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Early Detection of Tuberculosis Outbreaks among the San Francisco Homeless

Description

San Francisco has the highest rate of TB in the US. Although in recent years the incidence of TB has been declining in the San Francisco general population, it has remained relatively constant in the homeless population. Spatial investigations of disease outbreaks seek to identify and determine the significance of spatially localized disease clusters by partitioning the underlying geographic region. The level of such regional partitioning can vary depending on the available geospatial data on cases including towns, counties, zip codes, census tracts, and exact longitude-latitude coordinates. It has been shown for syndromic surveillance data that when exact patients’ geographic coordinates are used, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. While the benefits of using a finer spatial resolution, such as patients’ individual addresses, have been examined in the context of spatial epidemiology, the effect of varying spatial resolution on detection timeliness and the amount of historical data needed have not been investigated.

 

Objective

The objective of this study is to investigate the effect of varying the spatial resolution in a variant of space-time permutation scan statistic applied to the tuberculosis data on the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed for training the model.

Submitted by elamb on