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Published in: BMC Public Health 1/2023

Open Access 01-12-2023 | Tuberculosis | Research

Risk factors for poor engagement with a smart pillbox adherence intervention among persons on tuberculosis treatment in Ethiopia

Authors: Amare Worku Tadesse, Martina Cusinato, Gedion Teferra Weldemichael, Tofik Abdurhman, Demelash Assefa, Hiwot Yazew, Demekech Gadissa, Amanuel Shiferaw, Mahilet Belachew, Mamush Sahile, Job van Rest, Ahmed Bedru, Nicola Foster, Degu Jerene, Katherine Linda Fielding

Published in: BMC Public Health | Issue 1/2023

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Abstract

Background

Non-adherence to tuberculosis treatment increases the risk of poor treatment outcomes. Digital adherence technologies (DATs), including the smart pillbox (EvriMED), aim to improve treatment adherence and are being widely evaluated. As part of the Adherence Support Coalition to End TB (ASCENT) project we analysed data from a cluster-randomised trial of DATs and differentiated care in Ethiopia to examine individual-factors for poor engagement with the smart pillbox.

Methods

Data were obtained from a cohort of trial participants with drug-sensitive tuberculosis (DS-TB) whose treatment started between 1 December 2020 and 1 May 2022, and who were using the smart pillbox. Poor engagement with the pillbox was defined as (i) > 20% days with no digital confirmation and (ii) the count of days with no digital confirmation, and calculated over a two evaluation periods (56-days and 168-days). Logistic random effects regression was used to model > 20% days with no digital confirmation and negative binomial random effects regression to model counts of days with no digital confirmation, both accounting for clustering of individuals at the facility-level.

Results

Among 1262 participants, 10.8% (133/1262) over 56-days and 15.8% (200/1262) over 168-days had > 20% days with no digital confirmation. The odds of poor engagement was less among participants in the higher stratum of socio-economic position (SEP) over 56-days. Overall, 4,689/67,315 expected doses over 56-days and 18,042/199,133 expected doses over 168-days were not digitally confirmed. Compared to participants in the poorest SEP stratum, participants in the wealthiest stratum had lower rates of days not digitally confirmed over 168-days (adjusted rate ratio [RRa]:0.79; 95% confidence interval [CI]: 0.65, 0.96). In both evaluation periods (56-days and 168-days), HIV-positive status (RRa:1.29; 95%CI: 1.02, 1.63 and RRa:1.28; 95%CI: 1.07, 1.53), single/living independent (RRa:1.31; 95%CI: 1.03, 1.67 and RRa:1.38; 95%CI: 1.16, 1.64) and separated/widowed (RRa:1.40; 95%CI: 1.04, 1.90 and RRa:1.26; 95%CI: 1.00, 1.58) had higher rates of counts of days with no digital confirmation.

