Environmental heterogeneity in human health studies. A compositional methodology for Land Use and Land cover data

Abstract A variety of metrics assessing the environment are frequently showcased in the study of the relationship between the environment and human health. Among them, Land Use and Land Cover (LULC) data are gradually becoming more notable. However, little research has acknowledged the compositional nature of these data. The goal of the present study is to explore, for the first time, the independent effect of eight LULC categories (agricultural land, bare land, coniferous forest, broad-leaved forest, sclerophyll forest, grassland and shrubs, urban areas and waterbodies) on three selected common health conditions: type 2 diabetes mellitus (T2DM), asthma and anxiety, using a compositional methodological approach and leveraging observational health data of Catalonia at area level. Three covariates (socioeconomic status, age group and sex) were used for segmentation in order to fix the risk exposure scenario and assess the independent effect of the eight LULC categories on the three health conditions. Our results show that each LULC category would affect distinctively on the three health conditions, and that this effect would be clearly mediated by the three covariates. This compositional approach has led us to plausible results supported by the existing literature, highlighting the relevance of environmental heterogeneity in health studies. In this sense, we argue that different types of environment possess exclusive elements (humidity, temperature, type of flora and fauna, accessibility, walkability, openness, presence of water, sounds, air compounds and air quality, heat, and noise, light contamination and even chemical exposure) affecting distinctively on human health. We believe that our contribution might help researchers to approach the environment in a more multidimensional scope, allowing environmental heterogeneity to be brought into the equation.