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Adherence interventions and outcomes of tuberculosis treatment: A systematic review and meta-analysis of trials and observational studies

Author

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  • Narges Alipanah
  • Leah Jarlsberg
  • Cecily Miller
  • Nguyen Nhat Linh
  • Dennis Falzon
  • Ernesto Jaramillo
  • Payam Nahid

Abstract

Background: Incomplete adherence to tuberculosis (TB) treatment increases the risk of delayed culture conversion with continued transmission in the community, as well as treatment failure, relapse, and development or amplification of drug resistance. We conducted a systematic review and meta-analysis of adherence interventions, including directly observed therapy (DOT), to determine which approaches lead to improved TB treatment outcomes. Methods and findings: We systematically reviewed Medline as well as the references of published review articles for relevant studies of adherence to multidrug treatment of both drug-susceptible and drug-resistant TB through February 3, 2018. We included randomized controlled trials (RCTs) as well as prospective and retrospective cohort studies (CSs) with an internal or external control group that evaluated any adherence intervention and conducted a meta-analysis of their impact on TB treatment outcomes. Our search identified 7,729 articles, of which 129 met the inclusion criteria for quantitative analysis. Seven adherence categories were identified, including DOT offered by different providers and at various locations, reminders and tracers, incentives and enablers, patient education, digital technologies (short message services [SMSs] via mobile phones and video-observed therapy [VOT]), staff education, and combinations of these interventions. When compared with DOT alone, self-administered therapy (SAT) was associated with lower rates of treatment success (CS: risk ratio [RR] 0.81, 95% CI 0.73–0.89; RCT: RR 0.94, 95% CI 0.89–0.98), adherence (CS: RR 0.83, 95% CI 0.75–0.93), and sputum smear conversion (RCT: RR 0.92, 95% CI 0.87–0.98) as well as higher rates of development of drug resistance (CS: RR 4.19, 95% CI 2.34–7.49). When compared to DOT provided by healthcare providers, DOT provided by family members was associated with a lower rate of adherence (CS: RR 0.86, 95% CI 0.79–0.94). DOT delivery in the community versus at the clinic was associated with a higher rate of treatment success (CS: RR 1.08, 95% CI 1.01–1.15) and sputum conversion at the end of two months (CS: RR 1.05, 95% CI 1.02–1.08) as well as lower rates of treatment failure (CS: RR 0.56, 95% CI 0.33–0.95) and loss to follow-up (CS: RR 0.63, 95% CI 0.40–0.98). Medication monitors improved adherence and treatment success and VOT was comparable with DOT. SMS reminders led to a higher treatment completion rate in one RCT and were associated with higher rates of cure and sputum conversion when used in combination with medication monitors. TB treatment outcomes improved when patient education, healthcare provider education, incentives and enablers, psychological interventions, reminders and tracers, or mobile digital technologies were employed. Our findings are limited by the heterogeneity of the included studies and lack of standardized research methodology on adherence interventions. Conclusion: TB treatment outcomes are improved with the use of adherence interventions, such as patient education and counseling, incentives and enablers, psychological interventions, reminders and tracers, and digital health technologies. Trained healthcare providers as well as community delivery provides patient-centered DOT options that both enhance adherence and improve treatment outcomes as compared to unsupervised, SAT alone. In this systematic review and meta-analysis, Payam Nahid and colleagues investigate results from trials and observational studies to identify methods for encouraging patients to complete tuberculosis treatment that are associated with improved TB outcomes.Why was this study done?: What did the researchers do and find?: What do these findings mean?:

Suggested Citation

  • Narges Alipanah & Leah Jarlsberg & Cecily Miller & Nguyen Nhat Linh & Dennis Falzon & Ernesto Jaramillo & Payam Nahid, 2018. "Adherence interventions and outcomes of tuberculosis treatment: A systematic review and meta-analysis of trials and observational studies," PLOS Medicine, Public Library of Science, vol. 15(7), pages 1-44, July.
  • Handle: RePEc:plo:pmed00:1002595
    DOI: 10.1371/journal.pmed.1002595
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    Cited by:

    1. Kwang-Sim Jang & Jeong-Eun Oh & Gyeong-Suk Jeon, 2022. "Effects of Simulated Laughter Therapy Using a Breathing Exercise: A Study on Hospitalized Pulmonary Tuberculosis Patients," IJERPH, MDPI, vol. 19(16), pages 1-13, August.
    2. Natasha C H Ting & Nicole El-Turk & Michael S H Chou & Claudia C Dobler, 2020. "Patient-perceived treatment burden of tuberculosis treatment," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-13, October.
    3. Kirubel Manyazewal Mussie & Solomon Abebe Yimer & Tsegahun Manyazewal & Christoph Gradmann, 2019. "Exploring local realities: Perceptions and experiences of healthcare workers on the management and control of drug-resistant tuberculosis in Addis Ababa, Ethiopia," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-16, November.
    4. Seup Park & Ilham Sentissi & Seung Jae Gil & Won-Seok Park & ByungKwon Oh & Ah Reum Son & Young Ju Kong & Sol Park & Eunseong Paek & Yong Joon Park & Seung Heon Lee, 2019. "Medication Event Monitoring System for Infectious Tuberculosis Treatment in Morocco: A Retrospective Cohort Study," IJERPH, MDPI, vol. 16(3), pages 1-9, January.
    5. Ninfa Marlen Chaves Torres & Jecxy Julieth Quijano Rodríguez & Pablo Sebastián Porras Andrade & María Belen Arriaga & Eduardo Martins Netto, 2019. "Factors predictive of the success of tuberculosis treatment: A systematic review with meta-analysis," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-24, December.
    6. Paulo Ruiz-Grosso & Rodrigo Cachay & Adriana de la Flor & Alvaro Schwalb & Cesar Ugarte-Gil, 2020. "Association between tuberculosis and depression on negative outcomes of tuberculosis treatment: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-13, January.
    7. Seup Park & Narae Moon & Byungkwon Oh & Miyeon Park & Kilho Kang & Ilham Sentissi & Sung-Heui Bae, 2021. "Improving Treatment Adherence with Integrated Patient Management for TB Patients in Morocco," IJERPH, MDPI, vol. 18(19), pages 1-12, September.
    8. Abdullah A. Saati & Muhammad Khurram & Hani Faidah & Abdul Haseeb & Marcello Iriti, 2021. "A Saudi Arabian Public Health Perspective of Tuberculosis," IJERPH, MDPI, vol. 18(19), pages 1-37, September.
    9. Kim, Min Kyong & Bhattacharya, Jayanta & Bhattacharya, Joydeep, 2024. "Is income inequality linked to infectious disease prevalence? A hypothesis-generating study using tuberculosis," Social Science & Medicine, Elsevier, vol. 345(C).
    10. Melese Yeshambaw Teferi & Ziad El-Khatib & Minyahil Tadesse Boltena & Azeb Tarekegn Andualem & Benedict Oppong Asamoah & Mulatu Biru & Hawult Taye Adane, 2021. "Tuberculosis Treatment Outcome and Predictors in Africa: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 18(20), pages 1-15, October.
    11. Jonathan Salcedo & Monica Rosales & Jeniffer S Kim & Daisy Nuno & Sze-chuan Suen & Alicia H Chang, 2021. "Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-15, July.
    12. Brian McKay & Maria Castellanos & Mark Ebell & Christopher C Whalen & Andreas Handel, 2019. "An attempt to reproduce a previous meta-analysis and a new analysis regarding the impact of directly observed therapy on tuberculosis treatment outcomes," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-13, May.

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