Spatiotemporal patterns and agroecological risk factors for cutaneous and renal glomerular vasculopathy (Alabama Rot) in dogs in the UK

• Published in: Veterinary record Vol. 183; no. 16; p. 502
• Main Authors: Stevens, Kim B, Jepson, Rosanne, Holm, Laura Phillipa, Walker, David John, Cardwell, Jacqueline Martina
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group Limited 27.10.2018; Blackwell Publishing Ltd
• Subjects: Alabama Rot; Anemia; Animals; boosted regression trees; Cattle; Climate change; CRGV; Data analysis; Datasets; Disease; Disease Outbreaks - statistics & numerical data; Disease Outbreaks - veterinary; Dogs; Epidemiology; Habitats; Histopathology; Kidney Diseases - epidemiology; Kidney Diseases - veterinary; Kidney Glomerulus - pathology; Kidneys; Risk Factors; Seasons; Skin Diseases - epidemiology; Skin Diseases - veterinary; Soil fertility; space‐time permutation statistic; Spatial analysis; Spatial data; Spatio-Temporal Analysis; United Kingdom - epidemiology; Vascular Diseases - epidemiology; Vascular Diseases - veterinary; Veterinary medicine; United Kingdom; England; United Kingdom > UK; Alabama; United States > US; Wales; Alabama Rot; CRGV; epidemiology; space-time permutation statistic; risk factors; boosted regression trees
• ISSN: 0042-4900; 2042-7670
• DOI: 10.1136/vr.104892

The annual outbreaks of cutaneous and renal glomerular vasculopathy (CRGV) reported in UK dogs display a distinct seasonal pattern (November to May) suggesting possible climatic drivers of the disease. The objectives of this study were to explore disease clustering and identify associations between agroecological factors and CRGV occurrence. Kernel-smoothed maps were generated to show the annual reporting distribution of CRGV, Kuldorff’s space–time permutation statistic used to identify significant spatiotemporal case clusters and a boosted regression tree model developed to quantify associations between CRGV case locations and a range of agroecological factors. The majority of diagnoses (92 per cent) were reported between November and May while the number of regions reporting the disease increased between 2012 and 2017. Two significant spatiotemporal clusters were identified—one in the New Forest during February and March 2013, and one adjacent to it (April 2015 to May 2017)—showing significantly higher and lower proportions of cases than the rest of the UK, respectively, for the indicated time periods. A moderately significant high-risk cluster (P=0.087) was also identified in the Manchester area of northern England between February and April 2014. Habitat was the predictor with the highest relative contribution to CRGV distribution (20.3 per cent). Cases were generally associated with woodlands, increasing mean maximum temperatures in winter, spring and autumn, increasing mean rainfall in winter and spring and decreasing cattle and sheep density. Understanding of such factors may help develop causal models for CRGV occurrence.