Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake.

Autor: Mena, Emily, Bolte, Gabriele, on behalf of the Advance Gender study group, Holmberg, Christine, Jaehn, Philipp, Merz, Sibille, Rommel, Alexander, Saß, Anke-Christine, Pöge, Kathleen, Strasser, Sarah, Advance Gender study group
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Zdroj: BMC Public Health; 11/4/2021, Vol. 21 Issue 1, p1-14, 14p, 2 Diagrams, 3 Charts
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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