A practical approach to calculate sample size for herd prevalence surveys (original) (raw)
Related papers
Estimating herd prevalence on the basis of aggregate testing of animals
Journal of the Royal Statistical Society: Series A (Statistics in Society), 2011
It is common practice that some or all animals in a group of animals, e.g. a herd, are tested for their health status by using a diagnostic test to investigate whether the herd is infected by a disease. Several obstacles complicate the estimation of herd prevalence on the basis of test results of the animals. First, diagnostic tests are often imperfect, resulting in a misclassification of the animal's disease status. It is well known how to correct the animal's apparent prevalence by using the diagnostic sensitivity and specificity of the animal test, but the effects on herd prevalence are less clear. Second, in practice, a herd is often defined as positive when at least one sampled animal tested positively. This definition is ambiguous and is also different from the herd prevalence that is based on having at least one diseased animal in the herd. The paper provides a discussion of these aspects and proposes a method to estimate the true herd prevalence on the basis of the health status of (all or a sample of) animals within a herd corrected for the sensitivity and specificity of the individual test, the number of animals that are tested in the herd and the uncertainty of the diagnostic test characteristics.
Preventive Veterinary Medicine, 2008
Paratuberculosis is a chronic infection affecting cattle and other ruminants. In the dairy industry, losses due to paratuberculosis can be substantial in infected herds and several countries have implemented national programmes based on herd-classification to manage the disease. The aim of this study was to develop a method to estimate the probability of low within-herd prevalence of paratuberculosis for Danish dairy herds. A stochastic simulation model was developed using the R ® programming environment. Features of this model included: use of age-specific estimates of test-sensitivity and specificity; use of a distribution of observed values (rather than a fixed, low value) for design prevalence; and estimates of the probability of low prevalence (Pr Low ) based on a specific number of test-positive animals, rather than for a result less than or equal to a specified cut-point number of reactors.
Preventive Veterinary Medicine, 2007
Diagnostic inference by use of assays such as ELISA is usually done by dichotomizing the optical density (OD)-values based on a predetermined cut-off. For paratuberculosis, a slowly developing infection in cattle and other ruminants, it is known that laboratory factors as well as animal specific covariates influence the OD-value, but while laboratory factors are adjusted for, the animal specific covariates are seldom utilized when establishing cut-offs. Furthermore, when dichotomizing an OD-value, information is lost. Considering the poor diagnostic performance of ELISAs for diagnosis of paratuberculosis, a framework for utilizing the continuous OD-values as well as known coavariates could be useful in addition to the traditional approaches, e.g. for estimating within-herd prevalences.The objective of this study was to develop a Bayesian mixture model with two components describing the continuous OD response of infected and non-infected cows, while adjusting for known covariates. Based on this model, four different within-herd prevalence indicators were considered: the mean prevalence in the herd; the age adjusted prevalence of the herd for better between-herd comparisons; the rank of the age adjusted prevalence to better compare across time; and a threshold-based prevalence to describe differences between herds. For comparison, the within-herd prevalence and associated rank using a traditional dichotomization approach based on a single cut-off for an OD corrected for laboratory variation was estimated in a Bayesian model with priors for sensitivity and specificity.The models were applied to the OD-values of a milk ELISA using samples from all lactating cows in 100 Danish dairy herds in three sampling rounds 13 months apart. The results of the comparison showed that including covariates in the mixture model reduced the uncertainty of the prevalence estimates compared to the cut-off based estimates. This allowed a more informative ranking of the herds where low ranking and high ranking herds were easier to identify.
Prevalence estimates for paratuberculosis adjusted for test variability using Bayesian analysis
Preventive Veterinary Medicine, 2003
The ELISA tests that are available to detect an infection with Mycobacterium avium subsp. paratuberculosis (MAP) have a limited validity expressed as the sensitivity (Se) and specificity (Sp). In many studies, the Se and Sp of the tests are treated as constants and this will result in an underestimation of the variability of the true prevalence (TP). Bayesian inference provided a natural framework for using information on the test variability (i.e., the uncertainty) in the estimates of test Se and Sp when estimating the TP. Data from two prevalence studies for MAP using an ELISA in several regions in two locations were available for the analyses. In location 1, all cattle of at least 3 years of age were sampled in approximately 90 randomly sampled herds in each of the four regions of the country. In location 2, in 30 randomly sampled herds in each of three regions, approximately 30 randomly selected cows were sampled. Information about the unknown test Se and Sp and MAP prevalence was incorporated into a Bayesian model by joint prior probability distributions. Posterior estimates were obtained by combining the actual likelihood with the prior distributions using Bayes' formula. The corrected cow-level TP (proportion of infected cows in a herd) was low, 5.8 and 3.6% in locations 1 and 2, respectively. Certain regions within a location differed significantly in herd-level TP (proportion of infected herds). The herd-level TP was 54.3% in location 1 (95% credible interval (CI) 46.1, 63.3%) and 32.9% in location 2 (95% CI: 14.4, 73.3%). The variation in the herd-level TP estimate for location 2 was more than three times as large as the variation in location 1 mainly because of the relatively small number of investigated herds in location 2. In future prevalence studies for MAP,
Determining the infection status of a herd
Journal of Agricultural, Biological, and Environmental Statistics, 2003
This article presents hierarchical models for determining infection status and prevalence of infection within a herd given a hypergeometric or binomial sample of animals that have been screened with an imperfect test. Expert prior information on the infection status of the herd, diagnostic test accuracy, and herd prevalence is incorporated into the model. Posterior probabilities versus prior probabilities of infection are presented in the novel form of a curve, summarizing the probability of infection over a range of possible prior probability values. We demonstrate the model with serologic data for Mycobacterium paratuberculosis(Johne's disease) in dairy herds.
