Tuberculosis (TB) causes significant morbidity and mortality in U.S. cities, particularly in poor, transient populations. During a TB outbreak in Fulton County, Atlanta, GA, we aimed to determine if local maps created from multiple locations of personal activity per case would differ significantly from traditional maps created from single residential address. Data were abstracted for patients with TB disease diagnosed 2008-2014 receiving care at Fulton County Health Department. Clinical and activity location data were abstracted from charts. Kernel density methods, activity space analysis, and overlay with homeless shelter locations were used to characterize case spatial distribution when using single versus multiple addresses.

Data was collected for 198 TB cases, with over 30% homeless U.S. born cases included. Greater spatial dispersion of cases was found when utilizing multiple versus single addresses per case. Activity spaces of homeless and isoniazid (INH) resistant cases were more spatially congruent with one another than non-homeless and INH-susceptible cases (p<0.0001, p<0.0001 respectively).

Innovative spatial methods allowed us to more comprehensively capture the geography of TB-infected homeless persons, who made up a large portion of the Fulton County outbreak. We demonstrate how activity space analysis, prominent in exposure science and chronic disease, supports that routine capture of multiple location TB data may facilitate spatially different public health interventions than traditional surveillance maps.