Children as sentinels of tuberculosis transmission: Disease mapping of programmatic data

Abstract

Background: Identifying hotspots of tuberculosis transmission can inform spatially targeted active case-finding interventions. While national tuberculosis programs maintain notification registers which represent a potential source of data to investigate transmission patterns, high local tuberculosis incidence may not provide a reliable signal for transmission because the population distribution of covariates affecting susceptibility and disease progression may confound the relationship between tuberculosis incidence and transmission. Child cases of tuberculosis and other endemic infectious disease have been observed to provide a signal of their transmission intensity. We assessed whether local overrepresentation of child cases in tuberculosis notification data corresponds to areas where recent transmission events are concentrated. Methods: We visualized spatial clustering of children < 5 years old notified to Peru's National Tuberculosis Program from two districts of Lima, Peru, from 2005 to 2007 using a log-Gaussian Cox process to model the intensity of the point-referenced child cases. To identify where clustering of child cases was more extreme than expected by chance alone, we mapped all cases from the notification data onto a grid and used a hierarchical Bayesian spatial model to identify grid cells where the proportion of cases among children < 5 years old is greater than expected. Modeling the proportion of child cases allowed us to use the spatial distribution of adult cases to control for unobserved factors that may explain the spatial variability in the distribution of child cases. We compare where young children are overrepresented in case notification data to areas identified as transmission hotspots using molecular epidemiological methods during a prospective study of tuberculosis transmission conducted from 2009 to 2012 in the same setting. Results: Areas in which childhood tuberculosis cases are overrepresented align with areas of spatial concentration of transmission revealed by molecular epidemiologic methods. Conclusions: Age-disaggregated notification data can be used to identify hotspots of tuberculosis transmission and suggest local force of infection, providing an easily accessible source of data to target active case-finding intervention.