Epidemiological Characteristics and Diagnostic Outcomes of Suspected Occupational Noise-Induced Deafness - Guangdong Province, China, 2014-2023.

INTRODUCTION

Suspected occupational noise-induced deafness (ONID) represents the most prevalent suspected occupational disease in Guangdong Province and is among the most frequently reported nationwide. Given its public health significance, we conducted a systematic investigation of suspected ONID cases in Guangdong from 2014 to 2023, analyzing their epidemiological characteristics and diagnostic outcomes to inform evidence-based policies for ONID prevention and management.

METHODS

Data on suspected ONID cases reported in Guangdong Province from 2014 to 2023 were extracted from the "Occupational Diseases and Health Hazard Factors Monitoring Information System." Cases were analyzed using descriptive epidemiological methods, with joinpoint regression analysis employed to assess long-term trends.

RESULTS

From 2014 to 2023, 16,987 suspected ONID cases were reported in Guangdong Province, comprising 65.22% of all suspected occupational disease cases (26,044). Cases exhibited a significant increasing trend (Average annual percentage change: 11.8%, 95% CI: 2.9%-22.3%, P = 0.013). The Pearl River Delta region accounted for 87.9% of all cases, with manufacturing being the predominant industry (90.1%). Within manufacturing, the metal products industry represented the highest proportion (15.2%). Males constituted 87.7% (14,905/16,987) of cases. Analysis of diagnostic outcomes from 2020 to 2023 revealed an overall diagnostic procedure initiation rate of 45.1%, with a subsequent confirmation rate of 48.9%.

CONCLUSIONS

Guangdong Province demonstrates high occurrence patterns of suspected ONID cases, particularly concentrated in the Pearl River Delta region and manufacturing sectors. The low rates of diagnostic procedure initiation and confirmation highlight the urgent need for enhanced regulatory oversight of diagnostic procedures and the development of expert consensus on suspected ONID identification criteria to improve diagnostic confirmation rates.