Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance

BMC Veterinary Research

Olaf Berke³  Bruce McNab¹  Ken G Bateman²  David L Pearl²  Gillian D Alton² 

[1] Ontario Ministry of Agriculture & Food, Guelph, ON N1G 4Y2, Canada;Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON N1G 2W1, Canada;Department of Mathematics and Statistics, University of Guelph, Guelph, ON N1G 2W1, Canada

关键词: Condemnations;    Abattoir;    Syndromic surveillance;    Scan statistic;   

Others  :  1119407
DOI  :  10.1186/1746-6148-9-231

 received in 2013-06-12, accepted in 2013-11-13,  发布年份 2013

【 摘 要 】

Background

Abattoir condemnation data show promise as a rich source of data for syndromic surveillance of both animal and zoonotic diseases. However, inherent characteristics of abattoir condemnation data can bias results from space-time cluster detection methods for disease surveillance, and may need to be accounted for using various adjustment methods. The objective of this study was to compare the space-time scan statistics with different abilities to control for covariates and to assess their suitability for food animal syndromic surveillance. Four space-time scan statistic models were used including: animal class adjusted Poisson, space-time permutation, multi-level model adjusted Poisson, and a weighted normal scan statistic using model residuals. The scan statistics were applied to monthly bovine pneumonic lung and “parasitic liver” condemnation data from Ontario provincial abattoirs from 2001–2007.

Results

The number and space-time characteristics of identified clusters often varied between space-time scan tests for both “parasitic liver” and pneumonic lung condemnation data. While there were some similarities between isolated clusters in space, time and/or space-time, overall the results from space-time scan statistics differed substantially depending on the covariate adjustment approach used.

Conclusions

Variability in results among methods suggests that caution should be used in selecting space-time scan methods for abattoir surveillance. Furthermore, validation of different approaches with simulated or real outbreaks is required before conclusive decisions can be made concerning the best approach for conducting surveillance with these data.

【 授权许可】

   
2013 Alton et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Robertson C, Nelson TA, MacNab YC, Lawson AB: Review of methods for space–time disease surveillance. Spat Spattemporal Epidemiol 2010, 1:105-116.
• [2]Pearl DL, Louie M, Chui L, Dore K, Grimsrud KM, Leedell D, Martin SW, Michel P, Svenson LW, McEwen SA: The use of outbreak information in the interpretation of clustering of reported cases ofEscherichia coli 0157 in space and time in Alberta, Canada, 2000–2002. Epidemiol Infect 2006, 134:699-711.
• [3]Odoi A, Martin SW, Michel P, Middleton D, Holt J, Wilson JB: Investigation of clusters of giardiasis using GIS and a spatial scan statistic. Int J Health Geogr 2004, 3:11. BioMed Central Full Text
• [4]Scott K, Abhinav K, Stanton B, Johnston C, Turner M, Ampong A, Sakel M, Orrell R, Howard R, Shaw C, Leigh P, Al-Chalabi A: Geogrphical clustering of amyotrophic lateral sclerosis in south-east England: a population study. Neuroepidemiology 2009, 32:81-88.
• [5]Klassen A, Kulldorff M, Curriero F: Geographical clustering of prostate cancer grade and stage at diagnosis, before and after adjustment for risk factors. Int J Health Geogr 2005, 4:1. BioMed Central Full Text
• [6]Kleinman K, Abrams A, Kulldorff M, Platt R: A model-adjusted space-time scan statistic with an application to syndromic surveillance. Epidemiol Infect 2005, 133:409-419.
• [7]Perez AM, Zeng D, Tseng C, Chen H, Whedbee Z, Paton D, Thurmond MC: A web-based system for near real-time surveillance and space-time cluster analysis of foot-and-mouth disease and other animal diseases. Prev Vet Med 2009, 91:39-45.
• [8]Kulldorff M, Athas W, Feuer E, Miller B, Key C: Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos. Am J Public Health 1998, 88:1377-1380.
• [9]Lawson AB, Kleinman K: Spatial and syndromatic surveillance for public health. Chichester, West Sussex, Hoboken, NJ: J. Wiley; 2005.
• [10]Edge VL, Pollari F, Ng LK, Michel P, McEwen SA, Wilson JB, Jerrett M, Sockett PN, Martin SW: Syndromic surveillance of Norovirususing over-the-counter sales of medications related to gastrointestinal illness. Can J Infect Dis Med Microbiol 2006, 17:235-241.
• [11]Hope K, Durrheim DN, Muscatello D, Merritt T, Zheng W, Massey P, Cashman P, Eastwood K: Identifying pneumonia outbreaks of public health importance: can emergency department data assist in earlier identification? Aust N Z J Public Health 2008, 32:361-364.
• [12]van Dijk A, McGuinness D, Rolland E, Moore KM: Can telehealth Ontario respiratory call volume be used as a proxy for emergency department respiratory visit surveillance by public health? CJEM 2008, 10:18-24.
• [13]Vourc’h G, Bridges VE, Gibbens J, De Groot BD, McIntyre L, Poland R, Barnouin J: Detecting emerging diseases in farm animals through clinical observations. Emerg Infect Dis 2006, 12:204-210.
• [14]Van Metre DC, Barkey DQ, Salman MD, Morley PS: Development of a syndromic surveillance system for detection of disease among livestock entering an auction market. JAVMA 2009, 234:658-664.
• [15]Vilas VJDR, Bohning D, Kuhnert R: A comparison of the active surveillance of scrapie in the European Union. Vet Res 2008, 39:37-52.
• [16]Weber WD: Development of an animal health monitoring system based on slaughter condemnation data. Miami: Proceedings of the Eighth International Society for Disease Surveillance Conference; 2009:3-4. December
• [17]Alton GD, Pearl DL, Bateman KG, McNab WB, Berke O: Factors associated with whole carcass condemnation rates in provincially-inspected abattoirs in Ontario 2001–2007: implications for food animal syndromic surveillance. BMC Vet Res 2010, 6:42. BioMed Central Full Text
• [18]Kulldorff M, Heffernan R, Hartman J, Assunção R, Mostashari F: A space-time permutation scan statistic for disease outbreak detection. PLoS Med 2005, 2:216-224.
• [19]Alton GD, Pearl DL, Bateman KG, McNab WB, Berke O: Suitability of portion condemnations at Ontario provincially-inspected abbatoirs for food animal syndromic surveillance. BMC Vet Res 2012, 8:88. BioMed Central Full Text
• [20]Huang L, Tiwari R, Zou Z, Kulldorff M, Feuer E: Weighted normal spatial scan statistic for heterogeneous population data. J Am Stat Assoc 2009, 104(487):886-898.