An Investigation of Epidemiological Approaches for

An Investigation of Epidemiological Approaches for Syndromic Surveillance

of Cattle Health using Ontario Condemnation Data

by

Gillian Denise Alton

A Thesis

presented to

The University of Guelph

In partial fulfilment of requirements

for the degree of

Doctor of Philosophy

in

Population Medicine

Guelph, Ontario, Canada

© Gillian D. Alton, June, 2014

ABSTRACT

AN INVESTIGATION OF EPIDEMIOLOGICAL APPROACHES FOR

SYNDROMIC SURVEILLANCE OF CATTLE HEALTH USING

ONTARIO CONDEMNATION DATA

Gillian Denise Alton Advisors: Dr. O. Berke & Dr. D.L. Pearl

University of Guelph, 2014

This thesis is an investigation of quantitative methods for food animal syndromic

surveillance utilizing bovine abattoir condemnation data as a case study to illustrate approaches

to using Ontario provincial abattoir data. There has been little investigation into the suitability of

bovine abattoir condemnation data in Ontario for its use in a food animal syndromic surveillance

system and the quantitative methods necessary for this type of system.

Overall, it was found bovine condemnation data from provincially inspected abattoirs to

be useful for food animal syndromic surveillance since they provided a more regionally detailed

picture of emerging diseases in Ontario than data from federal abattoirs. Disease-related and non-

disease factors such as season and sales price were shown to have an impact on condemnation

rates, and accounting for relevant predictable factors considerably affects the results of

quantitative cluster detection methods. This was demonstrated by comparison of various space-

time scan statistics with distinct options to control for covariate information. The results from

this study found that model-adjusted approaches for controlling for covariates in scan statistics

appeared to perform best in terms of ability to include all important covariates and suitability for

use with bovine abattoir condemnation data. Furthermore, the efficiency of syndromic

surveillance was investigated by comparing various sentinel abattoir selection approaches to

reduce the number of sample sites while still maintaining the overall trends in the full dataset.

The most effective sentinel selection approach utilized data from abattoirs in operation all weeks

of the year, and this approach shows promise for the integration of sentinel sites into a sentinel

syndromic surveillance system.

While these findings suggest that bovine abattoir condemnation data would be suitable

for integration into a food animal syndromic surveillance system, there are some limitations

including data quality issues and current methodological approaches. Future research is

recommended to focus on the following before formalizing a food animal syndromic surveillance

system in Ontario: (i) developing an improved meat inspector training program; (ii) finding ways

to harmonize the condemnation process by standardizing definitions for reasons for

condemnation; and (iii) validating methodological findings from this thesis by studying

simulated and/or documented historical outbreak data.

iv

ACKNOWLEDGEMENTS

I am fortunate to have so many supporters who have helped me along my PhD. I would

like to thank my advisory committee Dr. Olaf Berke, Dr. David Pearl, Dr. Bruce McNab and Dr.

Ken Bateman for all of their support and guidance throughout my graduate studies. To my co-

advisors, Dr. Berke and Dr. Pearl, Dr. Berke, thank you for your support and guidance

throughout my graduate studies. I cannot thank you enough for introducing me to the world of

spatial epidemiology, which I have grown to love, and for encouraging me to pursue a PhD even

on the first day of my Masters. Dr. Pearl, thank you for your creative thinking and encouraging

me to try out new ideas. I appreciate all of the support and advice you have both given me, both

academically and professionally as I have started my career in Public Health. Dr. McNab, thank

you for your advice and insight during the course of my program and for connecting me with

helpful contacts at the Ontario Ministry of Agriculture and Food (OMAF). Dr. Bateman, thank

you for assisting me with connecting my statistical results with practical animal health

explanations. I could not have completed my PhD without such a supportive team as an advisory

committee.

I would also like to thank OMAF for funding and data support for this project as well as

funding through the OMAF/University of Guelph Research Program. I would also like to

especially thank OMAF staff including Dr. Ab Rehmtulla, Dr. Alexandra Reid, Dr. Leslie

Woodcock, Jeff Perkins and Amy Rutgers-Kelly for their assistance with data retrieval and

assisting with questions over the course of this project. I would like to acknowledge the Canada

Foundation for Innovation (CFI) and the Ontario Research Fund for their support for

v

infrastructure and computing equipment in Dr. David Peal’s computer lab. In addition, I would

like to thank the Ontario Graduate Scholarship for funding to help support my graduate studies.

Thank you to my family for their constant love and support. It has been a very long road,

especially during the past two years, and I could not have done this without your continual

support and encouragement. I would like to especially thank my parents for encouraging me to

work hard and for supporting all of my dreams, even if my aspirations involved spending 12

years in school!

To my wonderful partner Jayson who has been by my side through all of my

undergraduate and graduate studies. You were always there to cheer me up when things were not

going as planned, and encouraged me to keep working when I wanted to throw my computer out

the window, and most importantly, you were there to celebrate all of my successes. I could not

have completed this thesis without your love and support.

vi

STATEMENT OF WORK

Data for this project were collected by the Ontario Ministry of Agriculture and Food

(OMAF) and provided to the University of Guelph for this thesis. All data cleaning, merging and

assessment were performed by Gillian Alton in consultation with personnel from the OMAF.

OMAF personnel also provided assistance and clarification with respect to data quality and

issues throughout this thesis. All data analyses were performed by Gillian Alton. Writing and

manuscript generation was performed by Gillian Alton. Editing and advice concerning analyses

for the thesis in its entirety was received from Drs. David Pearl, Olaf Berke, Ken Bateman and

Bruce McNab.

vii

TABLE OF CONTENTS ABSTRACT .................................................................................................................................................. ii

ACKNOWLEDGEMENTS ......................................................................................................................... iv

STATEMENT OF WORK .......................................................................................................................... vi

TABLE OF CONTENTS ............................................................................................................................ vii

LIST OF TABLES ........................................................................................................................................ x

LIST OF FIGURES .................................................................................................................................... xii

CHAPTER ONE ........................................................................................................................................... 1

INTRODUCTION, LITERATURE REVIEW, STUDY RATIONALE AND OBJECTIVES .................... 1

INTRODUCTION .................................................................................................................................... 1

LITERATURE REVIEW ......................................................................................................................... 3

Overview of the Ontario provincial abattoir system and abattoir surveillance in Ontario........................ 3

Current and historical uses of abattoir data ............................................................................................... 5

Recent major health events in Canadian animal agriculture ................................................................. 5

Use of abattoirs for surveillance ........................................................................................................... 7

Types of surveillance systems................................................................................................................... 8

Active vs. passive surveillance ............................................................................................................. 8

Syndromic surveillance ......................................................................................................................... 9

Sentinel surveillance ........................................................................................................................... 13

Quantitative methods for disease surveillance ........................................................................................ 15

Tests for space-time interaction .......................................................................................................... 15

Cumulative sum (CUSUM) methods .................................................................................................. 16

Scan statistics ...................................................................................................................................... 16

Model-based approaches ..................................................................................................................... 17

STUDY RATIONALE AND OBJECTIVES ......................................................................................... 19

REFERENCES ....................................................................................................................................... 21

CHAPTER TWO ........................................................................................................................................ 27

FACTORS ASSOCIATED WITH WHOLE CARCASS CONDEMNATION RATES IN

PROVINCIALLY-INSPECTED ABATTOIRS IN ONTARIO 2001 – 2007: IMPLICATIONS FOR

FOOD ANIMAL SYNDROMIC SURVEILLANCE ................................................................................. 27

ABSTRACT ............................................................................................................................................ 27

viii

INTRODUCTION .................................................................................................................................. 29

METHODS ............................................................................................................................................. 32

Data source and variables ................................................................................................................... 32

Statistical analysis ............................................................................................................................... 34

RESULTS ............................................................................................................................................... 35

Descriptive statistics ........................................................................................................................... 35

Statistical models ................................................................................................................................ 37

DISCUSSION ......................................................................................................................................... 38

CONCLUSIONS..................................................................................................................................... 42

REFERENCES .................................................................................................................................... 43

CHAPTER THREE .................................................................................................................................... 59

SUITABILITY OF BOVINE PORTION CONDEMNATIONS AT PROVINCIALLY-INSPECTED

ABATTOIRS IN ONTARIO CANADA FOR FOOD ANIMAL SYNDROMIC SURVEILLANCE ...... 59

ABSTRACT ............................................................................................................................................ 59

BACKGROUND .................................................................................................................................... 60

METHODS ............................................................................................................................................. 63


Statistical analysis ............................................................................................................................... 65

RESULTS ............................................................................................................................................... 67

Descriptive Statistics ........................................................................................................................... 67

Statistical models ................................................................................................................................ 68

DISCUSSION ......................................................................................................................................... 71

CONCLUSIONS..................................................................................................................................... 77

ACKNOWLEDGEMENTS .................................................................................................................... 78

REFERENCES ....................................................................................................................................... 79

CHAPTER FOUR ................................................................................................................................... 93

COMPARISON OF COVARIATE ADJUSTMENT METHODS USING SPACE-TIME SCAN

STATISTICS FOR FOOD ANIMAL SYNDROMIC SURVEILLANCE ............................................. 93

ABSTRACT ............................................................................................................................................ 93

BACKGROUND .................................................................................................................................... 95

METHODS ............................................................................................................................................. 98

ix


Space-time scan statistic ..................................................................................................................... 99

RESULTS ............................................................................................................................................. 101

“Parasitic liver” data ......................................................................................................................... 102

Pneumonic lung data ......................................................................................................................... 102

DISCUSSION ....................................................................................................................................... 103

CONCLUSIONS................................................................................................................................... 107

ACKNOWLEDGEMENTS .................................................................................................................. 108

REFERENCES ..................................................................................................................................... 109

CHAPTER FIVE .................................................................................................................................. 118

SUITABILITY OF SENTINEL ABATTOIRS FOR SYNDROMIC SURVEILLANCE USING

PROVINCIALLY-INSPECTED BOVINE ABATTOIR CONDEMNATION DATA ....................... 118

ABSTRACT .......................................................................................................................................... 118

BACKGROUND .................................................................................................................................. 121

METHODS ........................................................................................................................................... 123

Data source ........................................................................................................................................ 123

Descriptive analyses .......................................................................................................................... 125

Statistical analyses ............................................................................................................................ 126

RESULTS ............................................................................................................................................. 127

Descriptive Statistics ......................................................................................................................... 127

Negative binomial models ................................................................................................................ 129

DISCUSSION ....................................................................................................................................... 130

CONCLUSIONS................................................................................................................................... 132

ACKNOWLEDGEMENTS .................................................................................................................. 133

REFERENCES ..................................................................................................................................... 134

CHAPTER SIX ..................................................................................................................................... 148

SUMMARY, DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS ............................... 148

REFERENCES ..................................................................................................................................... 155

x

LIST OF TABLES

CHAPTER 2:

Table 2.1 – Number of provincially inspected abattoirs in Ontario 2001 – 2007.........................45

Table 2.2 – Reason for whole carcass condemnation in provincially inspected abattoirs in

Ontario 2001-2007.........................................................................................................................46

Table 2.3 – Summary of number of cattle processed, number weeks open, audit rating and animal

class................................................................................................................................................48

Table 2.4 – Univariable negative binomial models using GEE approach……………………….49

Table 2.5 – Comparison of GEE fitted models using QIC statistic……………………………...51

Table 2.6 – Multivariable negative binomial model using GEE approach………………………52

Table 2.7 – Linear combinations of condemnation rates in cows and calves……………………55

CHAPTER 3:

Table 3.1 – Summary of Ontario provincial abattoir bovine portion condemnations 2001-

2007................................................................................................................................................82

Table 3.2 – Summary of number cattle processed, number weeks open, audit rating and animal

class………………………………………………………………………………………………83

Table 3.3 – Multivariable multi-level Poisson regression using pneumonic lung condemnation

rates………………………………………………………………………………………………84

Table 3.4 – Multivariable multi-level Poisson regression using “parasitic liver” condemnation

rates………………………………………………………………………………………………86

CHAPTER 4:

Table 4.1 – Results of the space-time scan statistic using “parasitic liver” condemnation

data……………………………………………………………………………………………...111

Table 4.2 – Results of the space-time scan statistic using pneumonic lung condemnation

data……………………………………………………………………………………………...114

CHAPTER 5:

Table 5.1 – Summary of sentinel abattoirs selection approaches for Ontario provincial abattoirs

(2001 – 2007)…………………………………………………………………………………...136

xi

Table 5.2 – Percentage of shared abattoirs between sentinel selection approaches……………137

Table 5.3 – Distribution of provincial abattoirs 2001 – 2007 among census agricultural regions in

Ontario based on three sentinel selection approaches…………………………………………..138

Table 5.4 – Negative binomial regression models comparing monthly pneumonic lung

condemnation rates for all abattoirs for each animal class with three methods of sentinel abattoir

selection………………………………………………………………………………………...139

Table 5.5 – Multivariable multilevel negative binomial regression model examining seasonal and

annual variability in monthly pneumonic lung condemnations for three sentinel selection

approaches………………………………………………………………………………………140

xii

LIST OF FIGURES

CHAPTER 2:

Figure 2.1 – Condemnation rates per 1000 cattle from Ontario provincial abattoirs 2001-

2007………………………………………………………………………………………………56

Figure 2.2 – Animal class condemnation rates per 1000 cattle from Ontario provincial abattoirs

2001-2007………………………………………………………………………………………..57

Figure 2.3 – Choropleth map of percentage of abattoirs processing cattle in Ontario per census

agricultural region………………………………………………………………………………..58

CHAPTER 3:

Figure 3.1 – Map of provincially-inspected abattoirs in Ontario 2001-2007……………………88

Figure 3.2 – “Parasitic liver” condemnation rates per 1000 slaughtered cattle from Ontario

provincial abattoirs 2001-2007…………………………………………………………………..89

Figure 3.3 - Pneumonic lung condemnation rates per 1000 slaughtered cattle from Ontario

provincial abattoirs 2001-2007…………………………………………………………………..90

Figure 3.4 – Model expected pneumonic lung condemnation rates based on multi-level Poisson

model……………………………………………………………………………………………..91

Figure 3.5 – Model expected “parasitic liver” condemnation rates based on multi-level Poisson

model……………………………………………………………………………………………92

CHAPTER 4:

Figure 4.1 – Results of space-time scan statistic using “parasitic liver” portion condemnation

data from Ontario provincial abattoirs 2001 – 2007 using four approaches for covariate

adjustment compared to the unadjusted data including: A) Unadjusted Poisson space-time scan

test, B) Animal-adjusted Poisson space-time scan test, C) Model-adjusted space-time scan test

and D) Space-time permutation scan test………………………………………………………116

Figure 4.2 – Space-time scan statistic using pneumonic lung condemnation data from Ontario

provincial abattoirs 2001 -2007 using four approaches for covariate adjustment compared to the

unadjusted data including: A) Unadjusted Poisson space-time scan test, B) Animal-adjusted

Poisson space-time scan test, C) Model-adjusted space-time scan test and D) Space-time

permutation scan test and E) Space-time scan test of residuals………………………………...117

xiii

CHAPTER 5:

Figure 5.1 – Pneumonic lung condemnation rates per 1000 slaughtered cattle for each animal

class for all abattoirs processing cattle throughout the study period…………………………...142

Figure 5.2 – Boxplots comparing the pneumonic lung condemnation rates per 1000 slaughtered

cattle for each sentinel abattoir selection approach…………………………………………….143

Figure 5.3 – Comparison of pneumonic lung condemnation rates in calves for sentinel site

selection approaches and full dataset 2001 – 2007……………………………………………..144

Figure 5.4 - Comparison of pneumonic lung condemnation rates in cows for sentinel site


Figure 5.5 - Comparison of pneumonic lung condemnation rates in heifers for sentinel site


Figure 5.6 - Comparison of pneumonic lung condemnation rates in steers for sentinel site

selection approaches and full dataset 2001 – 2007……………………………………………147

1

CHAPTER ONE:

INTRODUCTION, LITERATURE REVIEW, STUDY RATIONALE AND

OBJECTIVES

INTRODUCTION

With the emergence and re-emergence of novel infectious and zoonotic diseases

in recent years, there is an increased concern and awareness surrounding novel methods

for disease surveillance for the purposes of outbreak detection at the human-animal

interface [1]. Syndromic surveillance is one such novel approach that has been used to a

greater extent in various public health applications [2-4] in the past 20 years, but has been

explored more recently for the detection of disease outbreaks in animal health

applications [5-7]. In an effort to improve the early detection of animal disease outbreaks

in Ontario, the Ontario Ministry of Agriculture and Food (OMAF) was interested in

determining the suitability of utilizing novel data sources for animal disease surveillance

[7-9], including the use of bovine abattoir condemnation data for food animal syndromic

surveillance and the use of cluster detection methods for the rapid identification of

potential outbreaks of infectious animal diseases.

A great deal of research has been conducted on the application of quantitative

methods for disease surveillance [10]. However, it is important to remember that animal

health data, and in particular, provincial abattoir condemnation data have unique

characteristics which can create “noise” in the data. In provincial abattoir condemnation

data, for instance, non-disease factors such as season, price of animal class, animal

throughput, and processing capacity of abattoir need to be considered in understanding

2

the factors that influence condemnation rates. The selection, application and

interpretation of quantitative methods for disease surveillance needs to account for both

disease and non-disease specific factors that can lead to biases in the surveillance system.

This is particularly important when using animal health data to minimize the occurrence

of false alarms, and to avoid missing true disease outbreaks.

This thesis focuses on the novel approach of investigating the use of bovine

abattoir condemnation data from provincially-inspected abattoirs in Ontario for

syndromic surveillance of emerging infectious and zoonotic diseases, and assesses the

suitability of various quantitative cluster detection methods for integration into a food

animal syndromic surveillance system. The aim of the following narrative review is to

highlight recent examples from the past 20 years of veterinary syndromic surveillance

and key considerations for the application of quantitative cluster detection methods for

Ontario provincial abattoir condemnation data through the proceeding topics:

1. Overview of the Ontario provincial abattoir system and abattoir surveillance in

Ontario;

2. Current and historical applications of abattoir data;

3. Overview of surveillance approaches; and

4. Quantitative methods for disease surveillance.

3

LITERATURE REVIEW

Overview of the Ontario provincial abattoir system and abattoir surveillance in

Ontario

According to the 2011 Census of Agriculture, the Ontario cattle population

consisted of approximately 1.7 million cattle from 20,349 farms, representing

approximately 14% of the Canadian cattle population and 24% of Canadian farms.

Ontario has two types of abattoirs: provincial and federal [11]. The provincial abattoirs

process livestock for consumption within provincial boundaries [12]. These abattoirs are

generally small to medium sized establishments in terms of processing capacity, yet more

abundant in numbers compared to federal abattoirs. Federal abattoirs process livestock

for distribution inter-provincially and internationally [12]. As of September 2013, there

were seven federally-inspected [13] and 100 provincially inspected [14] meat

establishments processing cattle in Ontario and 95% of Canadian cattle were slaughtered

at federal abattoirs [15].

Meat inspection is the largest component of Canada’s food processing industry

[15]. Meat inspection regulations in Ontario are enforced by both federal and provincial

jurisdictions. Since 1997, federal abattoirs are licensed and regulated by the Canadian

Food Inspection Agency (CFIA). The CFIA continually performs inspection activities in

all federally registered abattoirs to ensure that meat products are processed according to

the Meat Inspection Act and Meat Inspection Regulations. Alternatively, the Food

Inspection Branch of the Ontario Ministry of Agriculture and Food (OMAF) regulates

meat inspection at provincial abattoirs through the Food Safety and Quality Act

(“FSQA”) and Regulation 31/05 (“Regulation”) [16]. OMAF has inspectors who inspect

the abattoirs and free standing meat plants and auditors who audit the plants. The meat

4

inspection regulations stipulate that every carcass and half carcass at the plant is subject

to a post-mortem inspection by a provincial inspector before being approved for use as

food [16]. Slaughter and condemnation data from all provincially-inspected abattoirs are

collected on a daily basis by OMAF using the Food Safety Decision Support System

(FSDSS) [12]. This system represents a potential wealth of information for food animal

syndromic surveillance, yet has been under-utilized to date.

In a variety of countries, abattoirs have previously been utilized for surveillance

of a variety of diseases important to both human and animal health [17-22]. While

abattoirs have been previously used to monitor animal diseases and lesions, these

programs were mainly designed to provide feedback to producers and stakeholders about

production performance, disease occurrence, and the impact of disease at the herd and

national levels [23]. However, little research has been done to examine the usefulness of

abattoir condemnation data for application in a food animal syndromic surveillance

system, particularly in cattle in Ontario, with the specific purpose to outbreak detection.

Compared to federal abattoirs, provincial abattoir condemnation data present a unique

opportunity to be utilized for surveillance as these data appear to be spatially sensitive.

Anecdotal evidence has suggested that cattle shipped to provincial abattoirs originate

from relatively local farms [12]. However, as farm of origin data are not currently

associated with Ontario provincial abattoir condemnation data this has never been

substantiated in the literature. This is an important characteristic of provincial abattoirs in

Ontario and pertinent spatial-temporal surveillance, as detection of disease clusters at the

abattoir may signal problems which originate in the surrounding cattle population. In

5

addition, due to international trade regulations at federal abattoirs, cattle of poorer quality

(i.e., poor-doers) may be more likely to be sent to provincial abattoirs [24].

Current and historical uses of abattoir data

Recent major health events in Canadian animal agriculture

Many countries worldwide, including Canada and specifically Ontario, have

experienced a re-emergence of infectious diseases in food animal populations over the

past decade. This includes outbreaks of a new strain of porcine circovirus type II (PCV-2)

in swine in 2004 [25] and outbreaks of bovine viral diarrhea (BVD) with enhanced

virulence in cattle from 1993-1995 [26]. BVD is an acute, highly contagious viral disease

in cattle. Infected animals usually exhibit signs of fever, diarrhea and mucosal lesions

[26]. The disease was first described in the literature in 1946 in New York State [27].

Although some reports have described the disease to be severe, prior to the early 1990’s,

the disease was considered to be mild, short in duration and have low mortality rates in

cattle of all ages [28, 29]. However, in 1993, bovine viral diarrhea virus (BVDV) with

enhanced virulence caused unprecedented outbreaks of severe BVD in Ontario and

Quebec. The outbreak lasted 3 years and caused an estimated $40,000 - $100,000 in

economic losses per herd due to lost animals, decreased milk production and abortion

[26]. A retrospective review of the outbreak data in Ontario by Carman et al. found

unusual and significantly higher median crude mortality rates in adult dairy cattle of

infected herds [26]. This finding suggests that perhaps the monitoring of dead stock or

mortality rates in cattle could have been used as an early warning system for the

outbreak.

6

Animal disease outbreaks are not only detrimental to the animals; they can have

devastating and lasting effects on the producers and processors, particularly if they

impact international trade. This type of event was seen when a case of bovine spongiform

encephalopathy (BSE), was detected in Alberta, Canada in 2003. Bovine spongiform

encephalopathy is a progressive and fatal prion infection in cattle causing damage to the

central nervous system. It belongs to a larger family of diseases called transmissible

spongiform encephalopathies (TSEs) which includes scrapie in sheep and goats, chronic

wasting disease in deer, elk and moose, and classic and variant Creutzfeldt-Jakob disease

in humans [30]. The incubation period for BSE is quite long with most cattle exhibiting

signs approximately 3-6 years after infection. Signs of BSE include behavioural changes,

coordination problems, weight loss and decreased milk production [30]. Though much

research has been completed, currently there is no test to diagnose BSE in live animals.

Diagnosis is generally made through clinical signs and confirmed under Western Blot for

the presence of prions in the brain tissue [31].

Bovine spongiform encephalopathy is speculated to have originated in the early

1970’s from the use of rendered ruminant protein containing tissue of infected animals in

the United Kingdom [31]. While there is still more to be learned about the transmission

of BSE, it is believed the primary source of infection is through contaminated feed

through the use of meat and bone meal containing protein from rendered infected

ruminants [30]. Within hours of the confirmation of the first BSE case in Canada, the

United States government announced an immediate ban of all imports of Canadian beef

[31]. The closure of international trade borders to Canadian cattle lasted for 26 months.

This caused the price of cattle to drop significantly throughout the country, and it took

7

several years to recover [32]. These economic impacts may be associated with the quality

of animals being sent to slaughter, as producers consider shipping costs against possible

return of shipping an animal of suspect health, thus impacting condemnation rates. The

story of BSE in Canada raises the importance of early identification and prevention of

infectious and zoonotic diseases in food animals, as they have the ability to have lasting

and far-reaching effects on the agricultural industry and international trade.

Use of abattoirs for surveillance

The use of abattoirs for disease surveillance is not a novel concept. Abattoirs have

been utilized successfully as a source of data collection and surveillance for many years.

Historically, abattoirs have been used primarily for targeted surveillance of a specific

pathogen, or to quantify the presence/absence of certain lesions or condemnations [33-

39]. Studies by Van Donkersgoed et al. [38] and Schamber et al. [39] in the 1980’s and

1990’s described the condemnations in cattle presented for slaughter at federally-

inspected abattoirs in Canada and the United States. These studies highlighted liver [38]

and pneumonic lung condemnations [39] as being among the most frequently reported

reasons for condemnation of cattle. Similar studies have also been conducted using other

species including broilers in Danish abattoirs [34], or sheep and goats from abattoirs in

Tanzania [36].

Other studies using abattoir data have focused on specific pathogen surveillance

to improve food safety. For example, a study by Alban et al. [40] used data from the

Danish Salmonella surveillance and control programme for finisher pigs and found that

meat-juice samples taken from finisher pigs at the time of slaughter was an effective way

to identify high-risk herds for Salmonella. Similarly, a study by Osaili et al. [35],

8

collected samples from cattle slaughtered in Amman to determine the prevalence of

Escherichia coli O157:H7 and respective antimicrobial susceptibility.

