Bayesian estimation of the true prevalence of paratuberculosis in Hungarian dairy cattle herds

Seroprevalence of Mycobacterium avium subsp. paratuberculosis in Swiss dairy herds and risk factors for a positive herd status and within-herd prevalence

Introduction

Bovine paratuberculosis (PTB) is a chronic enteric disease caused by Mycobacterium avium subsp. paratuberculosis (MAP). Control of PTB is important given its negative economic consequences and the potential zoonotic role of MAP in Crohn's disease in humans.

Methods

To determine the seroprevalence of MAP in Swiss dairy herds and to identify risk factors associated with seropositive herd status and high within-herd seroprevalence, 10,063 serum samples collected from cattle over 12 months of age in 171 Swiss dairy farms were analyzed using a commercial ELISA test. Eight herds were excluded due to non-interpretable ELISA results. Risk factors associated with seropositive herd status and high within-herd seroprevalence were investigated with regression models using results from a questionnaire on management practices possibly associated with the introduction or spread of MAP in the remaining 163 herds. Univariable logistic regression was performed, carrying forward for multivariable regression analysis when p < 0.2.

Results

The calculated between-herd true seroprevalence was 3.6% (95% CI, 0.96-8.4%). Due to the low within-herd seroprevalence, it was not possible to calculate the true seroprevalence at animal level; the apparent within-herd seroprevalence ranged from 2.3 to 5.5% with a median of 3.6% in nine positive farms. Herd size (p = 0.037) and the common grazing of lactating cows with cows from other herds (p = 0.014) were associated with seropositive herd status, while heifers sharing alpine pasture with dairy cattle from other herds were associated with a decreased probability of the herd to test seropositive (p = 0.042). Reliable identification of significant risk factors associated with MAP spread and high seroprevalence of PTB within seropositive herds was not possible due to low observed seroprevalence within herds and low sensitivity of the ELISA test.

Discussion

These results highlight the limitation of serology for MAP diagnosis in small herds with low infection prevalence.

Bayesian latent class modelling of true prevalence in animal subgroups with application to bovine paratuberculosis infection

The prevalence of an infectious disease of animals living in separate groups (e.g. herds) is naturally analyzed using a Bayesian hierarchical latent class model. We propose an extension to this methodology by including subgroup level prevalence measures within the groups of animals. As an application illustrating the merits of our methodology, we reassessed the prevalence of bovine paratuberculosis (PTBC) infection in Hungarian commercial dairy farms. Our aim was to consolidate previous findings using a large amount of recent data and priors based on historical data. To model the subgroup level infection prevalence within animal groups, we considered correlated prevalences following beta distributions derived from independent normally distributed random herd effects. In the application, infection status of herds was handled as latent classes, multiparous and primiparous cows as within-herd subgroups. The novel methodology allows us to estimate both the mean and median conditional within-herd true prevalence (CWHP) related to each animal subgroup as well as other measures characterizing the interrelation of subgroups. The results of the application aligned with the findings of the former PTBC study, while the more recent and considerably larger dataset and the use of historical priors increased the reliability of the results. The STAN and JAGS codes of the application are available in Supplementary material.

Quantitative bias analysis of prevalence under misclassification: evaluation indicators, calculation method and case analysis

Prevalence estimates are fundamental to epidemiological studies. Although they are highly vulnerable to misclassification bias, the risk of bias assessment of prevalence estimates is often neglected. Quantitative bias analysis (QBA) can effectively estimate misclassification bias in epidemiological studies; however, relatively few applications are identified. One reason for its low usage is the lack of knowledge and tools for these methods among researchers. To expand existing evaluation methods, based on the QBA principles, three indicators are proposed. One is the relative bias that quantifies the bias direction through its signs and the bias magnitude through its quantity. The second is the critical point of positive test proportion in case of a misclassification bias that is equal to zero. The third is the bound of positive test proportion equal to adjusted prevalence at misclassification bias level α. These indicators express the magnitude, direction and uncertainty of the misclassification bias of prevalence estimates, respectively. Using these indicators, it was found that slight oscillations of the positive test proportion within a certain range can lead to substantial increases in the misclassification bias. Hence, researchers should account for misclassification error analytically when interpreting the significance of adjusted prevalence for epidemiological decision making. This highlights the importance of applying QBA to these analyses. In this article, we have used three real-world cases to illustrate the characteristics and calculation methods of presented indicators. To facilitate application, an Excel-based calculation tool is provided.

Bayesian estimation of prevalence of Johne's disease in dairy herds in Southern Italy

Johne's disease (JD) is a chronic granulomatous disease caused by Mycobacterium avium subsp. paratuberculosis (MAP) causing important losses on dairy farms. In Italy, voluntary programs to control MAP infection in dairy cattle are implemented in the Northern part of the country, where several studies have been carried out. Conversely, the disease status has not been fully investigated in the Southern regions. The aims of this study were to (i) determine the herd-level true prevalence (HTP) and (ii) the conditional within herd animal-level prevalence (CWHP) of JD in selected dairy cattle herds in Southern Italy. Serum samples were taken from 27 farms and analysed using a commercial ELISA test. A Bayesian model was fitted to the data. The estimated posterior mean of HPT was 0.46 (89 % CI 0.25-0.67), while the mean CWHP was 0.03 (89 % CI: 0.012-0.045). The results presented in this study call for designing and implementing an effective JD control program at national level.