Evidence map›Paper›PMID 25409038›Full record

ArticlePloS one2014

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 A Lyons, Owen G Bodger, Ann John, Huw Brunt, Kerina Jones, Mike B Gravenor, Sinead Brophy

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Shang-Ming ZhouInstitute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
Ronan A LyonsInstitute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
Owen G BodgerInstitute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
Ann JohnInstitute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
Huw BruntPublic Health Wales, Temple of Peace and Health, Cathays Park, Cardiff, United Kingdom.
Kerina JonesInstitute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
Mike B GravenorInstitute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.
Sinead BrophyInstitute of Life Science, College of Medicine, Swansea University, Swansea, United Kingdom.

Funding

Arthritis Research UKBritish Heart Foundation MR/KO232331/1Cancer Research UKChief Scientist OfficeMedical Research Council MR/K006525/1Medical Research Council MR/K023233/1Wellcome Trust
6 · The paper itself

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.

Indexed as

Health StatusModels, TheoreticalSocioeconomic FactorsChildDatabases, FactualHousingHumansIncomeRisk FactorsSocial Environment

Identifiers

PMID25409038
PMCPMC4237439

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.