Bayesian evaluation of budgets for endemic disease control: an example using management changes to reduce milksomatic cell count early in the first lactation of Irish dairy cows

The aim of this research was to determine budgets for specific management interventions to control heifer mastitis in Irish dairy herds as an example of evidence synthesis and 1-step Bayesian micro-simulation in a veterinary context. Budgets were determined for different decision makers based on their willingness to pay. Reducing the prevalence of heifers with a high milk somatic cell count (SCC) early in the first lactation could be achieved through herd level management interventions for pre- and peri-partum heifers, however the cost effectiveness of these interventions is unknown. A synthesis of multiple sources of evidence, accounting for variability and uncertainty in the available data is invaluable to inform decision makers around likely economic outcomes of investing in disease control measures. One analytical approach to this is Bayesian micro-simulation, where the trajectory of different individuals undergoing specific interventions is simulated. The classic micro-simulation framework was extended to encompass synthesis of evidence from 2 separate statistical models and previous research, with the outcome for an individual cow or herd assessed in terms of changes in lifetime milk yield, disposal risk, and likely financial returns conditional on the interventions being simultaneously applied. The 3 interventions tested were storage of bedding inside, decreasing transition yard stocking density, and spreading of bedding evenly in the calving area. Budgets for the interventions were determined based on the minimum expected return on investment, and the probability of the desired outcome. Budgets for interventions to control heifer mastitis were highly dependent on the decision maker's willingness to pay, and hence minimum expected return on investment. Understanding the requirements of decision makers and their rational spending limits would be useful for the development of specific interventions for particular farms to control heifer mastitis, and other endemic diseases. Keywords Bayesian; Micro-simulation; Dairy heifer; Mastitis control; Cost-effectiveness; Decision makin

Duurzaamheid veestapels in project Koeien & Kansen

Duurzaamheid wordt ook in de melkveehouderij steeds belangrijker. Duurzaamheid omvat veel verschillende aspecten die uiteindelijk bij elkaar komen en gewogen dienen te worden. Dit is zeker nog niet uitgekristalliseerd en diverse verkenningen zijn dan ook nodig. Daarnaast ligt er de vraag om duurzaamheid ook praktisch toepasbaar te maken als managementinstrument voor melkveehouders. Deze twee aspecten komen in dit rapport bij elkaar. Het doel van deze rapportage is dan ook inzicht te krijgen in de duurzaamheid van de veestapels van Koeien & Kansen-veehouders en de Duurzaamheidsmonitor van CRV toe te passen op een praktisch vraagstuk. In de Duurzaamheidsmonitor is een economisch kengetal opgenomen om een economische uitwerking van levensproductie, het Netto Dag Rendement (NDR), te laten zien

Association between somatic cell count and serial locomotion score assessments in UK dairy cows

This research investigated the effect of lameness, measured by locomotion score (LS) on the somatic cell count (SCC) of UK dairy cows. The data set consisted of 11,141 records of SCC and LS collected monthly on 12 occasions from 1,397 cows kept on 7 farms. The data were analyzed to account for the correlation of repeated measures of SCC within cow. Results were controlled for farm of origin, stage of lactation, parity, season, and test-day milk yield. Compared with the geometric mean SCC for cows with LS 1 on each farm, cows on farm 3 with LS 2 produced milk with 28,000 fewer somatic cells/mL, and cows with LS 2 on farm 6 produced milk with 30,000 fewer somatic cells/mL at a test day within 10 d. Cows that would have LS 3 six months later produced milk with 16,000 fewer somatic cells/mL compared with the geometric mean SCC for cows that would have LS 1 in 6 mo time. These results illustrate differences in disease dynamics between farms, highlight potential conflict between lameness and mastitis control measures, and emphasize the importance of developing farm-specific estimates of disease costs, and hence, health management plans in clinical practice

Current management practices and interventions prioritised as part of a nationwide mastitis control plan

The objectives of this study were to report performance and management data taken from a sample of UK dairy farms that have participated in the Agriculture and Horticulture Development Board Dairy Mastitis Control Plan (DMCP) and to identify important mastitis prevention practices that are not currently widely implemented. A total of 234 UK dairy herds were included in the study from which farm management and udder health data were collected. Herds were grouped according to their mastitis epidemiology and could be classed as (i) environmental dry period (EDP) (i.e. environmental pathogen with majority of infections being acquired during the dry period), (ii) environmental lactation (EL), (iii) contagious dry period (CDP) or (iv) contagious lactation (CL). The results of this study showed that many mastitis-related management practices that are generally considered to be important were not widely performed. A better understanding of those practices not widely adopted by UK dairy farmers at present may aid practitioners in identifying and overcoming potential barriers to improved mastitis control

Prediction of Streptococcus uberis clinical mastitis risk using Matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) in dairy herds

The purpose of this study was to evaluate whether the risk of Streptococcus uberis clinical mastitis at cow level could be predicted from the historical presence of specific strains of S. uberis on dairy farms. Matrix-assisted laser desorption ionization time of flight mass spectrometry was used to identify S. uberis isolates potentially capable of contagious transmission. Data were available from 10,652 cows from 52 English and Welsh dairy farms over a 14 month period, and 521 isolates of S. uberis from clinical mastitis cases were available for analysis. As well as the temporal herd history of clinical mastitis associated with particular S. uberis strains, other exposure variables included cow parity, stage of lactation, milk yield, and somatic cell count. Observations were structured longitudinally as repeated weekly measures through the study period for each cow. Data were analyzed in a Bayesian framework using multilevel logistic regression models. Similarity of mass spectral profiles between isolates of S. uberis from consecutive clinical cases of mastitis in herds was used to indicate potential for contagious phenotypic characteristics. Cross validation showed that new isolates with these characteristics could be identified with an accuracy of 90% based on bacterial protein mass spectral characteristics alone. The cow-level risk in any week of these S. uberis clinical mastitis cases increased with the presence of the same specific strains of S. uberis in other cows in the herd during the previous 2 weeks. The final statistical model 29 indicated there would be a 2 to 3 fold increase in the risk of S. uberis clinical mastitis associated with particular strains if these occurred in the herd 1 and 2 weeks previously. The results suggest that specific strains of S. uberis may be involved with contagious transmission, and predictions based on their occurrence could be used as an early warning surveillance system to enhance the control of S. uberis mastitis