The introduction of video-enabled directly observed therapy (video-DOT) for patients with drug-resistant TB disease in Eswatini amid the COVID-19 pandemic - a retrospective cohort study.
COVID-19
DOT
Drug-resistant TB
Video-enabled DOT
Journal
BMC health services research
ISSN: 1472-6963
Titre abrégé: BMC Health Serv Res
Pays: England
ID NLM: 101088677
Informations de publication
Date de publication:
03 Jun 2024
03 Jun 2024
Historique:
received:
03
07
2023
accepted:
28
05
2024
medline:
4
6
2024
pubmed:
4
6
2024
entrez:
3
6
2024
Statut:
epublish
Résumé
Video-enabled directly observed therapy (video-DOT) has been proposed as an additional option for treatment provision besides in-person DOT for patients with drug-resistant TB (DRTB) disease. However, evidence and implementation experience mainly originate from well-resourced contexts. This study describes the operationalization of video-DOT in a low-resourced setting in Eswatini facing a high burden of HIV and TB amid the emergence of the COVID-19 pandemic. This is a retrospectively established cohort of patients receiving DRTB treatment during the implementation of video-DOT in Shiselweni from May 2020 to March 2022. We described intervention uptake (vs. in-person DOT) and assessed unfavorable DRTB treatment outcome (death, loss to care) using Kaplan-Meier statistics and multivariable Cox-regression models. Video-related statistics were described with frequencies and medians. We calculated the fraction of expected doses observed (FEDO) under video-DOT and assessed associations with missed video uploads using multivariable Poisson regression analysis. Of 71 DRTB patients eligible for video-DOT, the median age was 39 (IQR 30-54) years, 31.0% (n = 22) were women, 67.1% (n = 47/70) were HIV-positive, and 42.3% (n = 30) were already receiving DRTB treatment when video-DOT became available. About half of the patients (n = 37; 52.1%) chose video-DOT, mostly during the time when COVID-19 appeared in Eswatini. Video-DOT initiations were lower in new DRTB patients (aHR 0.24, 95% CI 0.12-0.48) and those aged ≥ 60 years (aHR 0.27, 95% CI 0.08-0.89). Overall, 20,634 videos were uploaded with a median number of 553 (IQR 309-748) videos per patient and a median FEDO of 92% (IQR 84-97%). Patients aged ≥ 60 years were less likely to miss video uploads (aIRR 0.07, 95% CI 0.01-0.51). The cumulative Kaplan-Meier estimate of an unfavorable treatment outcome among all patients was 0.08 (95% CI 0.03-0.19), with no differences detected by DOT approach and other baseline factors in multivariable analysis. Implementing video-DOT for monitoring of DRTB care provision amid the intersection of the HIV and COVID-19 pandemics seemed feasible. Digital health technologies provide additional options for patients to choose their preferred way to support treatment taking, thus possibly increasing patient-centered health care while sustaining favorable treatment outcomes.
Sections du résumé
BACKGROUND
BACKGROUND
Video-enabled directly observed therapy (video-DOT) has been proposed as an additional option for treatment provision besides in-person DOT for patients with drug-resistant TB (DRTB) disease. However, evidence and implementation experience mainly originate from well-resourced contexts. This study describes the operationalization of video-DOT in a low-resourced setting in Eswatini facing a high burden of HIV and TB amid the emergence of the COVID-19 pandemic.
METHODS
METHODS
This is a retrospectively established cohort of patients receiving DRTB treatment during the implementation of video-DOT in Shiselweni from May 2020 to March 2022. We described intervention uptake (vs. in-person DOT) and assessed unfavorable DRTB treatment outcome (death, loss to care) using Kaplan-Meier statistics and multivariable Cox-regression models. Video-related statistics were described with frequencies and medians. We calculated the fraction of expected doses observed (FEDO) under video-DOT and assessed associations with missed video uploads using multivariable Poisson regression analysis.
