DOI QR코드

DOI QR Code

Improving Tuberculosis Medication Adherence: The Potential of Integrating Digital Technology and Health Belief Model

  • Mohd Fazeli Sazali (Department of Public Health Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Syed Sharizman Syed Abdul Rahim (Department of Public Health Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Ahmad Hazim Mohammad (Department of Public Health Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Fairrul Kadir (Department of Emergency Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Alvin Oliver Payus (Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Richard Avoi (Department of Public Health Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Mohammad Saffree Jeffree (Department of Public Health Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Azizan Omar (Department of Public Health Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Mohd Yusof Ibrahim (Department of Public Health Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Azman Atil (Department of Public Health Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Nooralisa Mohd Tuah (Faculty of Computing and Informatics, Universiti Malaysia Sabah) ;
  • Rahmat Dapari (Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia) ;
  • Meryl Grace Lansing (Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Ahmad Asyraf Abdul Rahim (Department of Public Health Medicine, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah) ;
  • Zahir Izuan Azhar (Department of Public Health Medicine, Faculty of Medicine, MARA Technological University)
  • Received : 2022.11.11
  • Accepted : 2022.12.16
  • Published : 2023.04.30

Abstract

Tuberculosis (TB) is a significant public health concern. Globally, TB is among the top 10 and the leading cause of death due to a single infectious agent. Providing standard anti-TB therapy for at least 6 months is recommended as one of the crucial strategies to control the TB epidemic. However, the long duration of TB treatment raised the issue of non-adherence. Non-adherence to TB therapy could negatively affect clinical and public health outcomes. Thus, directly observed therapy (DOT) has been introduced as a standard strategy to improve anti-TB medication adherence. Nonetheless, the DOT approach has been criticized due to inconvenience, stigma, reduced economic productivity, and reduced quality of life, which ultimately could complicate adherence issues. Apart from that, its effectiveness in improving anti-TB adherence is debatable. Therefore, digital technology could be an essential tool to enhance the implementation of DOT. Incorporating the health belief model (HBM) into digital technology can further increase its effectiveness in changing behavior and improving medication adherence. This article aimed to review the latest evidence regarding TB medication non-adherence, its associated factors, DOT's efficacy and its alternatives, and the use of digital technology and HBM in improving medication adherence. This paper used the narrative review methodology to analyze related articles to address the study objectives. Conventional DOT has several disadvantages in TB management. Integrating HBM in digital technology development is potentially effective in improving medication adherence. Digital technology provides an opportunity to improve medication adherence to overcome various issues related to DOT implementation.

Keywords

Acknowledgement

The authors would like to thank the University of Malaysia of Sabah for permission to publish this paper.

