Estimating miners at risk for occupational noise-induced hearing loss: A review of data from a South African platinum mine

BACKGROUND: Occupational noise-induced hearing loss (ONIHL) is a complex, but preventable, health problem for South African miners. Meticulously collected data should be made use of to design interventions to address this health issue. OBJECTIVES: A single mine’s electronic data were reviewed in a secondary data review to determine, from the records, factors that hearing conservation practitioners deemed useful for identifying ‘at risk’ miners and to establish factors that would pave the way for the integration of the 2014 hearing conservation programme (HCP) milestones into the mine’s current proactive data management system (PDMS). The objectives of this article were to establish how miners with published risk factors associated with ONIHL were managed by the mine’s hearing conservation practitioners as part of the HCP; to determine if the mine’s hearing conservation practitioners could estimate miners’ risk of ONIHL using baseline percentage loss of hearing (PLH) as a hearing conservation measure; and to estimate the contribution of noise exposure to ONIHL risk. METHOD: In a secondary data review design, records in a platinum mine’s two electronic data sets were reviewed: the first contained diagnostic audiometry records (N = 1938) and the second comprised a subset of miners diagnosed with ONIHL (n = 73). Data were available for the period 2014–2017 and included demographic, occupational, audiometry and ONIHL diagnosis data. Miners’ risk factors associated with ONIHL were identified using the functional risk management structure. A logistic regression model was used for the baseline PLH margins of 0% – 40% (in 5% increments) to estimate the adjusted predictions for miners at risk of developing ONIHL. The contribution of noise exposure as a risk for ONIHL was estimated using a two-way sample proportion test. RESULTS: The mean age of the miners (all male candidates) was 47 ± 8.5 years; more than 80% had worked for longer than 10 years. Valid baseline audiometry records were available for only 34% (n = 669) of the miners. Miners with a 0% baseline PLH had a 20% predicted risk of ONIHL, and a 45% predicted risk if they had a 40% baseline PLH – these employees were referred. The noise exposure risk rankings revealed that 64.9% (n = 1250) of the miners were exposed to 91 dBA – 105 dBA noise exposure levels and that 59 (80.8%) diagnosed with ONIHL were exposed to noise levels of up to 104 dBA. CONCLUSION: These findings indicate significant gaps in the mine’s PDMS, requiring attention. Nonetheless, the mine’s current data capturing may be used to identify miners at risk of developing ONIHL. The PLH referral cut-off point (≥2.5%) used by the mine’s hearing conservation practitioners, when used in conjunction with baseline PLH shifts, was the major factor in early identification of ONIHL in miners exposed to ≥85 dBA noise. An inclusive integrative data management programme that includes the medical surveillance data set of the miners’ noise exposure levels, occupations, ages and medical treatments for tuberculosis and human immunodeficiency syndrome is recommended, as these are important risk indicators for developing ONIHL, particularly within the South African context.