Latent class evaluation of the performance of serological tests for exposure to Brucella spp. in cattle, sheep, and goats in Tanzania.
Animals
Bayes Theorem
Brucella
/ immunology
Brucellosis
/ epidemiology
Cattle
Cattle Diseases
/ epidemiology
Enzyme-Linked Immunosorbent Assay
Female
Goat Diseases
/ epidemiology
Goats
Latent Class Analysis
Male
Rose Bengal
Seroepidemiologic Studies
Serologic Tests
Sheep
Sheep Diseases
/ epidemiology
Tanzania
Journal
PLoS neglected tropical diseases
ISSN: 1935-2735
Titre abrégé: PLoS Negl Trop Dis
Pays: United States
ID NLM: 101291488
Informations de publication
Date de publication:
08 2021
08 2021
Historique:
received:
07
01
2021
accepted:
06
07
2021
entrez:
24
8
2021
pubmed:
25
8
2021
medline:
17
11
2021
Statut:
epublish
Résumé
Brucellosis is a neglected zoonosis endemic in many countries, including regions of sub-Saharan Africa. Evaluated diagnostic tools for the detection of exposure to Brucella spp. are important for disease surveillance and guiding prevention and control activities. Bayesian latent class analysis was used to evaluate performance of the Rose Bengal plate test (RBT) and a competitive ELISA (cELISA) in detecting Brucella spp. exposure at the individual animal-level for cattle, sheep, and goats in Tanzania. Median posterior estimates of RBT sensitivity were: 0.779 (95% Bayesian credibility interval (BCI): 0.570-0.894), 0.893 (0.636-0.989), and 0.807 (0.575-0.966), and for cELISA were: 0.623 (0.443-0.790), 0.409 (0.241-0.644), and 0.561 (0.376-0.713), for cattle, sheep, and goats, respectively. Sensitivity BCIs were wide, with the widest for cELISA in sheep. RBT and cELISA median posterior estimates of specificity were high across species models: RBT ranged between 0.989 (0.980-0.998) and 0.995 (0.985-0.999), and cELISA between 0.984 (0.974-0.995) and 0.996 (0.988-1). Each species model generated seroprevalence estimates for two livestock subpopulations, pastoralist and non-pastoralist. Pastoralist seroprevalence estimates were: 0.063 (0.045-0.090), 0.033 (0.018-0.049), and 0.051 (0.034-0.076), for cattle, sheep, and goats, respectively. Non-pastoralist seroprevalence estimates were below 0.01 for all species models. Series and parallel diagnostic approaches were evaluated. Parallel outperformed a series approach. Median posterior estimates for parallel testing were ≥0.920 (0.760-0.986) for sensitivity and ≥0.973 (0.955-0.992) for specificity, for all species models. Our findings indicate that Brucella spp. surveillance in Tanzania using RBT and cELISA in parallel at the animal-level would give high test performance. There is a need to evaluate strategies for implementing parallel testing at the herd- and flock-level. Our findings can assist in generating robust Brucella spp. exposure estimates for livestock in Tanzania and wider sub-Saharan Africa. The adoption of locally evaluated robust diagnostic tests in setting-specific surveillance is an important step towards brucellosis prevention and control.
Sections du résumé
BACKGROUND
Brucellosis is a neglected zoonosis endemic in many countries, including regions of sub-Saharan Africa. Evaluated diagnostic tools for the detection of exposure to Brucella spp. are important for disease surveillance and guiding prevention and control activities.
METHODS AND FINDINGS
Bayesian latent class analysis was used to evaluate performance of the Rose Bengal plate test (RBT) and a competitive ELISA (cELISA) in detecting Brucella spp. exposure at the individual animal-level for cattle, sheep, and goats in Tanzania. Median posterior estimates of RBT sensitivity were: 0.779 (95% Bayesian credibility interval (BCI): 0.570-0.894), 0.893 (0.636-0.989), and 0.807 (0.575-0.966), and for cELISA were: 0.623 (0.443-0.790), 0.409 (0.241-0.644), and 0.561 (0.376-0.713), for cattle, sheep, and goats, respectively. Sensitivity BCIs were wide, with the widest for cELISA in sheep. RBT and cELISA median posterior estimates of specificity were high across species models: RBT ranged between 0.989 (0.980-0.998) and 0.995 (0.985-0.999), and cELISA between 0.984 (0.974-0.995) and 0.996 (0.988-1). Each species model generated seroprevalence estimates for two livestock subpopulations, pastoralist and non-pastoralist. Pastoralist seroprevalence estimates were: 0.063 (0.045-0.090), 0.033 (0.018-0.049), and 0.051 (0.034-0.076), for cattle, sheep, and goats, respectively. Non-pastoralist seroprevalence estimates were below 0.01 for all species models. Series and parallel diagnostic approaches were evaluated. Parallel outperformed a series approach. Median posterior estimates for parallel testing were ≥0.920 (0.760-0.986) for sensitivity and ≥0.973 (0.955-0.992) for specificity, for all species models.
CONCLUSIONS
Our findings indicate that Brucella spp. surveillance in Tanzania using RBT and cELISA in parallel at the animal-level would give high test performance. There is a need to evaluate strategies for implementing parallel testing at the herd- and flock-level. Our findings can assist in generating robust Brucella spp. exposure estimates for livestock in Tanzania and wider sub-Saharan Africa. The adoption of locally evaluated robust diagnostic tests in setting-specific surveillance is an important step towards brucellosis prevention and control.
