods suffer from lower life expectancy and other chronic diseases and live in the country's 10% most deprived neighbourhoods