A model of osteoporosis impact in Switzerland 2000-2020

The aim of our study was to develop a modeling framework suitable to quantify the incidence, absolute number and economic impact of osteoporosis-attributable hip, vertebral and distal forearm fractures, with a particular focus on change over time, and with application to the situation in Switzerland from 2000 to 2020. A Markov process model was developed and analyzed by Monte Carlo simulation. A demographic scenario provided by the Swiss Federal Statistical Office and various Swiss and international data sources were used as model inputs. Demographic and epidemiologic input parameters were reproduced correctly, confirming the internal validity of the model. The proportion of the Swiss population aged 50 years or over will rise from 33.3% in 2000 to 41.3% in 2020. At the total population level, osteoporosis-attributable incidence will rise from 1.16 to 1.54 per 1,000 person-years in the case of hip fracture, from 3.28 to 4.18 per 1,000 person-years in the case of radiographic vertebral fracture, and from 0.59 to 0.70 per 1,000 person-years in the case of distal forearm fracture. Osteoporosis-attributable hip fracture numbers will rise from 8,375 to 11,353, vertebral fracture numbers will rise from 23,584 to 30,883, and distal forearm fracture numbers will rise from 4,209 to 5,186. Population-level osteoporosis-related direct medical inpatient costs per year will rise from 713.4 million Swiss francs (CHF) to CHF946.2 million. These figures correspond to 1.6% and 2.2% of Swiss health care expenditures in 2000. The modeling framework described can be applied to a wide variety of settings. It can be used to assess the impact of new prevention, diagnostic and treatment strategies. In Switzerland incidences of osteoporotic hip, vertebral and distal forearm fracture will rise by 33%, 27%, and 19%, respectively, between 2000 and 2020, if current prevention and treatment patterns are maintained. Corresponding absolute fracture numbers will rise by 36%, 31%, and 23%. Related direct medical inpatient costs are predicted to increase by 33%; however, this estimate is subject to uncertainty due to limited availability of input data.

The importance of glacier and forest change in hydrological climate-impact studies

Changes in land cover alter the water balance components of a catchment, due to strong interactions between soils, vegetation and the atmosphere. Therefore, hydrological climate impact studies should also integrate scenarios of associated land cover change. To reflect two severe climate-induced changes in land cover, we applied scenarios of glacier retreat and forest cover increase that were derived from the temperature signals of the climate scenarios used in this study. The climate scenarios were derived from ten regional climate models from the ENSEMBLES project. Their respective temperature and precipitation changes between the scenario period (2074–2095) and the control period (1984–2005) were used to run a hydrological model. The relative importance of each of the three types of scenarios (climate, glacier, forest) was assessed through an analysis of variance (ANOVA). Altogether, 15 mountainous catchments in Switzerland were analysed, exhibiting different degrees of glaciation during the control period (0–51%) and different degrees of forest cover increase under scenarios of change (12–55% of the catchment area). The results show that even an extreme change in forest cover is negligible with respect to changes in runoff, but it is crucial as soon as changes in evaporation or soil moisture are concerned. For the latter two variables, the relative impact of forest change is proportional to the magnitude of its change. For changes that concern 35% of the catchment area or more, the effect of forest change on summer evapotranspiration is equally or even more important than the climate signal. For catchments with a glaciation of 10% or more in the control period, the glacier retreat significantly determines summer and annual runoff. The most important source of uncertainty in this study, though, is the climate scenario and it is highly recommended to apply an ensemble of climate scenarios in the impact studies. The results presented here are valid for the climatic region they were tested for, i.e., a humid, mid-latitude mountainous environment. They might be different for regions where the evaporation is a major component of the water balance, for example. Nevertheless, a hydrological climate-impact study that assesses the additional impacts of forest and glacier change is new so far and provides insight into the question whether or not it is necessary to account for land cover changes as part of climate change impacts on hydrological systems.

