A predictive modelling tool for assessing climate, land use and hydrological change on reservoir physicochemical and biological properties

Reservoirs are fundamental for water and energy supply but vulnerable to impacts including climate change. This paper outlines the steps in the development of a model to predict how climate, land use and hydrological change could affect the physiochemical and ecological quality of reservoirs in Portugal’s Douro region. Climatic data will be downscaled for subsequent finer spatial scale models to develop scenarios and outputs. Field observations and satellite imagery analysis will create dynamic maps providing data on change in land use and vegetation cover, while Artificial Neural Networks will determine how climate, land use and vegetation cover change may influence catchment hydrology. Data from field surveys of biological indicators, greenhouse gas emissions plus additional research will be applied in the Stochastic Dynamic Methodology, a sequential modelling process based on statistical parameter estimation, developed to predict and model physiochemical and ecological changes in reservoirs. This interdisciplinary approach will provide vital modelling tools for end users essential for water resource management in Portugal and to comply with the EU Water Framework Directive

30-day morbidity and mortality of sleeve gastrectomy, Roux-en-Y gastric bypass and one anastomosis gastric bypass: a propensity score-matched analysis of the GENEVA data

Background: There is a paucity of data comparing 30-day morbidity and mortality of sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and one anastomosis gastric bypass (OAGB). This study aimed to compare the 30-day safety of SG, RYGB, and OAGB in propensity score-matched cohorts. Materials and methods: This analysis utilised data collected from the GENEVA study which was a multicentre observational cohort study of bariatric and metabolic surgery (BMS) in 185 centres across 42 countries between 01/05/2022 and 31/10/2020 during the Coronavirus Disease-2019 (COVID-19) pandemic. 30-day complications were categorised according to the Clavien–Dindo classification. Patients receiving SG, RYGB, or OAGB were propensity-matched according to baseline characteristics and 30-day complications were compared between groups. Results: In total, 6770 patients (SG 3983; OAGB 702; RYGB 2085) were included in this analysis. Prior to matching, RYGB was associated with highest 30-day complication rate (SG 5.8%; OAGB 7.5%; RYGB 8.0% (p = 0.006)). On multivariate regression modelling, Insulin-dependent type 2 diabetes mellitus and hypercholesterolaemia were associated with increased 30-day complications. Being a non-smoker was associated with reduced complication rates. When compared to SG as a reference category, RYGB, but not OAGB, was associated with an increased rate of 30-day complications. A total of 702 pairs of SG and OAGB were propensity score-matched. The complication rate in the SG group was 7.3% (n = 51) as compared to 7.5% (n = 53) in the OAGB group (p = 0.68). Similarly, 2085 pairs of SG and RYGB were propensity score-matched. The complication rate in the SG group was 6.1% (n = 127) as compared to 7.9% (n = 166) in the RYGB group (p = 0.09). And, 702 pairs of OAGB and RYGB were matched. The complication rate in both groups was the same at 7.5 % (n = 53; p = 0.07). Conclusions: This global study found no significant difference in the 30-day morbidity and mortality of SG, RYGB, and OAGB in propensity score-matched cohorts. © 2021, The Author(s)