Objective To investigate negative mental and behavior problems in children with short stature and analyze their relationship with family function and quality of life. Methods A total of 347 cases of children with dwarfism received from 358 cases in Chengdu Jingjiang Hospital for Women and Children Health from January 2019 to December 2023 were selected as the dwarfism group were included in this study. The two groups were compared on negative mental and behavior problems [Mental Health Scale for Child and Adolescent (MHS-CA)], family function [Family Adaptability and Cohesion Evaluation Scale II-Chinese Version (FACES II-CV)] and quality of Life [Pediatric Quality of Life Inventory 4.0 (PedQL4.0)]. Correlation analysis was performed. Results MHS-CA scores, FACES II-CV scores and PedQL4.0 scores of the short stature group were lower than those of the control group (P<0.05). With MHS-CA score ≤ 57 as the critical value, 347 children with short stature were divided into healthy state group (256 cases) and unhealthy state group (91 cases). FACES II-CV scores and PedsQL4.0 scores of children in unhealthy state were lower than those of children in healthy state (P<0.05). Pearson correlation analysis found that mental health problems were positively correlated with family function and quality of life in children with short stature (r=0.217, 0.386, both P=0.000). Conclusion Mental health problems in children with short stature are significantly positively correlated with family function and quality of life.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective:To provide strategical reference for large-scale colorectal cancer screening with full regional coverage by analyzing the results of the first colorectal cancer screening in the target population in Linhai city,Zhejiang Province. Methods:The target population of 50-74 years old in Linhai were invited to take part in the colorectal cancer screening program from 2020 to 2021.The risk of colorectal cancer of the participants were preliminarily evaluated by questionnaire and qualitative fecal occult blood test(FOBT),and participants with positive screening results were suggested to take colonoscopy test for further evaluation.The screening results were collected and analyzed. Results:A total of 71 942 people were screened from 2020 to 2021,and 15 1 70 of them were found positive in preliminary screening.The positive rate in males was significantly higher than that in females(x 2=724.005,P＜0.001),and the positive rate was highest in the population of 60-69 years old during preliminary screening.The compliance rate of colonoscopy was 24.1 9％with no significant difference between males and females(x 2=0.256,P=0.61 3),showing a decreasing trend as the age increases.From 2020 to 2021,the detection rate of lesions by colonoscopy was 52.92％,with 47 case of colorectal cancer(CRC),333 case of advanced colorectal adenoma,561 case of non-advanced colorectal adenoma and 1 001 case of benign lesions.The detection rate of lesions in males was much higher than that in females(x 2=82.451,P＜0.001).The detection rates of lesions,advanced colorectal adenoma,and non-advanced adenoma showed increasing trends with the age.The compliance rate of colonoscopy,the detection rate of lesions,and the detection rate of CRC,advanced colorectal adenoma,and non-advanced adenoma were 32.94％,69.53％,2.87％,1 6.85％and 1 9.71％,respectively,in participants who were both assessed as high-risk according to questionnaire evaluation and FOBT positive,the highest among all participants.The compliance rate of colonoscopy in 2021 was obviously higher than that in 2020(32.11％vs 1 9.09％,P＜0.001),but no significant difference was found in the detection rate of lesions between 2021 and 2020(P＞0.05). Conclusion:From 2020 to 2021,the compliance rate of colonoscopy was low and the detection rate of colorectal lesions was high during the screening of colorectal cancer in the target population in Linhai,Zhejiang Province.It is necessary to enhance the public awareness of the importance of colorectal cancer screening,standardize the enrollment criteria,and improve the compliance of colonoscopy,in orderto give full play to primary screening in the general public.