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Equine syndromic surveillance in Colorado using veterinary laboratory testing order data.
Abstract: 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. 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. 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. 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.
Publication Date: 2019-03-01 PubMed ID: 30822346PubMed Central: PMC6396905DOI: 10.1371/journal.pone.0211335Google Scholar: Lookup The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
- Journal Article
- Research Support
- Non-U.S. Gov't
- Animal Health
- Diagnosis
- Disease control
- Disease Diagnosis
- Disease Etiology
- Disease Management
- Disease Outbreaks
- Disease Prevention
- Disease Surveillance
- Disease Transmission
- Disease Treatment
- Epidemiology
- Equine Diseases
- Equine Health
- Horses
- Laboratory Methods
- Public Health
- Veterinary Care
- Veterinary Medicine
- Veterinary Research
- Veterinary Science
Summary
This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.
The research focuses on building an equine surveillance system in Colorado to monitor livestock health data. The system is based on weekly counts of laboratory test orders to detect outbreaks of diseases in horses using statistical methods. Data from 12 years of equine test records was analysed and groups formed based on the risk identification unit (RIU) experience and published literature.
Methods
- Initial efforts focused on 12 years of equine test records from three state labs.
- The team formed trial syndrome groups based on experience and published literature.
- The data was analysed and the groups modified with stakeholder input and laboratory workflow details to eliminate bias.
- Customized detection methods were identified for effective monitoring based on the expected presentation and health significance of diseases associated with each syndrome.
- Test orders associated with government regulations, teaching hospital protocols, or research projects were excluded.
- A rule set was produced where records were directly classified into syndromes using the test name where possible and otherwise, the body system related to the specimen type was used with test name.
Data Analysis
- Weekly counts of tests included in the candidate syndrome groups were analyzed.
- This was followed by an iterative process of data analysis and veterinary consultation for syndrome refinement and record filters.
- Data transformation and formation of syndromes focused on test battery types, test names, the organization submitting the tests, and the type of specimen.
Testing and Results
- A testbed was constructed for sets of 1,000 statistical trials and a stochastic injection process was applied to choose an alerting algorithm required for each resulting syndrome.
- The research team studied the outcomes associated with each syndrome and specified the detection of single-week signals for most syndromes, 8-week signals for neurological and tick-borne monitoring, 6-week signals for respiratory, and 4-week signals for systemic fungal.
- The recommended methods, settings, and thresholds were derived from the test-bed trials.
Conclusion
- Understanding of laboratory submission sources, laboratory workflow and syndrome-related outcomes are crucial to form syndrome groups for routine monitoring without artifactual alerting.
- The choices of methods, parameters, and thresholds for the surveillance system varied by syndrome and depended strongly on the specifications set by veterinary epidemiologists.
Cite This Article
APA
Burkom H, Estberg L, Akkina J, Elbert Y, Zepeda C, Baszler T.
(2019).
Equine syndromic surveillance in Colorado using veterinary laboratory testing order data.
PLoS One, 14(3), e0211335.
https://doi.org/10.1371/journal.pone.0211335 Publication
Researcher Affiliations
- The Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
- Center for Epidemiology and Animal Health, US Department of Agriculture, Ft. Collins, Colorado, United States of America.
- Center for Epidemiology and Animal Health, US Department of Agriculture, Ft. Collins, Colorado, United States of America.
- The Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
- Center for Epidemiology and Animal Health, US Department of Agriculture, Ft. Collins, Colorado, United States of America.
- Veterinary Diagnostic Laboratory, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, Colorado, United States of America.
MeSH Terms
- Algorithms
- Animals
- Clinical Laboratory Techniques / trends
- Clinical Laboratory Techniques / veterinary
- Colorado
- Disease Outbreaks / veterinary
- Horse Diseases / diagnosis
- Horses
- Population Surveillance
- Sentinel Surveillance / veterinary
Conflict of Interest Statement
The authors have declared that no competing interests exist.
References
This article includes 18 references
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Citations
This article has been cited 3 times.- Lustgarten JL, Zehnder A, Shipman W, Gancher E, Webb TL. Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA). JAMIA Open 2020 Jul;3(2):306-317.
- De Nardi M, Küker S, Salah S, Qtananni I, Rosso F, Baiomy S. Animal health syndromic surveillance system in Jordan, a road map for a pilot model. Front Vet Sci 2025;12:1538347.
- Oliveira VHS, Dórea FC, Dean KR, Bang Jensen B. Exploring Options for Syndromic Surveillance in Aquaculture: Outbreak Detection of Salmon Pancreas Disease Using Production Data from Norwegian Farms. Transbound Emerg Dis 2024;2024:9861677.
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