Impact of Imperfect Disease Detection on the Identification of Risk Factors in Veterinary Epidemiology.

Risk factors are key epidemiological concepts that are used to explain disease distributions. Identifying disease risk factors is generally done by comparing the characteristics of diseased and non-diseased populations. However, imperfect disease detectability generates disease observations that do not necessarily represent accurately the true disease situation. In this study, we conducted an extensive simulation exercise to emphasize the impact of imperfect disease detection on the outcomes of logistic models when case reports are aggregated at a larger scale (e.g., diseased animals aggregated at farm level). We used a probabilistic framework to simulate both the disease distribution in herds and imperfect detectability of the infected animals in these herds. These simulations show that, under logistic models, true herd-level risk factors are generally correctly identified but their associated odds ratio are heavily underestimated as soon as the sensitivity of the detection is less than one. If the detectability of infected animals is not only imperfect but also heterogeneous between herds, the variables associated with the detection heterogeneity are likely to be incorrectly identified as risk factors. This probability of type I error increases with increasing heterogeneity of the detectability, and with decreasing sensitivity. Finally, the simulations highlighted that, when count data is available (e.g., number of infected animals in herds), they should not be reduced to a presence/absence dataset at the herd level (e.g., presence or not of at least one infected animal) but rather modeled directly using zero-inflated count models which are shown to be much less sensitive to imperfect detectability issues. In light of these simulations, we revisited the analysis of the French bovine abortion surveillance data, which has already been shown to be characterized by imperfect and heterogeneous abortion detectability. As expected, we found substantial differences between the quantitative outputs of the logistic model and those of the zero-inflated Poisson model. We conclude by strongly recommending that efforts should be made to account for, or at the very least discuss, imperfect disease detectability when assessing associations between putative risk factors and observed disease distributions, and advocate the use of zero-inflated count models if count data is available.

Modelling African swine fever presence and reported abundance in the Russian Federation using national surveillance data from 2007 to 2014.

African swine fever (ASF) is a viral disease of swine that has been present in the Russian Federation since 2007. Counts of ASF outbreaks reported in the Southern regions of the country (2007-2014) were aggregated to a grid of hexagons, and a zero-inflated Poisson model accounting for spatial dependence between hexagons was used to identify factors associated with the presence of ASF outbreaks and factors associated with the number of ASF reports in affected hexagons. Increasing density of pigs raised on low biosecurity farms was found to be positively associated with the probability of occurrence of at least one ASF outbreak in a hexagon and with the average number of reported ASF outbreaks amongst affected hexagons. Increasing human population density and increasing distance from the closest diagnostic laboratory were additional variables associated with number of reported ASF outbreaks amongst affected hexagons. The model was shown to have good predictive ability.