1. Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake
- Author
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Gabriele Bolte, Emily Mena, Alexander Rommel, Christine Holmberg, Philipp Jaehn, Anke-Christine Saß, Sarah Strasser, Kathleen Pöge, and Sibille Merz
- Subjects
Cart ,Intersectionality ,medicine.medical_specialty ,Intersectional Framework ,Population ,Gender roles ,Public health monitoring ,Sex/gender ,Sex Factors ,Population Groups ,Epidemiology ,Vegetables ,medicine ,Humans ,CART ,education ,education.field_of_study ,CIT ,Public health ,business.industry ,Research ,Public Health, Environmental and Occupational Health ,Public health reporting ,Health promotion ,Cross-Sectional Studies ,Telephone interview ,Socioeconomic Factors ,Vegetable intake ,Educational Status ,Biostatistics ,Public aspects of medicine ,RA1-1270 ,business ,Demography - Abstract
Background Daily vegetable intake is considered an important behavioural health resource associated with improved immune function and lower incidence of non-communicable disease. Analyses of population-based data show that being female and having a high educational status is most strongly associated with increased vegetable intake. In contrast, men and individuals with a low educational status seem to be most affected by non-daily vegetable intake (non-DVI). From an intersectionality perspective, health inequalities are seen as a consequence of an unequal balance of power such as persisting gender inequality. Unravelling intersections of socially driven aspects underlying inequalities might be achieved by not relying exclusively on the male/female binary, but by considering different facets of gender roles as well. This study aims to analyse possible interactions of sex/gender or sex/gender related aspects with a variety of different socio-cultural, socio-demographic and socio-economic variables with regard to non-DVI as the health-related outcome. Method Comparative classification tree analyses with classification and regression tree (CART) and conditional inference tree (CIT) as quantitative, non-parametric, exploratory methods for the detection of subgroups with high prevalence of non-DVI were performed. Complete-case analyses (n = 19,512) were based on cross-sectional data from a National Health Telephone Interview Survey conducted in Germany. Results The CART-algorithm constructed overall smaller trees when compared to CIT, but the subgroups detected by CART were also detected by CIT. The most strongly differentiating factor for non-DVI, when not considering any further sex/gender related aspects, was the male/female binary with a non-DVI prevalence of 61.7% in men and 42.7% in women. However, the inclusion of further sex/gender related aspects revealed a more heterogenous distribution of non-DVI across the sample, bringing gendered differences in main earner status and being a blue-collar worker to the foreground. In blue-collar workers who do not live with a partner on whom they can rely on financially, the non-DVI prevalence was 69.6% in men and 57.4% in women respectively. Conclusions Public health monitoring and reporting with an intersectionality-informed and gender-equitable perspective might benefit from an integration of further sex/gender related aspects into quantitative analyses in order to detect population subgroups most affected by non-DVI.
- Published
- 2021