Cost Effective Drug Testing in the Transportation Industry

Abstract

Random testing for drugs and alcohol has become a critical issue for agencies and firms who employ 'safety-sensitive' transportation workers. The recent tightening of industry standards by the Department of Transportation in which firms operating in this industry are required to test 25% of their employees each year at an estimated annual industry cost of $200 million provides an incentive to evaluate the effectiveness of ad hoc random approaches to drug testing. In this paper we propose a Bayesian acceptance sampling approach for the problem of random drug testing in the transportation industry. The model recognizes the dependence of the technique on the prior distribution of users in the population and on the outcome of the test itself. The approach offers a minimum expected total cost solution and a decision rule for testing, based upon the optimal sampling plan derived, which may then be used to determine future testing schedules and outcomes. The comparative cost of sampling plans derived with the Bayesian approach are compared with that obtained with a random, non-economic approach. The results show that use of an economic approach can generate savings of from 8% to 90%. The approach is applied to the Los Angeles County Metropolitan Transit Authority as a method of monitoring and randomly testing a population of 4000 bus drivers. In comparison with their existing approach and utilizing cost inputs provided by the Authority, acceptance sampling would allow a significant increase in the amount of testing possible and provide a more proactive drug testing policy toward drivers who use drugs