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Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments

机译:评估复杂数据中风险因素的微观影响的本地建模技术:了解儿童受教育程度中的健康和社会经济不平等现象

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摘要

Although inequalities in health and socioeconomic status have an important influence on childhood educational performance, the interactions between these multiple factors relating to variation in educational outcomes at micro-level is unknown, and how to evaluate the many possible interactions of these factors is not well established. This paper aims to examine multi-dimensional deprivation factors and their impact on childhood educational outcomes at micro-level, focusing on geographic areas having widely different disparity patterns, in which each area is characterised by six deprivation domains (Income, Health, Geographical Access to Services, Housing, Physical Environment, and Community Safety). Traditional health statistical studies tend to use one global model to describe the whole population for macro-analysis. In this paper, we combine linked educational and deprivation data across small areas (median population of 1500), then use a local modelling technique, the Takagi-Sugeno fuzzy system, to predict area educational outcomes at ages 7 and 11. We define two new metrics, “Micro-impact of Domain” and “Contribution of Domain”, to quantify the variations of local impacts of multidimensional factors on educational outcomes across small areas. The two metrics highlight differing priorities. Our study reveals complex multi-way interactions between the deprivation domains, which could not be provided by traditional health statistical methods based on single global model. We demonstrate that although Income has an expected central role, all domains contribute, and in some areas Health, Environment, Access to Services, Housing and Community Safety each could be the dominant factor. Thus the relative importance of health and socioeconomic factors varies considerably for different areas, depending on the levels of each of the other factors, and therefore each component of deprivation must be considered as part of a wider system. Childhood educational achievement could benefit from policies and intervention strategies that are tailored to the local geographic areas' profiles.
机译:尽管健康和社会经济状况的不平等对儿童的教育表现有重要影响,但与微观水平的教育成果变化相关的多个因素之间的相互作用尚不清楚,如何评估这些因素的许多可能相互作用尚不明确。本文旨在从微观层面研究多维剥夺因素及其对儿童教育成果的影响,重点关注差异模式迥异的地理区域,其中每个区域都具有六个剥夺域(收入,健康,服务,住房,自然环境和社区安全)。传统的健康统计研究倾向于使用一种全局模型来描述整个人群,以进行宏观分析。在本文中,我们将小地区(中位数人口为1500)的教育和贫困数据链接在一起,然后使用局部建模技术Takagi-Sugeno模糊系统来预测7岁和11岁时的地区教育成果。我们定义了两个指标“领域的微影响”和“领域的贡献”,以量化多维因素对小区域教育成果的局部影响的变化。这两个指标强调了不同的优先级。我们的研究揭示了剥夺域之间复杂的多方相互作用,而传统的基于单一全局模型的健康统计方法无法提供这种相互作用。我们证明,尽管收入发挥了预期的核心作用,但所有领域都发挥了作用,在某些领域,健康,环境,服务获取,住房和社区安全可能都是主导因素。因此,健康和社会经济因素的相对重要性在不同地区有很大不同,这取决于其他因素的水平,因此,必须将贫困的每个组成部分视为更广泛系统的一部分。根据当地地理区域的情况制定的政策和干预策略可以使儿童的教育成就受益。

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