RESULTS
RESULTS
Of 71 DRTB patients eligible for video-DOT, the median age was 39 (IQR 30-54) years, 31.0% (n = 22) were women, 67.1% (n = 47/70) were HIV-positive, and 42.3% (n = 30) were already receiving DRTB treatment when video-DOT became available. About half of the patients (n = 37; 52.1%) chose video-DOT, mostly during the time when COVID-19 appeared in Eswatini. Video-DOT initiations were lower in new DRTB patients (aHR 0.24, 95% CI 0.12-0.48) and those aged ≥ 60 years (aHR 0.27, 95% CI 0.08-0.89). Overall, 20,634 videos were uploaded with a median number of 553 (IQR 309-748) videos per patient and a median FEDO of 92% (IQR 84-97%). Patients aged ≥ 60 years were less likely to miss video uploads (aIRR 0.07, 95% CI 0.01-0.51). The cumulative Kaplan-Meier estimate of an unfavorable treatment outcome among all patients was 0.08 (95% CI 0.03-0.19), with no differences detected by DOT approach and other baseline factors in multivariable analysis.
CONCLUSIONS
CONCLUSIONS
Implementing video-DOT for monitoring of DRTB care provision amid the intersection of the HIV and COVID-19 pandemics seemed feasible. Digital health technologies provide additional options for patients to choose their preferred way to support treatment taking, thus possibly increasing patient-centered health care while sustaining favorable treatment outcomes.
Identifiants
pubmed: 38831356
doi: 10.1186/s12913-024-11151-4
pii: 10.1186/s12913-024-11151-4
doi:
Substances chimiques
Antitubercular Agents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
699Informations de copyright
© 2024. The Author(s).
Références
Global tuberculosis report 2023. Geneva, Switzerland: World Health Organization; 2023.
Global tuberculosis report 2021. Geneva, Switzerland: World Health Organization; 2022.
Farley JE, Ram M, Pan W, Waldman S, Cassell GH, Chaisson RE et al. Outcomes of Multi-Drug Resistant Tuberculosis (MDR-TB) among a Cohort of South African Patients with High HIV Prevalence. PLoS ONE. 2011 Jul 22 [cited 2013 Jul 25];6(7). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3142109/
Wells CD, Cegielski JP, Nelson LJ, Laserson KF, Holtz TH, Finlay A, et al. HIV infection and multidrug-resistant tuberculosis—the Perfect Storm. J Infect Dis. 2007;196(s1):S86–107.
doi: 10.1086/518665
pubmed: 17624830
Zumla A, Raviglione M, Hafner R. Fordham Von Reyn C. Tuberculosis. N Engl J Med. 2013;368(8):745–55.
doi: 10.1056/NEJMra1200894
pubmed: 23425167
Bloom BR, Atun R, Cohen T, Dye C, Fraser H, Gomez GB et al. Tuberculosis. In: Holmes KK, Bertozzi S, Bloom BR, Jha P, editors. Major Infectious Diseases. 3rd ed. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2017. http://www.ncbi.nlm.nih.gov/books/NBK525174/
Frieden TR, Sbarbaro JA. Promoting adherence to treatment for tuberculosis: the importance of direct observation. Bull World Health Organ. 2007;85:407–9.
doi: 10.2471/BLT.06.038927
pubmed: 17639230
pmcid: 2636637
Macq JCM, Theobald S, Dick J, Dembele M. An exploration of the concept of directly observed treatment (DOT) for tuberculosis patients: from a uniform to a customised approach. Int J Tuberc Lung Dis. 2003;7(2):103–9.
pubmed: 12588009
WHO consolidated guidelines on tuberculosis. Module 4: treatment - drug-resistant tuberculosis treatment. Geneva, Switzerland: World Health Organization; 2020.
Alipanah N, Jarlsberg L, Miller C, Linh NN, Falzon D, Jaramillo E, et al. Adherence interventions and outcomes of tuberculosis treatment: a systematic review and meta-analysis of trials and observational studies. PLoS Med. 2018;15(7):e1002595.
doi: 10.1371/journal.pmed.1002595
pubmed: 29969463
pmcid: 6029765
Getahun B, Wubie M, Dejenu G, Manyazewal T. Tuberculosis care strategies and their economic consequences for patients: the missing link to end tuberculosis. Infect Dis Poverty. 2016;5:93.
doi: 10.1186/s40249-016-0187-9
pubmed: 27799063
pmcid: 5088676
Am K, Bj APBJNR, C N. M, Economic burden of tuberculosis in Tanzania: a national survey of costs faced by tuberculosis-affected households. BMC Public Health. 2022 Mar 29 [cited 2022 Apr 25];22(1). https://pubmed.ncbi.nlm.nih.gov/35351063/
Garfein RS, Doshi RP. Synchronous and asynchronous video observed therapy (VOT) for tuberculosis treatment adherence monitoring and support. J Clin Tuberc Mycobact Dis. 2019;17:100098.
