Dairy herd mastitis and reproduction: Using simulation to aid interpretation of results from discrete time survival analysis

- Simulation model constructed based on a previously published statistical model.
- Used in a probabilistic sensitivity analysis to explore drivers of herd fertility.
- Mastitis highly unlikely to have meaningful impact on herd-level reproduction.
- Background submission and pregnancy rate appear to be key drivers.
- PSA a highly useful tool to contextualise results from complex statistical models.

Probabilistic sensitivity analysis (PSA) is a simulation-based technique for evaluating the relative importance of different inputs to a complex process model. It is commonly employed in decision analysis and for evaluation of the potential impact of uncertainty in research findings on clinical practice, but has a wide variety of other possible applications. In this example, it was used to evaluate the association between herd-level udder health and reproductive performance in dairy herds.Although several recent studies have found relatively large associations between mastitis and fertility at the level of individual inseminations or lactations, the current study demonstrated that herd-level intramammary infection status is highly unlikely to have a clinically significant impact on the overall reproductive performance of a dairy herd under typical conditions. For example, a large increase in incidence rate of clinical mastitis (from 92 to 131 cases per 100 cows per year) would be expected to increase a herd's modified FERTEX score (a cost-based measure of overall reproductive performance) by just £4.501 per cow per year. The herd's background level of submission rate (proportion of eligible cows served every 21 days) and pregnancy risk (proportion of inseminations leading to a pregnancy) correlated strongly with overall reproductive performance and explained a large proportion of the between-herd variation in performance.PSA proved to be a highly useful technique to aid understanding of results from a complex statistical model, and has great potential for a wide variety of applications within the field of veterinary science.