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Impact of imperfect test sensitivity on determining risk factors : the case of bovine tuberculosis

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Szmaragd, Camille, Green, Laura E., Medley, Graham and Browne, William J.. (2012) Impact of imperfect test sensitivity on determining risk factors : the case of bovine tuberculosis. PLoS ONE, Vol.7 (No.8). e43116. ISSN 1932-6203

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Official URL: http://dx.doi.org/10.1371/journal.pone.0043116

Abstract

Background
Imperfect diagnostic testing reduces the power to detect significant predictors in classical cross-sectional studies. Assuming that the misclassification in diagnosis is random this can be dealt with by increasing the sample size of a study. However, the effects of imperfect tests in longitudinal data analyses are not as straightforward to anticipate, especially if the outcome of the test influences behaviour. The aim of this paper is to investigate the impact of imperfect test sensitivity on the determination of predictor variables in a longitudinal study.

Methodology/Principal Findings
To deal with imperfect test sensitivity affecting the response variable, we transformed the observed response variable into a set of possible temporal patterns of true disease status, whose prior probability was a function of the test sensitivity. We fitted a Bayesian discrete time survival model using an MCMC algorithm that treats the true response patterns as unknown parameters in the model. We applied our approach to epidemiological data of bovine tuberculosis outbreaks in England and investigated the effect of reduced test sensitivity in the determination of risk factors for the disease. We found that reduced test sensitivity led to changes to the collection of risk factors associated with the probability of an outbreak that were chosen in the ‘best’ model and to an increase in the uncertainty surrounding the parameter estimates for a model with a fixed set of risk factors that were associated with the response variable.

Conclusions/Significance
We propose a novel algorithm to fit discrete survival models for longitudinal data where values of the response variable are uncertain. When analysing longitudinal data, uncertainty surrounding the response variable will affect the significance of the predictors and should therefore be accounted for either at the design stage by increasing the sample size or at the post analysis stage by conducting appropriate sensitivity analyses.

Item Type:

Journal Article

Subjects:

S Agriculture > SF Animal culture

Divisions:

Faculty of Science > Life Sciences (2010- )

Library of Congress Subject Headings (LCSH):

Diseases -- Risk factors -- Mathematical models, Tuberculosis in cattle -- Risk factors -- Mathematical models, Diagnostic errors

Journal or Publication Title:

PLoS ONE

Publisher:

PLOS

ISSN:

1932-6203

Official Date:

2012

Dates:

Date	Event
2012	Published

Volume:

Vol.7

Number:

No.8

Page Range:

e43116

Identification Number:

10.1371/journal.pone.0043116

Status:

Peer Reviewed

Publication Status:

Published

Access rights to Published version:

Restricted or Subscription Access

Funder:

Great Britain. Dept. for Environment, Food & Rural Affairs (DEFRA)

Grant number:

SE3239 (DEFRA)

References:

