title: Potential impact of spatially targeted adult tuberculosis vaccine in Gujarat, India.
abstract: Some of the most promising vaccines in the pipeline for tuberculosis (TB) target adolescents and adults. Unlike for childhood vaccines, high-coverage population-wide vaccination is significantly more challenging for adult vaccines. Here, we aimed to estimate the impact of vaccine delivery strategies that were targeted to high-incidence geographical 'hotspots' compared with randomly allocated vaccination. We developed a spatially explicit mathematical model of TB transmission that distinguished these hotspots from the general population. We evaluated the impact of targeted and untargeted vaccine delivery strategies in India--a country that bears more than 25% of global TB burden, and may be a potential early adopter of the vaccine. We collected TB notification data and conducted a demonstration study in the state of Gujarat to validate our estimates of heterogeneity in TB incidence. We then projected the impact of randomly vaccinating 8% of adults in a single mass campaign to a spatially targeted vaccination preferentially delivered to 80% of adults in the hotspots, with both strategies augmented by continuous adolescent vaccination. In consultation with vaccine developers, we considered a vaccine efficacy of 60%, and evaluated the population-level impact after 10 years of vaccination. Spatial heterogeneity in TB notification (per 100,000/year) was modest in Gujarat: 190 in the hotspots versus 125 in the remaining population. At this level of heterogeneity, the spatially targeted vaccination was projected to reduce TB incidence by 28% after 10 years, compared with a 24% reduction projected to achieve via untargeted vaccination--a 1.17-fold augmentation in the impact of vaccination by spatially targeting. The degree of the augmentation was robust to reasonable variation in natural history assumptions, but depended strongly on the extent of spatial heterogeneity and mixing between the hotspot and general population. Identifying high-incidence hotspots and quantifying spatial mixing patterns are critical to accurate estimation of the value of targeted intervention strategies.
authors.affiliation: Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD 21205, USA sourya@jhu.edu.
authors.forename: Sourya
authors.identifier: http://orcid.org/0000-0002-6106-6834
authors.initials: S
authors.lastname: Shrestha
authors.affiliation: Public Health Foundation of India, Delhi, India.
authors.forename: Susmita
authors.initials: S
authors.lastname: Chatterjee
authors.affiliation: Department of International Health, Johns Hopkins School of Public Health, Baltimore, MD 21205, USA.
authors.forename: Krishna D
authors.initials: KD
authors.lastname: Rao
authors.affiliation: Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD 21205, USA.
authors.forename: David W
authors.initials: DW
authors.lastname: Dowdy
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D007194:India; D008297:Male; D008875:Middle Aged; D014376:Tuberculosis / Q000453:epidemiology / Q000517:prevention & control / Q000635:transmission; D032581:Tuberculosis Vaccines / Q000008:administration & dosage*; D014611:Vaccination
chemical_list: D032581:Tuberculosis Vaccines
keywords: mathematical model; spatially targeted tuberculosis vaccine; tuberculosis; tuberculosis in India