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

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.

Do Multiple Sex/Gender Dimensions Play a Role in the Association of Green Space and Self-Rated Health? Model-Based Recursive Partitioning Results from the KORA INGER Study

“…We consider sex/gender from an intersectional perspective and follow an intercategorical intersectional approach [ 58 ] to explore differences between different intersections of sex/gender and further social categories. Decision trees have been identified as promising methods when interested in comparing large numbers of intersections [ 56 ]; however, decision tree methods, such as CARTs [ 28 ] and CITs [ 29 ], which have so far been used [ 59 , 60 ] and compared [ 57 ] in quantitative intersectional research, can only be used for descriptive intersectional research [ 31 ]. In this study, however, we wanted to explore whether we would be able to identify intersections with differential effects of an environmental exposure on a health outcome, and thus followed a more analytic intersectional approach as defined by Bauer and Scheim [ 31 ].…”

Lysmeral Exposure in Children and Adolescences Participating in the German Environmental Survey (2012–2015): Integrating Sex/Gender into Analysis

“…In the second part of this study we performed a multivariate analysis of the available sex/gender variables in order to identify previously undetected relations with chemical exposure. To prevent assumptions from influencing the selection of covariates for the statistical model, we used decision trees, which have been successfully used in health research, for example in epidemiology [ 46 ], mental health [ 47 ], and public health [ 48 , 49 ]. As they do not have any presumptions about the data at all, they can be used for analyses which have to deal with many covariates and data structures and can detect non-linear and complex correlations.…”

Intersectional analysis of inequalities in self-reported breast cancer screening attendance using supervised machine learning and PROGRESS-Plus framework

“…Only a few public health studies have used decision trees to explore sociodemographic inequalities. Mena et al used classification and regression trees (CARTs) and conditional inference trees (CITs) to identify groups with a higher prevalence of non-daily vegetable intake (22). Eagle et al employed chi-squared automatic interaction detection trees (CHAID) to explore the associations between race and ethnicity, sex, depression, and concussion history with reported suicide attempts among adolescents in the US (23).…”