1. Lachish S, Gopalaswamy AM, Knowles SCL, Sheldon BC (2012) Siteoccupancy
modelling as a novel framework for assessing test sensitivity and
estimating wildlife disease prevalence from imperfect diagnostic tests. Methods in
Ecology and Evolution 3: 339–348.
2. Diggle PJ (2011) Estimating prevalence using an imperfect test. Epidemiology
Research International 2011: 5 pages.
3. Cannon R (2001) Sense and sensitivity – designing surveys based on an
imperfect test. Preventive Veterinary Medicine 49: 141–163.
4. Steele F (2008) Multilevel models for longitudinal data. Journal of the Royal
Statistical Society: Series A (Statistics in Society) 171: 5–19.
5. Independent Scientific Group on Cattle TB (2007) Bovine TB: The scientific
evidence. Technical report, DEFRA. http://www.defra.gov.uk/foodfarm/
farmanimal/diseases/atoz/tb/isg/report/final_report.pdf.
6. Defra (2011). Historical overview of bovine TB. http://www.defra.gov.uk/
foodfarm/farmanimal/disease/atoz/tb/abouttb/index/htm.
7. Christley R, Robinson S, Moore B, Setzkorn C, Donald I (2011) Responses of
farmers to introduction in England and Wales of pre-movement testing for
bovine tuberculosis. Preventive Veterinary Medicine 100: 126–133.
8. Animal Health Defra (2010). Dealing with TB in your herd. http://
animalhealth.defra.gov.uk/about/publications/advice-guidance/documents/1_
Bovine_TB.pdf.
9. Green LE, Cornell SJ (2005) Investigations of cattle herd breakdowns with
bovine tuberculosis in four counties of England and Wales using VETNET data.
Preventive Veterinary Medecine 70: 293–311.
10. Gilbert M, Mitchell A, Bourn D, Mawdsley J, Clifton-Hadley R, et al. (2005)
Cattle movements and bovine tuberculosis in Great Britain. Nature 435: 491–
496.
11. Gopal R, Goodchild A, Hewinson G, de la Rua Domenech R, Clifton-Hadley R
(2006) Introduction of bovine tuberculosis to north-east England by bought-in
cattle. Veterinary Record 159: 265–271.
12. Ramı´rez-Villaescusa A, Medley G, Mason S, Green L (2010) Risk factors for
herd breakdown with bovine tuberculosis in 148 cattle herds in the south west of
England. Preventive Veterinary Medicine 95: 224–230.
13. Reilly LA, Courtenay O (2007) Husbandry practices, badger sett density and
habitat composition as risk factors for transient and persistent bovine
tuberculosis on UK cattle farms. Preventive Veterinary Medecine 80: 129–142.
14. Wolfe D, Berke O, More S, Kelton D, White P, et al. (2009) The risk of a
positive test for bovine tuberculosis in cattle purchased from herds with and
without a recent history of bovine tuberculosis in Ireland. Preventive Veterinary
Medicine 92: 99–105.
15. Carrique-Mas JJ, Medley GF, Green LE (2008) Risks for bovine tuberculosis in
British cattle farms restocked after the foot and mouth disease epidemic of 2001.
Preventive Veterinary Medecine 84: 85–93.
16. Donnelly CA, Woodroffe R, Cox DR, Bourne FJ, Cheeseman CL, et al. (2006)
Positive and negative effects of widespread badger culling on tuberculosis in
cattle. Nature 439: 843–846.
17. Griffin JM, Williams DH, Kelly GE, Clegg TA, O’Boyle I, et al. (2005) The
impact of badger removal on the control of tuberculosis in cattle herds in
Ireland. Preventive Veterinary Medecine 67: 237–266.
18. Monaghan M, Doherty M, Collins J, Kazda J, Quinn P (1994) The tuberculin
test. Veterinary Microbiology 40: 111–124.
19. de la Rua-Domenech R, Goodchild A, Vordermeier H, Hewinson R,
Christiansen K, et al. (2006) Ante mortem diagnosis of tuberculosis in cattle:
A review of the tuberculin tests, c-interferon assay and other ancillary diagnostic
techniques. Research in Veterinary Science 81: 190–210.
20. Clegg TA, Duignan A, Whelan C, Gormley E, Good M, et al. (2011) Using
latent class analysis to estimate the test characteristics of the c-interferon test, the
single intradermal comparative tuberculin test and a multiplex immunoassay
under irish conditions. Veterinary Microbiology 151: 68–76.
21. Alvarez J, Perez A, Bezos J, Marque´s S, Grau A, et al. (2012) Evaluation of the
sensitivity and specificity of bovine tuberculosis diagnostic tests in naturally
infected cattle herds using a Bayesian approach. Veterinary Microbiology 155(1):
38–43.
22. Thom M, Morgan J, Hope J, Villarreal-Ramos B, Martin M, et al. (2004) The
effect of repeated tuberculin skin testing of cattle on immune responses and
disease following experimental infection with Mycobacterium bovis. Veterinary
Immunology and Immunopathology 102: 399–412.
23. Independent Scientific Review Group (1997) Bovine tuberculosis in Cattle and
badgers (‘‘the Krebs Report’’). Technical report. http://www.defra.gov.uk/
foodfarm/farmanimal/diseases/atoz/tb/publications/krebs.htm.
24. Lunn D, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS – A Bayesian
modelling framework: Concepts, structure, and extensibility. Statistics and
Computing 10(4): 325–337.
25. Cox DR, Wermuth N (1996) Multivariate dependencies - Models, Analysis and
Interpretation. Chapman & Hall, 255 p.