ArticleBMC infectious diseases2022
Estimating tuberculosis drug resistance amplification rates in high-burden settings.
Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Expanding diagnostic testing for drug-resistant tuberculosis in high burden settings: a cost-effectiveness analysis.BMC public health · 2025Article
- Dynamic modelling of improved diagnostic testing for drug-resistant tuberculosis in high burden settings.BMC infectious diseases · 2024Article
- Combating antimicrobial resistance in malaria, HIV and tuberculosis.Nature reviews. Drug discovery · 2024Review
- AI-Driven Enhancements in Drug Screening and Optimization.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Molecular investigations of Mycobacterium tuberculosis genotypes among baseline and follow-up strains circulating in four regions of Eswatini.BMC infectious diseases · 2023Article
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundAntimicrobial resistance develops following the accrual of mutations in the bacterial genome, and may variably impact organism fitness and hence, transmission risk. Classical representation of tuberculosis (TB) dynamics using a single or two strain (DS/MDR-TB) model typically does not capture elements of this important aspect of TB epidemiology. To understand and estimate the likelihood of resistance spreading in high drug-resistant TB incidence settings, we used epidemiological data to develop a mathematical model of Mycobacterium tuberculosis (Mtb) transmission.
methodsA four-strain (drug-susceptible (DS), isoniazid mono-resistant (INH-R), rifampicin mono-resistant (RIF-R) and multidrug-resistant (MDR)) compartmental deterministic Mtb transmission model was developed to explore the progression from DS- to MDR-TB in The Philippines and Viet Nam. The models were calibrated using data from national tuberculosis prevalence (NTP) surveys and drug resistance surveys (DRS). An adaptive Metropolis algorithm was used to estimate the risks of drug resistance amplification among unsuccessfully treated individuals.
resultsThe estimated proportion of INH-R amplification among failing treatments was 0.84 (95% CI 0.79-0.89) for The Philippines and 0.77 (95% CI 0.71-0.84) for Viet Nam. The proportion of RIF-R amplification among failing treatments was 0.05 (95% CI 0.04-0.07) for The Philippines and 0.011 (95% CI 0.010-0.012) for Viet Nam.
conclusionThe risk of resistance amplification due to treatment failure for INH was dramatically higher than RIF. We observed RIF-R strains were more likely to be transmitted than acquired through amplification, while both mechanisms of acquisition were important contributors in the case of INH-R. These findings highlight the complexity of drug resistance dynamics in high-incidence settings, and emphasize the importance of prioritizing testing algorithms which allow for early detection of INH-R.
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