 I'm going to present to you a study on examining association of standard threshold shifts for occupational hearing loss among minors at a large-scale platinum mine in South Africa. My name is Diopoulon Lagann and the study that I'm presenting to you is out of my PhD and comes from some of the thoughts around the missing links in our understanding on the prevention of occupational nursing just hearing loss. The global burden of occupational nursing just hearing loss on the adult population is worrisome and as we have seen in, as we have seen the numbers on the latest world hearing report, this should be of concern. In South Africa, error prevalence rate of greater than 30%, especially in the mining industry, as hearing conservation practitioners, we are similarly worried. Previous studies have indicated that there are multiple risk factors associated with occupational nursing just hearing loss, and this include sociodemographic factors, genetic predisposition, history of recreational exposure, noise exposure, platinum mine dust, chemicals, and specific to the South African context, medical conditions such as HIV and TB. From these findings, we should be able to diversify our research methods in order to identify risk factors associated with occupational nursing just hearing loss, and we are hoping that this will bring up solutions for the prevention of occupational nursing just hearing loss. Also from previous studies, we know that there are challenges around audiometry surveillance programs implemented by the industry with insufficient hearing conservation programs, poor data quality of which in 10 affect how hearing function is and can be tracked efficiently. The use of reliance on subjective audiometry measures also works against hearing conservation program outcomes. Having said that, how do we predict or how can we predict any signs of occupational nursing just hearing loss and this to the methods for our study. The objective here was to examine the association of standard threshold shifts with exposure to noise and platinum mine dust for miners employed at this particular mine, and we looked at the period from 2014 to 2018 in order to identify early signs of hearing deterioration, which is associated with occupational nursing just hearing loss. This was an analysis of individual miners hearing screening, as well as occupational hygiene data, which was collected between 2014 and 2018. And so looking at how we proceeded here, we collected all those electronic records, then we cleaned data, excluding all incomplete records and leaving only the records that we could work with. Then we wanted to be able to describe miners age sex percentage loss of hearing dust and noise exposure data in percentages and to look at the median and range in order to describe the continuous standard threshold shifts and percentage loss of hearing in order to look at the hearing deterioration. And again here, looking at the differences between males and females. Then finally, looking at how to estimate the association, we developed the linear regression, the linear mixed effects regression model. And here we wanted to see the deterioration and what are the risk factors and how to risk rank those risk factors. And we looked at the statistical significance with the p-value that was less than 0.05 and a goodness of fit was tested on the final model. Looking at the results, I'll just provide the highlights of our findings. Although occupational noise exposure levels were still excessive, there is a double effect brought by the levels of platinum minedust, which may increase risk of occupational hearing loss for this miners. And the second point, male miners seem to have been more at risk of occupational noise induced hearing loss, but their hearing deterioration compared to their female counterparts was similar. Therefore, similar interventions for prevention should be applied for both. We were able to, from this results to benchmark the miners hearing function and to show a level of hearing deterioration using longitudinal data. And here, the median level of deterioration was sitting at 1.7 dB from 2014 to 2018. It may seem minute, however, this could have been due to the exposure variables that we used. And so we do acknowledge that there was limited data that we used and that gave rise to the results that we're presenting to you. And so looking at the different deterioration levels, we can see that although male miners would start first, their level of deterioration was almost similar to their female counterparts. And in South Africa, this is due to other factors of employment with the male counterparts being employed way before their female counterparts. And I'm sure this is not unique to South Africa. And so looking at our conclusions and our conclusion and recommendations, we can safely say that the exposure variables that we used here, age, sex, years of exposure to noise, and noise exposure levels, combined effects and their strength of association can be used to predict early hearing deterioration for this group of miners. And again, data that we used here was limited. The scope could have been broader by including medical surveillance records, which was our limitation. And so the recommendations for this particular mine is to shift their focus from diagnosing for compensation, but rather to look at their records and say, how can we prevent occupational noise and just hearing loss? How can we prevent occupational hearing loss? There is a need for inclusive data. In this case, hearing conservation program data should include medical conditions, chronic medical conditions such as HIV, cancer, TB are important to be included in the model for us to be able to track hearing deterioration accurately. And to improve data quality in order to accurately predict hearing deterioration. And in this case, this was observed with the amount of data cleaning that we did and how much we were able to, and how much we excluded in order for us to come up with the results that we've just presented. This are some of the references that we used in our study, and I thank you for your time. I hope to get more questions at the end of this presentation. Thank you very much.