References

  1. World Health Organization. Global tuberculosis report 2020 [Internet]. Geneva: WHO; 2020 [cited 2022 Dec 26]. Available from: https://www.who.int/publications/i/item/9789240013131.
  2. World Health Organization. Global tuberculosis report [Internet]. Geneva: WHO; 2022 [cited 2022 Dec 26]. Available from: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022.
  3. Cohen A, Mathiasen VD, Schon T, Wejse C. The global prevalence of latent tuberculosis: a systematic review and meta-analysis. Eur Respir J 2019;54:1900655.
  4. Ministry of Health Malaysia, Academy of Medicine Malaysia, Malaysian Thoracic Society, World Health Organization. Clinical practice guidelines management of tuberculosis 3rd ed [Internet]. Putrajaya: Ministry of Health Malaysia; 2012 [cited 2022 Dec 26]. Available from: https://www.moh.gov.my/moh/attachments/8612.pdf.
  5. Choi R, Jeong BH, Koh WJ, Lee SY. Recommendations for optimizing tuberculosis treatment: therapeutic drug monitoring, pharmacogenetics, and nutritional status considerations. Ann Lab Med 2017;37:97-107. https://doi.org/10.3343/alm.2017.37.2.97
  6. Naidoo P, Theron G, Rangaka MX, Chihota VN, Vaughan L, Brey ZO, et al. The South African tuberculosis care cascade: estimated losses and methodological challenges. J Infect Dis 2017;216(Suppl 7):S702-13. https://doi.org/10.1093/infdis/jix335
  7. Iribarren S, Beck S, Pearce PF, Chirico C, Etchevarria M, Cardinale D, et al. TextTB: a mixed method pilot study evaluating acceptance, feasibility, and exploring initial efficacy of a text messaging intervention to support TB treatment adherence. Tuberc Res Treat 2013;2013:349394.
  8. Van LH, Phu PT, Vinh DN, Son VT, Hanh NT, Nhat LT, et al. Risk factors for poor treatment outcomes of 2266 multidrug-resistant tuberculosis cases in Ho Chi Minh city: a retrospective study. BMC Infect Dis 2020;20:164.
  9. World Health Organization. The End TB Strategy [Internet]. Geneva: World Health Organization; 2015 [cited 2022 Dec 26]. Available from: https://www.who.int/publications/i/item/WHO-HTM-TB-2015.19.
  10. Subbaraman R, de Mondesert L, Musiimenta A, Pai M, Mayer KH, Thomas BE, et al. Digital adherence technologies for the management of tuberculosis therapy: mapping the landscape and research priorities. BMJ Glob Health 2018;3:e001018.
  11. Karumbi J, Garner P. Directly observed therapy for treating tuberculosis. Cochrane Database Syst Rev 2015;5:CD003343.
  12. Salehitali S, Noorian K, Hafizi M, Dehkordi AH. Quality of life and its effective factors in tuberculosis patients receiving directly observed treatment short-course (DOTS). J Clin Tuberc Other Mycobact Dis 2019;15:100093.
  13. McLaren ZM, Milliken AA, Meyer AJ, Sharp AR. Does directly observed therapy improve tuberculosis treatment? More evidence is needed to guide tuberculosis policy. BMC Infect Dis 2016;16:537.
  14. World Health Organization. Adherence to long-term therapies: evidence for action [Internet]. Geneva: WHO; 2003 [cited 2022 Dec 26]. Available from: https://apps.who.int/ iris/handle/10665/42682.
  15. Valencia S, Leon M, Losada I, Sequera VG, Fernandez Quevedo M, Garcia-Basteiro AL. How do we measure adherence to anti-tuberculosis treatment? Expert Rev Anti Infect Ther 2017;15:157-65. https://doi.org/10.1080/14787210.2017.1264270
  16. Vernon A, Fielding K, Savic R, Dodd L, Nahid P. The importance of adherence in tuberculosis treatment clinical trials and its relevance in explanatory and pragmatic trials. PLoS Med 2019;16:e1002884.
  17. Wahyuni AS, Soeroso NN, Wahyuni DD. Analysis of concordance of medication-taking behaviour in tuberculosis patients in Medan, Indonesia. Open Access Maced J Med Sci 2018;6:1699-701. https://doi.org/10.3889/oamjms.2018.380
  18. Dickinson D, Wilkie P, Harris M. Taking medicines: concordance is not compliance. BMJ 1999;319:787.
  19. Lei X, Huang K, Liu Q, Jie YF, Tang SL. Are tuberculosis patients adherent to prescribed treatments in China? Results of a prospective cohort study. Infect Dis Poverty 2016;5:38.
  20. Kigozi G, Heunis C, Chikobvu P, Botha S, van Rensburg D. Factors influencing treatment default among tuberculosis patients in a high burden province of South Africa. Int J Infect Dis 2017;54:95-102. https://doi.org/10.1016/j.ijid.2016.11.407
