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A machine learning approach to determine the influence of specific health conditions on self-rated health across education groups

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dc.contributor.author Gumà, Jordi
dc.contributor.author Arpino, Bruno
dc.date.accessioned 2023-05-11T06:24:25Z
dc.date.available 2023-05-11T06:24:25Z
dc.date.issued 2023
dc.identifier.citation Gumà‑Lao J, Arpino B. A machine learning approach to determine the influence of specific health conditions on self-rated health across education groups. BMC Public Health. 2023;23:131. DOI: 10.1186/s12889-023-15053-8
dc.identifier.issn 1471-2458
dc.identifier.uri http://hdl.handle.net/10230/56774
dc.description.abstract Background: Self-rated health, a subjective health outcome that summarizes an individual’s health conditions in one indicator, is widely used in population health studies. However, despite its demonstrated ability as a predictor of mortality, we still do not full understand the relative importance of the specific health conditions that lead respondents to answer the way they do when asked to rate their overall health. Here, education, because of its ability to identify different social strata, can be an important factor in this self-rating process. The aim of this article is to explore possible differences in association pattern between self-rated health and functional health conditions (IADLs, ADLs), chronic diseases, and mental health (depression) among European women and men between the ages of 65 and 79 according to educational attainment (low, medium, and high). Methods: Classification trees (J48 algorithm), an established machine learning technique that has only recently started to be used in social sciences, are used to predict self-rated health outcomes. The data about the aforementioned health conditions among European women and men aged between 65 and 79 comes from the sixth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE) (n = 27,230). Results: It is confirmed the high ability to predict respondents’ self-rated health by their reports related to their chronic diseases, IADLs, ADLs, and depression. However, in the case of women, these patterns are much more heterogeneous when the level of educational attainment is considered, whereas among men the pattern remains largely the same. Conclusions: The same response to the self-rated health question may, in the case of women, represent different health profiles in terms of the health conditions that define it. As such, gendered health inequalities defined by education appear to be evident even in the process of evaluating one’s own health status.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher BMC
dc.relation.ispartof BMC Public Health. 2023;23:131.
dc.rights © The Author(s) 2023. Open Access This 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.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title A machine learning approach to determine the influence of specific health conditions on self-rated health across education groups
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1186/s12889-023-15053-8
dc.subject.keyword Self-rated health
dc.subject.keyword Health conditions
dc.subject.keyword Education
dc.subject.keyword Machine learning
dc.subject.keyword SHARE survey
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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