Is IPT more effective in high-burden settings? Modelling the effect of tuberculosis incidence on IPT impact
OBJECTIVE: To describe the relationship between TB incidence and IPT effectiveness.
METHODS: We constructed a model of TB transmission dynamics to investigate IPT effectiveness under various epidemiological settings. The model structure was intended to be highly adaptable to uncertainty in both input parameters and the mechanism of action of IPT. To determine the optimal setting for IPT use, we identified the lowest number needed to treat (NNT) with IPT to prevent one case of active TB.
RESULTS: We found that the NNT as a function of TB incidence shows a ‘U-shape', whereby IPT impact is greatest at an intermediate incidence and attenuated at both lower and higher incidence levels. This U-shape was observed over a broad range of parameter values; the optimal TB incidence was between 500 and 900 cases per 100 000 per year.
CONCLUSIONS: TB burden is a critical factor to consider when making decisions about communitywide implementation of IPT. We believe that the total disease burden should not preclude programmatic application of IPT.
Keywords: latent tuberculous infection; optimal impact; preventive therapy
Document Type: Research Article
Affiliations: 1: Department of Medicine, Royal Melbourne Hospital/Western Hospital, University of Melbourne, Parkville, Centre for Population Health, Burnet Institute, Melbourne, Australia 2: Department of Medicine, Royal Melbourne Hospital/Western Hospital, University of Melbourne, Parkville, Australia; Centre for Population Health, Burnet Institute, Melbourne, Victorian Tuberculosis Program, Melbourne Health, Melbourne, Victoria, Australia 3: Department of Medicine, Royal Melbourne Hospital/Western Hospital, University of Melbourne, Parkville, Centre for Population Health, Burnet Institute, Melbourne, Australia; Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia 4: Department of Infectious Disease Epidemiology, TB Modelling Group, TB Centre, and Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK 5: Victorian Tuberculosis Program, Melbourne Health, Melbourne, Victoria, Australia; Department of Microbiology and Immunology, University of Melbourne, Melbourne, Australia; Victorian Infectious Diseases Service, Royal Melbourne Hospital, Parkville, Victoria, Australia 6: Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, USA
Publication date: 01 January 2017
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