Evidence map›Paper›PMID 34499668›Full record

ArticlePloS one2021

Costs and cost-effectiveness of a comprehensive tuberculosis case finding strategy in Zambia.

Youngji Jo, Mary Kagujje, Karl Johnson, David Dowdy, Peter Hangoma, Lophina Chiliukutu, Monde Muyoyeta, Hojoon Sohn

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 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

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. From DOTS to DOST: a new framework for TB elimination in India.The Lancet regional health. Southeast Asia · 2026
    Review
  4. Article
  5. Article
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  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
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  13. Article
  14. Community tuberculosis screening, testing and care, Uganda.Bulletin of the World Health Organization · 2024
    Article
  15. Article
  16. Article
  17. Article
  18. The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2023
    Article
  19. Cost to perform door-to-door universal sputum screening for TB in a high-burden community.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2023
    Article
  20. 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

8 authors.

Youngji JoJohns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.ORCID 0000-0002-1440-2786
Mary KagujjeCentre For Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia.
Karl JohnsonUniversity of North Carolina School of Global Public Health, Chapel Hill, North Carolina, United States of America.
David DowdyJohns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Peter HangomaSchool of Public Health, University of Zambia, Lusaka, Zambia.
Lophina ChiliukutuCentre For Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia.
Monde MuyoyetaCentre For Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia.
Hojoon SohnJohns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionActive-case finding (ACF) programs have an important role in addressing case detection gaps and halting tuberculosis (TB) transmission. Evidence is limited on the cost-effectiveness of ACF interventions, particularly on how their value is impacted by different operational, epidemiological and patient care-seeking patterns.

methodsWe evaluated the costs and cost-effectiveness of a combined facility and community-based ACF intervention in Zambia that utilized mobile chest X-ray with computer-aided reading/interpretation software and laboratory-based Xpert MTB/RIF testing. Programmatic costs (in 2018 US dollars) were assessed from the health system perspective using prospectively collected cost and operational data. Cost-effectiveness of the ACF intervention was assessed as the incremental cost per TB death averted over a five-year time horizon using a multi-stage Markov state-transition model reflecting patient symptom-associated care-seeking and TB care under ACF compared to passive care.

resultsOver 18 months of field operations, the ACF intervention costed $435 to diagnose and initiate treatment for one person with TB. After accounting for patient symptom-associated care-seeking patterns in Zambia, we estimate that this one-time ACF intervention would incrementally diagnose 407 (7,207 versus 6,800) TB patients and avert 502 (611 versus 1,113) TB-associated deaths compared to the status quo (passive case finding), at an incremental cost of $2,284 per death averted over the next five-year period. HIV/TB mortality rate, patient symptom-associated care-seeking probabilities in the absence of ACF, and the costs of ACF patient screening were key drivers of cost-effectiveness.

conclusionsA one-time comprehensive ACF intervention simultaneously operating in public health clinics and corresponding catchment communities can have important medium-term impact on case-finding and be cost-effective in Zambia. The value of such interventions increases if targeted to populations with high HIV/TB mortality, substantial barriers (both behavioral and physical) to care-seeking exist, and when ACF interventions can optimize screening by achieving operational efficiency.

Indexed as

Cost-Benefit AnalysisHumansMass ScreeningTuberculosisZambia

Identifiers

PMID34499668
PMCPMC8428570

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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.