Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
Animal Health Laboratory, Laboratory Services Division, University of Guelph, Guelph, Ontario, N1H, Canada
Abstract
Background
Animal disease monitoring and surveillance are crucial for ensuring the health of animals, humans and the environment. Many studies have investigated the utility of monitoring syndromes associated with data from veterinary laboratory submissions, but no research has focused on how negative test results from a veterinary diagnostic laboratory data can be used to improve our knowledge of disease outbreaks. For example, if a diagnostic laboratory was seeing a disproportionate number of negative test results for a known disease could this information be an indication of a novel disease outbreak? The objective of this study was to determine the association between the porcine circovirus associated disease (PCVAD) outbreak in Ontario 2004–2006 and the results of porcine reproductive and respiratory syndrome virus (PPRSV) enzymelinked immunosorbent assay (ELISA) and the results of PRRSV polymerase chain reaction (PCR) diagnostic tests requested by veterinarians.
Results
Retrospective data were collected from the Animal Health Laboratory (AHL) at the University of Guelph, Guelph, Ontario Canada and were comprised of weekly counts of PRRSV ELISA and PRRSV PCR diagnostic tests requested by swine practitioners from 2000–2007. The results of the PRRSV ELISA and PRRSV PCRs were analysed separately in two models using logistic regression with the dependent variables being: the weekly probability of PRRSV ELISA positivity, and the weekly probability of PRRSV PCR positivity, respectively. The weekly probability of PRRSV PCR positivity decreased during the PVCAD outbreak (OR=0.66,
Conclusions
The results of this study showed that during the PCVAD outbreak in Ontario from December 2004May 2006, the probability of a positive PRRSV PCR at the AHL decreased. We conclude that when a decrease in test positivity occurs for a known disease, it may suggest that a new disease agent is emerging in the population. Hence, monitoring the test results of commonly used firstorder tests for a known disease (e.g. PRRSV) has the potential to be a unique form of syndromic data for the timely identification of novel disease outbreaks in swine populations.
Background
The information captured by veterinary diagnostic laboratories generates an immense database of animal health information and has contributed significantly to the collective knowledge of animal diseases. In addition to playing a role in determining disease etiology, the data are crucial in providing essential health information for disease monitoring and passive disease surveillance systems of livestock industries worldwide
To the authors’ knowledge, no research has documented the association between the proportion of positive or negative test results for known swine diseases and the occurrence of a novel swine disease outbreak. For example, if a diagnostic laboratory experiences a disproportionate number of negative test results i.e., more tests with negative results than expected, could this information indicate that practicing veterinarians are seeing an unknown disease that represents a novel or reemerging disease outbreak?
Porcine reproductive and respiratory syndrome virus (PRRSV) has challenged the global swine industry for years; and despite herd, region, or country eradication programs, remains a significant swine disease challenge
Numerous diagnostic tests are available for detection of PRRSV antigens or antibodies. However, the PRRSV enzymelinked immunosorbent assay (ELISA) and the PRRSV polymerase chain reaction (PCR) are common firstorder tests requested by swine veterinarians
In the late fall of 2004, an outbreak of porcine circovirus associated disease (PCVAD) caused by a highly pathogenic variant of porcine circovirus type2, (PCV2) occurred in Ontario, Canada
Infection with PCV2 causes a wide range of systemic clinical signs similar to some clinical signs associated with PRRSV infection. Severe weight loss (wasting), failuretothrive, and pneumonia are clinical signs common to both PRRS and PCV2
Methods
Data source and variables
Retrospective AHL diagnostic test data requested by swine veterinarians were compiled from January 1, 2000 to April 30, 2007 and collapsed into weekly counts. The AHL provides services for researchers as well as for private practitioners. For the purposes of this study, diagnostic test data associated with research cases were excluded as were tests used for herd monitoring and those associated with semen specimens. Firstorder PRRSV tests were considered for potential inclusion in the analysis and included the PRRSV ELISA and the PRRSV PCRs offered by the AHL during the study period. Diagnostic tests associated with followup requests, such as gene typing or sequencing, were not considered in the current study, as they have a slower turnaround time and do not represent firstorder tests.
The PRRSV ELISA offered at the AHL laboratory did not change with respect to its test performance during the study period. In 2002, AHL’s PRRSV ELISA was modified to include two recombinant protein preparations representing United States and European strains, but equivalent test performance and cutoff were maintained.