  21. Tok PS, Liew SM, Wong LP, Razali A, Loganathan T, Chinna K, et al. Determinants of unsuccessful treatment outcomes and mortality among tuberculosis patients in Malaysia: a registry-based cohort study. PLoS One 2020;15:e0231986.
  22. Woimo TT, Yimer WK, Bati T, Gesesew HA. The prevalence and factors associated for anti-tuberculosis treatment non-adherence among pulmonary tuberculosis patients in public health care facilities in South Ethiopia: a cross-sectional study. BMC Public Health 2017;17:269.
  23. Ajema D, Shibru T, Endalew T, Gebeyehu S. Level of and associated factors for non-adherence to anti-tuberculosis treatment among tuberculosis patients in Gamo Gofa zone, southern Ethiopia: cross-sectional study. BMC Public Health 2020;20:1705.
  24. Mekonnen HS, Azagew AW. Non-adherence to anti-tuberculosis treatment, reasons and associated factors among TB patients attending at Gondar town health centers, Northwest Ethiopia. BMC Res Notes 2018;11:691.
  25. 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;393:1216-24. https://doi.org/10.1016/S0140-6736(18)32993-3
  26. Kayigamba FR, Bakker MI, Mugisha V, De Naeyer L, Gasana M, Cobelens F, et al. Adherence to tuberculosis treatment, sputum smear conversion and mortality: a retrospective cohort study in 48 Rwandan clinics. PLoS One 2013;8:e73501.
  27. Nguyen TA, Pham MT, Nguyen TL, Nguyen VN, Pham DC, Nguyen BH, et al. Video directly observed therapy to support adherence with treatment for tuberculosis in Vietnam: a prospective cohort study. Int J Infect Dis 2017;65:85-9. https://doi.org/10.1016/j.ijid.2017.09.029
  28. Fang XH, Shen HH, Hu WQ, Xu QQ, Jun L, Zhang ZP, et al. Prevalence of and factors influencing anti-tuberculosis treatment non-adherence among patients with pulmonary tuberculosis: a cross-sectional study in Anhui Province, Eastern China. Med Sci Monit 2019;25:1928-35. https://doi.org/10.12659/MSM.913510
  29. Du L, Chen X, Zhu X, Zhang Y, Wu R, Xu J, et al. Determinants of Medication adherence for pulmonary tuberculosis patients during continuation phase in Dalian, Northeast China. Patient Prefer Adherence 2020;14:1119-28. https://doi.org/10.2147/PPA.S243734
  30. Gavrilova A, Bandere D, Rutkovska I, Smits D, Maurina B, Poplavska E, et al. Knowledge about disease, medication therapy, and related medication adherence levels among patients with hypertension. Medicina (Kaunas) 2019;55:715.
  31. Tola HH, Garmaroudi G, Shojaeizadeh D, Tol A, Yekaninejad MS, Ejeta LT, et al. The effect of psychosocial factors and patients' perception of tuberculosis treatment non-adherence in Addis Ababa, Ethiopia. Ethiop J Health Sci 2017;27:447-58. https://doi.org/10.4314/ejhs.v27i5.2
  32. Tesfahuneygn G, Medhin G, Legesse M. Adherence to anti-tuberculosis treatment and treatment outcomes among tuberculosis patients in Alamata District, northeast Ethiopia. BMC Res Notes 2015;8:503.
  33. Zhang J, Yang Y, Qiao X, Wang L, Bai J, Yangchen T, et al. Factors influencing medication nonadherence to pulmonary tuberculosis treatment in Tibet, China: a qualitative study from the patient perspective. Patient Prefer Adherence 2020;14:1149-58. https://doi.org/10.2147/PPA.S252448
  34. Tola HH, Shojaeizadeh D, Tol A, Garmaroudi G, Yekaninejad MS, Kebede A, et al. Psychological and educational intervention to improve tuberculosis treatment adherence in Ethiopia based on health belief model: a cluster randomized control trial. PLoS One 2016;11:e0155147.
  35. Albasheer OB, Mahfouz MS, Solan Y, Khan DA, Muqri MA, Almutairi HA, et al. Depression and related risk factors among patients with type 2 diabetes mellitus, Jazan area, KSA: a cross-sectional study. Diabetes Metab Syndr 2018;12:117-21. https://doi.org/10.1016/j.dsx.2017.09.014
  36. Engidaw NA, Wubetu AD, Basha EA. Prevalence of depression and its associated factors among patients with diabetes mellitus at Tirunesh-Beijing general hospital, Addis Ababa, Ethiopia. BMC Public Health 2020;20:266.
  37. Azniza MR, Draman N, Siti Suhaila MY, Muhamad R. Depression and potential risk factors among the elderly with type 2 diabetes mellitus in Kedah, Malaysia. Med J Malaysia 2019;74:103-8.
