Modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in Ethiopia
Background Digital adherence technologies (DATs) with associated differentiated care are potential tools to improve tuberculosis (TB) treatment outcomes and reduce associated costs for both patients and healthcare providers. However, the balance between epidemiological and economic benefits remains...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2024-12-01
|
| Series: | BMJ Global Health |
| Online Access: | https://gh.bmj.com/content/9/12/e016997.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841526504060616704 |
|---|---|
| author | Richard White Katherine L Fielding Salome Charalambous Nicola Foster Degu Jerene Andrew Mganga Taye Letta Job van Rest Amare Worku Tadesse Rein MGJ Houben Ahmed Bedru Kristian van Kalmthout Christopher Finn McQuaid Lara Goscé Jense van der Wal Martin J Harker Norma Madden Tofik Abdurhman Demekech Gadissa Tanyaradzwa N Dube Jason Alacapa Natasha Deyanova |
| author_facet | Richard White Katherine L Fielding Salome Charalambous Nicola Foster Degu Jerene Andrew Mganga Taye Letta Job van Rest Amare Worku Tadesse Rein MGJ Houben Ahmed Bedru Kristian van Kalmthout Christopher Finn McQuaid Lara Goscé Jense van der Wal Martin J Harker Norma Madden Tofik Abdurhman Demekech Gadissa Tanyaradzwa N Dube Jason Alacapa Natasha Deyanova |
| author_sort | Richard White |
| collection | DOAJ |
| description | Background Digital adherence technologies (DATs) with associated differentiated care are potential tools to improve tuberculosis (TB) treatment outcomes and reduce associated costs for both patients and healthcare providers. However, the balance between epidemiological and economic benefits remains unclear. Here, we used data from the ASCENT trial to estimate the potential long-term epidemiological and economic impact of DAT interventions in Ethiopia.Methods We developed a compartmental transmission model for TB, calibrated to Ethiopia and parameterised with patient and provider costs. We compared the epidemiological and economic impact of two DAT interventions, a digital pillbox and medication labels, to the current standard of care, assuming each was introduced at scale in 2023. We projected long-term TB incidence, mortality and costs to 2035 and conducted a threshold analysis to identify the maximum possible epidemiological impact of a DAT intervention by assuming 100% treatment completion for patients on DAT.Findings We estimated small and uncertain epidemiological benefits of the pillbox intervention compared with the standard of care in Ethiopia, with a difference of −0.4% (95% uncertainty interval (UI) −1.1%; +2.0%) incident TB episodes and −0.7% (95% UI −2.2%; +3.6%) TB deaths. However, our analysis also found large total provider and patient cost savings (US$163 (95% UI US$118; US$211) and US$3 (95%UI: US$1; US$5), respectively, over 2023–2035), translating to a 50.2% (95% UI 35.9%; 65.2%) reduction in total cost of treatment. Results were similar for the medication label intervention. The maximum possible epidemiological impact a theoretical DAT intervention could achieve over the same timescale would be a 3% (95% UI 1.4%; 5.5%) reduction in incident TB and an 8.2% (95% UI 4.4%; 12.8%) reduction in TB deaths.Interpretation DAT interventions, while showing limited epidemiological impact, could substantially reduce TB treatment costs for both patients and the healthcare provider. |
| format | Article |
| id | doaj-art-8089550ac7644518ad0e7aa1f52e9b98 |
| institution | Kabale University |
| issn | 2059-7908 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Global Health |
| spelling | doaj-art-8089550ac7644518ad0e7aa1f52e9b982025-01-16T18:25:09ZengBMJ Publishing GroupBMJ Global Health2059-79082024-12-0191210.1136/bmjgh-2024-016997Modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in EthiopiaRichard White0Katherine L Fielding1Salome Charalambous2Nicola Foster3Degu Jerene4Andrew Mganga5Taye Letta6Job van Rest7Amare Worku Tadesse8Rein MGJ Houben9Ahmed Bedru10Kristian van Kalmthout11Christopher Finn McQuaid12Lara Goscé13Jense van der Wal14Martin J Harker15Norma Madden16Tofik Abdurhman17Demekech Gadissa18Tanyaradzwa N Dube19Jason Alacapa20Natasha Deyanova21Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine Faculty of Epidemiology and Population Health, London, UKDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine Faculty of Epidemiology and Population Health, London, UKThe Aurum Institute, Johannesburg, South AfricaDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine Faculty of Epidemiology and Population Health, London, UK1 Arba Minch University, Arba Minch, EthiopiaKNCV Tuberculosis Foundation, Dar es Salaam, TanzaniaEthiopian Ministry of Health, Addis Ababa, EthiopiaKNCV Tuberculosis Foundation, Den Haag, NetherlandsDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine Faculty of Epidemiology and Population Health, London, UKTB modelling Group, TB centre, London School of Hygiene & Tropical Medicine Faculty of Epidemiology and Public Health, London, UKKNCV Tuberculosis Foundation, Addis Ababa, EthiopiaKNCV Tuberculosis Foundation, Den Haag, NetherlandsTB modelling Group, TB centre, London School of Hygiene & Tropical Medicine Faculty of Epidemiology and Public Health, London, UKTB modelling Group, TB centre, London School of Hygiene & Tropical Medicine