School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, 225007, China.
Jiangsu Key Laboratory of Zoonosis, Yangzhou, China.
Malar J. 2023 Jun 6;22(1):175. doi: 10.1186/s12936-023-04604-4.
Predicting the risk of malaria in countries certified malaria-free is crucial for the prevention of re-introduction. This review aimed to identify and describe existing prediction models for malaria re-introduction risk in eliminated settings.
A systematic literature search following the PRISMA guidelines was carried out. Studies that developed or validated a malaria risk prediction model in eliminated settings were included. At least two authors independently extracted data using a pre-defined checklist developed by experts in the field. The risk of bias was assessed using both the prediction model risk of bias assessment tool (PROBAST) and the adapted Newcastle-Ottawa Scale (aNOS).
A total 10,075 references were screened and 10 articles describing 11 malaria re-introduction risk prediction models in 6 countries certified malaria free. Three-fifths of the included prediction models were developed for the European region. Identified parameters predicting malaria re-introduction risk included environmental and meteorological, vectorial, population migration, and surveillance and response related factors. Substantial heterogeneity in predictors was observed among the models. All studies were rated at a high risk of bias by PROBAST, mostly because of a lack of internal and external validation of the models. Some studies were rated at a low risk of bias by the aNOS scale.
Malaria re-introduction risk remains substantial in many countries that have eliminated malaria. Multiple factors were identified which could predict malaria risk in eliminated settings. Although the population movement is well acknowledged as a risk factor associated with the malaria re-introduction risk in eliminated settings, it is not frequently incorporated in the risk prediction models. This review indicated that the proposed models were generally poorly validated. Therefore, future emphasis should be first placed on the validation of existing models.
预测已认证无疟疾国家的疟疾风险对于防止疟疾再次传入至关重要。本综述旨在确定和描述已消除地区疟疾再次传入风险的现有预测模型。
按照 PRISMA 指南进行了系统的文献搜索。纳入了在消除地区开发或验证疟疾风险预测模型的研究。至少两名作者使用专家制定的预定义清单独立提取数据。使用预测模型风险偏倚评估工具 (PROBAST) 和改编的 Newcastle-Ottawa 量表 (aNOS) 评估偏倚风险。
共筛选出 10075 篇参考文献，其中有 10 篇文章描述了 6 个已认证无疟疾国家的 11 种疟疾再次传入风险预测模型。纳入的预测模型中有三分之二是为欧洲地区开发的。预测疟疾再次传入风险的参数包括环境和气象、媒介、人口迁移以及监测和应对相关因素。模型之间的预测因素存在很大的异质性。所有研究均被 PROBAST 评为高偏倚风险，主要是因为缺乏对模型的内部和外部验证。一些研究被 aNOS 量表评为低偏倚风险。
许多已消除疟疾的国家仍存在疟疾再次传入的风险。确定了多个可预测消除地区疟疾风险的因素。尽管人口迁移被认为是与消除地区疟疾再次传入风险相关的重要因素，但它并未经常纳入风险预测模型。本综述表明，所提出的模型通常验证不足。因此，未来应首先重视现有模型的验证。
