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The Effectiveness of Tuberculosis Control Strategies that Target Social Determinants of Health in Three First Nations and Métis Communities: A Mathematical Modeling Approach

  • Author / Creator
    Varughese, Marie B
  • BACKGROUND: Despite the overall decline in tuberculosis (TB) incidence in Canada, rates among Indigenous peoples have not decreased since the late 1990s. On-going transmission associated with the time from the onset of symptoms to treatment have been identified as major contributor to the persistence of TB in Canadian Indigenous communities. The social determinants of health associated with time to treatment represent additional challenges faced by Indigenous communities. OBJECTIVES: a) describe TB transmission across the Prairie Provinces of Canada (Alberta, Saskatchewan, and Manitoba), b) determine a baseline estimate for time to treatment (analogous to transmission period or total delay in diagnosis) and to identify its associated risk factors, c) construct a TB transmission agent based model (ABM) that integrates multivariate relationships between the time to treatment and associated risk factors that includes the social determinants of health in three First Nations and Métis communities in Alberta and Saskatchewan (TB-ABM), and d) to simulate three TB control strategies and assess their impact on TB cases (latent and active) and transmission. These control strategies include reductions in comorbidities, improved healthcare access, and an LTBI screening and treatment strategy. METHODS: Data management and statistical analysis (descriptive and multivariable logistic regression) was conducted using SAS 9.3 (SAS Institute Inc., Cary, NC, USA) and Microsoft Excel 2011. The TB-ABM was constructed, validated, and simulated using MATLAB 2015a (The MathWorks, Inc.). RESULTS: Evidence of on-going transmission was highest among First Nations living in northern reserves. The median estimated time to treatment was 30 days and durations that exceeded this cut-off value were defined as a “delayed time to treatment”. Factors that increase the odds of delayed time to treatment (>30 days) in the multivariate model included having a regular family doctor and not having a working x-ray machine and technician in a community. Simulations using the TB-ABM indicated that decreasing latent TB infection (LTBI) in high burden communities could have significant impacts on incidence overall. Reductions in LTBI among those infected within 5 years ranged between 40% and 71% based on screening 50% of people every six months (assuming a compliance of 60%). Healthcare access in the TB-ABM was defined as the location people first sought care for TB symptoms (either within or outside the community). Simulations from the TB-ABM estimated a 10% to 16% decrease in transmission events based on a 75% reduction of people accessing services outside the community. CONCLUSION: Interventions that directly interrupt on-going transmission in Indigenous communities through improving the social determinants of health such as healthcare access is important to help decrease TB burden. The overall use of mathematical modeling can provide insight and numerical evidence of the impact that risk factors including the social determinants of health can have on TB transmission and case-rates among First Nations and Métis peoples across the Prairies.

  • Subjects / Keywords
  • Graduation date
    2017-11
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3DV1D27T
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Public Health Sciences
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Applied Mathematics and Public Health
  • Supervisor / co-supervisor and their department(s)
    • Dr. Richard Long (School of Public Health and Department of Medicine)
    • Dr. Michael Li (Department of Mathematical and Statistical Sciences)
  • Examining committee members and their departments
    • Dr. Julien Arino (Department of Mathematical and Statistical Sciences - External Examiner)
    • Dr. Larry Svenson (School of Public Health and Alberta Health)
    • Dr. James Talbot (School of Public Health)
    • Dr. Sabrina Plitt (School of Public Health)
    • Dr. James Muldowney (Department of Mathematical and Statistical Sciences-Chair)