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 a...

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Autores principales:	Emily Mena, Gabriele Bolte, on behalf of the Advance Gender study group
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Intersectionality Public health monitoring Public health reporting Sex/gender Gender roles Vegetable intake Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/56355ac263f4430d9e8a29b8f51c6f3e
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spelling	oai:doaj.org-article:56355ac263f4430d9e8a29b8f51c6f3e2021-11-08T10:43:51ZClassification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake10.1186/s12889-021-12043-61471-2458https://doaj.org/article/56355ac263f4430d9e8a29b8f51c6f3e2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12889-021-12043-6https://doaj.org/toc/1471-2458Abstract 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.Emily MenaGabriele Bolteon behalf of the Advance Gender study groupBMCarticleIntersectionalityPublic health monitoringPublic health reportingSex/genderGender rolesVegetable intakePublic aspects of medicineRA1-1270ENBMC Public Health, Vol 21, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Intersectionality Public health monitoring Public health reporting Sex/gender Gender roles Vegetable intake Public aspects of medicine RA1-1270
spellingShingle	Intersectionality Public health monitoring Public health reporting Sex/gender Gender roles Vegetable intake Public aspects of medicine RA1-1270 Emily Mena Gabriele Bolte on behalf of the Advance Gender study group Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake
description	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.
format	article
author	Emily Mena Gabriele Bolte on behalf of the Advance Gender study group
author_facet	Emily Mena Gabriele Bolte on behalf of the Advance Gender study group
author_sort	Emily Mena
title	Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake
title_short	Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake
title_full	Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake
title_fullStr	Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake
title_full_unstemmed	Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake
title_sort	classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake
publisher	BMC
publishDate	2021
url	https://doaj.org/article/56355ac263f4430d9e8a29b8f51c6f3e
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Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake

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