Prioritizing Opportunities to Reduce the Risk of Foodborne Illness: A Conceptual Framework

Determining the best use of food safety resources is a difficult task faced by public policymakers, regulatory agencies, state and local food safety and health agencies, as well as private firms. The Food Safety Research Consortium (FSRC) has developed a conceptual framework for priority setting and resource allocation for food safety that takes full account of the food system’s complexity and available data but is simple enough to be workable and of practical value to decisionmakers. The conceptual framework addresses the question of how societal resources, both public and private, can be used most effectively to reduce the public health burden of foodborne illness by quantitatively ranking risks and considering the availability, effectiveness, and cost of interventions to address these risks. We identify two types of priority-setting decisions: Purpose 1 priority setting that guides risk-based allocation of food safety resources, primarily by government food safety agencies, across a wide range of opportunities to reduce the public health impact of foodborne illness; and Purpose 2 priority setting that guides the choice of risk management actions and strategies with respect to particular hazards and commodities. It is essential that such a framework be grounded in a systems approach, multi-disciplinary in approach and integration of data, practical, flexible, and dynamic by including ongoing evaluation and continuous updating of risk rankings and other elements. The conceptual framework is a synthesis of ideas and information generated in connection with and during the three FSRC workshops convened under a project funded by the Cooperative State Research, Education, and Extension Service of USDA. Workshop materials are available on the project website: http://www.card.iastate.edu/food_safety/.

Maquiladoras, Air Pollution, and Human Health in Ciudad Juarez and El Paso

Ciudad Juárez, Chihuahua, is home to the U.S.–Mexico border’s largest maquiladora labor force, and also its worst air pollution. We marshal two types of evidence to examine the link between maquiladoras and air pollution in Ciudad Juárez, and in its sister city, El Paso, Texas. First, we use a publicly available sector-level emissions inventory for Ciudad Juárez to determine the importance of all industrial facilities (including maquiladoras) as a source of air pollution. Second, we use original plantlevel data from two sample maquiladoras to better understand the impacts of maquiladora air pollution on human health. We use a series of computational models to estimate health damages attributable to air pollution from these plants, we compare these damages to estimates of damages from non-maquiladora industrial polluters, and we use regression analysis to determine whether the poor suffer disproportionately from maquiladora air pollution. We find that air pollution from maquiladoras has serious consequences for human health, including respiratory disease and premature mortality. However, maquiladoras are clearly not the leading cause of air pollution in Ciudad Juárez and El Paso. Moreover, most maquiladoras are probably less important sources of dangerous air pollution than at least one notoriously polluting Mexican-owned industry. Finally, we find no evidence to suggest that maquiladora air pollution affects the poor disproportionately.maquiladora, air pollution, human health, environmental justice, U.S.-Mexico border, Ciudad Juárez, El Paso

Ranking the Risks: The 10 Pathogen-Food Combinations With the Greatest Burden on Public Health

Examines food-borne pathogens with the highest disease burdens and the top ten foods most commonly contaminated by them, such as salmonella in poultry, toxoplasma in pork, and listeria in deli meats. Makes policy recommendations for improving prevention

Harnessing Knowledge to Ensure Food Safety: Opportunities to Improve the Nation\u27s Food Safety Information Infrastructure

Those working in the food industry face an abundance of information generated by diverse institutions and individuals. Ensuring the safety of food is critically important to the public\u27s health and a challenge for policy-makers seeking to enhance the government\u27s role in this arena. Although the food industry has an inherent duty to make food safe, the effectiveness of what they do is highly dependent on the quality of the information they receive on potential hazards and good practices. In this context, the Robert Wood Johnson Foundation funded a project under the auspices of the Food Safety Research Consortium to examine and make recommendations for improving the food safety information infrastructure (FSII). Through this initiative, a collection of food safety experts met for a series of workshops to discuss information needs around food safety. Key Findings: Establish a national FSII policy and program. Build a database for tracking research and data collection. Provide broader public access to journal articles and to complete data from research projects. Create a networking Web site. In this report, whose recommendations are based on those workshop discussions, the authors explore the constraints facing today\u27s FSII. These include the diversity of the information currently available, the plethora of information sources, and the numerous agencies and actors involved in generating data

Shade-Grown Coffee: Simulation and Policy Analysis for Coastal Oaxaca, Mexico

Shade-grown coffee provides a livelihood to many farmers, protects biodiversity, and creates environmental services. Many shade-coffee farmers have abandoned production in recent years, however, in response to declines in international coffee prices. This paper builds a farmer decision model under price uncertainty and uses simulation analysis of that model to examine the likely impact of various policies on abandonment of shade-coffee plantations. Using information from coastal Oaxaca, Mexico, this paper examines the role of various constraints in abandonment decisions, reveals the importance of the timing of policies, and characterizes the current situation in the study region.coffee farming, decision analysis, numerical modeling, Monte Carlo, price variability

