Mobility Patterns, Activity Spaces, and Tuberculosis in Nairobi, Kenya

Background: Tuberculosis (TB) remains one of the leading infectious causes of morbidity and mortality globally. Annually, over 3 million people develop TB but are not diagnosed and not initiated on treatment. This case-detection gap critically impedes global efforts to end the TB epidemic, as individuals with undiagnosed TB may be contributing to ongoing transmission. Active case finding (ACF) offers one approach to close this gap by screening and diagnosing TB in the community and reaching priority groups. Highly mobile individuals are one population that may be at higher risk for TB and could potentially benefit from targeted ACF. A second challenge for implementing ACF is to determine where to deploy ACF activities in a community. Activity spaces, which are locations where an individual spends their time, can provide possible venues in which to conduct ACF. Our goal in this analysis of mobility patterns and activity spaces is to evaluate TB with regards to geographic risk factors in an urban, high-burden setting. Methods: This analysis leverages data from a population-based TB prevalence survey conducted in Nairobi, Kenya, from May to December 2022. Location-based activity spaces data and data on mobility patterns were collected as part of a questionnaire administered to eligible study participants. The primary outcome of the survey was the prevalence of bacteriologically-confirmed pulmonary TB among those ages 15 years and older. Several dimensions of mobility were captured, including mode of transportation, distance and length of commute, and use of transit hubs. Latent class analysis was performed to identify two latent mobility classes. Reported activity spaces were recorded and categorized into six broad categories. Logistic regression was used to evaluate the association between mobility patterns and categorized activity spaces and odds of pulmonary TB. The adjusted analysis included age and sex. Secondary analysis explored odds of positive symptom screen. Results: The prevalence survey enrolled a total of 6371 participants, with 23.6% completing the work commute questions and 89.3% reporting activity spaces. Overall, the majority of respondents walked to work (68.3%) or used a bus or taxi (28.8%). There was a significant difference in transportation and commute between men and women and among age groups. Reported activity space also varied significantly by age and sex. In the unadjusted analysis, older age groups, male sex (OR 2.94, 95%CI 1.89, 4.58), and reporting a social activity space (OR 2.15, 95%CI 1.15, 4.03) were all significantly associated with a diagnosis of active TB. Mobility factors were not significantly associated with active TB diagnosis, nor was membership in the ‘mobile’ latent class. Adjusting for age and sex: age group 45 – 54 (OR 2.44, 95%CI 1.05, 5.69), male sex (OR 2.88, 95%CI 1.71, 4.85), and reporting a social activity space (OR 1.95, 95%CI 1.02, 3.72) were significantly associated with higher odds of active TB. Conclusions: In this analysis, we did not find a significant association between mobility patterns and TB. However, we did find a strong positive association between reported ‘social’ activity spaces and active TB diagnosis. Identification of ‘social’ activity spaces provides important insight into possible venues for ACF activities, both in Nairobi, Kenya, and possibly other high-burden urban settings. Investigating the extent of this association and other geographic risk factors can help form a data-driven approach for optimizing spatially-targeted ACF.