A machine learning approach to determine the influence of specific health conditions on self-rated health across education groups
Year: 2023
Type: article
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.
Source: BMC Public Health
Authors: Jordi Gumà-Lao, Bruno Arpino
Institutions: Umeå University, University of Florence
Language: English
Subfield: Health
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,990
Awards: Multinational Advancement of Research Infrastructures on Ageing, G1299585435, G1330667123, G1428006373, G1565542540, G1750703574, G1778268290, G1781191216, G1820526844, G2077389594, G2343781458, G24207250, G2465118858, G2881429984, G3166864496, G323020161, G3361099077, G3440788226, G3457581384, G3687180791, G3906179026, HEALTH AND ECONOMIC STATUS IN OLDER POPULATIONS, RAND Center for the Study of Aging, Upgrading the Survey of Health, Ageing and Retirement in Europe – preparatory phase, G4569152855, G4607009072, G4847675667, G5021238699, G5618942462, G598697797, G6767647029, G6978085111, G751768989, DEVELOPING AN ISRAELI VERSION OF THE HRS/SHARE PROJECT, G7664789006, Longitudinal Enhancement and Access imProvement of the SHARE infrastructure, G8957671751, G9000113159, Understanding and Improving Healthcare Decision-Making and Outcomes for People Living with Alzheimer's Disease and Related Dementias, Health and Retirement Study: Yrs 35-40, G93458789, G938892667, G939349750
OpenAlex ID: W4317354880