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

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.

The Influence of Economic Factors on the Relationship between Partnership Status and Health: A Gender Approach to the Spanish Case.

This study explores the relevance of economic factors (e.g., a household's economic capacity and the prevailing economic context) to understand the relationship between the partnership status and the health of Spanish adult women and men (age 30-59). To do so, it draws on cross-sectional data from the Spanish sample of the European Union Statistics on Income and Living Conditions (EU-SILC) for the years 2005, 2010, and 2015 (i.e., before, during, and after the 2008-2012 economic recession). The results reveal dissimilar patterns of association between partnership status and both the health of, and the economic difficulties faced by Spanish women and men in each of the three years studied. Most notably, the partnership status of Spanish women has a greater impact on their likelihood of experiencing economic difficulties and poor health than does that of their male counterparts. Additionally, women are also more likely to experience economic difficulties during and after the economic recession. The disadvantageous situation of Spanish women in the public sphere is shown to have a negative impact on their ability to cope with the economic difficulties associated with the end of a union and a contextual recession.