Economic evaluations of non-communicable disease interventions

Background Demographic projections suggest a major increase in non-communicable disease (NCD) mortality over the next two decades in developing countries. In a climate of scarce resources, policy-makers need to know which interventions represent value for money. The prohibitive cost of performing multiple economic evaluations has generated interest in transferring the results of studies from one setting to another. This paper aims to bridge the gap in the current literature by critically evaluating the available published data on economic evaluations of NCD interventions in developing countries. Methods We identified and reviewed the methodological quality of 32 economic evaluations of NCD interventions in developing countries. Developing countries were defined according to the World Bank classification for low- and lower middle-income countries. We defined NCDs as the 12 categories listed in the 1993 World Bank report Investing in Health. English language literature was searched for the period January 1984 and January 2003 inclusive in Medline, Science Citation Index, HealthStar, NHS Economic Evaluation Database and Embase using medical subheading terms and free text searches. We then assessed the quality of studies according to a set of pre-defined technical criteria. Results We found that the quality of studies was poor and resource allocation decisions made by local and global policy-makers on the basis of this evidence could be misleading. Furthermore we have identified some clear gaps in the literature, particularly around injuries and strategies for tackling the consequences of the emerging tobacco epidemic. Conclusion In the face of poor evidence the role of so-called generalised cost-effectiveness analyses has an important role to play in aiding public health decision-making at the global level. Further research is needed to investigates the causes of variation among cost, effects and cost-effectiveness data within and between settings. Such analyses still need to take a broad view, present data in a transparent manner and take account of local constraints

Using learned action models in execution monitoring

Planners reason with abstracted models of the behaviours they use to construct plans. When plans are turned into the instructions that drive an executive, the real behaviours interacting with the unpredictable uncertainties of the environment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a behaviour has diverged so far from the expected behaviour that it can be considered to be a failure. In this paper we present further developments of the work described in (Fox et al. 2006), where models of behaviours were learned as Hidden Markov Models. Execution of behaviours is monitored by tracking the most likely trajectory through such a learned model, while possible failures in execution are identified as deviations from common patterns of trajectories within the learned models. We present results for our experiments with a model learned for a robot behaviour

The identification and exploitation of almost symmetry in planning problems

Previous work in symmetry detection for planning has identified symmetries between domain objects and shown how the exploitation of this information can help reduce search at plan time. However these methods are unable to detect symmetries between objects that are almost symmetrical: where the objects must start (or end) in slightly different configurations but for much of the plan their behaviour is equivalent. In the paper we outline a method for identifying such symmetries and discuss how this symmetry information can be positively exploited to help direct search during planning we have implemented this method and integrated it with the FF-v2.3 planner and in the paper we present results of experiments with this approach that demonstrate its potential

Abstraction-based action ordering in planning

Many planning problems contain collections of symmetric objects, actions and structures which render them difficult to solve efficiently. It has been shown that the detection and exploitation of symmetric structure in planning problems can dramatically reduce the size of the search space and the time taken to find a solution. We present the idea of using an abstraction of the problem domain to reveal symmetric structure and guide the navigation of the search space. We show that this is effective even in domains in which there is little accessible symmetric structure available for pruning. Proactive exploitation represents a flexible and powerfulalternative to the symmetry-breaking strategies exploited in earlier work in planning and CSPs. The notion of almost symmetry is defined and results are presented showing that proactive exploitation of almost symmetry can improve the performance of a heuristic forward search planner

One-step dual purpose joining technique

This fastener used in induction heating is a wire screen basically of an eddy current carrying material such as carbon steel. Selected wires in the screen are copper, sheathed in an insulating material. The screen is placed between two sheets of thermoplastics. When inductively heated, the composite softens and flows around the apertures of the screen. After this heating and joining, the copper wires may be used to conduct electricity

Multi-Task Policy Search for Robotics

© 2014 IEEE.Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled approaches to share and transfer knowledge among similar tasks. We present a novel approach for learning a nonlinear feedback policy that generalizes across multiple tasks. The key idea is to define a parametrized policy as a function of both the state and the task, which allows learning a single policy that generalizes across multiple known and unknown tasks. Applications of our novel approach to reinforcement and imitation learning in realrobot experiments are shown

In situ analysis for intelligent control

We report a pilot study on in situ analysis of backscatter data for intelligent control of a scientific instrument on an Autonomous Underwater Vehicle (AUV) carried out at the Monterey Bay Aquarium Research Institute (MBARI). The objective of the study is to investigate techniques which use machine intelligence to enable event-response scenarios. Specifically we analyse a set of techniques for automated sample acquisition in the water-column using an electro-mechanical "Gulper", designed at MBARI. This is a syringe-like sampling device, carried onboard an AUV. The techniques we use in this study are clustering algorithms, intended to identify the important distinguishing characteristics of bodies of points within a data sample. We demonstrate that the complementary features of two clustering approaches can offer robust identification of interesting features in the water-column, which, in turn, can support automatic event-response control in the use of the Gulper