Suchergebnisse

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. ; Quim Zaldo-Aubanell was supported by AGAUR FI fellowship (DOGC num. 7720, of 5.10.2018). Isabel Serra acknowledges support from FIS2015-71851-P and PGC-FIS2018-099629-B-I00 from Spanish MINECO and MICINN, and was partially funded by the grant RTI2018- 096072-B-I00 from the Spanish Ministry of Science, Innovation and Universities. Jordina Belmonte was supported by the Spanish Ministry of Science and Technology through the project CTM2017-86565-C2-1-O and by the Catalan Government AGAUR through 2017SGR1692. Pepus Daunis-i-Estadella acknowledges support from the project RTI2018- 095518-B-C21 Methods for Compositional analysis of Data (CODAMET), Ministerio de Ciencia, Innovación y Universidades, Spain.

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. This compositional approach has yielded 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 biotic and abiotic elements affecting distinctively on human health. We believe our contribution might help researchers approach the environment in a more multidimensional manner integrating environmental heterogeneity in the analysis. ; Quim Zaldo-Aubanell was supported by AGAUR FI fellowship (DOGC num. 7720, of 5.10.2018). Isabel Serra acknowledges support from FIS2015-71851-P and PGC-FIS2018-099629-B-I00 from Spanish MINECO and MICINN, and was partially funded by the grant RTI2018-096072-B-I00 from the Spanish Ministry of Science, Innovation and Universities. Jordina Belmonte was supported by the Spanish Ministry of Science and Technology through the project CTM2017-86565-C2-1-O and by the Catalan Government AGAUR through 2017SGR1692. Pepus Daunis-i-Estadella acknowledges support from the project RTI2018-095518-B-C21 Methods for Compositional analysis of Data (CODAMET), Ministerio de Ciencia, Innovación y Universidades, Spain. The funding sources did not participate in the design or conduct of the study, the collection, management, analysis, or interpretation of the data, or the preparation, review, or approval of the manuscript. We want to thank Marcel Bach-Pagès for his insights in the manuscript. ; Peer Reviewed ; Postprint (published version)