Supporting the needs of public health decision-makers and review authors in the UK

Introduction Systematic reviews, through the synthesis of multiple primary research studies, can be powerful tools in enabling evidence-informed public health policy debate, development and action. In seeking to optimize the utility of these reviews, it is important to understand the needs of those using them. Previous work has emphasized that researchers should adopt methods that are appropriate to the problems that public health decision-makers are grappling with, as well as to the policy context in which they operate.1,2 Meeting these demands poses significant methodological challenges for review authors and prompts a reconsideration of the resources, training and support structures available to facilitate the efficient and timely production of useful, comprehensive reviews. The Cochrane Public Health Group (CPHG) was formed in 2008 to support reviews of complex, upstream public health topics. The majority of CPHG authors are from the UK, which has historically been at the forefront of efforts to promote the production and use of systematic reviews of research relevant to public health decision-makers. The UK therefore provides a suitably mature national context in which to examine (i) the current and future demands of decision-makers to increase the use, value and impact of evidence syntheses; (ii) the implications this has for the scope and methods of reviews and (iii) the required action to build and support capacity to conduct such reviews.

Bridging the gap: Exploring the barriers to using economic evidence in healthcare decision making and strategies for improving uptake

Evidence from economic evaluations is often not used to inform healthcare policy despite being well regarded by policy makers and physicians. This article employs the accessibility and acceptability framework to review the barriers to using evidence from economic evaluation in healthcare policy and the strategies used to overcome these barriers. Economic evaluations are often inaccessible to policymakers due to the absence of relevant economic evaluations, the time and cost required to conduct and interpret economic evaluations, and lack of expertise to evaluate quality and interpret results. Consistently reported factors that limit the translation of findings from economic evaluations into healthcare policy include poor quality of research informing economic evaluations, assumptions used in economic modelling, conflicts of interest, difficulties in transferring resources between sectors, negative attitudes to healthcare rationing, and the absence of equity considerations. Strategies to overcome these barriers have been suggested in the literature, including training, structured abstract databases, rapid evaluation, reporting checklists for journals, and considering factors other than cost effectiveness in economic evaluations, such as equity or budget impact. The factors that prevent or encourage decision makers to use evidence from economic evaluations have been identified, but the relative importance of these factors to decision makers is uncertain.

Decision making in social neurobiological systems modeled as transitions in dynamic pattern formation

Extant models of decision making in social neurobiological systems have typically explained task dynamics as characterized by transitions between two attractors. In this paper, we model a three-attractor task exemplified in a team sport context. The model showed that an attacker–defender dyadic system can be described by the angle x between a vector connecting the participants and the try line. This variable was proposed as an order parameter of the system and could be dynamically expressed by integrating a potential function. Empirical evidence has revealed that this kind of system has three stable attractors, with a potential function of the form V(x)=−k1x+k2ax2/2−bx4/4+x6/6, where k1 and k2 are two control parameters. Random fluctuations were also observed in system behavior, modeled as white noise εt, leading to the motion equation dx/dt = −dV/dx+Q0.5εt, where Q is the noise variance. The model successfully mirrored the behavioral dynamics of agents in a social neurobiological system, exemplified by interactions of players in a team sport.

Exploring urban consumers’ attitudes and intentions to purchase local food in Chile

Although local food consumption is growing in importance there remains a lack of research addressing local food consumption preferences in less-developed countries. This paper aims to examine the drivers of local food purchase intentions for Chilean consumers. A model of local food behavioral intention was developed from consumer behavior theory. The model was tested using structural equation modeling with data from Chilean shoppers located in Santiago (n=283). The analysis revealed that Chilean consumers are willing to purchase local food based on their positive attitude towards buying local food and their feelings of connectedness with the environment, but not because they have a desire to support local businesses. These findings have implications for retailers, marketers and food producers.

