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

本研究旨在从人-环境的角度系统地考察城市社区健康相关差异的关键决定因素及其相互关系，从而弥合上述知识差距。如果能够获得合作伙伴提供的不同地理参照卫生数据集，并促进规划学者和公共卫生从业人员之间的跨学科研究，这项研究将大大受益。拟议的研究将解决两个主要问题：我们如何理解不同社区健康相关福祉的空间变化模式；决定因素的不同维度如何相互关联并影响不同社区居民的健康相关福祉？有五个主要研究目标：(1)通过采用侧重于人类环境视角的健康成因方法，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）及其与健康相关的相互关系的理论理解和实证建模。(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，实施与健康有关的关键福祉决定因素的多变量措施。(3)通过GIS制图和空间聚类分析，建立不同社区不同慢性疾病和健康结果的多样性地理格局和时间变化，并了解其与不同健康相关决定因素的空间相关性。(4)应用多层次空间建模框架，建立不同空间层次上不同维度决定因素之间的相互关系，并确定其在解释不同社区居民健康幸福感差异中的相对重要性。(5)提出证据基础，为解决保健不平等问题的政策和做法发展提供信息，并促进地方当局和联合当局的联合工作，减少案例研究领域内外的保健不平等现象。我们的合作伙伴经营的Tameside和Glossop是案例研究领域。虽然案例研究区域由具有不同社会空间背景的社区组成，但许多社区的居民预期寿命较低，患有其他慢性疾病，生活在该国10%最贫困的社区

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