ArticleBMC public health2023
A machine learning approach to determine the influence of specific health conditions on self-rated health across education groups.
Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Intersection of gender and class in the assessment of self-rated health in the Spanish population: a classification tree study.International journal for equity in health · 2026Article
- Exploring Factors Associated with Health Status and Dietary Supplement Use Among Portuguese Adults: A Cross-Sectional Online Survey.Healthcare (Basel, Switzerland) · 2025Article
- Association of reported sleep disturbances with objectively assessed mild cognitive impairment among adults in the United States.SAGE open medicine · 2025Article
- Physical therapy interventions for people experiencing homelessness to improve pain and self-perceived health status.BMC public health · 2024Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundSelf-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).
methodsClassification 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).
resultsIt 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.
conclusionsThe 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.
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