Prevalence Estimation and Geographic Distribution of Scrapie in the Canadian

Sheep Population via Abattoir Surveillance

By

Jue Tang

A Thesis

Presented to

The University of Guelph

In partial fulfillment of requirements

for the degree of

Master of Science in

Population Medicine

Guelph, Ontario, Canada

○c Jue Tang, June, 2014

ABSTRACT

PREVALENCE ESTIMATION AND GEOGRAPHIC DISTRIBUTION OF SCRAPIE IN THE

CANADIAN SHEEP POPULATION VIA ABATTOIR SURVEILLANCE

Jue Tang Advisor:

University of Guelph, 2014 Dr. Olaf Berke

Classical scrapie, a federally reportable disease in Canada, is a fatal neurodegenerative

disease of sheep and goats. In order to inform future scrapie eradication programs for Canada, a

study estimating the national prevalence of scrapie was conducted from Nov 2010 to Dec 2012;

seven cases were detected among 11,702 sheep. The prevalence at the individual level is

estimated to be 0.06% (CI from 0.03% to 0.12%); at the farm-level it is estimated to be 0.22%

(CI from 0.11% to 0.45%).

A sampling information index was developed which measures the available sampling

information at the Census Division (CD) level. A choropleth map is used to show the spatial

distribution of this index. CDs with a low information index value cluster in the West Coast, the

southern border between British Columbia and Alberta, southern Manitoba, northern Ontario and

the Atlantic Provinces. These areas should be targeted in future surveillance activities.

iii

ACKNOWLEDGEMENTS

I would like to thank my committee members, Dr. Olaf Berke and Dr. Paula Menzies, for

their guidance in the completion of this thesis. Dr. Berke, I appreciate your time and effort in

teaching and guiding me through this project, especially the encouragement to complete my

work. Dr. Menzies, thank you for your knowledge of the Canadian sheep industry and the

etiology of scrapie, which was invaluable in the completion of this project.

I would like to thank Heather Brown from the Canadian Food Inspection Agency (CFIA)

for compiling the data and continuously tracing back the origins of sheep whenever I

encountered problems. This project could not have been completed without your data support.

As well, I would like to acknowledge Dr. Hernan Ortegon from Alberta Agriculture and Rural

Development (AARD) for the data compiling, which made this project possible. I would also

like to acknowledge Agriculture Canada and the Canadian Sheep Federation for generously

providing the funding for this project.

Sincere thanks to all the colleagues and friends who provided help on data analysis,

namely Michelle Edwards and Lucia Constanzo from the Data Resource Center, University of

Guelph Library, Bimal Chhetri, Theresa Procter, Jessica Cha, and Herbert Tang. I would like to

thank you for replying to my never ending emails of questions and assisting me through all the

off- hour communications.

To my friends and colleagues who helped me editing the thesis, I understand the time it

took to edit probably was as long as my writing time. Thank you for sacrificing your own time to

read my thesis and taking the time to understand and polish every single sentence. Special thanks

to the ones who have accompanied me during my driving trips between Toronto and Guelph.

iv

Thanks for keeping me awake on the way and making the crazily crowded 401 driving

experiences not lonely but enjoyable.

Lastly, I want to thank my family members for all the care and love you have spoiled me

with, even with all the conflicts I have caused constantly. Thanks to my cats Snow and Pumpkin

who have never said that they regretted joining my crazy life, and kept my papers safe and warm

by lying on them. My greatest love and thanks go to my parents. I felt I could go wrong and

explore the world in any way because you would always protect me. Thanks for letting me

become the person I want to be. Thank you!

v

STATEMENT OF WORK DONE

Health status data from sheep sampled at abattoirs across Canada and diagnosed at

Canadian Food Inspection Agency (CFIA) laboratories were provided by the CFIA in electronic

format. This data came over time in form of quarterly samples. I compiled all the data sets from

different time periods into one data set, reviewed the accuracy of data entry, identified missing

values as well as double entries and corrected where necessary and possible. Any changes to the

original data were made in consultation with the CFIA in person of Heather Brown. The cleaned

data was then subjected to statistical analysis using R 3.1.0 (R development team, 2014). I

performed all statistical data analysis on my own.

The computer program to estimate the stratified Wilson Confidence Interval in chapter 2

was programmed independently by Bimal Chhetri and me in R and the results were confirmed by

Herbert Tang via Matlab. The results of the two programs agreed exactly.

In order to construct the choropleth map for chapter 3, postal codes information was

converted into Census District information with the help of Michelle Edwards from the Data

Resource Center, University of Guelph Library. However, some of the postal codes turned out to

be corresponding to multiple CD regions. I identified the problematic postal codes. After

consultation with Heather Brown and a CFIA GIS specialist, we had those data records revised

using exact farm addresses. They were disclosed to me subject to confidentiality agreements.

vi

Table of Contents ABSTRACT ...................................................................................................................................... ii

ACKNOWLEDGEMENTS................................................................................................................ iii

STATEMENT OF WORK DONE ....................................................................................................... v

LIST OF TABLES ............................................................................................................................ ix

LIST OF FIGURES ............................................................................................................................ x

Chapter 1 Literature review and objectives ...........................................................................................1

1.1 Introduction ..............................................................................................................................1

1.2 Scrapie......................................................................................................................................2

1.2.1 Clinical signs and transmission ............................................................................................4

1.2.2 Etiology and Pathogenesis ...................................................................................................4

1.2.3 Genetics of scrapie ..............................................................................................................5

1.2.4. Diagnosis ..........................................................................................................................7

1.2.5. The relation of scrapie to livestock TSEs ........................................................................... 10

1.3 The Canadian sheep industry .................................................................................................... 14

1.3.1 Canadian sheep population ................................................................................................ 15

1.3.2 Slaughter plants ................................................................................................................ 16

1.4. Scrapie surveillance, control and prevention ............................................................................. 17

1.4.1. Scrapie surveillance ......................................................................................................... 17

1.4.2. Current scrapie control and prevention .............................................................................. 20

1.5 Methodology for epidemiological studies .................................................................................. 22

1.5.1 Sampling methods............................................................................................................. 22

1.5.2 Prevalence estimation........................................................................................................ 25

1.5.3 Confidence interval ........................................................................................................... 26

1.6 Study rationale and objectives .................................................................................................. 26

1.6.1 Objectives ........................................................................................................................ 27

1.7 References .............................................................................................................................. 29

Chapter 2: Prevalence Estimation of Scrapie in the Canadian Sheep Population by Active Surveillance in

Animals at Slaughter......................................................................................................................... 42

Abstract........................................................................................................................................ 42

2.1 Introduction ............................................................................................................................ 42

2.2 Materials and methods ............................................................................................................. 44

vii

2.2.1 Study design ..................................................................................................................... 44

2.2.2 Sampling .......................................................................................................................... 45

2.2.3 Diagnostic testing ............................................................................................................. 48

2.2.4 Data management ............................................................................................................. 49

2.2.5 Prevalence and confidence interval estimation .................................................................... 49

2.3 Results .................................................................................................................................... 54

2.4 Discussion .............................................................................................................................. 56

2.5 Conclusion .............................................................................................................................. 60

2.6 Acknowledgements ................................................................................................................. 61

2.7 References: ............................................................................................................................. 62

Chapter 3: Choropleth mapping the sampling intensity of a Canadian scrapie prevalence study .............. 66

Abstract........................................................................................................................................ 66

3.1 Introduction ............................................................................................................................ 67

3.2 Materials and methods ............................................................................................................. 68

3.2.1 Study design ..................................................................................................................... 68

3.2.2 Sampling procedures ......................................................................................................... 69

3.2.3 Data management ............................................................................................................. 70

3.2.4 Sampling information index............................................................................................... 71

3.2.5 Choropleth mapping .......................................................................................................... 73

3.3 Results .................................................................................................................................... 74

3.4 Discussion .............................................................................................................................. 76

3.5 Conclusion .............................................................................................................................. 79

3.6 Acknowledgements ................................................................................................................. 79

3.7 References .............................................................................................................................. 80

Chapter 4: General Summary and Conclusion ..................................................................................... 87

4.1 Motivations for conducting scrapie surveillance ........................................................................ 87

4.2 Review of the results ............................................................................................................... 88

4.3 Implications of the study .......................................................................................................... 89

4.4 Strengths and limitations .......................................................................................................... 92

4.5 Conclusion .............................................................................................................................. 94

4.6 References .............................................................................................................................. 95

Appendix A: Stratified Wilson Confidence Interval calculation R code, using sheep level data .............. 97

viii

Appendix B: Stratified Wilson Confidence Interval calculation Matlab code, using sheep level data .... 101

Appendix C: Scrapie surveillance sampling data ranking by index values ........................................... 104

ix

LIST OF TABLES

Table 1.1 .............................................................................................................................. 34

Sheep flocks infected with scrapie in Canada: 1984 – 2011 (CFIA, 2012b)

Table 1.2 .............................................................................................................................. 35

Scrapie genotyping risk groups categorized by UK (Baylis et al., 2001)

Table 1.3 .............................................................................................................................. 36

Canadian sheep population as of January 1, 2013, by province (Statistics Canada, 2013)

Table 2.1 .............................................................................................................................. 62

Prevalence estimation at sheep-level of classical scrapie in 10 provinces of Canada (data

from November 2010 to December 2012).

Table 2.2 .............................................................................................................................. 63

Prevalence estimation at farm-level of classical scrapie in 10 provinces of Canada (data

from November 2010 to December 2012).

x

LIST OF FIGURES

Figure 1.1 ............................................................................................................................ 37

Sheep population from 2000 to 2011 as of July 1st of each year (in thousands of heads)

(Stats Canada, 2011)

Figure 1.2 ............................................................................................................................ 38

Canada’s sheep inventories as of January 1, 2013 and July 1, 2012 (Statistics Canada,

2013)

Figure 1.3 ............................................................................................................................ 39

Figure 1.3 Estimates of prevalence of infection of classical scrapie in the sheep

population in Great Britain for the period 2002 -2011 with 95% confidence intervals

(Ortiz-Pelaez et al., 2012)

Figure 3.1 ............................................................................................................................ 79

Choropleth map of Canadian provinces based on the sampling information index of

scrapie study, using Azimuthal equidistant projection: CDs having no samples collected

from are outlined in purple; CDs having no sheep farms recorded are outlined in blue.

CDs with an index less than 0.1 are outlined in black.

Figure 3.2 ............................................................................................................................ 80

Enlargement of Figure 3.1: Choropleth map of sampling information index for British

Columbia and Alberta. CDs having no samples collected from are outlined in purple;

CDs having no sheep farms recorded are outlined in blue. CDs with an index less than 0.1

are outlined in black.

Figure 3.3 ............................................................................................................................ 81

Enlargement of Figure 3.1: Choropleth map of sampling information index for

Saskatchewan and Manitoba. CDs having no samples collected from are outlined in

purple; CDs having no sheep farms recorded are outlined in blue. CDs with an index less

than 0.1 are outlined in black.

Figure 3.4 ............................................................................................................................ 82

Enlargement of Figure 3.1: Choropleth map of sampling information index for Ontario.

CDs having no samples collected from are outlined in purple; CDs having no sheep farms

recorded are outlined in blue. CDs with an index less than 0.1 are outlined in black.

Figure 3.5 ............................................................................................................................ 83

xi

Enlargement of Figure 3.1: Choropleth map of sampling information index for Quebec

and the Atlantic Provinces. CDs having no samples collected from are outlined in purple;

CDs having no sheep farms recorded are outlined in blue. CDs with an index less than 0.1

are outlined in black.

1

Chapter 1 Literature review and objectives

1.1 Introduction

Scrapie is a transmissible neurodegenerative disease in sheep and goats. It is one of a

group of diseases known collectively as transmissible spongiform encephalopathies (TSEs),

which are characterized by findings of abnormal prion protein in the central nervous system.

Examples of TSE diseases include bovine spongiform encephalopathy (BSE) in cattle, chronic

wasting disease (CWD) in elk and deer, and Creutzfeldt-Jakob disease (CJD) and kuru in

humans. There is no effective treatment for scrapie, as for any of the other TSEs, thus leading to

a 100% case fatality rate if left to develop.

Scrapie has been diagnosed in sheep and goats in many countries around the world

(Hunter and Cairns, 1998). The long incubation of the disease, its infectious nature, and regular

animal movement require rapid and accurate tracing of the origin and movement of infected

animals, as well as correct classification of the scrapie status of flocks and herds in order to

achieve scrapie control. A sheep and goat identification (ID) system which allows for complete

traceability of livestock such as is in place for the UK (Department for Environment Food and

Rural Affairs, 2010) and Quebec (CFIA, 2012c) is therefore required. Currently, the mandatory

national sheep ID program (Canadian Sheep Identification Program) is in transition to a traceable

system. The national ID system for goats is voluntary. Canada’s national scrapie surveillance

program and data collection have not kept pace with that of other countries.

