Tuberculosis Infection in Oakland, CA 1997-2001: At-Risk Populations and Predicting "Hot Spots", An Analysis Method Combining Kernel Density Estimation Mapping with Regression

Produced for the NNIP cross-site initiative on Neighborhoods and Health, this analysis was conducted in collaboration with the Alameda County Public Health Department and focuses on the relationship between neighborhood conditions and the incidence of tuberculosis.  A problem has been that simply counting the incidence of tuberculosis cases (and other health indicators) for Health Districts, and even for units as small as census tracts, has not been finely grained enough to support efficient spatial targeting of services. The "kernel density" method the researchers applied depicts disease intensities in the form of contour intervals (like elevations on a topographic map), which are more sensitive for this purpose. Analysis with the method showed strong associations between tuberculosis and poverty rates and other neighborhood characteristics (the immigrant share of the population turned out to be the strongest predictor). Briefings and prevention planning sessions were held with the department's Neighborhood Health Teams in areas where the risk of tuberculosis was found to be particularly high.