Evaluating the health status of herds based on tests applied to individuals
Preventive Veterinary Medicine, 1992
Martin, S.W., Shoukri, M. and Thorburn, M.A., 1992. Evaluating the health status of herds based on tests applied to individuals. Prey. Vet. Med., The effects of test sensitivity and specificity, and the impact of true prevalence of disease, on test results at the individual level are well known. When individuals are tested to ascertain if an aggregate of animals (e.g. a herd) is affected by a condition of interest, the number of animals tested and the critical number of reactors used to decide the health status of the herd become very important in influencing the herd-level sensitivity and specificity.
Frontiers in Veterinary Science, 2021
Various European Member States have implemented control or eradication programmes for endemic infectious diseases in cattle. The design of these programmes varies between countries and therefore comparison of the outputs of different control programmes is complex. Although output-based methods to estimate the confidence of freedom resulting from these programmes are under development, as yet there is no practical modeling framework applicable to a variety of infectious diseases. Therefore, a data collection tool was developed to evaluate data availability and quality and to collect actual input data required for such a modeling framework. The aim of the current paper is to present the key learnings from the process of the development of this data collection tool. The data collection tool was developed by experts from two international projects: STOC free (Surveillance Tool for Outcome-based Comparison of FREEdom from infection, www.stocfree.eu) and SOUND control (Standardizing OUtpu...
Use of pooled serum samples to assess herd disease status using commercially available ELISAs
Tropical Animal Health and Production
Pooled samples are used in veterinary and human medicine as a cost-effective approach to monitor disease prevalence. Nonetheless, there is limited information on the effect of pooling on test performance, and research is required to determine the appropriate number of samples which can be pooled. Therefore, this study aimed to evaluate the use of pooled serum samples as a herd-level surveillance tool for infectious production-limiting diseases: bovine viral diarrhoea (BVD), infectious bovine rhinotracheitis (IBR), enzootic bovine leukosis (EBL) and Neospora caninum (NC), by investigating the maximum number of samples one can pool to identify one positive animal, using commercial antibody-detection ELISAs. Four positive field standards (PFS), one for each disease, were prepared by pooling highly positive herd-level samples diagnosed using commercially available ELISA tests. These PFS were used to simulate 18 pooled samples ranging from undiluted PFS to a dilution representing 1 posit...
Environmental sampling is an effective method for estimating regional dairy herd-level prevalence of infection with Mycobacterium avium ssp. paratuberculosis (MAP). However, factors affecting prevalence estimates based on environmental samples are not known. The objective was to determine whether odds of environmental samples collected on farm changed culture status over 2 sampling times and if changes were specific for location and type of housing (freestall, tiestall, or loose housing), the sample collected (i.e., manure of lactating, dry, or sick cows; namely, cow group), and effects of herd size. In 2012–2013 [sampling 1 (S1)] and 2015–2017 [sampling 2 (S2)], 6 environmental samples were collected and cultured for MAP from all 167 (99%) and 160 (95%) farms, respectively, in the province of Saskatchewan, Canada. Only the 148 dairy farms sampled at both sampling periods were included in the analysis. A mixed effects logistic regression was used to determine whether differences between sampling periods were associated with herd size and sample characteristics (cow group contributing to environmental sample, type of housing, and location). In S1 and S2, 55 and 34%, respectively, of farms had at least 1 MAP-positive environmental sample. Correcting for sensitivity of environmental sampling, estimated true prevalence in S1 and S2 was 79 and 48%, respectively. Herds with >200 cows were more often MAP-positive than herds with <51 cows in both S1 and S2. The percentage of positive samples was lower in S2 compared with S1 for all sampled areas, cow groups contributing to samples, types of housing where samples were collected, and herd size categories. However, samples collected from dry cow areas had the largest decrease in MAP-positive samples in S2 compared with all other cow group samples. Herds that were MAP-negative in S1 with a herd size 51 to 100 or 101 to 150 were more likely to stay MAP-negative, whereas MAP-positive herds with >200 cows more frequently stayed MAP-positive. No difference was observed in the odds of a sample being MAP-positive among housing types or location of sample collection in both sample periods. Of all farms sampled, 104 (70%) did not change status from S1 to S2. In conclusion, when herd-level MAP prevalence decreased over the 3-yr interval, the change in prevalence differed among herd size categories and was larger in samples from dry cow areas. It was, however, not specific to other characteristics of environmental samples collected.
When counting cattle is not enough: multiple perspectives in agricultural and veterinary research
Acta Veterinaria Scandinavica, 2011
From Databases in veterinary medicine: validation, harmonisation and application. The 24th Symposium of the Nordic Committee for Veterinary Abstract A traditional approach in agricultural and veterinary research is focussing on the biological perspective where large cattle-databases are used to analyse the dairy herd. This approach has yielded valuable insights. However, recent research indicates that this knowledge-base can be further increased by examining agricultural and veterinary challenges from other perspectives. In this paper we suggest three perspectives that may supplement the biological perspective in agricultural and veterinary research; the economic-, the managerial-, and the social perspective. We review recent studies applying or combining these perspectives and discuss how multiple perspectives may improve our understanding and ability to handle cattle-health challenges.