Gender inequity in the provision of care for hip disease: population-based cross-sectional study

Objectives To examine gender differences along the care pathway to total hip replacement. Methods We conducted a population-based cross-sectional study of 26,046 individuals aged 35 years and over in Avon and Somerset. Participants completed a questionnaire asking about care provision at five milestones on the pathway to total hip replacement. Those reporting hip disease were invited to a clinical examination. We estimated odds ratios (ORs) [95% confidence intervals (CI)] for provision of care to women compared with men. Results 3169 people reported hip pain, 2018 were invited for clinical examination, and 1405 attended (69.6%). After adjustment for age and disease severity, women were less likely than men to have consulted their general practitioner (OR 0.78, 95%-CI 0.61–1.00), as likely as men to have received drug therapy for hip pain in the previous year (OR 0.96, 95%-CI 0.74–1.24), but less likely to have been referred to specialist care (OR 0.53, 95%-CI 0.40–0.70), to have consulted an orthopaedic surgeon (OR 0.50, 95%-CI 0.32–0.78), or to be on a waiting list for total hip replacement (OR 0.41, 95%-CI 0.20–0.87). Differences remained in the 746 people who had sought care from their general practitioner, and after adjustment for willingness and fitness for surgery. Conclusions There are gender inequalities in provision of care for hip disease in England, which are not fully accounted for by gender differences in care seeking and treatment preferences. Differences in referral to specialist care by general practitioners might unwittingly contribute to this inequity. Accurate information about availability, benefits and risks of hip replacement for providers and patients, and continuing education to ensure that clinicians interpret and correct patients' assumptions could help reduce inequalities.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-temporal credit assignment in population learning

We present a model for plasticity induction in reinforcement learning which is based on a cascade of synaptic memory traces. In the cascade of these so called eligibility traces presynaptic input is first corre lated with postsynaptic events, next with the behavioral decisions and finally with the external reinforcement. A population of leaky integrate and fire neurons endowed with this plasticity scheme is studied by simulation on different tasks. For operant co nditioning with delayed reinforcement, learning succeeds even when the delay is so large that the delivered reward reflects the appropriateness, not of the immediately preceeding response, but of a decision made earlier on in the stimulus - decision sequence . So the proposed model does not rely on the temporal contiguity between decision and pertinent reward and thus provides a viable means of addressing the temporal credit assignment problem. In the same task, learning speeds up with increasing population si ze, showing that the plasticity cascade simultaneously addresses the spatial problem of assigning credit to the different population neurons. Simulations on other task such as sequential decision making serve to highlight the robustness of the proposed sch eme and, further, contrast its performance to that of temporal difference based approaches to reinforcement learning.

A feasibility study for the provision of electronic healthcare tools and services in areas of Greece, Cyprus and Italy

Background Through this paper, we present the initial steps for the creation of an integrated platform for the provision of a series of eHealth tools and services to both citizens and travelers in isolated areas of thesoutheast Mediterranean, and on board ships travelling across it. The platform was created through an INTERREG IIIB ARCHIMED project called INTERMED. Methods The support of primary healthcare, home care and the continuous education of physicians are the three major issues that the proposed platform is trying to facilitate. The proposed system is based on state-of-the-art telemedicine systems and is able to provide the following healthcare services: i) Telecollaboration and teleconsultation services between remotely located healthcare providers, ii) telemedicine services in emergencies, iii) home telecare services for "at risk" citizens such as the elderly and patients with chronic diseases, and iv) eLearning services for the continuous training through seminars of both healthcare personnel (physicians, nurses etc) and persons supporting "at risk" citizens. These systems support data transmission over simple phone lines, internet connections, integrated services digital network/digital subscriber lines, satellite links, mobile networks (GPRS/3G), and wireless local area networks. The data corresponds, among others, to voice, vital biosignals, still medical images, video, and data used by eLearning applications. The proposed platform comprises several systems, each supporting different services. These were integrated using a common data storage and exchange scheme in order to achieve system interoperability in terms of software, language and national characteristics. Results The platform has been installed and evaluated in different rural and urban sites in Greece, Cyprus and Italy. The evaluation was mainly related to technical issues and user satisfaction. The selected sites are, among others, rural health centers, ambulances, homes of "at-risk" citizens, and a ferry. Conclusions The results proved the functionality and utilization of the platform in various rural places in Greece, Cyprus and Italy. However, further actions are needed to enable the local healthcare systems and the different population groups to be familiarized with, and use in their everyday lives, mature technological solutions for the provision of healthcare services.

