Association between somatic cell count early in the first lactation and the lifetime milk yield of cows in Irish dairy herds

Change in lifetime milk yield is an important component of the cost of diseases in dairy cows. Knowledge of the likelihood and scale of potential savings through disease prevention measures is important to evaluate how much expenditure on control measures is rational. The aim of this study was to assess the association between somatic cell count (SCC) at 5 to 30 d in milk during parity 1 (SCC1), and lifetime milk yield for cows in Irish dairy herds. The data set studied included records from 53,652 cows in 5,922 Irish herds. This was split into 2 samples of 2,500 and 3,422 herds at random. Linear models with lifetime milk yield and first-lactation milk yield as the outcomes and random effects to account for variation between herds were fitted to the data for the first sample of herds; data for the second sample were used for cross-validation. The models were developed in a Bayesian framework to include all uncertainty in posterior predictions and parameters were estimated from 10,000 Markov chain Monte Carlo simulations. The final model was a good fit to the data and appeared generalizable to other Irish herds. A unit increase in the natural logarithm of SCC1 was associated with a median decrease in lifetime milk yield of 864kg, and a median decrease in first-lactation milk yield of 105kg. To clarify the meaning of the results in context, microsimulation was used to model the trajectory of individual cows, and evaluate the expected outcomes for particular changes in the herd-level prevalence of cows with SCC1 ≥400,000cells/mL. Differences in mean lifetime milk yield associated with these changes were multiplied by an estimated gross margin for each cow to give the potential difference in milk revenue. Results were presented as probabilities of savings; for example, a 75% probability of savings of at least€97 or€115/heifer calved into the herd existed if the prevalence of cows with SCC1 ≥400,000cells/mL was reduced from ≥20 to <10 or <5%, respectively, and at least€71/heifer calved into the herd if the prevalence of cows with SCC1 ≥400,000cells/mL was reduced from ≥10 to <5%. The results indicate large differences in lifetime milk yield, depending on SCC early in the first lactation and the findings can be used to assess where specific interventions to control heifer mastitis prepartum are likely to be cost effective. Key words: dairy heifer, somatic cell count, lifetime milk yiel

Association of season and herd size with somatic cell count for cows in Irish,English, and Welsh dairy herds

The aims of this study were to describe associations of time of year, and herd size with cow somatic cell count (SCC) for Irish, English, and Welsh dairy herds. Random samples of 497 and 493 Irish herds, and two samples of 200 English and Welsh (UK) herds were selected. Random effects models for the natural logarithm of individual cow test day SCC were developed using data from herds in one sub-dataset from each country. Data from the second sub-datasets were used for cross validation. Baseline model results showed that geometric mean cow SCC (GSCC) in Irish herds was highest from February to August, and ranged from 111,000 cells/mL in May to 61,000 cells/mL in October. For cows in UK herds, GSCC ranged from 84,000 cells/mL in February and June, to 66,000 cells/mL in October. The results highlight the importance of monitoring cow SCC during spring and summer despite low bulk milk SCC at this time for Irish herds. GSCC was lowest in Irish herds of up to 130 cows (63,000 cells/mL), and increased for larger herds, reaching 68,000 cells/mL in herds of up to 300 cows. GSCC in UK herds was lowest for herds of 130–180 cows (60,000 cells/mL) and increased to 63,000 cells/mL in herds of 30 cows, and 68,000 cells/mL in herds of 300 cows. Importantly, these results suggest expansion may be associated with increased cow SCC, highlighting the importance of appropriate management, to benefit from potential economies of scale, in terms of udder health

The SPINK gene family and celiac disease susceptibility

The gene family of serine protease inhibitors of the Kazal type (SPINK) are functional and positional candidate genes for celiac disease (CD). Our aim was to assess the gut mucosal gene expression and genetic association of SPINK1, -2, -4, and -5 in the Dutch CD population. Gene expression was determined for all four SPINK genes by quantitative reverse-transcription polymerase chain reaction in duodenal biopsy samples from untreated (n = 15) and diet-treated patients (n = 31) and controls (n = 16). Genetic association of the four SPINK genes was tested within a total of 18 haplotype tagging SNPs, one coding SNP, 310 patients, and 180 controls. The SPINK4 study cohort was further expanded to include 479 CD cases and 540 controls. SPINK4 DNA sequence analysis was performed on six members of a multigeneration CD family to detect possible point mutations or deletions. SPINK4 showed differential gene expression, which was at its highest in untreated patients and dropped sharply upon commencement of a gluten-free diet. Genetic association tests for all four SPINK genes were negative, including SPINK4 in the extended case/control cohort. No SPINK4 mutations or deletions were observed in the multigeneration CD family with linkage to chromosome 9p21-13 nor was the coding SNP disease-specific. SPINK4 exhibits CD pathology-related differential gene expression, likely derived from altered goblet cell activity. All of the four SPINK genes tested do not contribute to the genetic risk for CD in the Dutch population

