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Understanding local ethnic inequalities in childhood BMI through cross-sectional analysis of routinely collected local data
BACKGROUND: Local-level analysis of ethnic inequalities in health is lacking, prohibiting a comprehensive understanding of the health needs of local populations and the design of effective health services. Knowledge of ethnic disparities in child weight status is particularly limited by overlooking...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6883566/ https://www.ncbi.nlm.nih.gov/pubmed/31779606 http://dx.doi.org/10.1186/s12889-019-7870-2 |
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author | Murphy, Marie Johnson, Rebecca Parsons, Nicholas R. Robertson, Wendy |
author_facet | Murphy, Marie Johnson, Rebecca Parsons, Nicholas R. Robertson, Wendy |
author_sort | Murphy, Marie |
collection | PubMed |
description | BACKGROUND: Local-level analysis of ethnic inequalities in health is lacking, prohibiting a comprehensive understanding of the health needs of local populations and the design of effective health services. Knowledge of ethnic disparities in child weight status is particularly limited by overlooking both the heterogeneity within ethnic groupings; and the complex ecological contexts in which obesity arises. This study aimed to establish whether there was variation in childhood BMI across ethnic groups in Coventry, and the influence of individual, school and neighbourhood contexts, using routinely collected local data. METHODS: National Child Measurement Programme data were compiled for the period 2007/8–2014/15 and combined with routinely collected local data reflecting school performance and demographics, and school and neighbourhood physical environments. Multi-level modelling using Monte Carlo Markov Chain methods was used to account for the clustering of children within schools and neighbourhoods. Ethnic group differences in BMI z-score (zBMI) were explored at 4–5 years and 10–11 years for girls and boys alongside individual, school and neighbourhood covariates. RESULTS: At age 4–5 years (n = 28,407), ethnic group differences were similar for boys and girls, with children from South Asian, White other, Chinese and ‘any other’ ethnic groups having a significantly lower zBMI, and Black African children having a higher zBMI, versus White British (WB) children. Patterns differed considerably at age 10–11 years (n = 25,763) with marked sex differences. Boys from White other, Bangladeshi and Black African groups had a significantly higher zBMI than WB boys. For girls, only children from Black ethnic groups showed a significantly higher zBMI. Area-level deprivation was the only important school or neighbourhood covariate, but its inclusion did not explain ethnic group differences in child zBMI. CONCLUSION: This analysis contributes to the existing literature by identifying nuanced patterns of ethnic disparities in childhood adiposity in Coventry, supporting the targeting of early obesity prevention for children from Black African groups, as well as girls from Black Caribbean and Black other ethnic backgrounds; and boys from Bangladeshi and White other ethnic backgrounds. It also demonstrates the utility of exploring routinely collected local data sets in building a comprehensive understanding of local population needs. |
format | Online Article Text |
id | pubmed-6883566 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68835662019-12-03 Understanding local ethnic inequalities in childhood BMI through cross-sectional analysis of routinely collected local data Murphy, Marie Johnson, Rebecca Parsons, Nicholas R. Robertson, Wendy BMC Public Health Research Article BACKGROUND: Local-level analysis of ethnic inequalities in health is lacking, prohibiting a comprehensive understanding of the health needs of local populations and the design of effective health services. Knowledge of ethnic disparities in child weight status is particularly limited by overlooking both the heterogeneity within ethnic groupings; and the complex ecological contexts in which obesity arises. This study aimed to establish whether there was variation in childhood BMI across ethnic groups in Coventry, and the influence of individual, school and neighbourhood contexts, using routinely collected local data. METHODS: National Child Measurement Programme data were compiled for the period 2007/8–2014/15 and combined with routinely collected local data reflecting school performance and demographics, and school and neighbourhood physical environments. Multi-level modelling using Monte Carlo Markov Chain methods was used to account for the clustering of children within schools and neighbourhoods. Ethnic group differences in BMI z-score (zBMI) were explored at 4–5 years and 10–11 years for girls and boys alongside individual, school and neighbourhood covariates. RESULTS: At age 4–5 years (n = 28,407), ethnic group differences were similar for boys and girls, with children from South Asian, White other, Chinese and ‘any other’ ethnic groups having a significantly lower zBMI, and Black African children having a higher zBMI, versus White British (WB) children. Patterns differed considerably at age 10–11 years (n = 25,763) with marked sex differences. Boys from White other, Bangladeshi and Black African groups had a significantly higher zBMI than WB boys. For girls, only children from Black ethnic groups showed a significantly higher zBMI. Area-level deprivation was the only important school or neighbourhood covariate, but its inclusion did not explain ethnic group differences in child zBMI. CONCLUSION: This analysis contributes to the existing literature by identifying nuanced patterns of ethnic disparities in childhood adiposity in Coventry, supporting the targeting of early obesity prevention for children from Black African groups, as well as girls from Black Caribbean and Black other ethnic backgrounds; and boys from Bangladeshi and White other ethnic backgrounds. It also demonstrates the utility of exploring routinely collected local data sets in building a comprehensive understanding of local population needs. BioMed Central 2019-11-28 /pmc/articles/PMC6883566/ /pubmed/31779606 http://dx.doi.org/10.1186/s12889-019-7870-2 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Murphy, Marie Johnson, Rebecca Parsons, Nicholas R. Robertson, Wendy Understanding local ethnic inequalities in childhood BMI through cross-sectional analysis of routinely collected local data |
title | Understanding local ethnic inequalities in childhood BMI through cross-sectional analysis of routinely collected local data |
title_full | Understanding local ethnic inequalities in childhood BMI through cross-sectional analysis of routinely collected local data |
title_fullStr | Understanding local ethnic inequalities in childhood BMI through cross-sectional analysis of routinely collected local data |
title_full_unstemmed | Understanding local ethnic inequalities in childhood BMI through cross-sectional analysis of routinely collected local data |
title_short | Understanding local ethnic inequalities in childhood BMI through cross-sectional analysis of routinely collected local data |
title_sort | understanding local ethnic inequalities in childhood bmi through cross-sectional analysis of routinely collected local data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6883566/ https://www.ncbi.nlm.nih.gov/pubmed/31779606 http://dx.doi.org/10.1186/s12889-019-7870-2 |
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