Assessment of Area-Level Disease Control and Surveillance Vulnerabilities: An Application to Visceral Leishmaniasis in Brazil
Open Access
- 3 July 2019
- journal article
- research article
- Published by American Society of Tropical Medicine and Hygiene in The American Journal of Tropical Medicine and Hygiene
- Vol. 101 (1) , 93-100
Abstract
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.Keywords
This publication has 13 references indexed in Scilit:
- Integration of animal health and public health surveillance sources to exhaustively inform the risk of zoonosis: An application to visceral leishmaniasis data in BrazilSpatial and Spatio-temporal Epidemiology, 2019
- Optimal information networks: Application for data-driven integrated health in populationsScience Advances, 2018
- Modeling Area‐Level Health RankingsHealth Services Research, 2015
- Mapping malaria risk and vulnerability in the United Republic of Tanzania: a spatial explicit modelPopulation Health Metrics, 2015
- Visceral leishmaniasis in Brazil: rationale and concerns related to reservoir controlRevista de Saúde Pública, 2014
- Assessing the vulnerability of Brazilian municipalities to the vectorial transmission of Trypanosoma cruzi using multi-criteria decision analysisActa Tropica, 2014
- Assessment of Yellow Fever Epidemic Risk: An Original Multi-criteria Modeling ApproachPLOS Neglected Tropical Diseases, 2009
- Bayesian Factor Analysis for Spatially Correlated Data, With Application to Summarizing Area-Level Material Deprivation From Census DataJournal of the American Statistical Association, 2004
- Modelling risk from a disease in time and spaceStatistics in Medicine, 1998
- Inference from Iterative Simulation Using Multiple SequencesStatistical Science, 1992