The Detection And Influencing Factors Of Pneumoconiosis In Hebei Province Based On Geographic Information Technology

Objective:To explore the spatial distribution pattern and clustering characteristics of pneumoconiosis detected cases in Hebei province from 2009 to 2018;to explore the application value of spatial regression model in screening the influencing factors of pneumoconiosis detection,to provide a basis for prevention and control of pneumoconiosis,and to provide some references for the analysis and research of other influencing factors of diseases with spatial autocorrelation.Methods : The regional distribution of pneumoconiosis was shown by regional classification map.The global spatial autocorrelation method was used to explore the spatial autocorrelation and local aggregation of pneumoconiosis.Early after three rounds of the Delphi method to build the pneumoconiosis containing 18 indexes to estimate index system,selection of occupational health survey data in 2011 11 indicators as independent variable,because of lag of pneumoconiosis,choose pneumoconiosis diagnosis and suspected cases in 2011-2015 as the dependent variable,with the help of Lagrange multipliers to choose appropriate spatial regression model,screening of pneumoconiosis disease detection,the influence factors of and evaluate compare different model fitting effect.Results:From 2009 to 2018,a total of 6,099 cases of pneumoconiosis were detected in Hebei province,showing an overall upward trend.The top three cases were silicosis,coal worker's pneumoconiosis and potter's pneumoconiosis.The number of pneumoconiosis cases was spatially clustered,and the high detection areas were concentrated in some counties such as Zhangjiakou City and Chengde City.The results of spatial autocorrelation analysis showed that there was a positive spatial correlation between pneumoconiosis in Hebei province from 2009 to 2018.The local aggregation types were mainly high-high aggregation and low-high aggregation,and the high-low aggregation and low-low aggregation had a small distribution range.The fitting results of the ordinary linear regression model showed that the number of dust exposure in the district,the knowledge rate of prevention and treatment of pneumoconiosis among dust exposure personnel,the qualified rate of dust monitoring points in the district and the physical examination rate of dust exposure in the district had statistical significance.The fitting results of the spatial lag model showed that there was a positive correlation between the number of dust exposure,the awareness rate of occupational disease prevention knowledge of dust exposure personnel and the physical examination rate of dust exposure of enterprises in the jurisdiction and the detection of pneumoconiosis,while there was a negative correlation between the qualified rate of dust monitoring sites of enterprises in the jurisdiction and the detection of pneumoconiosis.Conclusion : The detection of pneumoconiosis in Hebei province showed an overall upward trend from 2009 to 2018.The distribution of pneumoconiosis cases has a global spatial autocorrelation,and there are different aggregation types in local areas,mainly high-high aggregation and low-high aggregation.On the whole,spatial lag model is better than the ordinary linear regression model in analyzing the influencing factors of pneumoconiosis detection,so it is more appropriate to adopt spatial model to study the influencing factors of pneumoconiosis detection.The detection of pneumoconiosis was affected by the number of dust exposure and the physical examination rate of dust exposure in enterprises.The influence of local socioeconomic factors on pneumoconiosis should be considered when formulating the prevention and treatment measures.