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Feral swine populations in the United States (US) are capable of carrying diseases that threaten the health of the domestic swine industry. Performing routine, near-real time monitoring for an unusual rise in feral swine slaughter condemnation will increase situational awareness and early detection of potential animal health issues, trends, and emerging diseases. In preparation to add feral swine to APHIS weekly monitoring, a descriptive analysis of feral swine slaughter and condemnations was conducted to understand the extent of commercial feral swine slaughter in the US at federally inspected slaughter establishments and to determine which condemnation reasons should be included. There were 17 establishments that slaughtered 242,198 feral swine across seven states from 2017 to 2019. For all 17 establishments combined, feral swine accounted for 63% of slaughtered animals. A total of 23 types of condemnation reasons were noted: Abscess/Pyemia, Arthritis, Contamination, Deads, Emaciation, General Miscellaneous, Icterus, Injuries, Metritis, Miscellaneous Degenerative & Dropsical Condition, Miscellaneous Inflammatory Diseases, Miscellaneous Parasitic Conditions, Moribund, Nephritis/Pyelitis, Non-ambulatory, Pericarditis, Pneumonia, Residue, Sarcoma, Septicemia, Sexual Odor, Toxemia, and Uremia. Exploratory analysis was conducted to determine which condemnation reasons should be included for weekly monitoring. For most condemn reasons, weeks of unusually high condemnations were noted. For example, a period of high pneumonia condemnations occurred from December 2, 2018 through February 3, 2019 with a spike on January 6, 2019 and a spike in dead swine occurred on November 3, 2019. The seasonal impacts on limited quality food resources, seasonal variation in the pathogen(s) causing pneumonia, and harsher weather are suspected to have an impact on the higher condemnation rates of pneumonia and dead swine during the winter months. Based on condemnation frequencies and the likelihood of enabling situational awareness and early detection of feral swine health emerging diseases, the following were selected for weekly monitoring: abscess/pyemia, contamination/peritonitis, deads, emaciation, injuries, miscellaneous parasitic conditions, moribund, pneumonia and septicemia. Detection of notable increases in condemnation reasons strongly suggestive of foreign animal or emerging diseases should contribute valuable evidence toward the overall disease discovery process when the anomalies are both confirmed with follow up investigation and combined with other types of surveillance.
RESUMO
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.
RESUMO
INTRODUCTION: The Risk Identification Unit (RIU) of the US Dept. of Agriculture's Center for Epidemiology and Animal Health (CEAH) conducts weekly surveillance of national livestock health data and routine coordination with agricultural stakeholders. As part of an initiative to increase the number of species, health issues, and data sources monitored, CEAH epidemiologists are building a surveillance system based on weekly syndromic counts of laboratory test orders in consultation with Colorado State University laboratorians and statistical analysts from the Johns Hopkins University Applied Physics Laboratory. Initial efforts focused on 12 years of equine test records from three state labs. Trial syndrome groups were formed based on RIU experience and published literature. Exploratory analysis, stakeholder input, and laboratory workflow details were needed to modify these groups and filter the corresponding data to eliminate alerting bias. Customized statistical detection methods were sought for effective monitoring based on specialized laboratory information characteristics and on the likely presentation and animal health significance of diseases associated with each syndrome. METHODS: Data transformation and syndrome formation focused on test battery type, test name, submitter source organization, and specimen type. We analyzed time series of weekly counts of tests included in candidate syndrome groups and conducted an iterative process of data analysis and veterinary consultation for syndrome refinement and record filters. This process produced a rule set in which records were directly classified into syndromes using only test name when possible, and otherwise, the specimen type or related body system was used with test name to determine the syndrome. Test orders associated with government regulatory programs, veterinary teaching hospital testing protocols, or research projects, rather than clinical concerns, were excluded. We constructed a testbed for sets of 1000 statistical trials and applied a stochastic injection process assuming lognormally distributed incubation periods to choose an alerting algorithm with the syndrome-required sensitivity and an alert rate within the specified acceptable range for each resulting syndrome. Alerting performance of the EARS C3 algorithm traditionally used by CEAH was compared to modified C2, CuSUM, and EWMA methods, with and without outlier removal and adjustments for the total weekly number of non-mandatory tests. RESULTS: The equine syndrome groups adopted for monitoring were abortion/reproductive, diarrhea/GI, necropsy, neurological, respiratory, systemic fungal, and tickborne. Data scales, seasonality, and variance differed widely among the weekly time series. Removal of mandatory and regulatory tests reduced weekly observed counts significantly-by >80% for diarrhea/GI syndrome. The RIU group studied outcomes associated with each syndrome and called for detection of single-week signals for most syndromes with expected false-alert intervals >8 and <52 weeks, 8-week signals for neurological and tickborne monitoring (requiring enhanced sensitivity), 6-week signals for respiratory, and 4-week signals for systemic fungal. From the test-bed trials, recommended methods, settings and thresholds were derived. CONCLUSIONS: Understanding of laboratory submission sources, laboratory workflow, and of syndrome-related outcomes are crucial to form syndrome groups for routine monitoring without artifactual alerting. Choices of methods, parameters, and thresholds varied by syndrome and depended strongly on veterinary epidemiologist-specified performance requirements.
