Application of whole genome sequence analysis to the study of Mycobacterium tuberculosis in Nunavut, Canada.

Canada has one of the lowest rates of tuberculosis (TB) in the world, however, among certain sub-populations, disease incidence rates approach those observed in sub-Saharan Africa, and other high incidence regions. In this study, we applied mycobacterial interspersed repetitive unit (MIRU) variable number of tandem repeat (VNTR) and whole genome sequencing (WGS) to the analysis of Mycobacterium tuberculosis isolates obtained from Northern communities in the territory of Nunavut. WGS was carried out using the Illumina MiSeq, with identified variants used to infer phylogenetic relationships and annotated to infer functional implications. Additionally, the sequencing data from these isolates were augmented with publically available WGS to evaluate data from the Nunavut outbreak in the broader Canadian context. In this study, isolates could be classified into four major clusters by MIRU-VNTR analysis. These could be further resolved into sub-clusters using WGS. No evidence for antimicrobial resistance, either genetic or phenotypic, was observed in this cohort. Among most subjects with multiple samples, reactivation/incomplete treatment likely contributed to recurrence. However, isolates from two subjects appeared more likely to have occurred via reinfection, based on the large number of genomic single nucleotide variants detected. Finally, although quite distinct from previously reported Canadian MTB strains, isolates obtained from Nunavut clustered most closely with a cohort of samples originating in the Nunavik region of Northern Quebec. This study demonstrates the benefit of using WGS for discriminatory analysis of MTB in Canada, especially in high incidence regions. It further emphasizes the importance of focusing epidemiological intervention efforts on interrupting transmission chains of endemic TB throughout Northern communities, rather than relying on strategies applied in regions where the majority of TB cases result from importation of foreign strains.

Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities.

BACKGROUND: In Canada, active tuberculosis (TB) disease rates remain disproportionately higher among the Indigenous population, especially among the Inuit in the north. We used mathematical modeling to evaluate how interventions might enhance existing TB control efforts in a region of Nunavut. METHODS: We developed a stochastic, agent-based model of TB transmission that captured the unique household and community structure. Evaluated interventions included: (i) rapid treatment of active cases; (ii) rapid contact tracing; (iii) expanded screening programs for latent TB infection (LTBI); and (iv) reduced household density. The outcomes of interest were incident TB infections and total diagnosed active TB disease over a 10- year time period. RESULTS: Model-projected incidence in the absence of additional interventions was highly variable (range: 33-369 cases) over 10 years. Compared to the 'no additional intervention' scenario, reducing the time between onset of active TB disease and initiation of treatment reduced both the number of new TB infections (47% reduction, relative risk of TB = 0.53) and diagnoses of active TB disease (19% reduction, relative risk of TB = 0.81). Expanding general population screening was also projected to reduce the burden of TB, although these findings were sensitive to assumptions around the relative amount of transmission occurring outside of households. Other potential interventions examined in the model (school-based screening, rapid contact tracing, and reduced household density) were found to have limited effectiveness. CONCLUSIONS: In a region of northern Canada experiencing a significant TB burden, more rapid treatment initiation in active TB cases was the most impactful intervention evaluated. Mathematical modeling can provide guidance for allocation of limited resources in a way that minimizes disease transmission and protects population health.