CoChat
Sign Up
Cited by 23Tuberculosis Research and EpidemiologyInfectious Diseases and TuberculosisLung Cancer Diagnosis and Treatment

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Comparing Tuberculosis Diagnostic Yield in Smear/Culture and Xpert® MTB/RIF-Based Algorithms Using a Non-Randomised Stepped-Wedge Design

Open access
Naidoo, Pren, Dunbar, Rory, Lombard, Carl, du Toit, Elizabeth, Caldwell, Judy, Detjen, Anne, Squire, S. Bertel, Enarson, Donald A., Beyers, Nulda·2016·PLOS ONE
DOI 10.1371/journal.pone.0150487PMC PMC4773132
Open this paper in CoChat — free accountOpen PDF

Abstract

SETTING: Primary health services in Cape Town, South Africa. STUDY AIM: To compare tuberculosis (TB) diagnostic yield in an existing smear/culture-based and a newly introduced Xpert® MTB/RIF-based algorithm. METHODS: TB diagnostic yield (the proportion of presumptive TB cases with a laboratory diagnosis of TB) was assessed using a non-randomised stepped-wedge design as sites transitioned to the Xpert® based algorithm. We identified the full sequence of sputum tests recorded in the electronic laboratory database for presumptive TB cases from 60 primary health sites during seven one-month time-points, six months apart. Differences in TB yield and temporal trends were estimated using a binomial regression model. RESULTS: TB yield was 20.9% (95% CI 19.9% to 22.0%) in the smear/culture-based algorithm compared to 17.9% (95%CI 16.4% to 19.5%) in the Xpert® based algorithm. There was a decline in TB yield over time with a mean risk difference of -0.9% (95% CI -1.2% to -0.6%) (p<0.001) per time-point. When estimates were adjusted for the temporal trend, TB yield was 19.1% (95% CI 17.6% to 20.5%) in the smear/culture-based algorithm compared to 19.3% (95% CI 17.7% to 20.9%) in the Xpert® based algorithm with a risk difference of 0.3% (95% CI -1.8% to 2.3%) (p = 0.796). Culture tests were undertaken for 35.5% of smear-negative compared to 17.9% of Xpert® negative low MDR-TB risk cases and for 82.6% of smear-negative compared to 40.5% of Xpert® negative high MDR-TB risk cases in respective algorithms. CONCLUSION: Introduction of an Xpert® based algorithm did not produce the expected increase in TB diagnostic yield. Studies are required to assess whether improving adherence to the Xpert® negative algorithm for HIV-infected individuals will increase yield. In light of the high cost of Xpert®, a review of its role as a screening test for all presumptive TB cases may be warranted.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0150487

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In Naidoo et al. 2016, practical, financial or logistic constraints prevented simultaneous introduction of Xpert-based algorithm to all facilities.

    “Practical, financial or logistic constraints prevented simultaneous introduction of Xpert-based algorithm to all facilities”

    From Applications and implementation considerations for stepped-wedge designs in sub-Saharan Africa: a systematic review (2026) · cites this paper as [91]

    The Discussion (Strengths and limitations; page unavailable) says stepped-wedge analysis is appropriate where practical, financial or logistic constraints prevent simultaneous introduction, and that health services scheduled this study’s Xpert rollout according to operational requirements. The Methods (Study design, population and timeframes; page unavailable) confirms that facilities adopted the algorithm at different times. The paper does not explicitly say those constraints prevented simultaneous introduction in this study. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Metadata

Authors
Naidoo, P., Dunbar, R., Lombard, C., du Toit, E., Caldwell, J., Detjen, A., Squire, S. B., Enarson, D. A., & Beyers, N.
Affiliations
  • Stellenbosch University (ZA)
  • South African Medical Research Council (ZA)
  • City of Cape Town (ZA)
  • International Union Against Tuberculosis and Lung Disease (FR)
  • Liverpool School of Tropical Medicine (GB)
Corresponding author
Naidoo, Pren
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2016-03-01
Volume
11
Issue
3
Pages
e0150487
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0150487
PMC
PMC4773132
OpenAlex
W2289838397
Cited by
23
References
30
Citations by year
2026: 1 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 4 · 2020: 2 · 2019: 2 · 2018: 3 · 2017: 3 · 2016: 1
FWCI
2.77
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • LSTM Online Archive (Liverpool School of Tropical Medicine) (acceptedVersion)
  • SUNScholar (Stellenbosch University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
United States Agency for International Development (GHN-A-00-08-00004-00, GHN‐A‐00‐08‐00004)
Topics
  • Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
  • Health Sciences › Medicine › Infectious Diseases › Infectious Diseases and Tuberculosis
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
Keywords
tuberculosis, Xpert MTB/RIF, smear-negative tuberculosis, diagnostic yield, multidrug-resistant tuberculosis, primary care, people living with HIV, tuberculosis screening, stepped wedge design, Cape Town
MeSH terms
Adolescent, Adult, Aged, Aged, 80 and over, Algorithms, Child, Child, Preschool, Female, Humans, Infant, Male, Middle Aged, Sensitivity and Specificity, South Africa, Sputum, Tuberculosis, Pulmonary, Tuberculosis, Multidrug-Resistant, Clinical Laboratory Techniques, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review