Envisioning sustainable water futures in a transdisciplinary learning process: combining normative, explorative, and participatory scenario approaches

Competing water demands for household consumption as well as the production of food, energy, and other uses pose challenges for water supply and sustainable development in many parts of the world. Designing creative strategies and learning processes for sustainable water governance is thus of prime importance. While this need is uncontested, suitable approaches still have to be found. In this article we present and evaluate a conceptual approach to scenario building aimed at transdisciplinary learning for sustainable water governance. The approach combines normative, explorative, and participatory scenario elements. This combination allows for adequate consideration of stakeholders’ and scientists’ systems, target, and transformation knowledge. Application of the approach in the MontanAqua project in the Swiss Alps confirmed its high potential for co-producing new knowledge and establishing a meaningful and deliberative dialogue between all actors involved. The iterative and combined approach ensured that stakeholders’ knowledge was adequately captured, fed into scientific analysis, and brought back to stakeholders in several cycles, thereby facilitating learning and co-production of new knowledge relevant for both stakeholders and scientists. However, the approach also revealed a number of constraints, including the enormous flexibility required of stakeholders and scientists in order for them to truly engage in the co-production of new knowledge. Overall, the study showed that shifts from strategic to communicative action are possible in an environment of mutual trust. This ultimately depends on creating conditions of interaction that place scientists’ and stakeholders’ knowledge on an equal footing.

Single hadron response measurement and calorimeter jet energy scale uncertainty with the ATLAS detector at the LHC

The uncertainty on the calorimeter energy response to jets of particles is derived for the ATLAS experiment at the Large Hadron Collider (LHC). First, the calorimeter response to single isolated charged hadrons is measured and compared to the Monte Carlo simulation using proton-proton collisions at centre-of-mass energies of root s = 900 GeV and 7 TeV collected during 2009 and 2010. Then, using the decay of K-s and Lambda particles, the calorimeter response to specific types of particles (positively and negatively charged pions, protons, and anti-protons) is measured and compared to the Monte Carlo predictions. Finally, the jet energy scale uncertainty is determined by propagating the response uncertainty for single charged and neutral particles to jets. The response uncertainty is 2-5 % for central isolated hadrons and 1-3 % for the final calorimeter jet energy scale.

Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set

Stepwise uncertainty reduction (SUR) strategies aim at constructing a sequence of points for evaluating a function  f in such a way that the residual uncertainty about a quantity of interest progressively decreases to zero. Using such strategies in the framework of Gaussian process modeling has been shown to be efficient for estimating the volume of excursion of f above a fixed threshold. However, SUR strategies remain cumbersome to use in practice because of their high computational complexity, and the fact that they deliver a single point at each iteration. In this article we introduce several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel. Such criteria are of particular interest when f is costly to evaluate and several CPUs are simultaneously available. We also manage to drastically reduce the computational cost of these strategies through the use of closed form formulas. We illustrate their performances in various numerical experiments, including a nuclear safety test case. Basic notions about kriging, auxiliary problems, complexity calculations, R code, and data are available online as supplementary materials.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

Networks of Information Exchange Under Uncertainty: The Case of Unconventional Gas Development in the United Kingdom

Modern policy-making is increasingly influenced by different types of uncertainty. Political actors are supposed to behave differently under the context of uncertainty then in “usual” decision-making processes. Actors exchange information in order to convince other actors and decision-makers, to coordinate their lobbying activities and form coalitions, and to get information and learn on the substantive issue. The literature suggests that preference similarity, social trust, perceived power and functional interdependence are particularly important drivers of information exchange. We assume that social trust as well as being connected to scientific actors is more important under uncertainty than in a setting with less uncertainty. To investigate information exchange under uncertainty analyze the case of unconventional shale gas development in the UK from 2008 till 2014. Our study will rely on statistical analyses of survey data on a diverse set of actors dealing with shale gas development and regulation in the UK.