Conclusion

Poorest SEP stratum, HIV-positive status, single/living independent and separated/ widowed were associated with poor engagement with smart pillbox among people with DS-TB in Ethiopia. Differentiated care for these sub-groups may reduce risk of non-adherence to TB treatment.
Literature
1.
go back to reference WHO, World Health Organisation. Adherence to long-term therapies: evidence for action. Geneva: World Health Organization; 2003. WHO, World Health Organisation. Adherence to long-term therapies: evidence for action. Geneva: World Health Organization; 2003.
2.
go back to reference Ali AOA, Prins MH. Patient characteristics associated with non-adherence to tuberculosis treatment: a systematic review. J Tuberc Res. 2020;08(02):73–92.CrossRef Ali AOA, Prins MH. Patient characteristics associated with non-adherence to tuberculosis treatment: a systematic review. J Tuberc Res. 2020;08(02):73–92.CrossRef
3.
go back to reference Dogah E, et al. Factors influencing adherence to tuberculosis treatment in the Ketu North District of the Volta Region. Ghana Tuberc Res Treat. 2021;2021:6685039.PubMed Dogah E, et al. Factors influencing adherence to tuberculosis treatment in the Ketu North District of the Volta Region. Ghana Tuberc Res Treat. 2021;2021:6685039.PubMed
4.
go back to reference Fang XH, 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.CrossRefPubMed Fang XH, 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.CrossRefPubMed
5.
go back to reference Gashu KD, Gelaye KA, Tilahun B. Adherence to TB treatment remains low during continuation phase among adult patients in Northwest Ethiopia. BMC Infect Dis. 2021;21(1):725.CrossRefPubMedPubMedCentral Gashu KD, Gelaye KA, Tilahun B. Adherence to TB treatment remains low during continuation phase among adult patients in Northwest Ethiopia. BMC Infect Dis. 2021;21(1):725.CrossRefPubMedPubMedCentral
6.
go back to reference Nezenega ZS, Perimal-Lewis L, and Maeder AJ. Factors influencing patient adherence to tuberculosis treatment in Ethiopia: a literature review. Int J Environ Res Public Health. 2020; 17(15). Nezenega ZS, Perimal-Lewis L, and  Maeder AJ. Factors influencing patient adherence to tuberculosis treatment in Ethiopia: a literature review. Int J Environ Res Public Health. 2020; 17(15).
7.
go back to reference Ruru Y, et al. Factors associated with non-adherence during tuberculosis treatment among patients treated with DOTS strategy in Jayapura, Papua Province, Indonesia. Glob Health Action. 2018;11(1):1510592.CrossRefPubMedPubMedCentral Ruru Y, et al. Factors associated with non-adherence during tuberculosis treatment among patients treated with DOTS strategy in Jayapura, Papua Province, Indonesia. Glob Health Action. 2018;11(1):1510592.CrossRefPubMedPubMedCentral
8.
go back to reference Stagg HR, et al. Temporal factors and missed doses of tuberculosis treatment. A causal associations approach to analyses of digital adherence data. Ann Am Thorac Soc. 2020; 17(4):438–449. Stagg HR, et al. Temporal factors and missed doses of tuberculosis treatment. A causal associations approach to analyses of digital adherence data. Ann Am Thorac Soc. 2020; 17(4):438–449.
10.
go back to reference Zegeye A, et al. Prevalence and determinants of anti-tuberculosis treatment non-adherence in Ethiopia: a systematic review and meta-analysis. PLOS ONE. 2019;14(1):e0210422. Zegeye A, et al. Prevalence and determinants of anti-tuberculosis treatment non-adherence in Ethiopia: a systematic review and meta-analysis. PLOS ONE. 2019;14(1):e0210422.
11.
go back to reference WHO. Handbook for the use of digital technologies to support tuberculosis medication adherence. 2017, Geneva: World Health Organization. WHO. Handbook for the use of digital technologies to support tuberculosis medication adherence. 2017, Geneva: World Health Organization.
12.
go back to reference Tadesse AW, et al. Evaluation of implementation and effectiveness of digital adherence technology with differentiated care to support tuberculosis treatment adherence and improve treatment outcomes in Ethiopia: a study protocol for a cluster randomised trial. BMC Infect Dis. 2021;21(1):1149.CrossRefPubMedPubMedCentral Tadesse AW, et al. Evaluation of implementation and effectiveness of digital adherence technology with differentiated care to support tuberculosis treatment adherence and improve treatment outcomes in Ethiopia: a study protocol for a cluster randomised trial. BMC Infect Dis. 2021;21(1):1149.CrossRefPubMedPubMedCentral
13.
