Analysis Of Influencing Factors Of Posttraumatic Stress Disorder In Firefighters

This paper uses the cross-sectional research method to understand the epidemic characteristics of firefighters’Post traumatic stress disorder（PTSD）and explore the influencing factors of PTSD and its main symptoms.Using the method of stratified and cluster sampling,1028 in-service firefighters of Chongqing fire brigade were selected as the research object for questionnaire survey.A questionnaire on the influencing factors of PTSD in firefighters was designed,the formal version of the questionnaire was determined after the reliability and validity were tested by pre-experiment.The self-made general situation questionnaire was used to investigate the demographic characteristics and occupational situation of the subjects,The PTSD Checklist Civilian Version（PCL-C）was selected to evaluate PTSD status,Maslach Burnout Inventory-General Survey（MBI-GS）evaluate job burnout,Patient Health Questionnaire（PHQ-9）evaluate the symptoms of depression,Social Support Rate Scale（SSRS）evaluate social support,Trait Coping Style Questionnaire（TCSQ）evaluate coping style,Pittsburgh Sleep Quality Index（PSQI）evaluate sleep quality.The database of questionnaire data was established by Excel and the data were statistically analyzed by SPSS 23.0.The measurement data are statistically described in the form of X±S.T-test was used to quantitatively compare the mean of the two groups,covariance analysis was used to quantitatively compare the mean of multiple groups,logistic regression and neural network were used to establish PTSD early warning model.The main results are as follows:1.The pre experimental data analysis showed that PCL-C,MBI-GS,PHQ-9 and TCSQ questionnaires had good reliability and validity..2.Among the 1028 firefighters formally investigated,the prevalence rates of PTSD and sub syndrome PTSD were 4.9%and 22.4%.The prevalence of PTSD symptom groups in firefighters was as follows:reexperience（group B）were 27.8%,avoidance and numbness（group C）were 19.6%,and increased alertness（group D）were 37.7%.3.The results of Covariance analysis are as follows:injury,physical pain,self-rated health status,social support,coping style,depressive symptoms,job burnout and sleep disorder are the influencing factors of firefighters’PTSD（P<0.05）.Injury,self-rated health status,social support,coping style,depressive symptoms,job burnout and sleep disorder are the common influencing factors of the three symptoms of PTSD（P<0.05）.In addition,the number of task participation and forest fire prevention are the influencing factors of group B（P<0.05）,physical pain is the influencing factor of group C（P<0.05）,and physical pain and fire rescue are the influencing factors of group D（P<0.05）.4.The impact factors included in the firefighter PTSD early warning model based on binary logistic regression are:sleep quality（X₁）、self-rated health status（X₂）、physical pain（X₃）、coping style（X₄）、social support（X₅）.The model equation is:Logist（Y）=-6.836+0.783X₁+0.752X₂+0.372X₃+0.813X₄-0.686X₅By Hosmer and lemeshow test,theχ² value of the model is 3.730,and the P value is 0.810.The AUC value of the model is 0.920,and the prediction accuracy is 95.3%.5.The firefighter PTSD early warning model based on neural network constructed in this paper includes an input layer（including 6 units）,a hidden layer（including 3 units）and an output layer（including 2 units）.The importance of PTSD influencing factors in the model is sorted as follows:self-rated health status,coping style,sleep quality,social support,physical pain,injury.The AUC value of the model is 0.921,and the prediction accuracy is 95.2%.6.The prediction accuracy and AUC value of the two models are basically the same,and the normalization importance trend of independent variables is roughly the same.In practical application,when the sample size is small,the result of neural network model is unstable,it is suggested to use logistic regression to construct the model.When the sample size is sufficient,it is suggested that logistic regression should be used to screen the real independent variables,and then combined with artificial intelligence neural network to construct the model.Based on the above results,the suggestions to reduce the PTSD of firefighters are put forward.Those include:try to recruit firefighters with positive coping characteristics,improve firefighters’sleep quality and self-care awareness,enhance firefighters’social support,establish alarm information platform and reasonably allocate posts.The results show that the prevalence of PTSD among firefighters in Chongqing is high.They are high-risk groups of PTSD.Injured,physical pain,self-rated health status,depressive symptoms,job burnout and sleep disorders are the main risk factors,while social support and active coping are buffer factors,The PTSD early warning model based on logistic regression and neural network can accurately predict the PTSD status of firefighters.The research results can provide a certain basis for the prevention and intervention of firefighters’PTSD.