LocoMMotion:a prospective, non-interventional, multinational study of real-life current standards of care in patients with relapsed and/or refractory multiple myeloma

Despite treatment advances, patients with multiple myeloma (MM) often progress through standard drug classes including proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), and anti-CD38 monoclonal antibodies (mAbs). LocoMMotion (ClinicalTrials.gov identifier: NCT04035226) is the first prospective study of real-life standard of care (SOC) in triple-class exposed (received at least a PI, IMiD, and anti-CD38 mAb) patients with relapsed/refractory MM (RRMM). Patients (N = 248; ECOG performance status of 0–1, ≥3 prior lines of therapy or double refractory to a PI and IMiD) were treated with median 4.0 (range, 1–20) cycles of SOC therapy. Overall response rate was 29.8% (95% CI: 24.2–36.0). Median progression-free survival (PFS) and median overall survival (OS) were 4.6 (95% CI: 3.9–5.6) and 12.4 months (95% CI: 10.3–NE). Treatment-emergent adverse events (TEAEs) were reported in 83.5% of patients (52.8% grade 3/4). Altogether, 107 deaths occurred, due to progressive disease (n = 74), TEAEs (n = 19), and other reasons (n = 14). The 92 varied regimens utilized demonstrate a lack of clear SOC for heavily pretreated, triple-class exposed patients with RRMM in real-world practice and result in poor outcomes. This supports a need for new treatments with novel mechanisms of action

Bayesian evaluation of budgets for endemic disease control: An example using management changes to reduce milk somatic 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\u27s 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

Association of season and herd size with somatic cell count for cows in Irish, English, and Welsh dairy herds

The aims of this study were to describe associations of time of year, and herd size with cow somatic cell count (SCC) for Irish, English, and Welsh dairy herds. Random samples of 497 and 493 Irish herds, and two samples of 200 English and Welsh (UK) herds were selected. Random effects models for the natural logarithm of individual cow test day SCC were developed using data from herds in one sub-dataset from each country. Data from the second sub-datasets were used for cross validation. Baseline model results showed that geometric mean cow SCC (GSCC) in Irish herds was highest from February to August, and ranged from 111,000 cells/mL in May to 61,000 cells/mL in October. For cows in UK herds, GSCC ranged from 84,000 cells/mL in February and June, to 66,000 cells/mL in October. The results highlight the importance of monitoring cow SCC during spring and summer despite low bulk milk SCC at this time for Irish herds. GSCC was lowest in Irish herds of up to 130 cows (63,000 cells/mL), and increased for larger herds, reaching 68,000 cells/mL in herds of up to 300 cows. GSCC in UK herds was lowest for herds of 130–180 cows (60,000 cells/mL) and increased to 63,000 cells/mL in herds of 30 cows, and 68,000 cells/mL in herds of 300 cows. Importantly, these results suggest expansion may be associated with increased cow SCC, highlighting the importance of appropriate management, to benefit from potential economies of scale, in terms of udder health

Association between somatic cell count early in the first lactation and the longevity of Irish dairy cows

Reduced longevity of cows is an important component of mastitis costs, and increased somatic cell count (SCC) early in the first lactation has been reported to increase culling risk throughout the first lactation. Generally, cows must survive beyond the first lactation to break even on their rearing costs. The aim of this research was to assess the association between SCC of primiparous cows at 5 to 30 days in milk (SCC1), and survival over a 5-y period for cows in Irish dairy herds. The data set used for model development was based on 147,458 test day records from 7,537 cows in 812 herds. Cows were censored at their last recording if identified at a later date in other herds or if recorded at the last available test date for their herd, otherwise, date of disposal was taken to be at the last test date for each cow. Survival time was calculated as the number of days between the dates of first calving and the last recording, which was split into 50-d intervals. Data were analyzed in discrete time logistic survival models that accounted for clustering of 50-d intervals within cows, and cows within herds. Models were fitted in a Bayesian framework using Markov chain Monte Carlo simulations. Model fit was assessed by comparison of posterior predictions to the observed disposal risk for cows aggregated by parameters in the model. Model usefulness was assessed by cross validation in a separate data set, which contained 144,113 records from 7,353 cows in 808 herds, and posterior predictions were compared with the observed disposal risk for cows aggregated by parameters of biological importance. Disposal odds increased by a factor of 5% per unit increase in ln SCC1. Despite this, posterior predictive distributions revealed that the probability of reducing replacement costs by >€10 per heifer calved, through decreasing the herd level prevalence of cows with SCC1 ≥400,000 cells/mL (from an initial prevalence of ≥20 t