Introduction: In this study, we aim to compare spatial statistic models to estimate the spatial distribution of Zika and Chikungunya infections in the city of Recife, Brazil. We also aim to establish the relationship between the diseases and the analyzed geographical conditions. Methods: The models were defined by combining three categories: type of spatial unit, calculation of the dependent variable format, and estimation methods (Geographical Weighted Regression [GWR] and Ordinary Least Square [OLS]). We identified the most accurate model to estimate the spatial distribution of the diseases. After selecting the model that provided best results, the relationship between the geographical conditions and the incidence of the diseases was analyzed. Results: It was observed that the matrix of 100 meters (as the spatial unit) showed the highest efficiency to estimate the diseases. The best results were observed in the models that utilized the kernel density estimation (as the calculation of the dependent variable). In all models, the GWR method showed the best results. By considering the OLS coefficient values, it was observed that all geographical conditions are related to the incidence of Zika and Chikungunya, while the GWR coefficient values showed where this relationship was more noticeable. Conclusions: The model that utilized the combination of the matrix of 100 meters, kernel density estimation (as the calculation of the dependent variable) and GWR method showed the highest efficiency in estimating the spatial distribution of the diseases. The coefficient values showed that all analyzed geographical conditions are related to the illnesses' incidence.
The carbon footprint reveals the emission profile of a healthcare building and, when quantified properly, is useful for the design of effective mitigation plans. This article aims to determine the global warming potential associated with the healthcare centre building process in Spain at a 100-year perspective. To this end, six healthcare centres built between 2007 and 2010 were analysed, and the emissions associated with the manufacturing, transport and placement of materials on site – including the final tests and commissioning of the building – were calculated. The results show that the average CO2 equivalent emission per m2 built is 1122.30 kg (standard deviation = 136.46), 1.24 kg (standard deviation = 0.19) per euro spent and 71.35 kg (standard deviation = 7.13) per hour spent on construction. Emissions per user, worker, electrical power and energy consumed were also classified. The material manufacturing and installation stages generate the most emissions, and healthcare centres larger than 2000 m2 appear to emit less CO2 equivalent per m2 when being built than smaller centres. The construction elements that caused most greenhouse gas emissions were also identified. These parameters allow extracting and designing proposals for improvements in environmental management.
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