Before treating onychomycosis, it is important to exclude other conditions such as lichen planus and psoriasis. The purpose of this study was to evaluate physician preferences and uses of diagnostic tests for toenail onychomycosis (TO) by surveying dermatologists (D), podiatrists (P) and family practitioners (FP) in the United States. Surveys were mailed to approximately 1000 randomly sampled physicians from each of the three specialities. The questionnaire consisted of 15 items regarding physician and practice characteristics, number of patients with TO seen and treated, tests used to diagnose TO and reasons for using the tests. Results were analysed using several statistical methods. Response rates were low (D33.7%; P16.6%; FP28.4%). Ds and Ps (75.2%) and FPs (43.4%) reported feeling 'very confident' at diagnosing onychomycosis. KOH was the preferred diagnostic test for all three specialities. More Ds (75.4%) felt 'very confident' interpreting potassium hydroxide (KOH) exams than Ps (24.9%) and FPs (18.5%). Use of KOH exams was statistically associated with confidence interpreting exams (P P = 0.04092; D & FP P < 0.0001). Some FPs (46.6%) and Ps (21.6%) did not obtain a confirmatory diagnostic test prior to the treatment of onychomycosis while 63.6% of Ds 'almost always/always' did. While limited by low-response rate, this study provides pilot information on the diagnostic preferences for TO by American D, P and FP.

a b s t r a c tObjectives: The objectives of this study were to: (1) explore the proportion of HTx centers that have a multidisciplinary team and (2) assess the relationship between multidisciplinarity and the level of chronic illness management (CIM). Background: The International Society for Heart and Lung Transplantation (ISHLT) recommends a multidisciplinary approach in heart transplant (HTx) follow-up care but little is known regarding the proportion of HTx centers that meet this recommendation and the impact on patient care. HTx centers

This paper compares methods for modeling the probability of removal when variable amounts of removal effort are present. A hierarchical modeling framework can produce estimates of animal abundance and detection from replicated removal counts taken at different locations in a region of interest. A common method of specifying variation in detection probabilities across locations or replicates is with a logistic model that incorporates relevant detection covariates. As an alternative to this logistic model, we propose using a catch-effort (CE) model to account for heterogeneity in detection when a measure of removal effort is available for each removal count. This method models the probability of detection as a nonlinear function of removal effort and a removal probability parameter that can vary spatially. Simulation results demonstrate that the CE model can effectively estimate abundance and removal probabilities when average removal rates are large but both the CE and logistic models tend to produce biased estimates as average removal rates decrease. We also found that the CE model fits better than logistic models when estimating wild turkey abundance using harvest and hunter counts collected by the Minnesota Department of Natural Resources during the spring turkey hunting season.

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Logistic regression models—or “sightability models”—fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.