A Bayesian micro-simulation to evaluate the cost-effectiveness of interventions for mastitis control during the dry period in UK dairy herds

The use of machine learning to predict somatic cell count status in dairy cows post-calving

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

Approaches for disease prioritization and decision-making in animal health, 2000-2021: a structured scoping review

This scoping review identifies and describes the methods used to prioritize diseases for resource allocation across disease control, surveillance, and research and the methods used generally in decision-making on animal health policy. Three electronic databases (Medline/PubMed, Embase, and CAB Abstracts) were searched for articles from 2000 to 2021. Searches identified 6, 395 articles after de-duplication, with an additional 64 articles added manually. A total of 6, 460 articles were imported to online document review management software (sysrev.com) for screening. Based on inclusion and exclusion criteria, 532 articles passed the first screening, and after a second round of screening, 336 articles were recommended for full review. A total of 40 articles were removed after data extraction. Another 11 articles were added, having been obtained from cross-citations of already identified articles, providing a total of 307 articles to be considered in the scoping review. The results show that the main methods used for disease prioritization were based on economic analysis, multi-criteria evaluation, risk assessment, simple ranking, spatial risk mapping, and simulation modeling. Disease prioritization was performed to aid in decision-making related to various categories: (1) disease control, prevention, or eradication strategies, (2) general organizational strategy, (3) identification of high-risk areas or populations, (4) assessment of risk of disease introduction or occurrence, (5) disease surveillance, and (6) research priority setting. Of the articles included in data extraction, 50.5% had a national focus, 12.3% were local, 11.9% were regional, 6.5% were sub-national, and 3.9% were global. In 15.2% of the articles, the geographic focus was not specified. The scoping review revealed the lack of comprehensive, integrated, and mutually compatible approaches to disease prioritization and decision support tools for animal health. We recommend that future studies should focus on creating comprehensive and harmonized frameworks describing methods for disease prioritization and decision-making tools in animal health.

Investing in health to improve the sustainability of cattle production in the United Kingdom: A narrative review

Livestock health is a key concern for all food system stakeholders and has considerable impacts upon sustainable food production. Improving productivity means that a set quantity of milk or meat may be produced at a lower economic cost, using fewer resources and with reduced greenhouse gas emissions (GHGe); however, diseases that reduce yield, growth or fertility have the opposite effect. The purpose of this narrative review was to assess the breadth of economic and environmental sustainability information relating to cattle health within the literature and to discuss related knowledge gaps within the literature. The mechanisms by which improved awareness and investment can lead to improved cattle health both on-farm and across the wider cattle industry are also appraised; concluding with the opportunities and challenges still outstanding in improving sustainability through livestock health. The economic and environmental impacts of cattle health have not been sufficiently quantified in the literature to draw valid conclusions regarding the sustainability impact of different diseases. Where available, economic data tended to be dated or extremely variable. Furthermore, environmental analyses did not use consistent methodologies and principally focused on GHGe, with little attention paid to other metrics. Although reducing disease severity or occurrence reduced GHGe, published impacts of disease varied from 1% to 40% with little apparent association between GHGe and industry-wide economic cost. An urgent need therefore exists to standardise methodologies and quantify disease impacts using a common baseline with up-to-date data inputs. Given the threat of antimicrobial resistance, improving cattle health through technology adoption and vaccine use would be expected to have positive impacts on social acceptability, especially if these improvements rendered milk and meat more affordable to the consumer. Therefore, it is important for cattle producers and allied industry to take a proactive approach to improving cattle health and welfare, with particular focus on diseases that have the greatest implications for sustainability.

Bacteriological outcomes following random allocation to quarter-level selection based on California Mastitis Test score or cow-level allocation based on somatic cell count for dry cow therapy