Garfein RS, Liu L, Cuevas-Mota J, Collins K, Muñoz F, Catanzaro DG, et al. Tuberculosis treatment monitoring by video directly observed therapy in 5 health districts, California, USA. Emerg Infect Dis. 2018;24(10):1806–15.
doi: 10.3201/eid2410.180459
pubmed: 30226154
pmcid: 6154139
Story A, Aldridge RW, Smith CM, Garber E, Hall J, Ferenando G, et al. Smartphone-enabled video-observed versus directly observed treatment for tuberculosis: a multicentre, analyst-blinded, randomised, controlled superiority trial. Lancet. 2019;393(10177):1216–24.
doi: 10.1016/S0140-6736(18)32993-3
pubmed: 30799062
pmcid: 6429626
Wade VA, Karnon J, Eliott JA, Hiller JE. Home videophones improve direct observation in tuberculosis treatment: a mixed methods evaluation. PLoS ONE. 2012;7(11):e50155.
doi: 10.1371/journal.pone.0050155
pubmed: 23226243
pmcid: 3511425
Sinkou H, Hurevich H, Rusovich V, Zhylevich L, Falzon D, de Colombani P, et al. Video-observed treatment for tuberculosis patients in Belarus: findings from the first programmatic experience. Eur Respir J. 2017;49(3):1602049.
doi: 10.1183/13993003.02049-2016
pubmed: 28331042
pmcid: 5380873
Au-Yeung KY, DiCarlo L. Cost comparison of wirelessly vs. directly observed therapy for adherence confirmation in anti-tuberculosis treatment. Int J Tuberc Lung Dis off J Int Union Tuberc Lung Dis. 2012;16(11):1498–504.
doi: 10.5588/ijtld.11.0868
Mirsaeidi M, Farshidpour M, Banks-Tripp D, Hashmi S, Kujoth C, Schraufnagel D. Video directly observed therapy for treatment of tuberculosis is patient-oriented and cost-effective. Eur Respir J. 2015;46(3):871–4.
doi: 10.1183/09031936.00011015
pubmed: 25792632
pmcid: 4558232
Perry A, Chitnis A, Chin A, Hoffmann C, Chang L, Robinson M, et al. Real-world implementation of video-observed therapy in an urban TB program in the United States. Int J Tuberc Lung Dis. 2021;25(8):655–61.
doi: 10.5588/ijtld.21.0170
pubmed: 34330351
pmcid: 8327629
Hoffman JA, Cunningham JR, Suleh AJ, Sundsmo A, Dekker D, Vago F, et al. Mobile direct observation treatment for tuberculosis patients: a technical feasibility pilot using mobile phones in Nairobi, Kenya. Am J Prev Med. 2010;39(1):78–80.
doi: 10.1016/j.amepre.2010.02.018
pubmed: 20537846
Lester R, Park JJ, Bolten LM, Enjetti A, Johnston JC, Schwartzman K, et al. Mobile phone short message service for adherence support and care of patients with tuberculosis infection: evidence and opportunity. J Clin Tuberc Mycobact Dis. 2019;16:100108.
Western Cape Department of Health in collaboration with the National Institute for Communicable Diseases, South Africa. Risk factors for Coronavirus Disease 2019 (COVID-19) death in a Population Cohort Study from the Western Cape Province, South Africa. Clin Infect Dis off Publ Infect Dis Soc Am. 2021;73(7):e2005–15.
doi: 10.1093/cid/ciaa1198
Eswatini Population-based HIV Impact. Assessment 3 2021 (SHIMS3 2021): final report. Mbabane, Eswatini: Ministry of Health of Eswatini; 2023.