Types of surveillance systems

Active vs. passive surveillance

Disease surveillance is the ongoing systematic monitoring of health of a

population, based on the collection, interpretation and dissemination of health data and

information [41]. Disease surveillance is a broad term which encompasses many different

approaches for surveillance. Each surveillance approach has advantages and

disadvantages which make them suitable for certain circumstances. These are defined by

the overall objective, context and the available resources under which a surveillance

program is conducted [42, 43]. Broadly, surveillance systems are classified into active

and passive. Passive surveillance involves the reporting of a particular disease as it is

diagnosed. Health authorities do not stimulate reporting by reminding health care workers

to report disease or provide feedback to individual health care workers. For example, in

human medicine, Canada has a published list of notifiable diseases and health care

workers are supposed to report a case of a notifiable disease to the local health

department on a case-by-case basis [44]. Similarly, in veterinary medicine, the Canadian

Food Inspection Agency (CFIA) has a list of reportable diseases outlined in the Health of

Animals Act and Reportable Disease Regulations which are to be reported to a CFIA

district veterinarian on a case-by-case basis [45]. The advantage of this approach to

surveillance is that it is simple and not burdensome to the health authority; however,

there can be an issue with incompleteness and variability in reporting. As a result, passive

9

surveillance systems may not be representative and may not always be sensitive enough

to identify outbreaks. In contrast, active surveillance involves regular outreach to data

reporters, such as health care workers and stimulates the reporting of specific diseases

[43, 44]. Active surveillance systems can be very costly and labour-intensive; therefore,

active surveillance systems are often used for limited time periods and for specific

purposes. Other major descriptors of surveillance include syndromic and sentinel

surveillance which may also be described as active or passive [43, 44].

Syndromic surveillance

Syndromic surveillance involves the grouping of large numbers of similar

signs/symptoms or surrogate data utilizing non-traditional data sources. The groupings of

signs/symptoms are loosely designated as ‘syndromes’. This information can be used to

track disease trends in a population and signal an aberration that may warrant further

investigation [46]. The objective of syndromic surveillance is to detect clusters of

symptoms earlier than traditional surveillance data sources (e.g., the detection of specific

pathogens through laboratory surveillance). Syndromic surveillance is a relatively new

tool for animal and public health. The concept was derived from a gastrointestinal disease

outbreak in Milwaukee in 1993. The outbreak was eventually traced to Cryptosporidium

in the water supply. However, after the outbreak was over, it was discovered that the

sales of over-the-counter anti-diarrheal medications had increased more than three-fold

weeks before health officials were alerted to the outbreak [47]. Since 1993, much

advancement has been made in the area of syndromic surveillance, in terms of

quantitative methods and practical applications of the methodology. Systems have been

developed to monitor data on school absenteeism, sales of over-the-counter products,

10

calls to nurse hotlines and counts of hospital emergency room admissions [2-4]. In

theory, syndromic surveillance shows great promise for the detection of disease outbreaks

in a variety of settings. However, in practical applications, syndromic surveillance

systems can be burdened with continuous false-alarms [48].

While syndromic surveillance has been utilized in public health settings for nearly

two decades, the application of such methods in an animal health context have only

recently been explored within the last few years using a variety of novel data inputs [5-8].

Amezcua et al. conducted a pilot study to determine the feasibility of utilizing a

veterinary-based syndromic surveillance system to detect disease outbreaks in Ontario

swine [7]. The surveillance system was evaluated in terms of timeliness, compliance,

geographic coverage and data quality. The study found that obtaining information from

veterinarians can be valuable, reliable and relatively inexpensive for the collection of data

from a large proportion of Ontario’s swine farms. However, it was found that the

timeliness of data transmission was too slow to identify disease trends on a weekly basis

and more work needed to be done to improve timeliness of data and drop-offs in

compliance [7]. Similarly, clinical data from veterinary practitioners have been

investigated in other countries including France, New Zealand, United States, The

Netherlands and United Kingdom [49].

Recent veterinary syndromic surveillance research investigated the use of novel

data sources for the timely detection of disease outbreaks, as well as employing

laboratory submission requests, rather than the actual laboratory results in animal disease

syndromic surveillance. Studies by Dórea et al. [50-52], have been investigating the use

of laboratory submission counts for veterinary syndromic surveillance based on

11

submissions to the Animal Health Laboratory at the University of Guelph. As there is no

standard classification for veterinary syndromes, the researchers created 17 syndromic

groups based on the type of sample submitted and diagnostic test requested by the

veterinarian for cattle [50]. The researchers also investigated the performance of various

time-series algorithms for use in detection of potential outbreaks using simulated data and

found the developed system provided high sensitivity for detection of potential outbreak

signals for cattle [52]. Similarly, a study by O’Sullivan et al. [9], investigated the use of

veterinary diagnostic laboratory data from the Animal Health Laboratory at the

University of Guelph, with a focus on monitoring negative test results in swine. The

study hypothesized that the presence of a disproportionate number of negative test results

for a specific disease could be an indication of a novel disease outbreak. The study found

that during the PCV-2 associated disease outbreak which occurred in Ontario from

December 2005 – May 2006, the probability of a positive porcine reproductive and

respiratory syndrome virus (PRRSV) polymerase chain reaction at the Animal Health

Laboratory decreased. The study concluded that in order to detect a new disease agent

emerging in a particular population, it may be advantageous to monitor negative test

results of commonly used first-order tests for a known disease [9].

Other researchers have used alternative approaches to veterinary-based

surveillance and have investigated the use of clinical observations of animals entering a

livestock auction. A study by Van Metre et al. used visual inspection of animals outside

pens at a livestock auction as a potential data source for syndromic surveillance [5]. This

study utilized data from multiple species including cattle, sheep, goats, horses and pigs.

Livestock were visually inspected for clinical signs of disease by a veterinarian and

12

clinical signs were categorized into 12 pre-determined disease syndromes. The

frequencies of clinical signs and disease syndromes were then calculated. The most

commonly observed disease syndromes for all animal species were respiratory tract

diseases, thin body condition, and abnormal ambulation or posture, and together

accounted for approximately 93% of all clinical signs observed. Further studies are

needed to determine whether these methods are sensitive and specific enough to detect

disease outbreaks [5].

Ontario provincial abattoir condemnation data show great potential for the

monitoring of emerging infectious and zoonotic diseases, however, these data have been

rarely used, particularly for cattle in Ontario. A study by Thomas-Bachli et al.

investigated the suitability and limitations of using abattoir condemnation data for

syndromic surveillance of emerging diseases of swine in Ontario [8]. This study found

clusters of high condemnation rates for kidneys with nephritis in time and space-time

which preceded the timeframe during which case clusters of PCV-2 were detected using

traditional laboratory data. In addition, the trends found in kidney condemnation rates

related to nephritis lesions in Eastern Ontario were consistent with documented disease

outbreaks during that time period [8]. The findings from this study support the further

investigation of using abattoir condemnation data for food animal syndromic

surveillance. In addition, studies in the United States have also utilized abattoir data for

syndromic surveillance. A study by Engle [53], used condemnation data from the

electronic Animal Disposition Reporting System (eADRS) and found that a swine

erysipelas outbreak in Iowa and Minnesota during July 2001 could have been identified

approximately 10 months earlier if an automated surveillance system had been in place.

13

A similar study in the United States evaluated the use of condemnation data from swine

to set up an animal health monitoring system [53]. More recently, studies in Switzerland

[33] and France [54], have investigated the use of both portion and whole carcass

condemnations from abattoirs for syndromic surveillance purposes. No studies have

investigated the use of bovine abattoir condemnation data from provincially-inspected

abattoirs in Ontario.

False-alarm rate is an issue with livestock data, and in particular for Ontario

provincial abattoir condemnation data. There are many characteristics, such as the

changing background population and small throughput of animals at certain facilities,

which can contribute to false-alarms if there are predictable covariates that are not

corrected for in quantitative cluster detection methods. These false-alarms can mask the

presence of a true outbreak and use additional governmental time and resources for

investigation. The study by Thomas-Bachli et al. identified a number of non-disease

factors associated with lung and kidney condemnation rates, including abattoir processing

capacity, agricultural region and season which should be taken into account when

applying cluster detection methods for abattoir condemnation data [8].

Sentinel surveillance

Sentinel surveillance is a form of surveillance which involves a limited number of

recruited participants or organizations, such as farms, veterinarians, abattoirs, healthcare

providers or hospitals, which report on specific health events to identify disease presence

and/or trends in the general population [40]. Sentinel surveillance is an alternative to

population-based surveillance and is often used when population-based data collection is

not feasible [44]. Sentinel surveillance enables the collection of timely information in a

14

relatively inexpensive manner, rather than sampling information from the general

population. Sentinel surveillance has been previously used in both human and animal

health settings for a variety of health outcomes. Sentinel surveillance has been used to

monitor or identify outbreaks of infectious diseases and to monitor the activity of certain

health conditions which can change due to environmental conditions. Some notable

examples in human health include the Canadian Primary Care Sentinel Surveillance

Network (CPCSSN), which is Canada’s first multi-disease electronic record sentinel

surveillance system funded by the Public Health Agency of Canada. CPCSSN includes

10 practice-based research networks associated with departments of family medicine

across the country [55]. This system collects information on chronic diseases from a

select group of family doctors. A similar approach utilizing sentinel practitioners has

been employed in the United States by the United States Influenza Sentinel Physicians

Surveillance Network which provides weekly reports on the total number of patients seen

and the number of patients with influenza-like illness by age group [43]. Though used

less often in animal health applications, sentinel surveillance has been used successfully

for surveillance in various applications. For example, following the emergence of

Bluetongue virus serotype 8 in Central Europe in 2006, causing a large scale outbreak in

several countries in Europe in 2007, a Bluetongue sentinel surveillance program was

established in Belgium in 2010. This surveillance program intended to demonstrate the

absence of Bluetongue virus [56]. This program randomly selected a total of 300 dairy

herds, with 30 herds selected from each of the Belgian provinces. The criteria for

selection of herds was based on dairy herds that were expected to have a minimum of 15

animals present between 4 and 12 months of age at the start of the sentinel program [56].

15

Other studies have combined both sentinel and syndromic surveillance and utilized data

collected from sentinel veterinarians or veterinary practices on farms for syndromic

surveillance of emerging infectious diseases in animals [7, 49].

Quantitative methods for disease surveillance

With the emergence of previously unidentified infectious and zoonotic diseases in

recent years in both animal and public health, the need for sensitive and specific

quantitative methods for disease surveillance is growing. A variety of quantitative

methods for disease surveillance exist depending on the available data and the question to

be answered. Quantitative methods for spatio-temporal disease surveillance generally

seek to determine whether the incidence of the disease of interest in a spatially and

temporally defined subset is unusual compared to the incidence in the rest of the study

population [10]. There are various broad classifications for quantitative methods

available including: tests for space-time interaction, cumulative sum methods, scan

statistics and model-based approaches [10].

Tests for space-time interaction

Tests for space-time interaction involve the number of cases of disease that are

related in space and time and compare this to a null hypothesis of no interaction [57].

Examples of tests for space-time interaction include the Knox test [58], Mantel’s test [59]

and the space-time K-function [60]. The space-time interaction tests are only suitable for

testing cases of a single disease and can only report the presence/absence of spatio-

temporal dependence or clustering in the data [10].

16

Cumulative sum (CUSUM) methods

Cumulative sum (CUSUM) methods maintain a running total of the deviations

between observed and expected values [10]. If this total deviates beyond pre-determined

threshold levels, then an alarm will indicate a potential disease outbreak [46]. CUSUM

methods are designed to detect sudden changes in the mean value and originated from

industrial process control applications to monitor production quality [46]. This statistic

was expanded by Rogerson and Yamada in 2004 [61] to create a multivariate CUSUM

where the statistic is derived for a singular disease over multiple sub-regions, but could

also be used to monitor multiple diseases over multiple sub-regions. CUSUM methods

are effective for prospective disease surveillance; however, due to the sum of deviations,

given a long enough time period false alarms are inevitable. The false-positive rate is

controlled by the expected time it takes for a false alarm to be signalled (in-control

average run length), however, in practice this value can be difficult to specify and

remains a key problem with CUSUM methods [10].

Scan statistics

Cluster detection methods have become popular due to the increased availability

of geo-referenced health data and the desire to detect not only when a potential disease

outbreak is occurring, but also the geographical region where it is occurring. One such

method is the space-time scan statistic; which has been previously utilized in a variety of

human and animal health-related applications [62-64]. The scan statistic detects a local

excess of events and then tests if this excess may have occurred due to chance [46]. The

17

scan statistic was first studied with time-series data [65]. Since this time, the scan

statistic method has been extended to include the 2-dimensional scan statistics to detect

purely spatial disease clusters [66], and 3-dimensional scan statistics to detect disease

clusters in space and time [63]. The space-time scan statistic uses a cylindrical window

with a circular base corresponding to space and the height corresponding to time [63].

The cylinder moves in space and time so that for each possible geographic location and

size, it also visits each possible time interval. This creates a number of overlapping

cylinders of different sizes covering the entire study area. The scanning window

compares the number of cases inside potential cluster space to the expected number of

cases outside that space based on the population at risk and estimates a likelihood for

each cylinder. The test is based on the assumption of constant risk inside the scanning

window compared to outside the scanning window under the null hypothesis. Statistical

significance of the cluster is determined by Monte Carlo-based simulations [63]. The

scan statistic can be conducted using a variety of probability models including: Poisson,

Bernoulli, multinomial, ordinal, exponential and normal depending on the type of data

being used.

Model-based approaches

While spatial, temporal and spatio-temporal scan statistics have been widely used

in the literature, limitations in the ability of these statistical methods to adjust for

covariates remains an issue. Currently, the implementations of the scan statistic allow for

the adjustment of categorical covariates without interactions. Model-based quantitative

methods for disease surveillance address the current limitations of spatio-temporal cluster

detection methods and allow for the inclusion of further covariates that may influence the

18

incidence of the disease of interest. For example, the incidence of some diseases can vary

with age and gender and would need to be accounted for in the model [10]. Regression

models allow for the incorporation of continuous and categorical confounding variables,

as well as, complex interaction terms and adjust the disease risk in space and time. In

addition, the covariates can be evaluated for statistical significance with the outcome of

interest to ensure only statistically significant variables are being included in the model.

Generalized linear mixed models (GLMMs) are one example of a broad classification of

statistical models that have been used for space-time disease surveillance [10]. The

GLMM is a statistical regression-based approach to model an outcome of interest, such as

disease counts or rates using a variety of statistical distributions [10]. The GLMM is an

extension of the generalized linear model that includes random effects to account for one

or more levels of clustering or spatial hierarchy within the data [67]. A study by

Kleinman et al. proposed a general approach to evaluate whether observed counts in a

relatively small area are larger than would be expected on the basis of a history of

naturally occurring disease utilizing a generalized linear mixed model [68]. The statistical

methods described in the above study were of particular use for syndromic surveillance

of bioterrorism agents, specifically anthrax, where the initial symptoms are difficult to

distinguish from those of naturally occurring disease [68]. However, in comparison to

spatio-temporal cluster detection methods, model-based surveillance approaches require

additional tests to determine if incoming data differ from modelled pattern of cases and

are unable to pin-point the location of a significant cluster. Thus, surveillance workers

may use models to estimate a relative risk and then combine the results with specific

cluster detection methods. An example of this multiple-method approach is illustrated by

19

Kleinman et al. [48]. Their study introduced a method to adjust for naturally occurring

temporal trends or geographical patterns in lower respiratory tract complaints in humans

reported to a large group medical practice. The study demonstrated the importance of

adjusting for predictable non-disease factors such as day of week, month, holidays, as

well as disease factors related to the local history of disease, and found the number of

false alarms could be reduced by adjusting for “noise” in the data [48]. However, these

methods have never been applied to bovine abattoir condemnation data, and specifically

in Ontario, and may be helpful at reducing the background “noise” in abattoir

condemnation data and improve its use in a food animal syndromic surveillance system.

STUDY RATIONALE AND OBJECTIVES

In order to conduct sensitive surveillance of infectious and zoonotic diseases and

identify potential threats to animal and public health, government surveillance agencies

are seeking novel ways to detect disease outbreaks in a timely manner. Syndromic

surveillance has been demonstrated in human health settings to address the issue of

timely identification of disease outbreaks and now animal health surveillance workers

have started to follow suit. However, there has been little investigation into the suitability

of bovine abattoir condemnations for use in a food animal syndromic surveillance system

and the quantitative methods necessary for this type of system.

This thesis focuses on the application of quantitative methods for food animal

syndromic surveillance utilizing bovine abattoir condemnation data as a case study to

illustrate approaches to using Ontario provincial abattoir data. The following objectives

will be addressed to assess the usage and analysis of these data for syndromic

surveillance:

20

1) Estimate approximate distance cattle are shipped for slaughter to ensure cattle at

abattoirs are originating from ‘local’ farms (Chapter 2);

2) Identify potential predictable covariates in abattoir condemnation rate data and

their potential impact on food animal syndromic surveillance (Chapter 2, 3);

3) Compare the impact of correcting for predictable covariates during quantitative

cluster detection for syndromic surveillance using abattoir condemnation data

(Chapter 4); and

4) Assess the performance of sentinel site selection approaches in terms of regional

representation, and capturing temporal and demographic patterns (Chapter 5).

21

REFERENCES

1. Brown C: Emerging zoonoses and pathogens of public health significance - an

overview. Rev sci tech Off int Epiz 2004, 23:435-442.

2. Edge V, L., Pollari F, Ng LK, Michel P, McEwen SA, Wilson JB, Jerrett M, Sockett

PN, Martin SW: Syndromic surveillance of Norovirus using over-the-counter sales of

medications related to gastrointestinal illness. Can J Infect Dis Med Microbiol 2006,

17:235-241.

3. 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.

4. 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.

5. 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. J Am Vet Med Assoc 2009, 234:658-664.

6. 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.

7. Amezcua M, Pearl D, Friendship R, McNab W: Evaluation of a veterinary-based

syndromic surveillance system implemented for swine. CJVR 2010, 74:241-250.

8. Thomas-Bachli AL, Pearl DL, Friendship RM, Berke O: Suitability and limitations

of portion-specific abattoir data as part of an early warning system for emerging

diseases of swine in Ontario. BMC Vet Res 2012, 8:3.

9. O'Sullivan T, Friendship R, Pearl D, McEwen B, Dewey C: Identifying an outbreak

of a novel swine disease using test requests for porcine reproductive and respiratory

syndrome as a syndromic surveillance tool. BMC Vet Res 8:192.

10. Robertson C, Nelson TA, MacNab YC, Lawson AB: Review of methods for space–

time disease surveillance. Spat Spattemporal Epidemiol 2010, 1:105-116.

11. Farm and farm operator data 2011[http://www29.statcan.gc.ca/ceag-

web/eng/community-agriculture-profile-profil

agricole?geoId=350000000&selectedVarIds=239%2C240%2C241%2C232%2C234%2C

242%2C233%2C236%2C235%2C238%2C237%2C]

22

12. Haines RJ: Report of the Meat Regulatory and Inspection Review: Farm to Fork - A

Stratgey for Meat Safety in Ontario. Toronto, ON: Ontario Ministry of the Attorney

General; 2004.

13. List of Federally Registered Meat Establishments and their Licensed Operators

[http://active.inspection.gc.ca/scripts/meavia/reglist/reglist.asp?lang=e]

14. Provincially Licensed Meat Plants

[http://www.omafra.gov.on.ca/english/food/inspection/meatinsp/licenced_operators_list.h

tm]

15. Canada's Meat Inspection System [http://inspection.gc.ca/food/information-for-

consumers/fact-sheets/specific-products-and-risks/meat-and-poultry-products/meat-

inspection-system/eng/1374559586662/1374559587537]

16. Meat Plant Guidelines

[http://www.omafra.gov.on.ca/english/food/inspection/meatinsp/manual/introduction.htm

]

17. Vilas VJDR, Bohning D, Kuhnert R: A comparison of the active surveillance of

scrapie in the European Union. Vet Res 2008, 39:37-52.

18. Hadorn DC, Stark KDC: Evaluation and optimization of surveillance systems for

rare and emerging infectious diseases. Vet Res 2008, 39:57-69.

19. Karama M, Johnson RP, Holtslander R, McEwen SA, Gyles CL: Prevalence and

characterization of verotoxin-producing Escherichia coli (VTEC) in cattle from an

Ontario abattoir. Can J Vet Res 2008, 72:297-302.

20. Kaneene JB, Miller R, Meyer RM: Abattoir surveillance: The U.S. experience. Vet

Microbiol 2006, 112:273-282.

21. Ansong-Danquah J: A survey of carcass condemnation at a poultry abattoir and

its application to disease management. Can Vet J 1987, 28:53-56.

22. Benschop J, Stevenson MA, Dahl J, Morris RS, French NP: Temporal and

longitudinal analysis of Danish Swine Salmonellosis Control Programme data:

implications for surveillance. Epidemiol Infect 2008, 136:1511-1520.

23. Christensen J, Ellegaard B, Kirkegaard Petersen BK, Willeberg P, Mousing J: Pig

Health and production surveillance in Denmark: sampling design, data recording,

and measures of disease frequency. Prev Vet Med 1994, 20:47-61.

24. Alton GD, Pearl DL, Bateman KG, McNab WB, Berke O: Factors associated with

whole carcass condemnation rates in provincially-inspected abattoirs in Ontario

http://active.inspection.gc.ca/scripts/meavia/reglist/reglist.asp?lang=e

http://www.omafra.gov.on.ca/english/food/inspection/meatinsp/licenced_operators_list.htm


http://ovc.uoguelph.ca/



http://www.omafra.gov.on.ca/english/food/inspection/meatinsp/manual/introduction.htm

http://www.omafra.gov.on.ca/english/food/inspection/meatinsp/manual/introduction.htm

23

2001-2007: implications for food animal syndromic surveillance. BMC Vet Res 2010,

6:42.

25. Poljak Z, Dewey CE, Rosendal T, Friendship RM, Young B, Berke O: Spread of

porcine circovirus associated disease (PCVAD) in Ontario (Canada) swine herds:

Part I. Exploratory spatial analysis. BMC Vet Res 2010, 5:59.

26. Carman S, van Dreumel T, Ridpath J, Hazlett M, Alves D, Dubovi E, Tremblay R,

Bolin S, Godkin A, Anderson N: Severe acute bovine viral diarrhea in Ontario, 1993-

1995. J Vet Diagn Invest 1998, 10:27-35.

27. Olafson P, MacCallum A, Fox F: An apparently new transmissible disease of

cattle. Cornell Vet 1946, 36:205-213.

28. Duffel S, Harkness J: Bovine virus diarrhea-mucosal disease infection in cattle.

Vet Rec 1985, 117:240-245.

29. Radostits O, Littlejohns I: New concepts in the pathogenesis, diagnosis and control

of diseases caused by the bovine viral diarrhea virus. Can Vet J 1988, 29:513-528.

30. Animal Disease Information

[http://www.aphis.usda.gov/animal_health/animal_diseases/bse/index.shtml]

31. Overview of Canada's BSE Safeguards

[http://www.inspection.gc.ca/animals/terrestrial-

animals/diseases/reportable/bse/safeguards/eng/1363896195473/1363896681768]

32. Beef Farmers of Ontario [http://www.cattle.guelph.on.ca/statistics/f-

p_inspectcattle.asp]

33. Vial F, Reist M: Evaluation of Swiss slaughterhouse data for integration in a

syndromic surveillance system. BMC Vet Res 2014, 10:33.

34. Lund VP, Kyvsgaard NC, Christensen JP, Bisgaard M: Pathological manifestations

observed in dead-on-arrival broilers at a Danish abattoir. Br Poult Sci 2013, 54:430-

440.

35. Osaili TM, Alaboudi AR, Rahahlah M: Prevalence and antimicrobial susceptibility

of Escherichia coli O157:H7 on beef cattle slaughtered in Amman abattoir. Meat Sci

2013, 93:463-468.

36. Mellau LS, Nonga HE, Karimuribo ED: A slaughterhouse survey of lung lesions in

slaughtered stocks at Arusha, Tanzania. Prev Vet Med 2010, 97:77-82.

http://www.aphis.usda.gov/animal_health/animal_diseases/bse/index.shtml

http://www.inspection.gc.ca/animals/terrestrial-animals/diseases/reportable/bse/safeguards/eng/1363896195473/1363896681768

http://www.inspection.gc.ca/animals/terrestrial-animals/diseases/reportable/bse/safeguards/eng/1363896195473/1363896681768

http://www.cattle.guelph.on.ca/statistics/f-p_inspectcattle.asp

http://www.cattle.guelph.on.ca/statistics/f-p_inspectcattle.asp

24

37. Dorjee S, H., Dorjee S, Heuer C, Jackson R, West D, Collins-emerson J, Midwinter

A, Ridler A: Prevalence of pathogenic Leptospira spp. in sheep in a sheep-only

abattoir in New Zealand. N Z Vet J 56:164-170.

38. Van Donkerdgoed J, Jewison G, Mann M, Cherry B, Altwasser B, Lower R, Wiggins

K, Dejonge R, Thorlakson B, Moss E, Mills C, Grogan H: Canadian beef quality audit.

Can Vet J 1997, 38:217-225.

39. Schamber G, Olson C, Witt L: Condemnations in Cattle Presented for Slaughter.

Bovine Practice 1981, 2:19-21.

40. Alban L, B., Alban L, Baptista FM, Møgelmose V, Sørensen LL, Christensen H,

Aabo S, Dahl J: Salmonella surveillance and control for finisher pigs and pork in

Denmark - A case study. Food Res Int 45:656-665.

41. Teutsch SM, Churchill RE: Principles and Practice of Public Health Surveillance:

2nd

Ed. Oxford ; New York: Oxford University Press; 2000.

42. Halliday Jeb, Halliday JEB: Animal sentinel surveillance: evaluating domestic

dogs as sentinels for zoonotic pathogen surveillance.

http://hdl.handle.net.subzero.lib.uoguelph.ca/ 2010, :.

43. Salman MD: Animal Disease Surveillance and Survey Systems Methods and

Applications: Ames, Iowa : Iowa State Press; 2003.

44. Lee LM, Teutsch SM, Thacker BS, St. Louis ME: Principles and Practice of Public

Health Surveillance: 3rd

Ed. Oxford; New York: Oxford University Press; 2010.

45. Reportable Diseases [http://www.inspection.gc.ca/animals/terrestrial-

animals/diseases/reportable/eng/1303768471142/1303768544412]

46. Lawson AB, Kleinman K: Spatial and Syndromatic Surveillance for Public Health:

Chichester, West Sussex ; Hoboken, NJ: J. Wiley; 2005.

47. Stoto MA: Syndromic surveillance. Issues in Science & Technology 2005, 21:49-56.

48. 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.

49. Dórea F, Sanchez J, Revie CW: Veterinary syndromic surveillance: Current

initatives and potential for development. Prev Vet Med 2011, 101:1-17.

50. Dórea FC, Muckle CA, Kelton D, Mcclure JT, Mcewen BJ, Mcnab WB, Sanchez J,

Revie CW: Exploratory analysis of methods for automated classification of

http://hdl.handle.net.subzero.lib.uoguelph.ca/

http://www.inspection.gc.ca/animals/terrestrial-animals/diseases/reportable/eng/1303768471142/1303768544412

http://www.inspection.gc.ca/animals/terrestrial-animals/diseases/reportable/eng/1303768471142/1303768544412

25

laboratory test orders into syndromic groups in veterinary medicine. PloS one

8(3):e57334.

