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Open Forum Infect Dis. 2014 Aug 30;1(2):ofu073. doi: 10.1093/ofid/ofu073. eCollection 2014 Sep.

Drivers and trajectories of resistance to new first-line drug regimens for tuberculosis.

Author information

1
Department of Epidemiology , Johns Hopkins School of Public Health , Baltimore, Maryland.
2
TB Modelling Group, Department of Infectious Disease Epidemiology , London School of Hygiene and Tropical Medicine , United Kingdom.
3
Division of Global Health Equity , Brigham and Women's Hospital , Boston, Massachusetts.
4
Amsterdam Institute for Global Health and Development , The Netherlands.

Abstract

BACKGROUND:

New first-line drug regimens for treatment of tuberculosis (TB) are in clinical trials: emergence of resistance is a key concern. Because population-level data on resistance cannot be collected in advance, epidemiological models are important tools for understanding the drivers and dynamics of resistance before novel drug regimens are launched.

METHODS:

We developed a transmission model of TB after launch of a new drug regimen, defining drug-resistant TB (DR-TB) as resistance to the new regimen. The model is characterized by (1) the probability of acquiring resistance during treatment, (2) the transmission fitness of DR-TB relative to drug-susceptible TB (DS-TB), and (3) the probability of treatment success for DR-TB versus DS-TB. We evaluate the effect of each factor on future DR-TB prevalence, defined as the proportion of incident TB that is drug-resistant.

RESULTS:

Probability of acquired resistance was the strongest predictor of the DR-TB proportion in the first 5 years after the launch of a new drug regimen. Over a longer term, however, the DR-TB proportion was driven by the resistant population's transmission fitness and treatment success rates. Regardless of uncertainty in acquisition probability and transmission fitness, high levels (>10%) of drug resistance were unlikely to emerge within 50 years if, among all cases of TB that were detected, 85% of those with DR-TB could be appropriately diagnosed as such and then successfully treated.

CONCLUSIONS:

Short-term surveillance cannot predict long-term drug resistance trends after launch of novel first-line TB regimens. Ensuring high treatment success of drug-resistant TB through early diagnosis and appropriate second-line therapy can mitigate many epidemiological uncertainties and may substantially slow the emergence of drug-resistant TB.

KEYWORDS:

Mycobacterium tuberculosis; TB drug regimens; TB drug resistance; TB mathematical model

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