Mobilising community networks for early identification of tuberculosis and treatment initiation in Cambodia: an evaluation of a seed-and-recruit model

Abstract

Background and objectivesThe effects of active case finding (ACF) models that mobilise community networks for early identification and treatment of tuberculosis (TB) remain unknown. We investigated and compared the effect of community-based ACF using a seed-and-recruit model with one-off roving ACF and passive case finding (PCF) on the time to treatment initiation and identification of bacteriologically confirmed TB.MethodsIn this retrospective cohort study conducted in 12 operational districts in Cambodia, we assessed relationships between ACF models and: 1) the time to treatment initiation using Cox proportional hazards regression; and 2) the identification of bacteriologically confirmed TB using modified Poisson regression with robust sandwich variance.ResultsWe included 728 adults with TB, of whom 36% were identified via the community-based ACF using a seed-and-recruit model. We found community-based ACF using a seed-and-recruit model was associated with shorter delay to treatment initiation compared to one-off roving ACF (hazard ratio 0.81, 95% CI 0.68–0.96). Compared to one-off roving ACF and PCF, community-based ACF using a seed-and-recruit model was 45% (prevalence ratio (PR) 1.45, 95% CI 1.19–1.78) and 39% (PR 1.39, 95% CI 0.99–1.94) more likely to find and detect bacteriologically confirmed TB, respectively.ConclusionMobilising community networks to find TB cases was associated with early initiation of TB treatment in Cambodia. This approach was more likely to find bacteriologically confirmed TB cases, contributing to the reduction of risk of transmission within the community.