Preventing a shortage of general practitioners in Austria

Background: In Austria, almost half of GPs will reach retirement age within the upcoming ten years. Nevertheless, the specific number of young physicians who intend to replace them is unknown and might be insufficient. Avoiding a GP shortage is therefore an indispensable challenge. Aims. Developing a comprehensive catalogue of potentially effective measures to prevent a GP shortage, assessing these measures’ understandability, effectiveness and feasibility, identifying practical considerations for implementing prioritised measures and analysing current GP shortage reform processes. Methods: Firstly, a search strategy for international policy documents and literature reviews included bibliographical databases, institutional websites, an internet search engine, and references of included publications. Three experts reviewed extracted measures. Secondly, identified measures were assessed for understandability, effectiveness and feasibility by a two-phase expert panel process. Ten relevant experts performed structured assessments through e-mail and a face-to-face workshop. Thirdly, previously prioritised measures were assessed for practical considerations concerning implementation by six relevant experts through a semi-structured questionnaire. Fourthly, 26 semi-structured interviews were performed with key experts from different stakeholders and regions and supplemented with a documentary analysis. Theories on agenda setting and research utilization informed a framework analysis. Results: Firstly, ten policy documents and 32 literature reviews informed a catalogue of 95 potentially effective measures. Secondly, seven measures were considered effective and feasible by key experts. Thirdly, several practical considerations were identified concerning implementing prioritised measures. Fourthly, the GP shortage receives public and stakeholder attention but there seems to be little agreement on its definition, severity, causes and solutions. Attention was reportedly raised by alarmed mayors and media coverage but less by advocacy efforts or policy entrepreneurship. Research studies apparently increased recognition of the problem and policy alternatives. Conclusions: This thesis offers a comprehensive catalogue of assessed measures to prevent a GP shortage. The current reform processes indicate room for improvement

Changing risks of simultaneous global breadbasket failure

The risk of extreme climatic conditions leading to unusually low global agricultural production is exacerbated if more than one global ‘breadbasket’ is subject to climatic extremes at the same time. Such shocks can pose a risk to the global food system amplifying threats to global food security and have the potential to trigger other systemic risks. So far, while the possibility of climatic extremes hitting more than one breadbasket has been postulated little is known about the actual risk. Here we present quantitative risk estimates of simultaneous breadbasket failures due to climatic extremes and show how risk has changed over time. We combine region-specific data on agricultural production with spatial statistics of climatic extremes to quantify the changing risk of low production for the major food producing regions (‘breadbaskets’) in the world. We find evidence that there is increasing risk of simultaneous failure of wheat, maize and soybean crops, across the breadbaskets analyzed. For rice, risks of simultaneous adverse climate conditions have decreased in the breadbaskets analyzed in this study in the recent past mostly owing to solar radiation changes favoring rice growth. Depending on the correlation structure between the breadbaskets, spatial dependence between climatic extremes globally can mitigate or aggravate the risks for the global food production. Our analysis can provide the basis for more efficient allocation of resources to contingency plans and/or strategic crop reserves that would enhance the resilience of the global food system

A typology of community flood resilience

Flood risk is increasing worldwide and there is a growing need to better understand the co-benefits of investments in disaster resilience. Utilizing a multinational community flood resilience dataset, this paper takes a systems approach to understanding community-level flood resilience. Using a cluster analysis and bivariate correlation methods, we develop a typology of community flood resilience capacity based on community characteristics and five capitals (human, financial, natural, physical, and social). Our results reinforce the importance of context-specific policymaking and give recommendations of four distinct clusters to investigate the relationship between flood resilience and prevailing development conditions. We especially find that communities with higher interactions between their capital capacities tend to have higher flood resilience levels. Additionally, there are indications that stronger interactions between community capacities can help to induce multiple co-benefits when investing in disaster resilience. Our results also have important policy implications on the individual community level. For example, based on our results, we suggest that communities with lower flood resilience capacities and interactions can best build resilience on leveraging their relatively higher human capital capacities to strengthen the financial and social capitals. Negative effects might happen for urban communities when co-benefits of natural and physical capital are not fully integrated. The highest flood resilience capacity is found in communities with a well-balanced household income distribution which is likely a contributing factor to the importance of financial capital for this cluster. Our results emphasize the importance of an integrative approach to management when implementing systematic flood disaster resilience metrics and development measures

