Epidemiological Characteristics of Classical Scrapie Outbreaks in 30 Sheep Flocks in the United Kingdom

Most previous analyses of scrapie outbreaks have focused on flocks run by research institutes, which may not reflect the field situation. Within this study, we attempt to rectify this deficit by describing the epidemiological characteristics of 30 sheep flocks naturally-infected with classical scrapie, and by exploring possible underlying causes of variation in the characteristics between flocks, including flock-level prion protein (PrP) genotype profile. In total, the study involved PrP genotype data for nearly 8600 animals and over 400 scrapie cases.We found that most scrapie cases were restricted to just two PrP genotypes (ARQ/VRQ and VRQ/VRQ), though two flocks had markedly different affected genotypes, despite having similar underlying genotype profiles to other flocks of the same breed; we identified differences amongst flocks in the age of cases of certain PrP genotypes; we found that the age-at-onset of clinical signs depended on peak incidence and flock type; we found evidence that purchasing infected animals is an important means of introducing scrapie to a flock; we found some evidence that flock-level PrP genotype profile and flock size account for variation in outbreak characteristics; identified seasonality in cases associated with lambing time in certain flocks; and we identified one case that was homozygous for phenylalanine at codon 141, a polymorphism associated with a very high risk of atypical scrapie, and 28 cases that were heterozygous at this codon.This paper presents the largest study to date on commercially-run sheep flocks naturally-infected with classical scrapie, involving 30 study flocks, more than 400 scrapie cases and over 8500 PrP genotypes. We show that some of the observed variation in epidemiological characteristics between farms is related to differences in their PrP genotype profile; although much remains unexplained and may instead be attributed to the stochastic nature of scrapie dynamics

Epidemiological Characteristics of Classical Scrapie Outbreaks in 30 Sheep Flocks in the United Kingdom

Most previous analyses of scrapie outbreaks have focused on flocks run by research institutes, which may not reflect the field situation. Within this study, we attempt to rectify this deficit by describing the epidemiological characteristics of 30 sheep flocks naturally-infected with classical scrapie, and by exploring possible underlying causes of variation in the characteristics between flocks, including flock-level prion protein (PrP) genotype profile. In total, the study involved PrP genotype data for nearly 8600 animals and over 400 scrapie cases.We found that most scrapie cases were restricted to just two PrP genotypes (ARQ/VRQ and VRQ/VRQ), though two flocks had markedly different affected genotypes, despite having similar underlying genotype profiles to other flocks of the same breed; we identified differences amongst flocks in the age of cases of certain PrP genotypes; we found that the age-at-onset of clinical signs depended on peak incidence and flock type; we found evidence that purchasing infected animals is an important means of introducing scrapie to a flock; we found some evidence that flock-level PrP genotype profile and flock size account for variation in outbreak characteristics; identified seasonality in cases associated with lambing time in certain flocks; and we identified one case that was homozygous for phenylalanine at codon 141, a polymorphism associated with a very high risk of atypical scrapie, and 28 cases that were heterozygous at this codon.This paper presents the largest study to date on commercially-run sheep flocks naturally-infected with classical scrapie, involving 30 study flocks, more than 400 scrapie cases and over 8500 PrP genotypes. We show that some of the observed variation in epidemiological characteristics between farms is related to differences in their PrP genotype profile; although much remains unexplained and may instead be attributed to the stochastic nature of scrapie dynamics

A critical analysis of multi-criteria models for the prioritisation of health threats

© 2019 Elsevier B.V. Multi-criteria assessments are increasingly being employed in the prioritisation of health threats, supporting decision processes related to health risk management. The use of multi-criteria analysis in this context is welcome, as it facilitates the consideration of multiple impacts of health threats, it can encompass the use of expert judgment to complement and amalgamate the evidence available, and it permits the modelling of policy makers’ priorities. However, these assessments often lack a clear multi-criteria conceptual framework, in terms of both axiomatic rigour and adequate procedures for preference modelling. Such assessments are ad hoc from a multi-criteria decision analysis perspective, despite the strong health expertise used in constructing these models. In this paper we critically examine some key assumptions and modelling choices made in these assessments, comparing them with the best practices of multi-attribute value analysis. Furthermore, we suggest a set of guidelines on how simulation studies might be employed to assess the impact of these modelling choices. We apply these guidelines to two relevant studies available in the health threat prioritisation domain. We identify severe variability in our simulations due to poor modelling choices, which could cause changes in the ranking of threats being assessed and thus lead to alternative policy recommendations than those suggested in their reports. Our results confirm the importance of carefully designing multi-criteria evaluation models for the prioritisation of health threats

Policy analytics : an agenda for research and practice

The growing impact of the “analytics” perspective in recent years, which integrates advanced data-mining and learning methods, is often associated with increasing access to large databases and with decision support systems. Since its origin, the field of analytics has been strongly business-oriented, with a typical focus on data-driven decision processes. In public decisions, however, issues such as individual and social values, culture and public engagement are more important and, to a large extent, characterise the policy cycle of design, testing, implementation, evaluation and review of public policies. Therefore public policy making seems to be a much more socially complex process than has hitherto been considered by most analytics methods and applications. In this paper, we thus suggest a framework for the use of analytics in supporting the policy cycle—and conceptualise it as “Policy Analytics”