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

Alvin Kuo Jing Teo; Kiesha Prem; Sovannary Tuot; Chetra Ork; Sothearith Eng; Tripti Pande; Monyrath Chry; Li Yang Hsu; Siyan Yi

doi:10.1183/23120541.00368-2019

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

ERJ Open Res. 2020 May 4;6(2):00368-2019. doi: 10.1183/23120541.00368-2019. eCollection 2020 Apr.

Authors

Alvin Kuo Jing Teo¹, Kiesha Prem^{1

2}, Sovannary Tuot³, Chetra Ork³, Sothearith Eng³, Tripti Pande⁴, Monyrath Chry⁵, Li Yang Hsu^{1

6}, Siyan Yi^{1

3

7

8}

Affiliations

¹ Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore.
² Dept of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
³ KHANA Centre for Population Health Research, Phnom Penh, Cambodia.
⁴ McGill International TB Centre, Montreal, Canada.
⁵ Cambodia Anti-Tuberculosis Association, Phnom Penh, Cambodia.
⁶ Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore.
⁷ Center for Global Health Research, Touro University California, Vallejo, CA, USA.
⁸ School of Public Health, National Institute of Public Health, Phnom Penh, Cambodia.

Abstract

Background and objectives: The 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.

Methods: In 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.

Results: We 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.

Conclusion: Mobilising 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.