Combining education and income into a socioeconomic position score for use in studies of health inequalities ...

Abstract 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 combinati...

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Main Authors: Lindberg, Marie Hella, Chen, Gang, Olsen, Jan Abel, Abelsen, Birgit
Format: Article in Journal/Newspaper
Language:unknown
Published: figshare 2022
Subjects:
Online Access:https://dx.doi.org/10.6084/m9.figshare.c.5995766
https://springernature.figshare.com/collections/Combining_education_and_income_into_a_socioeconomic_position_score_for_use_in_studies_of_health_inequalities/5995766
id ftdatacite:10.6084/m9.figshare.c.5995766
record_format openpolar
spelling ftdatacite:10.6084/m9.figshare.c.5995766 2024-04-28T08:32:28+00:00 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 2022 https://dx.doi.org/10.6084/m9.figshare.c.5995766 https://springernature.figshare.com/collections/Combining_education_and_income_into_a_socioeconomic_position_score_for_use_in_studies_of_health_inequalities/5995766 unknown figshare Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Space Science Medicine Environmental Sciences not elsewhere classified Sociology FOS Sociology Biological Sciences not elsewhere classified Science Policy Collection article 2022 ftdatacite https://doi.org/10.6084/m9.figshare.c.5995766 2024-04-02T12:06:05Z Abstract 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 ... Article in Journal/Newspaper Northern Norway DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Space Science
Medicine
Environmental Sciences not elsewhere classified
Sociology
FOS Sociology
Biological Sciences not elsewhere classified
Science Policy
spellingShingle Space Science
Medicine
Environmental Sciences not elsewhere classified
Sociology
FOS Sociology
Biological Sciences not elsewhere classified
Science Policy
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 ...
topic_facet Space Science
Medicine
Environmental Sciences not elsewhere classified
Sociology
FOS Sociology
Biological Sciences not elsewhere classified
Science Policy
description Abstract 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 ...
format Article in Journal/Newspaper
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
title 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_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_sort combining education and income into a socioeconomic position score for use in studies of health inequalities ...
publisher figshare
publishDate 2022
url https://dx.doi.org/10.6084/m9.figshare.c.5995766
https://springernature.figshare.com/collections/Combining_education_and_income_into_a_socioeconomic_position_score_for_use_in_studies_of_health_inequalities/5995766
genre Northern Norway
genre_facet Northern Norway
op_rights Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
op_doi https://doi.org/10.6084/m9.figshare.c.5995766
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