Competition and its influence on consumer decision making in social marketing

This paper extends the largely conceptual understanding of competition in social marketing by empirically investigating, from a consumer perspective, the nature of competition and its influence on decision making at the individual level. Two phases of qualitative inquiry in Australia, comprising 30 and 20 semi-structured interviews respectively, examined the role of competition in young adults’ decision to adopt and maintain help-seeking for mental ill-health. The findings from thematic analysis suggest that competition operates at both the behavioural and goal level to influence consumers’ decision to perform behaviour and that the types of competition in operation may vary from the adoption to the maintenance of behaviour. The findings are integrated into a framework that social marketers could employ to identify, analyse and address competition.

Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process

One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.

Knowledge gain or system benefit in environmental decision making?

The quality of environmental decisions are gauged according to the management objectives of a conservation project. Management objectives are generally about maximising some quantifiable measure of system benefit, for instance population growth rate. They can also be defined in terms of learning about the system in question, in such a case actions would be chosen that maximise knowledge gain, for instance in experimental management sites. Learning about a system can also take place when managing practically. The adaptive management framework (Walters 1986) formally acknowledges this fact by evaluating learning in terms of how it will improve management of the system and therefore future system benefit. This is taken into account when ranking actions using stochastic dynamic programming (SDP). However, the benefits of any management action lie on a spectrum from pure system benefit, when there is nothing to be learned about the system, to pure knowledge gain. The current adaptive management framework does not permit management objectives to evaluate actions over the full range of this spectrum. By evaluating knowledge gain in units distinct to future system benefit this whole spectrum of management objectives can be unlocked. This paper outlines six decision making policies that differ across the spectrum of pure system benefit through to pure learning. The extensions to adaptive management presented allow specification of the relative importance of learning compared to system benefit in management objectives. Such an extension means practitioners can be more specific in the construction of conservation project objectives and be able to create policies for experimental management sites in the same framework as practical management sites.

Robust conservation decision-making

Decision-making for conservation is conducted within the margins of limited funding. Furthermore, to allocate these scarce resources we make assumptions about the relationship between management impact and expenditure. The structure of these relationships, however, is rarely known with certainty. We present a summary of work investigating the impact of model uncertainty on robust decision-making in conservation and how this is affected by available conservation funding. We show that achieving robustness in conservation decisions can require a triage approach, and emphasize the need for managers to consider triage not as surrendering but as rational decision making to ensure species persistence in light of the urgency of the conservation problems, uncertainty, and the poor state of conservation funding. We illustrate this theory by a specific application to allocation of funding to reduce poaching impact on the Sumatran tiger Panthera tigris sumatrae in Kerinci Seblat National Park, Indonesia. To conserve our environment, conservation managers must make decisions in the face of substantial uncertainty. Further, they must deal with the fact that limitations in budgets and temporal constraints have led to a lack of knowledge on the systems we are trying to preserve and on the benefits of the actions we have available (Balmford & Cowling 2006). Given this paucity of decision-informing data there is a considerable need to assess the impact of uncertainty on the benefit of management options (Regan et al. 2005). Although models of management impact can improve decision making (e.g.Tenhumberg et al. 2004), they typically rely on assumptions around which there is substantial uncertainty. Ignoring this 'model uncertainty', can lead to inferior decision-making (Regan et al. 2005), and potentially, the loss of the species we are trying to protect. Current methods used in ecology allow model uncertainty to be incorporated into the model selection process (Burnham & Anderson 2002; Link & Barker 2006), but do not enable decision-makers to assess how this uncertainty would change a decision. This is the basis of information-gap decision theory (info-gap); finding strategies most robust to model uncertainty (Ben-Haim 2006). Info-gap has permitted conservation biology to make the leap from recognizing uncertainty to explicitly incorporating severe uncertainty into decision-making. In this paper we present a summary of McDonald-Madden et al (2008a) who use an info-gap framework to address the impact of uncertainty in the functional representations of biological systems on conservation decision-making. Furthermore, we highlight the importance of two key elements limiting conservation decision-making - funding and knowledge - and how they interact to influence the best management strategy for a threatened species. Copyright © ASCE 2011.