Evidence map›Paper›PMID 39653521›Full record

ArticleBMJ global health2024

Modelling the epidemiological and economic impact of digital adherence technologies with differentiated care for tuberculosis treatment in Ethiopia.

Lara Goscé, Amare Worku Tadesse, Nicola Foster, Kristian van Kalmthout, Job van Rest, Jense van der Wal, Martin J Harker, Norma Madden, Tofik Abdurhman, Demekech Gadissa and 12 more

Abstract read
In one paragraph

Article in BMJ global health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Lara GoscéTB modelling Group, TB centre, London School of Hygiene & Tropical Medicine Faculty of Epidemiology and Public Health, London, UK lara.gosce@lshtm.ac.uk.ORCID http://orcid.org/0000-0003-2392-6271
Amare Worku TadesseDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine Faculty of Epidemiology and Population Health, London, UK.ORCID http://orcid.org/0000-0002-7805-3191
Nicola FosterDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine Faculty of Epidemiology and Population Health, London, UK.
Kristian van KalmthoutKNCV Tuberculosis Foundation, Den Haag, Netherlands.
Job van RestKNCV Tuberculosis Foundation, Den Haag, Netherlands.
Jense van der WalKNCV Tuberculosis Foundation, Den Haag, Netherlands.
Martin J HarkerTB modelling Group, TB centre, London School of Hygiene & Tropical Medicine Faculty of Epidemiology and Public Health, London, UK.ORCID http://orcid.org/0000-0001-6154-3517
Norma MaddenKNCV Tuberculosis Foundation, Den Haag, Netherlands.ORCID http://orcid.org/0000-0002-7667-2108
Tofik AbdurhmanKNCV Tuberculosis Foundation, Addis Ababa, Ethiopia.
Demekech GadissaKNCV Tuberculosis Foundation, Addis Ababa, Ethiopia.
Ahmed BedruKNCV Tuberculosis Foundation, Addis Ababa, Ethiopia.
Tanyaradzwa N DubeThe Aurum Institute, Johannesburg, South Africa.
Jason Alacapa7KNCV Tuberculosis Foundation, Makati City, Metro Manila, Philippines.
Andrew MgangaKNCV Tuberculosis Foundation, Dar es Salaam, Tanzania.
Natasha DeyanovaProgram for Appropriate Technology in Health (PATH), Kyiv, Ukraine.
Salome CharalambousThe Aurum Institute, Johannesburg, South Africa.
Taye LettaEthiopian Ministry of Health, Addis Ababa, Ethiopia.
Degu JereneKNCV Tuberculosis Foundation, Den Haag, Netherlands.
Richard WhiteTB modelling Group, TB centre, London School of Hygiene & Tropical Medicine Faculty of Epidemiology and Public Health, London, UK.ORCID http://orcid.org/0000-0003-4410-6635
Katherine L FieldingDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine Faculty of Epidemiology and Population Health, London, UK.ORCID http://orcid.org/0000-0002-6524-3754
Rein Mgj HoubenTB modelling Group, TB centre, London School of Hygiene & Tropical Medicine Faculty of Epidemiology and Public Health, London, UK.ORCID http://orcid.org/0000-0003-4132-7467
Christopher Finn McQuaidTB modelling Group, TB centre, London School of Hygiene & Tropical Medicine Faculty of Epidemiology and Public Health, London, UK.ORCID http://orcid.org/0000-0001-6199-0931

Funding

Defining drivers of TB transmission in the era of universal ART, and implications for finding the walking wellR01AI147321 · NIAID · LONDON SCH/HYGIENE & TROPICAL MEDICINE · PI GRANT, ALISON · 2019 to 2024
$2.3M
NIAID NIH HHS R01 AI147321Wellcome Trust
6 · The paper itself

Abstract

backgroundDigital 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.

methodsWe 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.

findingsWe 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.

interpretationDAT interventions, while showing limited epidemiological impact, could substantially reduce TB treatment costs for both patients and the healthcare provider.

Indexed as

Antitubercular AgentsMedication AdherenceTuberculosisAdolescentAdultCost-Benefit AnalysisEthiopiaFemaleHumansIncidenceMaleMiddle AgedYoung AdultAntitubercular AgentsHealth economicsMathematical modellingTuberculosis

Identifiers

PMID39653521
PMCPMC11628985

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.