go back to reference Jerene D, et al. Effectiveness of digital adherence technologies in improving tuberculosis treatment outcomes in four countries: a pragmatic cluster randomised trial protocol. BMJ Open. 2023;13(3): e068685.CrossRefPubMedPubMedCentral Jerene D, et al. Effectiveness of digital adherence technologies in improving tuberculosis treatment outcomes in four countries: a pragmatic cluster randomised trial protocol. BMJ Open. 2023;13(3): e068685.CrossRefPubMedPubMedCentral
14.
go back to reference Global tuberculosis report 2022. Geneva: World Health Organization. 2022. Licence: CC BY-NC-SA 3.0 IGO. Global tuberculosis report 2022. Geneva: World Health Organization. 2022. Licence: CC BY-NC-SA 3.0 IGO.
15.
go back to reference Gebremariam RB, Wolde M, Beyene A. Determinants of adherence to anti-TB treatment and associated factors among adult TB patients in Gondar city administration, Northwest, Ethiopia: based on health belief model perspective. J Health Popul Nutr. 2021;40(1):49.CrossRefPubMedPubMedCentral Gebremariam RB, Wolde M, Beyene A. Determinants of adherence to anti-TB treatment and associated factors among adult TB patients in Gondar city administration, Northwest, Ethiopia: based on health belief model perspective. J Health Popul Nutr. 2021;40(1):49.CrossRefPubMedPubMedCentral
16.
go back to reference 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(1):691.CrossRefPubMedPubMedCentral 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(1):691.CrossRefPubMedPubMedCentral
17.
go back to reference Krieger N. Theories for social epidemiology in the 21st century: an ecosocial perspective. Int J Epidemiol. 2001;30(4):668–77.CrossRefPubMed Krieger N. Theories for social epidemiology in the 21st century: an ecosocial perspective. Int J Epidemiol. 2001;30(4):668–77.CrossRefPubMed
18.
go back to reference Atkinson AB, et al. Measuring Poverty around the World. Micklewright J, Brandolini A, editors. Princeton University Press; 2019. Atkinson AB, et al. Measuring Poverty around the World. Micklewright J, Brandolini A, editors. Princeton University Press; 2019.
19.
go back to reference Martel P, Mbofana F, Cousens S. The polychoric dual-component wealth index as an alternative to the DHS index: Addressing the urban bias. J Glob Health. 2021;11:04003.CrossRefPubMedPubMedCentral Martel P, Mbofana F, Cousens S. The polychoric dual-component wealth index as an alternative to the DHS index: Addressing the urban bias. J Glob Health. 2021;11:04003.CrossRefPubMedPubMedCentral
20.
go back to reference Peduzzi P, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9.CrossRefPubMed Peduzzi P, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9.CrossRefPubMed
21.
22.
go back to reference Liu X, et al. Effectiveness of Electronic Reminders to Improve Medication Adherence in Tuberculosis Patients: A Cluster-Randomised Trial. PLoS Med. 2015;12(9): e1001876.CrossRefPubMedPubMedCentral Liu X, et al. Effectiveness of Electronic Reminders to Improve Medication Adherence in Tuberculosis Patients: A Cluster-Randomised Trial. PLoS Med. 2015;12(9): e1001876.CrossRefPubMedPubMedCentral
23.
go back to reference Charalambous S, et al. TB treatment adherence amongst drug-susceptible TB persons using medication monitor and differentiated care approach versus standard of care in South Africa. in IJTLD. 2022. EP-27–870. Charalambous S, et al. TB treatment adherence amongst drug-susceptible TB persons using medication monitor and differentiated care approach versus standard of care in South Africa. in IJTLD. 2022. EP-27–870.
24.
go back to reference Lynch J, et al. Is income inequality a determinant of population health? Part 1. A systematic review Milbank Q. 2004;82(1):5–99.CrossRefPubMed Lynch J, et al. Is income inequality a determinant of population health? Part 1. A systematic review Milbank Q. 2004;82(1):5–99.CrossRefPubMed
25.
go back to reference Pickett KE, Wilkinson RG. Income inequality and health: a causal review. Soc Sci Med. 2015;128:316–26.CrossRefPubMed Pickett KE, Wilkinson RG. Income inequality and health: a causal review. Soc Sci Med. 2015;128:316–26.CrossRefPubMed
26.
go back to reference Wilkinson RG, Pickett KE. Income inequality and population health: a review and explanation of the evidence. Soc Sci Med. 2006;62(7):1768–84.CrossRefPubMed Wilkinson RG, Pickett KE. Income inequality and population health: a review and explanation of the evidence. Soc Sci Med. 2006;62(7):1768–84.CrossRefPubMed
27.