Even though the incidence of scrapie in Canada, as reported by Statistics Canada, has

been historically low (Table 1.1), the Canadian government has been recommended by the

scrapie eradication steering committee, which is made up of producers, industry groups,

academia and government agencies, that the eradication of scrapie needs to be planned and

2

achieved in the very near future in Canada (Scrapie Canada, 2014). In part, this is motivated by a

concern that scrapie may have been the cause of the BSE epidemic in the UK (Wilesmith et al.,

1991). When BSE was diagnosed in a Canadian cattle herd, a ban on importation of Canadian

sheep, goat and cattle products was announced by the US government on May 20, 2003 (Library

of Parliament, 2012) resulting in a loss of markets and income for the small ruminant industries

of Canada. Currently, animals less than one year of age may cross the border but must be

slaughtered prior to turning one year of age. Breeding stock may not be exported to the US (CSF,

2011a). As this restriction will not be lifted until Canada is proven to be scrapie free according to

OIE standard, determining the prevalence and distribution of scrapie in Canada is critical.

Therefore, this study examines both the individual level and farm level prevalence of scrapie in

Canadian sheep population.

The following sections of this chapter will describe scrapie in sheep in more detail along

with the Canadian sheep industry, current scrapie distribution, some statistical methods used in

epidemiology studies, and objectives of the project.

1.2 Scrapie

The pathodiagnostic classifications of scrapie are “classical” and “atypical”. In

publications and in this thesis, the word “scrapie” refers to classical scrapie unless specifically

noted. Classical scrapie has been reported since the 18th century (Woodhouse et al, 2001). Its

highly contagious nature has resulted in a world-wide distribution with few countries categorized

by the World Animal Health Association (OIE) as “scrapie-free”. However, recent studies have

shown the prevalence in various regions of scrapie is low. For example, in Finland, there have

been no cases detected from 2002 to 2008 (Hautaniemi et al., 2012). A study in France estimated

the prevalence of classical scrapie to be 0.44% (Vergne et al., 2012). In Great Britain, the flock-

3

level prevalence of classical scrapie declined by around 40% between 2003 and 2007 (from 0.6-

0.7% to 0.3-0.4%), as a result of various national mandatory control schemes (Gubbins and

McIntyre, 2009). The most recent European Commission’s annual report for 2011 from all its 27

member-states reported a prevalence of 0.38% (1,416 ovine positive of 369,417 ovine tested)

(The European Commission, 2012). In the United States 2002-2003, the prevalence was

estimated at a level of 0.2% (United States Department of Agriculture, 2004).

A new variant of scrapie, atypical or Nor98, was first described in Norway in 1998

(Benestad et al., 2008). Even though clinical signs are similar, the prion protein (PrP) lesions are

different from that of classical scrapie (Green et al, 2007). Involvement of the dorsal motor

nucleus of the vagus nerve (DMNV) is the primary finding in classical scrapie but is not affected

in case of atypical scrapie (Benestad et al., 2008). Additionally, sheep genetically resistant to

scrapie and the cattle BSE agents (i.e. genotypes ARR/ARR and ARR/ARQ), do not appear to be

similarly resistant to atypical scrapie (Andréoletti, 2011). So far, atypical scrapie cases have been

identified worldwide, including several in New Zealand, which is considered free of classical-

scrapie (Saunders et al, 2006). Epidemiological studies have indicated that atypical scrapie does

not conform to the behaviour of an infectious disease; rather it has been proposed that it is a

spontaneous disease (Benestad et al., 2008) and has been found to have restricted abilities to

spread into the environment or between individuals (Andréoletti, 2011). Therefore, as atypical

scrapie is not a major concern in infectivity studies, this study focuses only on classical scrapie.

The first recorded scrapie case in Canada was described in 1938 in a Suffolk sheep, a ewe

imported from Britain (Greenwood, 2002). As of 1945, scrapie became a reportable disease in

Canada under the Health of Animals Act, which means any suspected case must be reported to

4

the CFIA (Canadian Food Inspection Agency, 2012e). At this time, a national control program

was instituted.

1.2.1 Clinical signs and transmission

The term “scrapie” refers to the behaviour of affected animals scraping and rubbing their

coats against fences and walls due to intense pruritus. Clinical signs are not limited to pruritis,

however; signs of scrapie infection include chronic weight loss with reduced appetite, wool

pulling, biting at limbs or side, ataxia and hypermetria, sensitivity to noise and movement,

occasional blindness, tremor and eventually inability to stand (Foster et al., 2001). Scrapie has a

long incubation period prior to a sheep developing clinical signs, from a minimum of 1 year up

to several years. The majority of infected animals show clinical signs between the ages of 2 and

4 years; duration of disease is generally one to two months after the disease onset with a 100%

case fatality rate (Detwiler, 1992).

The disease agent can be transmitted both horizontally to sheep and goats in the same

environment, and vertically to offspring (Woolhouse et al., 1999). Usually it is spread from an

infected female’s placental tissue and birth fluids to her offspring at birth, and to other animals

exposed to the same birth environment, most susceptible are the lambs / kids born in the same

cohort (Wiggins, 2009). Researchers also found scrapie agent present in colostrum and milk

from infected sheep and goats (Lacroux et al., 2008), this may be a source of infection to young

stock. Although rams develop scrapie, they do not transmit the disease agent.

1.2.2 Etiology and Pathogenesis

Research has shown that animals affected by scrapie have characteristics very similar to

Creutsfeldt – Jakob disease (CJD) and kuru in human beings (Bendheim, 1985). When bovine

spongiform encephalopathy (BSE) was confirmed in 1986 (Becher et al., 2008), researchers

5

examined these diseases which showed similar clinical signs and discovered that the pathological

characteristics of kuru, CJD, BSE and scrapie were very similar, i.e. spongy degeneration in the

brain and spinal cord of the infected individual and that accumulations of proteinaceous material

filled the holes of these cavernous bodies. These diseases were subsequently identified as

transmissible spongiform encephalopathies (TSE’s). The word “spongiform”, refers to the

characteristic spongy appearance of the brain cortex that results from tiny holes seen when the

cortex tissue in the brain is viewed histologically.

The accumulations of protein that cause the spongiform appearance of the cortex were

found to be an abnormal form of the prion protein. Therefore, TSEs are also called “prion

diseases”. The word “prion” was named by Stanley B. Prusiner in 1982 as an abbreviation for

“protein of infection” (Kim, 2007). Specifically, abnormal prion protein (PrPSc) is a malformed

protein of its precursor isoform cellular prion protein (PrPC), and behaves like an infectious agent

that causes the normal proteins to misfold when replicating (Benestad et al, 2008). The prion is a

distinct form of infectious agent because it contains neither DNA nor RNA as do viruses,

bacteria, fungi and parasites (Kim, 2007). Currently, the pathogenesis of prion diseases is not

completely understood and no effective treatment is available. What causes the protein to change

to a neurodegenerative prion is unknown, but it is commonly accepted that scrapie is caused by

an infectious agent and that the genetic make-up of the exposed animal determines the

development of the disease (Woolhouse et al., 1999).

1.2.3 Genetics of scrapie

The presence of scrapie-resistant genotypes in sheep populations provides an opportunity

to control the disease through selectively breeding those sheep carrying the resistant genotypes.

Polymorphisms in the amino-acid sequence of PrP gene play a significant role in determining

6

whether individual sheep are susceptible or resistant to scrapie after being exposed to the

transmissible agent (Baylis et al., 2000). Three polymorphisms have been identified in sheep as

determining resistance or susceptibility: valine (V) or alanine (A) at codon 136; arginine (R) or

histidine (H) at codon 154; and glutamine (Q), arginine (R) or histidine (H) at codon 171 (Baylis

et al., 2000). Among those, V at codon 136, F at codon 154, and Q and H at codon 171 are

linked to susceptibility while the rest are linked to resistance to the scrapie agent (Baylis et al.,

2004). Although there are 12 possible genotype combinations, only 5 occur naturally with any

frequency: ARR (short notation for A136R154R171), ARQ, AHQ, ARH and VRQ, which in

different combinations can result in 15 common genotypes in sheep as there are two alleles at

one codon (Baylis et al., 2004).

Among all the naturally occurring genotypes, researchers have found that sheep of

genotype ARR/ARR appear to be the most resistant to scrapie, whereas sheep of VRQ/VRQ

genotype are highly susceptible (Belt et al., 1995). Due to the fact that the latter is a very rare

genotype and extremely susceptible to scrapie, it was hypothesized that scrapie may be primarily

a genetic disease and animals with this genotype would invariably develop scrapie regardless of

environmental exposures (Parry, 1983). However, sheep with the VRQ/VRQ genotype were

later proven to be able to live a normal life-span in a scrapie-free environment supporting the

hypothesis that an infectious agent was indeed necessary to cause scrapie (Foster, 2006). The

classification adapted by Department of Environment, Food and Rural Affairs (DEFRA) in UK

of each genotype’s risk of developing disease when exposed to the scrapie agent is shown in

Table 1.2 (Baylis et al., 2001; Baylis et al., 2004). The classification used by the CFIA ignores

the codon at 154; the most resistant genotypes are (136AA 171RR) and (136AA 171QR); while

the most susceptible as (136AA 171QQ), (136AV 171QQ), (136VV 171QQ) and (136AV

7

171QR) (CFIA, 2012d). Within some genotypes, the level of prevalence of susceptible

genotypes varies, however, based on different regions, farm types (hill, upland or lowland), flock

types (pedigree, pure-bred or commercial) and sheep breeds (Baylis et al., 2004).

1.2.4. Diagnosis

Scrapie can be diagnosed by biopsy of affected tissues, or blood-based assays in live

sheep or by necropsy in deadstock (deaths on farm). In live sheep, scrapie can be detected by the

age of 14 months (O’Rourke et al., 2002). Researchers have been diagnosing scrapie based on

the accumulation of PrPSc in biopsy of lymphoid tissues of the tonsil or third eyelid (O’Rourke et

al., 2002). More recently, recto-anal mucosa associated lymphoid tissues (RAMALT) have been

used to indicate an infection of scrapie in sheep. In a sheep infected with scrapie, PrPSc

accumulates more in RAMALT than in lymphoid tissue samples from other body parts. For this

reason as well as the convenient accessibility of RAMALT for sampling, it is considered a better

screening test compared to third eyelid lymphoid tissues (Dennis et al., 2009). After the samples

are collected, immunohistochemistry (IHC) staining is performed, by which antibodies bind

specifically to scrapie antigens. The sensitivity (Se) of the IHC tests using RAMALT was found

to be not significantly different from the one using the third eyelid (Dennis et al., 2009). In this

study, the Se of IHC testing for PrPSc in RAMALT collected from live sheep was between

85.3%and 89.4%, depending on the site from which RAMALT was obtained, compared to 87%

Se for eyelid tissues. The reference test system used is the results from necropsy diagnosis using

either tonsil and brain tissue or retropharyngeal lymph nodes, or both considered in parallel.

Blood-based assays in live animals are also used in the diagnosis of scrapie. It is difficult

to detect the infection in the early stages because of much lower PrPSc concentrations found in

blood compared to brain (Everest et al., 2007). An example of such a test is the immunocapillary

8

electrophoresis (ICE, or capillary electrophoresis fluoroimmunoassay). The test appears to be

capable of detecting the presence of PrPSc in blood with low PrPSc concentrations (Everest et al.,

2007). However, the sensitivity (Se) and specificity (Sp) of the test vary greatly: the range is 0%

to 66.7% for Se and 66.7% to 100% for Sp (Everest et al., 2007). Due to the low test

performance, especially low sensitivity, this test is not used as often as the IHC assay.

There are some weaknesses in detecting scrapie in live animals. Although the methods

applied to detect scrapie in live sheep have previously detected PrPSc in some subclinically

affected sheep, they have technical drawbacks and cannot be used to screen large populations of

sheep in the field (González et al., 2006). In addition, some PrPSc genotypes do not accumulate

PrPSc in their lymphoid tissues, or do so weakly or inconsistently (van Keulen et al., 1996;

Schereuder et al., 1998; Andreoletti et al., 2000). There is a higher accumulation of PrPSc in the

obex tissue which is available for collection only in dead animals.

Scrapie diagnosis in dead animals is more accurate and is the most commonly used

method, regardless of whether the animal has succumbed to scrapie or for another reason –

including slaughter of healthy animals. It can be accomplished using one or more of the

following tests: ELISA (enzyme-linked immunosorbent assay); IHC; western blot (WB); and

luminescence immunoassay (LIA). Tissues sampled at necropsy are the obex in medulla

oblongata (tissues from central nervous system [CNS]) and specific lymphoid tissues, i.e. tonsil

tissues and retropharyngeal lymph nodes (Monleón et al., 2005). By comparing the results,

Monleón and colleges (2005) suggested even though testing lymphoid tissue or CNS alone

would detect scrapie, it is more accurate when both lymphoid tissues and obex were used for

parallel testing to increase sensitivity without sacrificing specificity.

9

Currently, the Bio-Rad ELISA (Bio-Rad Laboratories, Inc., Hercules, California, USA) is

the most commonly used test for screening for evidence of scrapie at necropsy; it is used by all

CFIA certified laboratories and also used in many European countries, Japan and the US (CFIA

– OLF Standard Operating Procedure, SOP #SS-PR012.02, 2010, personal communication).