Cost-Effective NOx Control in the Eastern United States

Reducing nitrogen oxide (NOx) emissions in the eastern United States has become the focus of efforts to meet ozone air quality goals and will be useful for reducing particulate matter (PM) concentrations in the future. This paper addresses many aspects of the debate over the appropriate approach for obtaining reductions in NOx emissions from point sources beyond those called for in the Clean Air Act Amendments of 1990. Data on NOx control technologies and their associated costs, spatial models linking NOx emissions and air quality, and benefit estimates of the health effects of changes in ozone and PM concentrations are combined to allow an analysis of alternative policies in thirteen states in the eastern United States. The first part of the study examines the cost and other consequences of a command-and-control approach embodied in the Environmental Protection Agency’s (EPA) NOx SIP call, which envisions large reductions in NOx from electric utilities and other point sources. These results are compared to the alternative policy of ton-for-ton NOx emissions trading, similar to that proposed by the EPA for utilities. We find that emission reduction targets can be met at roughly 50% cost savings under a trading program when there are no transaction costs. The paper examines a number of alternative economic incentive policies that have the potential to improve upon the utility NOx trading plan proposed by EPA, including incorporation of other point sources in the trading program, incorporation of ancillary PM benefits to ozone reductions in the trading program, and trading on the basis of ozone exposures that incorporates the spatial impact of emissions on ozone levels. For the latter analysis, we examine spatially differentiated permit systems for reducing ozone exposures under different and uncertain meteorological conditions, including an empirical analysis of the trade-off between the reliability (or degree of certainty) of meeting ozone exposure reduction targets and the cost of NOx control. Finally, several policies that combine costs and health benefits from both ozone and PM reductions are compared to command-and-control and single-pollutant trading policies. The first of these is a full multipollutant trading system that achieves a health benefit goal, with the interpollutant trading ratios governed by the ratio of unit health benefits of ozone and PM. Then, a model that maximizes aggregate benefits from both ozone and PM exposure reductions net of the costs of NOx controls is estimated. EPA’s program appears to be reasonably cost-effective compared to all of the other more complex trading programs we examined. It may even be considered an optimal policy that maximizes net aggregate benefits if the high estimate of benefits is used in which mortality risk is linked to ozone exposure. Without this controversial assumption, however, we find that EPA’s NOx reduction target is far too large.

Identifying the Most Significant Microbiological Foodborne Hazards to Public Health: A New Risk Ranking Model

In order to help facilitate a risk-based food safety system, we developed the Foodborne Illness Risk Ranking Model (FIRRM), a decisionmaking tool that quantifies and compares the relative burden to society of 28 foodborne pathogens. FIRRM estimates the annual number of cases, hospitalizations, and fatalities caused by each foodborne pathogen, subsequently estimates the economic costs and QALY losses of these illnesses, and, lastly, attributes these pathogen-specific illnesses and costs to categories of food vehicles, based on outbreak data and expert judgment. The model ranks pathogen-food combinations according to five measures of societal burden. FIRRM incorporates probabilistic uncertainty within a Monte Carlo simulation framework and produces confidence intervals and statistics for all outputs. Gaps in data, most importantly in regards to food attribution and the statistical uncertainty of incidence estimates, currently limit the utility of the model. Once we address these and other problems, however, FIRRM will be a robust and useful decisionmaking tool.foodborne illness, risk ranking, pathogens, health valuation, QALYs, cost of illness, uncertainty, modeling, Monte Carlo

Linking Illness to Food: Summary of a Workshop on Food Attribution

To identify and prioritize effective food safety interventions, it is critical not only to identify the pathogens responsible for illness, but also to attribute cases of foodborne disease to the specific food vehicle responsible. A wide variety of such “food attribution” approaches and data are used around the world, including the analysis of and extrapolation from outbreak and other surveillance data, case-control studies, microbial subtyping and source-tracking methods, and expert judgment, among others. The Food Safety Research Consortium sponsored the Food Attribution Data Workshop in October 2003 to discuss the virtues and limitations of these approaches and to identify future options for the collection of food attribution data in the United States. This discussion paper summarizes workshop discussions and identifies challenges that affect progress in this critical component of a risk-based approach to improving food safety.foodborne illness, food attribution, outbreaks, case-control studies, microbial fingerprinting, microbial subtyping, FoodNet

La Educación Bilingue: Propuesta de un Programa de Inmersión Dual

Indiana University-Purdue University Indianapolis (IUPUI)La educación bilingüe en los Estados Unidos es un tema que puede causar y que ha causado muchos problemas entre los anglohablantes y los no anglohablantes (especialmente los hispanohablantes) que viven juntos en este país tan diverso e inmenso. La educación bilingüe es un problema porque crea preguntas sobre la identidad nacional, el federalismo, el poder, la etnicidad, y la pedagogía

Attributing Illness to Food

Identification and prioritization of effective food safety interventions require an understanding of the relationship between food and pathogen from farm to consumption. Critical to this cause is food attribution, the capacity to attribute cases of foodborne disease to the food vehicle or other source responsible for illness. A wide variety of food attribution approaches and data are used around the world, including the analysis of outbreak data, case-control studies, microbial subtyping and source tracking methods, and expert judgment, among others. The Food Safety Research Consortium sponsored the Food Attribution Data Workshop in October 2003 to discuss the virtues and limitations of these approaches and to identify future options for collecting food attribution data in the United States. We summarize workshop discussions and identify challenges that affect progress in this critical component of a risk-based approach to improving food safety