### Abstract

The ELISA tests that are available to detect an infection with Mycobacterium avium subsp. paratuberculosis (MAP) have a limited validity expressed as the sensitivity (Se) and specificity (Sp). In many studies, the Se and Sp of the tests are treated as constants and this will result in an underestimation of the variability of the true prevalence (TP). Bayesian inference provided a natural framework for using information on the test variability (i.e., the uncertainty) in the estimates of test Se and Sp when estimating the TP. Data from two prevalence studies for MAP using an ELISA in several regions in two locations were available tor the analyses. In location 1, all cattle of at least 3 years of age were sampled in approximately 90 randomly sampled herds in each of the four regions of the country. In location 2, in 30 randomly sampled herds in each of three regions, approximately 30 randomly selected cows were sampled. Information about the unknown test Se and Sp and MAP prevalence was incorporated into a Bayesian model by joint prior probability distributions. Posterior estimates were obtained by combining the actual likelihood with the prior distributions using Bayes' formula. The corrected cow-level TP (proportion of infected cows in a herd) was low, 5.8 and 3.6% in locations 1 and 2, respectively. Certain regions within a location differed significantly in herd-level TP (proportion of infected herds). The herd-level TP was 54.3% in location 1 (95% credible interval (CI) 46.1, 63.3%) and 32.9% in location 2 (95% CI: 14.4, 73.3%). The variation in the herd-level TP estimate for location 2 was more than three times as large as the variation in location 1 mainly because of the relatively small number of investigated herds in location 2. In future prevalence studies for MAP, sample size calculations should be based on a very low cow-level prevalence. Approximately 50 and 90% of the herds in the current study had an estimated cow-level TP below 4 and 10%, respectively.

Original language | English (US) |
---|---|

Pages (from-to) | 281-295 |

Number of pages | 15 |

Journal | Preventive Veterinary Medicine |

Volume | 60 |

Issue number | 4 |

DOIs | |

State | Published - Sep 12 2003 |

Externally published | Yes |

### Fingerprint

### Keywords

- Bayesian analysis
- Paratuberculosis
- Prevalence
- Sensitivity
- Specificity

### ASJC Scopus subject areas

- Animal Science and Zoology
- veterinary(all)

### Cite this

*Preventive Veterinary Medicine*,

*60*(4), 281-295. https://doi.org/10.1016/S0167-5877(03)00157-0

**Prevalence estimates for paratuberculosis adjusted for test variability using Bayesian analysis.** / Van Schaik, G.; Schukken, Y. H.; Crainiceanu, Ciprian M; Muskens, J.; VanLeeuwen, J. A.

Research output: Contribution to journal › Article

*Preventive Veterinary Medicine*, vol. 60, no. 4, pp. 281-295. https://doi.org/10.1016/S0167-5877(03)00157-0

}

TY - JOUR

T1 - Prevalence estimates for paratuberculosis adjusted for test variability using Bayesian analysis

AU - Van Schaik, G.

AU - Schukken, Y. H.

AU - Crainiceanu, Ciprian M

AU - Muskens, J.

AU - VanLeeuwen, J. A.

PY - 2003/9/12

Y1 - 2003/9/12

N2 - The ELISA tests that are available to detect an infection with Mycobacterium avium subsp. paratuberculosis (MAP) have a limited validity expressed as the sensitivity (Se) and specificity (Sp). In many studies, the Se and Sp of the tests are treated as constants and this will result in an underestimation of the variability of the true prevalence (TP). Bayesian inference provided a natural framework for using information on the test variability (i.e., the uncertainty) in the estimates of test Se and Sp when estimating the TP. Data from two prevalence studies for MAP using an ELISA in several regions in two locations were available tor the analyses. In location 1, all cattle of at least 3 years of age were sampled in approximately 90 randomly sampled herds in each of the four regions of the country. In location 2, in 30 randomly sampled herds in each of three regions, approximately 30 randomly selected cows were sampled. Information about the unknown test Se and Sp and MAP prevalence was incorporated into a Bayesian model by joint prior probability distributions. Posterior estimates were obtained by combining the actual likelihood with the prior distributions using Bayes' formula. The corrected cow-level TP (proportion of infected cows in a herd) was low, 5.8 and 3.6% in locations 1 and 2, respectively. Certain regions within a location differed significantly in herd-level TP (proportion of infected herds). The herd-level TP was 54.3% in location 1 (95% credible interval (CI) 46.1, 63.3%) and 32.9% in location 2 (95% CI: 14.4, 73.3%). The variation in the herd-level TP estimate for location 2 was more than three times as large as the variation in location 1 mainly because of the relatively small number of investigated herds in location 2. In future prevalence studies for MAP, sample size calculations should be based on a very low cow-level prevalence. Approximately 50 and 90% of the herds in the current study had an estimated cow-level TP below 4 and 10%, respectively.

AB - The ELISA tests that are available to detect an infection with Mycobacterium avium subsp. paratuberculosis (MAP) have a limited validity expressed as the sensitivity (Se) and specificity (Sp). In many studies, the Se and Sp of the tests are treated as constants and this will result in an underestimation of the variability of the true prevalence (TP). Bayesian inference provided a natural framework for using information on the test variability (i.e., the uncertainty) in the estimates of test Se and Sp when estimating the TP. Data from two prevalence studies for MAP using an ELISA in several regions in two locations were available tor the analyses. In location 1, all cattle of at least 3 years of age were sampled in approximately 90 randomly sampled herds in each of the four regions of the country. In location 2, in 30 randomly sampled herds in each of three regions, approximately 30 randomly selected cows were sampled. Information about the unknown test Se and Sp and MAP prevalence was incorporated into a Bayesian model by joint prior probability distributions. Posterior estimates were obtained by combining the actual likelihood with the prior distributions using Bayes' formula. The corrected cow-level TP (proportion of infected cows in a herd) was low, 5.8 and 3.6% in locations 1 and 2, respectively. Certain regions within a location differed significantly in herd-level TP (proportion of infected herds). The herd-level TP was 54.3% in location 1 (95% credible interval (CI) 46.1, 63.3%) and 32.9% in location 2 (95% CI: 14.4, 73.3%). The variation in the herd-level TP estimate for location 2 was more than three times as large as the variation in location 1 mainly because of the relatively small number of investigated herds in location 2. In future prevalence studies for MAP, sample size calculations should be based on a very low cow-level prevalence. Approximately 50 and 90% of the herds in the current study had an estimated cow-level TP below 4 and 10%, respectively.

KW - Bayesian analysis

KW - Paratuberculosis

KW - Prevalence

KW - Sensitivity

KW - Specificity

UR - http://www.scopus.com/inward/record.url?scp=0041783600&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0041783600&partnerID=8YFLogxK

U2 - 10.1016/S0167-5877(03)00157-0

DO - 10.1016/S0167-5877(03)00157-0

M3 - Article

C2 - 12941553

AN - SCOPUS:0041783600

VL - 60

SP - 281

EP - 295

JO - Preventive Veterinary Medicine

JF - Preventive Veterinary Medicine

SN - 0167-5877

IS - 4

ER -