The Establishment of Job Exposure Matrix in Shanghai 工种接触矩阵的建立 Shouzheng XUE ( 薛寿征 教授 ) National Cancer Institute 美国国家肿瘤研究所, 职业与环境流行病学处 Shanghai Municipal.

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Presentation transcript:

The Establishment of Job Exposure Matrix in Shanghai 工种接触矩阵的建立 Shouzheng XUE ( 薛寿征 教授 ) National Cancer Institute 美国国家肿瘤研究所, 职业与环境流行病学处 Shanghai Municipal CDC 上海市疾病控制中心

Subject of Hazard Monitoring FactoriesTotalExposedRate % District5,6555, % County11,6849, % Total17,33915, %

Employees Exposed to Occupational Hazards, Shanghai 1986 DistrictTotalExposedRate Worker2,654,127694, % Contractor89,1272, % Total2,743,254697, % County Worker1,601,307485, % Contractor93,06235, % Total1,694,369521, % Total Worker4,255,4341,180, % Contractor182,18959, % Total4,437,6231,240, %

Collect and Bending Record

Transcript and coding

Processes Collecting, Bending, Transcript Coding (Trade, Occupation, Hazard) Keying and Checking Cleaning-up Assign Sample Source Exclude ELV and Duplicate Rating IPF (trade code & occupation code) Regression and Prediction (Modification) Validation Individual and Group TWA estimation

Coding, Reviewing, Keying

First Prediction of Benzene JEM PeriodRateio=0io=1io=2io=3total 1ii= ii= ii= ii= total

Second Prediction of Benzene JEM paired t test, t=1.4346, P= PeriodRateio=0io=1io=2io=3total 1ii= ii= ii= ii= total

Calculate TWA from Spot Sample Using rate of Frequency of exposure to modifying the exposed level, Intensity for calculate TWA (time weighed average) Using rate of Possibility of employee exposed to modifying the individual TWA obtain TWA of job group (JTWA) Using validated data take TWA as gold Standard to verifying the calculated TWA and JTWA

Usefulness of the JEM of Occupational Hazard Monitoring Establish the database for hazards Evaluate the status of occup. health Supporting the diagnosis of occupa- tion diseases and occupation cancer Setting priority and direction on work of occupation health Basis for occupation epidemiology

Examples of JEM for Main Occupational Hazards Shanghai, Average Mean of Concentration by Time; Measured and Predicted Data in Curves: Measured Record Collapsed Numerical 1st and 2nd Prediction

SiO 2 ,Concentration by Year

SiO 2 JEM Curves

Foundry Dust, Conc. By Year

Foundry Dust JEM Curves

Asbestos, Conc. By Year

Asbestos JEM Curves

Carbon Black, Conc. By Year

Carbon Black JEM Curves

Benzene Conc. By Year

Benzene JEM Curves

TCE, Concentration by Year

TCE JEM Curves

Lead Fume, Conc. By Year

Lead Fume JEM Curves

Formaldehyde, Conc. By Year

Formaldehyde JEM Curves

Aniline, Conc. By Year

Aniline JEM Curves

Thank You! End.