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

Spatial and spatiotemporal dynamics of visceral leishmaniasis in an endemic North-eastern region of Brazil

Visceral Leishmaniasis (VL) is a neglected disease with increasing incidence in Brazil, particularly in the North-eastern. The aim of this study was to analyze the spatial and spatiotemporal dynamics of VL in an endemic region of North-eastern Brazil, between 2009 and 2017. Using spatial analysis techniques, an ecological and time series study was made regarding VL cases in Sergipe filed as notifiable disease events. With data from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística, IBGE), a digital population and cartographic baseline was established. Segmented linear regression was used to examine the temporal trends. The statistical analysis methods of Global and Local Moran' I, local Bayesian empirical methodology and spatial-temporal scanning were used to produce thematic maps. High instances were found among adults, males, urban residents, non-Whites and persons with low levels of education. A decrease in the recovery rate and an increase in the proportion of urban cases and lethality was found. A heterogeneous VL distribution with spatiotemporal agglomeration on the seaside of the state was seen in Sergipe. To better manage the disease, new research is encouraged together with development of public health strategies. Further, improving health care networks, especially primary care, is suggested as this approach has a key role in health promotion, prevention and monitoring of the most prevalent diseases.

High seroprevalence and associated factors for visceral leishmaniasis in dogs in a transmission area of Paraíba state, Northeastern Brazil

Dogs have been implicated as main reservoirs for visceral leishmaniasis (VL) in urban areas. Therefore, studies on this species provide important data for public health. Thus, the objective of the present study was to ascertain the seroprevalence of canine VL (CVL) and the associated factors in the Brejo Paraibano microregion, northeastern Brazil. A total of 409 dogs were sampled from the eight municipalities of the microregion: Alagoa Grande, Alagoa Nova, Areia, Bananeiras, Borborema, Matinhas, Pilões and Serraria. The diagnosis of CVL was made using an enzyme-linked immunosorbent assay (ELISA S7®), through which the prevalence observed was 29.3%. By robust Poisson regression analysis two factors were associated with seroprevalence: illiterate/incomplete elementary owner's education level (prevalence ratio = 1.57; 95% CI = 1.05-2.34; p-value = 0.027) and tick infestation (prevalence ratio = 1.82; 95% CI = 1.27-2.61; p-value = 0.001). It is concluded that the seroprevalence of CVL in the Brejo Paraibano microregion is high. The factors associated with seroprevalence indicated the importance to develop socioeducational actions on the population, and the finding that tick infestation was associated with seroprevalence shows that there is a need for investigation regarding the role of ticks in the epidemiology of CVL.

Integrated Early Warning Surveillance: Achilles' Heel of One Health?

Emerging and re-emerging infectious diseases and zoonoses indicate the importance of the One Health (OH) approach for early warning. At present, even when surveillance data are available, they are infrequently timeously shared between the health sectors. In the context of the MediLabSecure (MLS) Project, we investigated the collection of a set of surveillance indicators able to provide data for the implementation of integrated early warning systems in the 22 MLS countries of the Mediterranean, Black Sea and Sahel regions. We used an online questionnaire (covering vector, human, and animal sectors), focusing on seven relevant arboviruses, that was submitted to 110 officially appointed experts. Results showed that West Nile virus was perceived as the most relevant zoonotic pathogen, while Dengue virus was the most relevant non-zoonotic pathogen in the study area. Data collection of early warning indicators is in place at a different level for all the investigated pathogens and in almost all the MLS Countries. Further assessments on the reliability of the collection in place and on the feasibility of piloting an integrated early warning system for arbovirus could verify if integrated early warning really represents the Achilles' heel of OH.

Assessment of Area-Level Disease Control and Surveillance Vulnerabilities: An Application to Visceral Leishmaniasis in Brazil

The large number of activities contributing to zoonoses surveillance and control capability, on both human and animal domains, and their likely heterogeneous implementation across administrative units make assessment and comparisons of capability performance between such units a complex task. Such comparisons are important to identify gaps in capability development, which could lead to clusters of vulnerable areas, and to rank and subsequently prioritize resource allocation toward the least capable administrative units. Area-level preparedness is a multidimensional entity and, to the best of our knowledge, there is no consensus on a single comprehensive indicator, or combination of indicators, in a summary metric. We use Bayesian spatial factor analysis models to jointly estimate and rank disease control and surveillance capabilities against visceral leishmaniasis (VL) at the municipality level in Brazil. The latent level of joint capability is informed by four variables at each municipality, three reflecting efforts to monitor and control the disease in humans, and one variable informing surveillance capability on the reservoir, the domestic dog. Because of the large volume of missing data, we applied imputation techniques to allow production of comprehensive rankings. We were able to show the application of these models to this sparse dataset and present a ranked list of municipalities based on their overall VL capability. We discuss improvements to our models, and additional applications.