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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

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  • Shang-Ming Zhou
  • Ronan A Lyons
  • Owen G Bodger
  • Ann John
  • Huw Brunt
  • Kerina Jones
  • Mike B Gravenor
  • Sinead Brophy

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 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.

Suggested Citation

  • Shang-Ming Zhou & Ronan A Lyons & Owen G Bodger & Ann John & Huw Brunt & Kerina Jones & Mike B Gravenor & Sinead Brophy, 2014. "Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-14, November.
  • Handle: RePEc:plo:pone00:0113592
    DOI: 10.1371/journal.pone.0113592
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    References listed on IDEAS

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    1. Sandra E. Black & Paul J. Devereux & Kjell G. Salvanes, 2007. "From the Cradle to the Labor Market? The Effect of Birth Weight on Adult Outcomes," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(1), pages 409-439.
    2. Shang-Ming Zhou & Ronan A Lyons & Sinead Brophy & Mike B Gravenor, 2012. "Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-14, December.
    3. repec:ucn:wpaper:10197/317 is not listed on IDEAS
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    1. Shang-Ming Zhou & Fabiola Fernandez-Gutierrez & Jonathan Kennedy & Roxanne Cooksey & Mark Atkinson & Spiros Denaxas & Stefan Siebert & William G Dixon & Terence W O’Neill & Ernest Choy & Cathie Sudlow, 2016. "Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.
    2. Mahyuddin K. M. Nasution, 2022. "World on Data Perspective," World, MDPI, vol. 3(3), pages 1-17, September.

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