Abstract
Blowfly strike is a devastating and often rapidly fatal disease in rabbits. In Great Britain (GB), Lucilia sericata is the primary causative species. Despite its severity, there has been minimal investigatory work into the disease in rabbits. Here we used text mining to screen electronic health records (EHRs) from a large sentinel network of 389 veterinary practices in GB between March 2014 and April 2017 for confirmed cases of blowfly strike in rabbits. Blowfly strike was identified in 243 of 42,226 rabbit consultations (0.6%), affecting 205 individual rabbits. The anatomical site of recorded blowfly strike lesions was overwhelmingly the perineal area (n = 109, 52.4%). Less commonly lesions were observed affecting other areas of the body (n = 9, 4.3%) and head (n = 8, 3.8%); in 83 consultations (39.9%), the affected area was not specified. Of the rabbits presenting with blowfly strike, 44.7% were recorded as being euthanized or died. A case control study was used to identify risk factors for blowfly strike in this population. Whilst sex and neuter status in isolation were not significantly associated with blowfly strike, entire female rabbits showed a 3.3 times greater odds of being a case than neutered female rabbits. Rabbits five years of age and over were more than 3.8 times likely to present for blowfly strike. For every 1 °C rise in environmental temperature between 4.67 °C and 17.68 °C, there was a 33% increase risk of blowfly strike, with cases peaking in July or August. Overall blowfly strike cases started earlier and peaked higher in the south of Great Britain. The most northerly latitude studied was at lower risk of blowfly strike than the most southerly (OR = 0.50, p < 0.001). There appeared to be no significant relationship between blowfly strike in rabbits and either the sheep density or rural and urban land coverage types. The results presented here can be used for targeted health messaging to reduce the impact of this deadly disease for rabbits. We propose that real-time temporal and spatial surveillance of the rabbit disease may also help inform sheep control, where the seasonal profile is very similar, and where routine surveillance data is also not available. Our results highlight the value of sentinel databases based on EHRs for research and surveillance.