Journal
CLINICAL INFECTIOUS DISEASES
Volume 61, Issue -, Pages S147-S154Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/cid/civ579
Keywords
tuberculosis; antibiotic resistance; fitness costs; mathematical modeling
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Funding
- Bill & Melinda Gates Foundation [21675/OPP1084276, 19790.01]
- Medical Research Council (UK) [MR/J005088/1]
- Centers for Disease Control and Prevention/President's Emergency Plan for AIDS Relief via Aurum Institute [U2GPS0008111]
- US Agency for International Development/International Union Against Tuberculosis and Lung Disease/The Union North America (TREAT TB: Technology, Research, Education, and Technical Assistance for Tuberculosis)
- Engineering and Physical Sciences Research Council [EPSRC EP/K026003/1]
- Medical Scientist Training Program from National Institutes of Health [T32 GM007309]
- Paul and Daisy Soros Fellowships for New Americans scholarship
- Engineering and Physical Sciences Research Council [EP/K026003/1] Funding Source: researchfish
- Medical Research Council [MR/J005088/1] Funding Source: researchfish
- EPSRC [EP/K026003/1] Funding Source: UKRI
- MRC [MR/J005088/1] Funding Source: UKRI
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Background. Drug resistance poses a serious challenge for the control of tuberculosis in many settings. It is well established that the expected future trend in resistance depends on the reproductive fitness of drug-resistant Mycobacterium tuberculosis. However, the variability in fitness between strains with different resistance-conferring mutations has been largely ignored when making these predictions. Methods. We developed a novel approach for incorporating the variable fitness costs of drug resistance-conferring mutations and for tracking this distribution of fitness costs over time within a transmission model. We used this approach to describe the effects of realistic fitness cost distributions on the future prevalence of drug-resistant tuberculosis. Results. The shape of the distribution of fitness costs was a strong predictor of the long-term prevalence of resistance. While, as expected, lower average fitness costs of drug resistance-conferring mutations were associated with more severe epidemics of drug-resistant tuberculosis, fitness distributions with greater variance also led to higher levels of drug resistance. For example, compared to simulations in which the fitness cost of resistance was fixed, introducing a realistic amount of variance resulted in a 40% increase in prevalence of drug-resistant tuberculosis after 20 years. Conclusions. The differences in the fitness costs associated with drug resistance-conferring mutations are a key determinant of the future burden of drug-resistant tuberculosis. Future studies that can better establish the range of fitness costs associated with drug resistance-conferring mutations will improve projections and thus facilitate better public health planning efforts.
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