Modeling social, environmental and biological determinants of TB

Murray, M., Oxlade, O. and Lin, H.H., 2011. Modeling social, environmental and biological determinants of tuberculosis. The International Journal of Tuberculosis and Lung Disease, 15(6), pp.S64-S70. [TB0040]

  • Mathematical models have improved understanding of infectious disease dynamics and are useful for comparing control scenarios when interventional studies are not feasible or ethical.
  • HIV has significantly impacted TB incidence rates over the past decades. Among HIV-infected individuals, low CD4 counts and high viral loads increase TB risk, while highly active antiretroviral therapy reduces it.
  • Tobacco-associated illness and premature death create major economic burdens in low- and middle-income countries (LMICs). Higher rates of TB infection, disease, and mortality are observed among smokers.
  • There is a potential increased risk of TB among solid-fuel users.
  • Patients with diabetes mellitus (DM) are more than three times more likely to have TB than controls.
  • TB disease rates are about three times higher, and pulmonary TB is four times more frequent, in heavy drinkers compared to controls.
  • TB incidence increases exponentially as body mass index (BMI) decreases.
  • Lifelong urban residents are 1.5 times more likely to test positive for TB than lifelong rural residents.
  • Migrants from high TB burden countries are at increased risk of TB after arriving in low-burden countries, especially within the first year.
  • Aging populations may have increased TB risk due to cumulative exposure and progression risk. Elderly institutionalization can lead to nosocomial TB, with high rates in settings like nursing homes.
  • TB infection is diagnosed by measuring the host’s immune response via tuberculin skin test (TST) or interferon-gamma release assays. Risk factors for TB can reduce immune response, leading to false-negative tests.
  • Removing a TB determinant prevents both primary and secondary cases, reducing overall disease burden. Failing to account for the indirect effect of risk factor reduction on secondary cases underestimates the attributable risk and potential intervention impact.

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