According to CFIA – Ottawa Laboratory in Fallowfield (CFIA-OLF) and Alberta Agriculture,

Food and Rural Development (AAFRD) TSE Laboratory in Edmonton, the Se and Sp of the Bio-

Rad ELISA are high, ranging from 95% to 100% and from 99.41% to 99.71% respectively

(CFIA – OLF Standard Operating Procedure, SOP #SS-PR012.02, 2010, personal

communication). The Bio-Rad ELISA uses a series of antibodies to which the target antigen

(PrPSc) can bind. The unbound antibodies as well as other proteins are washed away several

times before adding an enzymatic substrate, which binds to the antibody-antigen complex. The

result is the ELISA shows a colour change, indicating the quantity of antigen in the sample.

WB and IHC tests are also widely used diagnostic tests for scrapie. WB separates normal

and abnormal PrP through the use of the proteinase K (PK) enzyme which is then followed by

electrophoresis. After, the sample proteins are transferred to the immunoblotting membrane

where a highly specific monoclonal antibody is used to detect PrPSc. WB is an essential test in

scrapie because it can distinguish distinct banding patterns of classical and atypical scrapie strain

Nor98 (CFIA-OLF Standard Operating Procedure, SOP #SS-PR012.02, 2010, personal

communication). The IHC test involves microscopic examination of the obex and/or

lymphreticular tissues that have been first treated with antibodies directed against the abnormal

protein PrPSc; staining those tissues containing this protein and thus identifying animals that are

disease positive (Spiropoulos et al., 2007). This requires two to three days to complete and either

fixed or frozen tissue can be used for testing. LIA is a chemiluminescence sandwich ELISA,

10

which indicates positivity by emission of light as a result of chemical reaction (Bolea et al.,

2005). A comparison study done by Bolea and colleagues (2005) showed that WB and LIA tests

are able to detect PrPSc in the obex, cervical spinal cord, and thalamus from all the scrapie-

positive sheep, but unable to detect PrPSc in other areas of the brain where a weak

immunohistochemical staining was observed. This might result in a slightly lower Se of WB and

LIA compared to IHC in these regions. Serial diagnostic testing, where initially testing all

samples using the Bio-Rad ELISA followed by confirming cases through parallel testing using

WB and IHC, is considered to be the gold standard of scrapie diagnosis with a high Se and a

100% Sp (CFIA-OLF Standard Operating Procedure, SOP #SS-PR012.02, 2010, personal

communication); thus was used in this study.

Because scrapie diagnosis is less accurate when performed on biopsies of peripheral

lymphoid tissues compared to necropsy samples, control of the disease in live populations has

problems of accurate and early detection of infected animals. However, reliance on scrapie

testing in animals suspected of dying of scrapie may mean that there is a delay in the

implementation of control strategies, impeding their effectiveness. For this reason, detection of

scrapie by means of active surveillance (e.g. at the abattoir in slaughtered healthy animals)

appears to be an important tool in scrapie control and eventual eradication.

1.2.5. The relation of scrapie to livestock TSEs

Although scrapie is not a zoonotic disease, it is related to other zoonotic TSEs and there

is concern specifically around transmission of the scrapie agent from sheep to other species.

BSE, a TSE which occurs mostly in cattle has been well-described as an epidemic

occurring in the United Kingdom during the late 1980’s and early 1990’s (Bons et al., 1999). The

clinical signs of BSE are similar to scrapie and include abnormal posture, altered mental

11

status/behaviour, gait deficits, wasting, and finally death (Iulini et al., 2012). These two diseases

are hard to differentiate by clinical signs alone, but BSE exhibits pathological lesions and

distribution that are markedly different than scrapie and which can be distinguished by the

molecular features of different PrP’s. This can be tested by WB and examining molecular size

and the glycosylation profile (Thuring et al., 2004).

It has been confirmed that both sheep and goats are susceptible to experimental infection

with the BSE agent and secondary natural transmission can occur in sheep, although pathological

lesions are different than those with scrapie (Bellworthy et al, 2005). To date, no sheep raised in

flocks in a natural environment have been identified with BSE infection. However, two

confirmed BSE cases in naturally infected goats have been reported respectively in France in

2005 and the UK in 2006, the latter had been misdiagnosed by scrapie due to these two diseases’

similarity (Eloit et al., 2005, Jeffrey et al., 2006, Spriopoulos et al., 2011).

The agent of BSE has been found to cause disease in other species including humans,

which is called variant Creuzfelt Jakob disease (vCJD). It is believed that humans become

infected by consumption of infected beef products (Bruce et al., 1997; Migliore et al., 2011). The

rise in incidence of BSE followed by a rise in diagnosed cases of vCJD (World Organization for

Animal Health—OIE, 2013a; NCJDRSU, 2013) particularly in the UK suggests a significant

causal association between BSE and vCJD. Because of the BSE agent’s zoonotic potential, BSE

is considered a public health risk, and extensive measures have been established to detect and

eliminate the disease. The risk of BSE in sheep and goats, however, is negligible.

Studies conducted in the UK, 1988, by Wilesmith and colleagues (1991) suggested that

BSE may have arisen as a result of the feeding of “meat and bone meal” (MBM) to cattle. MBM

products that were usually sold as protein supplements are suspected to have been contaminated

12

with materials from scrapie-infected sheep and/or BSE-infected cattle (Wilesmith el al., 1991).

Therefore, the European Union (EU) introduced a feed ban on the use of processed animal

protein (PAP) in the feed for cattle, sheep and goats in July 1994 (The European Commission,

2013). The ban was expanded in January 2001 with the feeding of all processed animal proteins

to all farmed animals being prohibited, with certain limited exceptions (The European

Commission, 2013; WHO, 2011). This is to ensure that there is no cross-contamination between

species. MBM was banned in Canada in 1997 with the exception of MBM made exclusively

from pork or horse meat (Library of Parliament – LOP, 2005). Even though the number of BSE

cases decreased significantly after the feed ban (World Organisation for Animal health—OIE,

2013a), research has not yet identified a direct causal relationship between MBM containing

sheep tissue and BSE outbreaks.

The relationship between scrapie and chronic wasting disease (CWD) in elk and deer has

been investigated. It has been found that white-tailed deer are susceptible to the scrapie agent

through intracerebral inoculation (Greenlee et al, 2011), and that it is also possible to transmit

CWD to cattle, goats and sheep (Hamir et al, 2006). However, a study done by Hamir and

colleagues (2006) resulted in the observation that only sheep with genotypes ARQ/VRQ and

ARQ/ARQ appear to be susceptible to CWD: the only sheep inoculated with CWD agent and

further developed the disease has a genotype that corresponded to a susceptible genotype for

scrapie in sheep. Therefore, in the natural environment, the chance of cross-infection is possible.

Other TSEs such as feline spongiform encephalopathy (FSE) in cats, transmissible mink

encephalopathy (TME) in mink, and exotic ungulate encephalopathy (EUE) in zoo animals such

as nyala and greater kudu (Bendheim et al., 1985) have been linked to BSE (Sigurdson and

Miller, 2003). From the late 1980s until the early 1990s when BSE became an epidemic, 15

13

species were first diagnosed with TSE (Bons et al., 1999; Pearson et al., 1992). Affected animals

were either fed cattle-derived protein supplements or had been in contact with prion-infected

individuals of the same species (Kirkwood et al., 1994). Even though some researchers believe

scrapie-contaminate feedstuffs may have contributed to the BSE outbreaks in cattle, and further

cattle-derived protein supplements have affected other zoological or domestic animals, the direct

oral transmission of the scrapie agent to other animals has not been confirmed as a source of

infection (Sigurdson and Miller, 2003).

So far, BSE is the only confirmed zoonotic animal TSE. There is no epidemiological

evidence showing either scrapie or CWD can be transmitted naturally to humans. As a result, the

zoonotic risk remains unproven but should continue to be evaluated (Da Costa Dias et al., 2011;

Spiropoulos et al., 2011). Therefore, the role of scrapie in the transmission of BSE warrants

further research even though it is not a zoonotic disease.

The best known form of TSE in humans is Creutzfeldt Jakob disease (CJD). It is a rare,

degenerative and invariably fatal brain disorder with two forms, “classic CJD” and the

previously mentioned vCJD. Whereas classic CJD is the common type of CJD which occurs

worldwide and is either familial or sporadic in the elderly population, vCJD is a rare disease

which occurs primarily in young people and is associated with consumption of products from

BSE affected cattle (WHO, 2013). The first case of vCJD was reported in 1996 in the UK

(Mackay et al., 2011). The majority of cases worldwide have been identified in individuals

residing in the UK during the BSE outbreak period (NCJDRSU, 2011). Furthermore, a study has

reported that no cases of vCJD have been detected among people born after 1989, which was

after BSE risk materials were not sold for human consumption in 1987 (Mackay et al., 2011).

14

The temporal relationship suggests a causal association between BSE and vCJD which further

studies have confirmed (Mackay et al., 2011).

Kuru is another TSE of humans that was known to occur only in a restricted area in the

New Guinea highlands (Gajdusek, 2008). In recent years, the incidence has declined and the

disease has now almost disappeared (Gajdusek, 2008). Kuru is thought to be transmitted

between humans through the consumption of brains of people dying of the disease. Even though

the kuru agent has been experimentally transmitted to primates causing disease, as has the classic

CJD agent to chimpanzees, research has not achieved successful transmission of the scrapie

agent from sheep to other primates (Chou and Martin, 1971, Gibbs et al., 1980), suggesting host

specificity of those agents. However, knowledge regarding host specificity of TSE agents is still

unclear, and the precautionary principle should be applied when making assumptions regarding

the risk of scrapie transmission to other species.

Understanding the prevalence and distribution of scrapie in the Canadian sheep industry

is important because scrapie causes severe health problems in sheep. More importantly, infected

animals may provide a reservoir of pathogens and represent a risk to healthy populations of

animals and possibly people. Because of this, scrapie eradication should be achieved on national

bases.

1.3 The Canadian sheep industry

Reports show that in the year 2002 alone, the revenue from small ruminants (sheep and

goats) exported from Canada reached $12.5 million, and was expected to increase by 71% in

2003, with the USA being the most important trading partner (CSF et al., 2009). An international

ban on exports of Canadian sheep and goats along with cattle was implemented immediately

after the first Canadian BSE case was confirmed in Alberta in May, 2003. No sheep or lambs

15

were exported to the USA from 2004 to 2009. This ended in 2010 with the export of 3000 lambs

and 1,400 sheep for immediate slaughter (Statistics Canada, 2012). During 2011, the number of

sheep exported increased to 9,800 (Statistics Canada, 2012), suggesting a restoring of balanced

markets. However, the total number of exported sheep and lambs is still lower than the number

exported prior to 2003 (Statistics Canada, 2012). The market for cross-border trading of live

sheep for breeding purposes has remained closed however, causing economic difficulty for

producers and placing a burden on the government to support the producers (Library of

Parliament –LOP, 2012).

1.3.1 Canadian sheep population

The sheep population in Canada has remained around one million between 2000 and

2011 (Figure 1.1), with approximately half of the population being breeding sheep. There was

some decrease between 2003 and 2010, as a result of decreased access to international markets

and suppressed domestic prices caused by too many animals going to market in Canada. In 2011,

the sheep population finally increased slightly, reaching 1,070.3 thousand head on July 1, 2011, a

2.2% increase compared to the same period in 2010. It indicates the start of a post BSE recovery

for the sheep industry. Despite the increase between 2010 and 2011, the total stock of sheep still

hasn’t recovered to its 2002 pre-BSE level (Figure 1.1). Retention of a higher proportion of ewe

lambs for breeding, may have contributed to the total sheep population, because the sheep

industry believed that high prices would continue.

Statistics Canada conducts a semi-annual census on Canadian sheep population. The

national sheep population is divided into ewes, rams and lambs by province, and is recorded on

January 1 and July 1 of each year. Table 1.3 shows the sheep population in each province as of

January 1, 2013. Of the provinces, Ontario had 33% of the total mature sheep population,

16

making it the province with the highest sheep population, followed by Quebec and Alberta which

had 25% and 17% respectively. The three provinces with the most sheep farms according to

Statistics Canada’s 2006 Census of Agriculture are Quebec, Saskatchewan and Ontario (Table

1.3; Statistics Canada, 2007).

The population of lambs is higher at the July census than at the January census because

lambing commonly occurs in the spring and lambs are marketed in the fall. An example of the

sheep population in Canada broken down by growth stages and gender as of July 1, 2012, and as

of January 1, 2013, is shown in Figure 1.2. Mature sheep, which includes rams and ewes, are the

target population of this study. Mature sheep on July 1, 2012, and January 1, 2013, occupied

53% and 65% of the total population respectively. Ewes, as the possible source of transmission

of scrapie disease agents, on July 1, 2012, and January 1, 2013, accounted for 50% and 62%

respectively.

1.3.2 Slaughter plants

Sheep, goats and cattle are frequently transported long distances across Canada for

slaughter purposes. Provincial abattoirs receive animals from across the country but the meat can

only be sold within the same province. Federal abattoirs, on the other hand, process imported

animals as well and market their products across the country (Alton et al., 2012). Approximately

half of the market lambs born in Canada will be transported into Ontario for slaughter (Statistics

Canada, 2012). The market for lamb meat in Ontario is the largest in Canada, followed by

Quebec. This can be explained by the population concentration around in major cities such as

Toronto and Montreal. Therefore, the majority of sheep in Canada are being slaughtered in

Ontario.

