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Socioeconomic and ethnic inequalities increase the risk of type 2 diabetes

An analysis of NHS health check attendees in Birmingham

Bibliographic Data

ID22076217
AuthorsChung Him Au-Yeung (Birmingham City Council, corresponding author), David A Ellis (0000-0001-6172-3323), David Ellis (0000-0002-8311-0541, Birmingham City Council), Alexander Dallaway (0000-0002-2116-6654, University of Wolverhampton), Jenny Riley (Birmingham City Council), Justin Varney (0000-0002-8928-3291, Birmingham City Council), Rebecca Howell-Jones (0000-0002-9553-0367, Birmingham City Council)
Year2024
Volume12
Pages1477418-1477418
Publication date2024-11-27
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1477418
PMID39664526
OpenAlexW4404774360
LanguageEN
Citations received2
References cited65

Introduction Birmingham has a significantly higher type-II diabetes prevalence than the national average. This study aimed to investigate the association of socioeconomic deprivation and ethnicity on the risk of diabetes in Birmingham. Methods Data were included from 108,514 NHS Health Checks conducted in Birmingham between 2018 and 2023. Attributable fraction and multinomial logistic regression were used to estimate the number of events avoidable and the prevalence odds ratios (POR) of determinants respectively. Results Attributable fraction analysis estimated that 64% of diabetes and 44% of pre-diabetes cases could be attributed to socioeconomic deprivation. Specifically, if Asian attendees in the least deprived areas had the same risk as White individuals in the least deprived areas, there would have been 1,056 fewer cases of diabetes and 2,226 fewer cases of pre-diabetes. Diabetes was significantly associated with Asian ethnicity (POR = 5.43, p < 0.001), Black ethnicity (POR = 3.15, p < 0.001) and Mixed ethnicity (POR = 2.79, p < 0.001). Pre-diabetes was also significantly associated with Asian ethnicity (POR = 3.06, p < 0.001), Black ethnicity (POR = 2.70, p < 0.001) and Mixed ethnicity (POR = 2.21, p < 0.001). The interaction effects between ethnicity and deprivation posed a greater risk of diabetes, especially for Asian attendees in the first (POR = 9.34, p < 0.001) and second (POR = 6.24, p < 0.001) most deprived quintiles. Discussion The present findings demonstrate the association of ethnicity and socioeconomic deprivation on the risk of diabetes and pre-diabetes. It underscores the necessity for targeted interventions and policies to address these inequalities

Diabetes mellitus · Environmental health · Ethnic group · Inequality · Population · Socioeconomic status · Sociology · Type 2 diabetes · Cardiovascular Health and Risk Factors · Demography · Diabetes, Cardiovascular Risks, and Lipoproteins · Health Promotion and Cardiovascular Prevention · Mathematics · Medicine · Gerontology

  • Prevalence of type 2 diabetes from 2011 to 2023 by regional socioeconomic deprivation in Germany

    Open Access•Marielle Wirth, Stephan C Bischoff et al.•BMC Public Health•2025

  • Wider determinants of adverse birth outcomes in Birmingham and Solihull

    Open Access•David A Ellis, David Ellis et al.•Frontiers in Public Health•2025

  • Social Determinants of Health

    M G Marmot, Monique Bégin et al.•SOCIAL DETERMINANTS OF HEALTH;…•2005

  • Logistic Regression

    Open Access•David G Kleinbaum, Mitchel Klein•Logistic Regression•2010

  • Measuring bias in self-reported data

    Robert Rosenman, Vidhura Tennekoon et al.•International Journal of…•2011

  • Alcohol as a Risk Factor for Type 2 Diabetes

    Open Access•Dolly Baliunas, Dolly O Baliunas et al.•Diabetes Care•2009

  • Alcohol Consumption and Obesity

    Open Access•Gregory Traversy, Jean‐philippe Chaput•Current Obesity Reports•2015

  • Social Determinants of Health and Diabetes

    Open Access•Felicia Hill-Briggs, Felicia Hill‐Briggs et al.•Diabetes Care•2021

  • The Interpretation of Interaction in Contingency Tables

    Open Access•E H SIMPSON, Esther Simpson•Journal of the Royal Statistical…•1951

  • What Improves with Increased Missing Data Imputations?

    Todd E Bodner, Todd Bodner•Structural Equation Modeling: A…•2008

  • Prevalence odds ratio versus prevalence ratio: choice comes with consequences

    Open Access•Ashutosh Tamhane, Ashutosh R Tamhane et al.•Statistics in Medicine•2016

  • Health equity in England

    Open Access•Michael Marmot•BMJ•2020

  • Inference and missing data

    Donald B Rubin•Biometrika•1976

  • Multiple imputation using chained equations

    Open Access•I R White, Patrick Royston et al.•Statistics in Medicine•2011

  • How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory

    Open Access•John W Graham, J A Graham et al.•Prevention Science•2007

  • A Test of Missing Completely at Random for Multivariate Data with Missing Values

    Roderick J A Little, Roderick J Little•Journal of the American…•1988

  • A National Study of the Association Between Food Environments and County-Level Health Outcomes

    Open Access•Melissa Ahern, Cheryl Brown et al.•The Journal of Rural Health•2011

  • Which are the most suitable contextual indicators for use in widening participation to HE

    Stephen Gorard, Vikki Boliver et al.•Research Papers in Education•2019

Citations per year2
Citation span2025 - 2025 (1)
Highly citedNo

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