Occupational noise induced hearing loss in South African miners: risk assessment explored at a platinum mine in South Africa - Ntlhakana, Liepollo (My University, 2021)
Background: Occupational noise-induced hearing loss (ONIHL) is one of the most common occupational health diseases affecting miners in South Africa. There are challenges around the high prevalence of ONIHL in South African miners that have been linked with excessive noise exposure levels, and ineffective HCPs, with poor quality records that impede accurate monitoring of miners at risk of developing ONIHL. The main aim of this study is to explore risk assessment practices for ONIHL at a large-scale platinum mine in Limpopo province, South Africa, so to propose an early identification predictive model for ONIHL. Objectives: The objectives of this research were: 1. To describe the audiometry surveillance system (including record keeping) in a large South African mine over a five-year period (2014-2018) 2. To identify factors that impede the early identification of ONIHL, from mine records captured within an information management system, from 2014-2018 3. To determine if the mine used a proactive data management system (PDMS) to identify miners at risk of ONIHL 4. To establish how the mine manages miners presenting with risk factors associated with ONIHL, within the HCP 5. To propose an early identification predictive model for ONIHL in the mining industry Methods: This was a respective cohort study. Data were accessed from one platinum mine in Limpopo province, South Africa. The individual miners’ audiometry medical surveillance (health and safety) and occupational hygiene records (N=305) for the period 2014 to 2018 were analysed. Thereafter, the mine’s two datasets, which contained miners’ diagnostic audiometry records (N = 1 938) and a subset of records of miners diagnosed with ONIHL (n = 73) were analysed. STATA was used for data analysis, but the data were analysed differently for each study objective. For objectives 1 and 2, data were analysed using descriptive statistics. For objective 3, miners’ risk factors associated with ONIHL were identified and described using the functional risk management structure, thereafter, a logistic regression model was used with the baseline percentage loss of hearing (PLH) margins of 0% - 40% (in 5% increments) to estimate the adjusted predictions for miners at risk of developing ONIHL, and the contribution of noise exposure as a risk for ONIHL was estimated using a two-way sample proportion test. For objective 4, ethical principles prescribed by the Health Professions Council of South Africa, the Protection of Personal Information Act, and the National Health Act (Act No.61 of 2003), that guide data access for medical research were applied to the mine’s audiometry medical surveillance data to identify ethical challenges related to data access. For objective 5, miners’ demographic and occupational exposures (noise and platinum mine dust) were analysed to examine their association to standard threshold shift (STS) and miners’ age, sex, PLH, and dust and noise data were used to predict STS, using a linear mixed effects regression model. Results: The results are detailed in the papers published and submitted for publications. The abstract provides a summary of findings drawn from the papers. In paper 1, most of the miners were male (89.6%), and more than 50% were younger than 41 years. There was inaccurate and insufficient recording of risk factors for hearing loss in the medical surveillance records. Some miners were exposed to dangerously high noise levels (as high as 104 dBA). Miners as young as 21 years of age were diagnosed with ONIHL. In Paper 2, we identified and described risk factors associated with ONIHL and calculated risks for ONIHL. A linear regression model estimated miners’ risks of ONIHL at baseline. 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. Seventy-three miners had a confirmed diagnosis of ONIHL. Paper 3 showed changes in the miners’ standard threshold shift (STS) ranging from 8.3 dBHL at baseline (2014/2015) to 10 dBHL in 2016, with no changes thereafter. Less than 10% of the miners were at risk of ONIHL (>26 dBHL; STS). A linear mixed effects regression model estimated that male miners’ STS were more associated with ONIHL than their female counterparts. When combined, age, PLH, noise exposure and years of exposure were associated with STS at < 10%. There was no statistically significant association between PMD and STS in hearing. In the fourth paper, a lack of clearly defined medical ethics policies around data access for research, and Protection of Personal Information Act (POPIA) regulations and their application on miners’ audiometry, occupational hygiene, and medical data restricted data access for the latter. In this paper, different data access practices for different datasets, with stricter restrictions, were applied to miners’ medical surveillance records (medical conditions and treatments). Conclusion: Systems need to be set in place to ensure integrated accessing, analysing, and reporting miner-specific demographic, medical, occupational, and non-occupational exposure information. Annual surveillance records should be complete, accurate and should include any ear-related conditions, such as impacted wax and middle ear infections. The machine learning systems (MLSs) used for HCP risk assessment are partially effective in reducing risk and preventing ONIHL. To improve efficiency, all risk factors associated with ONIHL should be included in the mine’s electronic data recording system. The use of percentage loss of hearing (PLH) to track miners’ hearing may be sufficient for ONIHL compensation, but it is not adequate for identifying early signs of any type of occupational hearing loss, including ONIHL. Thus, the use of STS will ensure tracking of miners’ hearing and the identification of early signs of occupational hearing loss. Age, sex, years of exposure to noise, and noise exposure levels combined effects and strength of association can be used to predict STS for this group of miners. Our findings may be used to measure the efficiency of the mine’s HCP, and its efforts in preventing ONIHL among miners
A thesis submitted to the Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy (Public Health), 2021