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Interactive data visualizations of antibiotic use and resistance in North America and Europe
The notion that information has both statistical and pragmatic value dates back at least to the 1950s; in recent years, interest in the economic value of information has grown considerably. This book explores and analyzes innovative methodologies and applications of research on the value of information. Based on papers commissioned for a workshop hosted in 2010 by Resources for the Future, the book offers answers to important questions: What is meant by “value of information”? When does information have value? What are the stateof-the-practice methods for ascribing value to information? The book examines applications in two disparate fields linked by the importance of valuing information: public health and space. Researchers in the health field have developed some of the most innovative methodologies for valuing information, used to help determine, for example, the value of diagnostics in informing patient treatment decisions. In the field of space, recent applications of value-of-information methods are critical for informing decisions on investment in satellites that collect data about air quality, fresh water supplies, climate and other natural and environmental resources affecting global health and quality of life. The contributors identify five discrete approaches at the frontier of methodological advances: price- and cost-based derivation; Bayesian belief networks; regulatory cost-effectiveness evaluation; econometric modeling and estimation and simulation modeling and estimation. The authors advance terms to describe what is meant by “value” (which need not be expressed in monetary terms) and identify steps to ascribe, measure, and communicate value. The research presented here makes clear that those who invest in information collection must better understand the needs of those who use the information. What attributes of the information are most useful? How much precision or accuracy is most useful? What are the barriers to using information? How can the constraints on decision makers be reduced to enable them to make better use of information? The answers to these questions will help set priorities for information investment in areas that have the potential to produce the greatest economic and nonmarket value.
Alan Krupnick's research focuses on analyzing environmental issues, in
particular, the benefits, costs, and design of air pollution policies,
both in the United States and in developing countries. His research
also addresses the valuation of health and ecological improvements and,
more recently, the ancillary benefits of climate policy and urban
transportation and development problems.
The Centers for Disease Control and Prevention (CDC) established the Environmental Public Health Tracking Program (Tracking Program) in 2002, responding to the need for a central body which would facilitate the collection and analysis of data on noninfectious diseases and integrate the information to understand the linkages between the environment and public health.
How can natural resource management frameworks inform policies to preserve antibiotic effectiveness and limit resistance?
The problems facing those combating antibiotic resistance are similar to problems in managing renewable and nonrenewable resources. Many of the issues, as well as possible solutions, overlap. However, those involved in managing antibiotics face a few field-specific challenges, such as the nature of the health-care industry and the role of hospital infections.
Approaching the issue of antibiotic resistance from the perspective of economics and natural resource management is a logical, effective strategy that may lead to significant and lasting solutions.
Problems of optimal natural resource extraction that were first addressed by economists in the contexts of fisheries and forests have reemerged in the context of a newly recognized resource: antibiotic effectiveness. This review introduces economists to the growing literature on optimal use, innovation, and regulation of antibiotic effectiveness. Along the way, we draw links and parallels to similar problems in the management of other resources with which economists may be more familiar, and we address new questions that have arisen in the context of antibiotic effectiveness but that are also relevant to other resources.
What is meant by “value of information”? When does information have value? What are state-of-the-practice methods to ascribe value to information?
This report presents the results of a workshop on the "value of information," held at Resources for the Future in June 2010.
Interest in the economic value of information has taken center stage in recent years, as policymakers face the burden of justifying large public investment in data on health and environmental issues.
This report highlights the major conclusions and outcomes from a workshop held 28-29 June 2010 at Resources for the Future in Washington, DC, on methodological frontiers and new applications of valuing information and its social benefit. The participants provided answers to a series of questions: what is meant by “value of information”? When does information have value? What are state-of-the-practice methods to ascribe value to information? Participants also identified steps to ascribe, measure, and communicate value. The workshop included identification of five discrete approaches at the frontier of methodological advances: price- and cost-based derivation; Bayesian belief networks; regulatory cost- effectiveness evaluation; econometric modeling and estimation; and simulation modeling and estimation.
On a recent Value of Information workshop at Resources for the Future:
Canadian municipal water utilities have had to face many difficulties in the past few years, not the least of which has been an erosion of consumer confidence in the safety of publicly supplied drinking water. This paper discusses how economic theory is used to develop a methodology for determining consumersâ€™ or society's preferences for better quality drinking water and how these preferences are expressed in the trade-offs made between money and two different types of risk reductions: mortality and morbidity. These trade-offs are observed by examining actual consumer behavior and/or in structured (hypothetical) market choices. The information gained can be used to structure more efficient water pricing schemes for municipal water utilities and to aid these utilities in their infrastructure investment decisions.
We compare cost-of-illness (COI) and willingness-to-pay (WTP) estimates of the damages from minor respiratory symptoms associated with air pollution, using data from a study in Taiwan in 1991-92. A contingent valuation survey was conducted to estimate WTP to avoid minor respiratory illnesses. Health diaries were analyzed to predict the likelihood and cost of seeking relief from symptoms and of missing work. As predicted by economic theory, WTP is greater than the COI estimates, exceeding the latter by 1.61 to 2.26 times, depending on pollution levels. These ratios are similar to those for the United States, despite the differences between the two countries.
We discuss an application of probabilistic inversion techniques to a model of campylobacter transmission in chicken processing lines. Such techniques are indicated when we wish to quantify a model which is new and perhaps unfamiliar to the expert community. In this case there are no measurements for estimating model parameters, and experts are typically unable to give a considered judgment. In such cases, experts are asked to quantify their uncertainty regarding variables which can be predicted by the model. The experts' distributions (after combination) are then pulled back onto the parameter space of the model, a process termed "probabilistic inversion". This study illustrates two such techniques, iterative proportional fitting (IPF) and PARmeter fitting for uncertain models (PARFUM). In addition, we illustrate how expert judgement on predicted observable quantities in combination with probabilistic inversion may be used for model validation and/or model criticism.