Fed dairy cattle represent an important and growing component of the U.S. fed cattle market. However, little is known about factors affecting fed dairy cattle transaction prices. This study analyzes confidential transaction-level data collected by United States Department of Agriculture Agricultural (USDA) Marketing Service (AMS) under Livestock Mandatory Reporting to determine how data collected for price reporting explains price variation. Hedonic models are developed to illustrate potential use to enhance fed dairy cattle price reporting. However, important price variation remains unexplained suggesting factors not available in AMS data are associated with fed dairy cattle price variation. We suggest AMS collect and utilize additional data to enhance price reporting.

The United States Department of Agriculture Agricultural Marketing Service (USDA AMS) began publishing formula base price information in August 2021. Considerable variation in the types of cattle priced via formula has raised questions about the level of base price transparency that can be gleaned from formula base price reports. This study employs 6 years of transactions to estimate hedonic models assessing the capability of existing data to describe variation in formula base prices. Results suggest factors beyond those reported to USDA AMS by packers influence base prices. We offer suggestions for improved data collection to make hedonic modeling of base prices more effective for reporting market information.

This SHEA white paper identifies knowledge gaps and challenges in healthcare epidemiology research related to coronavirus disease 2019 (COVID-19) with a focus on core principles of healthcare epidemiology. These gaps, revealed during the worst phases of the COVID-19 pandemic, are described in 10 sections: epidemiology, outbreak investigation, surveillance, isolation precaution practices, personal protective equipment (PPE), environmental contamination and disinfection, drug and supply shortages, antimicrobial stewardship, healthcare personnel (HCP) occupational safety, and return to work policies. Each section highlights three critical healthcare epidemiology research questions with detailed description provided in supplementary materials. This research agenda calls for translational studies from laboratory-based basic science research to well-designed, large-scale studies and health outcomes research. Research gaps and challenges related to nursing homes and social disparities are included. Collaborations across various disciplines, expertise and across diverse geographic locations will be critical.

Lactoferrin (LTF) is a milk glycoprotein favorably associated with the immune system of dairy cows. Somatic cell count is often used as an indicator of mastitis in dairy cows, but knowledge on the milk LTF content could aid in mastitis detection. An inexpensive, rapid and robust method to predict milk LTF is required. The aim of this study was to develop an equation to quantify the LTF content in bovine milk using mid-infrared (MIR) spectrometry. LTF was quantified by enzyme-linked immunosorbent assay (ELISA), and all milk samples were analyzed by MIR. After discarding samples with a coefficient of variation between 2 ELISA measurements of more than 5% and the spectral outliers, the calibration set consisted of 2499 samples from Belgium (n = 110), Ireland (n = 1658) and Scotland (n = 731). Six statistical methods were evaluated to develop the LTF equation. The best method yielded a cross-validation coefficient of determination for LTF of 0.71 and a cross-validation standard error of 50.55 mg/l of milk. An external validation was undertaken using an additional dataset containing 274 Walloon samples. The validation coefficient of determination was 0.60. To assess the usefulness of the MIR predicted LTF, four logistic regressions using somatic cell score (SCS) and MIR LTF were developed to predict the presence of mastitis. The dataset used to build the logistic regressions consisted of 275 mastitis records and 13 507 MIR data collected in 18 Walloon herds. The LTF and the interaction SCS × LTF effects were significant (P < 0.001 and P = 0.02, respectively). When only the predicted LTF was included in the model, the prediction of the presence of mastitis was not accurate despite a moderate correlation between SCS and LTF (r = 0.54). The specificity and the sensitivity of models were assessed using Walloon data (i.e. internal validation) and data collected from a research herd at the University of Wisconsin – Madison (i.e. 5886 Wisconsin MIR records related to 93 mastistis events – external validation). Model specificity was better when LTF was included in the regression along with SCS when compared with SCS alone. Correct classification of non-mastitis records was 95.44% and 92.05% from Wisconsin and Walloon data, respectively. The same conclusion was formulated from the Hosmer and Lemeshow test. In conclusion, this study confirms the possibility to quantify an LTF indicator from milk MIR spectra. It suggests the usefulness of this indicator associated to SCS to detect the presence of mastitis. Moreover, the knowledge of milk LTF could also improve the milk nutritional quality.

Shiga toxin-producing Escherichia coli (STEC) can cause serious disease in human beings. Ruminants are considered to be the main reservoir of human STEC infections. However, STEC have also been isolated from other domestic animals, wild mammals and birds. We describe a cross-sectional study of wild birds in northern England to determine the prevalence of E. coli-containing genes that encode Shiga toxins (stx1 and stx2) and intimin (eae), important virulence determinants of STEC associated with human disease. Multivariable logistic regression analysis identified unique risk factors for the occurrence of each virulence gene in wild bird populations. The results of our study indicate that while wild birds are unlikely to be direct sources of STEC infections, they do represent a potential reservoir of virulence genes. This, coupled with their ability to act as long-distance vectors of STEC, means that wild birds have the potential to influence the spread and evolution of STEC.