go back to reference Gebremariam MK, Bjune GA, Frich JC. Barriers and facilitators of adherence to TB treatment in patients on concomitant TB and HIV treatment: a qualitative study. BMC Public Health. 2010;10:651.CrossRefPubMedPubMedCentral Gebremariam MK, Bjune GA, Frich JC. Barriers and facilitators of adherence to TB treatment in patients on concomitant TB and HIV treatment: a qualitative study. BMC Public Health. 2010;10:651.CrossRefPubMedPubMedCentral
28.
go back to reference Jaeggi AV, et al. Do wealth and inequality associate with health in a small-scale subsistence society? Elife. 2021;10:e59437 Jaeggi AV, et al. Do wealth and inequality associate with health in a small-scale subsistence society? Elife. 2021;10:e59437
29.
go back to reference Lonnroth K, et al. Drivers of tuberculosis epidemics: the role of risk factors and social determinants. Soc Sci Med. 2009;68(12):2240–6.CrossRefPubMed Lonnroth K, et al. Drivers of tuberculosis epidemics: the role of risk factors and social determinants. Soc Sci Med. 2009;68(12):2240–6.CrossRefPubMed
30.
go back to reference Whitehead M, Dahlgren G, Evans T. Equity and health sector reforms: can low-income countries escape the medical poverty trap? Lancet. 2001;358(9284):833–6.CrossRefPubMed Whitehead M, Dahlgren G, Evans T. Equity and health sector reforms: can low-income countries escape the medical poverty trap? Lancet. 2001;358(9284):833–6.CrossRefPubMed
31.
go back to reference Hailemichael Y, et al. Mental health problems and socioeconomic disadvantage: a controlled household study in rural Ethiopia. Int J Equity Health. 2019;18(1):121.CrossRefPubMedPubMedCentral Hailemichael Y, et al. Mental health problems and socioeconomic disadvantage: a controlled household study in rural Ethiopia. Int J Equity Health. 2019;18(1):121.CrossRefPubMedPubMedCentral
32.
go back to reference Awofeso N. Anti-tuberculosis medication side-effects constitute major factor for poor adherence to tuberculosis treatment. Bull World Health Organ. 2008; 86(3): p. B-D. Awofeso N. Anti-tuberculosis medication side-effects constitute major factor for poor adherence to tuberculosis treatment. Bull World Health Organ. 2008; 86(3): p. B-D.
33.
go back to reference Mindachew M, et al. Perceived barriers to the implementation of Isoniazid preventive therapy for people living with HIV in resource constrained settings: a qualitative study. Pan Afr Med J. 2014;17:26.CrossRefPubMedPubMedCentral Mindachew M, et al. Perceived barriers to the implementation of Isoniazid preventive therapy for people living with HIV in resource constrained settings: a qualitative study. Pan Afr Med J. 2014;17:26.CrossRefPubMedPubMedCentral
34.
go back to reference Zewude SB, Ajebe TM. Magnitude of optimal adherence and predictors for a low level of adherence among HIV/AIDS-infected adults in South Gondar zone, Northwest Ethiopia: a multifacility cross-sectional study. BMJ Open. 2022;12(1): e056009.CrossRefPubMedPubMedCentral Zewude SB, Ajebe TM. Magnitude of optimal adherence and predictors for a low level of adherence among HIV/AIDS-infected adults in South Gondar zone, Northwest Ethiopia: a multifacility cross-sectional study. BMJ Open. 2022;12(1): e056009.CrossRefPubMedPubMedCentral
35.
go back to reference Govender S, Mash R. What are the reasons for patients not adhering to their anti-TB treatment in a South African district hospital? South African Family Practice. 2014;51(6):512–6.CrossRef Govender S, Mash R. What are the reasons for patients not adhering to their anti-TB treatment in a South African district hospital? South African Family Practice. 2014;51(6):512–6.CrossRef
36.
go back to reference Muture BN, et al. Factors associated with default from treatment among tuberculosis patients in Nairobi province, Kenya: a case control study. BMC Public Health. 2011;11:696.CrossRefPubMedPubMedCentral Muture BN, et al. Factors associated with default from treatment among tuberculosis patients in Nairobi province, Kenya: a case control study. BMC Public Health. 2011;11:696.CrossRefPubMedPubMedCentral
37.
go back to reference Deshmukh RD, et al. Social support a key factor for adherence to multidrug-resistant tuberculosis treatment. Indian J Tuberc. 2018;65(1):41–7.CrossRefPubMed Deshmukh RD, et al. Social support a key factor for adherence to multidrug-resistant tuberculosis treatment. Indian J Tuberc. 2018;65(1):41–7.CrossRefPubMed
38.
