Canine visceral leishmaniasis in Araçatuba, state of São Paulo, Brazil, and its relationship with characteristics of dogs and their owners: a cross-sectional and spatial analysis using a geostatistical approach
Danielle Nunes Carneiro Castro Costa | Marta Blangiardo | Lilian Aparecida Colebrusco Rodas | Caris Maroni Nunes | Roberto Mitsuyoshi Hiramoto | José Eduardo Tolezano | Lucas Xavier Bonfietti | Patricia Marques Moralejo Bermudi | Rafael Silva Cipriano | Graziela Cândido Diniz Cardoso | Cláudia Torres Codeço | Francisco Chiaravalloti-Neto
Abstract Background The incidence of visceral leishmaniasis (VL), one of the most important neglected diseases worldwide, is increasing in Brazil. The objectives of this study were to determine the canine VL (CanL) seroprevalence in an urban area of Araçatuba municipality and to evaluate its relationship with the characteristics of dogs and their owners. Results The CanL seroprevalence in the study area was 0.081 (95% credible interval [CI]: 0.068–0.096). The following covariates/categories were positively associated with the occurrence of a seropositive dog: more than 10 dogs that had lived in the house (odds ratio [OR] = 2.36; 95% CI: 1.03–5.43) (baseline: 0–10 dogs); house with dogs that previously died of VL (OR = 4.85; 95% CI: 2.65–8.86) or died of causes other than old age (OR = 2.26; 95% CI: 1.12–4.46) (baseline: natural or no deaths); dogs that spent the day in a sheltered backyard (OR = 2.14; 95% CI: 1.05–4.40); dogs that spent the day in an unsheltered backyard or the street (OR = 2.67; 95% CI: 1.28–5.57) (baseline: inside home). Spatial dependence among observations occurred within about 45.7 m. Conclusions The number of dogs that had lived in the house, previous deaths by VL or other cause, and the place the dog stayed during the day were associated with the occurrence of a VL seropositive dog. The short-distance spatial dependence could be related to the vector characteristics, producing a local neighbourhood VL transmission pattern. The geostatistical approach in a Bayesian context using integrated nested Laplace approximation (INLA) allowed to identify the covariates associated with VL, including its spatially dependent transmission pattern.
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