Caractérisation des facteurs de risque à partir de données issues d'une surveillance imparfaite : comparaison des modèles de régression logistique et de Poisson enflés en zéro

Les facteurs de risque sont des concepts épidémiologiques permettant d’expliquer l’hétérogénéité de la distribution des maladies. L’imperfection de la détection des maladies génère cependant des observations qui ne représentent pas précisément la situation réelle. Des simulations ont été conduites afin d’évaluer l’impact d’une détection imparfaite sur les résultats des modèles statistiques de régression logistique et de Poisson enflés en zéro. Une situation où la sensibilité de détection est imparfaite et la spécificité parfaite a été simulée, et un facteur de risque influençant la prévalence de la maladie a été introduit ainsi qu’un facteur de confusion influençant la sensibilité de détection. Une situation où la spécificité de détection est imparfaite a aussi été simulée. A la lumière des résultats de simulation, des données de surveillance d’avortements bovins en France ont été analysées avec un modèle de régression logistique et un modèle enflé en zéro. Il apparaît que les modèles logistiques sont plus affectés par l’imperfection des données que les modèles de Poisson enflés en zéro

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