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Abstract. Diagnostic laboratories frequently select a subjective cutoff value for real-time amplification assays, above which a threshold cycle (Ct) value is deemed false. Commonly, higher Ct values are interpreted as amplification or fluorescence artifacts, or cross contaminations. Although the implementation of Ct cutoff might be reasonable, its justification and selection should be based on evidence. The current article reviewed evidence-based strategies to select Ct cutoffs grouped in analytical and epidemiologic approaches. Analytical strategies use criteria gathered during the assay development and include fluorescence threshold, reaction endcycle, limit of detection, and artifact investigation. Variability in amplification efficacy across test runs may induce some instability in an intended Ct cutoff and requires some standardization or normalization procedures. Epidemiologic strategies use criteria based on either the probability or the cost of a false test result associated with a specified cutoff. Cutoffs, depending on the intended purpose of the test, can be selected graphically to minimize the probability of either false-positive or false-negative results by using two-graph receiver operating characteristics curves. The assay's diagnostic sensitivity and specificity may vary with the tested population, thus, the estimated two-graph receiver operating characteristics curve is population dependent and should be established for the targeted population. Although the selection of a cutoff based on misclassification cost depends on infection prevalence, the selection based on predictive values does not. To optimize the test average diagnostic performance, the Ct cutoff should be selected when diagnostic odds ratio is maximal. Epidemiologic approaches were illustrated by selecting Ct cutoffs for a real-time assay for Infectious salmon anemia virus.

Bulk milk somatic cell count (BMSCC), individual cow somatic cell count (ICSCC), and incidence rate of clinical mastitis (IRCM) are all udder health parameters. So far, no studies have been reported on the effect of season on BMSCC, IRCM, and ICSCC in the same herds and period over multiple years. The objectives of this study were to determine the seasonal pattern over a 4-yr period of 1) BMSCC, 2) elevated ICSCC, 3) IRCM, and 4) pathogen-specific IRCM. Bulk milk somatic cell count, ICSCC, and pathogen-specific clinical mastitis data were recorded in 300 Dutch dairy farms. For the analyses of BMSCC, ICSCC, and IRCM, a mixed, a transitional, and a discrete time survival analysis model were used, respectively. Sine and cosine were included in the models to investigate seasonal patterns in the data. For all parameters, a seasonal effect was present. Bulk milk somatic cell count peaked in August to September in all 4 years. The probability of cows getting or maintaining a high ICSCC was highest in August and May, respectively. Older and late-lactation cows were more likely to develop or maintain a high ICSCC. Incidence rate of clinical mastitis was highest in December to January, except for Streptococcus uberis IRCM, which was highest in August. Totally confined herds had a higher Escherichia coli IRCM in summer than in winter. Compared with the major mastitis pathogens, the seasonal differences in IRCM were smaller for the minor pathogens. Distinguishing between Strep. uberis, Streptococcus dysgalactiae, Streptococcus agalactiae, and other streptococci is essential when identifying Streptococcus spp. because each of them has a unique epidemiology. Streptococcus uberis IRCM seemed to be associated with being on pasture, whereas E. coli IRCM was more housing-related.

Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster‐specific random effects that allow one to partition the total variation in the outcome into between‐cluster variation and between‐individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time‐to‐event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time‐to‐event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between‐cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. This content downloaded from 132.Summary 1. Surveys of the weed flora in Danish arable fields were conducted in 1967-70 and 1987-89. Frequencies of 67 common weed species in spring barley, spring rape, winter rye, winter wheat and grass ley were compared. 2. The comparison showed that the occurrence of common weed species has generally decreased during the last 20 years. 3. The frequency of some species such as Anagallis arvensis, Arenaria serpyllifolia, Atriplex patula, Cerastium caespitosum, Galium aparine, Plantago major and Silene noctifora has decreased remarkably, possibly because of intensive chemical weed control. 4. The dominating weed species in the 1987-89 survey were largely the same as in the 1967-70 survey, namely Capsella bursa-pastoris, Chenopodium album, Elymus repens, Myosotis arvensis, Poa annua, Polygonum aviculare, P. convolvulus, P. persicaria, Stellaria media, Taraxacum, Veronica arvensis, V. persica and Viola arvensis. 5. Some species occurred more frequently in 1987-89 than in 1967-70, but only Stellaria media increased significantly and only in grass ley.

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