Estimating tuberculosis drug resistance amplification rates in high-burden settings
Abstract Background Antimicrobial 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 doe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-01-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-022-07067-1 |
| _version_ | 1818306144755515392 |
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| author | Malancha Karmakar Romain Ragonnet David B. Ascher James M. Trauer Justin T. Denholm |
| author_facet | Malancha Karmakar Romain Ragonnet David B. Ascher James M. Trauer Justin T. Denholm |
| author_sort | Malancha Karmakar |
| collection | DOAJ |
| description | Abstract Background Antimicrobial 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. Methods A 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. Results The 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. Conclusion The 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. |
| first_indexed | 2024-12-13T06:37:49Z |
| format | Article |
| id | doaj.art-fa86f3f72dbd409bb035017eae0caf18 |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-12-13T06:37:49Z |
| publishDate | 2022-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-fa86f3f72dbd409bb035017eae0caf182022-12-21T23:56:29ZengBMCBMC Infectious Diseases1471-23342022-01-0122111210.1186/s12879-022-07067-1Estimating tuberculosis drug resistance amplification rates in high-burden settingsMalancha Karmakar0Romain Ragonnet1David B. Ascher2James M. Trauer3Justin T. Denholm4Computational Biology and Clinical Informatics, Baker Heart and Diabetes InstituteSchool of Public Health and Preventive Medicine, Monash UniversityComputational Biology and Clinical Informatics, Baker Heart and Diabetes InstituteSchool of Public Health and Preventive Medicine, Monash UniversityVictorian Tuberculosis Program and Department of Microbiology and Immunology, Doherty Institute of Infection and Immunity, University of MelbourneAbstract Background Antimicrobial 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. Methods A 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. Results The 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. Conclusion The 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.https://doi.org/10.1186/s12879-022-07067-1Drug resistant tuberculosisEpidemiological modellingFitness costTuberculosis transmission dynamicsResistance amplification |
| spellingShingle | Malancha Karmakar Romain Ragonnet David B. Ascher James M. Trauer Justin T. Denholm Estimating tuberculosis drug resistance amplification rates in high-burden settings BMC Infectious Diseases Drug resistant tuberculosis Epidemiological modelling Fitness cost Tuberculosis transmission dynamics Resistance amplification |
| title | Estimating tuberculosis drug resistance amplification rates in high-burden settings |
| title_full | Estimating tuberculosis drug resistance amplification rates in high-burden settings |
| title_fullStr | Estimating tuberculosis drug resistance amplification rates in high-burden settings |
| title_full_unstemmed | Estimating tuberculosis drug resistance amplification rates in high-burden settings |
| title_short | Estimating tuberculosis drug resistance amplification rates in high-burden settings |
| title_sort | estimating tuberculosis drug resistance amplification rates in high burden settings |
| topic | Drug resistant tuberculosis Epidemiological modelling Fitness cost Tuberculosis transmission dynamics Resistance amplification |
| url | https://doi.org/10.1186/s12879-022-07067-1 |
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