17

It appears from the sharp decline and slow recovery of sheep inventory numbers in

Canada after 2003, that BSE and scrapie have had a strong and prolonged negative impact on the

sheep industry. The producers should be vigilant regarding recognizing and reporting potential

scrapie cases to the CFIA. In addition, the constant movement of sheep from farms to abattoirs

between difference provinces especially requires a national ID system which allows accurate

tracing of animal movements.

1.4. Scrapie surveillance, control and prevention

1.4.1. Scrapie surveillance

By monitoring the spread of a disease and determining patterns of progression, disease

surveillance can not only help predict, observe and minimize the harm caused by diseases, but

also monitor changes in disease patterns and the effect of control programs. A good disease

surveillance program should reflect national disease control priorities and promote the best use

of public resources by maximizing effectiveness and efficiency (Lynn et al., 2007).

Scrapie surveillance programs test sheep samples that have been collected by both

passive surveillance and active surveillance system. Passive surveillance is the examination of

clinically identified suspected cases; when the case is diagnosed as positive, information about

the case is entered into a notification database. Passive surveillance does not require researchers

to actively search for individuals to test. Active surveillance, on the other hand, is conducted by

actively looking for animals to test, such as healthy sheep slaughtered at abattoirs or fallen stock

in sheep flocks. For scrapie testing, active surveillance is primarily conducted in slaughter

populations, while passive surveillance is conducted by testing suspected cases reported by

farmers or veterinarians (Lynn et al., 2007). An effective scrapie surveillance program requires

18

implementation of a mandatory traceability program, which includes identification of individual

animals, and of their locations and farm types.

Surveillance programs for scrapie have been in place in the US and European countries

for several years, and are critical in the control and eradication of scrapie and other TSE diseases.

The Canadian surveillance program started in May 2005, and received new funding and strong

endorsement in 2010 from Agriculture and Agri-food Canada (Scrapie Canada, 2013b). The

structure and scope of surveillance systems used in the UK, US and Canada are explained below.

In the UK in 2002, the Department of Environment, Food and Rural Affairs (DEFRA)

initiated a surveillance program. This surveillance system had developed five sampling sources

for scrapie control by 2004, including surveillance via the Scrapie Notification Database (SND],

Fallen Stock (FS), Dead in Transit (DIT), Abattoir survey (AS) and Compulsory Scrapie Flock

Scheme (CSFS) (Ortiz-Pelaez et al., 2011). Among the five, FS and DIT, often done on animals

dying of other reasons, depend on producers’ and veterinarians’ submission of clinical samples,

and positive results lead to more frequent surveillance. A traceability program has been fully

developed (Birch et al., 2010) and allows for accurate tracing of animals to the farm of origin.

Animal movements are tracked by tagging the individual animal when it leaves its birth flock

(Birch et al., 2010). Information is stored in a nationally administered database which allows

officials to act on risk targets efficiently and quickly, including locating and quarantining

exposed farms and livestock. Overall, the number of confirmed scrapie cases in sheep identified

by passive surveillance in Great Britain has been decreasing since the program started in 2002.

The estimated prevalence of classical scrapie in the Great Britain sheep population is shown in

Figure 1.3 (Ortiz-Pelaez et al., 2012).

19

In the US, the surveillance activities include the following: active surveillance, passive

observation/reporting, laboratory surveillance, focusing efforts to reach under-sampled flocks

and geographic areas, and increasing compliance with identification requirements (United States

Department of Agriculture et al., 2010). Laboratory surveillance requires appropriate samples

from targeted and clinical animals to be forwarded to the National Veterinary Services

Laboratories (NVSL) or an approved contract laboratory (United States Department of

Agriculture et al., 2010). Sheep and goats moved interstate are required to be officially identified

(USDA, 2013). When a positive case is found, the individual is traced back to its flock of origin.

In cases where the flock of birth cannot be determined, the most recent residing flock will be

used (Code of Federal Regulations, 2012). Since the surveillance system has been implemented,

the prevalence of scrapie has decreased greatly. The US national prevalence of scrapie in sheep

in 2009 was estimated to be 0.05% compared to 0.2% in 2003, primarily through active

slaughter-based surveillance (United States Department of Agriculture et al., 2010).

In Canada, the CFIA has implemented the Canadian National Scrapie Surveillance

Program, an active surveillance program that aims to discover and identify infected animals and

their farm of origin in a time-efficient manner. The animal samples are mainly collected at

slaughter facilities, but also farms, auction markets, animal diagnostic laboratories, and dead

stock facilities (CFIA, 2012a).

The Canadian Sheep Identification Program (CSIP) has been mandatory for all sheep

regardless of age and location since January 1, 2004 (CSF, 2011b). A national identification tag

is applied to the individual’s ear before leaving the flock of origin. Tags are purchased in

authorized retail stores which are responsible for submitting the purchasers’ information and tag

numbers to the CSF (CSF, 2011b). In 2012, CSIP introduced radio frequency identification

20

(RFID) tags to the list of CSIP-approved identifiers; these RFID tags allow for easier recording

of all animal movement (CSF, 2012a). Sheep producers in Canada are required to record sheep

movements, i.e. animals leaving and entering a farm as well as source of the animal (CSF,

2011b). Ideally, this assures all farms on which a particular sheep has lived can be traced and

identified. However, this goal is not currently achievable due to the size of sheep flocks, the

frequency of movements among farms and compliance. In addition, the farms are not properly

identified, i.e. geographically coded, but might be referenced only via phone numbers, owner

names, and addresses. Data are frequently out of date or missing information.

This current study is aiming to estimate the national prevalence of scrapie in Canada

through active surveillance conducted from November 2010 to December 2012. Using the

experiences of GB and the US, it is expected that scrapie would decrease in prevalence, compare

to the periods prior to the surveillance systems being fully implemented.

1.4.2. Current scrapie control and prevention

The scrapie control and prevention program in Canada is currently implemented by and

under supervision of CFIA. As previously mentioned, scrapie is a federally reportable disease

meaning that anyone suspecting a case of scrapie must by law report this to the CFIA (Canadian

Food Inspection Agency, 2012e). When scrapie is suspected in a live animal on a farm, affected

animals are humanely euthanized, the brain submitted for official testing and their carcasses are

disposed under CFIA’s supervision. If scrapie is confirmed, the rest of the flock will be under

quarantine immediately, adult sheep with susceptible genotypes euthanized and all lambs are

ordered to be slaughtered (Canadian Food Inspection Agency, 2012f). Once all destruction and

disposal activities have been completed and the facility properly disinfected according to CFIA

21

requirements, the quarantine will be removed and the farm will be under CFIA’s surveillance to

ensure no remaining scrapie case.

When a scrapie case is diagnosed in an animal that was slaughtered in an abattoir, CFIA

will trace the animal to be farm of origin using the CSIP ear tag, and follow the procedures

mentioned above. CFIA will compensate producers for a previously established sum

approximating market value of animals ordered destroyed (CFIA, 2011b). Besides the financial

burden for both producers and government, this slaughtering action is also an animal welfare

concern since a large numbers of animals may be humanely euthanized.

To allow scrapie status to be determined at the flock level, Scrapie Canada introduced a

Voluntary Scrapie Flock Certification Program (VSFCP) (Scrapie Canada, 2012). Due to the

long incubation period of scrapie, infection in a flock may go undetected for many years. VSFCP

is designed to perform flock level surveillance (on farm deaths and suspected cases), genetic

monitoring (resistant genotypes vs. susceptible genotypes), combined with biosecurity such as

prevention of higher risk animals from entering the flock. The purpose is to assess a flock over a

long term with respect to status and put in place biosecurity practices which will minimize the

risk of a flock becoming infected (Scrapie Canada, 2012).

Producers participating in VSFCP need to follow one of three pathways and a number of

regulations (Scrapie Canada, 2013a). The most recommended pathway is to use disease

surveillance and biosecurity procedures to achieve specified VSFCP certification in 5 years. The

second and the third pathways use live animal testing technologies, such as lymphoid tissue

testing and/or genotyping for resistance to scrapie, in addition to disease surveillance (Scrapie

Canada, 2012). Some examples of the regulations are that animals over 12 months of age that die

on the farms participating in VSFCP must be tested by the CFIA for scrapie, and that annual

22

inventories, supervised by an accredited veterinarian, confirm that animals are accounted for and

have been properly sampled and identified. Risk of infection can be reduced by increasing the

proportion of the flock with genetic resistance to the scrapie prion (Scrapie Canada, 2013a). This

tool will also reduce the incidence of disease within an infected flock.

In addition to controlling scrapie, CFIA regulations also attempt to prevent scrapie. The

VSFCP requires that, because the transmission of scrapie is mainly by ewes, sheep flocks must

be strictly closed to ewes from other flocks, except from those at an equivalent or higher

program status level (Scrapie Canada, 2012). Producers may introduce sheep with genetic

resistant genotypes into the flocks as a method of prevention. However, if sheep are selected for

genetic resistance, the producer is not selecting for more economically important traits such as

carcass composition or prolificacy.

Overall, Canada’s scrapie surveillance program has been in place for almost a decade,

and some aspects have been improved by learning from the programs in Great Britain and the

US. The current scrapie control and prevention actions in Canada are aiming to eradicate scrapie

in the long run and thus to recover the international sheep trade with the US.

1.5 Methodology for epidemiological studies

1.5.1 Sampling methods

In order to provide an accurate estimation of disease prevalence, sampling strategies

should be carefully designed based on but not limited to the nature of the disease, the

representativeness of the sample, the feasibility of the study and the cost of sampling.

Simple random sampling (SRS) is the method that provides the most representative

sample because it is based on the principle that every sample of size n has the same chance of

being sampled out of a population of size N. However, it is rarely feasible because it requires a

23

complete list of all individuals in the sampling population. Also, SRS can be an inefficient

sampling strategy when the risk of the target population is known to be variable between

identifiable groups, in which case stratified random sampling would be preferred. For the present

study, the target population is all mature Canadian sheep being slaughtered in Canadian

abattoirs, but no master list of all individual sheep exists and the prevalence of scrapie is

assumed to vary among provinces. Therefore, for this study, SRS was not used for sampling

sheep individuals but was used to select sampling dates.

Stratified random sampling is a common sampling method which splits the population

into non-overlapping groups called strata; then SRS can be used within each stratum. The

stratification principle is to divide the population according to a stratification variable so that

individuals within the strata are more homogeneous than the target population, and variation

between strata is maximized. By applying this method, the standard errors of prevalence

estimates are minimized. Stratified random sampling was used in this study at the first stage to

divide Canada into strata based on provincial borders.

Cluster sampling divides the target population into clusters so that each cluster is

representative of the target population. Clusters are usually created by geographic or size

characteristics. Only certain clusters are selected, and all of the elements in those clusters are

sampled. Because cluster sampling assesses only a portion of the population, it is cost efficient.

Also, it is more feasible than SRS since the list of elements in a cluster is easier to obtain than in

the entire sampling population. In this study, cluster sampling was used during the sheep

sampling process to select abattoirs.

Sampling with probability proportional to size (PPS) is used often when determining

which clusters to select. It determines the probability of selecting a sampling unit based on the

24

size of its population. This was used in this current study to sample the abattoirs according to

their capacity or animal throughput in the past. The ones with larger capacity were selected

because they are more representative compared to the ones with smaller capacity.

While SRS is the standard method, stratified sampling is more efficient, and cluster

sampling is more feasible. In practise, these methods are often combined to create so-called

multistage sampling schemes, which were applied in the current study.

Multistage sampling is a complex form of cluster sampling and is usually applied when

the populations have a hierarchical structure and when using all the sample elements in the

selected clusters may be prohibitively expensive or not necessary (Gregoire and Valentine,

2008). Instead, the researcher randomly selects elements from each cluster. Constructing the

clusters is the first stage; deciding what elements within the cluster to use is the second stage. In

some cases, several levels of cluster selection may be applied, through stratified sampling or

cluster sampling, before the final sample elements are identified. The sampling unit in the first

stage of sampling is known as a primary sampling unit or first stage sampling unit. The sampling

unit in the second stage of sampling is known as a secondary sampling unit or second stage

sampling unit. The technique is used frequently when a complete list of all members of the

population does not exist or is inaccurate. The current study used stratified sampling as the first

stage to divide the target population by provinces; cluster sampling was used as second stage in

Ontario and Quebec to select abattoirs based on their capacity; then PPS, being the third stage,

was applied to choose the abattoirs to be visited within selected clusters.

Another sampling method used for rare diseases study is inverse sampling, also called

negative binomial sampling. In inverse sampling, a series of Bernoulli trials, which have exactly

two outcomes of “success” or “failure”, are conducted from a sampling population until a

25

predefined r number of ”successes” occur. Under this design, the total sample size is a random

variable. A more detailed description can be found in Haldane’s “On a method of estimating

frequencies” (Haldane, 1945). This method was not used in the current study, because it was

unknown whether a certain number of positive cases could be achieved within the two year time

frame of the study.

1.5.2 Prevalence estimation

This study aimed to estimate the prevalence of scrapie within a certain period of time

through active surveillance. Prevalence, or prevalence proportion (p), is an epidemiological

measure of how commonly a disease or condition presents in a population at a particular time. It

is in contrast to incidence which measures the risk of developing new cases of disease or

condition in a population within a certain period (Dohoo et al, 2009). Because scrapie has a long

incubation period, measuring the incidence will not give useful information.

