Recently, attention has been drawn to the need to integrate sex/gender more comprehensively into environmental health research. Considering theoretical approaches, we define sex/gender as a multidimensional concept based on intersectionality. However, operationalizing sex/gender through multiple covariates requires the usage of statistical methods that are suitable for handling such complex data. We therefore applied two different decision tree approaches: classification and regression trees (CART) and conditional inference trees (CIT). We explored the relevance of multiple sex/gender covariates for the exposure to green spaces, measured both subjectively and objectively. Data from 3742 participants from the Cooperative Health Research in the Region of Augsburg (KORA) study were analyzed within the INGER (Integrating gender into environmental health research) project. We observed that the participants’ financial situation and discrimination experience was relevant for their access to high quality public green spaces, while the urban/rural context was most relevant for the general greenness in the residential environment. None of the covariates operationalizing the individual sex/gender self-concept were relevant for differences in exposure to green spaces. Results were largely consistent for both CART and CIT. Most importantly we showed that decision tree analyses are useful for exploring the relevance of multiple sex/gender dimensions and their interactions for environmental exposures. Further investigations in larger urban areas with less access to public green spaces and with a study population more heterogeneous with respect to age and social disparities may add more information about the relevance of multiple sex/gender dimensions for the exposure to green spaces.

Background: Physical and social neighbourhood characteristics can vary according to the neighbourhood socio-economic status (SES) and influence residents’ perceptions, behaviours and health outcomes both positively and negatively. Neighbourhood SES has been shown to be predictive of mental health, which is relevant for healthy ageing and prevention of dementia or depression. Positive affectivity (PA) is an established indicator of mental health and might indicate a positive emotional response to neighbourhood characteristics. In this study, we focussed on the association of neighbourhood SES with PA among older residents in Germany and considered social integration and environmental perceptions in this association. Methods: We used questionnaire-based data of the ongoing population-based Heinz Nixdorf Recall Study for our cross-sectional analysis, complemented by secondary data on social welfare rates in the neighbourhood of residents’ address. PA was assessed using the Positive and Negative Affect Schedule (PANAS) in 2016. Linear regression models were performed to estimate the associations and adjusted for socio-demographic variables. Results: Higher social welfare rates were associated with lower PA scores. The strongest negative association from the crude model (b = −1.916, 95%-CI [−2.997, −0.835]) was reduced after controlling for socio-demographic variables (b = −1.429, 95%-CI [−2.511, −0.346]). Social integration factors (b = −1.199, 95%-CI [−2.276, −0.121]) and perceived environmental factors (b = −0.875, 95%-CI [−1.971, 0.221]) additionally diminished the association of social welfare rates with PA in the full model (b = −0.945, 95%-CI [−2.037, 0.147]). Conclusion: Our results suggest that neighbourhoods have an influence on the occurrence and the extent of PA. Public health interventions that address socio-economic disadvantage in the neighbourhood environment could be an effective and far-reaching way to reduce the risk of depression and depressive symptoms due to low PA in older residents.

Exposure to green space has a positive impact on health. Whether sex/gender modifies the green space–health association has so far only been studied through the use of a binary sex/gender category; however, sex/gender should be considered more comprehensively as a multidimensional concept based on theoretical approaches. We therefore explored whether sex/gender, operationalized through multiple sex/gender- and intersectionality-related covariates, modifies the green space–self-rated health association. We collected data from participants involved in the German KORA study (Cooperative Health Research in the Region of Augsburg) in 2019. Self-rated health was assessed as a one-question item. The availability of green spaces was measured subjectively as well as objectively. The multiple sex/gender- and intersectionality-related covariates were measured via self-assessment. To analyze the data, we used model-based recursive partitioning, a decision tree method that can handle complex data, considering both multiple covariates and their possible interactions. We showed that none of the covariates operationalizing an individual sex/gender self-concept led to subgroups with heterogeneous effects in the model-based tree analyses; however, we found effect heterogeneity based on covariates representing structural aspects from an intersectionality perspective, although they did not show the intersectional structuring of sex/gender dimensions. In one identified subgroup, those with a lower education level or a feeling of discrimination based on social position showed a positive green space–self-rated health association, while participants with a higher education level or no feeling of discrimination based on social position had a high level of self-rated health regardless of the availability of green spaces. Model-based recursive partitioning has the potential to detect subgroups exhibiting different exposure–outcome associations, with the possibility of integrating multiple sex/gender- and intersectionality-related covariates as potential effect modifiers. A comprehensive assessment of the relevance of sex/gender showed effect heterogeneity based on covariates representing structural aspects from an intersectionality perspective.

Exposure to transportation noise is a highly prevalent health burden in urban areas. Social-spatially unequal distributions of transportation noise exposure can contribute to health inequalities (distributional environmental justice). Noise action planning according to the EU Environmental Noise Directive is the instrument for noise abatement at the local level. In this context, public participation in terms of procedural environmental justice becomes relevant. Socio-spatial indicators used to describe environmental justice mainly deal with the distributional aspect. The question is to what extent both unequal environmental quality on the one hand and unequal participation on the other can be traced by indicators and spatial units typically used in social monitoring at the local level. Using the distance measure index of German social welfare rates and the local voter turnout in 2014, the socio-spatial distribution of factors of objective environmental quality, subjective noise exposure and engagement against transportation noise among older residents in three major cities in the German Ruhr region was examined. In order to address the observed inequalities within noise action planning, distributional and procedural aspects have to be integrated in noise action planning.