Time Trend Analysis of Tuberculosis Treatment While Using Digital Adherence Technologies—An Individual Patient Data Meta-Analysis of Eleven Projects across Ten High Tuberculosis-Burden Countries

Liza M. de Groot, Royal Tropical Institute - KIT
Masja Straetemans, Royal Tropical Institute - KIT
Noriah Maraba, The Aurum Institute
Lauren Jennings, Desmond Tutu Health Foundation
Maria Tarcela Gler, De La Salle Medical and Health Sciences Institute
Danaida Marcelo, De La Salle Medical and Health Sciences Institute
Mirchaye Mekoro, Health Poverty Action
Pieter Steenkamp, Health Poverty Action
Riccardo Gavioli, Health Poverty Action
Anne Spaulding, Health Through Walls
Edwin Prophete, Health Through Walls
Margarette Bury, Health Through Walls
Sayera Banu, International Centre for Diarrhoeal Disease Research Bangladesh
Sonia Sultana, International Centre for Diarrhoeal Disease Research Bangladesh
Baraka Onjare, KNCV Tuberculosis Foundation
Egwuma Efo, KNCV Tuberculosis Foundation
Jason Alacapa, KNCV Tuberculosis Foundation
Jens Levy, KNCV Tuberculosis Foundation
Mona Lisa L. Morales, KNCV Tuberculosis Foundation
Achilles Katamba, School of Medicine, Makerere University College of Health Sciences
Aleksey Bogdanov, PATH
Kateryna Gamazina, PATH
Dzhumagulova Kumarkul, The Red Crescent National Society of the Kyrgyz Republic
Orechova Li Ekaterina, The Red Crescent National Society of the Kyrgyz Republic
Adithya Cattamanchi, UCSF School of Medicine
Amera Khan, Stop TB Partnership
Mirjam I. Bakker, Royal Tropical Institute - KIT


Worldwide, non-adherence to tuberculosis (TB) treatment is problematic. Digital adherence technologies (DATs) offer a person-centered approach to support and monitor treatment. We explored adherence over time while using DATs. We conducted a meta-analysis on anonymized longitudinal adherence data for drug-susceptible (DS) TB (n = 4515) and drug-resistant (DR) TB (n = 473) populations from 11 DAT projects. Using Tobit regression, we assessed adherence for six months of treatment across sex, age, project enrolment phase, DAT-type, health care facility (HCF), and project. We found that DATs recorded high levels of adherence throughout treatment: 80% to 71% of DS-TB patients had ≥90% adherence in month 1 and 6, respectively, and 73% to 75% for DR-TB patients. Adherence increased between month 1 and 2 (DS-TB and DR-TB populations), then decreased (DS-TB). Males displayed lower adherence and steeper decreases than females (DS-TB). DS-TB patients aged 15–34 years compared to those >50 years displayed steeper decreases. Adherence was correlated within HCFs and differed between projects. TB treatment adherence decreased over time and differed between subgroups, suggesting that over time, some patients are at risk for non-adherence. The real-time monitoring of medication adherence using DATs provides opportunities for health care workers to identify patients who need greater levels of adherence support.