  38. Yang TW, Park HO, Jang HN, Yang JH, Kim SH, Moon SH, et al. Side effects associated with the treatment of multidrug-resistant tuberculosis at a tuberculosis referral hospital in South Korea: a retrospective study. Medicine (Baltimore) 2017;96:e7482.
  39. Dar SA, Shah NN, Wani ZA, Nazir D. A prospective study on quality of life in patients with pulmonary tuberculosis at a tertiary care hospital in Kashmir, Northern India. Indian J Tuberc 2019;66:118-22. https://doi.org/10.1016/j.ijtb.2018.07.002
  40. Peh KQ, Kwan YH, Goh H, Ramchandani H, Phang JK, Lim ZY, et al. An adaptable framework for factors contributing to medication adherence: results from a systematic review of 102 conceptual frameworks. J Gen Intern Med 2021;36:2784-95. https://doi.org/10.1007/s11606-021-06648-1
  41. Pradipta IS, Houtsma D, van Boven JF, Alffenaar JC, Hak E. Interventions to improve medication adherence in tuberculosis patients: a systematic review of randomized controlled studies. NPJ Prim Care Respir Med 2020;30:21.
  42. Khachadourian V, Truzyan N, Harutyunyan A, Petrosyan V, Davtyan H, Davtyan K, et al. People-centred care versus clinic-based DOT for continuation phase TB treatment in Armenia: a cluster randomized trial. BMC Pulm Med 2020;20:105.
  43. Garfein RS, Liu L, Cuevas-Mota J, Collins K, Munoz F, Catanzaro DG, et al. Tuberculosis treatment monitoring by video directly observed therapy in 5 health districts, California, USA. Emerg Infect Dis 2018;24:1806-15. https://doi.org/10.3201/eid2410.180459
  44. Ratchakit-Nedsuwan R, Nedsuwan S, Sawadna V, Chaiyasirinroje B, Bupachat S, Ngamwithayapong-Yanai J, et al. Ensuring tuberculosis treatment adherence with a mobile-based CARE-call system in Thailand: a pilot study. Infect Dis (Lond) 2020;52:121-9. https://doi.org/10.1080/23744235.2019.1688862
  45. Guo P, Qiao W, Sun Y, Liu F, Wang C. Telemedicine technologies and tuberculosis management: a randomized controlled trial. Telemed J E Health 2020;26:1150-6. https://doi.org/10.1089/tmj.2019.0190
  46. Browne SH, Umlauf A, Tucker AJ, Low J, Moser K, Gonzalez Garcia J, et al. Wirelessly observed therapy compared to directly observed therapy to confirm and support tuberculosis treatment adherence: a randomized controlled trial. PLoS Med 2019;16:e1002891.
  47. Bahia K, Suardi S. Connected society: the state of mobile internet connectivity 2019 [Internet]. London: Global System for Mobile Communication Association; 2019 [cited 2022 Dec 26]. Available from: https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2019/07/ GSMA-State-of-Mobile-Internet-Connectivity-Report-2019.pdf.
  48. Subbaraman R, Nathavitharana RR, Mayer KH, Satyanarayana S, Chadha VK, Arinaminpathy N, et al. Constructing care cascades for active tuberculosis: a strategy for program monitoring and identifying gaps in quality of care. PLoS Med 2019;16:e1002754.
  49. Bestrashniy JR, Nguyen VN, Nguyen TL, Pham TL, Nguyen TA, Pham DC, et al. Recurrence of tuberculosis among patients following treatment completion in eight provinces of Vietnam: a nested case-control study. Int J Infect Dis 2018;74:31-7. https://doi.org/10.1016/j.ijid.2018.06.013
  50. Lee HK, Teo SSH, Barbier S, Tang SC, Yeo GH, Tan NC. The impact of direct observed therapy on daily living activities, quality of life and socioeconomic burden on patients with tuberculosis in primary care in Singapore. Proc Singap Healthc 2016;25:235-42. https://doi.org/10.1177/2010105816652148
  51. Goroh MM, Rajahram GS, Avoi R, Van Den Boogaard CH, William T, Ralph AP, et al. Epidemiology of tuberculosis in Sabah, Malaysia, 2012-2018. Infect Dis Poverty 2020;9:119.
  52. Bojorquez I, Salazar I, Garfein RS, Cerecer P, Rodwell TC. Surveillance or support: the experience of direct observation during tuberculosis treatment. Glob Public Health 2018;13:804-18. https://doi.org/10.1080/17441692.2016.1240823
  53. Furin J, Loveday M, Hlangu S, Dickson-Hall L, le Roux S, Nicol M, et al. "A very humiliating illness": a qualitative study of patient-centered care for rifampicin-resistant tuberculosis in South Africa. BMC Public Health 2020;20:76.
