背景:已经有许多关于输入性传染病的预测研究,采用航空旅行量或进口风险(IR)指数,这是来源国旅行量和疾病负担的产物,作为主要预测因素。然而,缺乏验证变量可预测性的研究,特别是对于很少报道的传染病。在这项研究中,我们分析了IR指数和航空旅行量的预测性能,以预测疾病输入。
方法:以狂犬病和非洲锥虫病为目标疾病。狂犬病和非洲锥虫病输入事件列表,两个特定国家之间的年度航空旅行量,从各种数据库中获得了来源国狂犬病和非洲锥虫病的发病率。
结果:Logistic回归分析显示IR指数与狂犬病输入风险显著相关(p值<0.001),但与非洲锥虫病的相关性不显著(p值=0.923)。单变量逻辑回归模型对狂犬病的预测表现合理(受试者工作特征曲线下面积[AUC]=0.734),但对非洲锥虫病的预测表现较差(AUC=0.641)。
结论:我们的研究发现,IR指数不能普遍适用于预测罕见输入事件。然而,通过在狂犬病模型中提出可接受的性能,显示了IR指数的潜在效用。建议进一步研究以探索IR指数适用性的普遍性,并提出特定疾病的预测模型。
There have been many prediction studies for imported infectious diseases, employing air-travel volume or the importation risk (IR) index, which is the product of travel-volume and disease burden in the source countries, as major predictors. However, there is a lack of studies validating the predictability of the variables especially for infectious diseases that have rarely been reported. In this study, we analyzed the prediction performance of the IR index and air-travel volume to predict disease importation.
Rabies and African trypanosomiasis were used as target diseases. The list of rabies and African trypanosomiasis importation events, annual air-travel volume between two specific countries, and incidence of rabies and African trypanosomiasis in the source countries were obtained from various databases.
Logistic regression analysis showed that IR index was significantly associated with rabies importation risk (p value < 0.001), but the association with African trypanosomiasis was not significant (p value = 0.923). The univariable logistic regression models showed reasonable prediction performance for rabies (area under curve for Receiver operating characteristic [AUC] = 0.734) but poor performance for African trypanosomiasis (AUC = 0.641).
Our study found that the IR index cannot be generally applicable for predicting rare importation events. However, it showed the potential utility of the IR index by suggesting acceptable performance in rabies models. Further studies are recommended to explore the generalizability of the IR index\'s applicability and to propose disease-specific prediction models.