Evidence map›Paper›PMID 29642897›Full record

ArticleBMC medicine2018

Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study.

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

Erratum issuedAbstract read
In one paragraph

Article in BMC medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Stewart T ChangInstitute for Disease Modeling, Bellevue, Washington, USA. stchang@idmod.org.ORCID http://orcid.org/0000-0002-7604-9797
Violet N ChihotaAurum Institute, Johannesburg, South Africa.
Katherine L FieldingSchool of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa.
Alison D GrantSchool of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa.
Rein M HoubenTB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, London, UK.
Richard G WhiteTB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, London, UK.
Gavin J ChurchyardAurum Institute, Johannesburg, South Africa.
Philip A EckhoffInstitute for Disease Modeling, Bellevue, Washington, USA.
Bradley G WagnerInstitute for Disease Modeling, Bellevue, Washington, USA.

Funding

Bill and Melinda Gates Foundation OPP1084276Bill and Melinda Gates Foundation OPP1110334Bill and Melinda Gates Foundation OPP1135288Bill and Melinda Gates Foundation OPP1137034Bill and Melinda Gates Foundation OPP1160830Medical Research Council MR/K012126/1Medical Research Council MR/P002404/1UNITAID PO 8477-0-600
6 · The paper itself

Abstract

backgroundGold 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.

methodsWe 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.

resultsWe 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.

conclusionsGold 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.

Indexed as

AdultEpidemicsFemaleGoldHumansIncidenceMaleMiningMycobacterium tuberculosisSouth AfricaTuberculosisGoldGlobal healthHIVHotspotsMiningRisk groupsSouth AfricaTuberculosis

Identifiers

PMID29642897
PMCPMC5896106

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.