Experimental Deer-To-Deer Transmission of Mycobacterium bovis

Objective—To determine whether Mycobacterium bovis can be transmitted from experimentally infected deer to uninfected in-contact deer. Animals—Twenty-three 6-month-old white-tailed deer. Procedure—On day 0, M bovis (2 X 108 colony-forming units) was administered by intratonsillar instillation to 8 deer; 3 control deer received saline (0.9% NaCl) solution. Eight in-contact deer were comingled with inoculated deer from day 21. On day 120, inoculated deer were euthanatized and necropsied. On day 180, 4 in-contact deer were euthanatized, and 4 new incontact deer were introduced. On day 360, all in-contact deer were euthanatized. Rectal, oral, and nasal swab specimens and samples of hay, pelleted feed, water, and feces were collected for bacteriologic culture. Tissue specimens were also collected at necropsy for bacteriologic culture and histologic analysis. Results—On day 90, inoculated and in-contact deer developed delayed-type hypersensitivity (DTH) reactions to purified protein derivative of M bovis. Similarly, new in-contact deer developed DTH reactions by 100 days of contact with original in-contact deer. Tuberculous lesions in in-contact deer were most commonly detected in lungs and tracheobronchial and medial retropharyngeal lymph nodes. Mycobacterium bovis was isolated from nasal secretions and saliva from inoculated and in-contact deer, urine and feces from in-contact deer, and hay and pelleted feed. Conclusions and Clinical Relevance—Mycobacterium bovis is efficiently transmitted from experimentally infected deer to uninfected in-contact deer through nasal secretions, saliva, or contaminated feed. Wildlife management practices that result in unnatural gatherings of deer may enhance both direct and indirect transmission of M bovis.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

INVESTIGATION OF PATIENT PERCEPTION OF HOSPITAL NOISE AND SOUND LEVEL MEASUREMENTS: BEFORE, DURING, AND AFTER RENOVATIONS OF A HOSPITAL WING

Acoustic conditions in hospitals have been shown to influence a patient’s physical and psychological health. Noise levels in an Omaha, Nebraska, hospital were measured and compared between various times: before, during, and after renovations of a hospital wing. The renovations included cosmetic changes and the installation of new in-room patient audio-visual systems. Sound pressure levels were logged every 10-seconds over a four-day period in three different locations: at the nurses' station, in the hallway, and in a nearby patient’s room. The resulting data were analyzed in terms of the hourly A-weighted equivalent sound pressure levels (