Integration of animal health and public health surveillance sources to exhaustively inform the risk of zoonosis: An application to visceral leishmaniasis data in Brazil

• Published in: Spatial and spatio-temporal epidemiology Vol. 29; pp. 177 - 185
• Main Authors: Boaz, R., Corberán-Vallet, A., Lawson, A., Ferreira Lima, F.E. de, Donato, L. Edel, Alves, R. Vieira, Machado, G., Carvalho M., Freire de, Pompei, Julio, Vilas, V.J. Del Rio
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.06.2019
• Subjects: Animals; Brazil - epidemiology; Demography; Dog Diseases - epidemiology; Dogs; Humans; Leishmaniasis; Leishmaniasis, Visceral - epidemiology; Leishmaniasis, Visceral - etiology; Public Health Surveillance; Risk Factors; Spatial/spatio-temporal analysis; Spatio-Temporal Analysis; Surveillance; Zoonoses - epidemiology; Zoonoses - etiology; Brazil; Surveillance; Spatial/spatio-temporal analysis; Leishmaniasis
• ISSN: 1877-5845; 1877-5853
• DOI: 10.1016/j.sste.2018.09.001

Visceral leishmaniasis (VL) is a parasitic disease that is endemic in more than 80 countries, and leads to high fatality rates when left untreated. We investigate the relationship of VL cases in dogs and human cases, specifically for evidence of VL in dogs leading to excess cases in humans. We use surveillance data for dogs and humans for the years 2007–2011 to conduct both spatial and spatio-temporal analyses. Several models are evaluated incorporating varying levels of dependency between dog and human data. Models including dog data show marginal improvement over models without; however, for a subset of spatial units with ample data, models provide concordant risk classification for dogs and humans at high rates (∼70%). Limited reported dog case surveillance data may contribute to the results suggesting little explanatory value in the dog data, as excess human risk was only explained by dog risk in 5% of regions in the spatial analysis.