Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis

Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be mo...
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Autor*in:	Vesga, Juan F. [verfasserIn] Lienhardt, Christian Nsengiyumva, Placide Campbell, Jonathon R. Oxlade, Olivia den Boon, Saskia Falzon, Dennis Schwartzman, Kevin Churchyard, Gavin Arinaminpathy, Nimalan

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Tuberculosis Preventive therapy Mathematical modelling

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: BMC medicine - London : BioMed Central, 2003, 20(2022), 1 vom: 18. Mai
Übergeordnetes Werk:	volume:20 ; year:2022 ; number:1 ; day:18 ; month:05

Links:	Volltext

DOI / URN:	10.1186/s12916-022-02378-1

Katalog-ID:	SPR050718371

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245	1	0	\|a Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
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520			\|a Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. Methods Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. Results Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. Conclusions As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB.
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700	1		\|a den Boon, Saskia \|4 aut
700	1		\|a Falzon, Dennis \|4 aut
700	1		\|a Schwartzman, Kevin \|4 aut
700	1		\|a Churchyard, Gavin \|4 aut
700	1		\|a Arinaminpathy, Nimalan \|4 aut
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allfields	10.1186/s12916-022-02378-1 doi (DE-627)SPR050718371 (SPR)s12916-022-02378-1-e DE-627 ger DE-627 rakwb eng Vesga, Juan F. verfasserin (orcid)0000-0003-1103-9587 aut Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. Methods Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. Results Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. Conclusions As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. Tuberculosis (dpeaa)DE-He213 Preventive therapy (dpeaa)DE-He213 Mathematical modelling (dpeaa)DE-He213 Lienhardt, Christian aut Nsengiyumva, Placide aut Campbell, Jonathon R. aut Oxlade, Olivia aut den Boon, Saskia aut Falzon, Dennis aut Schwartzman, Kevin aut Churchyard, Gavin aut Arinaminpathy, Nimalan aut Enthalten in BMC medicine London : BioMed Central, 2003 20(2022), 1 vom: 18. Mai (DE-627)377271225 (DE-600)2131669-7 1741-7015 nnns volume:20 year:2022 number:1 day:18 month:05 https://dx.doi.org/10.1186/s12916-022-02378-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2022 1 18 05
spelling	10.1186/s12916-022-02378-1 doi (DE-627)SPR050718371 (SPR)s12916-022-02378-1-e DE-627 ger DE-627 rakwb eng Vesga, Juan F. verfasserin (orcid)0000-0003-1103-9587 aut Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. Methods Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. Results Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. Conclusions As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. Tuberculosis (dpeaa)DE-He213 Preventive therapy (dpeaa)DE-He213 Mathematical modelling (dpeaa)DE-He213 Lienhardt, Christian aut Nsengiyumva, Placide aut Campbell, Jonathon R. aut Oxlade, Olivia aut den Boon, Saskia aut Falzon, Dennis aut Schwartzman, Kevin aut Churchyard, Gavin aut Arinaminpathy, Nimalan aut Enthalten in BMC medicine London : BioMed Central, 2003 20(2022), 1 vom: 18. Mai (DE-627)377271225 (DE-600)2131669-7 1741-7015 nnns volume:20 year:2022 number:1 day:18 month:05 https://dx.doi.org/10.1186/s12916-022-02378-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2022 1 18 05
allfields_unstemmed	10.1186/s12916-022-02378-1 doi (DE-627)SPR050718371 (SPR)s12916-022-02378-1-e DE-627 ger DE-627 rakwb eng Vesga, Juan F. verfasserin (orcid)0000-0003-1103-9587 aut Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. Methods Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. Results Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. Conclusions As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. Tuberculosis (dpeaa)DE-He213 Preventive therapy (dpeaa)DE-He213 Mathematical modelling (dpeaa)DE-He213 Lienhardt, Christian aut Nsengiyumva, Placide aut Campbell, Jonathon R. aut Oxlade, Olivia aut den Boon, Saskia aut Falzon, Dennis aut Schwartzman, Kevin aut Churchyard, Gavin aut Arinaminpathy, Nimalan aut Enthalten in BMC medicine London : BioMed Central, 2003 20(2022), 1 vom: 18. Mai (DE-627)377271225 (DE-600)2131669-7 1741-7015 nnns volume:20 year:2022 number:1 day:18 month:05 https://dx.doi.org/10.1186/s12916-022-02378-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2022 1 18 05