51. Dórea FC, Revie CW, McEwen BJ, McNab WB, Kelton D, Sanchez J: Retrospective

time series analysis of veterinary laboratory data: Preparing a historical baseline

for cluster detection in syndromic surveillance. Prev Vet Med 2013, 1:219-227.

52. Dórea FC, Mcewen BJ, Mcnab WB, Sanchez J, Revie CW: Syndromic surveillance

using veterinary laboratory data: algorithm combination and customization of

alerts. PloS one 8(12):e82183.

53. Weber WD: Development of an animal monitoring system based on slaughter

condemnation data. Miami: Proceedings of the Eighth International Society for Disease

Surveillance Conference; 2009. 3-4 December.

54. Dupuy C, Morignat E, Maugey X, Vinard J-L, Hendrikx P, Ducrot C, Calavas D, Gay

E: Defining syndromes using cattle meat inspection data for syndromic surveillance

purposes: a statistical approach with the 2005-2010 data from ten French

slaughterhouses. BMC Vet Res 9:88.

55. Canadian Primary Care Sentinel Surveillance Network

[http://www.cpcssn.ca/cpcssn/home-e.asp]

56. Vangeel ID, Vangeel I, De Leeuw I, Meroc E, Vandenbussche F, Riocreux F,

Hooyberghs J, Raemaekers M, Houdart P, Van dS, De Clercq K: Bluetongue sentinel

surveillance program and cross-sectional serological survey in cattle in Belgium in

2010-2011. Prev Vet Med 106:235-243.

57. Kulldorff M, Hjalmars U: The Knox Method and Other Tests for Space-Time

Interaction. Biometrics 1999, 55:544-552.

58. Knox E: The detection of space-time interactions. Appl Stat 1964, 13:25-29.

59. Mantel N: The detection of disease clustering and a generalized regression

approach. Cancer Res 1967, 27:209-220.

60. Diggle P, Chetwynd A, Haggkvist R, Morris S: Second-order analysis of space-time

clustering. Stat Methods Med Res 1995, 4:124-136.

61. Rogerson P, Yamada I. Statistical detection and surveillance of geographic

clusters. J Reg Sci 51:195.

62. 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 of Escherichia coli 0157 in space and time in Alberta,

Canada, 2000 - 2002. Epidemiol Infect 2006, 134:699-711.

http://www.cpcssn.ca/cpcssn/home-e.asp

26

63. 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.

64. 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.

65. Naus J: The distribution of the size of maximum cluster of points on a line. J Am

Stat Assoc 1965, 60:532-538.

66. Kulldorff M: A spatial scan statistic. Commun Statist - Theory Meth 1997, 26:1481-

1496.

67. McCulloch CE, Searle SR: Generalized Linear Mixed Models (GLMMs). In

Generalized, Linear, and Mixed Models. Edited by Anonymous New Jersey, USA: John

Wiley & Sons; 2001:220-246.

68. Kleinman K, Lazarus R, Platt R: A generalized linear mixed model approach for

detecting incident clusters of diease in small areas, with an application to biological

terrorism. Am J Epidemiol 2004, 159:217-224.

27

CHAPTER TWO:

FACTORS ASSOCIATED WITH WHOLE CARCASS CONDEMNATION

RATES IN PROVINCIALLY-INSPECTED ABATTOIRS IN ONTARIO 2001 –

2007: IMPLICATIONS FOR FOOD ANIMAL SYNDROMIC SURVEILLANCE

(Alton et al. as published in BMC Veterinary Research 2010; 6:42)

ABSTRACT

Ontario provincial abattoirs have the potential to be important sources of

syndromic surveillance data for emerging diseases of concern to animal health, public

health and food safety. The objectives of this study were to: (1) describe provincially

inspected abattoirs processing cattle in Ontario in terms of the number of abattoirs, the

number of weeks abattoirs process cattle, geographical distribution, types of whole

carcass condemnations reported, and the distance animals are shipped for slaughter; and

(2) identify various seasonal, secular, disease and non-disease factors that might bias the

results of quantitative methods, such as cluster detection methods, used for food animal

syndromic surveillance.

Data were collected from the Ontario Ministry of Agriculture, Food and Rural

Affairs and the Ontario Cattlemen’s Association regarding whole carcass condemnation

rates for cattle animal classes, abattoir compliance ratings, and the monthly sales-yard

price for various cattle classes from 2001-2007. To analyze the association between

condemnation rates and potential explanatory variables including abattoir characteristics,

season, year and commodity price, as well as animal class, negative binomial regression

models were fit using generalized estimating equations (GEE) to account for

autocorrelation among observations from the same abattoir. Results of the fitted model

28

found animal class, year, season, price, and audit rating are associated with condemnation

rates in Ontario abattoirs. In addition, a subset of data was used to estimate the average

distance cattle are shipped to Ontario provincial abattoirs. The median distance from the

farm to the abattoir was approximately 82 km, and 75% of cattle were shipped less than

100 km.

The results suggest that secular and seasonal trends, as well as some non-disease

factors will need to be corrected for when applying quantitative methods for syndromic

surveillance involving these data. This study also demonstrated that animals shipped to

Ontario provincial abattoirs come from relatively local farms, which is important when

considering the use of spatial surveillance methods for these data.

29

INTRODUCTION

The monitoring and surveillance of emerging infectious and zoonotic diseases in

food animals are important components of our food safety system. In recent years,

emerging zoonotic diseases have been of increased concern to both public and animal

health, following the emergence of H5N1 influenza and bovine spongiform

encephalopathy (BSE) [1]. Consequently, researchers are turning their attention to novel

approaches, such as syndromic surveillance, for detecting emerging diseases in food

animals at various points along the farm-to-fork continuum [2, 3].

Abattoirs have played an important role in the surveillance of various diseases of

human and animal health importance [4-6]. Surveillance at the abattoir allows for all

animals passing into the human food chain to be examined for unusual signs, lesions or

specific diseases. For instance, a study evaluating surveillance systems for bovine

tuberculosis in Switzerland found that surveillance during meat inspection at the

slaughterhouse had the highest sensitivity for identifying the disease compared to passive

clinical surveillance of humans or cattle on farms [5]. A relatively new application of

surveillance methods for animal health, food safety and public health is syndromic

surveillance of food animals.

Syndromic surveillance is the grouping of large numbers of signs/symptoms and

data regarding non-traditional sources of information. These groups of signs/symptoms

are loosely designated as ‘syndromes’. This information is then used to track disease

trends in a population and signal putative outbreaks that warrant further investigation [7].

Syndromic surveillance has been primarily used in public health practice [8-10] and has

had some success at the early detection of disease outbreaks in humans [11]. Recently,

30

syndromic surveillance has been applied to animal health using data from farms, sales-

yards, veterinary practitioners, and abattoir condemnation data [2, 3, 12]. Changes in the

incidence of lesions at slaughter may provide important information for syndromic

surveillance of diseases of animal, public health, and food safety significance.

The application of appropriate quantitative methods is important for any

surveillance system. A variety of statistical methods has been developed and is frequently

used for disease surveillance, including spatial, temporal and spatiotemporal methods [7].

In general, these methods use various statistical detection algorithms to analyze a

continuous stream of data and raise an alarm when the count is significantly greater than

expected, suggesting a possible disease outbreak [13]. Depending on the method used and

the data collected, one might be able to identify the area and/or time of the disease

outbreak. Although, these methods have been shown to be useful, surveillance systems

are only as good as the data provided to the system. Consideration of the quality of the

data and naturally occurring covariates need to be taken into account in the selection,

application and interpretation of quantitative methods for disease surveillance.

Recent literature has suggested the need for model-based approaches for

surveillance in order to include other variables into the specification of expected disease

incidence [14]. For many diseases, the incidence may vary with biological factors such as

sex and season. In addition, factors associated with the reporting of disease may also

impact the apparent incidence of disease. For example, price of the commodity may be

associated with the quality of animals being shipped to slaughter, which then in turn will

affect the condemnation rate in abattoirs. Being able to account for these known factors

prior to the application of cluster detection methods, may improve the sensitivity and

31

specificity of a quantitative syndromic surveillance system [15, 16]. The quality of spatial

data is also important to consider prior to the application of space or space-time cluster

detection methods [17]. These issues may be particularly true for Ontario provincial

abattoir data where factors, such as the capacity of an abattoir may be correlated with the

quality of animals they receive, and the location of the abattoir can only approximately

reflect the spatial location of an animal’s farm of origin.

It was hypothesized that a variety of factors may be associated with the

condemnation rates seen in abattoirs, and that these effects may vary in space and/or

time. Abattoir characteristics, such as the number of weeks abattoirs processed animals

and the number of animals processed, may be associated with condemnation rates, as the

speed of processing may impact inspection. An abattoir’s audit rating may reflect a

plant’s compliance to regulations and/or willingness to accept animals of poorer quality.

Region of the abattoir may be associated with condemnation rates in provincial abattoirs

due to regional differences in animal density and disease prevalence. Season and year

may also be associated with condemnation rates as many diseases in animals are thought

to have seasonal and secular variability. Animal class may be associated with

condemnation rates, as older animals are generally at higher risk of disease. Economic

factors, such as commodity price fluctuate greatly and may be associated with the quality

of animals being sent to slaughter, as producers consider shipping costs against the

possible return of shipping an animal of suspect health.

Consequently, the objectives of this study were to identify biological and non-

biological factors that may be associated with abattoir condemnations and possibly

influence cluster detection methods for quantitative syndromic surveillance systems.

32

Specifically, provincially inspected abattoirs which slaughter cattle in Ontario were

characterized in terms of number of abattoirs, the number of weeks abattoirs processed

cattle, geographical distribution, types of condemnations reported, and distance animals

are shipped to provincial abattoirs. Secondly, this study will determine how abattoir

characteristics, season, year and commodity price, and animal class may be associated

with whole carcass condemnation rates in provincial abattoirs. In addition, the suitability

of Ontario provincial abattoir data for spatial and spatio-temporal analyses will be

considered based on the results.

METHODS

Data source and variables

Whole carcass condemnation data were obtained from the Food Safety

Decision Support System (FSDSS) database maintained by the Ontario Ministry of

Agriculture, Food and Rural Affairs (OMAFRA). Data were extracted from the database

for cattle animal classes: bulls, calves, cows, heifers and steers from January 1, 2001 to

December 31, 2007. Missing geographical coordinates for abattoirs were approximated

using postal codes and/or addresses with the address geocoding software GeoPinpoint

Suite 6.4 (DMTI Spatial Inc., Markham, Ontario, Canada). Using the FSDSS database,

the following information was extracted for each month: abattoir identification number,

geographical coordinates of abattoir, year, season, number of weeks an abattoir was open

each year, total number of whole carcasses condemned, total number of cattle processed

each year, and animal class. Season was categorized by 3 month groupings as follows:

winter (December – February), spring (March – May), summer (June – August) and fall

(September – November). Animal class included five categories: bulls, cows, calves,

33

heifers, and steers. Bulls were excluded from subsequent statistical analyses due to

missing data and inconsistencies in the use of this classification. The number of weeks an

abattoir was open each year was determined by the total number of weeks in which at

least one bovine animal was processed. The total number of animals processed each year

was calculated by adding the total number of condemned cattle and the total number of

cattle fit for consumption each year. Linearity of continuous variables was assessed by

plotting the log of the condemnation rate against the covariate using a lowess smoother.

If there was no visible linear relationship between the outcome and the covariate, and the

association could not be adequately modeled with a quadratic term, or transformation,

then the variable was categorized.

Abattoir audit ratings were obtained for all abattoirs from the abattoir audit

program administered through OMAFRA. The audit program assesses each facility’s

food safety performance and compliance with the Ontario Meat Inspection Act.

Audits are conducted once a year and evaluate each premise on 14 food safety areas

based on the Standards of Compliance relating to food safety, animal welfare and

occupational health and safety with a letter grade given for each abattoir [18]. Annual

OMAFRA audit ratings were obtained for all abattoirs in the audit program from 2001-

2007. Abattoir audit ratings were classified according to the letter grade received from

best to poorest as follows: AAA, AA, A, B or C and unrated for abattoirs that had

missing data.

The price of cattle was obtained from the Ontario Cattlemen’s Association market

reports for 2001-2007. Prices were calculated to be the average price (in Canadian

dollars) per lbs based on sales records from Ontario sales-yards. A price was assigned for

34

each month and year by animal class. The most appropriate weight category was selected

to represent each animal class based on an average animal at the time of slaughter.

The agricultural region where the abattoir was located was classified as: central,

eastern, northern, southern or western Ontario using the Census Agricultural Region

boundaries (Statistics Canada, Census Agricultural Regions, Census year 2001). The

regional location of each abattoir was determined using the point-in-polygon technique

with geographic information system software ArcGIS 9.2 (ESRI, Redlands, California,

USA).

Travel distance between the animals’ farm and the abattoir was estimated using

data obtained from OMAFRA. Because farm location is not routinely recorded with

condemnation data, a subset of cattle, in which a sample was sent for laboratory testing,

were used to obtain geo-location information for the abattoir and farm. Like abattoir

location, owner address information was geo-coded according to the owner postal code

using geocoding software GeoPinpoint Canada. Distance from the farm to abattoir was

calculated using the Haversine distance formula, which calculates the great-circle

distances between two points on a sphere using their longitudes and latitudes [19].

Data from all sources were merged into one master dataset using Stata 10.1 (Stata

Corp., College Station, Texas, USA).

Statistical analysis

To model and evaluate their association with monthly whole carcass cattle

condemnation rates, the effect of year, season, annual audit rating, number of weeks

open, number of cattle processed, census agricultural region, animal class and sales price

of animal class were included in the model. All covariates were evaluated for statistical

significance individually and then in a multivariable model using generalized estimating

35

equations (GEE) to fit a negative binomial regression model with an exchangeable

correlation structure to account for repeated measurements from each abattoir. Wald tests

were performed on each covariate in the model to estimate the significance of each

categorical variable as a group. Non-significant covariates (p ≥ 0.05) based on the Wald

test were removed from the model. All excluded covariates were evaluated for their

potential confounding effect by evaluating if their removal produced a 20% or greater

change in the coefficient of significant variables in the model. Interactions between price

and year, year and number of animals processed, year and animal class, as well as season

and animal class were investigated. In addition, the covariates included in the model were

then fitted using GEE with both Poisson and negative binomial distributions and the

following correlation structures: exchangeable, first order autoregressive structure,

second order autoregressive structure, non-stationary, and stationary. All resulting models

were evaluated for how well the model fit the data using a quasi-log-likelihood under the

independence model information criterion (QIC) statistic for model selection [20]. The

model with the lowest QIC was selected as the final model. Robust standard errors were

used for all GEE fitted models. All statistical analyses were performed using Stata 10.1.

RESULTS

Descriptive statistics

There were 207 provincially-inspected abattoirs processing a total of 1,162,410

cattle from 2001 – 2007 with the following animal class distribution: 5.4% bulls, 12.6%

cows, 19.8% heifers, 30.8% calves and 31.3 % steers. The number of abattoirs processing

at least one bovine animal each year varied over the study period (Table 2.1). Of the total

number of processed cattle, 6875 carcasses were condemned for various reasons with

septicaemia and/or toxaemia being typically the most common reason for condemnation

36

(Table 2.2). The condemnation rate per 1000 animals fluctuated over the study period

with the most prominent decrease occurring during 2004-2005 (Figure 2.1). Average

overall condemnation rates were much greater in cows compared to other cattle classes

(Figure 2.2). However, the overall decreasing trend in condemnation rates was most

marked in cows in 2006 (Figure 2.2). The total number of animals being processed

peaked in cows, heifer and steers at some point during the 2004-2006 period (Figure 2.2).

The quartiles of total number of animals and the corresponding number of

processing abattoirs were tabulated for each year of the study period (Table 2.3a). With

the exception of 2004, 2005 and 2007, most of the abattoirs processed fewer than 500

cattle per year. The quartiles of the total number of weeks each year an abattoir processed

at least one animal and the corresponding number of abattoirs was tabulated for each year

of the study period (Table 2.3b). Over the study period, there was an increasing trend in

the number of abattoirs processing cattle more than 49 weeks per year. On average,

approximately 1.5% of abattoirs processed cattle only 1 week per year, 7% processed

cattle a quarter of the year or less, and 20% processed cattle up to half of the year. During

the study period only 19% of abattoirs processed cattle 52 weeks per year. The annual

OMAFRA audit rating scores and the corresponding number of abattoirs receiving those

scores are shown in Table 2.3c for each year during the study period. Throughout this

period, the majority of rated abattoirs were given an “A” rating. The median sales price

of each animal class was calculated for each year during the study period (Table 2.3d).

The median sales-price in all cattle classes was lowest in 2004. No continuous variables

were found to have a linear relationship with cattle carcass condemnation rates, therefore,

quartiles of the empirical distribution were used to categorize the total number of animals

37

processed and the number of weeks an abattoir was open (Table 2.3a and Table 2.3b,

respectively). Price was categorized into a dichotomous variable according to the yearly

median sales price for each animal class (Table 2.3d).

Provincial abattoirs are located throughout Ontario with the majority situated in

southern and western Ontario and fewest located in northern Ontario (Figure 2.3). The

distance between abattoir and farm was calculated for 2456 samples sent for laboratory

testing from 107 of the 207 abattoirs processing cattle from 2001-2007. Results indicated

that the median distance between the farm and abattoir was 82 km, with 25% of all farms

located within 34 km of the abattoir, and 75% within 94 km of the abattoir.

Statistical models

Results of the univariable GEE modeling approach indicated that animal class

(χ2 = 147.39, p < 0.001), region (χ2 = 12.83, p = 0.012), audit rating (χ2 = 351.23, p

<0.001), season (χ2 = 10.41, p = 0.015), and price (χ2 = 4.05, p = 0.044) all had

statistically significant associations with the outcome according to the Wald test

performed to determine the significance of the entire variable in the model (Table 2.4).

Year, the number of weeks an abattoir was open, and the total number of animals

processed were not significant based on Wald tests (χ2 = 11.82, p = 0.066; χ2 = 3.95, p =

0.267; and χ2 = 1.21, p = 0.751, respectively).

Multivariable Poisson and negative binomial models fit by GEE were investigated

using a variety of correlation structures (Table 2.5). Based on the QIC statistic, the best

fitting model was a multivariable negative binomial regression model using an

exchangeable correlation structure. Animal class, year, season, price and audit rating of

abattoirs were the only statistically significant variables in the model. The interaction

38

between animal class and year was the only statistically significant interaction term.

There was no evidence that excluded variables confounded the remaining variables. The

fitted model indicated that during 2005 - 2006 cows had the most prominent decrease in

condemnation rates in abattoirs compared to calves in 2001, based on the size of the cows

x 2005 and cows x 2006 interaction terms (Table 2.6). Condemnation rates for cows

ranged from approximately 3 to 8 times greater than calves throughout the study period

(Table 2.7). Condemnation rates were significantly lower in heifers during 2002

compared to calves in 2001 (Table 2.6). In comparison to winter, condemnation rates

were significantly lower in the summer and fall (Table 2.6). Condemnation rates were

also significantly higher in C rated abattoirs compared to higher rated abattoirs.

Condemnation rates were higher in cattle when the sales price of the animal class was

above the yearly median (Table 2.6).

DISCUSSION

Provincial abattoir data are useful for surveillance because they can provide a

more regionally specific picture of emerging diseases in Ontario. However, various

biological and non-biological factors were found to have an effect on condemnation rates.

Consequently, careful consideration should be given to how these factors may influence

quantitative methods designed for outbreak detection. If these variables are ignored,

quantitative methods designed to identify trends or disease clusters may not provide valid

results.

Results from this study regarding the distance animals are shipped to slaughter

indicated that the majority of cattle are shipped less than 100 km. Within the spatial scale

of the province of Ontario, which is approximately one million square kilometres [21],

39

we can conclude that cattle sent to provincial abattoirs come from relatively local farms.

This result is important for the application of quantitative spatial surveillance methods, as

the assumption that abattoir data reflects disease rates among locally slaughtered animals

appears to be valid.

Seasonal effects were noted in the results of the multivariable analysis with

summer and fall having lower condemnation rates compared to winter. This may be due

to a change in the quality of animals being submitted over the year. Animals which are

shipped during the winter may reflect those animals which grew slower due to certain

health issues causing delayed market readiness, thus, resulting in more condemnations at

slaughter during this season. More research on quality of animals and point in production

cycle is needed to confirm if this trend reflects “poor-doers”. It is important to identify

seasonal trends before the application of quantitative surveillance methods, as many

diseases have seasonal variability, and without correcting for this trend, any results from

temporal or spatial-temporal quantitative methods will be biased.

Commodity class and year were found to be associated with condemnation rates

in provincial abattoirs. It is suspected that the discovery of BSE in Alberta, Canada in

May 2003 [22] had an impact on the patterns of condemnations in Ontario provincial

abattoirs. Within hours of the confirmation of the first Canadian case, the United States

(US) government announced an immediate ban of all imports of Canadian beef. On July

24, 2003, new processing regulations were implemented in Canadian abattoirs outlining

that specified risk materials (SRMs), such as brain and spinal cord, must be removed

from cattle older than 30 months. After 26 months, the US ban on Canadian cattle

imports was lifted [23]. The effects of BSE in Canada, and subsequent changes to trade

40

and processing regulations are mirrored in the descriptive data, which showed a

decreasing trend over the study period in the number of abattoirs processing cattle, as

well as overall condemnation rates and condemnation rates by animal class. The

decreasing trend levels off in 2004 – 2006 where rates begin to increase again.

Condemnation rates in cows showed the largest drop, coinciding with regulations

implemented for cattle over 30 months [22]. These trends are also mirrored in the

interaction effect between year and animal class seen in the multivariable model.

Condemnation rates for cows ranged from approximately 3 to 8 times greater than calves

throughout the study period, with cows during 2005 – 2006 having the most prominent

decrease in condemnation rates in abattoirs compared to calves in 2001. Being older,

cows are at higher risk for disease and thus are condemned more frequently. The decline

in cow condemnation rates during this period occurred while the total number of cows

being processed by provincial abattoirs increased. In the case of cows, the decline in the

rate of condemnations most likely resulted from younger and healthier cows being

shipped for slaughter to provincial abattoirs, which ship their products intra-provincially,

rather than federal plants, which ship their products inter-provincially and internationally.

Commodity class is an important factor, which must be accounted for in a food animal

syndromic surveillance system. Older animals are not only more likely to be condemned

due to an increased incidence of disease, but are more likely to be processed in certain

abattoirs that specialize in this animal class. Consequently, accounting for animal class is

important in determining the expected or baseline condemnation rates at an abattoir.

Economic factors, such as commodity sales price, also appear to play a role in

condemnation rates, as we found that condemnation rates in cattle were higher when the

41

sales price was above the yearly median. This may be due to a difference in the quality of

animals being sent to slaughter depending on the sales price. If the price were too low,

the cost of shipping animals of questionable quality may have exceeded the potential

return for the producer. There was also an association found between condemnation rates

and audit rating. Condemnation rates tended to be higher in C-rated abattoirs compared to

those abattoirs with higher ratings. This may be due to certain abattoirs accepting a larger

proportion of older or poorer quality cattle; however, there were only a small number of

C-rated abattoirs during the study period. The unrated abattoirs were unlikely to have

influenced the model since the condemnation rates for abattoirs in this category were not

significantly different from abattoirs with other ratings. These results demonstrate the

need to consider not only biological factors, but also non-biological factors, which may

be associated with condemnation rates.

This study did not find a statistical association between condemnation rates and

factors dealing with abattoir throughput. However, it is also important to consider abattoir

characteristics, such as the number of weeks and/or the number of animals processed at

abattoirs. There was great variability in the number of weeks and the number of animals

processed in Ontario provincial abattoirs, which may affect quantitative data analysis. For

instance, a spatio-temporal surveillance method such as the spatio-temporal scan statistic

using a space-time permutation model assumes the background population remains

relatively stable over time [24]. This is not the case in Ontario provincial abattoir data, as

only 19% of abattoirs consistently process cattle throughout the year. If the background

population increases or decreases faster in certain areas, there is a risk of population shift

bias, which can cause biased p-values [24]. This bias may cause abattoirs to be identified

42

in a high rate cluster solely due to the irregular timing of processing at certain abattoirs,

not due to a true disease outbreak. It may be more appropriate to select sentinel abattoirs

from regions throughout Ontario, which have a more consistent and stable processing

capacity throughout the year to more accurately capture disease clusters among abattoirs.

It is important to understand the factors affecting and even biasing abattoir

surveillance data, before any quantitative methods can be chosen in the design of a

surveillance system. This study identified and discussed the implications of biological

and non-biological factors, which may affect quantitative surveillance methods. Once

these factors are identified, appropriate adjustments can be made to the quantitative

methods being used for outbreak detection. For instance, a study by Kleinman et al. [15]

compared the performance of the space-time scan statistic using unadjusted data for

lower respiratory complaints and model-adjusted data for day of week, month, holidays

and local history of illness. The study found significantly lower false detection rates in

the model-adjusted analysis compared to the unadjusted.

CONCLUSIONS

This study has identified various seasonal, secular, biological and non-biological

factors that may be associated with the expected incidence of disease and indicates the

potential importance of adjusting for these factors when applying quantitative methods

for any disease surveillance system. Specifically, this study found that animal class, year,

season, price, and audit rating impact condemnation rates from provincially inspected

cattle abattoirs in Ontario. Similarly, the quality of spatial data should also be part of the

assessment of potential biases that may arise from surveillance data prior to decisions

concerning the application of spatial and/or spatial-temporal surveillance techniques.

43

REFERENCES

1. Brown C. Emerging zoonoses and pathogens of public health significance - an

overview. Rev sci tech Off int Epiz 2004; 23:435-442.

2. Vourc'h G, Bridges VE, Gibbens J, De Groot BD, McIntyre L, Poland R, Barnouin J.


Infect Dis 2006; 12:204-210.

3. 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. J Am Vet Med Assoc 2009; 234:658-664.

4. Vilas VJDR, Bohning D, Kuhnert R. A comparison of the active surveillance of

scrapie in the European Union. Vet Res 2008; 39:37-52.

5. Hadorn DC, Stark KDC. Evaluation and optimization of surveillance systems

for rare and emerging infectious diseases. Vet Res 2008; 39:57-69.