Prevalence (p) is often expressed as a percentage with values between 0% and 100%. It is

calculated as:

p = cases / population-at-risk (Equation 1.1)

where “cases” is the number of cases of disease in a population at a point in time, and the

population-at-risk is the total number of individuals (or sampling units) at risk at the same point

in time (Dohoo et al, 2009).

For stratified sampling, in particular, the prevalence p for the entire population is a

weighted average of the individual stratum prevalence with weights proportional to the number

of elements in each stratum (Levy and Lemeshow, 2008). The equation is thus written as

∑

∑

(Equation 1.2)

26

where N is used to denote the number of individuals in the target population, Nh is the number of

sampling units in each stratum h, L is the number of strata, and Wh=Nh/N is the proportion of the

total population belonging to stratum h.

1.5.3 Confidence interval

A confidence interval (CI) for an estimator indicates a range of values which includes the

true value with a desired probability before sampling. It consists of a lower and an upper limit.

The size of the interval depends, among other things, on the sample size (n) and confidence level

(1 – α), where α denotes an acceptable error probability and is generally set to 5%. As prevalence

is essentially a probability, a proper CI is naturally bound between 0 and 1 (or expressed as

between 0% and 100%).

Several methods have been applied in the past to estimate CI of disease prevalence

depending on different aspects of the disease. Scrapie is a rare disease that varies geographically

and might go undetected in areas with low sheep populations; indeed no case of scrapie has ever

been detected among sheep from British Columbia as shown in Table 1.1 (CFIA, 2012b). When

the prevalence is equal to 0%, the normal CI estimation method, i.e. the Wald interval, will result

in a degenerated confidence interval at the point 0%, which does not give useful information.

Therefore, alternative methods to estimate the CI for the prevalence are required. For rare

diseases, methods to estimate CI, such as continuity corrected Wald interval, Clopper-Pearson

exact interval, Agresti-Coull interval, and Wilson score interval, will be explained in chapter 2.

1.6 Study rationale and objectives

Scrapie is a fatal disease, yet is shown to have low prevalence in the European countries,

in the US as well as in Canada. Controlling the spread of the disease is critical; however, an

infected flock may not be detected until extensive loss has occurred. The origins of infected

27

animals need to be confirmed, and the contaminated areas and disease-free areas need to be

identified. The US-Canada trading border for live sheep has remained closed since May 20,

2003, in part because Canada has not kept pace with other developed countries with respect to

implementation of a national surveillance program for scrapie. Economic loss from international

trade has affected small ruminant producers and has created a financial burden for the Canadian

government.

An eradication plan is in development with the aim that Canada achieves scrapie-free

status according to World Organization for Animal Health (OIE) standards. According to the

OIE, a country or zone can be considered scrapie free when a representative and sufficient

number of sheep and goats over 18 months of age (sample size assuming 0.1% prevalence) are

tested annually with no case found for at least seven years (World Organization for Animal

Health—OIE, 2013b). In order to achieve this goal, the CFIA, CSF, Canadian Sheep Breeders’

Association (CSBA), Canadian National Goat Federation (CNGF) and Agriculture and Agri-

Food Canada have partnered to support a “National Scrapie Prevalence Study”. Scrapie Canada,

a division of the CSF, is devoted to working on scrapie control in Canada. In spring 2010,

Scrapie Canada received funding through the Agri-Flexibility Fund for the National

Transmissible Spongiform Encephalopathy (TSE) Eradication Plan from Agriculture and Agri-

Food Canada. An extensive active surveillance program for scrapie was conducted between

November 2010 and December 2012, in order to sample sufficient animals to meet surveillance

targets, many more animals than had been previously sampled in prior years.

1.6.1 Objectives

The goal of this thesis project is to investigate the prevalence of classical scrapie in the

adult Canadian sheep population. Specific objectives are

28

(i) Review methods for point and confidence interval estimation appropriate for rare

disease conditions.

(ii) Estimate the prevalence of scrapie in Canada and its provinces at the individual sheep

and at the farm level

(iii) Assess the geographic distribution of available sample information to inform

activities for future disease surveillance and eradication.

29

1.7 References

Aguzzi, A., 2008. Unraveling prion strains with cell biology and organic chemistry. Proceedings

of the National Academy of Sciences of the United States of America 105 (1): 11–2.

Alton, G., Pearl, D., Bateman, K.G., McNab, W.B., Berke, O., 2012. Suitability of bovine

portion condemnations at provincially-inspected abattoirs in Ontario Canada for food

animal syndromic surveillance. BMC Veterinary Research 8:88.

Andreoletti, O., Berthon, P., Marc, D., Sarradin, P., Grosclaude, J., van Keulen, L., Schelcher, F.,

Elsen, J-M., Lantier, F., 2000. Early accumulation of PrPSc in gut-associated lymphoid

and nervous tissues of susceptible sheep from a Romanov flock with natural

scrapie.Journal of General Virology 81,3115-3126.

Andréoletti, O., Orgek L., Benestad, S.L., Beringue, V., Litaise, C., Simon, S., Le Dur, A.,

Laude, H., Simmons, H., Luga, S., Corbiere, F., Costes, P., Morel, N., Schelcher, F.,

Lacroux, C., 2011. Atypical/Nor98 scrapie infectivity in sheep peripheral tissues. PLoS

Pathog 7(2): e1001285. doi:10.1371/journal.ppat.1001285

Baylis, M., Houston, F., Goldmann, W., Hunter N., McLean, A.R., 2000. The signature of

scrapie: differences in the PrP genotype profile of scrapie-affected and scrapie-free UK

sheep flocks. Proc. R. Soc. Lond. B, 267:2029-2035.

Baylis, M., Chihota, C., Stevenson, E., Goldmann, W., Smith, A., Sivarn, K., Tongue, S.,

Gravenor, M.B., 2004. Risk of scrapie in British sheep of different prion protein

genotype. J. Gen. Virol, 85(9), 2735-2740.

Becher, G. S., Copeland, C. W., Lister, S. A., editors, 2008. Mad Cow Disease (Bovine

Spongiform Encephalopathy). Nova Science Publishers, Inc., New York, pp 2.

Bellworthy, S.J., Dexter, G., Stack, M., Chaplin, M., Hawkins, S.A.C., Simmons, M.M., Jeffrey,

M., Martin, S., Gonzalez, L., Hill, P., 2005. Natural transmission of BSE between sheep

within an experimental flock. Vet Rec, 157:206.

Belt, P.B., Muileman, I.H., Schreuder, B.E., Bos-de Ruijter, J., Gielkens, A. L., Smits, M.A.,

1995. Identification of five allelic variants of the sheep PrP gene and their association

with natural scrapie. J Gen Virol. 76 (Pt 3): 509-17.

Bendheim, P.E., Bockman, J.M., McKinley, M.P., Kingsbury, D.T., Prusiner, S.B., 1985. Scrapie

and Creutzfeldt-Jakob disease prion proteins share physical properties and antigenic

determinants. PNAS 82 (4): 997-1001

Benestad, S. L., Arsac, J., Goldmann, W., Noremark, M., 2008. Atypical/Nor98 scrapie:

properties of the agent, genetics, and epidemiology. Vet. Res., 39(4), 19.

Birch, C.P., Del Rio Vilas, V.J., Chikukwa, A.C., 2010. A “shotgun” method for tracing the birth

locations of sheep from flock tags, applied to scrapie surveillance in Great Britain. Prev.

Vet. Med., 96 (3-4), 218-25.

Bons, N., Mestre-Frances, N., Belli, P., Cathala, F., Gajdusek, D.C., Brown, P., 1999. Natural

and experimental oral infection of nonhuman primates by bovine spongiform

encephalopathy agents. PNAS 96 (7): 4046-4051.

Bruce, M. E., Will, R. G., Ironside, J. W.,McConnell, I., Drummond, D., Suttie, A., McCardle,

L., Chree, A., Hope, J., Birkett, C., Cousens, S., Fraser, H., Bostock, C. J., 1997.

Transmissions to mice indicate that ‘new variant' CJD is caused by the BSE agent. Nature

389: 498-501.

Canadian Food Inspection Agency (CFIA), 2011. Animal Health Compensation - What to expect

when an animal is ordered destroyed. Retrieved May 2013 from:

30

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

animals/diseases/compensation/eng/1313712524829/1313712773700

Canadian Food Inspection Agency (CFIA), 2012a. Scrapie surveillance: eradicating scrapie from

Canada. Retrieved Jan 2012 from:

http://www.inspection.gc.ca/english/anima/disemala/scrtre/surve.shtml

Canadian Food Inspection Agency (CFIA), 2012b. Flocks infected with scrapie in Canada in

2011. Retrieved Mar 2012 from: http://www.inspection.gc.ca/animals/terrestrial-

animals/diseases/reportable/2011/flocks-infected-in-

2011/eng/1329729421107/1329729572094

Canadian Food Inspection Agency (CFIA), 2012c. Canadian sheep identification program.

http://www.inspection.gc.ca/animals/terrestrial-animals/traceability/sheep-

identification/eng/1328852777479/1328852957523

Canadian Food Inspection Agency (CFIA), 2012d. Scrapie genotyping.

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

animals/diseases/reportable/scrapie/genotyping/eng/1356066354790/1356066497693

Canadian Food Inspection Agency (CFIA), 2012e. Fact sheet –

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

animals/diseases/reportable/scrapie/fact-sheet/eng/1356131973857/1356132310673

Canadian Food Inspection Agency (CFIA), 2012f. Scrapie - What to expect if your animals may

be infected. http://www.inspection.gc.ca/animals/terrestrial-

animals/diseases/reportable/scrapie/if-your-animals-may-be-

infected/eng/1355963623752/1355963789207

Canadian Sheep Federation (CSF), Canadian Sheep Breeders’ Association, Canadian

Cooperative Wool Growers, Canadian National Goat Federation, Canadian Livestock

Genetics Association, 2009. Briefing note to the minister of agriculture and agri-food: A

national strategy for scrapie eradication.

Canadian Sheep Federation (CSF), 2011a. Myth: Allowing sheep to transit through Canada from

the northern US states to Alaska puts the Canadian industry at a disadvantage. Retrieved

from: http://www.cansheep.ca/User/Docs/POV%20Summer%20Edition%202011.pdf

Canadian Sheep Federation (CSF), retrieved Nov 2011b. Scrapie Eradication Programs.

http://www.cansheep.ca/cms/en/Programs/NSPrograms/NSProgram.aspx

Canadian Sheep Federation (CSF), retrieved Aug 2012a. Key Milestones for Mandatory RFID.

http://www.cansheep.ca/cms/en/key_milestones.aspx

Canadian Sheep Federation (CSF), retrieved Aug 2012b. RFID Tagging Videos.

http://www.cansheep.ca/cms/en/tagvideos.aspx

Chou, S.M., Martin, J.D., 1971. Kuru-plaques in a case of Creutzfeldt-Jakob disease. Acta

Neuropathologica, Vol 17, No. 2, 150-155.

Code of Federal Regulations, 2012. Section 79.2 - Identification of sheep and goats in interstate

commerce.

Da Costa Dias, B., Javanovic, K., Weiss, S.F., 2011. Alimentary prion infections: Touchdown in

the intertine. Prion, 5(1), 6-9.

Dennis, M.M., Thomsen, B.V., Marshall, K.L., Hall, S.M., Wagner, B.A., Salman, M.D.,

Norden, D.K., Gaise,r C, Sutton, D.L., 2009. Evaluation of immunohistochemical

detection of prion protein in rectoanal mucosa-associated lymphoid tissue for diagnosis

of scrapie in sheep. Am J Vet Res 70:63-72

31

Department for Environment Food and Rural Affairs (DEFRA), 2010. ARCHIVE: BSE: Other

TSEs – Scrapie. Retrieved from:

http://archive.defra.gov.uk/foodfarm/farmanimal/diseases/atoz/bse/othertses/scrapie/

Detwiler, L.A., 1992. Scrapie. Rev. Sci. Tech. Off. Int. Epiz. 11, 491-537.

Detwiler, L.A., Baylis, M., 2003. The epidemiology of scrapie. Rev Sci Tech., 22(1),121-43.

Dohoo, I., Martin W., Stryhn, H., 2009. Veterinary Epidemiologic Research. (2nd ed.).

Charlottetown , PEI, Canada, pp: 33-53.

Eloit, M., Adious, K., Coulpier, M. Fontaine, J.J., Hamel, R. Lilin, T. Messiaen, S., Andreoletti,

Ol, Baron, T., Bencsik, A., Biacabe, A.G., Berinque, V., Laude, H., Le Dur, A., Vilotte,

J.L., Comoy, E., Deslys, J.P., Grassi, J., Simon, S., Lantier, F., Sarradin, P., 2005. BSE

agent signatures in a goat. Vet. Rec., 156(16), 523-4.

