Beverly McEwen

Diseases and Pathogens Associated with Mortality in Ontario Beef Feedlots

This study determined the prevalence of diseases and pathogens associated with mortality or severe morbidity in 72 Ontario beef feedlots in calves that died or were euthanized within 60 days after arrival. Routine pathologic and microbiologic investigations, as well as immunohistochemical staining for detection of bovine viral diarrhea virus (BVDV) antigen, were performed on 99 calves that died or were euthanized within 60 days after arrival. Major disease conditions identified included fibrinosuppurative bronchopneumonia (49%), caseonecrotic bronchopneumonia or arthritis (or both) caused by Mycoplasma bovis (36%), viral respiratory disease (19%), BVDV-related diseases (21%), Histophilus somni myocarditis (8%), ruminal bloat (2%), and miscellaneous diseases (8%). Viral infections identified were BVDV (35%), bovine respiratory syncytial virus (9%), bovine herpesvirus-1 (6%), parainfluenza-3 virus (3%), and bovine coronavirus (2%). Bacteria isolated from the lungs included M. bovis (82%), Mycoplasma arginini (72%), Ureaplasma diversum (25%), Mannheimia haemolytica (27%), Pasteurella multocida (19%), H. somni (14%), and Arcanobacterium pyogenes (19%). Pneumonia was the most frequent cause of mortality of beef calves during the first 2 months after arrival in feedlots, representing 69% of total deaths. The prevalence of caseonecrotic bronchopneumonia caused by M. bovis was similar to that of fibrinosuppurative bronchopneumonia, and together, these diseases were the most common causes of pneumonia and death. M. bovis pneumonia and polyarthritis has emerged as an important cause of mortality in Ontario beef feedlots.

Naturally Occurring Mycoplasma Bovis—Associated Pneumonia and Polyarthritis in Feedlot Beef Calves

Mycoplasma bovis is perceived as an emerging cause of mortality in feedlot beef cattle. This study examined the lesions and infectious agents in naturally occurring M. bovis–associated bronchopneumonia and arthritis and the relationship of this condition with bovine viral diarrhea virus (BVDV) infection. Standardized pathologic, immunohistochemical, and microbiologic investigations were conducted on 99 calves that died or were euthanized within 60 days after arrival in 72 feedlots. Cranioventral bronchopneumonia with multiple foci of caseous necrosis was identified in 54 of 99 calves, including 30 with concurrent fibrinosuppurative bronchopneumonia typical of pneumonic pasteurellosis. Mycoplasma bovis was consistently identified in these lesions by culture and immunohistochemistry, but also commonly in healthy lungs and those with pneumonia of other causes. Focal lesions of coagulation necrosis, typical of pneumonic pasteurellosis, were often infected with both Mannheimia haemolytica and M. bovis. Arthritis was present in 25 of 54 (46%) calves with M. bovis pneumonia, and all calves with arthritis had pneumonia. BVDV infection was more common in calves with lesions of bacterial pneumonia than in those dying of other causes, but BVDV infection was not more common in calves with caseonecrotic bronchopneumonia than those with fibrinosuppurative bronchopneumonia. Retrospective analysis identified cases of M. bovis pneumonia in the early 1980s that had milder lesions than the current cases. The findings suggest that, in at least some calves, M. bovis induces caseonecrotic bronchopneumonia within the lesions of pneumonic pasteurellosis.

Coxiella Burnetii Infection is Associated with Placentitis in Cases of Bovine Abortion

A positive score on a modified acid-fast (MAF)-stained smear test of fresh placenta was used to identify a group of bovine abortion submissions believed to be infected with Coxiella burnetii. Immunohistochemical (IHC) testing for Coxiella and Chlamydia antigens was performed on 14 MAF smear-positive cases as well as 29 MAF smear-negative cases Received during the study period. Nine MAF smear-positive cases as well as 1 MAF smear-negative case were Coxiella-positive via the IHC test. No placentas were positive for Chlamydia antigen. Various histopathologic features were categorized for all placentas and the presence or absence of selected risk categories was also graded for each case. The results between Coxiella IHC-positive cases and Coxiella IHC-negative/MAF-negative cases were compared using Fishers exact test (P value at 95% confidence). Significant associations were found between Coxiella IHC-positive cases and the presence of placental inflammation (P = 0.0027), placental necrosis (P = 0.012), fetal pneumonia (P = 0.0152), and the visibility of Coxiella-like organisms within trophoblasts on hematoxylin and eosin–stained sections (P < 0.0001). Histopathologic features of Coxiella IHC-positive placentas included infiltration of the chorionic stroma by mononuclear cells, necrosis of chorionic trophoblasts, and focal exudation of fibrin and neutrophils. The results indicate that MAF smears are a good screening tool for the presence of Coxiella in placentas from bovine abortion cases and that the detection of this pathogen in aborted placentas via traditional staining or IHC methods is usually associated with placentitis.

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt–Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel.

Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.

Background Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes–syndromic surveillance–using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users. Methods This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory. Results High performance (F1-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F1-macro), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F1-micro score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F1-micro = 0.994 and F1-macro = .955), however the classification process is not transparent to the domain experts. Conclusion The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input.