6. Karama M, Johnson RP, Holtslander R, McEwen SA, Gyles CL. Prevalence and

characterization of verotoxin-producing Escherichia coli (VTEC) in cattle from

an Ontario abattoir. Can J Vet Res 2008; 72:297-302.

7. Lawson AB, Kleinman K. Spatial and syndromic surveillance for public health.

Chichester,West Sussex; Hoboken,NJ: J. Wiley; 2005.

8. Edge VL, Pollari F, Ng LK, Michel P, McEwen SA, Wilson JB, Jerrett M, Sockett

PN, Martin SW: Syndromic surveillance of Norovirus using over-the-counter sales

of medications related to gastrointestinal illness. Can J Infect Dis Med Microbiol

2006; 17:235-241.



emergency department data assist in earlier identification? Aust N Z J Public

Health 2008; 32:361-364.


respiratory call volume be used as a proxy for emergency department

respiratory visit surveillance by public health? CJEM 2008; 10:18-24.

11. Wagner MM, Moore AW, Aryel RM. Handbook of Biosurveillance. Amsterdam;

Boston: Academic Press; 2006.

12. Weber WD: Development of an animal health monitroing system based on

slaughter condemnation data. In: Proceedings of the first International Society for

Disease Surveillance Conference: 3-4 December 2009; Miami.

44

13. Stoto MA, Fricker RDJ, Jain A, Diamond A, Davies-Cole JO, Glymph C, Kidane G,

Lum G, Jones L, Dehan K, Yuan C. Evaluating Statistical Methods for

Syndromic Surveillance. In: Statistical Methods in Counterterrorism. Edited by

Wilson AG, Wilson GD, Olwell DH. New York: Springer; 2006, pp 141-172.

14. Robertson C, Nelson TA, MacNab YC, Lawson AB: Review of methods for

space-time disease surveillance. Spat Spattemporal Epidemiol 2010; 1:105-116.

15. Kleinman K, Abrams AM, Kulldorff M, Platt R: A model-adjusted space-time

scan statistic with an application to syndromic surveillance. Epidemiol Infect

2005; 133:409-419.

16. Klassen AC, 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.

17. Teutsch SM, Churchill RE: Principles and practice of public health surveillance:

2nd Ed. Oxford; New York: Oxford University Press; 2000.

18. Audit Ratings of Provincially Licensed Meat Plants [Available from

http://www.omafra.gov.on.ca/english/food/inspection/meatinsp/abattoir_audit_ratings.ht

m]

19. Gottwald S, Hirsch KA, Reichardt H: VNR Concise Encyclopedia of Mathematics:

2nd ed. New York: Van Nostrand Reinhold; 1989.

20. Pan W: Akaike's information criterion in generalized estimating equations.

Biometrics 2001; 57:120-125.

21. Land and freshwater area, by province and territory

[http://www40.statcan.gc.ca/101/cst01/phys01-eng.htm]

22. Bovine Spongiform Encephalopathy (BSE) in North America

[http://www.inspection.gc.ca/english/anima/heasan/disemala/bseesb/bseesbindexe.

Shtml]

23. Timeline of BSE in Canada and the U.S.

[http://www.cbc.ca/news/background/madcow/timeline.html]

24. Kulldorff M, Hjalmars U: The Knox Method and other tests for space-time

interaction. Biometrics 1999; 55:544-552.

http://www.cbc.ca/news/background/madcow/timeline.html

45

Table 2.1- Number of provincially inspected abattoirs in Ontario 2001-2007

Year Number of abattoirs reporting bovine carcass counts

2001 163

2002 158

2003 147

2004 143

2005 148

2006 139

2007 129

Total 207

Number of provincially inspected abattoirs in Ontario processing at least one bovine

animal per year and the total number of provincially inspected abattoirs processing cattle

in Ontario from 2001-2007.

46

Table 2.2 - Reason for whole carcass condemnation in provincially inspected

abattoirs in Ontario 2001-2007

Condemnation reason Percentage of total yearly condemnations

Year

2001 2002 2003 2004 2005 2006 2007

Abscess 7.4 9.4 7.3 8.8 9.8 10.5 9.8

Animal arrived dead1 0 0 2.7 0.4 1.1 0.8 0.6

Animal found dead2 4.7 5.8 4.9 4.4 7.5 7.2 6.8

Arthritis 3.5 2.4 2.9 2.9 1.9 4.0 3.2

Bruising 9.1 9.8 7.8 3.0 2.0 1.4 1.1

Chemical Residue 0 0.1 0.0 0.0 0.0 0.0 0.0

Contamination 0.1 0.1 0.2 0.1 0.2 0.2 0.3

Cystericercus bovis 0.0 0 0.0 0.2 0.0 0.0 0.0

Drug residue 3.0 2.0 1.5 0.0 0.1 0.3 3.1

Edema 2.3 3.7 3.4 3.4 1.3 1.2 0.8

Emaciation 6.6 6.0 7.7 6.6 10.7 8.4 9.7

Icterus 0.6 1.2 1.5 1.6 1.7 1.2 1.5

Inadequate bleeding 0.4 0.1 0.9 3.0 1.5 0.5 0.8

Lymphadenitis 1.5 2.2 5.3 6.7 4.5 4.6 3.4

Lymphosarcoma neoplasm 12.6 13.1 12.4 6.6 4.4 2.7 4.3

Mastitis 0.0 0.1 0.2 0.2 0.6 0.2 0.3

Metritis 0.4 0.1 0.1 0.4 0.7 0.2 0.3

Moribund 1.4 1.3 0.7 1.0 1.1 0.9 1.8

Myositis 0.7 0.2 0.2 0.2 0.6 1.1 0.8

Neoplasm other 3.0 1.9 2.7 1.8 2.1 3.0 2.5

Nephritis 0.8 0.7 0.5 0.3 1.2 0.9 0.5

Odour 2.2 1.2 1.5 0.8 0.3 0.2 0.2

47

Operator requested

condemnation

0.0 0.0 0.0 4.5 10.6 7.9 8.9

Other disease 6.9 6.2 5.3 8.7 5.1 4.9 3.4

Pericarditis 2.0 1.7 1.1 1.3 1.9 2.1 1.7

Peritonitis 6.5 6.2 6.0 6.1 6.1 7.6 6.9

Pleuritis 0.4 0.8 0.6 0.5 1.3 1.4 0.6

Pneumonia 2.7 3.1 2.2 4.1 5.5 4.6 4.8

Pyelonephritis 0.3 0.2 0.9 0.0 1.2 1.4 2.5

Rabies 0.0 0.1 0.0 0.0 0.0 0.0 0.0

Septicaemia and/or toxemia 11.7 15.6 15.0 17.7 11.6 14.0 15.8

Squamous cell carcinoma

neoplasm

1.2 0.4 0.9 1.3 0.8 1.1 1.1

Toxaemia 6.0 3.2 2.5 2.3 0.7 4.4 0.5

Uremia 2.3 1.2 1.5 1.3 1.8 1.4 2.2

Total number of condemnations

per year

1047 1203 1236 1188 895 656 650

1 Refers to animals which are dead when the truck pulls into the plant premises

2 Inspector finds a dead animal in the pen that had arrived alive during ante-mortem

inspection in the barn

Reason and percentage of whole cattle carcasses condemned in provincially inspected

abattoirs in Ontario from 2001-2007.

48

Table 2.3 - Summary of number of cattle processed, number weeks open, audit

rating and animal class

a) Number of

animals processed

Number of Abattoirs

2001 2002 2003 2004 2005 2006 2007

1 - 286 73 72 36 24 29 40 38

287 - 498 42 35 40 28 34 34 25

499 - 841 25 24 36 40 43 32 36

842 - 27 286 22 26 33 49 41 32 30

b) Number of

weeks open

Number of Abattoirs

1 - 43 66 67 44 32 43 38 38

44 - 49 58 46 48 45 30 39 29

50 - 51 17 27 25 31 41 31 32

52 21 17 28 33 33 30 30

c) Audit Rating Number of Abattoirs

AAA 1 0 0 0 0 1 1

AA 6 6 7 13 16 23 28

A 61 72 79 78 78 78 72

B 15 8 8 5 8 6 9

C 2 1 0 0 1 0 0

Unrated 78 71 53 47 45 31 19

d) Animal Class Median sales-price of animal class per year 2001-2007

Calves 136.53 117.95 112.19 83.61 107.14 119.69 108.49

Cows 64.28 58.64 27.11 19.70 25.38 31.84 34.77

Heifers 112.01 101.22 82.16 73.61 87.74 90.87 91.09

Steers 113.24 102.57 83.15 75.60 91.07 93.55 92.56

Summary of the a) quartiles of the total number of cattle processed, b) quartiles of the number of weeks at least one bovine animal was

processed, c) annual OMAFRA audit rating, and d) median sales price of calves, cows, heifers and steers 2001-2007.

49

Table 2.4 - Univariable negative binomial models using GEE1

approach

Covariate IRR2 Robust Standard

Error

P-value 95% CI

Animal class

Calves --- --- --- ---

Cows 4.83 1.35 < 0.001 2.80 - 8.34

Heifers 0.43 0.09 < 0.001 0.28 - 0.64

Steers 0.51 0.11 0.01 0.34 - 0.77

Year

2001 --- --- --- ---

2002 1.02 0.14 0.90 0.77 - 1.34

2003 0.90 0.14 0.49 0.67 - 1.22

2004 0.70 0.15 0.10 0.46 - 1.06

2005 0.69 0.14 0.07 0.47 - 1.03

2006 0.75 0.16 0.17 0.50 - 1.13

2007 0.73 0.17 0.18 0.46 - 1.16

Season

Winter --- --- --- ---

Spring 0.94 0.05 0.20 0.85 - 1.03

Summer 0.86 0.06 0.02 0.76 - 0.98

Fall 0.76 0.07 0.01 0.64 - 0.91

3Audit rating

C --- --- --- ---

AAA 0.71 0.53 0.64 0.16 - 3.07

AA 0.90 0.37 0.80 0.40 - 2.01

A 1.19 0.48 0.67 0.54 - 2.61

B 1.01 0.42 0.98 0.45 - 2.28

50

unrated 2.69 1.09 0.01 1.22 - 5.94

Number of weeks open

1 - 43 --- --- --- ---

44 - 49 0.72 0.15 0.10 0.48 - 1.08

50 - 51 0.71 0.16 0.13 0.46 - 1.11

52 0.92 0.29 0.78 0.49 - 1.70

Number of animal processed

1 - 286 --- --- --- ---

287 - 498 1.01 0.15 0.95 0.75 - 1.35

499 - 841 1.14 0.19 0.44 0.82 - 1.58

842 - 27286 1.29 0.42 0.43 0.68 - 2.46

Region

Central --- --- --- ---

Eastern 9.88 6.83 0.01 2.55 - 38.26

Northern 3.78 2.49 0.04 1.04 - 13.74

Southern 7.91 6.56 0.01 1.56 - 40.21

Western 1.60 2.31 0.75 0.09 - 27.23

Price

Below median --- --- --- ---

Above median 1.11 0.06 0.04 1.00 - 1.23

1 Generalized estimating equation using an exchangeable correlation structure to

accommodate repeated measurements among provincial abattoirs 2

Incidence rate ratio 3

Model would not converge therefore results represent univariable negative binomial

model

Univariable negative binomial models using a GEE1

approach modeling the association

between condemnation rates in Ontario provincial abattoirs and animal class, year,

season, audit rating, the number of weeks an abattoir was open each year, the number of

cattle an abattoir processed each year, census agricultural region and median yearly sales-

price for animal class.

51

Table 2.5 - Comparison of GEE1 fitted models using QIC

2 statistic

Model Correlation Structure QIC

Negative binomial GEE2 Exchangeable 11328

Non-stationary 11626

2

nd order autoregressive 11647

Stationary 11664

1

st order autoregressive 11668

Poisson GEE2 2

nd order autoregressive 12007

Stationary 12056

1

st order autoregressive 12409

Exchangeable 13605

Non-stationary Did not converge

1Generalized estimating equation

2Quasi-log- likelihood under the independence model information criterion

Comparison of QIC2

statistic values for negative binomial or Poisson models using a

GEE1

approach to investigate the association between condemnation rates in Ontario

Provincial abattoirs and year, season, animal class, price, year-animal class interaction

and audit rating.

52

Table 2.6 - Multivariable negative binomial model using a GEE1 approach

Covariate IRR2

Robust Standard

Error P-value 95% CI

Animal class

Calves --- --- --- ---

Cows 6.73 2.40 < 0.001 3.35 - 13.52

Heifers 0.47 0.10 < 0.001 0.31 - 0.72

Steers 0.46 0.12 0.01 0.29 - 0.74

Year

2001 --- --- --- ---

2002 1.37 0.21 0.04 1.02 - 1.84

2003 0.70 0.16 0.11 0.45 - 1.09

2004 0.56 0.13 0.02 0.36 - 0.90

2005 0.93 0.20 0.74 0.61 - 1.43

2006 1.05 0.23 0.80 0.69 - 1.63

2007 1.16 0.29 0.56 0.71 - 1.88

Season

Winter --- --- --- ---

Spring 0.88 0.07 0.07 0.76 - 1.01

Summer 0.82 0.06 0.01 0.71 - 0.95

Fall 0.77 0.05 < 0.001 0.69 - 0.87

Audit rating

C --- --- --- ---

AAA 0.32 0.30 0.22 0.05 - 2.01

AA 0.43 0.12 0.01 0.25 - 0.74

A 0.64 0.11 0.01 0.46 - 0.89

B 0.56 0.11 0.01 0.39 - 0.82

53

unrated 1.41 0.35 0.17 0.87 - 2.31

Price

Below median --- --- --- ---

Above median 1.12 0.05 0.02 1.02 - 1.23

Year x animal class

Calves and 2001 --- --- --- ---

Cows and 2002 0.61 0.12 0.01 0.42 - 0.88

Cows and 2003 1.13 0.34 0.69 0.63 - 2.05

Cows and 2004 0.83 0.30 0.61 0.40 - 1.72

Cows and 2005 0.48 0.14 0.01 0.27 - 0.86

Cows and 2006 0.45 0.12 0.01 0.27 - 0.78

Cows and 2007 0.51 0.19 0.06 0.25 - 1.04

Heifers and 2002 0.61 0.14 0.03 0.39 - 0.95

Heifers and 2003 1.46 0.44 0.21 0.81 - 2.65

Heifers and 2004 1.05 0.30 0.85 0.61 - 1.83

Heifers and 2005 0.88 0.24 0.63 0.51 - 1.50

Heifers and 2006 0.93 0.22 0.77 0.59 - 1.48

Heifers and 2007 0.71 0.23 0.29 0.39 - 1.33

Steers and 2002 0.74 0.14 0.12 0.51 - 1.08

Steers and 2003 1.40 0.43 0.28 0.77 - 2.54

Steers and 2004 1.63 0.50 0.11 0.89 - 2.99

Steers and 2005 1.02 0.28 0.94 0.60 - 1.73

Steers and 2006 1.42 0.44 0.26 0.78 - 2.61

Steers and 2007 0.90 0.32 0.77 0.45 - 1.81

Correlation value = 0.067

1 Generalized estimating equation using an exchangeable correlation structure to account

for repeated measurements among provincial abattoirs

54

2 Incidence rate ratio

Multivariable negative binomial models using a GEE1

approach investigating the

association between condemnation rates in Ontario provincial abattoirs and animal class,

year, season, price, year-animal class interaction and audit rating based on the

multivariable GEE model.

55

Table 2.7 - Linear combinations of condemnation rates in cows and calves

Contrast IRR P-value 95% CI

Cows 2001 vs. Calves 2001 6.73 < 0.001 3.35 - 13.52

Cows 2002 vs. Calves 2002 4.09 < 0.001 2.23 - 7.50

Cows 2003 vs. Calves 2003 7.61 < 0.001 4.00 - 14.46

Cows 2004 vs. Calves 2004 5.56 < 0.001 2.81 - 10.99

Cows 2005 vs. Calves 2005 3.23 < 0.001 1.90 - 5.50

Cows 2006 vs. Calves 2006 3.06 < 0.001 1.86 - 5.01

Cows 2007 vs. Calves 2007 3.41 < 0.001 1.97 - 5.90

Linear combinations of condemnation rates in cows compared to calves in Ontario

Provincial abattoirs 2001-2007 based on model in Table 2.6.

56

Figure 2.1 - Condemnation rates per 1000 cattle from Ontario provincial abattoirs

2001 – 2007

57

Figure 2.2 - Animal class condemnation rates per 1000 cattle from Ontario provincial abattoirs 2001 - 2007

http://www.biomedcentral.com/1746-6148/6/42/figure/F2

58

Figure 2.3 - Choropleth map of percentage of abattoirs processing cattle in Ontario

per census agricultural region

59

CHAPTER THREE:

SUITABILITY OF BOVINE PORTION CONDEMNATIONS AT

PROVINCIALLY-INSPECTED ABATTOIRS IN ONTARIO CANADA FOR

FOOD ANIMAL SYNDROMIC SURVEILLANCE

(Alton et al. as published in BMC Veterinary Research 2012; 8:88)

ABSTRACT

Abattoir condemnations may play an important role in a food animal syndromic

surveillance system. Portion condemnation data may be particularly useful, as these data

can provide more specific information on health outcomes than whole carcass

condemnation data. Various seasonal, secular, disease, and non-disease factors have been

previously identified to be associated with whole carcass condemnation rates in Ontario

provincial abattoirs; and if ignored, may bias the results of quantitative disease

surveillance methods. The objective of this study was to identify various seasonal,

secular, and abattoir characteristic factors that may be associated with bovine portion

condemnation rates and compare how these variables may differ from previously

identified factors associated with bovine whole carcass condemnation rates.

Data were collected from the Ontario Ministry of Agriculture, Food and Rural

Affairs (OMAFRA) and the Ontario Cattlemen’s Association regarding “parasitic liver”

and pneumonic lung condemnation rates for different cattle classes, abattoir compliance

ratings, and the monthly sales-yard price for commodity classes from 2001-2007. To

control for clustering by abattoirs, multi-level Poisson modeling was used to investigate

the association between the following variables and “parasitic liver” as well as

pneumonic lung condemnation rates: year, season, annual abattoir audit rating,

60

geographic region, annual abattoir operating time, annual total number of animals

processed, animal class, and commodity sales price.

In this study, “parasitic liver” condemnation rates were associated with year,

season, animal class, audit rating, and region. Pneumonic lung condemnation rates were

associated with year, season, animal class, region, audit rating, number of cattle

processed per year, and number of weeks abattoirs processed cattle. Unlike previous

models based on whole carcass condemnations, commodity price was not associated with

partial condemnations in this study. The results identified material-specific predictor

variables for condemnation rates. This is important for syndromic surveillance based on

abattoir data and should be modeled and controlled for during quantitative surveillance

analysis on a portion specific basis.

BACKGROUND

Animal disease outbreaks can have a devastating effect, not only on animals, but

to the food-animal industry, public, economy, and international trade [1-3]; therefore,

research and development of novel animal disease surveillance systems is extremely

important. In recent years, Ontario has experienced the emergence of various infectious

animal diseases including a new strain of porcine circovirus type II (PCV-2) in 2004[4],

outbreaks of bovine viral diarrhea (BVD) with enhanced virulence in cattle in the early

90’s [3], and the impact of the identification of a small number of cases of bovine

spongiform encephalopathy (BSE) in Alberta, Canada in 2003[2]. The confirmation of

BSE in May 2003 is just one of many examples of how the emergence of an infectious

animal disease can cripple an industry and have profound lasting effects [2]. Though all

61

BSE cases in Canada were only identified in Alberta, Canada, closure of international

trade borders to Canadian cattle caused the price of cattle to drop significantly throughout

the country and took several years to recover [5]. New approaches to animal disease

surveillance, such as syndromic surveillance of condemnation data may be important for

the timely identification of infectious animal and zoonotic disease events in the future.

Syndromic surveillance, though an increasingly popular tool in public health

surveillance research [6-9] has only recently been explored as an option for animal health

surveillance. One novel way to target surveillance of infectious animal and zoonotic

diseases would be through the food system. In the past, abattoirs have been the focus for

surveillance at this human-animal interface, usually involving targeted surveillance of a

specific disease [10-13]. In recent years, research has been expanded to include

surveillance of food animal data from a variety of sources including on-farm surveillance

[14,15], sales-yard surveillance [16], as well as syndromic surveillance using abattoir

condemnation data [17-19].

Abattoir condemnation data have the potential to provide early warning of

emerging animal and zoonotic diseases, particularly provincial abattoir data and yet these

data have been under-utilized in the past. Portion condemnations and whole carcass

condemnation data have been previously described in the literature [20-22]; however, few

reports of potential usages of these data for syndromic surveillance purposes have been

implemented. Changes in portion and whole carcass condemnation rates could be

monitored over time and space, and when the condemnation rate reaches a certain

threshold it may signal a potential outbreak or quality control problem within an abattoir

62

and/or region. In Ontario, condemnation data from provincially inspected abattoirs are

particularly useful for syndromic surveillance, as they give a relatively local perspective

on the health of animals within the province. Ontario provincial abattoirs only distribute

their products within the province, compared to federal abattoirs, which ship their

products inter-provincially and internationally [23]. Anecdotal evidence has suggested

that cattle shipped to Ontario provincial abattoirs originate from relatively local farms.

This was confirmed by a previous study which found that 75% of cattle from Ontario

provincial abattoirs originated from farms less than 94 kilometres from the abattoir [17].

Previous research has investigated the use of whole carcass condemnation data for

syndromic surveillance [17]. It is unclear whether organ/body system data may be better

suited for syndromic surveillance, as these data may provide more specific information

on health outcomes than whole carcass condemnation data. By having a more specific

outcome, it is hypothesized that portion condemnation data should be more sensitive than

whole carcass data because inspectors condemn a carcass for one reason. However,

bovine carcasses may have several disease conditions causing the condemnation of the

carcass; whereas organs are less likely to have more than one reason for condemnation

and reflect diseases found in a specific organ system.

Generalized linear mixed models (GLM) have been previously used for human

disease surveillance [24], as well as clustered GLM’s fit by generalized estimating

equations (GEE) specifically for whole carcass condemnation data [17]. Various

seasonal, secular, disease, and non-disease factors were previously found to have a

statistically significant association with bovine whole carcass condemnation rates, and

should be controlled for in the application of quantitative surveillance methods to prevent

63

biased results (e.g., false alarms) [17]. It was unclear whether the same factors would also

be significantly associated with condemnation data when applied to portion

condemnation data.

The objective of this study was to identify various seasonal, secular, and abattoir

characteristic factors that may be associated with bovine portion condemnation rates and

compare how these may differ from factors associated with bovine whole carcass

condemnation rates. “Parasitic liver” and pneumonic lung condemnations were used in

this case study as these condemnation classes were a rich source of data.

METHODS


Portion condemnation data were obtained from the Food Safety Decision Support

System (FSDSS) database maintained by the Ontario Ministry of Agriculture, Food and

Rural Affairs (OMAFRA). The database contains information regarding the number and

reason for daily organs/body systems condemnations in provincially inspected abattoirs

in Ontario. The condemnation categories of pneumonic lungs and “parasitic livers” were

selected for this analysis, as these categories were among the most frequently reported

portion condemnations by provincial inspectors during the study period and represent

potential animal and public health concern. “Parasitic livers” is an inspection term used to

label bovine livers considered unfit for human consumption, and thus condemned due to

pathologies such as necrosis, fibrosis cirrhosis, atrophy, telangiectasia, and adhesions.

Although the term “parasitic liver” suggests truly parasitic infections such as fascioliasis,

the term covers non-parasitic conditions as well (personal communication Ab Rehmtulla,

64

DVM, OMAFRA, Stone Road, Guelph, Ontario). Pneumonic lung condemnation refers

to bovine lungs which were condemned for lesions indicative of a previous localized and

resolved antero-ventral pneumonia infection. Data were extracted from the database for

cattle animal classes: bulls, calves, cows, heifers, and steers from January 1, 2001 to

December 31, 2007. Missing geographical coordinates for abattoirs were approximated

using postal codes and/or addresses with the address geocoding software GeoPinpoint

Suite 6.4 (DMTI Spatial Inc., Markham, Ontario, Canada). Using the FSDSS database,

the following variables were created for each month: abattoir identification number,

geographical coordinates of abattoir, year, season, number of weeks an abattoir was

operating each year, total number of “parasitic liver” and pneumonic lung

condemnations, total number of cattle processed each year, and animal class. Season was

categorized by 3 month groupings as follows: winter (December - February), spring

(March - May), summer (June - August), and fall (September - November). Animal class

included five categories: bulls, cows, calves, heifers, and steers. Bulls were excluded

from subsequent analyses due to missing data and inconsistencies in the use of this

classification. The number of weeks an abattoir was operating each year was determined

by the total number of weeks in which at least one bovine animal was processed. The

total number of animals processed each year was calculated from the total number of

condemned cattle plus the number of cattle fit for consumption.

Abattoir audit ratings were obtained for all abattoirs through the abattoir audit

program administered through OMAFRA. The audit program assesses each facility’s

food safety performance and compliance with the Ontario Meat Inspection Act. Audits

are conducted once a year and evaluate each premise on 14 food safety areas based on the

65

Standards of Compliance relating to food safety, animal welfare and occupational health

and safety with a letter grade given for each abattoir [25]. Annual OMAFRA audit ratings

were obtained for all abattoirs in the audit program from 2001-2007. Abattoir audit

ratings were classified according to the letter grade received from best to poorest as

follows: AAA, AA, A, B or C and unrated for abattoirs that had missing data.

The price of cattle was obtained from the Ontario Cattlemen’s Association market

reports for 2001-2007. Prices are calculated to be the average price (in Canadian dollars)

per 100 lbs based on sales records from Ontario sales-yards. A price was assigned to each

month and year by animal class. The most appropriate weight category was selected to

represent each animal class based on an average animal at the time of slaughter.

The agricultural region where an abattoir was located was classified as: central,

eastern, northern, southern or western Ontario using the Ontario Census Agricultural

Region boundaries (Statistics Canada, Census Agricultural Regions, Census year 2001).

The regional location of each abattoir was determined using the point-in-polygon

technique with geographic information system software ArcGIS 9.2 (ESRI, Redlands,

California, USA).

Data from all sources were merged into one master dataset using Stata 10.1 (Stata

Corp., College Station, Texas, USA).

Statistical analysis

Multilevel Poisson regression modeling was used to evaluate the association of

monthly condemnation rates of pneumonic lungs and “parasitic livers” with the above

66

mentioned predictor variables. To model and evaluate their association with monthly

“parasitic liver” and pneumonic lung condemnation rates, the effect of year, season,

annual audit rating, number of weeks in operation, number of cattle processed, census

agricultural region, animal class, sales price of animal class were included in the model.

