ABSTRACT
Objective: To explore the present situation and epidemiological characteristics of pesticide poisoning in Chengdu City from 2012 to 2021, and to provide scientific evidence for further prevention and control. Methods: In January 2022, the pesticide poisoning report cards of Chengdu City from 2012 to 2021 were collected from the China Disease Control and Prevention Information System. The data of the report card was reorganized and the distribution characteristics of pesticide poisoning such as time, region, gender, age and pesticide types were analyzed. Results: 14326 cases of pesticide poisoning were reported in Chengdu City from 2012 to 2021, 651 deaths, and the fatality rate was 4.54%. The cases of productive pesticide poisoning and unproductive pesticide poisoning were 504 and 13822, respectively. The fatality rates of productive and unproductive pesticide poisoning were 1.39% and 4.66%, which were significant different (χ(2)=11.99, P=0.001). The highest reported cases of pesticide poisoning was in 2013 (1779) and the lowest in 2021 (1047). The number of reported cases showed a downward trend year by year (t=-12.30, P<0.001), and the fatality rates also showed a downward trend year by year (χ(2)(trend)=25.48, P<0.001). The fluctuation range of unproductive pesticide poisoning cases in each month of the year was small, and the productive pesticide poisoning mainly occurred from May to August. The regions with the largest number of reported poisoning cases were Pengzhou (1620), Jianyang (1393), Jintang (1266) and Qionglai (1158). The high incidence of poisoning was among 25-54 years old (50.21%, 7193/14326). The fatality rate in the age group 75-96 years old was the highest (8.98%, 95/1058), and the fatality rates increased gradually with age (χ(2)(trend)=186.03, P<0.001). The pesticides causing poisoning were mainly insecticide (43.86%, 6284/14326) and herbicides (35.75%, 5121/14326). Herbicides paraquat had the highest fatality rate (9.54%, 286/2998) . Conclusion: Pesticide poisoning in Chengdu City is mainly unproductive poisoning. Health education should be carried out for key areas and people, and the control of highly toxic pesticides such as insecticide and herbicides should be strengthened.
Subject(s)
Humans , Aged , Aged, 80 and over , Adult , Middle Aged , Insecticides , Pesticides , Herbicides , Paraquat , Cities , Poisoning/epidemiologyABSTRACT
Objective To develop a registration method for 3D human atlas models by using geometric information of the vertices so as to lay a foundation for statistical modeling of atlas. Methods Based on CT images of the normal human, thirty 3D models of human atlases were created and marked by the manual selected points, including 1 standard module, 20 training sets and 9 testing samples. The training sets were first registered with the standard module, including calculation on geometric information of the individual vertex and optimization process of weight coefficients in the registration models. By minimizing the energy function defined with the Euclidean distances between the automatic registered points and the manual selected points in training sets, the optimized weight coefficients could be obtained. The testing samples were then registered with the standard module to calculate the Euclidean distances between the automatic registered points and the manual selected points. The results were then compared with the training sets to evaluate the stability of the registration method. Results The registration function and the corresponding optimized weight coefficients were obtained, and the average errors for the training sets and testing samples were 1.983 mm and 2.045 mm, respectively. Further statistical analysis showed that there were no obvious differences in the error distributions among the training sets and testing samples. Conclusions The accuracy and stability of the proposed registration method meet the requirement in medical applications, and it can provide automatic registration of points of interest on human atlas models and be used for element classification in statistical modeling.