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Title: Understanding tuberculosis dynamics in the United Kingdom using mathematical modelling
Author: Keen, Adrienne Rae
Awarding Body: London School of Hygiene & Tropical Medicine
Current Institution: London School of Hygiene and Tropical Medicine (University of London)
Date of Award: 2013
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In the UK, tuberculosis incidence has risen from the mid-1980s until recently, with the proportion of cases in foreign-born patients increasing to more than 70% of total cases today. Because several features of tuberculosis epidemiology in the UK are unclear, a simulation model was applied to better understand the epidemiology of tuberculosis in the UK. The model was first used to estimate age- and birthplace-dependent risks of disease progression for those infected via the different disease progression pathways-recent infection, reinfection, and latent infection-by fitting the model to incident cases in England and Wales from 1999 - 2009. Results showed that UK-born risks were lower than previous estimates, though foreign-born risks were an estimated 2.5 times higher than UK-born risks. Estimates for the proportion of disease due to recent transmission were higher than previous estimates, at around 46%. Simulations also identified plausible assumptions for the contact rate and the infection status of migrants upon entry to the UK. Results informed a model fitted to Variable Number Tandem Repeat (VNTR) genotyping data from cases in the West Midlands from 2007 - 2011 , which was used to estimate the proportion of disease due to recent transmission in the UK and compare estimates to those based on genotyping data. Results showed that a n estimated 45 - 63% of cases in the West Midlands were due to recenttransmission i n the UK, which was underestimated by genotyping data-derived estimates of 35%. Results also identified plausible mutation rates for VNTR profiles and plausible strain type distributions for UK-born and foreign-born individuals. This work suggests there is a large proportion of cases due to recent transmission in the UK, which is underestimated by genotyping data. The study also provides current disease risk estimates and shows a need for better data on migrants to the UK. This work may help focus prevention efforts.
Supervisor: Vynnycky, E. ; White, R. G. Sponsor: Health Protection Agency (HPA) Modelling & Economics Unit
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral