A new methodology to extrapolate disease freedom to an area using surveillance results from selected aquatic populations.

According to Chapter 1.4 of the World Organisation for Animal Health (WOAH) Aquatic Animal Health Code, an entire country or zone can be classified as free of a disease only if there is compelling evidence that all susceptible populations within the country or zone are free. However, the methods for achieving freedom are not prescribed in the WOAH standards and guidelines. Within this context, this paper describes a novel methodology to determine if surveillance results can be extrapolated from a study population to a target population. A framework of six criteria was developed to standardize a method for extrapolating surveillance results to other susceptible populations that have not been sampled. Criteria 1 assesses the internal validity for the freedom claim on the source population. Criteria 2 assesses which other susceptible populations have a non-negligible probability of exposure. Criteria 3 assesses whether the risk of infection upon exposure of the source population is the same or greater than each of the other susceptible populations. Finally, Criteria 4, 5 and 6 assess if the other susceptible populations would transmit the infection to the source population or if they have the same exposure pathways as the source population. We illustrate the use of this novel methodology using two hypothetical case scenarios. The presented methodology has the advantage of being applicable either retrospectively or prospectively. When applied retrospectively, it can be used to assess if the surveillance results of the source population can be extrapolated to the target population. When applied prospectively it can be used to design a more efficient surveillance system by selecting source populations from which it is easier to extrapolate surveillance results to the rest of the target population. Conclusions drawn using this methodology depend on the validity of the assumptions made when working through the methodology. We therefore recommend cautious application of the criteria and thorough review of all assumptions.

Using pseudogold standards and latent-class analysis in combination to evaluate the accuracy of three diagnostic tests.

We previously reported our use of latent-class models to estimate the sensitivity (Se) and specificity (Sp) for each of three tests used to monitor farmed salmon for infectious salmon anaemia virus (ISAv). Those tests were reverse-transcriptase polymerase chain reaction (RT-PCR), virus isolation (VI), and an indirect immunofluorescent-antibody test (IFAT). We used tissues from 403 salmon from four populations presumed to have different prevalence of ISAv. However, no formal evaluation of the assumptions of conditional independence and constant accuracy had been carried out. In our present study, we "adjusted" that and used two pseudogold standards (a composite reference standard and a study pseudogold), as indicative of the true health status of each fish. The assumption of constant accuracy across populations was evaluated using separate random-effects logistic-regression models for fish classified as D+ or D- (disease positive or negative, according to the pseudogold standards) with study population included in the model to determine if it affected the probability of a positive test result. Where there was evidence of variation in test accuracy across populations, the issue was further investigated using separate latent-class models with informative priors for each study population. Our results suggested that only one PCR test had an accuracy that varied across populations. The assumption of conditional independence among tests was first evaluated using log-linear models of D+ and D- fish with significant interaction between test results indicative of conditional dependence. Latent-class models which incorporated up to two pairs of between-test dependencies were also fit using Bayesian methods. The two approaches showed considerable evidence of dependence between IFAT and VI and some evidence of dependence between one PCR and IFAT. Results obtained from both maximum-likelihood models and from Bayesian analyses of models allowing for conditional dependence between two pairs of tests were consistent with those obtained with the pseudogold standards. The results suggest that pseudogold standards can help in choosing a correct dependence structure and should be used in combination with latent-class models.