The firstorder PRRSV diagnostic tests selected for the analyses were the PRRSV ELISA and PCRs requested from January 1, 2000 until April 30, 2007. These two tests were considered unique and were analysed separately. The weekly count of positive PRRSV ELISAs and the total weekly count of requested PRRSV ELISAs were determined and used to represent the dependent variable, weekly probability of positive PRRSV ELISA results. Similarly, the weekly count of positive PRRSV PCRs and the total weekly count of requested PRRSV PCRs were determined and used to represent the dependent variable, weekly probability of positive PRRSV PCR results.
Two dichotomous variables were generated and coded (1= outbreak, 0= no outbreak) to represent the two disease outbreaks experienced by the Ontario swine industry during the study period: the PCVAD outbreak that occurred in Ontario from 2004–2006
Statistical analysis
All statistical analyses were conducted in Stata 11(Stata Corp., College Station, Texas, USA).
Descriptive statistics and univariable associations
The PRRSV ELISA and PCR results were analysed separately. The two dependent variables of interest were the “weekly probability of PRRSV ELISA positivity” and the “weekly probability of PRRSV PCR positivity”. The dependent variable “weekly probability of ELISA positivity” was created by taking the number of positive ELISAs per week and dividing that by the total number of ELISAs requested that week. Similarly, the dependent variable “weekly probability of PCR positivity” was created by taking the number of positive PCRs per week and dividing that by the total number of PCRs requested that week. The dependent variables were examined by graphing time series plots to observe the trend of the variables over time and by examining distribution plots. Standard descriptive statistics were calculated.
All of the above covariates were then evaluated for statistical significance with the dependent variable “weekly probability PRRSV ELISA positivity” using logistic regression in a generalized linear model (GLM) framework that used maximum likelihood (ML) estimation
Model A: logistic regression using a GLM approach
The independent variables previously identified by univariable associations as having a liberal significance of
Model fit using the Pearson Chisquare goodnessoffit test was performed. Graphical visualization of the scatterplot of the Pearson residuals against the predicted outcome was used to assess outliers. Subsequently, a partial autocorrelation function (PAF) plot was used to assess whether any autocorrelation remained in the Pearson residuals
Model B: logistic regression using a GLM approach
The same GLM modelbuilding process and model diagnostics used for Model A were repeated for the second model (Model B) using the PRRSV PCR positive results as the dependent variable. A logit link function and a binomial distribution were used with the total weekly count of PRRSV PCR tests used as the denominator.
Results
Descriptive statistics and univariable associations
A total of 7,092 PRRSV ELISA and 28,601 PRRSV PCRs were requested at the AHL from January 1, 2000  April 30, 2007. The means of the weekly count of PRRSV ELISA and PRRSV PCRs were 18.6 (SD=7.0) and 74.9 (SD=92.2), respectively. The total number of observed weeks was 382 and the overall mean of the weekly probability of positive PRRSV ELISA results was 42.1% (SD=16.2). The overall mean of the weekly probability of positive PRRSV PCR results was 24.8% (SD=19.2). The distribution of the weekly probability of PRRSV ELISA positivity and the weekly probability of PRRSV PCR positivity are presented in Figures
Distribution of the weekly probability of PRRSV ELISA positivity at the Animal Health Laboratory from January 1, 2000 to April 30, 2007
Distribution of the weekly probability of PRRSV ELISA positivity at the Animal Health Laboratory from January 1, 2000 to April 30, 2007.
Distribution of the weekly probability of PRRSV PCR positivity at the Animal Health Laboratory from January 1, 2000 to April 30, 2007
Distribution of the weekly probability of PRRSV PCR positivity at the Animal Health Laboratory from January 1, 2000 to April 30, 2007.
Time series plot of the weekly count of PRRSV ELISAs requested and the weekly probability of PRRSV ELISA positivity at the Animal Health Laboratory from January 1, 2000 to April 30, 2007
Time series plot of the weekly count of PRRSV ELISAs requested and the weekly probability of PRRSV ELISA positivity at the Animal Health Laboratory from January 1, 2000 to April 30, 2007.
Time series plot of the weekly count of PRRSV PCR tests requested and the weekly probability of PRRSV PCR positivity at the Animal Health Laboratory from January 1, 2000 to April 30, 2007
Time series plot of the weekly count of PRRSV PCR tests requested and the weekly probability of PRRSV PCR positivity at the Animal Health Laboratory from January 1, 2000 to April 30, 2007.
The independent variables considered for the full main effects multivariable GLM logistic regression model and their univariable associations with the dependent variable “weekly probability of PRRSV ELISA positivity,” are shown in Table
Variable
n
OR ^{ b }
95% CI
a. Logistic regression using generalized linear model ML estimation with the logit link, binomial distribution and total number of PRRSV ELISA tests ordered representing the denominator.
b. Odds ratio.
c. Suspected
PCVAD outbreak
0.87
0.77  0.97
0.02
PRRSV outbreak^{c}
1.19
1.04  1.37
0.01
Season^{c}
Fall
91
Referent


