Empiric antibiotic treatment of erythema migrans-like skin lesions as a function of geography: a clinical and cost effectiveness modeling study.
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The skin lesion of early Lyme disease, erythema migrans (EM), is so characteristic that routine practice is to treat all such patients with antibiotics. Because other skin lesions may resemble EM, it is not known whether presumptive treatment of EM is appropriate in regions where Lyme disease is rare. We constructed a decision model to compare the cost and clinical effectiveness of three strategies for the management of EM: Treat All, Observe, and Serology as a function of the probability that an EM-like lesion is Lyme disease. Treat All was found to be the preferred strategy in regions that are endemic for Lyme disease. Where Lyme disease is rare, Observe is the preferred strategy, as presumptive treatment would be expected to produce excessive harm and increased costs. Where Lyme disease is rare, clinicians and public health officials should consider observing patients with EM-like lesions who lack travel to Lyme disease-endemic areas.
Decision Support Techniques
Erythema Chronicum Migrans
Published Version (Please cite this version)10.1089/vbz.2013.1365
Publication InfoBrinkerhoff, RJ; Clemen, Robert T; Lantos, Paul; & Wormser, GP (2013). Empiric antibiotic treatment of erythema migrans-like skin lesions as a function of geography: a clinical and cost effectiveness modeling study. Vector Borne Zoonotic Dis, 13(12). pp. 877-883. 10.1089/vbz.2013.1365. Retrieved from http://hdl.handle.net/10161/13965.
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Robert T. Clemen is Professor of Decision Sciences at Duke University’s Fuqua School of Business. He has broad interests in the use of decision analysis for organizational decision making, and special interests in the psychology of judgment, assessing expert probabilities, the effectiveness of decision-making techniques, and using decision analysis to help organizations become environmentally sustainable. He has taught courses on decision making and environmental sustainability in Duke&rsq
Associate Professor of Medicine
I am interested in the spatial epidemiology of infectious diseases. My research utilizes geographic information systems (GIS) and geostatistical analyses to understand the spatial and spatiotemporal distribution of diseases, and their relationship with environmental and demographic factors. I currently have active studies evaluating the spatial distribution of numerous domestic and international infectious diseases, as well as studies of neighborhood health disparities in obstetrical care and bi
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