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

在撒哈拉以南非洲和亚洲，发烧是导致入院的最常见症状之一。然而，目前对这种情况下发烧的原因知之甚少，许多发热性疾病呈现非特定症状。过去，人们常常认为没有特定症状的发烧是由疟疾引起的；但随着全球疟疾的减少和更好的诊断，这一假设不再站得住脚。该项目将使用来自新获得的大型病例对照数据集的数据来调查撒哈拉以南非洲和亚洲发烧的原因。广泛人群发热疾病评估研究是一项在多个地点进行的大型国际合作，产生了大约15000个发烧病例和对照，并提供了大量潜在发烧原因的数据。有标准的方法可用于从病例对照数据中计算“归属分数”。然而，最近已经提出了基于贝叶斯技术的复杂的新方法。本项目将通过对FIEBRE数据的应用来使用和比较不同的方法。这个项目产生影响的可能性很高。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，而过度治疗会对抗菌素耐药性的发展产生重要后果。通过改善对发烧原因的了解，治疗将能够得到更适当的利用。

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.