Emergence of long memory in stock volatility from a modified Mike-Farmer model

The Mike-Farmer (MF) model was constructed empirically based on the continuous double auction mechanism in an order-driven market, which can successfully reproduce the cubic law of returns and the diffusive behavior of stock prices at the transaction level. However, the volatility (defined by absolute return) in the MF model does not show sound long memory. We propose a modified version of the MF model by including a new ingredient, that is, long memory in the aggressiveness (quantified by the relative prices) of incoming orders, which is an important stylized fact identified by analyzing the order flows of 23 liquid Chinese stocks. Long memory emerges in the volatility synthesized from the modified MF model with the DFA scaling exponent close to 0.76, and the cubic law of returns and the diffusive behavior of prices are also produced at the same time. We also find that the long memory of order signs has no impact on the long memory property of volatility, and the memory effect of order aggressiveness has little impact on the diffusiveness of stock prices.Comment: 6 pages, 6 figures and 1 tabl

The risk and consequences of multiple breadbasket failures: an integrated copula and multilayer agent-based modeling approach

Climate shocks to food systems have been thoroughly researched in terms of food security and supply chain management. However, sparse research exists on the dependent nature of climate shocks on food-producing breadbasket regions and their subsequent cascading impacts. In this paper, we propose that a copula approach, combined with a multilayer network and an agent-based model, can give important insights on how tail-dependent shocks can impact food systems. We show how such shocks can potentially cascade within a region through the behavioral interactions of various layers. Based on our suggested framework, we set up a model for India and show that risks due to drought events multiply if tail dependencies during extremes drought is explicitly taken into account. We further demonstrate that the risk is exacerbated if displacement also takes place. In order to quantify the spatial–temporal evolution of climate risks, we introduce a new measure of multilayer vulnerability that we term Vulnerability Rank or VRank. We find that with higher food production losses, the number of agents that are affected increases nonlinearly due to cascading effects in different network layers. These effects spread to the unaffected regions via large-scale displacement causing sudden changes in production, employment and consumption decisions. Thus, demand shifts also force supply-side adjustments of food networks in the months following the climate shock. We suggest that our framework can provide a more accurate picture of food security-related systemic risks caused by multiple breadbasket failures which, in turn, can better inform risk management and humanitarian aid strategies

Fiscal resilience challenged

The GAR Risk Atlas contributes to unveiling the hidden risk in national economies and their urban centres. Building on a multi-year effort by a consortium of leading scientific institutions coordinated by UNISDR, it provides a global vision of where and how disaster risk can undermine development. It estimates the probability of future disaster losses in the built environment for five major hazards (earthquakes, tsunamis, riverine floods, and tropical cyclones - winds and storm surge) and for every country and territory in the world and represents the results using robust risk metrics such as Average Annual Loss (AAL) and Probable Maximum Loss (PML). The GAR Risk Atlas (special report of the GAR series) presents the fully updated results of the global risk assessment in a visually appealing and innovative manner

Moderation of antipsychotic-induced weight gain by energy balance gene variants in the RUPP autism network risperidone studies.

Second-generation antipsychotic exposure, in both children and adults, carries significant risk for excessive weight gain that varies widely across individuals. We queried common variation in key energy balance genes (FTO, MC4R, LEP, CNR1, FAAH) for their association with weight gain during the initial 8 weeks in the two NIMH Research Units on Pediatric Psychopharmacology Autism Network trials (N=225) of risperidone for treatment of irritability in children/adolescents aged 4-17 years with autism spectrum disorders. Variants in the cannabinoid receptor (CNR)-1 promoter (P=1.0 × 10(-6)), CNR1 (P=9.6 × 10(-5)) and the leptin (LEP) promoter (P=1.4 × 10(-4)) conferred robust-independent risks for weight gain. A model combining these three variants was highly significant (P=1.3 × 10(-9)) with a 0.85 effect size between lowest and highest risk groups. All results survived correction for multiple testing and were not dependent on dose, plasma level or ethnicity. We found no evidence for association with a reported functional variant in the endocannabinoid metabolic enzyme, fatty acid amide hydrolase, whereas body mass index-associated single-nucleotide polymorphisms in FTO and MC4R showed only trend associations. These data suggest a substantial genetic contribution of common variants in energy balance regulatory genes to individual antipsychotic-associated weight gain in children and adolescents, which supersedes findings from prior adult studies. The effects are robust enough to be detected after only 8 weeks and are more prominent in this largely treatment naive population. This study highlights compelling directions for further exploration of the pharmacogenetic basis of this concerning multifactorial adverse event

