Establishment of a precise prevention and control model of occupational noise hazards based on occupational health big data

ObjectiveTo utilize big data analysis of occupational health to detect high-risk enterprises and early occupational health damage of workers in advance, block or delay the development of occupational diseases, and further prevent workers with early occupational health damage from developing to occupational disease patients for achieving precise prevention and control of occupational diseases.

MethodsInformation of occupational hazard declaration, occupational health files of enterprises, commissioned inspection of workplaces, occupational hazards monitoring, occupational disease identifications and claims, occupational health surveillance and reports were collected continually and systematically.We aimed to use noise hazard control as an example to explore the application of the big data in precise prevention and control of occupational diseases.

ResultsA total of 30 265 occupational health physical examinations were carried out by the health facilities in Songjiang District from 2017 to 2018.The re-examination rate was 9.57% and the occupational contraindication rate was 1.91%.There were 53 cases (0.40%) transferred to superior health facilities in 2017, and 5 cases suspected of noise deafness (0.03%) in 2018.There were 1 421 person-times (1 180 persons, accounting for 10.69% of the whole population) from 390 companies, whose average of binaural high-frequency hearing threshold was determined to be ≥40 dB (A) in 2017.There were 1 736 person-times (1 308 persons, accounting for 10.27%) from 413 companies, whose average of binaural high-frequency hearing threshold was ≥40 dB (A) in 2018.There was a huge gap between the qualified rate of noise commissioned testing and the qualified rate of active monitoring, which was significantly different (P < 0.001).There was also a gap between the qualified rate of noise commissioned test data and the findings of occupational health monitoring.

ConclusionUtilizing big data analysis of occupational health can improve the efficiency of occupational health supervision departments, which may contribute to making occupational health promotion and intervention by CDC.It can also directly evaluate the quality of commissioned testing, standardize occupational health examinations and diagnosis of occupational diseases, and implement more scientific and accurate prevention and decisions on controlling occupational diseases in enterprises.