Economic decision analysis model of a paratuberculosis test and cull program.
A spreadsheet program was written to perform decision tree analysis for control of paratuberculosis (Johne's disease), when testing all adults in a herd and culling all animals with positive test results. The program incorporated diagnostic test sensitivity, specificity, and test cost with the cost or value of each of the 4 possible outcomes; true-positive, true-negative, false-positive, and false-negative test results. The program was designed to repeat the analysis for the independent variable pretest paratuberculosis prevalence (0 to 100%). Model output was graphed as profit or loss in dollars vs pretest prevalence. The threshold was defined as the pretest prevalence at which benefit-cost equaled zero. Reed-Frost disease modeling techniques were used to predict the number of Mycobacterium paratuberculosis-infected replacement heifers resulting from infected cows during a control program. Sensitivity analysis was performed on variables of the decision tree model; test sensitivity, specificity, test cost, and factors affecting the cost of paratuberculosis to a commercial dairy. A test and cull program was profitable when paratuberculosis caused greater than or equal to 6% decrease in milk production if the pretest prevalence was greater than 6%, test sensitivity was 50%, test specificity was 98%, and the testing cost was $4/cow. Test specificities greater than 98% did not markedly affect the threshold for tests with a 50% sensitivity and costing $4/cow. Test sensitivity had minimal effect on the threshold. Using a diagnostic test with a 50% sensitivity and a 98% specificity as an example, test cost was shown to affect the threshold prevalence at which the test and cull program became profitable.(ABSTRACT TRUNCATED AT 250 WORDS)
Collins
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MT
Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin, Madison 53706.
Morgan
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IR
D000818:Animals; D002417:Cattle; D003365:Costs and Cost Analysis; D003658:Decision Making, Computer-Assisted; D003663:Decision Trees; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D010283:Paratuberculosis / Q000175:diagnosis* / Q000191:economics / Q000517:prevention & control; D011237:Predictive Value of Tests; D012984:Software