Identifiants
pubmed: 34428205
doi: 10.1371/journal.pntd.0009630
pii: PNTD-D-21-00028
pmc: PMC8384210
doi:
Substances chimiques
Rose Bengal
1ZPG1ELY14
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0009630Subventions
Organisme : FIC NIH HHS
ID : R01 TW009237
Pays : United States
Organisme : Biotechnology and Biological Sciences Research Council
ID : BBS/E/D/20002172
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/L018926/1
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/J010367/1
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/L018845/1
Pays : United Kingdom
Organisme : Medical Research Council
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/J010367
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/S013857/1
Pays : United Kingdom
Organisme : NIAID NIH HHS
ID : R01 AI121378
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Prev Vet Med. 2005 May 10;68(2-4):145-63
pubmed: 15820113
Sci Rep. 2020 Apr 27;10(1):7081
pubmed: 32341414
Clin Vaccine Immunol. 2009 May;16(5):765-71
pubmed: 19261777
Vet J. 2013 Jan;195(1):114-20
pubmed: 22831991
Vet Med Int. 2010 Sep 29;2010:
pubmed: 20953382
Vet Rec. 1999 Dec 18-25;145(25):735-6
pubmed: 10972112
Onderstepoort J Vet Res. 2016 May 24;83(1):a1032
pubmed: 27247075
J Clin Microbiol. 2011 Apr;49(4):1458-63
pubmed: 21270216
Parasitology. 2009 Mar;136(3):267-72
pubmed: 19154655
Int J Antimicrob Agents. 2010 Nov;36 Suppl 1:S8-11
pubmed: 20696557
PLoS Negl Trop Dis. 2011 Apr 19;5(4):e950
pubmed: 21526218
Vet Rec. 1994 Apr 16;134(16):415-20
pubmed: 8036772
PLoS One. 2016 Aug 26;11(8):e0161621
pubmed: 27564546
Croat Med J. 2010 Aug;51(4):296-305
pubmed: 20718082
Vet Med Int. 2016;2016:8032753
pubmed: 27595036
Vet Microbiol. 1995 Dec;47(3-4):271-80
pubmed: 8748542
Prev Vet Med. 2018 Mar 1;151:57-72
pubmed: 29496108
Prev Vet Med. 2020 Aug;181:105075
pubmed: 32622242
Biometrics. 1980 Mar;36(1):167-71
pubmed: 7370371
Biometrics. 2001 Mar;57(1):158-67
pubmed: 11252592
Clin Diagn Lab Immunol. 1999 Mar;6(2):269-72
pubmed: 10066666
Lancet Infect Dis. 2006 Feb;6(2):91-9
pubmed: 16439329
PLoS One. 2010 Apr 01;5(4):e9968
pubmed: 20376363
Zentralbl Veterinarmed B. 1997 Sep;44(7):425-36
pubmed: 9323930
Vet Immunol Immunopathol. 2011 May 15;141(1-2):58-63
pubmed: 21419497
J Immunol Methods. 2007 Mar 30;320(1-2):94-103
pubmed: 17258229
Emerg Infect Dis. 1997 Apr-Jun;3(2):213-21
pubmed: 9204307
Prev Vet Med. 2005 Apr;68(1):19-33
pubmed: 15795013
Vet Microbiol. 2007 Nov 15;125(1-2):187-92
pubmed: 17590540
Vet Microbiol. 2002 Dec 20;90(1-4):447-59
pubmed: 12414164
Rev Sci Tech. 2013 Apr;32(1):271-8
pubmed: 23837384
J Clin Microbiol. 2009 Oct;47(10):3098-107
pubmed: 19656980
Int J Public Health. 2013 Oct;58(5):791-5
pubmed: 23263198
Clin Vaccine Immunol. 2008 Jun;15(6):911-5
pubmed: 18385457
Vet Res. 2005 May-Jun;36(3):313-26
pubmed: 15845228
PLoS One. 2009;4(4):e5221
pubmed: 19381332
Epidemiol Infect. 2019 Jan;147:e242
pubmed: 31364555
BMC Vet Res. 2015 Jul 21;11:156
pubmed: 26195218
Acta Trop. 2017 Jan;165:33-39
pubmed: 27725154
PLoS One. 2020 Dec 30;15(12):e0229478
pubmed: 33378382
Vet Microbiol. 2005 Dec 20;111(3-4):211-21
pubmed: 16278056
J S Afr Vet Assoc. 2017 Feb 28;88(0):e1-e7
pubmed: 28281771
Biometrics. 1985 Dec;41(4):959-68
pubmed: 3830260
Int J Antimicrob Agents. 2010 Nov;36 Suppl 1:S12-7
pubmed: 20692128
Croat Med J. 2010 Aug;51(4):314-9
pubmed: 20718084
Acta Vet Scand. 2014 Jul 09;56:39
pubmed: 25007979
Vet Microbiol. 2002 Dec 20;90(1-4):111-34
pubmed: 12414138
BMC Vet Res. 2015 Aug 08;11:189
pubmed: 26253151
Rev Sci Tech. 2013 Apr;32(1):61-70
pubmed: 23837365
Prev Vet Med. 2006 Jun 16;74(4):309-22
pubmed: 16427711
Prev Vet Med. 2011 Nov 1;102(2):118-31
pubmed: 21571380