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. 2013 May 14;8(5):e63246.
doi: 10.1371/journal.pone.0063246. Print 2013.

Assessing the mandatory bovine abortion notification system in France using unilist capture-recapture approach

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Assessing the mandatory bovine abortion notification system in France using unilist capture-recapture approach

Anne Bronner et al. PLoS One. .

Abstract

The mandatory bovine abortion notification system in France aims to detect as soon as possible any resurgence of bovine brucellosis. However, under-reporting seems to be a major limitation of this system. We used a unilist capture-recapture approach to assess the sensitivity, i.e. the proportion of farmers who reported at least one abortion among those who detected such events, and representativeness of the system during 2006-2011. We implemented a zero-inflated Poisson model to estimate the proportion of farmers who detected at least one abortion, and among them, the proportion of farmers not reporting. We also applied a hurdle model to evaluate the effect of factors influencing the notification process. We found that the overall surveillance sensitivity was about 34%, and was higher in beef than dairy cattle farms. The observed increase in the proportion of notifying farmers from 2007 to 2009 resulted from an increase in the surveillance sensitivity in 2007/2008 and an increase in the proportion of farmers who detected at least one abortion in 2008/2009. These patterns suggest a raise in farmers' awareness in 2007/2008 when the Bluetongue Virus (BTV) was detected in France, followed by an increase in the number of abortions in 2008/2009 as BTV spread across the country. Our study indicated a lack of sensitivity of the mandatory bovine abortion notification system, raising concerns about the ability to detect brucellosis outbreaks early. With the increasing need to survey the zoonotic Rift Valley Fever and Q fever diseases that may also cause bovine abortions, our approach is of primary interest for animal health stakeholders to develop information programs to increase abortion notifications. Our framework combining hurdle and ZIP models may also be applied to estimate the completeness of other clinical surveillance systems.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Proportion of farmers who detected, reported or failed to report abortions during the 2006–2011 period.
The proportion of notifying farmers (formula image; dotted blue line and red diamond shape) was calculated as the ratio of the observed number of farmers who reported at least one abortion to the total number of farmers. The proportion of farmers who detected at least one abortion (formula image; grey) and the proportion of under-reporting farmers (formula image; light green) were estimated from the ZIP model. In 2010/2011, 60.6% [95% confidence interval: 52.4–72.2] of farmers detected at least one abortion (i.e. formula image;); among them, 20.0% reported at least one abortion (i.e. formula image). The proportion of under-reporting farmers formula imagewas estimated to be 66.4% [62.2–72.0].

References

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