Evidence map›Paper›PMID 30869026›Full record

ArticleEpidemiology and infection2019

Bayesian evaluation of three serological tests for the diagnosis of bovine brucellosis in Bangladesh.

A K M A Rahman, S Smit, B Devleesschauwer, P Kostoulas, E Abatih, C Saegerman, M Shamsuddin, D Berkvens, N K Dhand, M P Ward

Open access · hybridAbstract readComparative Study
In one paragraph

Article in Epidemiology and infection, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 14% of its field
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Risk Assessment ofVeterinary sciences · 2025
    Article
  5. Exploring bovine brucellosis in Bangladesh: Current scenario and future outlook.Journal of advanced veterinary and animal research · 2024
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Bovine brucellosis - a comprehensive review.The veterinary quarterly · 2021
    Review
  13. Article
  14. 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

10 authors at 7 institutions in 4 countries.

A K M A RahmanDepartment of Medicine,Bangladesh Agricultural University,Mymensingh-2202,Bangladesh.
S SmitDepartment of Biomedical Sciences,Institute of Tropical Medicine,Nationalestraat 155, B-2000 Antwerp,Belgium.
B DevleesschauwerDepartment of Epidemiology and Public Health, Sciensano, Rue Juliette Wytsmanstraat 14, 1050 Brussels,Belgium.ORCID 0000-0002-2867-6892
P KostoulasLaboratory of Epidemiology, Biostatistics and Animal Health Economics,School of Health Sciences, Faculty of Veterinary Science, University of Thessaly,Karditsa,224 Trikalon st. 43100,Greece.
E AbatihDepartment of Applied Mathematics, Computer Science and Statistics, Faculty of Sciences,Ghent University,281 Krijgslaan, B-9000, Ghent,Belgium.
C SaegermanResearch Unit of Epidemiology and Risk Analysis applied to Veterinary Science (UREAR-ULg), Fundamental and Applied Research for Animals & Health (FARAH) Center, Faculty of Veterinary Medicine, University of Liege,Quartier Vallée 2, Avenue de Cureghem 7A, B42, Sart-Tilman Liege,Belgium.
M ShamsuddinDepartment of Surgery and Obstetrics,Bangladesh Agricultural University,Mymensingh-2202,Bangladesh.
D BerkvensDepartment of Biomedical Sciences,Institute of Tropical Medicine,Nationalestraat 155, B-2000 Antwerp,Belgium.
N K DhandSydney School of Veterinary Science, The University of Sydney,425 Werombi Road, Camden, 2570 NSW,Australia.
M P WardSydney School of Veterinary Science, The University of Sydney,425 Werombi Road, Camden, 2570 NSW,Australia.ORCID 0000-0002-9921-4986
Instituut voor Tropische Geneeskunde · BEThe University of Sydney · AUUniversity of Liège · BEBangladesh Agricultural University · BDGhent University · BESciensano (Belgium) · BEUniversity of Thessaly · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We evaluated the performance of three serological tests - an immunoglobulin G indirect enzyme linked immunosorbent assay (iELISA), a Rose Bengal test and a slow agglutination test (SAT) - for the diagnosis of bovine brucellosis in Bangladesh. Cattle sera (n = 1360) sourced from Mymensingh district (MD) and a Government owned dairy farm (GF) were tested in parallel. We used a Bayesian latent class model that adjusted for the conditional dependence among the three tests and assumed constant diagnostic accuracy of the three tests in both populations. The sensitivity and specificity of the three tests varied from 84.6% to 93.7%, respectively. The true prevalences of bovine brucellosis in MD and the GF were 0.6% and 20.4%, respectively. Parallel interpretation of iELISA and SAT yielded the highest negative predictive values: 99.9% in MD and 99.6% in the GF; whereas serial interpretation of both iELISA and SAT produced the highest positive predictive value (PPV): 99.9% in the GF and also high PPV (98.9%) in MD. We recommend the use of both iELISA and SAT together and serial interpretation for culling and parallel interpretation for import decisions. Removal of brucellosis positive cattle will contribute to the control of brucellosis as a public health risk in Bangladesh.

Indexed as

Agglutination TestsAnimalsBangladeshBayes TheoremBrucellosis, BovineCattleEnzyme AssaysEnzyme-Linked Immunosorbent AssayRose BengalSerologic TestsRose BengalAnimal pathogensBayesian analysisbrucellosisinfectious disease epidemiologyveterinary epidemiology

Identifiers

PMID30869026
PMCPMC6518595
OpenAlexW2913566864

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.