Epidemiological Characteristics and Spatiotemporal Analysis of Occupational Noise–Induced Deafness From 2006 to 2022 in Guangdong, China: Surveillance Study

Abstract

Abstract BackgroundOccupational noise–induced deafness (ONID) has replaced occupational poisoning as the second most common occupational disease in China since 2015. However, there is a limited number of articles on epidemiological characteristics of legally diagnosed ONID. ObjectiveWe conducted a comprehensive analysis of the epidemiological and spatiotemporal characteristics of ONID in Guangdong Province from 2006 to 2022, with the aim of providing a scientific foundation for policy formulation and health resource allocation. MethodsSurveillance data of ONID cases in Guangdong Province from 2006 to 2022 were obtained from the “Occupational Diseases and Health Hazard Factors Monitoring Information System.” Joinpoint regression analysis was applied to assess the long-term trends in cases of ONID from 2006 to 2022. Global spatial autocorrelation analysis was performed to measure the overall degree of similarity of the attribute values of spatially adjacent or neighboring regional units. The local indicators of spatial autocorrelation (LISA) plots were then used to identify the local clusters of ONID in Guangdong. ResultsThere were 3761 ONID cases in Guangdong Province from 2006 to 2022, showing a significantly increased trend in cases across the entire study period (average annual percentage change 21.9, 95% CI 18.7-35.1). The Moran’s I values for the period of 2006 to 2022 ranged from 0.202 to 0.649 (all P ConclusionsSignificant spatiotemporal patterns of ONID in Guangdong Province from 2006 to 2022 were identified, characterized by a dramatic increase followed by stabilization in case numbers. ONID predominantly occur in manufacturing industries, domestically funded enterprises, among males, individuals aged 40‐49 years, and those with 5+ years of occupational noise exposure. Spatial analysis demonstrated significant clustering in the Pearl River Delta region, with consistent positive spatial autocorrelation across years. These results could help prioritize the allocation of resources for targeted prevention and control measures for ONID.