Productivity Burden of Occupational Noise-Induced Hearing Loss in Australia: A Life Table Modelling Study

Background: Occupational noise-induced hearing loss (ONIHL) is one of the most common yet preventable occupational diseases. The aim of this study was to estimate the economic burden of ONIHL in the Australian working population by quantifying and monetising ONIHL—related loss of Quality Adjusted Life Years (QALY) and Productivity Adjusted Life Years (PALYs). Methods: We simulated the number of moderate-to-severe ONIHL by multiplying the age-specific prevalence of occupational noise exposure by the excess risks of ONIHL. Life table modelling was applied to workers with ONIHL. The QALY and PALY weights attributable to hearing loss were sourced from published data. The 2016 Gross Domestic Product per full-time equivalent worker in Australia was used to estimate the cost of productivity loss due to ONIHL. The cost due to the loss of well-being was quantified using willingness to pay thresholds derived from an Australian longitudinal study. Results: Under current occupational noise exposure levels in Australia, we estimated that over 80,000 male workers and over 31,000 female workers would develop ONIHL over 10 years of exposure. Following this cohort until the age of 65 years, the estimated loss of QALYs and PALYs were 62,218 and 135,561 respectively, with a projected loss of AUD 5.5 billion and AUD 21.3 billion due to well-being and productivity loss, respectively. Reducing noise exposure at work would substantially reduce the economic burden of ONIHL. Conclusion: ONIHL imposes substantial burden on Australian economy. Interventions to reduce occupational noise exposure are warranted.

. Quantifying the risks of hearing loss resulting from noise exposure via simulation.

Background
Hearing loss is determined by measuring the hearing threshold levels (HTL) in decibels over selected audiometric frequencies within the human speech range. These threshold levels are usually tested in the six octaves; 250Hz, 500Hz, 1000Hz, 2000Hz, 4000Hz and 8000Hz [1].
High levels of noise exposure, an eight-hour weighted average exceeding 85 dB, is strongly associated with increased risks of hearing loss [2].
The ISO 7029 (International Organization for Standardization, 2000) and ISO 1999-2013 specifies algorithms/distributions for estimating the hearing threshold levels associated with age (HTLA) and noise-induced hearing threshold shift (NIHTS) based on exposure levels across the audiometric frequencies by age and gender [3]. The ISO 1999-2013 standard allows audiogram patters to be simulated with or without noise exposure.
The World Health Organisation (WHO) recommends the frequency combination of 500Hz, 1000Hz, 2000Hz and 4000Hz [4]. The mean of these four HTLs is used to categorise hearing loss into mild when the pure tone average (PTA) in the better ear is between 26 and 40 decibels (dBHL), moderate as 41-60 dBHL and severe as 61-80 dBHL [4].
We aimed to estimate the prevalence ratio (PR) of Noise-Induced Hearing Loss (NIHL) via a set of simulations transferring the ISO1999-2013 into hearing loss identification by applying the WHO definition of hearing loss.
Methods The distributions/formulas of threshold levels (including HTLA and NIHTS) at the four frequencies (500Hz, 1000Hz, 2000Hz and 4000Hz) were extracted from ISO1999-2013 [1] HTLA = a (Year-18) 2 +k S (1) a: gender and frequency specific parameter; k: Gaussian distribution parameter; S: a distribution parameter by gender and frequency. Note: this algorithm was derived from a highly screened ontologically normal population in accordance with ISO 7029.
NIHTS= [u + v lg (Ɵ)] (LEx,8h) 2 -kd (2) Ɵ: Years of noise exposure; LEx,8h: A-weighted noise exposure over an 8h working day; k: Gaussian distribution parameter; d: frequency specific noise exposure parameter Note: this formula applies to noise exposure of no less than ten years in length.
Combined hearing threshold levels (HTL)= NIHTS-HTLA*NIHTS/120 The formulas were applied to simulate the four frequency HTLs of 1000 individuals in a cohort. One random reading was allocated to an individual aged within the predefined 5-year age bands (assuming uniform distribution within each age band) from the four frequency distributions, respectively, to represent the HTL of one individual. Then the PTA was calculated for each individual. By applying the WHO criteria, we were able to determine the hearing status of this individual (i.e., no hearing loss, mild-severe hearing loss (>25dBHL) and moderate-severe hearing loss (>40dBHL)) [4]. We repeated the process 1000 times to represent 1000 individuals in a cohort. The prevalence of hearing loss was then determined in this cohort.
Occupational noise is usually measured with A-weighted decibel (dBA) over an 8-hour working day. Exposure levels of 85 dBA and 90 dBA were used as regulatory limits in developed and developing countries, respectively [4]. Noise exposure incurs cumulative damage to hearing over a long period. In this study, we apply this algorithm from ISO 1999-2013 to simulate the impact of noise exposure for at least 10 years of exposure [5,6].
Using the above method, we created 20 hypothetical sub-cohorts of the general population aged 20 to 69 years, stratified by gender and 5-year bands (thus ten different age groups), each of which comprised 1000 people. We ran simulations under three different scenarios: 1) no noise exposure (formula 1); 2) noise exposure at the level of 85-89 dBA for 10 years duration (formula 1-3); 3) noise -exposure at the level of 90-100 dBA for 10 years duration (formula 1-3) to estimate the prevalence of hearing loss for each scenario. The age and gender-specific prevalence of hearing loss under each of the three scenarios was summarised.
To represent the uncertainties of the simulated hearing loss prevalence in each sub-cohort and scenario, we repeated each cohort simulation 100 times to obtain 100 estimates of prevalence in each sub-cohort. The medians and the 2.5 and 97.5 percentiles (95% credibility intervals (CIs)) from the 100 simulations were summarised to represent the point estimate and variance of the hearing loss prevalence by age and gender. We then calculated the distribution of prevalence ratio (PR) of hearing loss associated with noise exposure by age and gender subgroups using the formula: The median prevalence was used to derive the point estimates of PRs, whereas the upper and lower limits of 95% CIs of Age-Related Hearing Loss (ARHL)+NIHL were used to derive the 95% CIs of PRs.
Results The prevalence of ARHL by age and gender are summarised in Table S2