Humans ; Paraquat ; poisoning ; Pulmonary Fibrosis ; chemically induced ; pathology

China ; Occupational Exposure ; adverse effects ; Organic Chemicals ; adverse effects ; Solvents ; adverse effects ; Workplace

Humans ; Occupational Health ; Occupational Health Services

Humans ; Occupational Health ; Physical Examination

Automobiles ; China ; Humans ; Occupational Exposure ; Risk Assessment

Animals ; Connective Tissue Growth Factor ; metabolism ; Fibrosis ; Herbicides ; poisoning ; Lentivirus ; Lung ; pathology ; Paraquat ; poisoning ; Poisoning ; pathology ; Pulmonary Fibrosis ; pathology ; RNA Interference ; RNA, Small Interfering ; Rats

Blood Pressure ; Hot Temperature ; Humans ; Hypertension ; Noise

Air Pollutants, Occupational ; adverse effects ; Dust ; Humans ; Lung ; drug effects ; physiopathology ; Welding

Adult ; Female ; Humans ; Male ; Occupational Exposure ; adverse effects ; Organic Chemicals ; adverse effects ; Paint ; adverse effects ; Solvents ; adverse effects

OBJECTIVETo explore the application of the gray series model GM (1, 1) in predicting trends in the incidence of pneumoconiosis and evaluate its degree of predicted precision.

METHODSAnalyzing the incidence of pneumoconiosis in this region from 2009 to 2013, and predicting the incidence of pneumoconiosis of the area in 2014-2016 by establishing GM (1, 1) according to the gray system theory.

RESULTSUsing occupational pneumoconiosis population data from 2009 to 2013, to establish GM (1, 1) model: yt = 1396.89e(0.12(t-1)), α = -0.12, µ = 147.2. The pneumoconiosis in 2014, 2015, 2016 were predicted respectively 51, 47, 43 cases based on the GM (1, 1) model, and C value of model is 0.15, P value is 1, all of them meet the requirements of model predictions. It shows the cases of pneumoconiosis are rising significantly.

CONCLUSIONGM (1, 1) model can be used to predict the recent trend in the incidence of pneumoconiosis.