The European Commission, 2012. Report on the monitoring of ruminants for the presence of

transmissible spongiform encephalopathies (TSEs) in the EU in 2011. Retrieved from:

http://ec.europa.eu/food/food/biosafety/tse_bse/monitoring_annual_reports_en.htm

The European Commission, 2013. TSE/BSE – Feed Ban. Retrieved Jan 2013 from:

http://ec.europa.eu/food/food/biosafety/tse_bse/feed_ban_en.print.htm

Everest, D.J., Waterhouse, S., Kelly, T., Velo-Rego, E., Sauer, M.J., 2007. Effectiveness of

capillary electrophoresis fluoroimniunoassay of Blood PrPSc for evaluation of

pathogenesis in sheep scrapie. J. Vet. Diagn. Invest., 19(5):552-7.

Foster J.D., Dickinson, A.G., 1989. Age at death from natural scrapie in a flock of Suffolk sheep.

Vet. Rec 125, 415–417.

Foster, J. D., Parnham, D., Chong, A., Goldmann, W., Hunter, N., 2001. Clinical signs,

histopathology and genetics of experimental transmission of BSE and natural scrapie to

sheep and goats. Vet. Rec., 148(6):165-171

Foster, J., McKenzie C., Parnham, D., Drummond, D., Goldmann, W., Stevenson, E., Hunter. N.,

2006. Derivation of a scrapie-free sheep flock from the progeny of a flock affected by

scrapie. Vet. Rec. 159, 42–45.

Gajdusek, D.C., 2008. Kuru and its contribution to medicine. Phil. Trans. R. Soc. B 27

363(1510): 3697-3700.

Gregoire, T. G., Valentine, H. T., 2008. Sampling strategies for natural resources and the

environment. Boca Raton, Florida, USA, Taylor & Francis Group, LLC, pp393-404.

Gibbs, C. J. Jr., Amyx, H. L., Bacote, A., Masters, C.L., Gajdusek, D.C., 1980. Oral transmission

of Kuru, Creutzfeldt-Jakob Disease, and scrapie to nonhuman primates. J. Infect. Dis.

142( 2), 205-208.

Goldmann, W., 2008. PrP genetics in ruminant transmissible spongiform encephalopathies. Vet

Res, 39(4):30.

González, L., Dagleish, M. P., Bellworthy, S. J., Sisó, S., Stack, M. J., Chaplin, M. J., Davis, L.

A., Hawkins, S. A. C., Hughes, J., Jeffrey, M., 2006. Postmortem diagnosis of preclinical

and clinical scrapie in sheep by the detection of disease-associated PrP in their rectal

mucosa. Veterinary Record,158: 325-331

Green, D.M., Vilas, V.J.D.R., Birch, C.P.D., Johnson, J., Kiss, I.Z., McCarthy, N.D., Kao, R.R.,

2007. Demographic risk factors for classical and atypical scrapie in Great Britain. J Gen

Viral, vol. 88 no. 12 3486-3492.

Greenlee, J. J., Smith, J.D., Kunkle, R.A., 2011. White-tailed deer are susceptible to the agent of

sheep scrapie by introcerebral inoculation. Veterinary Research 42:107.

Greenwood, P., 2002. Federal disease control. Can Vet J, 43(8):625-629.

32

Gubbins, S., McIntyre, K.M., 2009. Prevalence of sheep infected with classical scrapie in Great

Britian, 1993-2007. Epidemiol Infect., 136(6): 787-91.

Haldane J.B.S., (1945). On a method of estimating frequencies, Biometrika 33:222-225

Hamir, A.N., Kunkle, R.A., Cutlip, R.C., Miller, J.M., Williams, E.S., Richt, J.A., 2006.

Transmission of chronic wasting disease of mule deer to Suffolk sheep following

intracerebral inoculation. J Vet Diagn Invest. 18(6): 558-65.

Hautaniemi, M., Tapiovaara, H., Korpenfelt, S.L., Sihvonen, L., 2012. Genotyping and

surveillance for scrapie in Finnish sheep. BMC Vet Res., 8:122.

Hunter, N., Cairns, D., 1998. Scrapie-free Merino and Poll Dorset sheep from Australia and New

Zealand have normal frequencies of scrapie-susceptible PrP genotypes. J. Gen. Virol., 79

(Pt 8):2079-82.

Iulini, B., Maurella, C., Pintore, M.D., Vallino Dostassa, E., Corbellini, D., Porcario, C.,

Pautasso, A., Salata, C., Gelmetti, D., Avanzato, T., Palu, G., D’Angelo, A., Caramelli,

M., Casalone, C., 2012. Ten years of BSE surveillance in Italy: Newropathological

findings in clinically suspected cases. Res. Vet. Sci., 93(2), 872-878.

Jeffrey, M., Martin, S., Gonzalez, L., Foster, J., Langeveld, J.P., van Zijderveld, F.G., Grassi, J.,

Hunter, N., 2006. Immunohistochemical features of PrP(d) accumulation in natural and

experimental goat transmissible spongiform encephalopathies. J.Comp. Pathol., 134(2-3),

171-81.

Kim, K., 2007. The Social Construction of Disease. Routledge, New York, NY, USA, pp. 107-

126.

Kirkwood, J.K., Cunningham, A.A., 1994. Epidemiological observations on spongiform

encephalopathies in captive wild animals in the British Isles. Vet. Rec. 135, 296-303.

Lacroux, C., Simon, S., Benestad, S.L., Maillet, S., Mathey, J., Lugan, S., Corbière, F., Cassard,

H., Costes, P., Bergonier, D., Weisbecker, J., Moldal, T., Simmons, H., Lantier, F.,

Feraudet-Tarisse, C., Morel, N., Schelcher, F., Grassi, J., Andréoletti, O., 2008. Prions in

milk from ewes incubating natural scrapie. PLoS Pathog, 4(12): e1000238.

Levy, P. S., Lemeshow, S., 2008. Sampling of Populations: Methods and Applications (Forth

Edition). John Wiley & Sons, Inc., Hoboken, NJ, USA, pp. 121-139.

Library of Parliament (LOP), retrieved Mar 2012. Mad cow disease and Canada’s cattle industry.

http://www.parl.gc.ca/Content/LOP/ResearchPublications/prb0301-e.htm

Lynn, T., Grannis, J., Williams, M., Marshall, K., Miller, R., Bush, E., Bruntz, S., 2007. An

evaluation of scrapie surveillance in the United States. Prev Vet Medicine. 81:70-79

Mackay, G.A., Knight, R.S.G., Ironside, J.W., 2011. The molecular epidemiology of variant

CJD. Int J Mol Epidemial Genet. 2(3): 217-227.

Migliore, S., Esposite, E., Pirisinu, L., Marcon, S., Bari, M.D., Agostino, C.D., Chiappini, B.,

Conte, M., Sezzi, E., Grossi, L.D., Agrimi, U., Vaccari, G., Nonno, R., 2012. Effect of

PrP genotype and route of inoculation on the ability of discriminatory western blot to

distinguish scrapie from sheep BSE. J Gen Viral, 93(2): 450-455.

Monleón, E., Monzón, M., Hortells, P., Bolea, R., Acín, C., Vargas, F., Badiola, J.J., 2005.

Approaches to scrapie diagnosis by applying immunohistochemistry and rapid tests on

central nervous and lymphoreticular system. J Viral Methods. 125(2):165-171.

The National CJD Research & Surveillance Unit (NCJDRSU), 2013. Variant vCJD: current data,

June 201. Retrieved from: http://www.cjd.ed.ac.uk/documents/worldfigs.pdf

33

Ontario Sheep Marketing Agency2011. Retrieved June 2011 from:

http://www.ontariosheep.org/MARKETINFORMATION/MarketReports/MonthlyMarket

Summary.aspx

Ortiz-Pelaez, A., R. Warner, M. Arnold, 2011. Sheep and goats scrapie surveillance 2010. Joint

descriptive report for Great Britain. Retrieved Dec 2011 from:

http://vla.defra.gov.uk/science/docs/sci_tse_stats_scrapie.pdf

Ortiz-Pelaez, A., R. Warner, M. Arnold, 2012. Sheep and goats scrapie surveillance 2011. Joint

descriptive report for Great Britain. Retrieved Dec 2012 from:

http://www.defra.gov.uk/ahvla-en/files/pub-tse-stats-scrapie.pdf

O'Rourke, K.I., Duncan, J.V., Logan, J.R., Anderson, A.K., Norden, D.K., Williams, E.S.,

Combs, B.A., Stobart, R.H., Moss, G.E., Sutton, D.L., 2002. Active surveillance

for scrapie by third eyelid biopsy and genetic susceptibility testing of flocks of sheep in

Wyoming. Clin Diagn Lab Immunol. 9 (5):966-71.

Parry, H. B., 1983. Scrapie Disease in Sheep: Historical, Clinical, Epidemiological, Pathological

and Practical Aspects of the Natural Disease (Edited by D. R. Oppenheimer). Academic

press Inc. (London) LTD. New York, New York, pp. 93-102.

Pearson, G.R., Wyatt, J.M., Gruffydd-Jones, T.J., Hope, J., Chong, A., Higgins, R.J., Scott, A.C.,

Wells, G.A., 1992. Feline spongiform encephalopathy: fibril and PrP studies. Vet Rec

1992;131:307-310.

Petrie, A., Watson, P., 2006. Statistics for Veterinary and Animal Science (second edition).

Blackwell, Oxford, pp. 12-53.

Saunders, G.C., Cawthraw, S., Mountjoy, S.J., Hope, J., Windl, O., 2006. PrP genotypes of

atypical scrapie cases in Great Britain. J Gen Viral. 87 (11): 3141-3149.

Scheaffer, R.L., Mendenhall, W. III, Ott, R.L., Gerow, K. G., 2011. Elementary Survey

Sampling. (7th ed.). Boston, MA, USA.

Schreuder, B. E. C., van Keulen, L. J. M., Vromans, M. E. W., Langeveld, J. P. M., Smits,

M.A., 1998. Tonsillar biopsy and PrPSc detection in the preclinical diagnosis of scrapie.

Veterinary Record 142,564-568

Scrapie Canada, retrieved Aug 2012. Voluntary scrapie flock certification program,

http://www.scrapiecanada.ca/certification.html

Scrapie Canada, retrieved Jul 2013a. VSFCP National Standards (Revised March 2013).

http://www.scrapiecanada.ca/VSFCPrules-regs.html

Scrapie Canada, retrieved Jul 2013b. Welcome to Scrapie Canada.

http://www.scrapiecanada.ca/home.html

Scrapie Canada, retrieved Apr 2014. Strategic planning for scrapie eradication. Retrieved from:

http://www.scrapiecanada.ca/eradication.html

Sigurdson, C. J., Miller, M.W., 2003. Other animal prion diseases. Br Med Bull 66(1): 199-212.

Spiropoulos, J., Casalone, C., Caramelli, M., Simmons, M.M., 2007. Immunohistochemistry for

PrPSc in natural scrapie reveals patterns which are associated with the PrP genotype.

Neuropath Appl Neuro 33 (4): 398-409.

Spiropoulos, J., Lockey, R., Sallies, R.E., Terry, L.A., Thorne, L., Holder, T.M., Beck, K.E.,

Simmons, M.M., 2011. Isolation of Prion with BSE Properties from Farmed Goat.

Emerg. Infect. Dis., 17(12), 2253-2261.

Statistics Canada, 2007. Farms, by farm type and province (Census of Agriculture, 2006).

http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/agrc35a-eng.htm

34

Statistics Canada, 2012. Supply and demand reports from year 2006 to 2011, Retrieved Jun 2012

from: http://www.statcan.gc.ca/pub/23-011-x/2011002/tablesectlist-listetableauxsect-

eng.htm

Statistics Canada, retrieved May 2013. Sheep inventories, by province.

http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/prim52a-eng.htm

Thuring, C.M.A., Erkens, J.H.F., Jacobs, J.G., Bossers, A., Van Keulen, L.J.M., Garssen, G.J.,

Van Aijderveld, F.G., Ryder, S.J., Groschup, M.H., Sweeney, T., Langeveld, J.P.M.,

2004. Discrimination between scrapie and bovine spongiform encephalopathy in sheep

by molecular size, immunoreactivity, and glycoprofile of prion protein. J Clin Microbiol,

42(3): 972-980.

United States Department of Agriculture (USDA), 2004. Phase II: Scrapie: ovine slaughter

surveillance study 2002-2003. Retrieved from:

http://www.aphis.usda.gov/animal_health/animal_diseases/scrapie/downloads/sossphase2

.pdf

United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service

(APHIS), Veterinary Services Centers for Epidemiology and Animal Health (VSCEAH),

National Surveillance Unit (NSU) & Fort Collins, CO., 2010. National scrapie

surveillance plan. Retrieved from:

http://www.aphis.usda.gov/animal_health/animal_diseases/scrapie/downloads/national_s

crapie_surv_plan.pdf

United States Department of Agriculture (USDA), 2013. Final Rule: Traceability for Livestock

Moved Interstate. January 11, 2013. Summary of General Requirements by Species.

Effective Date: March 11, 2013. Retrieved from:

http://www.aphis.usda.gov/traceability/downloads/ADT_summary_species.pdf

van Keulen, L. J. M., Schreuder, B. E. C., Meloen, R. H., Mooijharkes, G., Vromans, M. E. W.

