title: Population-level impact of shorter-course regimens for tuberculosis: a model-based analysis.
abstract: Despite current control efforts, global tuberculosis (TB) incidence is decreasing slowly. New regimens that can shorten treatment hold promise for improving treatment completion and success, but their impact on population-level transmission remains unclear. Earlier models projected that a four-month regimen could reduce TB incidence by 10% but assumed that an entire course of therapy must be completed to derive any benefit. We constructed a dynamic transmission model of TB disease calibrated to global estimates of incidence, prevalence, mortality, and treatment success. To account for the efficacy of partial treatment, we used data from clinical trials of early short-course regimens to estimate relapse rates among TB patients who completed one-third, one-half, two-thirds, and all of their first-line treatment regimens. We projected population-level incidence and mortality over 10 years, comparing standard six-month therapy to hypothetical shorter-course regimens with equivalent treatment success but fewer defaults. The impact of hypothetical four-month regimens on TB incidence after 10 years was smaller than estimated in previous modeling analyses (1.9% [95% uncertainty range 0.6-3.1%] vs. 10%). Impact on TB mortality was larger (3.5% at 10 years) but still modest. Transmission impact was most sensitive to the proportion of patients completing therapy: four-month therapy led to greater incidence reductions in settings where 25% of patients leave care ("default") over six months. Our findings remained robust under one-way variation of model parameters. These findings suggest that novel regimens that shorten treatment duration may have only a modest effect on TB transmission except in settings of very low treatment completion.
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America; Medical Scientist Training Program, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America.
authors.forename: Mariam O
authors.initials: MO
authors.lastname: Fofana
authors.affiliation: TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: Gwenan M
authors.initials: GM
authors.lastname: Knight
authors.affiliation: Department of Global Health, University of Amsterdam, Amsterdam, The Netherlands; Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands.
authors.forename: Gabriela B
authors.initials: GB
authors.lastname: Gomez
authors.affiliation: TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: Richard G
authors.initials: RG
authors.lastname: White
authors.affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America; Center for Tuberculosis Research, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
authors.forename: David W
authors.initials: DW
authors.lastname: Dowdy
mesh_terms: D000465:Algorithms; D000995:Antitubercular Agents / Q000627:therapeutic use*; D018450:Disease Progression; D004334:Drug Administration Schedule; D006801:Humans; D008962:Models, Theoretical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012008:Recurrence; D013997:Time Factors; D014376:Tuberculosis / Q000188:drug therapy* / Q000517:prevention & control / Q000635:transmission
chemical_list: D000995:Antitubercular Agents