Development and empirical validation of a multi-criteria decision-making model for the assessment of noise-induced hearing loss (NIHL) in high-exposure industrial settings

Noise-Induced Hearing Loss (NIHL) is a common occupational health concern, especially in industries with high levels of noise exposure. This study aims to develop a comprehensive risk assessment model for NIHL by integrating qualitative expert opinions with quantitative data. The study employed a three-phase Delphi technique to reach a consensus on the key factors and sub-factors affecting NIHL risk. The Delphi process involved 26 subject matters experts (SMEs). In the second phase, the Delphi results were integrated into the Fuzzy Analytic Hierarchy Process (FAHP) to prioritize the factors based on expert consensus. Delphi used 80% agreement and CV < 20%; FAHP involved triangular fuzzy numbers, Chang’s extent analysis, and CR < 0.1. Finally, the model was validated at a metal parts manufacturing facility with 500 workers, where it was applied to assess real-world noise exposure and hearing protection measures. Validation involved comparing the model’s predictions with audiometric data. The Delphi study revealed that Noise Exposure Characteristics and Hearing Protection Measures are the most significant factors in preventing NIHL, with weights of 0.284 and 0.243, respectively. Individual Susceptibility and Organizational and Behavioral Factors followed, with weights of 0.217 and 0.165. Regulatory and Environmental Context was the least influential factor, with a weight of 0.091. The FAHP model confirmed these findings, identifying Exposure Duration and Work Rotation Schedules as the most critical sub-factors. The model was validated using real-world data, demonstrating strong predictive accuracy and a significant correlation between the model’s risk scores and actual hearing loss outcomes (p < 0.05). The findings underscore the importance of both environmental controls and individual factors in mitigating NIHL risk. This model offers a comprehensive tool for organizations to enhance hearing conservation strategies. Future research could focus on further refining the model and adapting it to other industries with diverse noise exposure profiles. Future iterations could integrate vibration and multi-stressor interactions to broaden applicability.