Intramammary infusion of antimicrobials at the end of lactation (dry cow therapy) has been a cornerstone of mastitis management for many years. However, as only a proportion of cows are infected at this time, treating only those cows likely to be infected is an important strategy to reduce antimicrobial usage and minimize risk of emergence of antimicrobial resistance. Such an approach requires the ability to discriminate between cows and quarters likely to be infected and uninfected. This study compared assignment of cows or quarters to antimicrobial treatment at the end of lactation based on cow composite somatic cell count (SCC; i.e., all quarters of cows with a maximum SCC across lactation >200,000 cells/mL received an antimicrobial; n = 891 cows, SCC-group) or assignment to quarter-level treatment based on a quarter level California Mastitis Test (CMT) score ≥ trace (n = 884 cows; CMT-group) performed immediately before drying off. All quarters of all cows also received an infusion of a bismuth-based internal teat sealant. Milk samples were collected for microbiology following the last milking, and again within 4 d of calving. Clinical mastitis records from dry off to 30 d into the subsequent lactation were collected. Multilevel, multivariable models were used to assess the effect of assignment to antimicrobial treatment. At drying off, a total of 575 (8.8%) and 147 (2.3%) of the 6,528 quarters had a minor, and a major intramammary infection (IMI), respectively. At drying off, 2089/3270 (63.9%) and 883/3311 (26.7%) of quarters were treated with dry cow therapy in the CMT and SCC-groups, respectively. Apparent bacteriological cure proportion for any IMI was higher in quarters assigned to the CMT than the SCC-group (349/368 (0.95, 95% CI 0.92-0.97) versus 313/346 (0.90, 95% CI 0.87-0.93)). New IMI proportion was lower among quarters assigned to the CMT than SCC-group [101/3,212 (0.032, 95% CI 0.025-0.038) versus 142/3,232 (0.044, 95% CI 0.036-0.051)]. The prevalence of any IMI postcalving was lower in quarters assigned to the CMT than SCC-group [119/3,243 (0.037, 95% CI: 0.030-0.044) versus 173/3,265 (0.054, 95% CI: 0.045-0.062)]. There was no difference in incidence of clinical mastitis between treatment groups. The total mass of antimicrobials used was 63% higher in the CMT-group than in the SCC-group (3.47 versus 2.12 mg/kg of liveweight). Selection of quarters for antimicrobial treatment at the end of lactation based on CMT resulted in greater proportion undergoing bacteriological cure, reduced risk of any new IMI and reduced post calving prevalence of any IMI compared with selection of cows based on SCC. However, CMT-based selection resulted in higher antimicrobial use compared with SCC-based selection, and further research is required to analyze the cost benefit and impact on risk of antimicrobial resistance of these 2 strategies.

Modifiable management practices to improve udder health in dairy cattle during the dry period and early lactation: A scoping review

The objective of this scoping review was to characterize all available literature on modifiable management practices used during the dry period that have been evaluated for their effects on udder health in dairy cattle during the dry period and the subsequent lactation. Five databases and two conference proceedings were searched for relevant literature. Articles published in or after 1990 were eligible for inclusion. Eligible interventions or exposures were restricted to modifiable management practices; however, antimicrobial and teat sealant products were enumerated but not further characterized, as systematic reviews have been published on this topic. Other modifiable management practices were reported in 229 articles. Nutrition (n = 79), which included ration formulation and delivery (n = 44) and vitamin and mineral additives (n = 35), was the most commonly reported practice, followed by vaccines (n = 40) and modification of dry period length (n = 27). Risk of clinical mastitis (CM) was the most commonly reported outcome (n = 151); however, reporting of outcome risk periods varied widely between articles. Cure of existing intramammary infections (IMI) over the dry period (n = 40) and prevention of new IMI over the dry period (n = 54) were most commonly reported with a risk period between calving and 30 d in milk. Future systematic reviews with meta-analyses could target management practices such as nutrition, vaccines, and dry period length to quantify their effects on improving udder health during the dry period and early lactation. However, the variation in reporting of time at risk for CM and other outcomes challenges the ability of future synthesis work to inform management decisions on the basis of efficacy to cure or prevent IMI and CM. Consensus on which core outcomes should be evaluated in mastitis research and the selection of consistent risk periods for specific outcomes in animal trials is imperative.

Antibiotic dry cow therapy, somatic cell count, and milk production: Retrospective analysis of the associations in dairy herd recording data using multilevel growth models

Antibiotic dry cow therapy (DCT) is an important part of most mastitis control programs. Updating DCT recommendations is an ongoing topic due to the global problem of antimicrobial resistance. Finland, along with other Nordic countries, has implemented selective DCT for decades. Our study analyzed Dairy Herd Improvement (DHI) information from 241 Finnish farmers who participated in a survey about their drying-off practices. The aim was to evaluate herd-level associations between milk somatic cell count (SCC), milk production, and various antimicrobial DCT approaches both cross-sectionally in 2016 and longitudinally in 2012-2016. The three DCT approaches in the study were selective, blanket, and no DCT use. An additional aim was to evaluate whether dynamic changes occurred in herd-average SCC and annual milk production over five years, and whether these potential changes differed between different DCT approaches. The method for the longitudinal analyses was growth modeling with random coefficient models. Differences in SCC and milk production between farms with different DCT approaches were minor. Regardless of the farm's DCT approach, annual milk production increased over the years, while average SCC was reasonably constant. The variability in SCC and milk production across all DCT groups was low between years, and most of the variability was between farms. Compared to other milking systems, farms with automatic milking system (AMS) had higher SCC, and in 2016 higher milk production. The results of this study suggest that it is possible to maintain low herd-average SCC and good milk production when using selective DCT and following the guidelines for prudent antimicrobial use. Average SCC and milk production varied across the herds, suggesting that advice on DCT practices should be herd-specific. The methodology of growth modeling using random coefficient models was applicable in analyzing longitudinal data, in which the time frame was relatively short and the number of herds was limited.