Linearity of continuous variables was assessed by plotting the log of the condemnation

rate per slaughtered cattle for both liver and lung condemnations against the covariate

using a locally weighted regression (lowess smoother) approach. If there was no visible

linear relationship between the outcome and the covariate, and the association could not

be adequately modeled with a quadratic term, or transformation, then the variable was

categorized. All covariates were evaluated for statistical significance individually and

then in a multivariable model using a multilevel Poisson regression model accounting for

clustering of observations within abattoirs. Backward selection was based on Wald tests

and non-significant covariates were removed from the model (α = 0.05). All excluded

covariates were evaluated for their potential confounding effect by evaluating if their

removal produced a 20% or greater change in the coefficient of the remaining variables in

the model. Interactions between region and animal class, year and animal class, as well as

season and animal class were investigated. The covariates included in the model were

fitted using a multilevel Poisson penalized quasi-likelihood model using a 2nd order

Taylor Series approximation (PQL-2). If convergence issues prohibited the multilevel

Poisson model being fit using the PQL-2 algorithm, a 1st order Taylor series

approximation (PQL-1) was employed. The premise’s identification number was used to

account for clustering at the level of the abattoir in both models. Fit of the models were

assessed visually by plotting the upper level residuals (at the level of the abattoir), also

67

known as the best linear unbiased predictors (BLUP’s) against the normal scores to

assess normality, as well as comparing the BLUP’s to the predicted outcome to assess

variance homogeneity. All multi-level statistical analyses were performed using MLwiN

2.17 (Centre for Multilevel Modeling, London, UK).

RESULTS

Descriptive Statistics

There were 211 provincially-inspected abattoirs slaughtering a total of 1,155,535

cattle from 2001-2007 (Figure 3.1). Of the total number of slaughtered cattle, 403,290

organs/portions were condemned for various reasons with the top four reasons being

evidence of pneumonia, liver abscesses, nephritis and “parasitic livers”. There were 14

different organs/body systems investigated for portion condemnations and 13

condemnation reasons (Table 3.1). Overall, the condemnation rate for “parasitic livers”

per 1000 slaughtered cattle increased over the study period, particularly in cows (Figure

3.2). In contrast, the condemnation rate for lungs per 1000 slaughtered cattle decreased

over the study period, especially in calves (Figure 3.3).

The quartiles of total number of animals and the corresponding number of

processing abattoirs were tabulated for each year of the study period (Table 3.2a). With

the exception of 2004 and 2005, most of the abattoirs processed fewer than 500 cattle per

year. The quartiles of the total number of weeks each year an abattoir processed at least

one animal and the corresponding number of abattoirs was tabulated for each year of the

study period (Table 3.2b). Over the study period, there was an increasing trend in the

number of abattoirs processing cattle more than 49 weeks per year. The annual

OMAFRA audit rating scores and the corresponding number of abattoirs receiving those

68

scores are shown in Table 3.2c for each year of the study. Throughout the study period,

the majority of rated abattoirs were given an “A” rating. The median sales price of each

animal class was calculated for each month during the study period (Table 3.2d). The

median sales-prices for all cattle classes were lowest in 2004. No continuous variables

were found to have a linear relationship with cattle carcass condemnation rates, therefore,

quartiles were used to categorize the total number of animals processed and number of

weeks an abattoir was open (Tables 3.2a and 3.2b, respectively). Price was categorized

into a dichotomous variable according to whether the price was less than or equal to the

annual median sales price for each animal class (Table 3.2d).

Statistical models

Pneumonic lung condemnation model

Results of the univariable Poisson regression models (PQL-2) indicated that

animal class (p < 0.01), year (p < 0.01), season (p < 0.01), agricultural region (p = 0.02),

number of weeks abattoirs processed cattle (p < 0.01) all had statistically significant

association with the condemnation rate of lungs according to the Wald test for the

variable. The number of animals processed per year (p < 0.01) was statistically

significant according to the Wald test for the variable using a MQL estimation method, as

the PQL algorithm would not converge. Price was not significantly associated with lung

condemnations (p = 0.79). Due to the convergence issues in the model fitting process the

univariable association between the outcome and audit rating could not be assessed.

Animal class, year, season, agricultural region, audit rating, number of weeks

abattoirs processed cattle, and number of animal processed per year were found to have a

statistically significant association with the outcome in the multivariable model (PQL-2)

69

(Table 3.3). There was no evidence that the excluded variable price confounded these

variables. Statistically significant interactions were found to exist between animal class

and season, animal class and region, as well as, animal class and year. The fitted model

indicated that lung condemnations tended to be lower in higher rated abattoirs compared

to C rated abattoirs (Table 3.3). Condemnation rates tended to be lower in abattoirs

processing a larger number of cattle each year compared to smaller processing abattoirs

(Table 3.3). Lung condemnation rates were higher in abattoirs open throughout the year

compared to abattoirs open fewer weeks during the year (Table 3.3). Due to the

complexity of the interaction terms between animal class and season, agricultural region

and year, relationships among these variables based on the predicted rates from the fitted

model were explored (Figure 3.4). According to predicted lung condemnation rates for

calves from the multilevel model, condemnation rates were highest in eastern, western

and central Ontario regions (Figure 3.4), with the highest condemnation rates found in

calves in eastern Ontario compared to all other regions and animal classes. A decreasing

trend in condemnation rates in calves was also seen in these same regions with the

exception of a small peak in 2003. The same decreasing trend and peak in 2003 was also

evident in heifers and steers in the same regions noted above (Figure 3.4). The same trend

was evident in cows, with the exception of a peak in 2003 and 2004 (Figure 3.4). In

comparison, calves, cows, heifers and steers in northern and southern Ontario regions had

consistently lower lung condemnation rates throughout the entire study period (Figure

3.4). The best linear unbiased predictors (BLUP’s) were visually inspected for the multi-

level model; there was no evidence to reject the assumptions of normally distributed

residuals and homogeneity of variance.

70

“Parasitic liver” condemnation model

Results of the univariable multi-level Poisson regression model (PQL-2) indicated

that animal class (p < 0.01), year (p < 0.01), season (p < 0.01), agricultural region (p <

0.01), audit rating (p < 0.01), the number of weeks an abattoir processed cattle (p < 0.01),

total number of cattle processed per year (p < 0.01) and price (p < 0.01) all had

statistically significant associations with “parasitic liver” condemnation rate according to

the Wald test for the variable.

In the multivariable multilevel Poisson model (PQL-1), year, animal class, season,

agricultural region, and audit rating were found to have a statistically significant

association with the outcome (Table 3.4). There was no evidence that the excluded

variables confounded the variables included in the model. Statistically significant

interactions were found to exist between animal class and season, animal class and

region, as well as animal class and year (Table 3.4). The fitted model indicated that liver

condemnation rates were higher in C-rated abattoirs compared to higher rated abattoirs

(Table 3.4). Due to the complexity of the interaction terms between animal class and

season, agricultural region and year, relationships among these variables based on

predicted rates from the fitted model were explored (Figure 3.5). According to predicted

liver condemnation rates for steers from the multilevel model, condemnation rates were

highest in eastern Ontario. Rates amongst steers tended to be higher in fall and winter.

The lowest rates in steers were found in northern Ontario (Figure 3.5). Liver

condemnation rates for calves were highest in central Ontario and lowest in eastern and

northern Ontario. Similar to steers, rates amongst calves tended to be higher in fall and

winter throughout the study period. Liver condemnation rates in cows had the highest

71

rates compared to all other animal classes in all regions of Ontario throughout the study

period (Figure 3.5). Condemnation rates in cows were highest in central and eastern

Ontario and lowest in southern Ontario. Condemnation rates amongst cows remained

fairly stable throughout the seasons; however, an increasing secular trend was seen in

cows with peaks in 2004 and 2007 for all regions of Ontario. Liver condemnation rates

for heifers were highest in eastern and western Ontario and lowest in northern and

southern Ontario (Figure 3.5). Condemnation rates in heifers remained fairly stable

within a region throughout the study period. The predicted random effects were subjected

to a visual diagnostic analysis and no evidence against the assumption of normality and

variance homogeneity was found.

DISCUSSION

Portion and whole carcass condemnation data may have an important role in the

development of a food animal syndromic surveillance system. These data provide insight

into lesions on carcasses and organs, which may lead to early detection of emerging

animal and zoonotic diseases. This study builds upon previous research investigating

biological and non-biological factors associated with bovine whole carcass condemnation

rates in Ontario provincial abattoirs during the same study period [17]. As with whole

carcass data, various seasonal, secular and abattoir characteristic factors were found to

have an association with liver and lung portion condemnations, and need to be taken into

account in the application of quantitative methods, such as cluster detection for disease

surveillance involving these data. In addition, the results show differences in the models

constructed for liver and lung portion condemnations, as well as between portion and

previously explored whole carcass condemnation data [17]. These findings suggest that

72

different variables may be associated with condemnation rates depending on the type of

material being condemned, and should be modeled and controlled for during quantitative

surveillance on a portion-specific basis. Previous studies have demonstrated the

importance of identification of potential confounding variables and different methods of

controlling for these variables in cluster detection methods for disease surveillance [26,

27]. For example, a study by Kleinman et al. [26] compared the performance of the

space-time scan statistic using unadjusted and covariate-adjusted respiratory complaint

data in humans to account for confounding temporal factors such as day of the week,

month and holidays. The study concluded that failure to adjust for confounding variables

can produce many false alarms and/or mask potential outbreaks.

Pneumonic lungs and “parasitic” livers were used as examples to explore the

modeling of biological and non-biological factors associated with portion condemnations.

These condemnation designations were selected as they represented two of the most

frequently reported reasons for portion condemnations by inspectors at provincial

abattoirs during the study period. Syndromic surveillance is based on non-traditional data

sources. Though this allows for early warning about potential disease outbreaks these

systems are generally less sensitive than traditional laboratory based surveillance systems

[28]. Therefore it is of utmost importance to (i) use highly predictive models (adjusted for

known risk factors and confounders), and (ii) preserve the robustness of the models by

adhering to the principle of parsimony. These are well known contradicting goals in

predictive modeling that make sufficient/large sample sizes an important requirement.

Therefore, pneumonic lung and “parasitic liver” condemnation rates were selected as they

were a rich data source from which to estimate trends. Livers are important from a public

73

health and economic standpoint, as they are a common edible portion in cattle and

represent a possible food safety concern. Lungs, though generally not consumed by the

average Ontarion, are also an important animal and public health concern, as lesions such

as tuberculosis granulomas may be found in inedible organs/tissues such as the lungs.

These are important factors to consider when selecting portion condemnation

designations for syndromic surveillance; a previous study by Thomas et al. [19],

investigating the use of portion condemnations in market hogs, noted that the quality of

data recording was poor for organs that were not considered to be economically

important or a concern for food safety.

It was interesting to find similarities and differences in terms of the significant

variables and the impact the variables had on condemnation rates in both the liver and

lung portion models, as well as the previously described models for whole carcass

condemnations [17]. The variables year, animal class, season and annual audit rating

were found to be significantly associated with condemnation rates in both portion models

as well as whole carcasses. It is not surprising that season and animal class were found to

be significant factors associated with abattoir condemnation rates in all three models, as

many animal diseases tend to have a distinct seasonality and high risk age groups

associated with the disease. For example, bovine respiratory disease complex more

commonly infects calves following a stressful event, such as sudden change in weather

conditions [29], and older cattle are generally at higher risk for being culled due to

disease and thus condemned more frequently. In the whole carcass condemnation data,

the variable year, identified patterns assumed to be associated with the discovery of

Bovine Spongiform Encephalopathy (BSE) in Alberta, Canada in 2003 [17]. In the

74

portion models, year also appeared to have a significant decreasing trend in calves for

pneumonic lung condemnations. It is suspected that these temporal trends in liver and

lung condemnations also stem from regulation changes due to BSE. Prior to BSE in

Canada, it was legal to load and transport downer animals with a veterinary certification

of fitness for slaughter. However, during 2004, changes were made to federal cattle

transportation regulations which forbid the transportation and slaughter of non-

ambulatory animals [30]. Ambulatory animals may be less likely to have lung pathology

than compromised and downer animals and are likely reflected in the marked decrease in

condemnation rates in pneumonic lungs (personal communication Ab Rehmtulla, DVM,

OMAFRA, Stone Road, Guelph, Ontario). In contrast, “parasitic liver” condemnations

increased in cows over the study period and may also reflect the type and quality of cattle

being sent to slaughter; however, it is unclear why an increase was seen over the study

period. Audit rating appeared to be an important variable for provincial abattoir

condemnation data. It was hypothesized that audit rating may reflect an abattoir’s

compliance to regulations and/or willingness to accept animals of poorer quality.

Although, different trends were found in these variables between all 3 models, the overall

statistically significant association of these variables with condemnation rates appears to

be “universal” within Ontario provincial abattoir data, and should be controlled for in

quantitative cluster detection methods for surveillance. Although the AAA and C

categories represent a small number of abattoirs over the study period, we felt that it was

important to not collapse the categories, as these abattoirs point to unique qualities among

these establishments. Since this study was conducted, the audit rating system was

simplified in September 2010 to a 3-grade system including pass, conditional pass and

75

fail to better reflect systems used in several jurisdictions for evaluating food safety at

restaurants [25]. This change in reporting may need to be accounted for in future studies

and/or in attempts to control for non-disease issues when conducting space-time cluster

analyses.

Agricultural region was found to be significantly associated with the portion

condemnation rates but not with whole carcass rates. It was interesting to note that

overall, predicted condemnation rates for bovine lungs and liver were lower in northern

and southern Ontario regions compared to other regions for all animal classes throughout

the study period. This pattern was particularly evident in the lung dataset. This regional

difference of condemnation rates in pneumonic lungs may reflect a genuine regional

difference; however, it likely stems from the quality of animals being sent to slaughter in

these regions. Abattoirs in southern and northern Ontario did not specialize and rarely

received non-ambulatory cattle. Prior to 2004, most of the so-called “downer plants”

were located in southwestern, central and eastern Ontario. While the regional differences

in “parasitic” livers may be due to a smaller concentration of dairy cattle in northern and

to some extent southern Ontario, corresponding with the lower condemnation rates in

these areas; it was surprising that variables related to abattoir processing capacity, such as

number of cattle processed each year and number of weeks an abattoir processed cattle

was only significant in the lung condemnation model. This may reflect issues with

processing speeds within abattoirs, perhaps the speeds impact lung inspection more than

livers since livers are more commonly considered to be an edible portion of cattle and

thus more carefully inspected.

76

It is important to identify and understand the factors which may cause “noise” in

the data before any quantitative methods can be chosen for disease surveillance. This

extra work beforehand can save valuable time and resources investigating “false alarms”

after the application of quantitative methods. Although differing results were found in the

two portion models as well as the previously described whole carcass model, common

themes arose from the results. Bovine abattoir condemnation data are sensitive to the

effects of regulatory and economic changes in the industry. Therefore it is important to

adjust models as regulations change over time. In addition, seasonal, secular, and non-

disease factors, such as commodity class, abattoir rating and processing capacity also

seemed to be important factors for bovine abattoir condemnation data and should be

adjusted in subsequent cluster detection analyses to prevent biased results. All analyses

thus far have been conducted retrospectively, facilitating the use of historical data to

highlight variables which need to be taken into account prior to applying outbreak

detection methods. However, the practical application of disease surveillance would be

conducted prospectively, and would have implications on the models. For example, the

variable year was found to be a very important variable in all of the models; however this

variable would not be applicable in a prospective analysis. The secular trend effect would

have to be accounted for in the analyses in some other way, for example using a trend

polynomial or trend filter. In addition, we were only able to account for clustering by

abattoir. However, it would be useful to explore the effect of clustering by inspector as

well. Unfortunately, these data were not available to explore the effect of inspector.

Specific training of inspectors may also improve the ability of a syndromic surveillance

system to detect unusual events. For example, the broad use of the category “parasitic

77

liver” may be more useful at detecting changes in disease if inspectors used more specific

criteria when condemning organs.

The suitability of portion vs. whole carcass condemnation data for syndromic

surveillance is difficult to ascertain in this study. It is suspected that for syndromic

surveillance purposes, portion condemnation data will be more specific and sensitive at

detecting changes in condemnation rates. However, validation of this conclusion is

difficult with no major documented disease outbreaks in Ontario cattle during the study

period. Nevertheless, it is encouraging that regulatory changes surrounding the

identification of BSE in Alberta, Canada was identified in both the whole carcass and

portion condemnation data. A similar study in pigs using Ontario provincial abattoir data

found that whole hog carcass condemnation data performed better than portion carcass

condemnation data at detecting disease clusters consistent with a documented porcine

circovirus-associated disease outbreak in Ontario [19]. Further investigations, perhaps

using simulated data, are needed to determine which types of data are more suitable for

the syndromic surveillance of specific types of diseases.

CONCLUSIONS

Findings from this study suggest that there are “universal” factors associated with

condemnation data, such as animal class, year, season and audit rating, additional

material-specific covariates are important and should be modeled and controlled for in

quantitative methods for disease surveillance, such as in cluster detection methods.

Validation of results is an issue with bovine portion abattoir condemnation data, as there

were no documented outbreaks in Ontario cattle during the study period. Further

investigations are needed to determine whether portion data would perform better than

78

whole carcass condemnation data for disease surveillance, and whether certain portions

are more appropriate than whole carcass for the surveillance of specific diseases.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the following organizations for their

support for infrastructure, data retrieval and funding for this project: Canada Foundation

for Innovation, the Ontario Research Fund, the Ontario Graduate Scholarship,

OMAFRA/University of Guelph Research Program and Ontario Ministry of Agriculture,

Food and Rural Affairs (OMAFRA).

79

REFERENCES

1. Bovine spongiform encephalopathy (BSE)

[http://www.omafra.gov.on.ca/english/food/inspection/meatinsp/abattoir_audit.htm].

2. Timeline of BSE in Canada and the U.S

[http://www.cbc.ca/news/background/madcow/timeline.html].

3. Carman S, van Dreumel T, Ridpath J, Hazlett M, Alves D, Dubovi E, Tremblay R,

Bolin S, Godkin A, Anderson N: Severe acute bovine viral diarrhea in Ontario, 1993-

1995. J Vet Diagn Invest 1998, 10:27–35.

4. Poljak Z, Dewey CE, Rosendal T, Friendship RM, Young B, Berke O: Spread of

porcine circovirus associated disease (PCVAD) in Ontario (Canada) swine herds:

Part I. Exploratory spatial analysis. BMC Vet Res 2010, 6:59.

5. Ontario Cattlemen's Association - Market Information Service

[http://www.cattle.guelph.on.ca/markets/marketinfo.asp].

6. Steiner-Sichel L, Greenko J, Heffernan R, Layton M, Weiss D: Field investigations of

emergency department syndromic surveillance signals. New York City: MMWR;

2004:184–189.

7. Heffernan R, Mostashari F, Das D, Besculides M: New York City syndromic

surveillance systems. New York City: MMWR; 2004:25–27.

8. Edge VL, Pollari F, Ng LK, Michel P, McEwen SA, Wilson JB, Jerrett M, Sockett PN,

Martin SW: Syndromic surveillance of Norovirus using over-the-counter sales of


17:235–241.



visit surveillance by public health? CJEM 2008, 10:18–24.

10. Hadorn DC, Stark KDC: Evaluation and optimization of surveillance systems for

rare and emerging infectious diseases. Vet Res 2008, 39:57–69.

11. Karama M, Johnson RP, Holtslander R, McEwen SA, Gyles CL: Prevalence and

characterization of verotoxin-producing Escherichia coli (VTEC) in cattle from an

Ontario abattoir. Can Vet Res 2008, 72:297–302.


scrapie in the European Union. Vet Res 2008, 39:37–52.

13. Kaneene JB, Miller R, Meyer RM: Abattoir surveillance: The U.S. experience. Vet

Microbiol 2006, 112:273–282.

80

14. Vourc'h G, Bridges VE, Gibbens J, De Groot BD, McIntyre L, Poland R, Barnouin J:


Infect Dis 2006, 12:204–210.


syndromic surveillance system implemented for swine. Can J Vet Res 2010, 74:241–

250.



market. JAVMA 2009, 234:658–664.




6:42.

18. Weber WD: Development of an animal health monitoring system based on slaughter






20. Van Donkersgoed J, Jewison G, Mann M, Cherry B, Altwasser B, Lower R, Wiggins

K, Dejonge R, Thorlakson B, Moss E, Mills C, Grogan H: Canadian beef quality audit.

Can Vet J 1997, 38:217–225.

21. Ansong-Danquah J: A survey of carcass condemnation at a poultry abattoir and

its application to disease management. Can Vet J 1987, 28:53-56.

22. Schamber G, Olson C, Witt L: Condemnations in cattle presented for slaughter.

Bovine Practice 1981, 2:19–21.

23. Your responsibilities under the meat inspection act (Ontario)

[https://ospace.scholarsportal.info/bitstream/1873/4777/2/10308452.pdf].

24. Robertson C, Nelson TA, MacNab YC, Lawson AB: Review of methods for space-

time disease surveillance. Spat Spattemporal Epidemiol 2010, 1:105–116.

25. The Meat Plant Audit Program

[http://www.omafra.gov.on.ca/english/food/inspection/meatinsp/abattoir_audit.htm].



133:409–419.

81

27. 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.

28. Lawson AB, Kleinman K: Spatial and Syndromatic Surveillance for Public Health:

Chichester, West Sussex; Hoboken. NJ: J. Wiley; 2005.

29. Ogilvie TH: Large Animal Internal Medicine: 1st Edition ed. Baltimore, Maryland:

Williams and Wilkens; 1998.

30. Overview of Canada's BSE Safeguards

[http://www.inspection.gc.ca/english/anima/disemala/bseesb/bseesbfs2e.shtml].

82

Table 3.1 – Summary of Ontario provincial abattoir bovine portion condemnations

2001 -2007.

Organ or body system

(# of condemnations / %)

Reason for condemnation Number of portion

condemnations (%)

Front quarter Arthritis 427 (0.11 %)

(28 318 / 7.02 %) Inflammation 701 (0.17 %)

Abscess 955 (0.24 %)

Bruising 12 947 (3.21 %)

Contamination 13 288 (3.29 %)

Head

(including tongue)

Xanthomatosis 40 (0.01%)

(9519 / 2.36 %) Actino 670 (0.17 %)

Contamination 2421 (0.60 %)

Abscess

Erosion

3723 (0.92 %)

2665 (0.66%)

Heart Adhesion 8261 (2.05 %)

Hind quarter Abscess 174 (0.04 %)

Hind quarter Inflammation 516 (0.13 %)

(49 858 / 12.36 %) Arthritis 2132 (0.53 %)

Abscess 4349 (1.08 %)

Contamination 14 053 (3.48 %)

Bruising 28 808 (7.14 %)

Kidneys Cystic 31 570 (7.83 %)

(93 589 / 23.21 %) Nephritis 62 019 (15.48% )

Liver Cirrhosis 1666 (0.41 %)

(168 183 / 41.70 %) Melanosis 8368 (2.07 %)

Adhesion 29 125 (7.22 %)

Abscess 57 511 (14.26 %)

Parasitic 71 513 (17.73 %)

Loin Abscess 456 (0.11 %)

Ribs Abscess 46 (0.01 %)

Shoulder/limb Arthritis 214 (0.05 %)

Stifle joint Arthritis 693 (0.17 %)

Trim Bruising 7096 (1.76 %)

Lungs Pneumonia 36 883 (9.15 %)

Total portions condemned 403 290

Total cattle slaughtered 1 155 535

83

Table 3.2 – Summary of number of cattle processed, number of weeks open, audit

rating and animal class

a) Number of

animals

processed

Number of Abattoirs

2001 2002 2003 2004 2005 2006 2007

1 – 286 73 72 36 24 30 40 38

287 – 498 43 35 40 28 36 34 26

499 – 841 24 24 36 40 40 32 35

842 – 27 286 22 26 33 49 41 32 30

b) Number of

weeks open

Number of Abattoirs

1 – 43 66 67 44 32 43 38 38

44 – 49 56 45 48 45 30 39 29

50 – 51 18 28 25 31 41 31 32

52 22 17 28 33 33 30 30

c) Audit

Rating

Number of Abattoirs

AAA 1 0 0 0 0 1 1

AA 6 6 7 13 15 22 28

A 61 71 77 76 78 78 72

B 14 8 8 5 8 6 9

C 2 1 0 0 1 0 0

Unrated 78 71 53 47 45 31 19

d) Animal

Class

1Median sales-price of animal class per year 2001-2007

Calves 136.53 117.95 112.19 83.61 107.14 119.69 108.49

Cows 64.28 58.64 27.11 19.70 25.38 31.84 34.77

Heifers 112.01 101.22 82.16 73.61 87.74 90.87 91.09

Steers 113.24 102.57 83.15 75.60 91.07 93.55 92.56 1Median sales-price in Canadian dollars per 100 lbs.

Summary of the a) quartiles of the number of cattle processed, b) quartiles of the number

of weeks at least one bovine animal was processed, c) annual OMAFRA audit rating, and

d) median sales price of calves, cows, heifers, steers 2001–2007 for portion

condemnations.

