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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 / Shang-ming Zhou, Ronan Lyons, Owen Bodger, Ann John, Huw Brunt, Kerina Jones, Mike B. Gravenor, Sinead Brophy, Michael Gravenor

PLoS ONE, Volume: 9, Issue: 11, Start page: e113592

Swansea University Authors: Shang-ming Zhou, Ronan Lyons, Owen Bodger, Ann John, Kerina Jones, Sinead Brophy, Michael Gravenor

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DOI (Published version): 10.1371/journal.pone.0113592

Abstract

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 th...

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Published: 2014
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title Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments
spellingShingle Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments
Shang-ming, Zhou
Ronan, Lyons
Owen, Bodger
Ann, John
Kerina, Jones
Sinead, Brophy
Michael, Gravenor
title_short Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments
title_full Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments
title_fullStr Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments
title_full_unstemmed Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments
title_sort Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments
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author_id_fullname_str_mv 118578a62021ba8ef61398da0a8750da_***_Shang-ming, Zhou
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan, Lyons
8096440ab42b60a86e6aba678fe2695a_***_Owen, Bodger
ed8a9c37bd7b7235b762d941ef18ee55_***_Ann, John
c13b3cd0a6f8cbac2e461b54b3cdd839_***_Kerina, Jones
84f5661b35a729f55047f9e793d8798b_***_Sinead, Brophy
70a544476ce62ba78502ce463c2500d6_***_Michael, Gravenor
author Shang-ming, Zhou
Ronan, Lyons
Owen, Bodger
Ann, John
Kerina, Jones
Sinead, Brophy
Michael, Gravenor
author2 Shang-ming Zhou
Ronan Lyons
Owen Bodger
Ann John
Huw Brunt
Kerina Jones
Mike B. Gravenor
Sinead Brophy
Michael Gravenor
format Journal article
container_title PLoS ONE
container_volume 9
container_issue 11
container_start_page e113592
publishDate 2014
institution Swansea University
doi_str_mv 10.1371/journal.pone.0113592
document_store_str 1
active_str 0
description 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.
published_date 2014-11-19T03:39:50Z
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