Faculty of Epidemiology and Public Health, London, UKKNCV Tuberculosis Foundation, Den Haag, NetherlandsTB modelling Group, TB centre, London School of Hygiene & Tropical Medicine Faculty of Epidemiology and Public Health, London, UKKNCV Tuberculosis Foundation, Den Haag, NetherlandsKNCV Tuberculosis Foundation, Addis Ababa, EthiopiaKNCV Tuberculosis Foundation, Addis Ababa, EthiopiaThe Aurum Institute, Johannesburg, South Africa7KNCV Tuberculosis Foundation, Makati City, Metro Manila, PhilippinesProgram for Appropriate Technology in Health (PATH), Kyiv, UkraineBackground Digital adherence technologies (DATs) with associated differentiated care are potential tools to improve tuberculosis (TB) treatment outcomes and reduce associated costs for both patients and healthcare providers. However, the balance between epidemiological and economic benefits remains unclear. Here, we used data from the ASCENT trial to estimate the potential long-term epidemiological and economic impact of DAT interventions in Ethiopia.Methods We developed a compartmental transmission model for TB, calibrated to Ethiopia and parameterised with patient and provider costs. We compared the epidemiological and economic impact of two DAT interventions, a digital pillbox and medication labels, to the current standard of care, assuming each was introduced at scale in 2023. We projected long-term TB incidence, mortality and costs to 2035 and conducted a threshold analysis to identify the maximum possible epidemiological impact of a DAT intervention by assuming 100% treatment completion for patients on DAT.Findings We estimated small and uncertain epidemiological benefits of the pillbox intervention compared with the standard of care in Ethiopia, with a difference of −0.4% (95% uncertainty interval (UI) −1.1%; +2.0%) incident TB episodes and −0.7% (95% UI −2.2%; +3.6%) TB deaths. However, our analysis also found large total provider and patient cost savings (US$163 (95% UI US$118; US$211) and US$3 (95%UI: US$1; US$5), respectively, over 2023–2035), translating to a 50.2% (95% UI 35.9%; 65.2%) reduction in total cost of treatment. Results were similar for the medication label intervention. The maximum possible epidemiological impact a theoretical DAT intervention could achieve over the same timescale would be a 3% (95% UI 1.4%; 5.5%) reduction in incident TB and an 8.2% (95% UI 4.4%; 12.8%) reduction in TB deaths.Interpretation DAT interventions, while showing limited epidemiological impact, could substantially reduce TB treatment costs for both patients and the healthcare provider.https://gh.bmj.com/content/9/12/e016997.full |
| spellingShingle | Richard White Katherine L Fielding Salome Charalambous Nicola Foster Degu Jerene Andrew Mganga Taye Letta Job van Rest Amare Worku Tadesse Rein MGJ Houben Ahmed Bedru Kristian van Kalmthout Christopher Finn McQuaid Lara Goscé Jense van der Wal Martin J Harker Norma Madden Tofik Abdurhman Demekech Gadissa Tanyaradzwa N Dube Jason Alacapa Natasha Deyanova Modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in Ethiopia BMJ Global Health |
| title | Modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in Ethiopia |
| title_full | Modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in Ethiopia |
| title_fullStr | Modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in Ethiopia |
| title_full_unstemmed | Modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in Ethiopia |
| title_short | Modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in Ethiopia |
| title_sort | modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in ethiopia |
| url | https://gh.bmj.com/content/9/12/e016997.full |
| work_keys_str_mv | AT richardwhite modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT katherinelfielding modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT salomecharalambous modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT nicolafoster modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT degujerene modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT andrewmganga modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT tayeletta modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT jobvanrest modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT amareworkutadesse modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT reinmgjhouben modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT ahmedbedru modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT kristianvankalmthout modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT christopherfinnmcquaid modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT laragosce modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT jensevanderwal modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT martinjharker modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT normamadden modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT tofikabdurhman modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT demekechgadissa modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT tanyaradzwandube modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT jasonalacapa modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia AT natashadeyanova modellingtheepidemiologicalandeconomicimpactofdigitaladherencetechnologieswithdifferentiatedcarefortuberculosistreatmentinethiopia |