Evidence map›Paper›PMID 29399596›Full record

ArticleOpen forum infectious diseases2018

Cost-effectiveness of WHO-Recommended Algorithms for TB Case Finding at Ethiopian HIV Clinics.

Max W Adelman, Deborah A McFarland, Mulugeta Tsegaye, Abraham Aseffa, Russell R Kempker, Henry M Blumberg

Abstract read
In one paragraph

Article in Open forum infectious diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 3 pooled it
–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

9 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Cost-effectiveness of TB diagnostic technologies in Ethiopia: a modelling study.Cost effectiveness and resource allocation : C/E · 2024
    Article
  6. Efficiency of TB service provision in the public and private health sectors in Ethiopia.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022
    Article
  7. Article
  8. Article
  9. 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

6 authors.

Max W AdelmanDepartment of Medicine, Massachusetts General Hospital, Boston, Massachusetts.
Deborah A McFarlandHubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia.
Mulugeta TsegayeTraining Division, ALERT Hospital, Addis Ababa, Ethiopia.
Abraham AseffaArmauer Hansen Research Institute, Addis Ababa, Ethiopia.
Russell R KempkerDivision of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Henry M BlumbergHubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia.

Funding

Implementing a Maternal health and PRegnancy Outcomes Vision for Everyone (IMPROVE)UL1TR002378 · NCATS · EMORY UNIVERSITY · PI Andres J Garcia, Elizabeth O. Ofili · 2017 to 2026
$92.1M
Ujima Mentoring ProgramP30MH062246 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Sheri Ann Lippman · 2001 to 2026
$57.4M
Atlanta Clinical and Translational Science Institute (ACTSI) RenewalUL1TR000454 · NCATS · EMORY UNIVERSITY · PI STEPHENS, DAVID S · 2012 to 2016
$25.8M
Ethiopia-Emory TB Research Training ProgramD43TW009127 · FIC · EMORY UNIVERSITY · PI HENRY M BLUMBERG, Kidist Bobosha · 2013 to 2026
$3.4M
Atlanta Clinical and Translational Science Institute (ACTSI) RenewalTL1TR000456 · NCATS · EMORY UNIVERSITY · PI STEPHENS, DAVID S · 2012 to 2016
$1.2M
Intra Cavitary Pharmacokinetics and Drug Resistance in Pulmonary TuberculosisK23AI103044 · NIAID · EMORY UNIVERSITY · PI KEMPKER, RUSSELL RYAN · 2013 to 2017
$910k
FIC NIH HHS D43 TW009127NCATS NIH HHS TL1 TR000456NCATS NIH HHS UL1 TR000454NCATS NIH HHS UL1 TR002378NIAID NIH HHS K23 AI103044NIMH NIH HHS P30 MH062246
6 · The paper itself

Abstract

backgroundThe World Health Organization (WHO) recommends active tuberculosis (TB) case finding and a rapid molecular diagnostic test (Xpert MTB/RIF) to detect TB among people living with HIV (PLHIV) in high-burden settings. Information on the cost-effectiveness of these recommended strategies is crucial for their implementation.

methodsWe conducted a model-based cost-effectiveness analysis comparing 2 algorithms for TB screening and diagnosis at Ethiopian HIV clinics: (1) WHO-recommended symptom screen combined with Xpert for PLHIV with a positive symptom screen and (2) current recommended practice algorithm (CRPA; based on symptom screening, smear microscopy, and clinical TB diagnosis). Our primary outcome was US$ per disability-adjusted life-year (DALY) averted. Secondary outcomes were additional true-positive diagnoses, and false-negative and false-positive diagnoses averted.

resultsCompared with CRPA, combining a WHO-recommended symptom screen with Xpert was highly cost-effective (incremental cost of $5 per DALY averted). Among a cohort of 15 000 PLHIV with a TB prevalence of 6% (900 TB cases), this algorithm detected 8 more true-positive cases than CRPA, and averted 2045 false-positive and 8 false-negative diagnoses compared with CRPA. The WHO-recommended algorithm was marginally costlier ($240 000) than CRPA ($239 000). In sensitivity analysis, the symptom screen/Xpert algorithm was dominated at low Xpert sensitivity (66%).

conclusionsIn this model-based analysis, combining a WHO-recommended symptom screen with Xpert for TB diagnosis among PLHIV was highly cost-effective ($5 per DALY averted) and more sensitive than CRPA in a high-burden, resource-limited setting.

Indexed as

cost-effectivenessdeveloping countriesEthiopiamodelingTB/HIV co-infection

Identifiers

PMID29399596
PMCPMC5788063

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.