Comparison of routine field epidemiology and whole genome sequencing to identify tuberculosis transmission in a remote setting. (4th February 2020)

• Record Type: Journal Article
• Authors: Guthrie, J. L.; Strudwick, L.; Roberts, B.; Allen, M.; McFadzen, J.; Roth, D.; Jorgensen, D.; Rodrigues, M.; Tang, P.; Hanley, B.; Johnston, J.; Cook, V. J.; Gardy, J.L.
• Is Part Of: Epidemiology and infection. Volume 148(2020)
• Publication Date: 2020-02-04
• Subjects: Epidemiology, -- programme assessment, -- transmission, -- tuberculosis (TB), -- whole genome sequencing; Communicable diseases -- Periodicals; Epidemiology -- Periodicals; 614.4
• DOI: 10.1017/S0950268820000072
• Languages: English
• ISSNs: 0950-2688

Abstract

Abstract: Yukon Territory (YT) is a remote region in northern Canada with ongoing spread of tuberculosis (TB). To explore the utility of whole genome sequencing (WGS) for TB surveillance and monitoring in a setting with detailed contact tracing and interview data, we used a mixed-methods approach. Our analysis included all culture-confirmed cases in YT (2005–2014) and incorporated data from 24-locus Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats (MIRU-VNTR) genotyping, WGS and contact tracing. We compared field-based (contact investigation (CI) data + MIRU-VNTR) and genomic-based (WGS + MIRU-VNTR + basic case data) investigations to identify the most likely source of each person's TB and assessed the knowledge, attitudes and practices of programme personnel around genotyping and genomics using online, multiple-choice surveys ( n = 4) and an in-person group interview ( n = 5). Field- and genomics-based approaches agreed for 26 of 32 (81%) cases on likely location of TB acquisition. There was less agreement in the identification of specific source cases (13/22 or 59% of cases). Single-locus MIRU-VNTR variants and limited genetic diversity complicated the analysis. Qualitative data indicated that participants viewed genomic epidemiology as a useful tool to streamline investigations, particularly in differentiating latent TB reactivation from the recent transmission. Based on this, genomic data could be used to enhance CIs, focus resources, target interventions and aid in TB programme evaluation.