Geospatial analysis of reported activity locations to identify sites for tuberculosis screening

Abstract

Mobile screening units can help close tuberculosis case detection gaps. Placing screening units where people at high risk for undiagnosed tuberculosis preferentially spend time could make screening more resource-effective. We conducted a case-control study in Lima, Peru to identify locations where people with tuberculosis were more likely to spend time than community controls. We surveyed participants about activity locations over the past 6 months. We used density-based clustering to assess how patient and control activity locations differed, and logistic regression to compare location-based exposures. We included 109 tuberculosis patients and 79 controls. In density-based clustering analysis, the two groups had similar patterns of living locations, but their work locations clustered in distinct areas. Both groups were similarly likely to use public transit, but patients predominantly used buses and were less likely to use rapid transit (adjusted odds ratio [aOR] 0.31, 95% confidence interval [CI] 0.10-0.96) or taxis (aOR 0.42, 95% CI 0.21-0.85). Patients were more likely to have spent time in prison (aOR 11.55, 95% CI 1.48-90.13). Placing mobile screening units at bus terminals serving locations where tuberculosis patients have worked and within and around prisons could help reach people with undiagnosed tuberculosis.

Figure 1

Density-based clustering of locations where participants reported having lived, slept, or spent the majority of the day during the past 6 months. Each dot represents a location reported by a study participant, with colors representing clusters and black dots representing non-clustered locations. The black outline indicates the study area. Reported locations mainly clustered in the study area for both (A) TB patients (red dots) and (B) control group participants (microclusters shown in different colors). Map was created by MBB using ArcGIS Pro Version 2.8.0 (Environmental Systems Research Institute, Redlands, California, USA).

Figure 2

Density-based clustering of locations where participants reported having worked during the past 6 months. Each dot represents a location reported by a study participant, with colors representing clusters and black dots representing non-clustered locations. The black outline indicates the study area. (A) For TB patients, locations clustered around the main avenue within the study area (green dots) with another large but disperse cluster closer to the port of Callao (blue dots). (B) For control group participants, clusters were identified within the study area (green dots), in a neighboring district (blue dots), and in the more affluent Central Lima region (red dots). Map was created by MBB using ArcGIS Pro Version 2.8.0 (Environmental Systems Research Institute, Redlands, California, USA).

Figure 3

Density-based clustering of locations where participants reported having socialized routinely during the past 6 months. Each dot represents a location reported by a study participant, with colors representing clusters and black dots representing non-clustered locations. The black outline indicates the study area. (A) Among TB patients, locations clustered mainly within the study area (red dots) with a smaller cluster in a neighboring district (blue dots). (B) No clusters were identified for control group participants. Map was created by MBB using ArcGIS Pro Version 2.8.0 (Environmental Systems Research Institute, Redlands, California, USA).