Spring
101
1.08
0.95  1.24
0.25
Summer
91
1.05
0.91  1.21
0.50
Winter
99
1.05
0.92  1.20
0.77
Year
2000

Referent


2001
52
0.89
0.72  1.10
0.28
2002
52
0.97
0.79  1.18
0.75
2003
52
0.77
0.63  0.94
0.01
2004
52
0.78
0.64  0.95
0.01
2005
52
0.60
0.49  0.73
<0.001
2006
52
0.37
0.31  0.45
<0.001
2007
18
0.25
0.19  0.32
<0.001
Variable
n
OR ^{ b }
95% CI
a. Logistic regression using generalized linear model ML estimation with the logit link, binomial distribution and total number of PRRSV PCR tests ordered representing the denominator.
b. Odds ratio.
c. Suspected
PCVAD outbreak
1.08
1.02  1.14
0.01
PRRSV outbreak^{c}
2.41
2.22  2.62
<0.001
Season^{c}
Fall
91
Referent


Spring
101
1.12
1.04  1.22
0.005
Summer
91
0.76
0.69  0.84
<0.001
Winter
99
1.05
0.97  1.14
0.22
Year
2000
52
Referent


2001
52
0.79
0.63  0.99
0.04
2002
52
1.05
0.84  1.31
0.68
2003
52
0.99
0.81  1.22
0.93
2004
52
1.79
1.50  2.15
<0.001
2005
52
1.14
0.97  1.35
0.11
2006
52
0.57
0.48  0.67
<0.001
2007
18
0.88
0.73  1.05
0.14
Final Model A: weekly probability of PRRSV ELISA positivity
The final multivariable GLM logistic regression model including all significant maineffects terms and interaction terms, is shown in Table
Variable
n
OR ^{ b }
95% CI
a. Logistic regression using generalized linear model ML estimation with the logit link, binomial distribution and total number of PRRSV PCR tests ordered representing the denominator.
b. Odds ratio.
c. Suspected
d. Interaction term.
bold text – significant variables.
PCVAD outbreak
1.33
0.97  1.83
0.08
PRRSV outbreak^{c}
0.83
0.56  1.24
0.36
Season^{c}
Fall
91
Referent


Spring
101
0.86
0.56  1.28
0.43
Summer
91
1.01
0.66  1.54
0.98
Winter
99
1.12
0.73  1.72
0.62
Year
2000
52
Referent


