Using mixed methods to investigate factors influencing reporting of livestock diseases: a case study among smallholders in Bolivia.

Livestock disease surveillance is particularly challenging in resource-scarce settings, where disease events are often unreported. Surveillance performance is determined as much by the quantifiable biological attributes of the disease, as it is by motivations and barriers perceived by livestock keepers for disease reporting. Mixed methods designs, which integrate the collection, analysis and interpretation of qualitative and quantitative data in a single study, are increasingly used across different disciplines. These designs allow for a deeper exploration of the topic under investigation, than can be achieved by either approach alone. In this study a mixed methods design was used in order to gain a greater understanding of the factors that influence reporting of livestock diseases in Bolivia. There is a need to strengthen passive surveillance in this country, among other reasons as part of an eradication programme for Foot and Mouth Disease (FMD). Findings revealed livestock keepers in the study area were extremely unlikely to report the occurrence of livestock health events to the Official Veterinary Services (OVS). Communication outside the local community occurs more often through alternative routes and this is positively correlated with disease awareness. The main barriers to disease reporting identified were a lack of institutional credibility and the conflicting priorities of the OVS and livestock keepers. As for other animal and human diseases across the developing world, passive surveillance of livestock diseases in Bolivia should be enhanced; this is urgent in view of the current FMD eradication programme. Increasing timeliness and smallholders' participation requires a detailed understanding of their likely actions and perceived barriers towards disease reporting. These insights are most likely to be developed through a holistic mixed methods approach of quantitative and qualitative analyses.

A comparison between Poisson and zero-inflated Poisson regression models with an application to number of black spots in Corriedale sheep.

Dark spots in the fleece area are often associated with dark fibres in wool, which limits its competitiveness with other textile fibres. Field data from a sheep experiment in Uruguay revealed an excess number of zeros for dark spots. We compared the performance of four Poisson and zero-inflated Poisson (ZIP) models under four simulation scenarios. All models performed reasonably well under the same scenario for which the data were simulated. The deviance information criterion favoured a Poisson model with residual, while the ZIP model with a residual gave estimates closer to their true values under all simulation scenarios. Both Poisson and ZIP models with an error term at the regression level performed better than their counterparts without such an error. Field data from Corriedale sheep were analysed with Poisson and ZIP models with residuals. Parameter estimates were similar for both models. Although the posterior distribution of the sire variance was skewed due to a small number of rams in the dataset, the median of this variance suggested a scope for genetic selection. The main environmental factor was the age of the sheep at shearing. In summary, age related processes seem to drive the number of dark spots in this breed of sheep.