IDEAS home Printed from https://ideas.repec.org/a/plo/pgph00/0002918.html
   My bibliography  Save this article

Identifying adolescents at risk for suboptimal adherence to tuberculosis treatment: A prospective cohort study

Author

Listed:
  • Silvia S Chiang
  • Joshua Ray Tanzer
  • Jeffrey R Starke
  • Jennifer F Friedman
  • Betsabe Roman Sinche
  • Katya León Ostos
  • Rosa Espinoza Meza
  • Elmer Altamirano
  • Catherine B Beckhorn
  • Victoria E Oliva Rapoport
  • Marco A Tovar
  • Leonid Lecca

Abstract

Adolescents account for an estimated 800,000 incident tuberculosis (TB) cases annually and are at risk for suboptimal adherence to TB treatment. Most studies of adolescent TB treatment adherence have used surveillance data with limited psychosocial information. This prospective cohort study aimed to identify risk factors for suboptimal adherence to rifampicin-susceptible TB treatment among adolescents (10–19 years old) in Lima, Peru. We collected psychosocial data using self-administered surveys and clinical data via medical record abstraction. Applying k-means cluster analysis, we grouped participants by psychosocial characteristics hypothesized to impact adherence. Then, we conducted mixed effects regression to compare suboptimal adherence–defined as 10% of doses)–between clusters. Treatment setting (facility vs. home) and drug formulation (single drug vs. fixed dose combination) were interaction terms. Of 249 participants, 90 (36.1%) were female. Median age was 17 (IQR: 15, 16.6) years. We identified three clusters–A, B, and C–of participants based on psychosocial characteristics. Cluster C had the lowest support from caregivers, other family members, and friends; had the weakest motivation to complete TB treatment; were least likely to live with their mothers; and had experienced the most childhood adversity. Among the 118 (47.4%) participants who received facility-based treatment with single drug formulations, adherence did not differ between Clusters A and B, but Cluster C had six-fold odds of suboptimal adherence compared to Cluster A. In Clusters B and C, adherence worsened over time, but only in Cluster C did mean adherence fall below 90% within six months. Our findings have implications for the care of adolescents with TB. When caring for adolescents with low social support and other risk factors, clinicians should take extra measures to reinforce adherence, such as identifying a community health worker or peer to provide treatment support. Implementing newly recommended shorter regimens also may facilitate adherence.

Suggested Citation

  • Silvia S Chiang & Joshua Ray Tanzer & Jeffrey R Starke & Jennifer F Friedman & Betsabe Roman Sinche & Katya León Ostos & Rosa Espinoza Meza & Elmer Altamirano & Catherine B Beckhorn & Victoria E Oliva, 2024. "Identifying adolescents at risk for suboptimal adherence to tuberculosis treatment: A prospective cohort study," PLOS Global Public Health, Public Library of Science, vol. 4(2), pages 1-16, February.
    • Handle: RePEc:plo:pgph00:0002918
    DOI: 10.1371/journal.pgph.0002918
    as

    Download full text from publisher

    File URL: https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0002918
    Download Restriction: no
    File URL: https://journals.plos.org/globalpublichealth/article/file?id=10.1371/journal.pgph.0002918&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pgph.0002918?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. repec:plo:pone00:0118457 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    3. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    4. Lin Lin & Rachel L Spreng & Kelly E Seaton & S Moses Dennison & Lindsay C Dahora & Daniel J Schuster & Sheetal Sawant & Peter B Gilbert & Youyi Fong & Neville Kisalu & Andrew J Pollard & Georgia D Tom, 2024. "GeM-LR: Discovering predictive biomarkers for small datasets in vaccine studies," PLOS Computational Biology, Public Library of Science, vol. 20(11), pages 1-23, November.
    5. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    6. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    7. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    8. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    9. repec:plo:pone00:0154450 is not listed on IDEAS
    10. Eunsil Seok & Akhgar Ghassabian & Yuyan Wang & Mengling Liu, 2024. "Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 435-458, July.
    11. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    12. Georges Steffgen & Philipp E. Sischka & Martha Fernandez de Henestrosa, 2020. "The Quality of Work Index and the Quality of Employment Index: A Multidimensional Approach of Job Quality and Its Links to Well-Being at Work," IJERPH, MDPI, vol. 17(21), pages 1-31, October.
    13. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    14. Esef Hakan Toytok & Sungur Gürel, 2019. "Does Project Children’s University Increase Academic Self-Efficacy in 6th Graders? A Weak Experimental Design," Sustainability, MDPI, vol. 11(3), pages 1-12, February.
    15. J M van Niekerk & M C Vos & A Stein & L M A Braakman-Jansen & A F Voor in ‘t holt & J E W C van Gemert-Pijnen, 2020. "Risk factors for surgical site infections using a data-driven approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    16. Stefkovics, Ádám & Krekó, Péter & Koltai, Júlia, 2024. "When reality knocks on the door. The effect of conspiracy beliefs on COVID-19 vaccine acceptance and the moderating role of experience with the virus," Social Science & Medicine, Elsevier, vol. 356(C).
    17. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    18. Lara Jehi & Xinge Ji & Alex Milinovich & Serpil Erzurum & Amy Merlino & Steve Gordon & James B Young & Michael W Kattan, 2020. "Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    19. Rawan Omar & Sooyun Caroline Tavolacci & Lathan Liou & Dillan F Villavisanis & Yoav Y Broza & Hossam Haick, 2024. "Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients," PLOS Global Public Health, Public Library of Science, vol. 4(3), pages 1-17, March.
    20. Matthew Carli & Mary H. Ward & Catherine Metayer & David C. Wheeler, 2022. "Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    21. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pgph00:0002918. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: globalpubhealth (email available below). General contact details of provider: https://journals.plos.org/globalpublichealth .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.