& Langeveld, J. P. M., 1996. Immunohistochemical detection of prion proteins in

lymphoid tissues of sheep with natural scrapie. Journal of Clinical Microbiology

34,1228-1231

Vergne, T., Calavas, D., Cazeau, G., Durand, B., Dufour, B., Grosbois, V., 2012. A Bayesian

zero-truncated approach for analysing capture-recapture count data from classical scrapie

surveillance in France. Prev Vet Med., 105 (1-2): 127-135.

Wilesmith, J.W., Ryan, J.B., Atkinson, M.J., 1991. Bovine spongiform encephalopathy：

epidemiological studies on the origin. Vet Rec;128:199-203

Wiggins, R.C., 2009. Prion Stability and Infectivity in the Environment. Neurochemical

Research, 34(1): 158-168.

World Health Organization (WHO), 2011. Retrieved Jun 2012 from:

http://www.who.int/mediacentre/factsheets/fs113/en/

World Health Organization (WHO), 2013. Retrieved April 2013 from:

http://www.who.int/mediacentre/factsheets/fs180/en/

World Organisation for Animal health (OIE), 2013a. BSE situation in the world and annual

incidence rate. Retrieved May 2013 from: http://www.oie.int/?id=504

World Organization for Animal Health—OIE, 2013b. Terrestrial Animal Health Code. Chapter

14.9. Scrapie.

http://www.oie.int/fileadmin/Home/eng/Health_standards/tahc/2010/en_chapitre_1.14.9.

htm

35

Woolhouse, M.E. J., Matthews, L., Coen, P., Stringer, S.M., Foster, J.D., Hunter, N., 1999.

Population dynamics of scrapie in a sheep flock. Philosophical Transactions: Biological

Sciences: Vol. 354, No. 1384.

Woolhouse, M.E.J., Coen, P., Matthews, L., Foster, J.D., Elsen, J., Lewis, R.M., Haydon, D.T.,

Hunter, N., 2001. A centuries-long epidemic of scrapie in British sheep? Trends

Microbiol., 9(2): 67-70.

36

Table 1.1: Sheep flocks infected with scrapie in Canada: 1984 – 2011 (CFIA, 2012b)

Year BC AB SK MB ON QC

Atlantic

Provinces Total

2011 0 0 0 0 3 3 0 6

2010 0 2 0 0 1 6 0 11

2009 0 0 0 0 0 2 0 6

2008 0 0 0 0 2 3 1 6

2007 0 0 0 0 1 0 0 2

2006 0 0 0 1 1 0 0 2

2005 0 0 0 2 0 2 0 4

2004 0 0 0 0 0 1 0 1

2003 0 1 6 0 1 4 0 12

2002 0 0 0 0 0 4 0 4

2001 0 0 0 8 0 4 0 12

2000 0 0 0 3 0 8 0 11

1999 0 0 3 1 2 8 0 14

1998 0 0 0 0 2 29 0 31

1997 0 0 0 0 2 14 0 16

1996 0 0 0 0 2 3 0 5

1995 0 0 0 0 1 2 0 3

1994 0 0 0 0 1 1 0 2

1993 0 1 0 0 3 4 1 9

1992 0 0 1 0 1 1 0 3

1991 0 0 0 0 3 2 1 6

1990 0 0 1 0 5 0 0 6

1989 0 0 2 0 5 2 0 9

1988 0 0 1 0 3 0 0 4

1987 0 0 1 0 0 0 0 1

1986 0 0 1 0 3 0 0 4

1985 0 0 1 0 2 1 0 4

1984 0 0 0 0 1 1 0 2

Total 0 4 17 15 45 105 3 196

37

Table 1.2: Scrapie genotyping risk groups categorized by UK (Baylis et al., 2001)

Risk to scrapie (from most

resistant to most acceptable)

Some genotypes representing the group (letters refer to codon

136, 157 and 171 respectively)

R1 (most resistant) ARR/ARR

R2 (resistant) ARR/ARQ, ARR/AHQ, ARR/ARH

R3 (have little resistance) ARQ/ARQ, ARH/ARH

R4 (susceptible) VRQ/ARR

R5 (highly susceptible) VRQ/AHQ, VRQ/ARQ, VRQ/ARH, VRQ/VRQ

38

Table 1.3 Canadian sheep population as of January 1, 2013, by province (Statistics Canada,

2013)

Province Mature

Sheep Lamb

AB 98.2 59.8

BC 27.7 19.3

MB 29.4 28.6

NB 4.7 2.9

NL 1.2 0.9

NS 12.5 7.1

ON 194.2 74.8

PE 4.1 3.3

QC 146.8 75.2

SK 62.1 39.9

Canada 580.9 311.8

Notes:

Ram: Male sheep.

Ewe: Female sheep which has borne young.

39

Figure 1.1 Sheep population from 2000 to 2011 as of July 1st of each year (in thousands of

heads) (Stats Canada, 2011)

0

200

400

600

800

1000

1200

1400

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tho

usa

nd

s o

f h

ead

s

Year

Total (sheep and lambs) 1 year or younger Ewes and rams

First case of BSE was

diagnosed in Canada

on May 20, 2003

40

Figure 1.2 Canada’s sheep inventories as of July 1, 2012 and January 1, 2013 (Statistics Canada,

2013)

Notes:

Unit: thousand head

Ram: Male sheep.

Ewe: Adult female sheep

0

200

400

600

800

1,000

1,200

Total Mature sheep Rams Ewes Lambs

Jul-12

Jan-13

41

Figure 1.3 Estimates of prevalence of infection of classical scrapie in the sheep population in

Great Britain for the period 2002 -2011 with 95% confidence intervals (Ortiz-Pelaez et al., 2012)

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

1.40%

1.60%

Esti

mat

ed

pre

vale

nce

Year

42

Chapter 2: Prevalence Estimation of Scrapie in the Canadian Sheep Population by Active

Surveillance in Animals at Slaughter

Abstract

Classical scrapie is a fatal neurodegenerative disease of sheep and goats which can result

in economic loss. This study was undertaken to estimate the national prevalence of scrapie using

data collected by the Canadian Food Inspection Agency (CFIA) during 2010 to 2012.

Abattoir surveillance was conducted in the 10 Canadian provinces and involved sheep

obex and lymph node tissues from randomly selected adult sheep at slaughter. Serial diagnostic

testing was conducted by testing all samples using the Bio-Rad ELISA. Cases were confirmed

through parallel testing using Western blot and immunohistochemistry. Prevalence estimates and

respective confidence intervals, including the Agresti-Coull prevalence estimate and Wilson

score interval, were compared. Both stratified and non-stratified analyses by province were

conducted and compared, but stratification by province did not improve the estimates.

A total of seven cases of classical scrapie were detected from 11,702 sheep, or 3,233

farms, out of a total of 12,367 sheep sampled. The national sheep-level prevalence is estimated at

0.06% with a 95% Wilson confidence interval ranging from 0.03% to 0.12%. The farm-level

prevalence is 0.22% with a 95% Wilson confidence interval ranging from 0.11% to 0.45%.

Classical scrapie among sheep in Canada is rare and occurs at a level of about 1 per

1,700 sheep, or 1 per 500 farms. The individual level prevalence is comparable to other

countries. A farm level prevalence has not been reported by other countries.

2.1 Introduction

Scrapie is a transmissible neurodegenerative disease in sheep and goats. This disease is

characterized by spongy degeneration and accumulation of abnormal protein-filled vacuolated

neurons in brain and spinal cord (Hunter and Cairns, 1998). There is no evidence that people can

43

contract scrapie from contact with livestock or by consuming sheep meat or products (CFIA,

2012a). Scrapie belongs to a group of diseases known collectively as transmissible spongiform

encephalopathies (TSE). Examples of TSE diseases include bovine spongiform encephalopathy

(BSE) in cattle, chronic wasting disease (CWD) in elk and deer, and Creutzfeldt Jakob disease

(CJD) and kuru in human beings. The disease is believed to be caused by an accumulation of an

abnormal or misfolded form of so-called prion proteins in tissues such as the brain in a way that

its function is disrupted; the term prion is derived from the words “protein” and “infection”

(Kim, 2007). Therefore, TSE diseases are also called prion diseases.

There are two types of scrapie: classical scrapie and atypical scrapie. In the scientific

literature, classical scrapie is usually referred to as scrapie, and atypical scrapie is specifically

noted as such. The history of classical scrapie can be traced back to the 18th century (Woolhouse

et al, 2001). Atypical scrapie was first confirmed in 1998 in Norway and differs in many ways

from classical scrapie (Benestad et al., 2008). Atypical scrapie, like classical scrapie, is a brain

degenerative condition in sheep and goats; however, the atypical form is spontaneously

occurring, is non-transmissible, occurs in older sheep and goats, and a genetic component has not

been found (Benestad et al., 2008). Because atypical scrapie is not a transmissible disease and is

not a major concern in infectivity studies, the current study focuses on classical scrapie.

Clinical signs of scrapie include but are not limited to weight loss, trembling, pruritus

without evidence of dermatitis, and lack of physical coordination. In the late stages of the

disease, the signs progress to paralysis and death (Foster and Dickinson, 1989). There is no

effective treatment for this disease. Scrapie, like all TSEs, has a 100% case fatality rate if

allowed to develop. The exact cause of the neurodegenerative changes in the prion protein (PrP)

is unknown, yet it is commonly accepted that scrapie is a naturally occurring infectious disease

with a genetic predisposition (Woolhouse et al, 1999).

44

Official scrapie surveillance programs have been established for many years in other

jurisdictions, e.g., the European Union since 2001 (The European Commission, 2012), the

United States (US) since 2002 for sheep and since 2007 for goats (the United States Department

of Agriculture, 2010). In Canada, scrapie has been categorized as a reportable disease since

1945 (CFIA, 2012a). A national surveillance program was initiated in May 2005 and restructured

to a more extended level in 2010 (Scrapie Canada, 2012). In May, 2003, live animal trade in

sheep and goats ceased between Canada and the USA due to a diagnosis of BSE in a bovine in

Alberta, Canada. The border has remained closed to sheep and goat breeding stock since this

date, partially because of a lack of prevalence data on scrapie (CSF, 2011).

The Canadian Food Inspection Agency (CFIA) implemented the mandatory Canadian

Sheep Identification Program (CSIP) in January, 2004 (Scrapie Canada, 2012). This program

requires all sheep to have approved CSIP ear tags applied before leaving the farm of origin. The

CSIP database includes identifying information on the producer who purchased the tags. If a

sheep is diagnosed with scrapie through active surveillance, the CSIP ID allows its identity to be

traced to the farm of origin, i.e. birth, thus providing geographic information as to the location of

potential scrapie positive flocks.

The objective of this study was to estimate the period prevalence of scrapie in the

Canadian sheep flock. Determining the prevalence will inform national strategies to lower this

prevalence so that Canada will reach compliance with the World Organization for Animal Health

(OIE) standards for scrapie-free status.

2.2 Materials and methods

2.2.1 Study design

A cross-sectional study was conducted over a two-year period from November 2010 to

December 2012 to obtain an estimate of the period prevalence of scrapie in the Canadian adult

45

sheep population in abattoirs. The period prevalence measures prevalence as the percentage of

cases in a sample that is collected over a period of time. The appropriate length of time is based

on characteristics and progress of the disease (Petrie and Watson, 2006). Scrapie is a rare and

chronic disease of adult sheep and usually manifests in animals that are two years of age or older.

For that reason, the target population was healthy adult sheep from Canadian farms that were

slaughtered at a Canadian abattoir.

The national scrapie prevalence study requires a large sample population due to the

disease’s low prevalence in Canada, as shown in Table 1.1 in Chapter 1 of this thesis. Only adult

sheep can be diagnosed with scrapie thus being collected in this study. The presence of

permanent incisors was used to determine the age of the sheep being sampled—when the first

permanent incisor erupts, the animal is one year old, thus an adult. However, only sheep with

readable CSIP tags, which enable CFIA agents to trace their flocks of origin, were included in

the analysis. This is to make sure the prevalence estimation for each province is accurate. To

ensure the samples were obtained strictly through active surveillance, only samples obtained at

the time of slaughter from healthy sheep at the abattoirs were used. Samples obtained by passive

surveillance (i.e. animals sampled directly from a farm or CFIA laboratory) were excluded. This

is because abattoir surveillance samples are considered to be from seemingly “healthy animals”,

whereas voluntary submissions through passive surveillance are considered to be from a higher

risk group for scrapie and therefore not representative of the period prevalence for Canada.

2.2.2 Sampling

The required sample size was determined to ensure that the prevalence can be accurately

estimated with 95% confidence. To achieve this, the target was to sample 15,000 sheep over a

period of two years (Olaf Berke, Scrapie among sheep in Canada-Sample size considerations for

prevalence estimation, unpublished report to the CFIA and CSF, University of Guelph, 2008).

46

CFIA agents were in charge of the sampling process, from both federally and provincially

inspected abattoirs, in all 10 Canadian provinces except Alberta. The three territories were not

included in the study because the respective sheep populations are considered negligible for the

purpose of this study. In Alberta, Alberta Agriculture and Rural Development (AARD) was in

charge of carrying out the sampling procedure and submitting information to CFIA. The detailed

sampling procedure for Alberta is described in “The Alberta Scrapie Prevalence Program”

(Hernan Ortegon, Alberta Agriculture and Rural Development, personal communication, 2011)

and for the rest of Canada in “Scrapie Prevalence Study Sampling Plan” (Heather Brown, CFIA,

Scrapie Prevalence Study Sampling Plan, personal communication, 2011). Both sampling frames

are summarized below.

