Bayesian estimation of the aetiology of fever using big case-control data

在撒哈拉以南非洲和亚洲，发烧是导致住院的最常见症状之一。然而，目前对这种情况下发热的原因知之甚少，许多发热性疾病表现为非特异性症状。过去，人们常常认为没有特定症状的发烧是由疟疾引起的；但是，随着全球疟疾的减少和更好的诊断，这种假设不再站得住脚。该项目将使用新获得的大型病例控制数据集的数据来调查撒哈拉以南非洲和亚洲的发热原因。广泛流行的发热性疾病评估（FIEBRE）研究是一项大型国际合作，在几个地点进行，产生了约15000例发热病例和对照，并提供了大量发热潜在原因的数据。根据病例对照数据计算“归因分数”存在标准和方法。然而，基于贝叶斯技术的复杂新方法最近被提出。本项目将通过对光纤数据的应用，使用和比较不同的方法。这个项目的潜在影响很大。目前对临床医生的指导往往导致可治疗的疾病得不到治疗或使用不适当的抗菌素治疗，而过度治疗对抗菌素耐药性的发展具有重要后果。通过改进对发热原因的了解，将能够更适当地利用治疗方法。

Fever is one of the most common symptoms leading to hospital admission in sub-Saharan Africa and Asia. However, little is currently known about the causes of fever in this setting and many febrile illnesses present with non-specific symptoms. In the past, it had often been assumed that fever without specific symptoms was caused by malaria; but this assumption is no longer tenable with a global reduction in malaria combined with better diagnostics. This project will use data from a newly available big case control data set to investigate the causes of fever in sub-Saharan Africa and Asia. The Febrile Illness Evaluation in a Broad Range of Endemicities (FIEBRE) study is a large international collaboration being conducted at several sites, resulting in around 15000 fever cases and controls with data on a large number of potential causes of fever. There exist standard, methods available to calculate "attributable fractions" from case control data. However, sophisticated new methods based on Bayesian techniques have recently been proposed. This project will use and compare the different methods through application to the FIEBRE data. The potential for impact from this project is high. Current guidance to clinicians often results in treatable diseases being left untreated or treated with inappropriate antimicrobials, while over-treatment has important consequences for the development of antimicrobial resistance. By improving what is known about the causes of fever, treatments will be able to be more appropriately utilised.