go back to reference Fetensa G, et al. Magnitude and determinants of delay in diagnosis of tuberculosis patients in Ethiopia: a systematic review and meta-analysis: 2020. Arch Public Health. 2022;80(1):78.CrossRefPubMedPubMedCentral Fetensa G, et al. Magnitude and determinants of delay in diagnosis of tuberculosis patients in Ethiopia: a systematic review and meta-analysis: 2020. Arch Public Health. 2022;80(1):78.CrossRefPubMedPubMedCentral
39.
go back to reference Li X, et al. Effectiveness of comprehensive social support interventions among elderly patients with tuberculosis in communities in China: a community-based trial. J Epidemiol Community Health. 2018;72(5):369–75.CrossRefPubMed Li X, et al. Effectiveness of comprehensive social support interventions among elderly patients with tuberculosis in communities in China: a community-based trial. J Epidemiol Community Health. 2018;72(5):369–75.CrossRefPubMed
40.
go back to reference Musiimenta A, et al. Mobile health technologies may be acceptable tools for providing social support to tuberculosis patients in rural Uganda: a parallel mixed-method study. Tuberc Res Treat. 2020;2020:7401045.PubMedPubMedCentral Musiimenta A, et al. Mobile health technologies may be acceptable tools for providing social support to tuberculosis patients in rural Uganda: a parallel mixed-method study. Tuberc Res Treat. 2020;2020:7401045.PubMedPubMedCentral
41.
go back to reference Tadesse S. Stigma against Tuberculosis Patients in Addis Ababa, Ethiopia. PLOS ONE. 2016;11(4):e0152900. Tadesse S. Stigma against Tuberculosis Patients in Addis Ababa, Ethiopia. PLOS ONE. 2016;11(4):e0152900.
42.
go back to reference Charalambous S, et al. Clinic-level factors influencing patient outcomes on antiretroviral therapy in primary health clinics in South Africa. AIDS. 2016;30(7):1099–109.CrossRefPubMed Charalambous S, et al. Clinic-level factors influencing patient outcomes on antiretroviral therapy in primary health clinics in South Africa. AIDS. 2016;30(7):1099–109.CrossRefPubMed
43.
go back to reference Paterson DL, et al. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med. 2000;133(1):21–30.CrossRefPubMed Paterson DL, et al. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med. 2000;133(1):21–30.CrossRefPubMed
44.
go back to reference Bastard M, et al. Effects of treatment interruption patterns on treatment success among patients with multidrug-resistant tuberculosis in Armenia and Abkhazia. J Infect Dis. 2015;211(10):1607–15.CrossRefPubMed Bastard M, et al. Effects of treatment interruption patterns on treatment success among patients with multidrug-resistant tuberculosis in Armenia and Abkhazia. J Infect Dis. 2015;211(10):1607–15.CrossRefPubMed
45.
go back to reference Menzies D, et al. Adverse events with 4 months of rifampin therapy or 9 months of isoniazid therapy for latent tuberculosis infection: a randomized trial. Ann Intern Med. 2008;149(10):689–97.CrossRefPubMed Menzies D, et al. Adverse events with 4 months of rifampin therapy or 9 months of isoniazid therapy for latent tuberculosis infection: a randomized trial. Ann Intern Med. 2008;149(10):689–97.CrossRefPubMed
46.
go back to reference Nackers F, et al. Adherence to Self-Administered Tuberculosis Treatment in a High HIV-Prevalence Setting: a Cross-Sectional Survey in Homa Bay, Kenya. PLOS ONE. 2012;7(3):e32140. Nackers F, et al. Adherence to Self-Administered Tuberculosis Treatment in a High HIV-Prevalence Setting: a Cross-Sectional Survey in Homa Bay, Kenya. PLOS ONE. 2012;7(3):e32140.
47.
go back to reference Van der Kop ML, et al. The effect of weekly text-message communication on treatment completion among patients with latent tuberculosis infection: study protocol for a randomised controlled trial (WelTel LTBI). BMJ Open. 2014;4(4): e004362.CrossRefPubMedPubMedCentral Van der Kop ML, et al. The effect of weekly text-message communication on treatment completion among patients with latent tuberculosis infection: study protocol for a randomised controlled trial (WelTel LTBI). BMJ Open. 2014;4(4): e004362.CrossRefPubMedPubMedCentral
Metadata
Title
Risk factors for poor engagement with a smart pillbox adherence intervention among persons on tuberculosis treatment in Ethiopia
Authors
Amare Worku Tadesse
Martina Cusinato
Gedion Teferra Weldemichael
Tofik Abdurhman
Demelash Assefa
Hiwot Yazew
Demekech Gadissa
Amanuel Shiferaw
Mahilet Belachew
Mamush Sahile
Job van Rest
Ahmed Bedru
Nicola Foster
Degu Jerene
Katherine Linda Fielding
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2023
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-023-16905-z

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