2001
52
0.92
0.61  1.38
0.68
2002
52
1.06
0.72  1.57
0.76
2003
52
0.66
0.44  0.99
0.047
2004
52
0.86
0.49  1.50
0.59
2005
52
0.36
0.22  0.59
<0.001
2006
52
0.22
0.15  0.34
<0.001
2007
18
0.22
0.15  0.34
<0.001
Year*Season interaction^{d}
2001*spring
0.84
1.15
0.64  2.07
0.64
2001*summer
0.88
0.58
0.46  1.54
0.58
2001*winter
1.10
0.66
0.49  1.58
0.66
2002*spring
0.89
0.73
0.63  1.92
0.73
2002*summer
0.69
0.69
0.51  1.57
0.69
2002*winter
1.17
0.19
0.39  1.21
0.19
2003*spring
1.13
0.58
0.67  2.05
0.58
2003*summer
1.32
0.68
0.63  2.04
0.68
2003*winter
1.65
0.33
0.75  2.33
0.33
2004*spring
0.88
0.08
0.95  2.87
0.08
2004*summer
0.76
0.65
0.50  1.55
0.65
2004*winter
1.70
0.41
0.40  1.45
0.41
2005*spring
1.09
0.05
0.99  2.90
0.05
2005*summer
1.46
0.75
0.63  1.90
0.75
2005*winter
2.24
0.23
0.79  2.72
0.23
2006*spring
1.50
0.007
1.25  4.00
0.007
2006*summer
1.25
0.16
0.86  2.63
0.16
2006*winter
1.26
0.47
0.69  2.26
0.47
2007*spring
0.84
0.42
0.69  2.31
0.42
Partial autocorrelation function plot of the Pearson residuals for Model A
Partial autocorrelation function plot of the Pearson residuals for Model A.
Final Model B: weekly probability of PRRSV PCR positivity
The final multivariable logistic regression model, including all significant maineffects terms and interaction terms, is displayed in Table
Variable
n
OR ^{ b }
95% CI
a. Logistic regression using generalized linear model ML estimation with the logit link, binomial distribution and total number of PRRSV PCR tests ordered representing the denominator.
b. Odds ratio.
c. Suspected
d. Interaction term.
bold text – significant variables.
PCVAD outbreak
0.66
0.58  0 .75
0.01
PRRSV outbreak ^{ c }
2.53
2.14  2.97
<0.001
Season^{c}
Fall
91
Referent

Spring
101
0.92
0.60  1.43
0.72
Summer
91
0.57
0.35  0.93
0.02
Winter
99
0.85
0.55  1.33
0.49
Year
2000
52
Referent


