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Combining education and income into a socioeconomic position score for use in studies of health inequalities
BACKGROUND: In studies of social inequalities in health, there is no consensus on the best measure of socioeconomic position (SEP). Moreover, subjective indicators are increasingly used to measure SEP. The aim of this paper was to develop a composite score for SEP based on weighted combinations of e...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9107133/ https://www.ncbi.nlm.nih.gov/pubmed/35562797 http://dx.doi.org/10.1186/s12889-022-13366-8 |
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author | Lindberg, Marie Hella Chen, Gang Olsen, Jan Abel Abelsen, Birgit |
author_facet | Lindberg, Marie Hella Chen, Gang Olsen, Jan Abel Abelsen, Birgit |
author_sort | Lindberg, Marie Hella |
collection | PubMed |
description | BACKGROUND: In studies of social inequalities in health, there is no consensus on the best measure of socioeconomic position (SEP). Moreover, subjective indicators are increasingly used to measure SEP. The aim of this paper was to develop a composite score for SEP based on weighted combinations of education and income in estimating subjective SEP, and examine how this score performs in predicting inequalities in health-related quality of life (HRQoL). METHODS: We used data from a comprehensive health survey from Northern Norway, conducted in 2015/16 (N = 21,083). A composite SEP score was developed using adjacent-category logistic regression of subjective SEP as a function of four education and four household income levels. Weights were derived based on these indicators’ coefficients in explaining variations in respondents’ subjective SEP. The composite SEP score was further applied to predict inequalities in HRQoL, measured by the EQ-5D and a visual analogue scale. RESULTS: Education seemed to influence SEP the most, while income added weight primarily for the highest income category. The weights demonstrated clear non-linearities, with large jumps from the middle to the higher SEP score levels. Analyses of the composite SEP score indicated a clear social gradient in both HRQoL measures. CONCLUSIONS: We provide new insights into the relative contribution of education and income as sources of SEP, both separately and in combination. Combining education and income into a composite SEP score produces more comprehensive estimates of the social gradient in health. A similar approach can be applied in any cohort study that includes education and income data. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13366-8. |
format | Online Article Text |
id | pubmed-9107133 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91071332022-05-15 Combining education and income into a socioeconomic position score for use in studies of health inequalities Lindberg, Marie Hella Chen, Gang Olsen, Jan Abel Abelsen, Birgit BMC Public Health Research BACKGROUND: In studies of social inequalities in health, there is no consensus on the best measure of socioeconomic position (SEP). Moreover, subjective indicators are increasingly used to measure SEP. The aim of this paper was to develop a composite score for SEP based on weighted combinations of education and income in estimating subjective SEP, and examine how this score performs in predicting inequalities in health-related quality of life (HRQoL). METHODS: We used data from a comprehensive health survey from Northern Norway, conducted in 2015/16 (N = 21,083). A composite SEP score was developed using adjacent-category logistic regression of subjective SEP as a function of four education and four household income levels. Weights were derived based on these indicators’ coefficients in explaining variations in respondents’ subjective SEP. The composite SEP score was further applied to predict inequalities in HRQoL, measured by the EQ-5D and a visual analogue scale. RESULTS: Education seemed to influence SEP the most, while income added weight primarily for the highest income category. The weights demonstrated clear non-linearities, with large jumps from the middle to the higher SEP score levels. Analyses of the composite SEP score indicated a clear social gradient in both HRQoL measures. CONCLUSIONS: We provide new insights into the relative contribution of education and income as sources of SEP, both separately and in combination. Combining education and income into a composite SEP score produces more comprehensive estimates of the social gradient in health. A similar approach can be applied in any cohort study that includes education and income data. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13366-8. BioMed Central 2022-05-13 /pmc/articles/PMC9107133/ /pubmed/35562797 http://dx.doi.org/10.1186/s12889-022-13366-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Lindberg, Marie Hella Chen, Gang Olsen, Jan Abel Abelsen, Birgit Combining education and income into a socioeconomic position score for use in studies of health inequalities |
title | Combining education and income into a socioeconomic position score for use in studies of health inequalities |
title_full | Combining education and income into a socioeconomic position score for use in studies of health inequalities |
title_fullStr | Combining education and income into a socioeconomic position score for use in studies of health inequalities |
title_full_unstemmed | Combining education and income into a socioeconomic position score for use in studies of health inequalities |
title_short | Combining education and income into a socioeconomic position score for use in studies of health inequalities |
title_sort | combining education and income into a socioeconomic position score for use in studies of health inequalities |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9107133/ https://www.ncbi.nlm.nih.gov/pubmed/35562797 http://dx.doi.org/10.1186/s12889-022-13366-8 |
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