  54. Fatima R, Haq MU, Yaqoob A, Mahmood N, Ahmad KL, Osberg M, et al. Delivering patient-centered care in a fragile state: using patient-pathway analysis to understand tuberculosis-related care seeking in Pakistan. J Infect Dis 2017;216(Suppl 7):S733-9. https://doi.org/10.1093/infdis/jix380
  55. Donahue ML, Eberly MD, Rajnik M. Tele-TB: using telemedicine to increase access to directly observed therapy for latent tuberculosis infection. Mil Med 2021;186(Suppl 1):25-31. https://doi.org/10.1093/milmed/usaa300
  56. Garfein RS, Collins K, Munoz F, Moser K, Cerecer-Callu P, Raab F, et al. Feasibility of tuberculosis treatment monitoring by video directly observed therapy: a binational pilot study. Int J Tuberc Lung Dis 2015;19:1057-64. https://doi.org/10.5588/ijtld.14.0923
  57. Ngwatu BK, Nsengiyumva NP, Oxlade O, Mappin-Kasirer B, Nguyen NL, Jaramillo E, et al. The impact of digital health technologies on tuberculosis treatment: a systematic review. Eur Respir J 2018;51:1701596.
  58. Klonoff DC. Behavioral theory: the missing ingredient for digital health tools to change behavior and increase adherence. J Diabetes Sci Technol 2019;13:276-81. https://doi.org/10.1177/1932296818820303
  59. Maiman LA, Becker MH. The health belief model: origins and correlates in psychological theory. Health Educ Monogr 1974;2:336-53.
  60. Ashraf M, Virk RN. Determinants of medication adherence in patients with HIV: application of the health belief model. J Pak Med Assoc 2021;71:1409-12.
  61. Wang MY, Shen MJ, Wan LH, Mo MM, Wu Z, Li LL, et al. Effects of a comprehensive reminder system based on the health belief model for patients who have had a stroke on health behaviors, blood pressure, disability, and recurrence from baseline to 6 months: a randomized controlled trial. J Cardiovasc Nurs 2020;35:156-64. https://doi.org/10.1097/JCN.0000000000000631
  62. Yue Z, Li C, Weilin Q, Bin W. Application of the health belief model to improve the understanding of antihypertensive medication adherence among Chinese patients. Patient Educ Couns 2015;98:669-73. https://doi.org/10.1016/j.pec.2015.02.007
  63. Kung PC, Yeh MC, Lai MK, Liu HE. Renal transplant recipients: the factors related to immunosuppressive medication adherence based on the health belief model. J Nurs Res 2017;25:392-7. https://doi.org/10.1097/JNR.0000000000000181
  64. Dempster NR, Wildman BG, Masterson TL, Omlor GJ. Understanding treatment adherence with the health belief model in children with cystic fibrosis. Health Educ Behav 2018;45:435-43. https://doi.org/10.1177/1090198117736346
  65. Alatawi YM, Kavookjian J, Ekong G, Alrayees MM. The association between health beliefs and medication adherence among patients with type 2 diabetes. Res Social Adm Pharm 2016;12:914-25. https://doi.org/10.1016/j.sapharm.2015.11.006
  66. Eakin MN, Riekert KA. The impact of medication adherence on lung health outcomes in cystic fibrosis. Curr Opin Pulm Med 2013;19:687-91. https://doi.org/10.1097/MCP.0b013e3283659f45
  67. Sellares J, de Freitas DG, Mengel M, Reeve J, Einecke G, Sis B, et al. Understanding the causes of kidney transplant failure: the dominant role of antibody-mediated rejection and nonadherence. Am J Transplant 2012;12:388-99. https://doi.org/10.1111/j.1600-6143.2011.03840.x
  68. Jai AN, Kassim ABM, Samad AA, Baharuddin A, Rosman A, Naidu BM, et al. National Health and Morbidity Survey 2016: maternal and child health [Internet]. Kuala Lumpur: Kementeri Kesihat Malaysia; 2016 [cited 2022 Dec 26]. Available from: http://www.iku.gov.my/images/IKU/Document/REPORT/2016/NHMS2016ReportVolumeII-MaternalChildHealthFindingsv2.pdf.
  69. Azizi N, Karimy M, Salahshour VN. Determinants of adherence to tuberculosis treatment in Iranian patients: application of health belief model. J Infect Dev Ctries 2018; 12:706-11. https://doi.org/10.3855/jidc.9653
  70. Prasetya H, Murti B, Anantanyu S, Syamsulhadi M. The effect of hypnosis on adherence to antituberculosis drugs using the health belief model. Int J Clin Exp Hypn 2018;66:211-27. https://doi.org/10.1080/00207144.2018.1421361
  71. World Health Organization. Digital technologies to support tuberculosis medication adherence [Internet]. Geneva: WHO; 2017 [cited 2022 Dec 26]. Available from: http://apps.who.int/iris/bitstream/10665/205222/1/WHO_HTM_TB_2015.21_eng.pdf?ua=1.