ERIC Number: EJ1019701
Record Type: Journal
Publication Date: 2013-Oct
Abstractor: As Provided
Reference Count: 30
Evaluating the Effectiveness of Contact Tracing on Tuberculosis Outcomes in Saskatchewan Using Individual-Based Modeling
Tian, Yuan; Osgood, Nathaniel D.; Al-Azem, Assaad; Hoeppner, Vernon H.
Health Education & Behavior, v40 n1 suppl p98S-110S Oct 2013
Tuberculosis (TB) is a potentially fatal disease spread by an airborne pathogen infecting approximately one third of the globe. For decades, contact tracing (CT) has served a key role in the control of TB and many other notifiable communicable diseases. Unfortunately, CT is a labor-intensive and time-consuming process and is often conducted by a small and overworked nursing staff. To help improve the effectiveness of CT, we introduce a detailed, individual-based model of CT for the Canadian province of Saskatchewan. The model captures the detailed operation of TB CT, including loss to follow-up, and prophylactic and case treatment. This representation is used to assess the impact on active TB cases and TB infection prevalence of differential scoping, speed, prioritization of the CT process, and reduced loss to follow-up. Scenario results are broadly consistent with--but provide many additional insights beyond--our previously reported findings using an aggregate model. In the context of a stylized northern community, findings suggest that age- and ethnicity-prioritized schemes could improve CT effectiveness compared to unprioritized schemes by dramatically reducing TB infection and preventing on average roughly 11% ("p" < .0001) of active TB cases over a period of 20 years. Reducing loss to follow-up to 10% could yield 5.4% ("p" = .02) TB cases prevented on average with lower prevalence of TB infection, but improving the CT speed does not yield significant improvement in TB outcomes. Finally, although the work emphasized the value of social network analysis, we found that caution should be exercised in directly translating social network analysis-observed associations into prioritization recommendations.
Descriptors: Foreign Countries, Communicable Diseases, Models, Computer Simulation, Prevention, Social Networks, Indigenous Populations, Canada Natives, Monte Carlo Methods, Disease Incidence, Health Personnel
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Authoring Institution: N/A
Identifiers - Location: Canada