The World Bank. The World Bank | World development indicators. Infrastructure. [cited 2022 Nov 13]. https://databank.worldbank.org/source/world-development-indicators
statistica | Eswatini. price for 1GB mobile data 2022. Statista. [cited 2022 Nov 13]. https://www.statista.com/statistics/1272806/price-for-mobile-data-in-eswatini/
Peresu E, Heunis CJ, Kigoz GN, De Grave D. Patient satisfaction with directly observed treatment and multidrug-resistant tuberculosis injection administration by lay health workers in rural Eswatini. Afr J Prim Health Care Fam Med. 2020;12(1):2257.
doi: 10.4102/phcfm.v12i1.2257
pubmed: 32501027
pmcid: 7284164
COVID-19 Data Explorer. Our World in Data. [cited 2022 Apr 2]. https://ourworldindata.org/coronavirus-data-explorer
The 2017 population. And housing census preliminary results. Mbabane, Eswatini: Central Statistical Office of Eswatini; 2017.
Kerschberger B, Telnov A, Yano N, Cox H, Zabsonre I, Kabore SM et al. Successful expansion of community-based drug-resistant TB care in rural Eswatini – a retrospective cohort study. Trop Med Int Health. 0(0). https://onlinelibrary.wiley.com/doi/abs/ https://doi.org/10.1111/tmi.13299
SureAdhere M. Technology, Inc. SureAdhere Mobile Technology, Inc. [cited 2022 Apr 25]. http://www.sureadhere.com
World Health Organization. Definitions and reporting framework for tuberculosis – 2013 revision: updated December 2014 and January 2020. Définitions et cadre de notification pour la tuberculose – révision 2013. Geneva: World Health Organization. 2013 [cited 2023 Nov 19]. https://iris.who.int/handle/10665/79199
Stata 17, Texas. US: StataCorp LLC; 2021.
Sekandi JN, Buregyeya E, Zalwango S, Dobbin KK, Atuyambe L, Nakkonde D, et al. Video directly observed therapy for supporting and monitoring adherence to tuberculosis treatment in Uganda: a pilot cohort study. ERJ Open Res. 2020;6(1):00175–2019.
doi: 10.1183/23120541.00175-2019
pubmed: 32280670
pmcid: 7132038
Our World in Data. | covid-19-data. GitHub. [cited 2022 Nov 4]. https://github.com/owid/covid-19-data
Mathieu E, Ritchie H, Rodés-Guirao L, Appel C, Giattino C, Hasell J et al. Coronavirus Pandemic (COVID-19) - COVID-19: Stringency Index. Our World in Data. 2020 [cited 2023 May 27]. https://ourworldindata.org/covid-stringency-index
Migliori GB, Thong PM, Akkerman O, Alffenaar JW, Álvarez-Navascués F, Assao-Neino MM, et al. Worldwide effects of coronavirus disease pandemic on tuberculosis services, January–April 2020. Emerg Infect Dis. 2020;26(11):2709–12.
doi: 10.3201/eid2611.203163
pubmed: 32917293
pmcid: 7588533
Visca D, Tiberi S, Pontali E, Spanevello A, Migliori GB. Tuberculosis in the time of COVID-19: quality of life and digital innovation. Eur Respir J. 2020;56(2):2001998.
doi: 10.1183/13993003.01998-2020
pubmed: 32513783
pmcid: 7278505
Guo X, Yang Y, Takiff HE, Zhu M, Ma J, Zhong T, et al. A comprehensive app that improves tuberculosis treatment management through video-observed therapy: Usability Study. JMIR MHealth UHealth. 2020;8(7):e17658.
doi: 10.2196/17658
pubmed: 32735222
pmcid: 7428914
Windish P, Luchenski S, Hall J, Appleby Y, Possas L, Hemming S, et al. Video observed therapy for multidrug-resistant tuberculosis: a qualitative study of patient perspectives. Lancet. 2015;386:S78.
doi: 10.1016/S0140-6736(15)00916-2
Bommakanti KK, Smith LL, Liu L, Do D, Cuevas-Mota J, Collins K, et al. Requiring smartphone ownership for mHealth interventions: who could be left out? BMC Public Health. 2020;20(1):81.
doi: 10.1186/s12889-019-7892-9
pubmed: 31959145
pmcid: 6971938
Fekadu G, Jiang X, Yao J, You JHS. Cost-effectiveness of video-observed therapy for ambulatory management of active tuberculosis during the COVID-19 pandemic in a high-income country. Int J Infect Dis. 2021;113:271–8.
doi: 10.1016/j.ijid.2021.10.029
pubmed: 34688946
pmcid: 8530791