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，许多发热性疾病都有非特异性症状。过去，人们常常认为没有具体症状的发烧是由疟疾引起的;但随着疟疾在全球范围内的减少以及诊断方法的改进，这种假设不再成立。该项目将使用新获得的大病例对照数据集的数据，调查撒哈拉以南非洲和亚洲的发热原因。在广泛的地方病（FIEBRE）研究发热性疾病评估是一个大型的国际合作正在进行的几个网站，导致约15000例发热病例和控制大量的潜在原因的发热数据。有标准的方法可以从病例对照数据中计算“可归因分数”。然而，最近提出了基于贝叶斯技术的复杂的新方法。本项目将通过应用于FIEBRE数据来使用和比较不同的方法。该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未经治疗或使用不适当的抗菌药物治疗，而过度治疗对抗菌药物耐药性的发展有重要影响。通过改善对发热原因的了解，治疗将能够更适当地使用。

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.