Improving disease surveillance in Australia’s sheep industries: investigations of syndromic surveillance, farmer behaviour and sheep trade networks
AuthorPfeiffer, Caitlin Nicole
AffiliationMelbourne Veterinary School
Document TypePhD thesis
Access StatusOpen Access
© 2018 Dr. Caitlin Nicole Pfeiffer
Designing and delivering effective, useful livestock health surveillance is a challenge for many countries. The observations of people in frequent contact with livestock, captured through passive surveillance, play an important role in many national surveillance systems. In Australia, the effectiveness of passive surveillance on sheep and beef farms has been limited by infrequent veterinary contact. Farm workers frequently observe signs of disease in livestock, but these observations are not captured by existing surveillance systems. This thesis therefore posed the question: can farmers’ observations be collected to generate useful surveillance information? Syndromic surveillance of farmers’ observations is one approach to increase data capture from extensive livestock farms. Chapter 3 describes the operation of a syndromic surveillance system collecting farmers’ observations of livestock health in Victoria, Australia, over its first two years of operation from 2014 to 2016. Survival analysis and classification and regression tree analysis were used to identify farm level factors associated with reliable participation, to inform future recruitment aimed at farmers who were willing and able to provide regular, timely reports. Farmers keeping only sheep were the most reliable and timely respondents, while farmers aged under 43 years or working full time on-farm had lower response rates than older farmers or part-time farmers. This chapter demonstrates that recording farmers’ observations of signs of disease using syndromes is a feasible and effective method to gather disease occurrence data. The utility of syndromic data is further investigated in Chapter 4, using the observations collected by the surveillance system to quantify ewe mortality on sheep farms in southern Australia. Ewe deaths were reported in 540 of 612 reports, describing 2106 individual deaths, with a median of 4 deaths per positive monthly report. Median mortality rates ranged between individual farms from 1 to 5 deaths/1000 ewes/month. The incidence rate ratio of mortality in the five months preceding and following lambing was 2.8 (95% CI 2.0 to 4.1) compared to the remaining seven months of the year. Overall ewe mortality could therefore be reduced through strategies targeted to improving peri-parturient ewe survival. In a subset of reports where veterinary contact was recorded, just 15% of reported deaths involved a veterinarian. Further investigation of how and why farmers respond to ewe deaths without veterinary support is needed, to determine the best farm management strategies to reduce mortality. Chapter 5 investigates Australian sheep farmers’ low rates of veterinary contact. The study aimed to understand why Australian sheep farmers chose not to contact veterinarians when their animals showed signs of disease, and what alternative approaches they took to managing unwell animals. Data were collected during three focus group discussions with sheep farmers in Victoria, Australia. Transcripts of those discussions were analysed using a modified grounded theory approach to develop a preliminary theory of Australian sheep farmers’ disease response behaviour. Critical steps in the decision-making process included the farmer recognising that action is needed, and then deciding what that action would be. The farmers reported having to decide whether they would act independently based on their previously experiences, or alternatively to seek advice. Veterinarians played a small but important role as potential advisors, alongside others including trusted farming friends and farmer discussion groups. Self-reliance and confidence in their knowledge and skills was highlighted as the main reason the farmers often chose not to seek veterinary advice. Rather than being seen as a barrier to effective passive surveillance, the actions that arise from farmers’ self-reliance when facing disease should be taken into account when designing novel surveillance approaches. A final consideration for observational disease surveillance is the selection of individuals to contribute data to the system. While characteristics associated with participation may guide recruitment as described in Chapter 3, it is also useful to target surveillance to farms that have increased risk of acquiring or disseminating disease. The movement of animals between farms contributes to infectious disease spread, and can be investigated through network analysis methods. Australia’s National Livestock Identification Scheme sheep movement records are suitable for such analyses, but are known to be a targeted subset of all sheep movement in the country. However, knowledge of the effect of sampling or incomplete network data on these studies is limited. In Chapter 6, a simulation algorithm is presented that provides an estimate of required sampling proportions based on predicted network size, density and degree value distribution. The algorithm may be applied a priori to ensure network analyses based on sampled or incomplete data provide population estimates of known precision. Results demonstrate that, for network degree metrics, sample size requirements vary with sampling method. Where simulated networks can be constructed to closely mimic the true network in a target population, this algorithm provides a straightforward approach to determining sample size under a given sampling procedure for a network metric of interest. Chapter 7 then presents analysis of National Livestock Identification Scheme sheep movement data for Victoria, Australia. The sheep movement network in Victoria shows typical livestock movement network characteristics including scale-free and small-world topology, small diameter and short average path lengths, supporting the assumption that disease could spread rapidly in the state through sheep movements if it were not detected rapidly. Victoria’s position as a net importer of sheep and sheep flow is confirmed, driven substantially by the activity of saleyards (livestock markets) and abattoirs. Little variation within or between years in overall movement patterns were detected. While most farms are connected to a very small number of properties in the network, small subsets of farms demonstrate high degree values (being directly connected to many other properties through incoming out outgoing animal movements) or high frequency of sheep purchases or sales. These farms may be useful targets for emerging surveillance methods that can be implemented on-farm. Together, these studies provide new information about the Australian sheep industry and the feasibility of new surveillance approaches to improve the effectiveness of surveillance. By describing farmer behaviour, livestock movements patterns and the feasibility of syndromic surveillance approaches to capture farmers’ observations of signs of disease, these studies justify further development and implementation of novel surveillance approaches in Australia and serve as an example for other countries facing similar surveillance challenges. While there is no ideal surveillance system, integrating new approaches into wider surveillance strategies can improve the quality of information generated by surveillance, to better describe true disease states in the population and drive appropriate response activities.
Keywordsanimal health surveillance; sheep; syndromic surveillance; farmer behaviour; grounded theory; ewe mortality; network analysis; temporal network; livestock movement network
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