Studies of the limit order book around large price changes

We study the dynamics of the limit order book of liquid stocks after experiencing large intra-day price changes. In the data we find large variations in several microscopical measures, e.g., the volatility the bid-ask spread, the bid-ask imbalance, the number of queuing limit orders, the activity (number and volume) of limit orders placed and canceled, etc. The relaxation of the quantities is generally very slow that can be described by a power law of exponent $\approx0.4$. We introduce a numerical model in order to understand the empirical results better. We find that with a zero intelligence deposition model of the order flow the empirical results can be reproduced qualitatively. This suggests that the slow relaxations might not be results of agents' strategic behaviour. Studying the difference between the exponents found empirically and numerically helps us to better identify the role of strategic behaviour in the phenomena.Comment: 19 pages, 7 figure

Standardized disaster and climate resilience grading: A global scale empirical analysis of community flood resilience

Suitable and standardized indicators to track progress in disaster and climate resilience are increasingly considered a key requirement for successfully informing efforts towards effective disaster risk reduction and climate adaptation. Standardized measures of resilience which can be used across different geographical and socioeconomic contexts are however sparse. We present and analyze a standardized community resilience measurement framework for flooding. The corresponding measurement tool is modelled based on and adapted from a so-called ‘technical risk grading’ approach as used in the insurance sector. The grading approach of indicators is based on a two-step process: (i) raw data is collected, and (ii) experts grade the indicators, called sources of resilience, based on this data. We test this approach using approximately 1.25 million datapoints collected across more than 118 communities in nine countries. The quantitative analysis is complemented by content analysis to validate the results from a qualitative perspective. We find that some indicators can more easily be graded by looking at raw data alone, while others require a stronger application of expert judgement. We summarize the reasons for this through six key messages. One major finding is that resilience grades related to subjective characteristics such as ability, feel, and trust are far more dependent on expert judgment than on the actual raw data collected. Additionally, the need for expert judgement further increases if graders must extrapolate the whole community picture from limited raw data. Our findings regarding the role of data and grade specifications can inform ways forward for better, more efficient and increasingly robust standardized assessment of resilience. This should help to build global standardized and comparable, yet locally contextualized, baseline estimates of the many facets of resilience in order to track progress over time on disaster and climate resilience and inform the implementation of the Paris Agreement, Sendai Framework, and the Sustainable Development Goals

The Australian wildfires from a systems dependency perspective

Wildfires are a normal occurrence in much of the world, with many fire adapted ecosystems and societies (Moritz et al 2014). However, a number of drivers appear to be increasing the fire risk and propensity for losses globally (Anon 2019). These drivers include global climate change which through heat and drying is increasing landscape flammability (Podur and Wotton 2010; IPCC 2019, Jones et al 2020). Exposure is being exacerbated through increasing use of fire prone landscapes for urban development, infrastructure and related activities. There is also widespread farmland abandonment, with the consequent loss of land and fire-risk management (Komac et al 2020). Importantly, there are now indications that wildfires are increasingly characterized by severe ecosystem impacts (Lewis 2020). While smaller wildfires often have a rejuvenating effect, the catastrophic fires recently seen in Australia, US and Indonesia seem to leave some ecosystems very seriously damaged (Duncombe 2020, Ward et al 2020). This also has important socio-economic implications, including health, tourism and economic development. How to assess and deal with extreme wildfire risks in the future is a key question that needs to be addressed at the local, country and even global level