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
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张雪军,公共卫生硕士,主治医师,主要研究方向:职业病流行病学

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R181

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Risk factors of malaria infection and risk prediction model research in in labor export in Langfang City
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    摘要:

    目的 分析廊坊市劳务输出至境外感染疟疾的影响因素,以期建立可视化工具辅助临床医生预测疟疾发生风险。方法 以2021年10月~2023年8月期间管道局项目外派员工4 774人作为研究对象,采用问卷调查的方式了解研究对象的性别、年龄、境外居住地区以及疟防知识知晓相关得分等信息,采用logistic回归模型筛选疟疾可能的风险因素;同时建立列线图预测模型,并将研究对象以2﹕1的比例分为训练组和验证组,分别绘制两组曲线下面积(ROC)以及决策曲线,对本研究预测模型的预测能力以及实用性进行评估。结果 在4774人研究对象中,共发生疟疾病例96例,发病率2.01%。学历为初中(OR=1.723,95% CI:1.361~2.173)、居住于农村地区(OR=2.091,95% CI: 1.760~3.100)为疟疾发生的风险因素(OR>1),而采取防护措施(OR=0.826,95% CI:0.781~0.901)以及疟防知识知晓得分高(OR=0.872,95% CI:0.621~0.899)为疟疾发生的保护因素(OR<1)。 列线图预测模型结果显示,预测组列线图预测模型曲线下面积为0.94(95% CI:0.85~1.00),而验证组列线图预测模型曲线下面积为0.93(95% CI:0.80~1.00)。决策曲线结果显示,当人群的阈值概率为0~0.9则使用列线图模型预测疟疾发生风险的净收入最高。结论 本研究建立并验证的列线图预测模型(变量包括性别、学历、地区、是否防护以及疟防知识知晓得分)对于临床医生筛选疟疾高危患者具有重要价值。

    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.

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  • 收稿日期:2024-12-06
  • 最后修改日期:2024-12-06
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  • 在线发布日期: 2025-01-15
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