Tuberculosis in Cape Town: An age-structured transmission model
Nello Blaser1, Cindy Zahnd1, Sabine Hermans2
1Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Epidemics
|March 15, 2016
Summary
Tuberculosis (TB) notification rates in Cape Town are driven by the protective effect of initial latent TB infection and faster disease progression in previously treated individuals. These factors explain the observed age patterns in TB cases.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- Tuberculosis (TB) is a leading cause of mortality in South Africa, with distinct age-related notification rate patterns in HIV-negative individuals.
- Understanding the drivers of these age-specific TB patterns is crucial for public health interventions.
Purpose of the Study:
- To investigate the key determinants of age-structured tuberculosis notification rates in Cape Town using an age-structured simulation model.
- To identify factors contributing to TB burden variations across different age groups.
Main Methods:
- Developed an age-structured simulation model of Mycobacterium tuberculosis (Mtb) transmission, incorporating states of TB and HIV progression.
- Utilized TB register data, social mixing patterns, and literature estimates for model parameterization.
- Conducted sensitivity analyses on age-structure-related parameters to identify key drivers.
Main Results:
- The model accurately replicated observed age patterns in HIV-negative TB notification rates in Cape Town.
- Sensitivity analyses revealed that the protective effect of latent TB explains the dip in rates after early adulthood.
- Retreatment TB was identified as the primary driver for increased notification rates from age 30 onwards.
Conclusions:
- The age structure of TB notification rates is significantly influenced by the protective immunity conferred by a first latent infection.
- Faster disease progression in previously treated TB patients is a critical factor in shaping age-specific TB epidemiology.
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