Evidence map›Paper›PMID 41491274›Full record

ArticleInternational journal of obesity (2005)2026

Intersectional inequality in general and central obesity: cross-sectional UK Biobank study.

Joseph Hutchinson, Natalie Darko, Rebecca Hardy, David Webb, Francesco Zaccardi, William Johnson

Abstract read
In one paragraph

Article in International journal of obesity (2005), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Joseph HutchinsonSchool of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK. j.Hutchinson@lboro.ac.uk.ORCID 0000-0001-8964-0984
Natalie DarkoDepartment of Population Health Sciences, University of Leicester. Director of Inclusion, Leicester NIHR Biomedical Research Centre, Leicester General Hospital, Leicester, UK.
Rebecca HardySchool of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK.
David WebbDiabetes Research Centre, University of Leicester. Consultant in Diabetes and Endocrinology, University Hospitals of Leicester NHS Trust, Leicester, UK.
Francesco ZaccardiLeicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK.
William JohnsonSchool of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK.ORCID 0000-0002-0347-4354

Funding

Wellcome Trust 223512/Z/21/Z
6 · The paper itself

Abstract

backgroundPeople exist at a combination of different individual and neighbourhood deprivations. Each of these combinations may have a unique impact on health. However, little is known about the intersectional inequality of these combinations on general and central obesity, including when considering their demographics. This study aims to answer these questions.

methodsThe sample comprised 452,339 participants from the UK Biobank study. Individuals were grouped into 320 intersectional strata according to their household income, neighbourhood deprivation, sex, ethnicity and age. Linear and logistic multilevel analysis of individual heterogeneity and discriminatory accuracy was used to establish the total, additive and interactive inequality of body mass index (BMI), fat mass index (FMI), and waist to height ratio (WHtR), as well as the associated obesity classifications.

results6.5%, 25.2% and 9.1% of the total variation in BMI, FMI and WHtR, respectively, was due to inequality between the strata. Of this, 26.5%, 3.5% and 22.0% is interactive. 79, 58 and 93 strata for BMI, FMI and WHtR demonstrate a significant interactive effect. We found some patterns; for example, affluent white women have an advantaged interactive effect, whilst deprived black women have a disadvantaged effect. Meanwhile men experience the inverse relationship. The relationship between individual and neighbourhood deprivation is not universally experienced by all strata. For example, black men living in areas of high deprivation have higher BMIs as their household income increases.

conclusionsA large proportion of variation in general and central obesity is due to intersectional inequality, with up to 26.5% being interactive. It is important that these intersectional effects are considered when designing policy interventions to avoid policy failure, such as by focussing on groups with high total and interactive risk.

Indexed as

Health Status DisparitiesObesityObesity, AbdominalAdultAgedBiological Specimen BanksBody Mass IndexCross-Sectional StudiesFemaleHumansMaleMiddle AgedSocioeconomic FactorsUK BiobankUnited Kingdom

Identifiers

PMID41491274
PMCPMC13056582

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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.