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Using the Welsh Index of Multiple Deprivation in research: estimating the effect of excluding domains on a routine health data study

Shamsudeen Mohammed, Grace A. Bailey, Ian W. Farr, Carys Jones, Anna Rawlings, Sarah Rees, Sean Scully, Ting Wang, Hywel Evans Orcid Logo

BMC Public Health, Volume: 25, Issue: 1

Swansea University Author: Hywel Evans Orcid Logo

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DOI (Published version): 10.1186/s12889-025-22369-0

Abstract

BackgroundThe Welsh Index of Multiple Deprivation (WIMD) is an area-based deprivation measure comprising eight domains, produced by the Welsh Government to rank Lower Layer Super Output Areas (LSOAs) in Wales. Researchers use the WIMD to account for deprivation, however, as one domain contains healt...

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Published in: BMC Public Health
ISSN: 1471-2458
Published: Springer Science and Business Media LLC 2025
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Researchers use the WIMD to account for deprivation, however, as one domain contains health indicators, there is a risk of endogeneity bias when using the WIMD in research on health outcomes. This study evaluated the effect on study results of removing the health domain from the overall WIMD or using only the income domain as deprivation measures.MethodsWIMD 2019 scores were linked to 2,760,731 individuals in the SAIL Databank. Original WIMD scores including decile and quintile rankings for each LSOA 2011 were obtained from Welsh Government. The first alternative method removed the health domain from the original WIMD scores. In the second alternative method, WIMD scores were based on only the income domain. Spearman&#x2019;s correlation and Cohen&#x2019;s kappa were used to assess the agreement of ranks, deciles, and quintiles between each method. To quantify the change in association between WIMD quintile and diabetes mellitus prevalence for each alternative method, binary logistic regression obtained age-adjusted odds ratios and 95% confidence intervals.ResultsRemoving the health domain from the original WIMD scores resulted in 17.28% of LSOAs changing decile (8.64% to a more deprived group and 8.64% to a less deprived group) and 9.00% changing quintile (4.50% more deprived, 4.50% less deprived). The income-domain-only method caused 50.49% of LSOAs to change decile (26.87% more deprived, 23.62% less deprived) as compared with the original WIMD, and 29.65% changed quintile (15.14% more deprived, 14.51% less deprived). There was a significant association between each of the three methods and diabetes prevalence, with odds ratios increasing with more deprived quintiles, but the 95% confidence intervals for each method showed little or no overlap with each other.ConclusionTo avoid biased estimates, researchers using WIMD in studies on health, education, housing, physical environment, income, employment, community safety, and access to services should consider how these domains are related to their outcomes. 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spelling 2025-04-09T15:04:41.1792021 v2 69145 2025-03-24 Using the Welsh Index of Multiple Deprivation in research: estimating the effect of excluding domains on a routine health data study 73cc98a5b8e4122fdfcee5d88208b0b7 0000-0001-6745-4187 Hywel Evans Hywel Evans true false 2025-03-24 MEDS BackgroundThe Welsh Index of Multiple Deprivation (WIMD) is an area-based deprivation measure comprising eight domains, produced by the Welsh Government to rank Lower Layer Super Output Areas (LSOAs) in Wales. Researchers use the WIMD to account for deprivation, however, as one domain contains health indicators, there is a risk of endogeneity bias when using the WIMD in research on health outcomes. This study evaluated the effect on study results of removing the health domain from the overall WIMD or using only the income domain as deprivation measures.MethodsWIMD 2019 scores were linked to 2,760,731 individuals in the SAIL Databank. Original WIMD scores including decile and quintile rankings for each LSOA 2011 were obtained from Welsh Government. The first alternative method removed the health domain from the original WIMD scores. In the second alternative method, WIMD scores were based on only the income domain. Spearman’s correlation and Cohen’s kappa were used to assess the agreement of ranks, deciles, and quintiles between each method. To quantify the change in association between WIMD quintile and diabetes mellitus prevalence for each alternative method, binary logistic regression obtained age-adjusted odds ratios and 95% confidence intervals.ResultsRemoving the health domain from the original WIMD scores resulted in 17.28% of LSOAs changing decile (8.64% to a more deprived group and 8.64% to a less deprived group) and 9.00% changing quintile (4.50% more deprived, 4.50% less deprived). The income-domain-only method caused 50.49% of LSOAs to change decile (26.87% more deprived, 23.62% less deprived) as compared with the original WIMD, and 29.65% changed quintile (15.14% more deprived, 14.51% less deprived). There was a significant association between each of the three methods and diabetes prevalence, with odds ratios increasing with more