allfieldsGer	10.1186/s12916-022-02378-1 doi (DE-627)SPR050718371 (SPR)s12916-022-02378-1-e DE-627 ger DE-627 rakwb eng Vesga, Juan F. verfasserin (orcid)0000-0003-1103-9587 aut Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. Methods Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. Results Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. Conclusions As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. Tuberculosis (dpeaa)DE-He213 Preventive therapy (dpeaa)DE-He213 Mathematical modelling (dpeaa)DE-He213 Lienhardt, Christian aut Nsengiyumva, Placide aut Campbell, Jonathon R. aut Oxlade, Olivia aut den Boon, Saskia aut Falzon, Dennis aut Schwartzman, Kevin aut Churchyard, Gavin aut Arinaminpathy, Nimalan aut Enthalten in BMC medicine London : BioMed Central, 2003 20(2022), 1 vom: 18. Mai (DE-627)377271225 (DE-600)2131669-7 1741-7015 nnns volume:20 year:2022 number:1 day:18 month:05 https://dx.doi.org/10.1186/s12916-022-02378-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2022 1 18 05
allfieldsSound	10.1186/s12916-022-02378-1 doi (DE-627)SPR050718371 (SPR)s12916-022-02378-1-e DE-627 ger DE-627 rakwb eng Vesga, Juan F. verfasserin (orcid)0000-0003-1103-9587 aut Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. Methods Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. Results Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. Conclusions As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. Tuberculosis (dpeaa)DE-He213 Preventive therapy (dpeaa)DE-He213 Mathematical modelling (dpeaa)DE-He213 Lienhardt, Christian aut Nsengiyumva, Placide aut Campbell, Jonathon R. aut Oxlade, Olivia aut den Boon, Saskia aut Falzon, Dennis aut Schwartzman, Kevin aut Churchyard, Gavin aut Arinaminpathy, Nimalan aut Enthalten in BMC medicine London : BioMed Central, 2003 20(2022), 1 vom: 18. Mai (DE-627)377271225 (DE-600)2131669-7 1741-7015 nnns volume:20 year:2022 number:1 day:18 month:05 https://dx.doi.org/10.1186/s12916-022-02378-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2022 1 18 05
language	English
source	Enthalten in BMC medicine 20(2022), 1 vom: 18. Mai volume:20 year:2022 number:1 day:18 month:05
sourceStr	Enthalten in BMC medicine 20(2022), 1 vom: 18. Mai volume:20 year:2022 number:1 day:18 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Tuberculosis Preventive therapy Mathematical modelling
isfreeaccess_bool	true
container_title	BMC medicine
authorswithroles_txt_mv	Vesga, Juan F. @@aut@@ Lienhardt, Christian @@aut@@ Nsengiyumva, Placide @@aut@@ Campbell, Jonathon R. @@aut@@ Oxlade, Olivia @@aut@@ den Boon, Saskia @@aut@@ Falzon, Dennis @@aut@@ Schwartzman, Kevin @@aut@@ Churchyard, Gavin @@aut@@ Arinaminpathy, Nimalan @@aut@@
publishDateDaySort_date	2022-05-18T00:00:00Z
hierarchy_top_id	377271225
id	SPR050718371
language_de	englisch
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author	Vesga, Juan F.
spellingShingle	Vesga, Juan F. misc Tuberculosis misc Preventive therapy misc Mathematical modelling Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
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topic_title	Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis Tuberculosis (dpeaa)DE-He213 Preventive therapy (dpeaa)DE-He213 Mathematical modelling (dpeaa)DE-He213
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title	Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
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title_full	Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
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author_browse	Vesga, Juan F. Lienhardt, Christian Nsengiyumva, Placide Campbell, Jonathon R. Oxlade, Olivia den Boon, Saskia Falzon, Dennis Schwartzman, Kevin Churchyard, Gavin Arinaminpathy, Nimalan
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title_sort	prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
title_auth	Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
abstract	Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. Methods Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. Results Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. Conclusions As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. © The Author(s) 2022
abstractGer	Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. Methods Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. Results Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. Conclusions As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. © The Author(s) 2022
abstract_unstemmed	Background Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. Methods Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. Results Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. Conclusions As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB. © The Author(s) 2022
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title_short	Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis
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Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis

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Zugang & Verfügbarkeit

Vorhandene Bände

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