Environmental and Farm Management Factors Associated with Tuberculosis on Cattle Farms in Northeastern Michigan

Objective—To identify major environmental and farm management factors associated with the occurrence of tuberculosis (TB) on cattle farms in northeastern Michigan. Design—Case-control study. Sample Population—17 cattle farms with infected cattle and 51 control farms. Procedure—Each case farm (laboratory confirmed diagnosis of Mycobacterium bovis infection) was matched with 2 to 4 control farms (negative whole-herd test results within previous 12 months) on the basis of type of farm (dairy or beef) and location. Cattle farm data were collected from in-person interviews and mailed questionnaires. Wildlife TB data were gathered through state wildlife surveillance. Environmental data were gathered from a satellite image-based geographic information system. Multivariable conditional logistic regression for matched analysis was performed. Results—Major factors associated with increased farm risk of TB were higher TB prevalence among wild deer and cattle farms in the area, herd size, and ponds or creeks in cattle housing areas. Factors associated with reduced farm risk of TB were greater amounts of natural open lands in the surrounding area and reducing deer access to cattle housing areas by housing cattle in barns, barnyards, or feedlots and use of electrified wire or barbed wire for livestock fencing. Conclusions and Clinical Relevance—Results suggest that certain environmental and management factors may be associated with risk of TB on cattle farms.

Distance software: design and analysis of distance sampling surveys for estimating population size

1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modeling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modeling analysis engine for spatial and habitat-modeling, and information about accessing the analysis engines directly from other software. 7. Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of- the-art software that implements these methods is described that makes the methods accessible to practicing ecologists.