Comparative assessment of passive surveillance in disease-free and endemic situation: example of Brucella melitensis surveillance in Switzerland and in Bosnia and Herzegovina

Abstract

Background: Globalization and subsequent growth in international trade in animals and animal products has increased the importance of international disease reporting. Efficient and reliable surveillance systems are needed in order to document the disease status of a population at a given time. In this context, passive surveillance plays an important role in early warning systems. However, it is not yet routinely integrated in the assessment of disease surveillance systems because different factors like the disease awareness (DA) of people reporting suspect cases influence the detection performance of passive surveillance. In this paper, we used scenario tree methodology in order to evaluate and compare the quality and benefit of abortion testing (ABT) for Brucella melitensis (Bm) between the disease free situation in Switzerland (CH) and a hypothetical disease free situation in Bosnia and Herzegovina (BH), taking into account DA levels assumed for the current endemic situation in BH.

Results: The structure and input parameters of the scenario tree were identical for CH and BH with the exception of population data in small ruminants and the DA in farmers and veterinarians. The sensitivity analysis of the stochastic scenario tree model showed that the small ruminant population structure and the DA of farmers were important influential parameters with regard to the unit sensitivity of ABT in both CH and BH. The DA of both farmers and veterinarians was assumed to be higher in BH than in CH due to the current endemic situation in BH. Although the same DA cannot necessarily be assumed for the modelled hypothetical disease free situation as for the actual endemic situation, it shows the importance of the higher vigilance of people reporting suspect cases on the probability that an average unit processed in the ABT-component would test positive.

Conclusion: The actual sensitivity of passive surveillance approaches heavily depends on the context in which they are applied. Scenario tree modelling allows for the evaluation of such passive surveillance system components under assumed disease free situation. Despite data gaps, this is a real opportunity to compare different situations and to explore consequences of changes that could be made.

Figure 1

Small ruminant population structure. Proportion of small ruminant flocks by flock size categories 1–19, 20–40, 41–60, 61–100 and > 100 animals over 12 months of age in Switzerland (CH) and Bosnia and Herzegovina (BH). The proportions of flock size categories for BH are derived from different sources [17,18].

Figure 2

Basic structure of the scenario tree for abortion testing. Structure of the scenario tree for the probability of Brucella melitensis (Bm) case detection based on testing of aborting small ruminants in Switzerland (CH) and Bosnia and Herzegovina (BH). 'FCallsV' means that the farmer calls a veterinarian and 'FCallsNoV' that he does not call a veterinarian. 'Sampl' means that the veterinarian takes samples to test for Bm and 'NoSampl' that he does not take samples. 'Test-POS' means a positive test result and 'Test-NEG' a negative test result.