A "Beyond WTO" Scenario for Swiss Agriculture: Consequences for Income Generation and the Provision of Public Goods

The conclusion of the Doha Round negotiations is likely to influence Swiss agricultural policy substantially. The same goes for a free trade agreement in agriculture and food with the European Communities. Even though neither of them will bring about duty-free and quota-free market access, or restrict domestic support measures to green box compatible support, both would represent a big step in that direction. There is no empirical evidence on the effect of such a counterfactual scenario for Swiss agriculture. We therefore use a normative mathematical programming model to illustrate possible effects for agricultural production and the corresponding agricultural income. Moreover, we discuss the results with respect to the provision of public goods under the assumption of continuing green box-compatible direct payments. The aim of our article is to bring more transparency into the discussion on the effects of freer and less distorted trade on the income generation by a multifunctional agriculture. The article will be organized as follows. In the first Section we specify the background of our study. In the second section, we focus on the problem statement and our research questions. In Section 3, we describe in detail a counterfactual scenario of “duty-free, quota-free and price support-free” agriculture from an economic as well as a legal perspective. Our methodology and the results are presented in Section 4 and 5 respectively. In Section 6, we discuss our results with respect to economic and legal aspects of multifunctional agriculture.

Evidence, lack of evidence, controversy, and debate in the provision and performance of the surgery of acute type A aortic dissection

Acute type A aortic dissection is a lethal condition requiring emergency surgery. It has diverse presentations, and the diagnosis can be missed or delayed. Once diagnosed, decisions with regard to initial management, transfer, appropriateness of surgery, timing of operation, and intervention for malperfusion complications are necessary. The goals of surgery are to save life by prevention of pericardial tamponade or intra-pericardial aortic rupture, to resect the primary entry tear, to correct or prevent any malperfusion and aortic valve regurgitation, and if possible to prevent late dissection-related complications in the proximal and downstream aorta. No randomized trials of treatment or techniques have ever been performed, and novel therapies-particularly with regard to extent of surgery-are being devised and implemented, but their role needs to be defined. Overall, except in highly specialized centers, surgical outcomes might be static, and there is abundant room for improvement. By highlighting difficulties and controversies in diagnosis, patient selection, and surgical therapy, our over-arching goal should be to enfranchise more patients for treatment and improve surgical outcomes.

Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

Potential and limitations of organic and fair trade cotton for improving livelihoods of smallholders: evidence from Central Asia

Cotton is a leading agricultural non-food commodity associated with soil degradation, water pollution and pesticide poisoning due to high levels of agrochemical inputs. Organic farming is often promoted as a means of addressing the economic, environmental and health risks of conventional cotton production, and it is slowly gaining ground in the global cotton market. Organic and fair trade cotton are widely seen as opportunities for smallholder farmers to improve their livelihoods thanks to higher returns, lower input costs and fewer risks. Despite an increasing number of studies comparing the profitability of organic and non-organic farming systems in developing and industrialized countries, little has been published on organic farming in Central Asia. The aim of this article is to describe the economic performance and perceived social and environmental impacts of organic cotton in southern Kyrgyzstan, drawing on a comparative field study conducted by the author in 2009. In addition to economic and environmental aspects, the study investigated farmers’ motivations toward and assessment of conversion to organic farming. Cotton yields on organic farms were found to be 10% lower, while input costs per unit were 42% lower; as a result, organic farmers’ cotton revenues were 20% higher. Due to lower input costs as well as organic and fair trade price premiums, the average gross margin from organic cotton was 27% higher. In addition to direct economic benefits, organic farmers enjoy other benefits, such as easy access to credit on favorable terms, provision of uncontaminated cottonseed cooking oil and cottonseed cake as animal feed, and marketing support as well as extension and training services provided by newly established organic service providers. The majority of organic farmers perceive improved soil quality, improved health conditions, and positively assess their initial decision to convert to organic farming. The major disadvantage of organic farming is the high manual labor input required. In the study area, where manual farm work is mainly women's work and male labor migration is widespread, women are most affected by this negative aspect of organic farming. Altogether, the results suggest that, despite the inconvenience of a higher workload, the advantages of organic farming outweigh its disadvantages and that conversion to organic farming improves the livelihoods of small-scale farmers.