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Statistical modelling to predict silicosis risk in deceased Southern African gold miners without medical evaluation
by
Thompson, Mary Lou
, Myers, Jonathan E.
in
Accuracy
/ Age
/ Classification
/ Classification accuracy
/ Compensation
/ Deeds
/ Dust
/ Exposure
/ Gold
/ gold miner
/ Litigation
/ Medical records
/ Miners
/ Occupational exposure
/ Occupational health
/ Prediction models
/ Regression analysis
/ Regression models
/ Risk prediction
/ Silicosis
/ Statistical analysis
/ Statistical model
/ Statistical models
/ Tuberculosis
2022
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Statistical modelling to predict silicosis risk in deceased Southern African gold miners without medical evaluation
by
Thompson, Mary Lou
, Myers, Jonathan E.
in
Accuracy
/ Age
/ Classification
/ Classification accuracy
/ Compensation
/ Deeds
/ Dust
/ Exposure
/ Gold
/ gold miner
/ Litigation
/ Medical records
/ Miners
/ Occupational exposure
/ Occupational health
/ Prediction models
/ Regression analysis
/ Regression models
/ Risk prediction
/ Silicosis
/ Statistical analysis
/ Statistical model
/ Statistical models
/ Tuberculosis
2022
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Do you wish to request the book?
Statistical modelling to predict silicosis risk in deceased Southern African gold miners without medical evaluation
by
Thompson, Mary Lou
, Myers, Jonathan E.
in
Accuracy
/ Age
/ Classification
/ Classification accuracy
/ Compensation
/ Deeds
/ Dust
/ Exposure
/ Gold
/ gold miner
/ Litigation
/ Medical records
/ Miners
/ Occupational exposure
/ Occupational health
/ Prediction models
/ Regression analysis
/ Regression models
/ Risk prediction
/ Silicosis
/ Statistical analysis
/ Statistical model
/ Statistical models
/ Tuberculosis
2022
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Statistical modelling to predict silicosis risk in deceased Southern African gold miners without medical evaluation
Journal Article
Statistical modelling to predict silicosis risk in deceased Southern African gold miners without medical evaluation
2022
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Overview
The Qhubeka Trust was established in 2016 in a legal settlement on behalf of former gold miners seeking compensation for silicosis contracted on the South African mines. Settlements resulting from lawsuits on behalf of gold miners aim to provide fair compensation. However, occupational exposure and medical records kept by South African mining companies for their employees have been very limited. Some claimants to the Qhubeka Trust died before medical evaluation was possible, thus potentially disadvantaging their dependants from receiving any compensation. With medical evaluation no longer possible, a statistical approach to this problem was developed. The records for claimants with medical evaluation were used to develop a logistic regression prediction model for the likelihood of silicosis, based on the potential predictors: cumulative exposure to respirable dust, age, years since first exposure, years of life lost prematurely, vital status at 31 December 2019, and a history of tuberculosis diagnosis. The prediction model allowed estimation of the likelihood of silicosis for each miner who had died without medical evaluation and is a novel approach in this setting. In addition, we were able to quantitatively evaluate the trade-offs of different silicosis risk classification thresholds in terms of true and false positives and negatives. A Microsoft Excel database with anonymised available information on all claimants was supplied by the Qhubeka Trust for the purpose of this study only. The data otherwise remain under the confidential control of the Trust. Clause 12.4 of the Qhubeka Trust Deed provides that:
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