84

Table 3.3 – Multivariable multi-level Poisson regression using pneumonic lung

condemnation rates

Variable Categories IRR1 Std. Err. P-value 95% CI

Year 2001 --- --- --- --- ---

2002 0.83 0.02 < 0.01 0.79 0.86

2003 1.14 0.02 < 0.01 1.09 1.19

2004 0.80 0.03 < 0.01 0.76 0.84

2005 0.75 0.03 < 0.01 0.71 0.79

2006 0.45 0.03 < 0.01 0.43 0.49

2007 0.31 0.04 < 0.01 0.28 0.34

Animal class Calves --- --- --- --- ---

Cows 0.30 0.23 < 0.01 0.19 0.47

Heifers 0.39 0.11 < 0.01 0.32 0.49

Steers 0.16 0.07 < 0.01 0.14 0.19

Season Winter --- --- --- --- ---

Spring 1.07 0.02 < 0.01 1.03 1.11

Summer 0.96 0.02 0.03 0.93 0.99

Fall 1.10 0.02 < 0.01 1.06 1.15

Region Central --- --- --- --- ---

Eastern 1.88 0.54 0.24 0.66 5.39

Northern 0.02 1.31 < 0.01 0.001 0.20

Southern 0.75 0.47 0.54 0.30 1.90

Western 0.98 0.46 0.97 0.40 2.42

Rating C --- --- --- --- ---

AAA 0.24 1.26 0.26 0.02 2.85

AA 0.18 0.64 0.01 0.05 0.62

A 0.11 0.63 < 0.01 0.03 0.37

B 0.15 0.63 < 0.01 0.04 0.50

unrated 0.29 0.64 0.048 0.08 0.99

# of animals 1-286 --- --- --- --- ---

287-498 0.70 0.12 < 0.01 0.56 0.89

499-841 0.36 0.15 < 0.01 0.27 0.48

842-27286 0.27 0.14 < 0.01 0.20 0.35

# of weeks 1-43 --- --- --- --- ---

44-49 1.72 0.07 < 0.01 1.50 1.97

50-51 2.84 0.06 < 0.01 2.55 3.16

52 2.31 0.05 < 0.01 2.11 2.54

Season x animal class Winter x calves --- --- --- --- ---

Spring x heifers 0.93 0.05 0.14 0.85 1.02

Summer x heifers 0.92 0.05 0.10 0.83 1.02

Fall x heifers 0.83 0.05 < 0.01 0.76 0.92

Spring x steers 0.91 0.04 0.01 0.84 0.98

Summer x steers 0.94 0.04 0.10 0.88 1.01

85

Fall x steers 0.82 0.04 < 0.01 0.77 0.88

Spring x cows 0.78 0.09 0.01 0.66 0.93

Summer x cows 0.73 0.09 < 0.01 0.61 0.88

Fall x cows 0.65 0.09 < 0.01 0.55 0.78

Region x animal class Central x calves --- --- --- --- ---

South x heifers 0.37 0.09 < 0.01 0.31 0.45

West x heifers 0.65 0.09 < 0.01 0.53 0.78

North x heifers 6.58 1.25 0.13 0.56 76.70

East x heifers 0.72 0.16 0.05 0.54 0.99

South x steers 0.68 0.07 < 0.01 0.60 0.78

West x steers 2.81 0.06 < 0.01 2.52 3.14

North x steers 67.90 1.06 < 0.01 8.44 546.45

East x steers 1.64 0.14 < 0.01 1.25 2.15

South x cows 0.26 0.27 < 0.01 0.15 0.45

West x cows 1.60 0.20 0.02 1.09 2.34

North x cows 7.39 1.49 0.18 0.40 136.25

East x cows 1.05 0.24 0.85 0.66 1.66

Year x animal class 2001 x calves --- --- --- --- ---

2002 x heifers 1.84 0.08 < 0.01 1.59 2.13

2003 x heifers 1.53 0.08 < 0.01 1.32 1.78

2004 x heifers 1.64 0.08 < 0.01 1.41 1.92

2005 x heifers 1.24 0.08 < 0.01 1.06 1.45

2006 x heifers 2.23 0.08 < 0.01 1.90 2.63

2007 x heifers 2.68 0.09 < 0.01 2.25 3.20

2002 x steers 2.05 0.05 < 0.01 1.85 2.27

2003 x steers 2.27 0.05 < 0.01 2.06 2.49

2004 x steers 2.44 0.05 < 0.01 2.20 2.69

2005 x steers 1.64 0.05 < 0.01 1.48 1.83

2006 x steers 2.60 0.06 < 0.01 2.32 2.92

2007 x steers 2.86 0.06 < 0.01 2.54 3.23

2002 x cows 1.07 0.19 0.74 0.73 1.55

2003 x cows 3.09 0.14 < 0.01 2.35 4.08

2004 x cows 3.63 0.14 < 0.01 2.76 4.77

2005 x cows 1.57 0.15 < 0.01 1.17 2.12

2006 x cows 2.57 0.16 < 0.01 1.89 3.49

2007 x cows 2.08 0.18 < 0.01 1.46 2.98 1Incidence rate ratio.

Multivariable multi-level Poisson regression model (PQL-2) investigating the association

between pneumonic lung condemnation rates in Ontario provincial abattoirs and year-

animal class interaction, season-animal class interaction, region-animal class interaction,

number of animals, number of weeks of processing and audit rating.

86

Table 3.4 – Multivariable multi-level Poisson regression using pneumonic lung

condemnation rates

Variable Categories IRR1 Std.

Err.

P-value

95% CI

Year 2001 --- --- --- --- ---

2002 1.08 0.04 0.08 0.99 1.17

2003 1.73 0.04 < 0.01 1.60 1.88

2004 1.81 0.04 < 0.01 1.67 1.96

2005 2.06 0.04 < 0.01 1.90 2.24

2006 2.29 0.04 < 0.01 2.12 2.49

2007 1.71 0.04 < 0.01 1.57 1.86

Animal class Calves --- --- --- --- ---

Cows 1.00 0.06 1.00 0.89 1.13

Heifers 1.45 0.05 < 0.01 1.30 1.60

Steers 1.29 0.08 < 0.01 1.10 1.50

Season Winter --- --- --- --- ---

Spring 0.88 0.03 < 0.01 0.83 0.94

Summer 1.08 0.03 < 0.01 1.02 1.14

Fall 1.22 0.03 < 0.01 1.15 1.29

Region Central --- --- --- --- ---

Southern 0.49 0.18 < 0.01 0.35 0.69

Western 0.61 0.18 < 0.01 0.43 0.86

Northern 0.22 0.31 < 0.01 0.12 0.41

Eastern 0.23 0.22 < 0.01 0.15 0.35

Rating C --- --- --- --- ---

AAA 0.53 0.13 < 0.01 0.41 0.69

AA 0.72 0.09 < 0.01 0.60 0.85

A 0.72 0.09 < 0.01 0.60 0.85

B 0.74 0.09 < 0.01 0.62 0.88

unrated 0.58 0.10 < 0.01 0.48 0.71

Season x animal class Winter x calves --- --- --- --- ---

Spring x heifers 1.04 0.04 0.37 0.96 1.12

Summer x heifers 0.96 0.04 0.33 0.90 1.04

Fall x heifers 0.90 0.04 0.00 0.84 0.97

Spring x steers 0.96 0.04 0.30 0.90 1.03

Summer x steers 0.89 0.03 < 0.01 0.84 0.95

Fall x steers 0.95 0.03 0.11 0.89 1.01

Spring x cows 1.13 0.04 < 0.01 1.05 1.21

Summer x cows 0.87 0.04 < 0.01 0.81 0.93

Fall x cows 0.75 0.03 < 0.01 0.70 0.80

Region x animal class Central x calves --- --- --- --- ---

South x heifers 1.90 0.05 < 0.01 1.71 2.10

West x heifers 1.82 0.05 < 0.01 1.65 2.00

North x heifers 2.96 0.17 < 0.01 2.11 4.15

East x heifers 7.34 0.09 < 0.01 6.10 8.82

South x steers 1.55 0.05 < 0.01 1.42 1.69

87

West x steers 1.76 0.04 < 0.01 1.63 1.91

North x steers 2.90 0.17 < 0.01 2.09 4.01

East x steers 6.09 0.09 < 0.01 5.10 7.28

South x cows 0.96 0.06 0.45 0.85 1.07

West x cows 1.38 0.05 < 0.01 1.26 1.57

North x cows 3.12 0.17 < 0.01 2.24 4.34

East x cows 5.96 0.09 < 0.01 4.96 7.17

Year x animal class 2001 x calves --- --- --- --- ---

2002 x heifers 0.88 0.06 0.02 0.79 0.98

2003 x heifers 0.52 0.06 < 0.01 0.47 0.58

2004 x heifers 0.57 0.05 < 0.01 0.51 0.63

2005 x heifers 0.58 0.05 < 0.01 0.53 0.65

2006 x heifers 0.55 0.05 < 0.01 0.50 0.61

2007 x heifers 0.72 0.05 < 0.01 0.65 0.80

2002 x steers 0.74 0.05 < 0.01 0.67 0.82

2003 x steers 0.65 0.05 < 0.01 0.59 0.72

2004 x steers 0.63 0.05 < 0.01 0.58 0.69

2005 x steers 0.60 0.05 < 0.01 0.55 0.66

2006 x steers 0.50 0.05 < 0.01 0.45 0.54

2007 x steers 0.64 0.05 < 0.01 0.59 0.71

2002 x cows 1.09 0.09 0.32 0.92 1.29

2003 x cows 1.06 0.08 0.46 0.91 1.23

2004 x cows 1.41 0.07 < 0.01 1.23 1.63

2005 x cows 0.91 0.07 0.17 0.79 1.04

2006 x cows 1.01 0.07 0.88 0.88 1.16

2007 x cows 2.12 0.07 < 0.01 1.83 2.44

1Incidence rate ratio

Multivariable multi-level Poisson regression model (PQL-1) investigating the association

between “parasitic liver” condemnation rates in Ontario provincial abattoirs and year-animal class

interaction, season-animal class interaction, region-animal class interaction and audit rating.

88

Figure 3.1 - Map of provincially-inspected abattoirs in Ontario


89

Figure 3.2 - “Parasitic liver” condemnation rates per 1000 slaughtered cattle from

Ontario provincial abattoirs 2001 – 2007


90

Figure 3.3 - Pneumonic lung condemnation rates per 1000 slaughtered cattle from

Ontario provincial abattoirs 2001 -2007


91

Figure 3.4 - Model expected pneumonic lung condemnation rates based on multi-level Poisson model


92

Figure 3.5 - Model expected “parasitic liver” condemnation rates based on multi-level Poisson model


93

CHAPTER FOUR:

COMPARISON OF COVARIATE ADJUSTMENT METHODS USING SPACE-

TIME SCAN STATISTICS FOR FOOD ANIMAL SYNDROMIC

SURVEILLANCE

(As published in BMC Veterinary Research 2013; 9:231 with minor editorial

revisions)

ABSTRACT

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.

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.

94

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.

95

BACKGROUND

With the development and availability of geographic information systems (GIS),

there has been an increasing trend in human and animal disease surveillance towards

capturing both temporal and spatial data for health and disease outcomes. Spatio-

temporal scan statistics are one of the most widely used methodologies [1] for

surveillance and have been shown to be useful for surveillance and outbreak detection in

both human and animal health applications [2-7]. Space-time scan statistics are one type

of spatio-temporal surveillance method which uses a cylindrical scanning window to scan

spatially by varying the size of the cylinder radius and scan temporally by varying the

height of the cylinder. Statistical significance of the cluster is determined by Monte Carlo

based simulations. Analysis can be conducted both retrospectively, as well as

prospectively, making it suitable for disease surveillance [8].

Syndromic surveillance is the amalgamation of signs/symptoms using data from

non-traditional sources [9]. The sign/symptom groupings are loosely designated as

‘syndromes’, and are used to track disease trends in populations and signal a possible

outbreak that warrants further investigation [9]. Historically, syndromic surveillance has

primarily been applied to human health data [10-12]. However, in recent years there has

been a growing trend towards the application of these methods for animal health

surveillance data [13-17]. Abattoir condemnation data are a rich source of information for

syndromic surveillance, and have the potential to provide early warning of emerging

animal and zoonotic disease but have been under-utilized in the past. Ontario provincial

abattoir data are particularly advantageous for syndromic surveillance and the application

96

of spatio-temporal methods, as they represent a fairly local picture of animal health

events, with cattle being shipped to abattoirs originating from farms less than 100 km

away [17].

Scan statistics identify the approximate locations of disease clusters in space and

time, and make use of a variety of statistical models [1,2], making them useful for a

variety of data. However, space-time scan statistics and current available software do

have some limitations and assumptions, which may be violated by the inherent

characteristics of provincial abattoir data. For example, while the space-time scan statistic

has the ability to control for covariates, at this time, this is only applicable to categorical

variables, thus limiting the type of variables one can control for in the analysis [1]. The

space-time permutation model inherently corrects for purely spatial and purely temporal

clusters, however, the expected rates of disease are dependent on a relatively stable

background population [18]. While this is generally true for human populations and

periods of a few years, this is generally not the case with abattoir data, where animal

population sizes can vary by season.

In recent years, model-based approaches have emerged to account for covariates

such as age, gender, and seasonality in expected rates of disease, in response to the

limited ability of space-time scan statistic software to include these covariate data [1].

Statistical modeling allows for adjustment of disease risk for both categorical and

continuous variables in space and time. By combining both methods, surveillance

researchers have the ability to account for relevant covariates, while locating clusters in

space and time. A study by Kleinman et al. [6], used this approach by conducting model-

97

adjusted space-time scan tests for syndromic surveillance of lower respiratory complaints

in a human health care setting. The study controlled for non-disease factors such as day

of week, month, and holidays and found that the number of false alarms could be reduced

by removing the “noise” of predictable covariates. However, this method has not been

applied to animal condemnation data for disease surveillance. Previous studies by Alton

et al. and Thomas et al. found that various seasonal, secular, disease and abattoir

characteristic factors were associated with condemnation rates in Ontario provincial

abattoirs; they stressed that these might be accounted for in the application of quantitative

space-time cluster detection methods for disease surveillance involving abattoir data [17-

19]. This study also highlights the importance of thinking beyond the typical age and sex

covariate adjustment and controlling for disease and non-disease factors such as animal

throughput at the abattoir and sales price of the animal class which may have a

considerable impact on the results, particularly for abattoir condemnation data.

Due to the variety of methods available for covariate-adjustment in cluster

detection, and their varying level of complexity in terms of analysis, a comparison study

of the space-time scan statistic on four different approaches at controlling for covariates

was used. If similarities in results were found between multiple approaches, then the most

parsimonious model could be recommended. Four covariate adjusted scan tests were

compared to results from the unadjusted space-time scan test including: 1) categorical

variable adjustment which stratifies on the covariate variable of interest within the space-

time scan statistic, 2) space-time permutation model which uses only case data and

inherently controls for purely spatial and purely temporal clusters, 3) multi-level model

adjusted approach which allows for adjustment of both categorical and continuous

98

variables, and 4) multi-level model residual-adjusted approach which uses the

standardized residuals from the above mentioned multi-level model to control for

covariates.

The objective of this study was to demonstrate four commonly used space-time

scan statistic approaches with different abilities to control for covariates that animal

health surveillance workers might consider when using statistical methods to identify

outbreaks of disease using abattoir condemnation data and assess their suitability for food

animal syndromic surveillance involving Ontario provincial abattoir condemnation data.

METHODS


Data regarding bovine “parasitic liver” and pneumonic lung condemnations were

extracted from the Food Safety Decision Support System (FSDSS) database maintained

by the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA). The

database contains information regarding the number and reason for daily portion

condemnations in provincially inspected abattoirs in Ontario. These portion

condemnation categories were selected for this analysis as an example dataset, as they

were among the most frequently reported portion condemnations by provincial inspectors

during the study period [19]. Additionally, as bovine livers are an edible portion, these

data may represent a potential food safety and/or quality concern. “Parasitic liver” is an

inspection term used to label bovine livers considered unfit for human consumption, due

to lesions such as necrosis, fibrosis, cirrhosis, atrophy, telangiectasia, and adhesions.

Although the term “parasitic liver” suggests truly parasitic infections such as fascioliasis,

99

the term covers non-parasitic conditions as well (personal communication Ab Rehmtulla,

DVM, OMAFRA, Stone Road, Guelph, Ontario). Pneumonic lung condemnation refers

to bovine lungs which were condemned for lesions indicative of a previous localized and

resolved antero-ventral pneumonia infection.

Data were extracted from the database for cattle animal classes: bulls, calves,

cows, heifers and steers from January 1, 2001 – December 31, 2007. Data from 45,148

bulls were excluded from subsequent analyses due to missing data and inconsistencies in

the use of this classification. Missing geographical coordinates for 54 abattoirs (26%)

were approximated using postal codes (76%) and/or addresses (24%) with the address

geocoding software GeoPinpoint Suite 6.4 (DMTI Spatial Inc., Markham, Ontario,

Canada).

Space-time scan statistic

The space-time scan statistic was used to identify abattoirs with high and low

“parasitic liver” and pneumonic lung condemnation rates in space-time using SaTScan

v8.0 (Kulldorff M. and Information Management Services Inc., 2009.), and were

visualized on maps using ArcGIS 9.2 (ESRI, Redlands, California, USA). Four different

approaches to control for confounding variables were compared to each other and an

unadjusted Poisson scan statistic including: (1) animal class adjusted Poisson scan

statistic, (2) space-time permutation, (3) multi-level model adjusted Poisson scan statistic,

and (4) a weighted normal scan statistic using model residuals. For all scan tests, latitude

and longitude coordinates for each abattoir, and premise identification number were used

to create the coordinates file. A maximum spatial cluster size of 50% of the population at

100

risk and maximum temporal cluster size of 50% of the study period were used. For all

scan tests, 9999 Monte Carlo replications were used to estimate the significance levels of

the clusters. For all analyses, the most likely (based on the size of the log-likelihood

ratio), non-overlapping in space-time, statistically significant (α = 0.05) clusters are

presented. Secondary clusters were set to allow some overlap as long as the secondary

cluster and a previously reported cluster did not both contain each other’s centroid. By

allowing some overlap, we were able to identify space-time clusters that overlapped in

space, but not time. Only the most likely non-overlapping clusters were reported to

simplify the presentation of the results. All the tests were run as two-sided tests scanning

for both high and low levels of disease to identify disease clusters as well as abattoirs

with unusually low condemnation rates.

For the unadjusted scan statistic, monthly raw counts of “parasitic

liver”/pneumonic lung condemnations and monthly number of cattle slaughtered were

used to create the case and population files respectively using a Poisson distribution. In

the animal-adjusted scan statistic, monthly raw counts of “parasitic liver”/pneumonic

lung condemnations and monthly number of cattle slaughtered were used to create the

case and population files respectively using a Poisson distribution. Cattle animal class

(e.g., calves, cows, heifers, steers) were adjusted with the space-time scan statistic by

stratifying on the variable within the case file. For the space-time permutation model, raw

case counts of “parasitic liver”/pneumonic lung condemnations were used. For the

model-adjusted scan test, a multi-level model was previously created to identify

economic, seasonal and abattoir processing capacity characteristics associated with

“parasitic liver” and pneumonic lung condemnation rates. The model identified year,

101

season, animal class, audit rating and region to be statistically associated with “parasitic

liver” condemnation rates [19]. For the pneumonic lung condemnation rates year, season,

animal class, region, audit rating, number of cattle processed per year, and number of

weeks abattoirs processed cattle were found to have a statistically significant association

[19]. For the model-adjusted scan statistic, standardized morbidity ratios were used in the

space-time scan statistic based on the observed number of condemnations and the model

predicted counts. For the model residual scan test, the observation level standardized

residuals from the multi-level model were analyzed. A Poisson model was used for the

model-adjusted scan test and a weighted normal model was used for the scan test using

the multi-level model residuals (at the level of the observation). The normal model

assumes that the quantitative variable (i.e., standardized residual) is independent and

identically distributed under the null hypothesis and therefore has the same variance.

Since the population at abattoirs changes greatly, the varying sample size at each abattoir

will cause the variance to be different for different abattoirs, thus a weighted normal

model was used to take into account the uncertainty of the observed rate [20]. The total

number of cattle slaughtered at each abattoir at each time period was used to account for

the variability.

RESULTS

A total of 211 provincially-inspected abattoirs, slaughtering a total of 1,155,535

cattle from 2001-2007 were included in this study. Provincially-inspected abattoirs can be

found throughout Ontario; however, over 80% of abattoirs processing cattle are located in

Southern, Western and Central Ontario regions. “Parasitic liver” and pneumonic lungs

condemnations were among the most frequently condemned portions and accounted for

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approximately 18% and 9% of total condemned portions for the study period,

respectively. A complete description of these data and abattoir locations can be found in

Alton et al., 2012 [69].

“Parasitic liver” data

Results of the unadjusted Poisson space-time scan statistic identified 1 high rate

cluster from July 2004 – December 2007, and 1 low rate cluster from March 2002 –

August 2005 (Figure 4.1a and Table 4.1a). Results of the animal class adjusted space-

time scan statistic identified 2 high rate clusters from August 2003 – January 2007 and

December 2006 – Dec 2007, and 1 low rate cluster from December 2001 – May 2005

(Figure 4.1b and Table 4.1b). The model-adjusted space-time scan identified 3 high rate

clusters from September 2001 – December 2001, March 2001 – June 2002, and

September 2003 – May 2004, and 3 low rate clusters from January 2001 – April 2001,

April 2004 – November 2006 and April 2005 – January 2006 (Figure 4.1c and Table

4.1c). The space-time permutation model identified 2 high rate clusters from January

2001 – September 2002 and December 2006 – December 2007, and 1 low rate cluster

from January 2001 – September 2002 (Figure 4.1d and Table 4.1d). Lastly, the space-

time scan test applied to Poisson model residuals did not identify any statistically

significant high or low rate clusters.

Pneumonic lung data

Results of the unadjusted Poisson space-time scan statistic identified 1 high rate

cluster from January 2001 – June 2004, and 1 low rate cluster from June 2004 to

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November 2007 (Figure 4.2a and Table 4.2a). Results of the animal class adjusted space-

time scan statistic identified 2 high rate clusters during January 2001 – June 2004 and

October 2001 to March 2005, and 1 low rate cluster during March 2004 – August 2007

(Figure 4.2b and Table 4.2b). The model-adjusted space-time scan statistic identified 1

high rate cluster during January 2001 – March 2002 and 3 low rate clusters during March

2004 – August 2007, January 2001 – March 2003 and April 2005 – November 2007

(Figure 4.2c and Table 4.2c). The space-time permutation model identified 1 high rate

cluster during Jan 2001 – February 2003, and 2 low rate clusters during January 2001 –

February 2003 and February 2005 – December 2007 (Figure 4.2d and Table 4.2d).

Lastly, the space-time scan statistic using multi-level model residuals identified 3 high

rate clusters during January 2001 – March 2003, July 2003 – June 2004 and January 2001

– February 2003, and 1 low rate cluster during January 2001 – March 2003 (Figure 4.2e

and Table 4.2e).

DISCUSSION

Provincial abattoir condemnation data may be useful for integration into a food

animal syndromic surveillance system; however, there are inherent characteristics in the

data which can bias the results of quantitative cluster detection methods. The results of

this methodological comparison study found differing results depending on the type of

covariate adjustment method used. By not considering or accounting for certain

covariates, such as non-disease factors (i.e. price, throughput), or selecting an

inappropriate statistical model for the data (i.e. space-time permutation model) the

subsequent results can lead to very different conclusions. These results suggest caution

should be exercised when arbitrarily selecting a space-time scan statistic model for

104

disease surveillance involving these data and highlight the importance of preliminary

validation studies using simulated or documented outbreak reports before a standard

cluster detection method is adopted by a surveillance system. A proper choice of the

method needs to take into account the properties of the sample data (e.g., its distribution,

case only vs. case and control data) and the question to be answered by the statistical test

(e.g., spatial versus space-time cluster location).

Farm location information is not routinely recorded for provincial abattoir

condemnation data. This lack of farm of origin location information for animals being

shipped to Ontario provincial abattoirs is a limitation in conducting spatial-temporal

cluster analyses. However, a previous study by Alton et al. [17], estimated the distance

between the animals’ farm and the abattoir using a subset of cattle, in which a sample

was sent for laboratory testing. The authors found cattle were shipped less than 100 km to

Ontario provincial abattoirs, and given the spatial scale of Ontario (1,000,000 km2),

abattoirs are considered to give an appropriate approximation of the disease rates among

locally slaughtered cattle. Over 75% of the abattoirs were geo-located by OMAFRA is

the FSDSS dataset; approximately 25% had missing co-ordinates and had to be geo-

located using addresses and/or postal code information. Of these, 54 abattoirs with

missing geo-locations, 76% were geo-located to the centroid of a postal code. However,

based on the potential difference between the abattoir location and centroid of its postal

code area in relation to the size of the study area, the impact would be negligible.

While there were differences between the four space-time scan statistic

approaches particularly for the “parasitic liver” condemnation data, there were some

105

similarities between the different approaches. For example, the “parasitic liver” data had

clusters which overlapped in space between the one high rate cluster and one low rate

cluster in the unadjusted and animal class adjusted approaches (Figure 4.1a and b) and

again between 2 high rate clusters and one low rate cluster in the model-adjusted and

space-time permutation model approaches (Figures 4.1c and d), however, none of these

clusters overlapped in time, and were at least a year apart. In contrast, pneumonic lung

data had a similar high rate cluster which overlapped in space and time between the

unadjusted and animal adjusted approaches (Figures 4.2a and b), a low rate cluster

between the model-adjusted and space-time permutation approach (Figures 4.2c and d)

and a low rate cluster between the model-adjusted, space-time permutation and residual

scan approaches (Figures 4.2c, d and e). There was also a similar high rate cluster in

space and time between the space-time permutation model and the residual scan (Figures

4.2d and e). While there were isolated clusters which overlapped between different

adjustment approaches, overall, the results of the scan statistics depicted very different

clusters between the different adjustment methods. The overall differences in results of

the covariate adjustment approaches suggest ignoring covariates beyond the animal level

may be unwise when using abattoir condemnation data for food animal syndromic

surveillance.

While each adjustment approach found differing results, it is also important to

consider assumptions of the scan statistic models in relation to the data being used. For

example, the space-time permutation model, which uses only case data, is advantageous

when population data are missing or difficult to obtain/sample. However, it is likely not

appropriate for use with provincial abattoir data, as the model assumes a stable

106

underlying population, which is not the case with these data. It was hypothesized that

perhaps the majority of the variation in condemnation rates may be attributed to animal

class, and perhaps by simply controlling for this categorical variable (using an adjustment

file when employing SaTScan) one would see similar results to that of the model-

adjusted approach. However, we found these approaches showed very different results.

We suspected that the model-adjusted and model residual adjustment approaches would

be the most appropriate for quantitative cluster detection involving provincial abattoir

condemnation data, as these methods are able to account for both categorical and

continuous variables, making it the most versatile of the adjustment approaches.

However, when the event is rare, as in the present example with condemnation rates, the

precision of the residuals using the normal model is unstable and may give inaccurate

results when applied to the space-time scan statistic [20]. Thus, this approach is not

appropriate for the application of provincial abattoir condemnation data unless counts are

aggregated to a higher temporal and/or spatial level. The model-adjusted approach using

the ratio of observed versus expected condemnations under the Poisson model would be

more appropriate when utilizing relatively rare events, as in the case of abattoir

condemnation data. However, this approach involves more complex analyses than some

of the other approaches. In addition, the current multi-level model includes temporal

variables such as, year and season, which are more conducive to retrospective analyses

and would be difficult to account for prospectively. Ultimately, to ensure the proper use

of these methods, validation of the different approaches with simulated or documented

outbreaks needs to be performed in selecting the most appropriate statistical test for these

data.

