Final Program - Society for Risk Analysis

esponse to requests from the EC, the European Parliament (EP) and EU MemberStates (MS). EFSA also undertakes scientific work on its own initiative, so-called selftasking.Current EU legislation on animal welfare covers calves, pigs, laying hensand broilers as well as experimental animals. Decisions on welfare requirements mustbe based on a sound science and appropriate risk assessment.. Since 2003, EFSA hasprovided scientific opinions and advice as well as technical support to risk managersin the area of animal welfare. The EFSA Animal Health and Welfare Panel has delivered36 scientific opinions on a variety of welfare issues. Although EFSA does notmake scientific research, it has played an important role on assessing the risk on animalwelfare and has provided important reviews of the current scientific knowledgeon animal welfare. The concept of animal welfare is not only related to the protectionand well-being of the animals but also to its relationship with animal and publichealth. Animal welfare indicators for the control and monitoring of animal welfareshould allow ranking of the welfare standards applied (from minimum to higher standards)in order to assist the development of improved animal welfare production andhusbandry methods and to facilitate their application at EU and international levels.EFSA scientific opinions have been and are used for international standard purposesconsidering the general reluctance to accept and enforce standards not supported byscience. The paper summarises the used Risk assessment methodologies by EFSAfuture possible trends.W1-G.3 Severtson DJ; djsevert@wisc.eduUW-MadisonDO MAPS PROMOTE WATER TESTING AMONG RESIDENTS WITHPRIVATE WELLS? THE INFLUENCE OF MAP FEATURES AND PER-CEIVED PROXIMITY TO MAPPED HAZARDS ON RISK BELIEFS, UN-CERTAINTY, AND TESTING INTENTIONS.The purpose of this study was to assess the influence of three map featuresand perceived proximity to mapped hazards on risk beliefs, perceived uncertaintyand protective behavior. Maps depicted private well water test results for arsenic. Featureswere map color (symbolic “stoplight” risk colors or non-risk colors), a hatchingsymbol to display map areas with no data (with or without hatching), and a table tosupplement map information (with or without table). This full factorial 2 x 2 x 2randomized trial resulted in 8 map interventions plus a 9th table only control. Thesample was homeowners with private wells from a county with some arsenic watertest results over the drinking water standard. Participants were spatially and randomlyselected from 8 townships that had different spatial distributions and amounts ofarsenic and no data. Of 1224 mailed surveys, 830 (67.8%) were returned. Structuralequation modeling will be used to examine the influence of map variables on behavioralintentions to test water for arsenic and how risk beliefs and uncertainty mediatethose relationships. These influences will be examined within a context of participants’characteristics that include prior risk beliefs, satisfaction with water aesthetics,skepticism about well water-related health risks, numeracy, gender, years in home,age, and education. Data collection is completed. Study results will be shared in thispresentation.P.78 Severtson DJ, Myers J; djsevert@wisc.eduUW-MadisonMAPPING MODELED HEALTH RISK FOR ENVIRONMENTAL HAZ-ARDS: THE INFLUENCE OF THREE MAP FEATURES ON RISK BE-LIEFS AND PERCEIVED UNCERTAINTY FOR MAPS OF MODELEDCANCER RISK FROM AIR POLLUTIONOften, models are used to estimate health risks from environmental hazardsand maps are used to display this information. The concrete nature of images, suchas maps, may convey more certainty than warranted for information estimated frommodels. Furthermore, maps using conventional stoplight colors to symbolize thesafety of these estimates may generate stronger risk beliefs than warranted for modeledinformation. In Wisconsin, natural resources and public health professionals developeda model to estimate cancer risk based on estimated air emissions and wantto display this information using maps. We selected two map features to convey thecertainty of modeled cancer risk (less vs. more certain): data classing (unclassed vs.classed) and how risk is expressed in the legend (relative risk vs. defined risk). Color(non-risk vs. stoplight risk colors) was a third feature. The purpose of this study wasto assess how these features influenced risk beliefs and uncertainty about risk beliefsat four map locations that varied by risk level. This full factorial 2 x 2 x 2 x 4 randomizedtrial used 32 maps that varied by study features and 4 risk levels. Maps werearranged into 8 blocks of 4 maps. Dependent variables included risk beliefs and perceiveduncertainty at personal and neighborhood levels. 776 university students, randomlyassigned to one block of four maps, participated in this online survey for extracredit. Structural equation modeling is used to assess the influence of map features onrisk beliefs and perceived uncertainty in the presence of participants’ characteristics(prior beliefs about air pollution, family cancer experience, academic major, numeracy,and gender). Data analysis is in progress. Results and implications will be included inthis poster presentation.W3-H.4 Shan X, Zhuang J; xshan@buffalo.eduUniversity at Buffalo, The State University of New YorkCOST OF EQUITY IN DEFENSIVE RESOURCE ALLOCATIONS INTHE FACE OF A POSSIBLY NON-STRATEGIC ATTACKERHundreds of billions of dollars have been spent in homeland security sinceSeptember 11, 2001. Many models have been developed to study games betweengovernments (defender) and terrorists (attacker), however, few studies consider thetradeoff between equity and efficiency in homeland security resource allocation. In171