A prospective study of sheep and goat abortion using real-time polymerase chain reaction and cut point estimation shows Coxiella burnetii and Chlamydophila abortus infection concurrently with other major pathogens

From 2009 to 2011, 163 sheep and 96 goat abortion submissions were received at the Animal Health Laboratory, University of Guelph, Ontario, Canada, for gross and histologic examination, as well as real-time polymerase chain reaction (PCR) testing for Chlamydophila abortus and/or Coxiella burnetii. Additional testing included immunohistochemistry for Toxoplasma gondii and Chlamydophila spp., routine bacterial culture and selective culture for Campylobacter spp., examination of modified acid-fast–stained placenta smears, enzyme-linked immunosorbent assay testing for Chlamydophila spp., and virus isolation. The final diagnosis made for each case by individual pathologists, based on gross and histologic lesions, as well as ancillary testing, was used as a standard to determine the significance of C. abortus and C. burnetii infection. Coxiella burnetii was identified by real-time PCR in 113 of 163 (69.0%) and 72 of 96 (75%) sheep and goat abortion submissions, respectively, but was considered to be significant in causing abortion in only 11 of 113 (10%) sheep and 15 out of 72 (21%) goat submissions that tested positive. Chlamydophila abortus was identified by real-time PCR in 42 of 162 (26%) and 54 of 92 (59%) sheep and goat submissions, respectively, but was considered the cause of the abortion in 16 of 42 (38%) sheep and 34 of 54 (63%) goat submissions that tested positive. Optimal sensitivity and specificity cut points for the real-time PCR copy number for C. abortus and C. burnetii were determined using the final pathology diagnosis as the reference test.

Application and Field Validation of a PCR Assay for the Detection of Mycoplasma Hyopneumoniae from Swine Lung Tissue Samples

A PCR assay was validated for the detection of Mycoplasma hyopneumoniae in porcine lung tissue. The detection limit of the assay was 0.18 colony-forming units/g of lung sample spiked with M. hyopneumoniae. In field validation, 426 pigs from 220 cases were examined for M. hyopneumoniae infection by M. hyopneumoniae PCR and a fluorescent antibody (FA) test. In total, 103 pig lungs (24.2%) were positive in the PCR test, and 69 pig lungs (16.2%) were positive in the FA test, among which, 62 pigs were positive for both PCR and FA test. Most of the PCR-positive but FA test-negative cases had lesions compatible with M. hyopneumoniae infection. With Bayesian modeling, the diagnostic sensitivity and specificity of the PCR were determined to be 97.3% and 93.0%, respectively.

Syndromic Surveillance Using Veterinary Laboratory Data: Algorithm Combination and Customization of Alerts

Background Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals. Methods This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed. Results The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described. Conclusion The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes.

Retrospective time series analysis of veterinary laboratory data: Preparing a historical baseline for cluster detection in syndromic surveillance

The practice of disease surveillance has shifted in the last two decades towards the introduction of systems capable of early detection of disease. Modern biosurveillance systems explore different sources of pre-diagnostic data, such as patients chief complaint upon emergency visit or laboratory test orders. These sources of data can provide more rapid detection than traditional surveillance based on case confirmation, but are less specific, and therefore their use poses challenges related to the presence of background noise and unlabelled temporal aberrations in historical data. The overall goal of this study was to carry out retrospective analysis using three years of laboratory test submissions to the Animal Health Laboratory in the province of Ontario, Canada, in order to prepare the data for use in syndromic surveillance. Daily cases were grouped into syndromes and counts for each syndrome were monitored on a daily basis when medians were higher than one case per day, and weekly otherwise. Poisson regression accounting for day-of-week and month was able to capture the day-of-week effect with minimal influence from temporal aberrations. Applying Poisson regression in an iterative manner, that removed data points above the predicted 95th percentile of daily counts, allowed for the removal of these aberrations in the absence of labelled outbreaks, while maintaining the day-of-week effect that was present in the original data. This resulted in the construction of time series that represent the baseline patterns over the past three years, free of temporal aberrations. The final method was thus able to remove temporal aberrations while keeping the original explainable effects in the data, did not need a training period free of aberrations, had minimal adjustment to the aberrations present in the raw data, and did not require labelled outbreaks. Moreover, it was readily applicable to the weekly data by substituting Poisson regression with moving 95th percentiles.

Trends in Domestic Animal Medico‐Legal Pathology Cases Submitted to a Veterinary Diagnostic Laboratory 1998–2010*

Abstract:  Pathologists at veterinary diagnostic laboratories receive medico‐legal cases from a variety of animal species for postmortem examination. A search of computerized records of the Animal Health Laboratory, University of Guelph, Guelph, Ontario, Canada from 1998 to 2010 identified 1706 medicolegal cases. These were categorized according to the history as criminal investigations, anesthetic‐related deaths, insurance, litigation, malpractice cases, and regulatory cases. Statistically significant linear trends in the proportion of medicolegal cases for all animals and criminal cases for companion animals were identified over the 12 year period. Companion animals had significantly greater odds of being a medicolegal case in all categories except for insurance and regulatory cases, compared to noncompanion animals. Based on pathology reports for the 271 criminal cases, 43.1% were consistent with neglect, 29.2% were compatible with non‐accidental injury, 4.80% were poisonings, 10.7% were deemed to be due to natural disease, and 11.43% were inconclusive.