Figure 1.
Population density and spatial distribution of leprosy in Castanhal.
(A) Population density per km2 in the urban census tracts. (B) Raw number of leprosy cases per census tract. (C) Number of cases normalized by the population of each census tract per year (annual raw case detection rate per 100,000 people), classifying areas according to their level of endemicity, from low to hyperendemic, according to official parameters. (D) Spatially empirical Bayes smoothed detection rate (based on a queen spatial weight matrix) to smooth the differences between contiguous areas.
Figure 2.
Clusters of leprosy in Castanhal.
(A) The spatial distribution of individual leprosy cases overlying the respective Kernel density estimation layer, representing areas with a high and low density of cases per km2. (B) LISA test (local Moran's I) characterizing areas with a statistically significant (p<0.05) positive spatial association according to the raw detection rate. The areas marked as high-high indicate a high rate in an area surrounded by high values of the weighted average rate of the neighboring areas, and low-low represents areas with a lower rate surrounded by lower values. (C) The most likely cluster of leprosy detected by the Kulldorff's spatial scan statistics (p<0.01).
Table 1.
Characteristics of the specific regions in the urban area of Castanhal.
Figure 3.
Spatial distribution of surveyed household contacts and school children.
The spatial distribution of surveyed household contacts and school children according to their level of antibodies compared to the level of endemicity of the different census tracts.
Figure 4.
Space-time links among cases and proximity to students.
An expanded view of a specific region identified as a cluster of leprosy (see Figure 2C, Kulldorff's spatial scan statistics), showing the space-time links among cases and the spatial relationship with a surveyed school and seropositive students.
Table 2.
Knox space-time clustering analysis for leprosy cases.*