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Age-test Markov chains for drug testing and detection
Authors:James P. Boyle  Theodore J. Thompson
Affiliation:

a Navy Personnel Research and Development Center, 53335 Ryne Road, San Diego, CA 92152-7250, U.S.A.

b Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation (K-10), 4770 Buford Highway, NE, Atlanta, GA 30341-3724, U.S.A.

Abstract:Random urinalysis strategies stratified by time since the last test are characterized with a set of Markov chain models. The probability of a person being tested depends on the amount of time since the person's last test. The Nuclear Regulatory Commission (NRC) has proposed a two strata drug testing strategy based on time since last test. The proposal included a high testing rate for people not yet tested in a given time period and a low testing rate for people testing negative in a given time period. Southern California Edison has implemented a variation of the NRC proposal. These strategies can be modeled within a Markov chain framework. Time to detection is calculated as a function of testing probabilities and drug usage levels. Drug user gaming strategies are discussed with illustrations. These models are implemented as part of a U.S. Navy drug policy analysis system.
Keywords:
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