Abstract
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.
Highlights
A disease risk factor is a variable that is associated with an increased likelihood of occurrence of a disease
To illustrate with a real example that logistic and zero-inflated Poisson (ZIP) models can lead to significantly different results as suggested by the simulations, we used the French bovine abortion surveillance data and revisited the analysis presented in Bronner et al [21]
Identification of X2 as a Risk Factor Expectedly, when the detection sensitivity was homogeneous between units, X2 was never identified by the logistic regression as a statistically significant risk factor
Summary
A disease risk factor is a variable that is associated with an increased likelihood of occurrence of a disease. Disease risk factors can be identified thanks to observational studies like cohort studies This approach compares the frequency of disease occurrence between a group of epidemiological units (e.g., animals or herds) exposed to the hypothetical risk factor and a non-exposed group. Disease risk factors can be identified thanks to case-control studies, in which the frequency of the hypothesized risk factor is compared between a group of diseased epidemiological units and a non-diseased group In both of these popular study designs, cases are identified based on a detection process that can be not perfectly sensitive, potentially leading to false negatives. This is true for case-control studies that rely on disease data generated by surveillance systems. Surveillance systems for endemic diseases, which aim at monitoring disease prevalence or at detecting cases to implement control measures, are generally composed of several surveillance components (like passive surveillance and active surveillance at the abattoir or on farms), all of which being likely imperfectly sensitive [2]
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