ArticleEpidemics2016
Tuberculosis in Cape Town: An age-structured transmission model.
Article in Epidemics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 42 citations in OpenAlex.
- Single-cell profiling reveals that dynamic lung immune responses distinguish protection from susceptibility to tuberculosis.PLoS pathogens · 2026Article
- A human YEATS4 variant confers resistance to TST and IGRA conversion despite Mycobacterium tuberculosis exposure.Genome medicine · 2025Article
- Molecular investigations of Mycobacterium tuberculosis genotypes among baseline and follow-up strains circulating in four regions of Eswatini.BMC infectious diseases · 2023Article
- Article
- Use of a Contained Mycobacterium tuberculosis Mouse Infection Model to Predict Active Disease and Containment in Humans.The Journal of infectious diseases · 2022Article
- Transmission, distribution and drug resistance-conferring mutations of extensively drug-resistant tuberculosis in the Western Cape Province, South Africa.Microbial genomics · 2022Article
- Automatic cough classification for tuberculosis screening in a real-world environment.Physiological measurement · 2021Article
- Accessing HIV care may lead to earlier ascertainment of comorbidities in health care clients in Khayelitsha, Cape Town.PLOS global public health · 2021Article
- Enhanced Detection ofFrontiers in veterinary science · 2021Article
- Peripheral Blood Mucosal-Associated Invariant T Cells in Tuberculosis Patients and Healthy Mycobacterium tuberculosis-Exposed Controls.The Journal of infectious diseases · 2020Article
- Contained Mycobacterium tuberculosis infection induces concomitant and heterologous protection.PLoS pathogens · 2020Article
- First-In-Human Trials of GamTBvac, a Recombinant Subunit Tuberculosis Vaccine Candidate: Safety and Immunogenicity Assessment.Vaccines · 2019Article
- Application of provincial data in mathematical modelling to inform sub-national tuberculosis program decision-making in South Africa.PloS one · 2019Article
- Four-Gene Pan-African Blood Signature Predicts Progression to Tuberculosis.American journal of respiratory and critical care medicine · 2018Article
- Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study.BMC medicine · 2018Article
- Epidemiology and outcomes ofBMJ global health · 2018Article
- Tuberculosis among older adults in Zambia: burden and characteristics among a neglected group.BMC public health · 2017Article
- Tuberculosis Infection: Occurrence and Risk Factors in Presumptive Tuberculosis Patients of the Serengeti Ecosystem in Tanzania.The East African health research journal · 2017Article
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Authors and funding
9 authors at 5 institutions in 5 countries.
Funding
Abstract
backgroundTuberculosis (TB) is the leading cause of death in South Africa. The burden of disease varies by age, with peaks in TB notification rates in the HIV-negative population at ages 0-5, 20-24, and 45-49 years. There is little variation between age groups in the rates in the HIV-positive population. The drivers of this age pattern remain unknown.
methodsWe developed an age-structured simulation model of Mycobacterium tuberculosis (Mtb) transmission in Cape Town, South Africa. We considered five states of TB progression: susceptible, infected (latent TB), active TB, treated TB, and treatment default. Latently infected individuals could be re-infected; a previous Mtb infection slowed progression to active disease. We further considered three states of HIV progression: HIV negative, HIV positive, on antiretroviral therapy. To parameterize the model, we analysed treatment outcomes from the Cape Town electronic TB register, social mixing patterns from a Cape Town community and used literature estimates for other parameters. To investigate the main drivers behind the age patterns, we conducted sensitivity analyses on all parameters related to the age structure.
resultsThe model replicated the age patterns in HIV-negative TB notification rates of Cape Town in 2009. Simulated TB notification rate in HIV-negative patients was 1000/100,000 person-years (pyrs) in children aged <5 years and decreased to 51/100,000 in children 5-15 years. The peak in early adulthood occurred at 25-29 years (463/100,000 pyrs). After a subsequent decline, simulated TB notification rates gradually increased from the age of 30 years. Sensitivity analyses showed that the dip after the early adult peak was due to the protective effect of latent TB and that retreatment TB was mainly responsible for the rise in TB notification rates from the age of 30 years.
conclusionThe protective effect of a first latent infection on subsequent infections and the faster progression in previously treated patients are the key determinants of the age-structure of TB notification rates in Cape Town.
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