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dc.contributor.authorArjun Chandnaen_US
dc.contributor.authorLisa J. Whiteen_US
dc.contributor.authorTiengkham Pongvongsaen_US
dc.contributor.authorMayfong Mayxayen_US
dc.contributor.authorPaul N. Newtonen_US
dc.contributor.authorNicholas P.J. Dayen_US
dc.contributor.authorYoel Lubellen_US
dc.contributor.otherLondon School of Hygiene & Tropical Medicineen_US
dc.contributor.otherUniversity of Oxforden_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherLaos-Oxford-Mahosot Hospital Wellcome Trust Research Uniten_US
dc.contributor.otherUniversity of Health Sciencesen_US
dc.date.accessioned2020-01-27T07:58:13Z-
dc.date.available2020-01-27T07:58:13Z-
dc.date.issued2019-01-01en_US
dc.identifier.citationWellcome Open Research. Vol.4, (2019)en_US
dc.identifier.issn2398502Xen_US
dc.identifier.other2-s2.0-85070458835en_US
dc.identifier.urihttp://repository.li.mahidol.ac.th/dspace/handle/123456789/50391-
dc.description.abstract© 2019 Chandna A et al. Background: Across Southeast Asia, declining malaria incidence poses a challenge for healthcare providers, in how best to manage the vast majority of patients with febrile illnesses who have a negative malaria test. In rural regions, where the majority of the population reside, empirical treatment guidelines derived from central urban hospitals are often of limited relevance. In these settings, relatively untrained health workers deliver care, often without any laboratory diagnostic support. In this paper, our aim was to model the impact on mortality from febrile illness of using point-of-care C-reactive protein testing to inform the decision to prescribe antibiotics and regional surveillance data to inform antibiotic selection, rooted in the real-world context of rural Savannakhet province, southern Laos. Methods: Our model simulates 100 scenarios with varying quarterly incidence of six key pathogens known to be prevalent in rural Laos. In the simulations, community health workers either prescribe antibiotics in-line with current practice as documented in health facilities in rural Laos, or with the aid of the two interventions. We provide cost-effectiveness estimates for each strategy alone and then for an integrated approach using both interventions. Results: We find that each strategy alone is predicted to be highly cost-effective, and that the combined approach is predicted to result in the biggest reduction in mortality (averting a predicted 510 deaths per year in rural Savannakhet, a 28% reduction compared to standard practice) and is highly cost-effective, with an incremental cost-effectiveness ratio of just $66 per disability-adjusted life year averted. Conclusions: Substantial seasonal variation in the predicted optimal empirical antibiotic treatment for febrile illness highlights the benefits of up-to-date information on regional causes of fever. In this modelling analysis, an integrated system incorporating point-of-care host biomarker testing and regional surveillance data appears highly cost-effective, and may warrant piloting in a real-life setting.en_US
dc.rightsMahidol Universityen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85070458835&origin=inwarden_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectMedicineen_US
dc.titleAccounting for aetiology: Can regional surveillance data alongside host biomarker-guided antibiotic therapy improve treatment of febrile illness in remote settings?en_US
dc.typeArticleen_US
dc.rights.holderSCOPUSen_US
dc.identifier.doi10.12688/wellcomeopenres.14976.1en_US
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85070458835&origin=inwarden_US
Appears in Collections:Scopus 2019

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