Development of Reporting Guidelines for Animal Health Surveillance-AHSURED

With the current trend in animal health surveillance toward risk-based designs and a gradual transition to output-based standards, greater flexibility in surveillance design is both required and allowed. However, the increase in flexibility requires more transparency regarding surveillance, its activities, design and implementation. Such transparency allows stakeholders, trade partners, decision-makers and risk assessors to accurately interpret the validity of the surveillance outcomes. This paper presents the first version of the Animal Health Surveillance Reporting Guidelines (AHSURED) and the process by which they have been developed. The goal of AHSURED was to produce a set of reporting guidelines that supports communication of surveillance activities in the form of narrative descriptions. Reporting guidelines come from the field of evidence-based medicine and their aim is to improve consistency and quality of information reported in scientific journals. They usually consist of a checklist of items to be reported, a description/definition of each item, and an explanation and elaboration document. Examples of well-reported items are frequently provided. Additionally, it is common to make available a website where the guidelines are documented and maintained. This first version of the AHSURED guidelines consists of a checklist of 40 items organized in 11 sections (i.e., surveillance system building blocks), which is available as a wiki at https://github.com/SVA-SE/AHSURED/wiki. The choice of a wiki format will allow for further inputs from surveillance experts who were not involved in the earlier stages of development. This will promote an up-to-date refined guideline document.

A modified TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) applied to choosing appropriate selection methods in ongoing surveillance for Avian Influenza in Canada

To achieve an appropriate and efficient sample in a surveillance program, the goals of the program should drive a careful consideration of the selection method or combination of selection methods to be applied. Therefore, the ongoing analysis and assessment of a surveillance system may include an assessment of the ability of the applied selection methods to generate an appropriate sample. There may be opinions from many technical experts (TEs) and many criteria to consider in a surveillance system so there is a need for methods to combine knowledge, priorities and preferences from a group of TEs. This paper proposes a modified weighted and unweighted TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis to choose selection methods in surveillance. An example from the Canadian Notifiable Avian Influenza surveillance (CanNAISS) is used to illustrate the method as this surveillance offers unique data with multiple selection methods and subpopulations. The primary objective was to assess the performance of the different selection methods applied in CanNAISS, from 2008 to 2013, in three subpopulations (A-C). A modified TOPSIS (weighted and unweighted) analyses is proposed to aggregate preferences from three TEs and to identify the selection method that was closest to the ideal solution agreed upon by the TEs. Criteria weights were provided individually by three TEs. For the group decision making, internal and external aggregation approaches were used with arithmetic and geometric means. The results of the weighted modified TOPSIS analysis showed that the selection methods that used farm registries yielded high estimates of the relative closeness to ideal-solution. The ranking of selection methods based on the modified TOPSIS weighted analysis, conducted at the individual and group decision making levels were similar. Regardless of the aggregation approach used (internal or external) in group decision making, the use of the arithmetic and geometric means yielded similar estimates of relative closeness to ideal-solution. The unweighted modified TOPSIS analysis yielded similar estimates of the relative closeness to the ideal-solution and therefore making the interpretation of the results difficult. The weighted modified TOPSIS analysis contributed to an informed decision on the best selection method to apply in CanNAISS. The weighted modified TOPSIS analysis is a straightforward and suitable technique to address decision making problems where the profile of the ideal and non-ideal solutions is known a priori by the decision makers.

Comparing capture-recapture methods for estimation of the size of small and medium-sized populations using empirical data on commercial turkey farms in Canada

The study objectives were (1) to conduct a systematic review of the performance of capture-recapture methods; (2) to use empirical data to estimate population size in a small-sized population (turkey breeder farms) and a medium-sized population (meat turkey farms) by applying two-source capture-recapture methods (the Lincoln-Petersen, the Chapman, and Chao's lower-bound estimators) and multi-source capture-recapture methods (the log-linear modeling and sample coverage approaches); and (3) to compare the performance of these methods in predicting the true population sizes (2007 data). Our set-up was unique in that we knew the population sizes for turkey breeder farms (99) and meat turkey farms (592) in Canada in 2007, which we applied as our true population sizes, and had surveillance data from the Canadian Notifiable Avian Influenza Surveillance System (2008-2012). We defined each calendar year of sampling as a data source. We confirmed that the two-source capture-recapture methods were sensitive to the violation of the local independence assumption. The log-linear modeling and sample coverage approaches yielded estimates that were closer to the true population sizes than were the estimates provided by the two-source methods for both populations. The performance of both multi-source capture-recapture methods depended on the number of data sources analyzed and the size of the population. Simulation studies are recommended to better understand the limits of each multi-source capture-recapture method.

Estimation of the population size of Canadian commercial poultry farms by log-linear capture-recapture analysis

The objective of this study was to estimate the population size of Canadian poultry farms in 3 subpopulations (British Columbia, Ontario, and Other) by poultry category. We used data for 2008 to 2011 from the Canadian Notifiable Avian Influenza (NAI) Surveillance System (CanNAISS). Log-linear capture-recapture models were applied to estimate the number of commercial chicken and turkey farms. The estimated size of farm populations was validated by comparing sizes to data provided by the Canadian poultry industry in 2007, which were assumed to be complete and exhaustive. Our results showed that the log-linear modelling approach was an appropriate tool to estimate the population size of Canadian commercial chicken and turkey farms. The 2007 farm population size for each poultry category was included in the 95% confidence intervals of the farm population size estimates. Log-linear capture-recapture modelling might be useful for estimating the number of farms using surveillance data when no comprehensive registry exists.

A population health surveillance theory

OBJECTIVES

Despite its extensive use, the term "Surveillance" often takes on various meanings in the scientific literature pertinent to public health and animal health. A critical appraisal of this literature also reveals ambiguities relating to the scope and necessary structural components underpinning the surveillance process. The authors hypothesized that these inconsistencies translate to real or perceived deficiencies in the conceptual framework of population health surveillance. This paper presents a population health surveillance theory framed upon an explicit conceptual system relative to health surveillance performed in human and animal populations.

METHODS

The population health surveillance theory reflects the authors' system of thinking and was based on a creative process.

RESULTS

Population health surveillance includes two broad components: one relating to the human organization (which includes expertise and the administrative program), and one relating to the system per se (which includes elements of design and method) and which can be viewed as a process. The population health surveillance process is made of five sequential interrelated steps: 1) a trigger or need, 2) problem formulation, 3) surveillance planning, 4) surveillance implementation, and 5) information communication and audit.

CONCLUSIONS

The population health surveillance theory provides a systematic way of understanding, organizing and evaluating the population health surveillance process.