Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study
Authored by Philip A Eckhoff, Richard G White, Bradley G Wagner, Stewart T Chang, Violet N Chihota, Katherine L Fielding, Alison D Grant, Rein M Houben, Gavin J Churchyard
Date Published: 2018
DOI: 10.1186/s12916-018-1037-3
Sponsors:
Bill and Melinda Gates Foundation
Platforms:
C++
EMOD
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
https://github.com/SCTX/mining_contribution
Abstract
Background: Gold mines represent a potential hotspot for Mycobacterium
tuberculosis (Mtb) transmission and may be exacerbating the tuberculosis
(TB) epidemic in South Africa. However, the presence of multiple factors
complicates estimation of the mining contribution to the TB burden in
South Africa.
Methods: We developed two models of TB in South Africa, a static risk
model and an individual-based model that accounts for longer-term
trends. Both models account for four populations - mine workers,
peri-mining residents, labor-sending residents, and other residents of
South Africa - including the size and prevalence of latent TB infection,
active TB, and HIV of each population and mixing between populations. We
calibrated to mine-and country-level data and used the static model to
estimate force of infection (FOI) and new infections attributable to
local residents in each community compared to other residents. Using the
individual-based model, we simulated a counterfactual scenario to
estimate the fraction of overall TB incidence in South Africa
attributable to recent transmission in mines.
Results: We estimated that the majority of FOI in each community is
attributable to local residents: 93.9\% (95\% confidence interval
92.4-95.1\%), 91.5\% (91.4-91.5\%), and 94.7\% (94.7-94.7\%) in gold
mining, peri-mining, and labor-sending communities, respectively.
Assuming a higher rate of Mtb transmission in mines, 4.1\% (2.6-5.8\%),
5.0\% (4.5-5.5\%), and 9.0\% (8.8-9.1\%) of new infections in South
Africa are attributable to gold mine workers, peri-mining residents, and
labor-sending residents, respectively. Therefore, mine workers with TB
disease, who constitute similar to 2.5\% of the prevalent TB cases in
South Africa, contribute 1.62 (1.04-2.30) times as many new infections
as TB cases in South Africa on average. By modeling TB on a longer time
scale, we estimate 63.0\% (58.5-67.7\%) of incident TB disease in gold
mining communities to be attributable to recent transmission, of which
92.5\% (92.1-92.9\%) is attributable to local transmission.
Conclusions: Gold mine workers are estimated to contribute a
disproportionately large number of Mtb infections in South Africa on a
per-capita basis. However, mine workers contribute only a small fraction
of overall Mtb infections in South Africa. Our results suggest that
curtailing transmission in mines may have limited impact at the country
level, despite potentially significant impact at the mining level.
Tags
HIV
tuberculosis
Community
South Africa
transmission
hotspots
Mycobacterium-tuberculosis
Mathematical-models
Hiv-infection
Molecular epidemiology
Mining
Risk groups
Global
health
Isoniazid preventive therapy
Antiretroviral
therapy
High prevalence
Cape-town