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An intersectional analysis providing more precise information on inequities in self-rated health

BACKGROUND: Intersectionality theory combined with an analysis of individual heterogeneity and discriminatory accuracy (AIHDA) can facilitate our understanding of health disparities. This enables the application of proportionate universalism for resource allocation in public health. Analyzing self-r...

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Autores principales: Wemrell, Maria, Karlsson, Nadja, Perez Vicente, Raquel, Merlo, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856780/
https://www.ncbi.nlm.nih.gov/pubmed/33536038
http://dx.doi.org/10.1186/s12939-020-01368-0
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author Wemrell, Maria
Karlsson, Nadja
Perez Vicente, Raquel
Merlo, Juan
author_facet Wemrell, Maria
Karlsson, Nadja
Perez Vicente, Raquel
Merlo, Juan
author_sort Wemrell, Maria
collection PubMed
description BACKGROUND: Intersectionality theory combined with an analysis of individual heterogeneity and discriminatory accuracy (AIHDA) can facilitate our understanding of health disparities. This enables the application of proportionate universalism for resource allocation in public health. Analyzing self-rated health (SRH) in Sweden, we show how an intersectional perspective allows for a detailed mapping of health inequalities while avoiding simplification and stigmatization based on indiscriminate interpretations of differences between group averages. METHODS: We analyzed participants (n=133,244) in 14 consecutive National Public Health Surveys conducted in Sweden in 2004–2016 and 2018. Applying AIHDA, we investigated the risk of bad SRH across 12 intersectional strata defined by gender, income and migration status, adjusted by age and survey year. We calculated odds ratios (with 95% confidence intervals) to evaluate between-strata differences, using native-born men with high income as the comparison reference. We calculated the area under the receiver operating characteristic curve (AU-ROC) to evaluate the discriminatory accuracy of the intersectional strata for identifying individuals according to their SRH status. RESULTS: The analysis of intersectional strata showed clear average differences in the risk of bad SRH. For instance, the risk was seven times higher for immigrated women with low income (OR 7.00 [95% CI 6.14–7.97]) than for native men with high income. However, the discriminatory accuracy of the intersectional strata was small (AU-ROC=0.67). CONCLUSIONS: The intersectional AIHDA approach provides more precise information on the existence (or the absence) of health inequalities, and can guide public health interventions according to the principle of proportionate universalism. The low discriminatory accuracy of the intersectional strata found in this study warrants universal interventions rather than interventions exclusively focused on strata with a higher average risk of bad SRH.
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spelling pubmed-78567802021-02-04 An intersectional analysis providing more precise information on inequities in self-rated health Wemrell, Maria Karlsson, Nadja Perez Vicente, Raquel Merlo, Juan Int J Equity Health Research BACKGROUND: Intersectionality theory combined with an analysis of individual heterogeneity and discriminatory accuracy (AIHDA) can facilitate our understanding of health disparities. This enables the application of proportionate universalism for resource allocation in public health. Analyzing self-rated health (SRH) in Sweden, we show how an intersectional perspective allows for a detailed mapping of health inequalities while avoiding simplification and stigmatization based on indiscriminate interpretations of differences between group averages. METHODS: We analyzed participants (n=133,244) in 14 consecutive National Public Health Surveys conducted in Sweden in 2004–2016 and 2018. Applying AIHDA, we investigated the risk of bad SRH across 12 intersectional strata defined by gender, income and migration status, adjusted by age and survey year. We calculated odds ratios (with 95% confidence intervals) to evaluate between-strata differences, using native-born men with high income as the comparison reference. We calculated the area under the receiver operating characteristic curve (AU-ROC) to evaluate the discriminatory accuracy of the intersectional strata for identifying individuals according to their SRH status. RESULTS: The analysis of intersectional strata showed clear average differences in the risk of bad SRH. For instance, the risk was seven times higher for immigrated women with low income (OR 7.00 [95% CI 6.14–7.97]) than for native men with high income. However, the discriminatory accuracy of the intersectional strata was small (AU-ROC=0.67). CONCLUSIONS: The intersectional AIHDA approach provides more precise information on the existence (or the absence) of health inequalities, and can guide public health interventions according to the principle of proportionate universalism. The low discriminatory accuracy of the intersectional strata found in this study warrants universal interventions rather than interventions exclusively focused on strata with a higher average risk of bad SRH. BioMed Central 2021-02-03 /pmc/articles/PMC7856780/ /pubmed/33536038 http://dx.doi.org/10.1186/s12939-020-01368-0 Text en © The Author(s) 2021 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/. 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 in a credit line to the data.
spellingShingle Research
Wemrell, Maria
Karlsson, Nadja
Perez Vicente, Raquel
Merlo, Juan
An intersectional analysis providing more precise information on inequities in self-rated health
title An intersectional analysis providing more precise information on inequities in self-rated health
title_full An intersectional analysis providing more precise information on inequities in self-rated health
title_fullStr An intersectional analysis providing more precise information on inequities in self-rated health
title_full_unstemmed An intersectional analysis providing more precise information on inequities in self-rated health
title_short An intersectional analysis providing more precise information on inequities in self-rated health
title_sort intersectional analysis providing more precise information on inequities in self-rated health
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856780/
https://www.ncbi.nlm.nih.gov/pubmed/33536038
http://dx.doi.org/10.1186/s12939-020-01368-0
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