基于职业卫生大数据的职业性噪声危害的精准防控模式

蒋元强; 尹艳; 盛峰松; 江松; 王丽英; 王慧; 顾晓旭; 王桂敏

doi:10.19428/j.cnki.sjpm.2020.19943

基于职业卫生大数据的职业性噪声危害的精准防控模式

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

摘要

摘要:
目的利用职业卫生大数据，提前发现高危企业和劳动者早期职业健康损害，阻断或延缓职业病发展进程，延缓或使早期职业健康损害的劳动者不至于发展为职业病，实现职业病防治的精准防控。
方法连续系统的收集上海市松江区职业病危害申报信息、企业职业卫生档案信息、作业场所委托检测信息、重点职业病危害因素主动监测信息、职业病工伤鉴定及理赔信息、劳动者职业健康监护信息和职业病报告信息等职业卫生数据，以噪声危害控制为例探讨职业卫生大数据在职业病精准防控中的应用和前景。
结果2017—2018年上海市松江区辖区体检机构共开展噪声在岗职业健康体检30 265人次，复查率为9.57%、职业禁忌证率为1.91%；2017年噪声转上级复查53例(0.40%)，2018年疑似噪声聋5例(0.03%)；2017年发现双耳高频平均听阈≥40 dB(A) 1 421人次(1 180人，占整体的10.69%)，分布在390家企业；2018年发现双耳高频平均听阈≥40 dB(A) 1 736人次(1 308人，占整体的10.27%)，分布在413家企；噪声委托检测合格率与主动监测合格率之间存在巨大差距，差异有统计学意义(P＜0.001)，并且噪声委托检测数据合格率与噪声职业健康监护结果也存在矛盾。
结论利用职业卫生大数据分析可以提升职业卫生监管部门监管效率、可使疾控部门职业卫生宣传和干预更加有的放矢、更直观的评估委托检测数据的质量、可以规范职业健康体检和职业病诊断、企业职业病防控和管理决策更加科学精准。

Abstract:
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.

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