deprived quintiles, but the 95% confidence intervals for each method showed little or no overlap with each other.ConclusionTo avoid biased estimates, researchers using WIMD in studies on health, education, housing, physical environment, income, employment, community safety, and access to services should consider how these domains are related to their outcomes. We describe a methodology for researchers to quantify any bias in their own studies. Journal Article BMC Public Health 25 1 Springer Science and Business Media LLC 1471-2458 28 3 2025 2025-03-28 10.1186/s12889-025-22369-0 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) This work was funded by the Economic and Social Research Council (ESRC) via Administrative Data Research Centre Wales (part of ADR UK) under grant ES/W012227/1. 2025-04-09T15:04:41.1792021 2025-03-24T15:41:17.2170537 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Shamsudeen Mohammed 1 Grace A. Bailey 2 Ian W. Farr 3 Carys Jones 4 Anna Rawlings 5 Sarah Rees 6 Sean Scully 7 Ting Wang 8 Hywel Evans 0000-0001-6745-4187 9 69145__33973__5fcc68954adf4640a9c5c1f1fd90326f.pdf 69145.VoR.pdf 2025-04-09T15:02:31.6810361 Output 2205912 application/pdf Version of Record true © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title Using the Welsh Index of Multiple Deprivation in research: estimating the effect of excluding domains on a routine health data study
spellingShingle Using the Welsh Index of Multiple Deprivation in research: estimating the effect of excluding domains on a routine health data study
Hywel Evans
title_short Using the Welsh Index of Multiple Deprivation in research: estimating the effect of excluding domains on a routine health data study
title_full Using the Welsh Index of Multiple Deprivation in research: estimating the effect of excluding domains on a routine health data study
title_fullStr Using the Welsh Index of Multiple Deprivation in research: estimating the effect of excluding domains on a routine health data study
title_full_unstemmed Using the Welsh Index of Multiple Deprivation in research: estimating the effect of excluding domains on a routine health data study
title_sort Using the Welsh Index of Multiple Deprivation in research: estimating the effect of excluding domains on a routine health data study
author_id_str_mv 73cc98a5b8e4122fdfcee5d88208b0b7
author_id_fullname_str_mv 73cc98a5b8e4122fdfcee5d88208b0b7_***_Hywel Evans
author Hywel Evans
author2 Shamsudeen Mohammed
Grace A. Bailey
Ian W. Farr
Carys Jones
Anna Rawlings
Sarah Rees
Sean Scully
Ting Wang
Hywel Evans
format Journal article
container_title BMC Public Health
container_volume 25
container_issue 1
publishDate 2025
institution Swansea University
issn 1471-2458
doi_str_mv 10.1186/s12889-025-22369-0
publisher Springer Science and Business Media LLC
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
active_str 0
description BackgroundThe Welsh Index of Multiple Deprivation (WIMD) is an area-based deprivation measure comprising eight domains, produced by the Welsh Government to rank Lower Layer Super Output Areas (LSOAs) in Wales. Researchers use the WIMD to account for deprivation, however, as one domain contains health indicators, there is a risk of endogeneity bias when using the WIMD in research on health outcomes. This study evaluated the effect on study results of removing the health domain from the overall WIMD or using only the income domain as deprivation measures.MethodsWIMD 2019 scores were linked to 2,760,731 individuals in the SAIL Databank. Original WIMD scores including decile and quintile rankings for each LSOA 2011 were obtained from Welsh Government. The first alternative method removed the health domain from the original WIMD scores. In the second alternative method, WIMD scores were based on only the income domain. Spearman’s correlation and Cohen’s kappa were used to assess the agreement of ranks, deciles, and quintiles between each method. To quantify the change in association between WIMD quintile and diabetes mellitus prevalence for each alternative method, binary logistic regression obtained age-adjusted odds ratios and 95% confidence intervals.ResultsRemoving the health domain from the original WIMD scores resulted in 17.28% of LSOAs changing decile (8.64% to a more deprived group and 8.64% to a less deprived group) and 9.00% changing quintile (4.50% more deprived, 4.50% less deprived). The income-domain-only method caused 50.49% of LSOAs to change decile (26.87% more deprived, 23.62% less deprived) as compared with the original WIMD, and 29.65% changed quintile (15.14% more deprived, 14.51% less deprived). There was a significant association between each of the three methods and diabetes prevalence, with odds ratios increasing with more deprived quintiles, but the 95% confidence intervals for each method showed little or no overlap with each other.ConclusionTo avoid biased estimates, researchers using WIMD in studies on health, education, housing, physical environment, income, employment, community safety, and access to services should consider how these domains are related to their outcomes. We describe a methodology for researchers to quantify any bias in their own studies.
published_date 2025-03-28T08:24:27Z
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