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

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.

Figure 1.

Average risk classification by municipality as applied by Brazil’s Ministry of Health. Period 2007–2011. (A) Minas Gerais, (B) Ceara. This figure appears in color at www.ajtmh.org.

Figure 2.

Probability of being in the least capable quintile of the posterior rank distribution for the two states. (A) Results from the naïve imputation method, (B) results from the posterior imputation method. This figure appears in color at www.ajtmh.org.

Figure 3.

Posterior means and 99% credible intervals using naïve imputation (x axis), of the municipalities’ ranks, for the two states, and comparison with the municipalities’ Z-scores (y axis). This figure appears in color at www.ajtmh.org.

Figure 4.

Posterior means and 99% credible intervals using posterior imputation (x axis), of the municipalities’ ranks, for the two states, and comparison with the municipalities’ Z-scores (y axis). This figure appears in color at www.ajtmh.org.