2001
52
0.81
0.51  1.30
0.38
2002
52
0.95
0.60  1.52
0.84
2003
52
0.90
0.60  1.35
0.61
2004
52
0.66
0.44  0.99
0.04
2005
52
1.04
0.71  1.50
0.85
2006
52
0.49
0.35  0.70
<0.001
2007
18
0.85
0.62  1.17
0.32
Year*Season interaction^{d}
2001*spring
0.58
0.30  1.12
0.11
2001*summer
2.98
1.51  5.86
0.002
2001*winter
0.66
0.35  1.25
0.20
2002*spring
0.95
0.51  1.76
0.86
2002*summer
0.97
0.46  2.01
0.93
2002*winter
1.34
0.72  2.49
0.35
2003*spring
0.77
0.44  1.36
0.37
2003*summer
0.66
0.34  1.31
0.23
2003*winter
2.06
1.18  3.06
0.01
2004*spring
0.85
0.51  1.40
0.52
2004*summer
1.22
0.69  2.17
0.49
2004*winter
2.24
1.34  3.74
0.002
2005*spring
2.19
1.39  3.47
0.001
2005*summer
2.06
1.23  3.48
0.006
2005*winter
0.98
0.61  1.59
0.94
2006*spring
1.61
1.01  2.57
0.045
2006*summer
1.09
0.65  1.85
0.74
2006*winter
1.57
0.97  2.54
0.07
2007*spring
0.93
0.59  1.45
0.74
Partial autocorrelation function plot of the Pearson residuals for Model B
Partial autocorrelation function plot of the Pearson residuals for Model B.
Discussion
The key finding of this study is that the weekly probability of PRRSV PCR positivity at the AHL decreased during the Ontario PCVAD outbreak. Thus, the results of PRRSV PCRs generated through laboratory test requests are an untapped source of swine health data that could be monitored for heightened swine disease outbreak awareness. A large proportion of negative test results do not specifically identify the novel disease or disease pathogen. However, monitoring the trends of such negative results could provide an early indication of disease diagnostic dilemmas occurring in the field. In other words, monitoring the results of such firstorder tests could be used as an early indicator of a disease outbreak, a form of syndromic data. This could improve the recognition of a novel outbreak without having to wait the extra time it takes to reach a definitive laboratory diagnosis through the use of followup tests. For the Ontario swine industry this could have beneficial implications for the timely detection of swine disease outbreaks and with identifying and utilizing novel data sources for such timely detection
The decrease in the weekly probability of positive PRRSV PCR results during the PCVAD outbreak could be extrapolated to suggest that practicing veterinarians were attempting to diagnose a new disease or syndrome (i.e., the PCVAD outbreak) by initially investigating for the presence of PRRSV through the use of the PRRSV PCR. Hence, monitoring PRRSV PCR requests, and more importantly, the results from these tests, has the potential to represent what veterinarians face in the field with respect to disease diagnosis. The PRRSV PCR used at the AHL did not change until after the PCVAD outbreak was resolved indicating that the changes in test positivity were not a result of changing test accuracy.
The results for the PRRSV ELISA model were not associated with the PCVAD outbreak likely due to data management issues identified in the study. Case submissions were not always clearly identified as to whether they were for monitoring or diagnostic purposes. Consequently, some submissions misclassified as diagnostic submissions were actually associated with routine farm monitoring and not part of a disease investigation. During the initial data management process, case submissions and test requests associated with semen specimens were dropped, as they were felt to represent herd monitoring for PRRSV instead of a disease investigation process. The ELISA, however, uses a serum sample that detects antibody, whereas the PCR, that detects antigen, is routinely performed on semen for boar stud herd PRRSV monitoring. Consequently, when cases associated with semen specimens were excluded, the PRRSV PCR data was likely more representative of true diagnostic cases versus monitoring cases.
The results of the PRRSV ELISA model may have also been influenced by many herd management and demographic changes that occurred in the Ontario swine industry during the study period. The number of total hogs in the province grew and the industry consolidated, with farms becoming larger and more species specialized
This study highlights the importance of data quality at the time of collection. Mandatory field requirements on laboratory submission forms, such as those used by Gibbens et al., (2008), could improve upon the classification of monitoring versus diagnostic type cases
The AHL is the largest veterinary diagnostic laboratory in Ontario and is the predominant laboratory used for swine diagnostics by practicing veterinarians in the province
A bias that presents itself in this study, as well as other studies using laboratory derived data for disease surveillance purposes, is that the pigs being tested by the AHL represent farms/producers that seek veterinary professional services. While a large proportion of herds in Ontario probably seek the advice of veterinarians
Future studies should employ surveillance monitoring and statistical tools to further investigate the usefulness of monitoring counts or clusters of negative test results. The application of cumulative sumbased (CUSUM) methods and other cluster detection techniques, such as the scan statistic, to the count or proportion of tests results should be considered
Conclusions
This study showed that during the PCVAD outbreak in Ontario from December 2004  May 2006, the probability of a positive PRRSV PCR at the AHL decreased. We conclude that when an increase in negative test results occurs (or decrease in positivity) it suggests that a new disease agent may be emerging in the population. PRRSV ELISA positivity did not yield a similar significant association possibility due to incomplete information associated with the test request at the time of submission. The results of this study support the importance of practitioners providing accurate and complete demographic and clinical history information on submission forms when requesting tests from a diagnostic laboratory.
Future research initiatives should be focused on CUSUMbased and cluster detection techniques for outbreak detection using the results of test requests made by veterinarians to diagnostic laboratories. To the authors’ knowledge this is the first study to document how a novel swine disease outbreak influenced the results of PRRSV tests requested by veterinarians.
Competing interest
The authors declare no conflict of interest.
Authors’ contributions
All authors were involved with the analysis and the interpretation of the data and with the revising of the manuscript critically for intellectual content. TO performed the cleaning and manipulation of the data, statistical analysis, and drafting and revising of the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors would like to thank the Ontario Ministry of Agriculture and Rural Affairs (OMAFRA), the Canadian Animal Health Surveillance Network (CAHSN), and the Ontario Veterinary College Fellowship Program for their funding support. The authors thank the Animal Health Laboratory for providing the laboratory test request data and for their assistance with this project.