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

The use of Land use and Land cover (LULC) data is gradually becoming more widely spread in studies relating the environment to human health. 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 independ...

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Detalles Bibliográficos
Autores: Zaldo-Aubanell, Quim, Serra, Isabel, Bach, Albert, Knobel, Pablo, Campillo López, Ferran, Belmonte, Jordina, Daunis-i-Estadella, Pepus, Maneja Zaragoza, Roser
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/83614
Acceso en línea:https://doi.org/10.1016/j.scitotenv.2021.150308
http://hdl.handle.net/10459.1/83614
Access Level:acceso abierto
Palabra clave:Land use and Land cover
Environmental heterogeneity
Compositional analysis
Type 2 diabetes mellitus
Asthma
Anxiety
Descripción
Sumario:The use of Land use and Land cover (LULC) data is gradually becoming more widely spread in studies relating the environment to human health. 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 (Spain) at area level. We fixed the risk exposure scenario using three covariates (socioeconomic status, age group, and sex). Then, we assessed the independent effect of the eight LULC categories on each health condition. Our results show that each LULC category has a distinctive effect on the three health conditions and that the three covariates clearly modify this effect.