Risk factors of malaria infection and risk prediction model research in in labor export in Langfang City
10.3969/j.issn.1006-2483.2025.01.027
- VernacularTitle:廊坊市劳务输出感染疟疾危险因素分析及风险预测模型研究
- Author:
Xuejun ZHANG
1
;
Kun ZHAO
2
;
Jing ZHAO
2
;
ZHUO WANG
2
;
Qiang GUO
1
;
Jie XIAO
1
;
Juanjuan GUO
1
;
Jinhong PENG
1
Author Information
1. Department of Occupational Health Management and Occupational Disease Prevention and Control , Hebei PetroChina Central Hospital , Langfang , Hebei 065000 , China
2. China Petroleum Pipeline Engineering Co., Ltd., Langfang , Hebei 065000 , China
- Publication Type:Journal Article
- Keywords:
Labor export;
Malaria;
Risk Prediction Models
- From:
Journal of Public Health and Preventive Medicine
2025;36(1):118-122
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the influencing factors of malaria infection of labor service exported to overseas in Langfang City, in order to establish a visualization tool to assist clinicians in predicting the risk of malaria. Methods A total of 4 774 expatriate employees of the Nibei Pipeline Project of the Pipeline Bureau from October 2021 to August 2023 were taken as the subjects, and the gender, age, overseas residence area and Knowledge of malaria controlscores of the study subjects were investigated by questionnaire survey, and the possible risk factors of malaria were screened by logistic regression model. At the same time, the nomogram prediction model was established, and the subjects were divided into the training group and the validation group at a ratio of 2:1, and the area under the curve (ROC) and the decision curve were plotted to evaluate the prediction ability and practicability of the prediction model in this study. Results Among the 4 774 study subjects, 96 cases of malaria occurred, and the detection rate was 2.01%. Junior school （OR=1.723，95% CI:1.361-2.173）， and residence in rural areas（OR=2.091，95%CI:1.760 -3.100）were risk factors (OR>1), while protective measures（OR=0.826，95% CI : 0.781 - 0.901） and high malaria education scores （OR=0.872，95% CI : 0.621 - 0.899）were protective factors.The nomogram prediction model results showed that the area under the curve of the nomogram prediction model in the training group was 0.94 (95% CI : 0.85 - 1.00), while the validation group was 0.93 (95% CI : 0.80 - 1.00). The results of the decision curve showed that when the threshold probability of the population was 0-0.9, the nomogram model was used to predict the risk of malaria occurrence with the highest net income. Conclusion The nomogram prediction model (including gender, education, region, protection and malaria education score) established and validated in this study is of great value for clinicians to screen high-risk patients with malaria.
