Imperial College London


Faculty of MedicineSchool of Public Health

Professor of Global Health



+44 (0)20 7594 1150timothy.hallett




Norfolk PlaceSt Mary's Campus






BibTex format

author = {Vesga, JF and Hallett, TB and Reid, MJA and Sachdeva, KS and Rao, R and Khaparde, S and Dave, P and Rade, K and Kamene, M and Omesa, E and Masini, E and Omale, N and Onyango, E and Owiti, P and Karanja, M and Kiplimo, R and Alexandru, S and Vilc, V and Crudu, V and Bivol, S and Celan, C and Arinaminpathy, N},
doi = {10.1016/s2214-109x(19)30037-3},
journal = {The Lancet Global Health},
pages = {e585--e595},
title = {Assessing tuberculosis control priorities in high-burden settings: a modelling approach},
url = {},
volume = {7},
year = {2019}

RIS format (EndNote, RefMan)

AB - Background:In the context of WHO's End TB strategy, there is a need to focus future control efforts on those interventions and innovations that would be most effective in accelerating declines in tuberculosis burden. Using a modelling approach to link the tuberculosis care cascade to transmission, we aimed to identify which improvements in the cascade would yield the greatest effect on incidence and mortality.Methods:We engaged with national tuberculosis programmes in three country settings (India, Kenya, and Moldova) as illustrative examples of settings with a large private sector (India), a high HIV burden (Kenya), and a high burden of multidrug resistance (Moldova). We collated WHO country burden estimates, routine surveillance data, and tuberculosis prevalence surveys from 2011 (for India) and 2016 (for Kenya). Linking the tuberculosis care cascade to tuberculosis transmission using a mathematical model with Bayesian melding in each setting, we examined which cascade shortfalls would have the greatest effect on incidence and mortality, and how the cascade could be used to monitor future control efforts.Findings:Modelling suggests that combined measures to strengthen the care cascade could reduce cumulative tuberculosis incidence by 38% (95% Bayesian credible intervals 27–43) in India, 31% (25–41) in Kenya, and 27% (17–41) in Moldova between 2018 and 2035. For both incidence and mortality, modelling suggests that the most important cascade losses are the proportion of patients visiting the private health-care sector in India, missed diagnosis in health-care settings in Kenya, and drug sensitivity testing in Moldova. In all settings, the most influential delay is the interval before a patient's first presentation for care. In future interventions, the proportion of individuals with tuberculosis who are on high-quality treatment could offer a more robust monitoring tool than routine notifications of tuberculosis.Interpretation:Linked to transmissi
AU - Vesga,JF
AU - Hallett,TB
AU - Reid,MJA
AU - Sachdeva,KS
AU - Rao,R
AU - Khaparde,S
AU - Dave,P
AU - Rade,K
AU - Kamene,M
AU - Omesa,E
AU - Masini,E
AU - Omale,N
AU - Onyango,E
AU - Owiti,P
AU - Karanja,M
AU - Kiplimo,R
AU - Alexandru,S
AU - Vilc,V
AU - Crudu,V
AU - Bivol,S
AU - Celan,C
AU - Arinaminpathy,N
DO - 10.1016/s2214-109x(19)30037-3
EP - 595
PY - 2019///
SN - 2214-109X
SP - 585
TI - Assessing tuberculosis control priorities in high-burden settings: a modelling approach
T2 - The Lancet Global Health
UR -
UR -
VL - 7
ER -