107

The comparison of the different methods for covariate adjustments highlights the

variability in the results for both pneumonic lung and “parasitic liver” portion

condemnation data. Overall, the results for both types of portion condemnation data

demonstrate that as you increase the detail in the covariate adjustment information, the

size of the cluster decreases. A study conducted by Kleinman et al. [6] compared the

space-time scan statistic using unadjusted and model-adjusted approaches for syndromic

surveillance of lower respiratory illness to account for confounding temporal and disease

variables, such as day of week, month, holidays and local history of illness. This study

found that during influenza season, large space-time clusters were identified almost every

weekday by the unadjusted approach compared to the model-adjusted approach, making

it unfeasible to investigate all ‘unusual’ events and diminishing the value of the tool for

surveillance. This adjustment effect could also be found in the current study, further

justifying the need for covariate adjustment with the space-time scan statistic for disease

surveillance purposes.

CONCLUSIONS

This study demonstrates the importance of identifying and adjusting for various

disease and non-disease factors which may bias the results of cluster detection methods

for disease surveillance. When selecting an adjustment method, it is important to consider

not only the inherent assumptions in the statistical method, but also these assumptions in

relation to the data being utilized. The variability in results stresses that there are a variety

of methods currently available for covariate adjustment, and that by simply selecting one

such method, without prior research and planning may yield very different and

potentially inaccurate results. Background studies such as this, which identify important

108

confounding factors and effectively correct for them may assist in improving the

sensitivity and specificity of outbreak detection by controlling for predictable clusters

creating false alarms and reducing the amount of time and resources needed for

investigation of potential clusters, as well as, identifying outbreaks that would have been

normally overlooked in the background “noise” of these data. Ultimately, validation of

different approaches with simulated or real outbreaks needs to be performed in selecting

the appropriate statistical test for these data for a food animal syndromic surveillance

context.

ACKNOWLEDGEMENTS

The authors would like to acknowledge Dr. Martin Kulldorff for his helpful

discussions concerning theoretical issues involving space-time scan statistics. The authors

would also like to acknowledge the following organizations for their support for

infrastructure, data retrieval and funding for this project: Canada Foundation for

Innovation (CFI), the Ontario Research Fund, the Ontario Graduate Scholarship,

OMAF/University of Guelph Research Program and Ontario Ministry of Agriculture &

Food (OMAF).

109

REFERENCES

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.

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.



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 Norovirus using over-the-counter sales of


17:235–241.



110

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


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:


Infect Dis 2006, 12:204–210.



market. JAVMA 2009, 234:658–664.


scrapie in the European Union. Vet Res 2008, 39:37–52.

16. Weber WD: Development of an animal health monitoring system based on slaughter


Surveillance Conference; 2009:3–4. December.



2001–2007: implications for food animal syndromic surveillance. BMC Vet Res 2010,

6:42.

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.

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.

111

Table 4.1 - Results of the space-time scan statistic using “parasitic liver” condemnation data

Date of

cluster

No. of

abattoirs in

cluster

Latitude

(°N)

Longitude

(°W) Radius (Km) No. of cases

Relative

risk1

P-value

a) Unadjusted Poisson space-time scan test

Jul 2004 - Dec

2007 16 44.52 80.92 62.45 12907 3.66 < 0.001

Mar 2002 -

Aug 2005 26 43.60 79.81 62.52 6385 0.36 < 0.001

b) Animal-adjusted Poisson space-time scan test

Dec 2006 -

Dec 2007 14 44.53 80.99 52.98 4973 3.90 < 0.001

Dec 2001 -

May 2005 51 42.91 79.50 117.97 8732 0.45 < 0.001

Aug 2003 -

Jan 2007 69 44.36 76.35 255.37 13849 1.59 < 0.001

c) Model-adjusted space-time scan test

Mar 2001 - 1 43.90 81.31 0 420 4.91 < 0.001

112

Date of

cluster

No. of

abattoirs in

cluster

Latitude

(°N)

Longitude


Relative

risk1

P-value

Jun 2002

Sep 2003 -

May 2004 71 43.18 80.51 98.22 4576 1.52 < 0.001

Sep 2001 -

Dec 2001 16 43.33 79.98 46.01 1335 2.19 < 0.001

Mar 2004 -

Nov 2006 2 44.11 80.80 0 2338 0.62 < 0.001

Jan 2001 -

Mar 2001 72 44.40 77.61 197.95 496 0.49 < 0.001

Mar 2005 -

Jan 2006 1 44.36 76.35 0 56 0.18 < 0.001

d) Space-time permutation scan test

Jan 2001 - Sep

2002 2 43.65 79.86 4 2335 6.76 < 0.001

Jan 2001 - Sep

2003 7 44.13 80.61 29.42 341 0.11 < 0.001

113

Date of

cluster

No. of

abattoirs in

cluster

Latitude

(°N)

Longitude


Relative

risk1

P-value

Sec 2006 -

Dec 2007 2 44.11 80.80 0 4431 3.06 < 0.001

e) Space-time scan test of residuals

No statistically significant clusters found

1The values in this column for the space-time permutation model refer to the observed/expected value.

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Table 4.2 - Results of the space-time scan statistic using pneumonic lung condemnation data

Date of cluster

No. of

abattoirs in

cluster

Latitude

(°N)

Longitude

(°W)

Radius

(Km) No. of cases

Relative

risk1

P-value

a) Unadjusted Poisson space-time scan test

Jan 2001 - Jun 2004 5 43.94 79.69 26.60 14896 4.98 < 0.001

Jun 2004 - Nov 2007 63 49.75 92.96 1149.60 54 0.01 < 0.001

b) Animal-adjusted Poisson space-time scan test

Jan 2001 - Jun 2004 5 43.96 79.69 26.60 14896 3.12 < 0.001

Mar 2004 - Aug 2007 21 43.01 79.97 49.09 61 0.01 < 0.001

Oct 2001 - Mar 2005 1 42.98 80.60 0 4466 5.14 < 0.001

c) Model-adjusted space-time scan test

Mar 2004 - Aug 2007 16 43.01 79.97 36.88 60 0.07 < 0.001

Jan 2001 - Mar 2003 1 43.91 79.82 0 1558 0.44 < 0.001

Jan 2001- Mar 2002 4 43.30 79.90 19.98 477 6.06 < 0.001

115

Table 4.2 - Results of the space-time scan statistic using pneumonic lung condemnation data

Date of cluster

No. of

abattoirs in

cluster

Latitude

(°N)

Longitude

(°W)

Radius

(Km) No. of cases

Relative

risk1

P-value

Apr 2005 - Nov 2007 4 43.96 79.19 20.93 273 0.26 < 0.001

d) Space-time permutation scan test

Jan 2001 - Feb 2003 11 44.14 79.94 28.20 1520 0.31 < 0.001

Jan 2001 - Feb 2003 32 42.95 79.10 121.36 10514 1.55 < 0.001

Feb 2005 - Dec 2007 10 43.96 79.19 38.19 363 0.20 < 0.001

e) Space-time scan test of residuals

Jan 2001 - Mar 2003 1 43.91 79.82 0 110 --- < 0.001

Jan 2001 – Feb 2003 8 43.17 79.73 26.91 370 --- < 0.001

Jul 2003 - Jun 2004 6 43.65 79.86 29.33 109 --- < 0.001

Jan 2001 - Mar 2003 36 43.99 80.67 68.49 2280 --- < 0.001

1The values in this column for the space-time permutation model refer to the observed/expected value.

116

Figure 4.1 - Results of space-time scan statistic using “parasitic liver” portion

condemnation data from Ontario provincial abattoirs 2001 – 2007 using four

approaches for covariate adjustment compared to the unadjusted data including: A)

Unadjusted Poisson space-time scan test, B) Animal-adjusted Poisson space-time

scan test, C) Model-adjusted space-time scan test and D) Space-time permutation

scan test


117

Figure 4.2 - Space-time scan statistic using pneumonic lung condemnation data from

Ontario provincial abattoirs 2001–2007 using four approaches for covariate

adjustment compared to the unadjusted data including: A) Unadjusted Poisson

space-time scan test, B) Animal-adjusted Poisson space-time scan test, C) Model-

adjusted space-time scan test and D) Space-time permutation scan test and e) Space-

time scan test of residuals


118

CHAPTER FIVE:

SUITABILITY OF SENTINEL ABATTOIRS FOR SYNDROMIC

SURVEILLANCE USING PROVINCIALLY-INSPECTED BOVINE ABATTOIR

CONDEMNATION DATA

(Alton et al. as submitted to BMC Veterinary Research, 2014)

ABSTRACT

Sentinel surveillance has previously been used to monitor and identify disease

outbreaks in both human and animal contexts. However, the use of sentinel abattoirs to

monitor provincially-inspected abattoir condemnation rates for food animal syndromic

surveillance has never been explored in Ontario, Canada. Three approaches for the

selection of sentinel sites are proposed and evaluated regarding their ability to capture

respiratory disease trends using provincial abattoir condemnation data for use in a

sentinel syndromic surveillance system. Consideration of how the sentinel abattoirs are

selected is important, as they are meant to represent the disease trends of all provincial

abattoirs. The objectives of this study were the following: (1) determine the suitability of

sentinel abattoirs for food animal syndromic surveillance using provincial abattoir

pneumonic lung portion data as an example; and (2) determine which sentinel abattoir

selection method best represented the pneumonic lung condemnation rate patterns from

all abattoirs operating throughout the study period.

Condemnation data for the bovine pneumonic lung classification from all

provincially-inspected abattoirs in Ontario were extracted from the Food Safety Decision

Support System (FSDSS) maintained by the Ontario Ministry of Agriculture and Food

(OMAF) from 2001 – 2007. Although there were 211 abattoirs processing cattle during

119

the study period, only 98 abattoirs were consistently operating throughout the study

period and were therefore used as the full set of abattoirs on which to make comparisons.

Pneumonic lung condemnation rates were calculated and plotted graphically. The

following three criteria to select sentinel abattoirs were compared to the full data

including: (1) abattoirs processing cattle all weeks of the year; (2) abattoirs processing at

least 6500 cattle per year; and (3) multi-criteria selection approach using data from

abattoirs that met a predefined set of criteria related to abattoir processing capacity,

animal classes represented, and operating over the entire study period. Negative binomial

regression models were used to compare monthly pneumonic lung condemnation rates

from the full set of abattoirs to the three sentinel abattoir selection methods for each

animal class, as well as to compare monthly pneumonic lung condemnation rates for each

sentinel selection approach while controlling for season, animal class and year to

determine if the overall patterns were similar between the sentinel selection approaches.

Descriptive graphs of pneumonic lung condemnation rates under the three sentinel

abattoir selection approaches showed some differences when compared to the

condemnation rates from the full set of abattoirs. However, all three approaches resulted

in the selection of groups of sentinel abattoirs that captured trends in condemnation rates

similar to those reported by the full set of abattoirs. Results of the negative binomial

regression models showed no difference between the 3 sentinel selection approaches and

the full set of abattoirs for calves, however, all selection approaches tended to

overestimate the condemnation rates of the full dataset by 1.4 to as high as 3.8 times for

cows, heifers and steers. The largest overestimation was consistently found with the

selection approach utilizing abattoirs processing at least 6500 cattle per year. In addition,

120

a negative binomial model found that the selection approach using abattoirs open all

weeks had similar temporal trends when compared to the full set of abattoirs.

Sentinel abattoirs show promise for integration into a food animal syndromic

surveillance system using Ontario provincial abattoir condemnation data. The selection of

sentinel abattoirs is extremely important, as they are meant to be representative of the

disease trends in the entire population. While all selection approaches tended to

overestimate the condemnation rates of the full dataset to some degree, the abattoirs open

all weeks selection approach appeared to best capture the overall seasonal and temporal

trends of the full dataset, and differences in condemnation rates among animal classes,

and would be the most suitable approach for sentinel abattoir selection. However, further

studies are needed to examine how the sentinel abattoir selection approaches perform

with simulated and/or historical outbreak data.

121

BACKGROUND

There are various approaches to conduct disease surveillance including sentinel

and syndromic surveillance. Sentinel surveillance is a form of surveillance which

involves a limited number of recruited participants or organizations, such as farms,

veterinarians, abattoirs, healthcare providers or hospitals, which report on certain health

events to give an indication of what may be happening in the general population [43].

Sentinel surveillance is a strategy used to sample timely data in a relatively inexpensive

manner rather than collect information on the general population, when population-based

data collection is unfeasible in a timely or cost-effective manner [44]. Syndromic

surveillance involves the amalgamation of signs/symptoms using data from non-

traditional data sources [46]; the signs/symptoms are grouped into classifications called

‘syndromes’ and are used to track disease trends in populations and signal a possible

outbreak that warrants further investigation [46].

Sentinel surveillance has been previously used in both human and animal health

settings for a variety of disease outcomes. Sentinel surveillance has been used to monitor

or identify outbreaks of infectious diseases and to monitor the activity of certain health

conditions which can change due to environmental conditions. Though used less often in

animal health applications than in human health, sentinel surveillance has been used

successfully for surveillance in various applications. For example, following the

emergence of Bluetongue virus serotype 8 in Central Europe in 2006, causing a large

scale outbreak in 2007 in several countries in Europe, a Bluetongue sentinel surveillance

program was established in Belgium in 2010. This surveillance program was intended to

demonstrate the absence of Bluetongue virus [70]. This program randomly selected a

122

total of 300 dairy herds, with 30 herds selected from each of the Belgian provinces. The

criteria for selection of herds was based on dairy herds that were expected to have a

minimum of 15 animals present between 4 and 12 months of age at the start of the

sentinel program [56]. Other studies have combined both sentinel and syndromic

surveillance and utilize data from sentinel veterinarians or veterinary practices [7, 49].

Animal health data including abattoir condemnation data have emerged recently

as a novel data stream for syndromic surveillance of diseases of animal and public health

importance [7, 9, 24, 53, 69, 71, 72]. Ontario provincial abattoir data have been recently

explored as a potential source of information for food animal syndromic surveillance [24,

69, 71]. However, there are approximately 100-150 provincial abattoirs in Ontario, many

of which are open sporadically and have differing processing capacities. It may be

beneficial to conduct targeted surveillance at select abattoirs in order to gather more

accurate data for syndromic surveillance in a cost-effective manner; sentinel surveillance

can be a cost-effective solution for disease surveillance. In addition, by selecting specific

sentinel abattoirs for inclusion in a sentinel syndromic surveillance system, it would

allow for more intensive and specialized training of inspectors for syndromic

surveillance. Furthermore, if sentinel abattoirs were selected properly, they could help to

reduce the cost of surveillance while still being representative of Ontario provincial

abattoirs. We proposed three sentinel site selection approaches and compared their ability

to detect respiratory disease trends in bovine abattoir condemnation data for use in a

sentinel-based syndromic surveillance system.

Pneumonic lung condemnation rates from all Ontario abattoirs processing cattle

throughout the 2001-2007 period were compared to those collected from sentinel sites

123

based on three selection approaches: (1) abattoirs processing cattle all weeks of the year;

(2) abattoirs processing at least 6500 cattle per year (based on data from abattoirs in the

upper 95th

percentile in processing capacity); and (3) multi-criteria selection approach of

abattoirs that met a predefined set of criteria related to abattoir processing capacity, and

animal classes represented.

The goal of the study was to design a sentinel surveillance system based on

provincially inspected abattoirs in Ontario. Specific objectives of this study were the

following: (1) determine the suitability of sentinel abattoirs for food animal syndromic

surveillance using provincial abattoir pneumonic lung condemnation data as an example;

and (2) determine which design is most efficient and representative for pneumonic lung

condemnation rate in terms of spatial distribution, temporal trends and relative

differences between animal classes when compared to data from all the abattoirs over the

study period.

METHODS

Data source

Pneumonic lung portion condemnation data were obtained from the Food Safety

Decision Support System (FSDSS) database maintained by the Ontario Ministry of

Agriculture, Food and Rural Affairs (OMAFRA). The database contains information

regarding the number and reason for daily organs/body systems condemnations in

provincially inspected abattoirs in Ontario. The condemnation category of pneumonic

lung was selected for these analyses, as this category represents a major health issue for

beef cattle and was among the most frequently reported portion condemnations by

provincial inspectors during the study period. Pneumonic lung condemnation refers to

124

bovine lungs which were condemned for lesions indicative of a previous localized and

resolved antero-ventral pneumonia infection (personal communication Abdul Rehmtulla,

DVM, OMAFRA, Stone Road, Guelph, Ontario).

Data were extracted from the Food Safety Decision Support System (FSDSS)

database for cattle animal classes: bulls, calves, cows, heifers, and steers from January 1,

2001 to December 31, 2007. Missing geographical coordinates for abattoirs were

approximated using postal codes and/or addresses with the address geocoding software

GeoPinpoint Suite 6.4 (DMTI Spatial Inc., Markham, Ontario, Canada). Using the

FSDSS database, further variables were created for each month: geographical coordinates

of abattoir, year, season, number of weeks an abattoir was operating each year, total

number of pneumonic lung condemnations, total number of cattle processed each year,

and animal class. Animal class included five categories: bulls, cows, calves, heifers, and

steers. Bulls were excluded from subsequent analyses due to missing data and

inconsistencies in the use of this classification. The number of weeks an abattoir was

operating each year was determined by the total number of weeks in which at least one

bovine animal was processed. The total number of animals processed each year was

calculated from the total number of condemned cattle plus the number of cattle fit for

consumption. The agricultural region where an abattoir was located was classified as:

central, eastern, northern, southern or western Ontario using the Ontario Census

Agricultural Region boundaries (Statistics Canada, Census Agricultural Regions, Census

year 2001). The regional location of each abattoir was determined using the point-in-

polygon technique with geographic information system software ArcGIS 9.2 (ESRI,

Redlands, California, USA). Abattoirs were excluded from subsequent analyses if the

125

abattoir did not slaughter at least 1 bovine animal for each year of the study period (2001

– 2007).

Descriptive analyses

Monthly bovine pneumonic lung condemnation rates were calculated using data

from all abattoirs slaughtering cattle during the study period of January 1, 2001 –

December 31, 2007. Condemnation rates were calculated by dividing the total number of

pairs of lungs condemned under the pneumonic lung classification each month by the

total number of slaughtered bovines for each animal class (e.g., calves, cows, heifers and

steers).

The number of abattoirs in operation during the study period varied from year to

year. There were 98 abattoirs consistently in operation during the study period and were

therefore used to represent the full set of abattoirs for this study. Three different design

approaches for a sentinel syndromic surveillance system were compared to all data from

the full set of 98 abattoirs. The first sentinel selection approach, which we refer to as

abattoirs open all weeks, uses data from abattoirs processing cattle 52 weeks per year.

The second approach, which we refer to as large abattoirs; uses data from abattoirs in the

upper 95th

percentile in processing capacity (processing at least 6500 cattle per year). The

third approach, which we refer to as multi-criteria approach, uses data from abattoirs that

met the following criteria for each year of the study period: processed at least 499 cattle

per year (representing the median total number of cattle processed based on data from all

211 abattoirs open from 2001 – 2007), processed at least 1 bovine carcass 44 weeks or

more each year (representing the median number of weeks cattle were processed based

126

on data from all 211 abattoirs open from 2001 - 2007), and processed cattle representing

all animal classes (calves, cows, heifers, and steers).

Data obtained using the three design approaches were summarized graphically

using the monthly condemnation rates of each animal class, as well as boxplots of the

rates. In addition, data were summarized in terms of the number of abattoirs included in

each selection approach, percentage of shared abattoirs between each selection approach,

and geographical representativeness of each selection approach according to the

distribution of abattoirs among census agricultural regions in Ontario.

Statistical analyses

A univariable negative binomial model was used to compare monthly pneumonic

lung condemnation rates from all sentinel site selection approaches and the full set of

abattoirs using a categorical variable for each of the 3 sentinel design approaches.

In addition, a separate multi-level negative binomial regression model was used with a

random intercept for abattoir using the xtnbreg command in Stata. This model allows the

random effect to follow a beta distribution and for the overdispersion parameter to vary

by abattoir. The negative binomial model was used to evaluate the association of monthly

condemnation rates of pneumonic lungs for each sentinel selection approach with year,

season and animal class. All covariates were evaluated for statistical significance

individually and then in a multivariable multi-level negative binomial model. Wald tests

were used to determine the significance of covariates. All covariates were forced into the

models regardless of univariable significance, due to the identification of these variables

127

as important predictors for pneumonic lung condemnation rates in a previous study [69]

and to evaluate overall temporal trends in the data.

For all regression models, the decision to use a negative binomial model instead

of a Poisson regression model was based on evaluating the Akaike Information Criterion

(AIC) value of both models and the significance of the over-dispersion term of the

negative binomial model. The offset of the negative binomial model was the natural log

of the total number of slaughtered cattle for the abattoirs included for each sentinel site

selection approach for each animal class and month-year period during the study period.

All statistical analyses were conducted using Stata 12 (Stata Corp., College Station,

Texas, USA).

RESULTS

Descriptive Statistics

There were a total of 211 provincial abattoirs slaughtering a total of 1,155,535

cattle from 2001-2007. However, there were only 98 abattoirs that remained in operation

for the entire study period and were used to represent the full set of abattoirs in this study.

This number is consistent with the current number of abattoirs processing cattle in

Ontario in 2014 [14]. During the study period there were 36,883 lungs condemned

representing approximately 9% of all portion condemnation and among the most

frequently reported condemnation in cattle. Further discussion of these data can be found

in a previous study by Alton et al.[69].

Three approaches for sentinel abattoir selection were compared to pneumonic

lung condemnation rates for each animal class from the full set of abattoirs (Table 5.1).

128

Abattoirs for each sentinel selection approach were chosen from a total of 98 abattoirs

processing cattle for the entire study period. The number of abattoirs selected for each

sentinel selection approach varied from 7 to 45. In assessing the percentage of overlap of

the selected abattoirs among the sentinel site selection approaches, the percentage of

shared abattoirs ranged from approximately 7% to 71%, with abattoirs open all weeks

and multi-selection criteria approaches having the highest percentage of abattoirs in

common (Table 5.2). All selection approaches led to surveillance systems based on

abattoirs representing all census agricultural regions across Ontario with the exception of

using the large abattoir criterion, which did not include abattoirs from eastern and

northern Ontario (Table 5.3).

The temporal condemnation rate graphs for all data indicate a gradual decrease in

pneumonic lung condemnation rates over time in calves from approximately 100

condemnations per 1000 slaughtered calves in 2001 to approximately 20 condemnations

per 1000 slaughtered calves in 2007 (Figure 5.1). In comparison, pneumonic lung

condemnation rates in cows, heifers and steers remained much more stable over the study

period (Figure 5.1). Boxplots of the monthly condemnation rates for the full set of

abattoirs compared to the 3 sentinel site selection approaches for each animal class had

similar distributions for calves, heifers and steers (Figure 5.2), with the exception of the

large abattoir selection approach, which tended to be have more variability when

compared to the other selection approaches and full set of abattoirs.

When pneumonic lung condemnation rates from the 3 sentinel site selection

approaches were graphically compared to condemnation rates from the full set of data,

some variation can be observed between the approaches, although similar overall trends

129

were present (Figures 5.3 – 5.6). Based on descriptive plots for calves (Figure 5.3), the

multi-criteria selection approach did not show a good fit with the full dataset, with

alternating trends of either overestimating or underestimating the condemnation rate of

the full dataset. However, the other two alternative selection approaches tended to

approximate the overall secular trends of the full dataset, but generally overestimated the

condemnation rates of the full dataset throughout the study period. Based on the

descriptive plots for cows, heifers and steers (Figures 5.4 – 5.6), all three design

approaches tended to overestimate the condemnation rate relative to the full set of data,

however, the large abattoir selection approach had the largest overestimation in all animal

classes.

Negative binomial models

We used a negative binomial regression model to compare the monthly

pneumonic lung condemnation rates from the 3 sentinel surveillance system selection

approaches for each animal class to the full dataset. The model involving calves (Table

5.4a) found a difference between the large abattoir selection approach and the full set of

data. In comparison, negative binomial modelling of cow data (Table 5.4b) found, that all

3 design approaches overestimated the condemnation rate by a factor of 2 to 4 times,

when compared to the full dataset. Similarly, relative to the full dataset, all the sentinel

selection approaches overestimated the condemnation rates for heifers (Table 5.4c) and

steers (Table 5.4d).

There were similar seasonal and temporal trends noted among all sentinel site

selection approaches and the full set of abattoirs (Tables 5.5a – d). However, the abattoirs

open all weeks sentinel selection approach tended to have coefficients and trends which

130

best resembled the seasonal and temporal trends of the full dataset. Abattoirs open all

weeks also tended to have the closest approximation of coefficients when compared to

the full dataset for the animal class variable.

DISCUSSION

Three sentinel site selection approaches for a sentinel syndromic surveillance

system using abattoir condemnation data were proposed and compared to the full set of

provincially inspected abattoirs in Ontario from 2001 - 2007. While all selection

approaches tended to overestimate the condemnation rates of the full dataset to some

degree, the abattoirs open all weeks selection approach appeared to best capture the

overall seasonal and temporal trends of the full dataset and would be the most suitable

approach for sentinel abattoir selection. This selection approach utilizes data from 45

abattoirs. It may be advantageous to conduct enhanced surveillance at carefully selected

sentinel abattoirs rather than at the approximately 100 abattoirs slaughtering cattle in

Ontario [14]. This allows for more intensive and specialized training of inspectors for

syndromic surveillance. Furthermore, if sentinel abattoirs were selected properly, sentinel

abattoirs could help to reduce the cost of surveillance while still being representative of

Ontario provincial abattoirs.

While there was not one design approach that perfectly fit the full dataset in all

analyses, the results of the descriptive and quantitative analyses found a fairly good fit

between abattoirs open all weeks and the full dataset. This sentinel selection approach

utilizes less than half of the abattoirs from the full set of abattoirs. By collecting data

from fewer abattoirs, the cost of data collection and analysis is reduced. This approach

also allows for the use of targeted training of inspectors to reduce any presence of

131

inspector bias on the data, which appeared to be a possible issue with these data based on

previous studies [24, 71]. The multi-criteria sentinel selection approach also utilizes data

from approximately the same number of abattoirs as the abattoirs open all weeks,

however, this approach had larger amount of overestimation for heifers and steers than

abattoirs open all weeks and did not approximate the overall seasonal, secular and animal

class condemnation trends of the full dataset as well as the abattoirs open all weeks. This

suggests that the number of abattoirs selected is not necessarily as important as the

criteria used to select the abattoirs. While the abattoirs open all weeks sentinel selection

approach drastically reduced the number of abattoirs needed to conduct syndromic

surveillance involving provincial abattoir condemnation data compared to the full dataset,

it is uncertain whether this reduction is sufficient and manageable to conduct intensive

and targeted surveillance. Further research is needed to determine if there are other

sentinel selection approaches that could reduce this number of abattoirs even further.

