Strategies to Protect the Health of Deployed U.S. Forces Detecting, Characterizing, and Documenting Exposures (2000) / Chapter Skim
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Appendix A Defining the Decision Framework and the Value of Exposure Information in Military Deployments
Appendix A Defining the Decision Framework and the Value of Exposure Information in Military Deployments
Pages 145-160
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Appendices
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Thus, an important issue for detecting and monitoring TICs is that the equipment and software be properly calibrated for detecting specific chemical agents. In addition, operators must be trained to monitor chemicals other than chemical warfare agents.
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The first question that must be considered is the purpose of tracking, detection, and monitoring information. Clearly, the information could be used for many purposes, including planning military missions, improving decisions on the battlefield, protecting soldiers from exposure to harmful agents, and making better decisions about medical care during and ~ The following material was prepared for the use of the principal investigator of this study.
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Since the emergence of the Gulf War, attention has been focused on correlations between reported symptoms and various types of exposures. DoD has initiated projects to track cumulative exposure information and collect health records for all military personnel as part of a comprehensive medical surveillance program.
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describes how a commander includes information about potential CB agents in the overall mission strategy to ensure that troops have full dimension protection. Exposure information is also important to other concepts in Joint Vision 2010, including dominant maneuver, focused logistics, precision engagement, and information superiority.
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the agent class of the substance to which troops are exposed, ranging from CB warfare agents to other agents available in the deployment environment, including TICs, endemic biological agents, and background chemical agents (e.g., high levels of naturally occurring metals, such as arsenic, lead, cadmium, etc.) All three distinctions are incorporated into the taxonomy of decisions and exposure information shown in Figure A-1.
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However, two important caveats must be kept in mind. First, reducing, or even eliminating, uncertainty in any one of the variables influencing exposure and health effects may not eliminate uncertainty about health effects.
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If the commander decides not to order the use of protective clothing and the harmful agent is present, 100 soldiers will be incapacitated for five days, resulting in a loss of 500 soldier days. If the commander decides to order his soldiers to use protective gear, their performance will be degraded, leading to an equivalent loss of soldier days.
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In this case, the decision to do nothing results in 100 lost soldier days, and the decision to use protective clothing results in only 12.5 lost soldier days. Thus, if there is a 20 percent chance of a real threat, using protective clothing is clearly the best decision.
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Clearly, if the agent is present, the best decision is to order troops to put on protective gear. The expected lost soldier days with perfect information are now reduced to 2.5 (compared to 12.5 with imperfect information)
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In the current context, we can assume that the commander will order the use of protective clothing if the sample "detects" an agent and that he will do nothing if the sample information "rejects" the presence of an agent. This leads to the somewhat unorthodox but simpler decision tree shown in Figure A-8.
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, and the difference between imperfect information and no special information is reduced to seven lost soldier days. The reduction of the VOI reflects the hit and false alarm rates.
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In Sample information I O 5.5 lost soldier days | Agent detected Protective clothing Agent / 0.98 ~ 22.3 lost soldier days | 0.20 ~ \ Agent \ mincer' Do nothing ma_ , | 12.5 lost soldier days, P = 0.20 | <1 | 500.0 Iost soldier days, P = 0.004 | False alarm Protective clothing \ No agent ,~ ~,0 12.5 lost soldier days, P= 0.08 Am} \ Agent 0.90 5.5 lost soldier days | Agent present Do ~ 0.20 nothing /, \ No agent present \ Imperfect / 0.80 \ information l \_ _ \ Protective clothing <1 | 0.0 Iost soldier days, P= 0.72 | 500.0 Iost soldier days | <| | 0.0 Iost soldier days 1 | 12.5 lost soldier days | FIGURE A-9 Analyzed decision tree with imperfect information (simplified)
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These differences in hit and false alarm rates completely determine the differences in the expected value of the old and new technologies and thus determine the incrementally higher VOI of the new technology. To conduct a formal VOI analysis for real monitoring, detection, and tracking technologies is, of course, impossible.
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A multiattribute utility analysis can also be combined with a VOI analysis so that all consequences of the alternative devices could be counted at the end of the decision tree. A multiattribute utility analysis would be used to convert the vector of consequences into a single utility number, and the VOI calculation would be based on the utility numbers.
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Using a matrix to compare alternative devices can help decision makers set priorities. References Clemen, R
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