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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 Orcid Logo, Ronan Lyons Orcid Logo, Owen Bodger Orcid Logo, Ann John Orcid Logo, Huw Brunt, Kerina Jones Orcid Logo, Mike B. Gravenor, Sinead Brophy Orcid Logo, Michael Gravenor Orcid Logo

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

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

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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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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, &#x201C;Micro-impact of Domain&#x201D; and &#x201C;Contribution of Domain&#x201D;, 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. 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spelling 2022-12-02T18:13:18.3372710 v2 24546 2015-11-19 Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 8096440ab42b60a86e6aba678fe2695a 0000-0002-4022-9964 Owen Bodger Owen Bodger true false ed8a9c37bd7b7235b762d941ef18ee55 0000-0002-5657-6995 Ann John Ann John true false c13b3cd0a6f8cbac2e461b54b3cdd839 0000-0001-8164-3718 Kerina Jones Kerina Jones true false 84f5661b35a729f55047f9e793d8798b 0000-0001-7417-2858 Sinead Brophy Sinead Brophy true false 70a544476ce62ba78502ce463c2500d6 0000-0003-0710-0947 Michael Gravenor Michael Gravenor true false 2015-11-19 BMS 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. Journal Article PLoS ONE 9 11 e113592 Local modelling, risk factors, epidemiological data,health and socioeconomic Inequality, educational attainments 19 11 2014 2014-11-19 10.1371/journal.pone.0113592 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University 2022-12-02T18:13:18.3372710 2015-11-19T15:07:48.6221993 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Shang-ming Zhou 0000-0002-0719-9353 1 Ronan Lyons 0000-0001-5225-000X 2 Owen Bodger 0000-0002-4022-9964 3 Ann John 0000-0002-5657-6995 4 Huw Brunt 5 Kerina Jones 0000-0001-8164-3718 6 Mike B. Gravenor 7 Sinead Brophy 0000-0001-7417-2858 8 Michael Gravenor 0000-0003-0710-0947 9 0024546-26042019161509.pdf Local_Modelling_Techniques.pdf 2019-04-26T16:15:09.5930000 Output 2047190 application/pdf Version of Record true 2019-04-26T00:00:00.0000000 Distributed under the terms of a Creative Commons Attribution (CC-BY-4.0) true eng
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
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publishDate 2014
institution Swansea University
doi_str_mv 10.1371/journal.pone.0113592
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hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
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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 2014-11-19T03:29:09Z
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