Characterizing Treatment Adherence Trajectories in the endTB Multisite Cohort of Drug-Resistant Tuberculosis Patients: An Application of Group-Based Trajectory Modeling.
| Title: | Characterizing Treatment Adherence Trajectories in the endTB Multisite Cohort of Drug-Resistant Tuberculosis Patients: An Application of Group-Based Trajectory Modeling. |
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| Authors: | Law S; McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.; Fulcher I; Delfina, San Francisco, California, USA.; Ashraf S; TB Control Programme-Sindh, National TB Control Programme, Karachi, Pakistan.; Bastard M; Global Programme on TB & Lung Health, World Health Organization, Geneva, Switzerland.; Docteur W; Partners In Health, Cange, Haiti.; Franke MF; Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA.; Partners In Health, Boston, Massachusetts, USA.; Guerra D; Socios En Salud Sucursal Peru, Lima, Peru.; Hewison C; Médecins Sans Frontières, Paris, France.; Huerga H; Epicentre, Paris, France.; Khan M; Interactive Research and Development, Durban, South Africa.; Khan P; Interactive Research and Development Global, Singapore, Singapore.; Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.; Khan U; Interactive Research and Development Global, Singapore, Singapore.; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.; Kliescikova J; Médecins sans Frontières, Dushanbe, Tajikistan.; Kumsa A; National TB, Leprosy and Other Lung Diseases Control Program, Addis Ababa, Ethiopia.; Lomtadze N; National Center for Tuberculosis and Lung Diseases, Tbilisi, Georgia.; Department of Medicine, David Tvildiani Medical University, Tbilisi, Georgia.; Department of Medicine, The University of Georgia, Tbilisi, Georgia.; Putri FA; Interactive Research & Development, Jakarta, Indonesia.; Rich ML; Partners In Health, Boston, Massachusetts, USA.; Division of Global Health Equity, Brigham and Women's Hospital, Boston, Massachusetts, USA.; Seung K; Division of Global Health Equity, Brigham and Women's Hospital, Boston, Massachusetts, USA.; Skrahina A; WHO Regional Office for Europe, Copenhagen, Denmark.; Tamirat M; Partners In Health, Boston, Massachusetts, USA.; Partners In Health Lesotho, Maseru, Lesotho.; Vo LNQ; Friends for International TB Relief, Ha Noi, Viet Nam.; Mitnick CD; Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA.; Partners In Health, Boston, Massachusetts, USA. |
| Source: | Clinical infectious diseases : an official publication of the Infectious Diseases Society of America [Clin Infect Dis] 2026 Mar 17; Vol. 82 (3), pp. e571-e579. |
| Publication Type: | Journal Article; Observational Study; Multicenter Study |
| Language: | English |
| Journal Info: | Publisher: Oxford University Press Country of Publication: United States NLM ID: 9203213 Publication Model: Print Cited Medium: Internet ISSN: 1537-6591 (Electronic) Linking ISSN: 10584838 NLM ISO Abbreviation: Clin Infect Dis Subsets: MEDLINE |
| Imprint Name(s): | Publication: Jan. 2011- : Oxford : Oxford University Press; Original Publication: Chicago, IL : The University of Chicago Press, c1992- |
| MeSH Terms: | Tuberculosis, Multidrug-Resistant*/drug therapy ; Antitubercular Agents*/therapeutic use ; Medication Adherence*; Rifampin/therapeutic use ; Humans ; Male ; Female ; Adult ; Middle Aged ; Treatment Outcome ; Cohort Studies |
| Abstract: | Background: In tuberculosis (TB) care, adherence is often assessed using a simple 80% threshold, which may overlook meaningful patterns. We analyzed adherence trajectories among individuals treated for rifampicin- or multidrug-resistant TB (RR/MDR-TB) in the endTB observational study to identify more informative patterns.; Methods: We applied a joint latent class mixed model to classify adherence trajectories and assess their relationship with treatment outcomes. Model performance was compared to common classification methods (eg 80% adherence threshold) using Kendall's τb and area under the receiver operating curve for predicting unsuccessful outcomes.; Results: Among 1787 individuals, we identified 4 adherence patterns: "consistently high" (72.5%), "high to low" (14.3%), "low to high" (7.3%), and "consistently low" (5.9%). Compared to the "consistently high" group, those in "high to low" (hazard ratio [HR] = 23.2; 95% confidence interval [CI]: 15.7-24.3) and "consistently low" (HR = 43.2; 95% CI: 26.2-71.5) groups had significantly higher risk of unsuccessful outcomes, while the "low to high" group did not (HR = 0.7; 95% CI: .1-3.8). Our trajectory model more accurately predicted outcomes than common classification methods (P < .01).; Conclusions: Group-based trajectory modeling provides more nuanced insights into adherence patterns than conventional classification methods. Our findings demonstrate that patients with RR/MDR-TB who exhibited initial poor adherence followed by subsequent improvement achieved clinical outcomes comparable to those with consistently high adherence throughout treatment. This finding challenges the prevailing assumption that sustained high adherence is necessary for treatment success, suggesting that adherence patterns, rather than overall adherence rates, may be more predictive of clinical outcomes in the management of RR/MDR-TB.; (© The Author(s) 2025. Published by Oxford University Press on behalf of Infectious Diseases Society of America.) |
| Competing Interests: | Potential conflicts of interest. C. D. M. received research funding from the National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health (NIH), speaker honorarium from the International Union Against Tuberculosis and Lung Disease—North America Region, and serves on the Data and Safety Monitoring Boards of the Centers for Disease Control and Prevention (CDC) Tuberculosis Trials Consortium and the Division of AIDS at the NIAID, and on the Scientific Advisory Board for Akagera Medicines. C. H. receives salary support from Médecins Sans Frontières. M. L. R. has received salary support from Partners In Health and Brigham and Women's Hospital and research funding from Unitaid, consults for the World Health Organization, and serves on the boards of Pivot and Plants Earth Life. All other authors report no potential conflicts. |
| Grant Information: | 001 International WHO_ World Health Organization; #258467 Harvard Data Science Initiative Postdoctoral Research Fund; #TS1-170663 Canada CAPMC CIHR; Unitaid; Médecins Sans Frontières |
| Contributed Indexing: | Keywords: MDR-TB; adherence; directly observed therapy; group-based trajectory models; tuberculosis |
| Substance Nomenclature: | 0 (Antitubercular Agents); VJT6J7R4TR (Rifampin) |
| Entry Date(s): | Date Created: 20250822 Date Completed: 20260316 Latest Revision: 20260328 |
| Update Code: | 20260328 |
| PubMed Central ID: | PMC13017710 |
| DOI: | 10.1093/cid/ciaf467 |
| PMID: | 40843468 |
| Database: | MEDLINE |
Journal Article; Observational Study; Multicenter Study