Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities

BMC Public Health

Research Article

Annie-Claude Bourgeois¹  Victor Gallant¹  Ashleigh R. Tuite²  Amy L. Greer³  Elaine Randell⁴ 

[1] Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, Ottawa, ON, Canada;Dalla Lana School of Public Health, University of Toronto, University of Toronto, Toronto, ON, Canada;Harvard T.H. Chan School of Public Health, 1633 Tremont Street, 02120, Boston, MA, United States;Department of Population Medicine, University of Guelph, Guelph, ON, Canada;Nunavut Department of Health, Iqaluit, NU, Canada;

关键词: Canada;    Latent TB infection;    Mathematical model;    Nunavut;    Public health;    Simulation;    TB;    Tuberculosis;   

DOI  :  10.1186/s12889-016-3996-7

 received in 2016-08-19, accepted in 2016-12-23,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundIn 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.MethodsWe 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.ResultsModel-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.ConclusionsIn 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.

【 授权许可】

CC BY   
© The Author(s). 2017