Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study
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...
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doaj-3ea3c4975e4a4cddb693c6aeb550a6f62020-11-25T00:26:10ZengBMCBMC Medicine1741-70152018-04-0116111210.1186/s12916-018-1037-3Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based studyStewart T. Chang0Violet N. Chihota1Katherine L. Fielding2Alison D. Grant3Rein M. Houben4Richard G. White5Gavin J. Churchyard6Philip A. Eckhoff7Bradley G. Wagner8Institute for Disease ModelingAurum InstituteSchool of Public Health, Faculty of Health Sciences, University of WitwatersrandSchool of Public Health, Faculty of Health Sciences, University of WitwatersrandTB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical MedicineTB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical MedicineAurum InstituteInstitute for Disease ModelingInstitute for Disease ModelingAbstract 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 ~ 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.http://link.springer.com/article/10.1186/s12916-018-1037-3TuberculosisHIVMiningSouth AfricaRisk groupsHotspots |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Stewart T. Chang Violet N. Chihota Katherine L. Fielding Alison D. Grant Rein M. Houben Richard G. White Gavin J. Churchyard Philip A. Eckhoff Bradley G. Wagner |
spellingShingle |
Stewart T. Chang Violet N. Chihota Katherine L. Fielding Alison D. Grant Rein M. Houben Richard G. White Gavin J. Churchyard Philip A. Eckhoff Bradley G. Wagner Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study BMC Medicine Tuberculosis HIV Mining South Africa Risk groups Hotspots |
author_facet |
Stewart T. Chang Violet N. Chihota Katherine L. Fielding Alison D. Grant Rein M. Houben Richard G. White Gavin J. Churchyard Philip A. Eckhoff Bradley G. Wagner |
author_sort |
Stewart T. Chang |
title |
Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study |
title_short |
Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study |
title_full |
Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study |
title_fullStr |
Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study |
title_full_unstemmed |
Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study |
title_sort |
small contribution of gold mines to the ongoing tuberculosis epidemic in south africa: a modeling-based study |
publisher |
BMC |
series |
BMC Medicine |
issn |
1741-7015 |
publishDate |
2018-04-01 |
description |
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 ~ 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. |
topic |
Tuberculosis HIV Mining South Africa Risk groups Hotspots |
url |
http://link.springer.com/article/10.1186/s12916-018-1037-3 |
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