Geographical representativeness is also an important consideration when

establishing sentinel selection criteria. While the sentinel selection approaches based on

abattoirs that are open all weeks and multi-criteria are geographically representative, the

large abattoir selection approach is geographically over-representative of abattoirs in

central and southern Ontario and has no representation of abattoirs in northern and

eastern Ontario. This could pose an issue for syndromic surveillance, as abattoirs

generally receive animals from relatively local farms [24], and a lack of representation

from abattoirs in these regions could lead to the inability to identify emerging health

issues in these under-represented areas. The distribution of abattoirs among regions is

almost identical for abattoirs open all weeks selection approach and the full dataset. In

132

addition, region has been shown to be an important variable associated with partial and

whole carcass condemnations [24, 69] and excluding certain regions could bias the

results of spatio-temporal cluster detection methods for syndromic surveillance involving

these data.

This study found bovine provincial abattoir condemnation data to be suitable for

sentinel surveillance assuming it is a cost-effective option. However, this study did not

investigate outbreak detection, as there were no documented animal health outbreaks in

cattle during the study period. Consequently, further research is needed to assess the

effectiveness of sentinel selection approaches for detecting emerging health issues using

documented or simulated outbreak data of both respiratory diseases as well as other

disease syndromes important to both animal health and food safety.

CONCLUSIONS

Sentinel abattoirs show promise for integration into a food animal syndromic

surveillance system using Ontario provincial abattoir condemnation data. The selection of

the sentinel abattoirs is extremely important in order to maintain representativeness of the

disease trends in the entire population. The information extracted from surveillance

systems varied by its respective selection approach and for each animal class. While all

selection approaches tended to overestimate the condemnation rates of the full dataset to

some degree, the abattoirs open all weeks selection approach appeared to best capture the

overall seasonal and temporal respiratory condemnation trends of the full dataset and

would be most suitable approach for sentinel abattoir selection involving these data.

Further studies should examine the performance of the proposed sentinel selection

approaches using simulated data that include disease outbreaks for both respiratory

133

diseases as well as other condemnation syndromes relevant to animal health and food

safety.

ACKNOWLEDGEMENTS

The authors would also like to acknowledge the following organizations for their

support for infrastructure, data retrieval and funding for this project: Canada Foundation

for Innovation (CFI), the Ontario Research Fund, the Ontario Graduate Scholarship, and

Ontario Ministry of Agriculture & Food (OMAF).

134

REFERENCES

1. Salman MD. Animal Disease Surveillance and Survey Systems: /methods and

Applications: Ames, Iowa: Iowa State Press; 2003.

2. Lee LM, Teutsch SM, Thacker SB, St. Louis ME. Principles and Practice of Public

Health Surveillance: 3rd

Ed.New York: Oxford University Press; 2010.

3. Lawson AB, Kleinman K: Spatial and Syndromic Surveillance for Public Health:

Chichester, West Sussex ; J. Wiley; 2005.

4. Vangeel I, De Leeuw I, Meroc E, Vandenbussche F, Riocreux F, Hooyberghs J,

Raemaekers M, Houdart P, Van dS, De Clercq K: Bluetongue sentinel surveillance

program and cross-sectional serological survey in cattle in Belgium in 2010-2011.

Prev Vet Med 106:235-243.


syndromic surveillance system implemented for swine. CJVR 2010, 74:241-250.

7. Dórea F, Sanchez J, Revie CW: Veterinary syndromic surveillance: current

initiatives and potential for development. Prev Vet Med 2011, 101:1-17.

8. 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.




6:42.

10. Thomas-Bachli AL, Pearl DL, Friendship RM, Berke O. Suitability and limitations






12. O'Sullivan T, Friendship RM, Pearl DL, McEwen B, Dewey CE: Identifying an

outbreak of a novel swine disease using test requests for porcine reproductive and

respiratory syndrome as a syndromic surveillance tool. BMC Vet Res 2012, 8:192.

135

13. Palmer S, Sully M, Fozdar F: Farmers, Animal Disease Reporting And The Effect

Of Trust: A Study Of West Australian Sheep And Cattle Farmers. Rural Society

2009, 19:32-48.

14. Provincially Licensed Meat Plants

[http://www.omafra.gov.on.ca/english/food/inspection/meatinsp/licenced_operators_list.h

tm]



136

Table 5.1 – Summary of sentinel abattoirs selection approaches for Ontario provincial abattoirs (2001 – 2007)

Sentinel site

selection approach

Selection description Number of abattoirs

Full dataset Abattoirs processing at least 1 bovine carcass each year of study period

(2001 – 2007)

98

Abattoirs open all

weeks

Abattoirs processing at least 1 bovine carcass 52 weeks per year 45

Multi-criteria Selection of abattoirs meeting the following criteria:

processed at least 499 cattle per year, processed at least 1 bovine carcass

44 weeks or more per year, processed cattle representing all animal

classes (calves, cows, heifers and steers)

44

Large abattoirs Abattoirs processing ≥ 6500 cattle per year 7

137

Table 5.2 – Percentage of shared abattoirs between sentinel selection approaches1

Selection

approaches

Full dataset2

(N=98)

Abattoirs open

all weeks3

(N=45)

Selection

criteria4

(N=44)

Large

abattoirs5

(N=7)

Full dataset2

(N=98)

100% (98) 45.9% (45) 44.9% (44) 7.1% (7)

Abattoirs open

all

weeks3(N=45)

45.9% (45) 100% (45) 71.1% (32) 15.6% (7)

Selection

criteria4 (N=44)

44.9% (44) 71.1% (32) 100% (44) 6.8% (3)

Large

abattoirs5

(N=7)

7.1% (7) 15.6% (7) 6.8% (3) 100% (7)

1To calculate the percentage of shared abattoirs, the largest number of abattoirs was used as the

denominator 2Abattoirs processing at least 1 bovine carcass each year of study period (2001 – 2007)

3Abattoirs processing at least 1 bovine carcass 52 weeks per year

4Selection of abattoirs meeting the following criteria: processed at least 499 cattle per year,

processed at least 1 bovine carcass 44 weeks or more per year, processed cattle representing all

animal classes (calves, cows, heifers and steers) 5Abattoirs processing ≥ 6500 cattle per year

138

Table 5.3 – Distribution of provincial abattoirs 2001 – 2007 among census agricultural

regions in Ontario based on three sentinel selection approaches

Selection approach Number of

abattoirs

Central

Ontario

Eastern

Ontario

Northern

Ontario

Southern

Ontario

Western

Ontario

Full dataset1 98 18% (18) 15% (15) 7% (7) 36% (35) 23% (23)

Abattoirs open all

weeks2

45 18% (8) 13% (6) 7% (3) 38% (17) 24% (11)

Multi-criteria3 44 9% (4) 18% (8) 5% (2) 39% (17) 30% (13)

Large abattoirs4 7 29% (2) 0% (0) 0% (0) 43% (3) 29% (2)

1Abattoirs processing at least 1 bovine carcass each year of study period (2001 – 2007)

2 Abattoirs processing at least 1 bovine carcass 52 weeks per year

3 Selection of abattoirs meeting the following criteria: processed at least 499 cattle per year,


animal classes (calves, cows, heifers and steers) 4 Abattoirs processing ≥ 6500 cattle per year

139

Table 5.4 – Negative binomial regression models comparing monthly pneumonic lung

condemnation rates for all abattoirs for each animal class with three methods of sentinel

abattoir selection

Sentinel selection method IRR 95% CI P-value

a) Calves

Full dataset1 --- --- ---

Abattoirs open all weeks2 1.12 0.95 – 1.32 0.16

Multi-criteria3 0.90 0.76 – 1.06 0.21

Large abattoirs4 1.25 1.06 – 1.47 0.01

b) Cows


Abattoirs open all weeks2 2.08 1.49 – 2.91 < 0.01

Multi-criteria3 2.06 1.47 – 2.89 < 0.01

Large abattoirs4 3.83 2.73 – 5.38 < 0.01

c) Heifers



Multi-criteria3 1.70 1.42 – 2.04 < 0.01

Large abattoirs4 2.86 2.38 – 3.43 < 0.01

d) Steers



Multi-criteria3 1.48 1.28 – 1.72 < 0.01

Large abattoirs4 2.02 1.74 – 2.34 < 0.01

1Abattoirs processing at least 1 bovine carcass each year of study period (2001 – 2007)





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Table 5.5 – Multivariable multilevel negative binomial regression model examining seasonal

and annual variability in monthly pneumonic lung condemnations for three sentinel

selection approaches

Model IRR 95% CI P-value

a) Full dataset1

Winter --- ---- ---

Spring 0.97 0.85 – 1.11 0.61

Summer 0.90 0.78 – 1.03 0.12

Fall 0.89 0.78 – 1.03 0.11

2001 --- --- ---

2002 0.93 0.79 – 1.08 0.34

2003 0.99 0.85 – 1.16 0.93

2004 0.53 0.44 – 0.63 < 0.01

2005 0.45 0.38 – 0.54 < 0.01

2006 0.39 0.32 – 0.47 < 0.01

2007 0.32 0.26 – 0.40 < 0.01

Calves --- --- ---

Cows 1.05 0.87 – 1.27 0.60

Heifers 0.68 0.57 – 0.80 < 0.01

Steers 0.75 0.66 – 0.85 < 0.01

b) Abattoirs open all weeks2

Winter --- --- ---

Spring 1.01 0.88 – 1.16 0.93

Summer 0.92 0.80 – 1.05 0.22

Fall 0.91 0.79 – 1.05 0.22

2001 --- --- ---

2002 0.94 0.80 – 1.11 0.50

2003 1.03 0.88 – 1.21 0.68

2004 0.57 0.48 – 0.68 < 0.01

2005 0.46 0.38 – 0.55 < 0.01

2006 0.40 0.38 – 0.49 < 0.01

2007 0.34 0.28 – 0.42 < 0.01

Calves --- --- ---

Cows 1.03 0.84 – 1.26 0.75

Heifers 0.73 0.62 – 0.87 < 0.01

Steers 0.75 0.66 – 0.86 < 0.01

c) Multi-criteria3

Winter --- --- ---

Spring 1.08 0.92 – 1.28 0.33

Summer 0.95 0.80 – 1.12 0.51

Fall 0.92 0.78- 1.09 0.35

2001 --- --- ---

2002 1.03 0.84 – 1.27 0.76

2003 1.30 1.11 – 1.63 < 0.01

2004 1.04 0.85 – 1.28 0.72

2005 0.67 0.54 – 0.84 < 0.01

2006 0.67 0.49 – 0.79 < 0.01

141

2007 0.62 0.62 – 0.96 < 0.01

Calves --- --- ---

Cows 0.77 0.62 – 0.96 0.02

Heifers 0.62 0.51 – 0.75 < 0.01

Steers 0.71 0.61 – 0.82 < 0.01

d) Large abattoirs4

Winter --- --- ---

Spring 0.97 0.82 – 1.15 0.74

Summer 0.92 0.77 – 1.09 0.32

Fall 0.99 0.84 – 1.18 0.94

2001 --- --- ---

2002 1.38 1.11 – 1.72 < 0.01

2003 1.56 1.26 – 1.93 < 0.01

2004 0.97 0.77 – 1.22 0.78

2005 0.75 0.59 – 0.94 0.01

2006 0.64 0.50 – 0.82 < 0.01

2007 0.64 0.50 – 0.82 < 0.01

Calves --- --- ---

Cows 1.22 0.92 – 1.62 0.16

Heifers 1.02 0.83 – 1.25 0.86

Steers 0.87 0.74 – 1.03 0.10 1Abattoirs processing at least 1 bovine carcass each year of study period (2001 – 2007)





142

Figure 5.1 – Pneumonic lung condemnation rates per 1000 slaughtered cattle for each animal class for all abattoirs processing cattle

throughout the study period

05

01

00

150

05

01

00

150

2000

/07

2001

/07

2002

/07

2003

/07

2004

/07

2005

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2006

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2007

/07

2000

/07

2001

/07

2002

/07

2003

/07

2004

/07

2005

/07

2006

/07

2007

/07

CALVES COWS

HEIFERS STEERS

Co

nd

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na

tio

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ate

per

100

0 s

laug

hte

red

ca

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Year/month

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Figure 5.2 – Boxplots comparing the pneumonic lung condemnation rates per 1000 slaughtered cattle for each sentinel abattoir selection

approach

0 50 100 150 200 0 50 100 150 200

CALVES COWS

HEIFERS STEERS

Full dataset (N=98) Abattoirs open all weeks (N=45)

Multi-criteria (N=44) Large abattoirs (N=7)

144

Figure 5.3 – Comparison of pneumonic lung condemnation rates in calves for sentinel site selection approaches and full dataset 2001 -

2007

05

01

00

150

Co

nd

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per

100

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laug

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2001

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2003

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2004

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2005

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2008

/01

Year/month



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Figure 5.4 - Comparison of pneumonic lung condemnation rates in cows for sentinel site selection approaches and full dataset 2001 - 2007

05

01

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150

200

Co

nd

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100

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/01

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/01

Year/month



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Figure 5.5 - Comparison of pneumonic lung condemnation rates in heifers for sentinel site selection approaches and full

dataset 2001 - 2007

05

01

00

150

Co

nd

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na

tio

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ate

per

100

0 s

laug

hte

red

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ttle

2001

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2002

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2003

/01

2004

/01

2005

/01

2006

/01

2007

/01

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/01

Year/month



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Figure 5.6 - Comparison of pneumonic lung condemnation rates in steers for sentinel site selection approaches and full dataset 2001 - 2007

05

01

00

150

Co

nd

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na

tio

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per

100

0 s

laug

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ttle

2001

/01

2002

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2003

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2004

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2006

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Year/month



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CHAPTER SIX:

SUMMARY, DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS

Syndromic surveillance is currently being utilized in human health settings to

address the issue of timely identification of disease outbreaks [2, 73, 74]. Research in the

area of syndromic surveillance has recently emerged for application in animal health

surveillance [7-9, 50, 54, 75]. However, there has been little investigation into the

suitability of bovine abattoir condemnation data in Ontario for use in a food animal

syndromic surveillance system and the quantitative methods necessary for this type of

system.

In this thesis, I investigated factors that influence condemnation rates, the

performance of different cluster detection methods, and various approaches for selecting

abattoirs for sentinel surveillance. Overall, it was found bovine condemnation data from

provincially inspected abattoirs to be useful for food animal syndromic surveillance,

since they provided a more regionally detailed picture of emerging diseases in Ontario.

Specifically, it was found most cattle were shipped less than 100 km for slaughter at

provincial abattoirs [24]. This validated anecdotal evidence suggesting that cattle shipped

to provincial abattoirs originate from relatively local farms. Furthermore, disease-related

factors (e.g., age of cattle and seasonal trends) and non-disease factors (e.g., sales price of

cattle, abattoir audit rating) were shown to be associated with both whole carcass and

portion condemnations from cattle [24, 76]. Non-disease factors such as abattoir audit

rating and sales price were shown to have an impact on condemnation rates and improve

quantitative cluster detection methods by minimizing “noise” in the data, thus reducing

the probability for false-alarms. The effect this “noise” can have on quantitative cluster

149

detection methods was then demonstrated by comparing the results of various space-time

scan statistics with varying abilities to control for covariates [77]. The results from this

study suggested that model-adjusted approaches for controlling for covariates in scan

statistics appeared to perform best in terms of the ability to include all important

covariates and suitability for use with bovine abattoir condemnation data. Lastly,

methods to improve the efficiency of syndromic surveillance were investigated by

comparing the use of various sentinel abattoir selection approaches to select sentinel

abattoirs to reduce the number of sample sites, while still maintaining the trends of the

full dataset. The most effective sentinel selection approach was utilizing data from

abattoirs processing cattle all weeks of the year, and this approach shows promise for

integration into a food animal sentinel syndromic surveillance system.

While these findings suggest that bovine abattoir condemnation data would be

suitable for integration in a food animal syndromic surveillance system, there are some

limitations in the data which have become apparent through this thesis including data

quality issues and limitations with current methodological approaches. During this study,

data quality emerged as a potential barrier for the use of these data for syndromic

surveillance. Provincially inspected abattoirs in Ontario use lay meat inspectors. These

inspectors have the ability to partially condemn carcasses in provincial abattoirs without

direct veterinary oversight [78]. A similar study investigating the suitability and

limitations of provincially inspected porcine abattoir condemnation data found that while

provincial meat inspectors undergo rigorous classroom and field training programs, and

efforts are underway to improve standardization of inspection procedures across the

province, there may be great variability in classification of lesions by non-veterinary

150

inspectors leading to an increased recording bias between inspectors and individual

abattoirs [8]. This unique regulation, along with the subjective nature of meat inspection

makes observational and operator bias a potential concern when using abattoir

condemnation data for syndromic surveillance [8]. This study also found similar concerns

during the investigation of bovine condemnation data. Condemnation classifications used

by inspectors are subjective and can represent multiple pathologies causing

misclassification bias within these data. For example, “parasitic liver” is a condemnation

category which can represent a truly parasitic infection, as well as non-parasitic

conditions such as necrosis, fibrosis, cirrhosis, atrophy, telangiectasia and adhesions. This

could result in misclassification of condemnations and bias the results of cluster detection

methods involving these data. However, data are not currently available in Ontario to

identify and account for variation at the inspector level. To prepare for a future food

animal syndromic surveillance system, further research regarding specialized training of

inspectors and standardization of condemnation classification is essential.

Disease-related (e.g., older cattle and seasonal trends), and non-disease factors

(e.g., sales price of cattle, abattoir audit rating) were found to be important predictable

factors associated with both whole carcass and portion bovine condemnations in Ontario

[24, 76]. By controlling for these variables in quantitative methods for outbreak

surveillance, there would be a reduction of “background noise” in the data and reduce the

frequency of false-alarms. This reduction in data “noise” has been previously

demonstrated in a human health application for syndromic surveillance of influenza [48].

In the study, a model-adjusted version of the space-time scan statistic was applied for

syndromic surveillance of lower respiratory complaints in a human health care setting.

151

The study controlled for non-disease factors such as day of the week, month and holidays

and found that the number of false alarms could be reduced by removing the “noise” of

predictable covariates [48]. I also demonstrated the effect these covariates have on the

results of spatio-temporal scan statistics using various approaches for controlling for

confounding variables [77]. While it was suspected that the model-adjusted and model

residual adjustment approaches would be the most suitable for quantitative cluster

detection involving provincial abattoir condemnation data, there were some limitations

with these methods when used in practical applications. These methods are the most

versatile of the adjustment approaches, as they are able to account for both categorical

and continuous covariates, however, when the event is rare, as in the case of abattoir

condemnations, the precision of the residuals using the normal model is unstable and may

give inaccurate results for the model residual adjustment approach [77]. Thus, this

approach is not appropriate for the application of provincial abattoir condemnation data

unless counts are aggregated in space and/or time. The model-adjusted approach using

the ratio of observed versus expected condemnations under the Poisson model would be

more appropriate when utilizing relatively rare events, as in the case of abattoir

condemnation data. However, this approach, like the residual approach, also involves

more complex analyses than some of the other methodological approaches. In addition,

the multi-level model included temporal variables, such as year and season, which are

more conducive to retrospective analyses and would be difficult to account for

prospectively.

While bovine abattoir condemnation data were shown here to be suitable and

useful for food animal syndromic surveillance, a limitation of this thesis is the lack of a

152

documented bovine outbreak or simulated outbreak data to validate the results of our

work. It is recommended that further research and validation are conducted before the

methods suggested in this thesis are applied in practice. In addition, rather than using the

full complement of approximately 100 – 150 abattoirs, it may be advantageous to conduct

enhanced surveillance at carefully selected sentinel abattoirs. Abattoir selection methods

investigated in this thesis found that pneumonic lung condemnation rates from 45

abattoirs open all weeks of the year provided a good approximation of the condemnation

rates of all 98 abattoirs open throughout the study period. While the abattoirs open all

weeks sentinel selection approach strongly reduced the number of abattoirs needed to

conduct syndromic surveillance with these data, it is uncertain whether this reduction is

sufficient and manageable to conduct intensive and targeted surveillance. It is

recommended that further research is conducted to determine if there are other sentinel

selection approaches that could reduce this number of abattoirs even further.

In conclusion, the use of abattoir condemnation data is promising for integration

into a food animal syndromic surveillance system in Ontario. Research regarding this

under-utilized data resource continues to emerge worldwide using data from various

species [8, 33, 53, 54]. A study by Thomas-Bachli et al. investigated the suitability and

limitations of using abattoir condemnation data for syndromic surveillance of emerging

diseases of swine in Ontario [8]. This study found several clusters of high condemnation

rates for kidneys with nephritis in time and space-time which preceded the time frame

during which case clusters of PCV-2 were detected using traditional laboratory

surveillance. In addition, the trends found in kidney condemnation rates related to

nephritis lesions in Eastern Ontario were consistent with documented disease outbreaks

153

during that time period [8]. These findings further support the investigation of using

abattoir condemnation data for food animal syndromic surveillance. In addition, studies

in the United States have also utilized abattoir data for syndromic surveillance. Engle

[53] used condemnation data from the electronic Animal Disposition Reporting System

(eADRS) and found that a swine erysipelas outbreak in Iowa and Minnesota during July

2001 could have been identified approximately 10 months earlier if an automated

surveillance system had been in place. A similar study in the United States evaluated the

use of condemnation data from swine to set up an animal health monitoring system [53].

In 2014, Vial et al. [33] evaluated the use of Swiss condemnation data for cattle, pigs and

small ruminants to determine the suitability of these data for integration in a syndromic

surveillance system. This study investigated the patterns in the number of animals

slaughtered and condemned, reasons for whole carcass condemnations and reporting

biases and regional effects of condemnation rates. While the study found these data

provided simultaneous coverage of cattle, pigs and small ruminants for the entire country,

as well as traceability of each condemnation to its farm of origin, there were issues with

the lack of timeliness of reporting (30-60 day delays between condemnation and

notification); a delay which currently limits the use of these data for early detection in

Switzerland [33]. In contrast, a study in France utilizing slaughterhouse data from cattle

concluded that it was possible to define groups of underlying reasons for condemnation

of portions and encouraged the increased use of meat inspection data for syndromic

surveillance [54]. The recent emergence of research studies, including this thesis,

investigating the use of abattoir condemnation data for syndromic surveillance reinforces

154

the potential importance these data may have for food animal disease surveillance and the

need for continued research in this area.

While the findings from this thesis support the use of bovine abattoir

condemnation data for integration into a food animal syndromic surveillance system, the

Ontario Ministry of Agriculture and Food (OMAF) should consider supporting further

research involving these data before formalizing a food animal syndromic surveillance

system in Ontario. Specifically, I would recommend improving the standardization of

abattoir condemnation classifications to improve the quality of these data. In addition, it

is recommended that model-adjusted space-time scan statistics be applied in a

prospective manner without the use of retrospective variables such as year to evaluate its

performance. Supplementary investigation of sentinel selection criteria to determine if the

number of sentinel abattoirs could be further reduced is also encouraged. In addition,

validation of methods discussed in this thesis using documented outbreak or simulated

data are strongly recommended before a formal food animal syndromic surveillance

system is established. Finally, it should be remembered that a valuable contribution of

surveillance beyond rapid detection of disease, is the value of health in support of trade.

Research investigating the use of advanced methods for disease surveillance in Ontario

and Canada demonstrates and reassures Canada’s trade partners how closely animal

health is monitored from various sources and Canada’s potential leadership in advancing

food animal surveillance.

155

REFERENCES

1. Edge VL, Pollari F, Ng LK, Michel P, McEwen SA, Wilson JB, Jerrett M, Sockett PN,

Martin SW: Syndromic surveillance of Norovirus using over-the-counter sales of


17:235-241.

2. Edge VL, Pollari F, Lim G, Aramini J: Syndromic surveillance of gastrointestinal

illness using pharmacy over-the-counter sales: A retrospective study of waterborne

Ouutbreaks in Saskatchewan and Ontario. Can J Public Health 2004, 95:446-446-50.

3. Steiner-Sichel L, Greenko J, Heffernan R, Layton M, Weiss D: Field Investigations of

Emergency Department Syndromic Surveillance Signals - New York City. MMWR

2004 :184-184-9.

4. O'Sullivan T, Friendship R, Pearl D, McEwen B, Dewey C: Identifying an outbreak

of a novel swine disease using test requests for porcine reproductive and respiratory

syndrome as a syndromic surveillance tool. BMC Vet Res 2012, 8:122.

5. Dupuy C, Morignat E, Maugey X, Vinard J-L, Hendrikx P, Ducrot C, Calavas D, Gay

E: Defining syndromes using cattle meat inspection data for syndromic surveillance

purposes: a statistical approach with the 2005-2010 data from ten French

slaughterhouses. BMC Vet Res 2013, 9:88.

6. Metre DC, Barkey DQ, Salman MD, Morley PS: Development of a syndromic


market. J Am Vet Med Assoc 2009, 234:658-664.


syndromic surveillance system implemented for swine. CJVR 2010, 74: 241-251.

8. Dórea FC, Muckle CA, Kelton D, Mcclure JT, Mcewen BJ, Mcnab WB, Sanchez J,

Revie CW: Exploratory analysis of methods for automated classification of

laboratory test orders into syndromic groups in veterinary medicine. PloS One

8(3):e57334.







6:42.

156

11. Alton G, P., Alton GD, Pearl DL, Bateman KG, McNab WB, Berke O: Suitability of

bovine portion condemnations at provincially-inspected abattoirs in Ontario

Canada for food animal syndromic surveillance. BMC Vet Res 8:88.

12. Alton GD, Pearl DL, Bateman KG, Mcnab B, Berke O, Alton GD, Pearl DL,

Bateman KG, McNab B, Berke O: Comparison of covariate adjustment methods

using space-time scan statistics for food animal syndromic surveillance. BMC Vet Res

9:231.

13. Food Safety and Qality Act, 2001 [http://www.e-

laws.gov.on.ca/html/statutes/english/elaws_statutes_01f20_e.htm]



133:409-419.

15. Vial F, Reist M: Evaluation of Swiss slaughterhouse data for integration in a

syndromic surveillance system. BMC Vet Res 10:33.




http://www.e-laws.gov.on.ca/html/statutes/english/elaws_statutes_01f20_e.htm

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