Four federal abattoirs across Canada slaughtered sheep during the study period: one in

Ontario, two in Quebec, and one in Alberta. At federally inspected abattoirs, CFIA inspectors are

always on-site; therefore, they were able to sample every mature sheep slaughtered in these

abattoirs.

Due to the large number and the wide geographical distribution of provincially inspected

abattoirs, the sampling methods varied among provinces. Because a comprehensive list of sheep

slaughtered in Canada enabling simple random sampling (SRS) was not available, multistage

sampling was performed.

At provincially inspected abattoirs, the primary sampling stage was stratified by

province. Each province was considered a stratum, or the primary sampling unit. The secondary

sampling unit was the abattoir, but sampling methods differed between provinces. Cluster

sampling was conducted in Ontario and Quebec; convenience sampling was conducted in the

Atlantic Provinces and Alberta; and all abattoirs were selected in British Columbia,

Saskatchewan and Manitoba.

47

Cluster sampling was conducted as the second stage in Ontario and Quebec because of

the large number of sheep slaughtered: over 50% of Canadian sheep are slaughtered in these two

provinces. Abattoirs were divided into three clusters depending on their slaughtering capacity:

those processing 500 or more sheep per year (cluster 1), those processing 50 to 499 sheep per

year (cluster 2), and those processing fewer than 50 sheep per year (cluster 3). Only abattoirs in

clusters 1 and 2 were visited in this study. SRS was used in those two clusters to select the

abattoirs for this study. The week for an abattoir to be sampled was determined using a random

number generator; a CFIA agent would then pick the most appropriate day of that week to

sample (i.e., the day likely to provide the most samples), as many abattoirs slaughter sheep only

on certain days. For abattoirs which slaughter on multiple days per week, if possible, the agent

picked different slaughter days when repeat sampling at a particular site. This was to avoid over-

representation by certain populations; for example, because culls shipped from western Canada

are most likely sold at the Ontario auction markets on Monday and slaughtered in Ontario

abattoirs on Mondays or Tuesdays, inspectors were to avoid always sampling on Mondays and

Tuesdays. All mature sheep, which were presented for slaughtering on the day the abattoirs were

visited by CFIA inspectors, were sampled.

Convenience sampling was used as second stage sampling method in Atlantic Provinces

(New Brunswick, Prince Edward Island, Nova Scotia, and Newfoundland and Labrador), CFIA

inspectors took samples whenever the opportunity arose. For example, only when a CFIA

inspector was present at an abattoir where a mature sheep was being slaughtered, was the animal

sampled. In Nova Scotia, most or all of the sheep slaughtered were sampled because the

provincial inspectors at the Nova Scotia provincial abattoirs agreed to collect samples on behalf

of the CFIA.

48

Convenience sampling conducted in Alberta was performed by AARD provincial

inspectors. There are 54 provincially inspected abattoirs in Alberta. However, at the start of the

prevalence study, only four were processing a significant number of mature sheep on a regular

basis. Of those four abattoirs, one closed before the study was completed and one refused to

participate in the project. Therefore, samples representing about 80% of all mature sheep

slaughtered in Alberta were collected from two abattoirs which slaughtered sheep from the entire

province. A random selection of sampling dates was provided to these participating abattoirs,

based on the number of samples to be collected within the time frame and excluding non-

working days.

In British Columbia, Saskatchewan, and Manitoba mature sheep were sampled at

provincial abattoirs, which are staffed full-time with CFIA inspectors. All adult sheep

slaughtered in those plants were sampled.

The Pearson χ2-test was applied to test the proportional representativeness of samples

from each province by comparing the proportion of sheep population in each province (expected

value) with that of the sample population (observed value).

Tissues from either or both the obex section of the medulla oblongata of the brain and

retropharyngeal lymph nodes, when available, were collected from each sheep. In general, the

scrapie prion protein is more likely to be detected in the obex than retropharyngeal lymph nodes.

Samples were all sent to CFIA laboratories for scrapie testing.

2.2.3 Diagnostic testing

Diagnostic tests were performed on both lymphoid and obex tissues when available. The

screening diagnostic test used for this study was Bio-Rad ELISA (Bio-Rad Laboratories,

Inc., Hercules, California, USA;, according to CFIA-OLF Standard Operating Procedure, SOP

#SS-PR012.02, Heather Brown, CFIA 2010, personal communication).

49

Samples that were classified as positive by the Bio-Rad ELISA were submitted for

confirmatory testing by Western blot (WB) and immunohistochemistry (IHC). In scrapie testing,

WB is the accepted regulatory method of differentiating between classical and atypical scrapie

acts strain Nor98 (Heather Brown, CFIA, personal communication, CFIA-OLF Standard

Operating Procedure, SOP #SS-PR012.02, 2010). A positive result for classical scrapie from WB

or IHC is interpreted in parallel, i.e. any positive result is considered positive for scrapie.

This testing system is considered gold standard. The sensitivity and specificity are both

close to 100% and thus assumed to be perfect in all calculations.

2.2.4 Data management

Data on animal’s national ID, postal codes of farms of origin, the farm ID (i.e. Public

Account No.) and other sample information were recorded by the CFIA and ARRD inspectors

according to the time frame and location being sampled, using an electronic spreadsheet

(Microsoft Office Excel®, 2007). Testing results from the Bio-Rad ELISA test were also

recorded in the spreadsheets, and results from the WB and IHC tests were entered as comments

of the first test results, confirming the first test results and distinguishing atypical and classical

scrapie. Data were shared by the CFIA, under the confidentiality agreement, with researchers at

the University of Guelph, where the accuracy of data entry was reviewed and corrected where

possible, and missing values were identified. The resulting cleaned data set was then subjected to

statistical analysis using R 2.15.3 (R development team, 2012).

2.2.5 Prevalence and confidence interval estimation

Prevalence can be estimated in several ways and even more methods exist for estimating

respective confidence intervals. Due to the diversity of sampling methods involved for the

various provinces or strata, stratified sample estimation methods were applied and compared to

non-stratified estimation methods.

50

Ideally stratification will lead to a proportional representation of the target population in

the sample and estimators reduce to simple averages. When the target population is however not

proportionally represented, then post sampling stratification is necessary, and estimators are

based on weighted averages, where the weights represent the proportion of the strata in the target

population.

The stratified prevalence estimator of the total sample population p is a weighted average

of the individual stratum prevalence estimator ph, with weights (Wh=Nh/N) proportional to the

number of elements in each stratum (Levy and Lemeshow, 2008). The equation is thus written as

∑

∑

(Equation 2.1)

where N is the total number of sheep in Canada, Nh is the number of sheep in each stratum h

(each province), L is the number of strata or provinces (L=10), and Wh=Nh/N is the proportion of

the total population belonging to stratum h. N and Nh were obtained from Statistics Canada’s

semi-annual survey for January 1, 2013 (Statistics Canada, 2013).

The prevalence of scrapie has been reported by various countries at low levels of about 1

case per 1000 animals or below. Therefore non-standard approaches to estimating the prevalence

and its confidence interval (CI) were considered in order to select an appropriate method.

The Wald CI is the most commonly used approximation to estimate the CI for a

proportion or prevalence:

(Equation 2.2)

where p denotes the estimate of the proportion (prevalence), standard error

√ where q equals (1-p), and z denotes the appropriate percentile of the standard Gaussian

distribution which for the usual two-sided 95% interval is 1.96. The major limitation of the Wald

CI for prevalence is the lower coverage than the nominal level (McV Messam et al., 2008). The

attained coverage was only 0.881 on average when the nominal coverage level 1-α was set at

51

0.95 in an experiment by Newcombe (2013). This coverage limit can be improved by using the

continuity-corrected Wald interval, for which the formula becomes

√

(Equation 2.3)

However, the increased coverage does not solve another problem of the Wald interval: the lower

or upper limit could go beyond the boundaries of 0 and 1. Therefore, the Wald and continuity-

corrected Wald interval approximation have poor performance when the true prevalence is close

to or equal to 0 or 1. Neither method is appropriate to estimate the CI in the current context.

The Agresti-Coull CI, or modified Wald CI, is especially designed for situations when

the true prevalence is expected to be close to 0. The prevalence is then estimated by the

shrinkage estimator:

(Equation 2.4)

where ψ is a pseudo frequency greater than 0 and is commonly set to be (Newcombe,

2013). For the default 95% CI, z=1.96 and ψ=1.92 2. Hence, the formula is reduced to

(Equation 2.5)

which represents the addition of 2 cases and 2 non-cases. Note that for small samples this

estimate is close to 0.5, but with increasing sample size it will move towards the true prevalence.

The Agresti-Coull CI is:

√{ ( )

} √{

( )

} (Equation 2.5)

This method reduces but does not eliminate the boundary violation problem. In the case where

the true prevalence is 0 or 1, a CI of a point (which happens in the case of Wald CI) is avoided.

However, its limits can still go beyond the boundaries of 0 and 1.

The Wilson score CI, although closely related to the Wald formula, corrects the boundary

anomalies problem. With a slight modification, the Wilson score CI performs well even when the

52

total sample size n is small, or when p or q is at or near zero (Newcombe, 2013). The standard

error imputed to p is √

instead of √

, where p is the prevalence estimator based

on the data observed and is the unknown true population prevalence. The lower and upper

limits are obtained by solving for π in the following equation:

√

(Equation 2.7)

The solution is thus obtained as

(

) (

)

√

where, as usual, q denotes 1-p. As shown in the formula, the Wilson score interval is asymmetric

around the generally reported maximum likelihood prevalence estimator and produces no

boundary anomalies. For rare diseases, when r>0, the lower limit is always positive; when r=0,

the lower limit is 0 and the upper limit becomes

.

While the Wald interval has low coverage, the Agresti-Coull interval and Wilson score

interval have closer coverage to the desired confidence level (Agresti and Coull, 1998). Given

that the Agresti-Coull interval might result in boundary violation for an extremely small

estimator, the Wilson score test is considered more suitable for estimating uncertainty in the

scrapie prevalence estimator.

After CIs of provincial prevalence were estimated, the CI of the national prevalence was

estimated based on stratification by province. The standard error for the national prevalence

estimator is obtained by the following equation

√∑ (

)

(

)

(Equation 2.8)

53

where is the total number of sheep in Canada, is the number of sheep in each stratum h

(each province), is the number of sheep sampled in stratum h, is the estimated prevalence

in stratum h (Levy and Lemeshow, 2008). The stratified Wald CI for the national prevalence can

be estimated by applying Equation 2.2.

As mentioned, the Wald interval has many limitations and is not a recommended method

in rare disease situations. Therefore, the stratified Wilson interval estimation method has been

developed by Yan and Su (2010) for stratified prevalence. The lower and upper limits of this

interval are obtained as follows:

∑ (

√

) (Equation 2.9)

∑ (

√

) (Equation 2.10)

where k is the number of strata, and the required normal percentile and weights are given

as

{

√∑

∑ √

⁄

}

(Equation 2.11)

( )

( )

∑

( )

⁄ (Equation 2.12)

In order to calculate weights, the following iterative procedure was followed:

1. The initial cut-off was chosen as with α=0.05 and k=10, and Equation 2.12 was

used to compute the initial weights .

54

2. The initial weights were substituted into Equation 2.11 to compute the new normal

percentile .

3. The percentile were substituted into Equation 2.12 to calculate the new weights .

4. The updated weights were substituted into Equation 2.11 to compute the new

normal percentile .

After all the steps, the updated percentile was substituted into Equation 2.9 and

Equation 2.10 to obtain the lower and upper bounds of the stratified Wilson interval.

A function in the R software environment (R development team, 2012) was written,

based on the formulas above, to estimate this stratified Wilson interval (Appendix A). The results

of the lower and upper CI were confirmed by MATLAB® (Appendix B).

In summary, the maximum likelihood estimator (MLE) (ph=r/nh) and Agresti-Coull

shrinkage estimator (Equation 2.5) were applied to estimate the provincial and the national non-

stratified prevalence. The CIs for these estimates were obtained by the Wilson score method and

the Agresti-Coull method. For the stratified prevalence analysis, only the MLE was applied,

since a stratified version of the Agresti-Coull shrinkage estimator is not available. Respectively,

the CIs for the national stratified prevalence were estimated using the stratified Wilson intervals.

Farm level prevalence and CIs were estimated using the same method as the individual

level ones. The total numbers of sheep farms in the 10 provinces were obtained from the 2011

Census of Agriculture by Statistics Canada.

2.3 Results

From November 2010 through December 2012, a total of 13,057 samples were collected

from all 10 provinces of Canada. However, 1,355 samples were excluded from the analysis, of

which 689 belonged to other species and 567 were missing animal ID information. Further

samples were excluded due to other reasons such as repeated entries or non-abattoir sampling.

55

The data relating to 11,702 samples were used for statistical analysis. Of those, 1,589 had only

results from one type of tissue; either because only one sample was suitable for testing or only

one sample was obtained at slaughter. Those records were included in the analysis as valid test

results were available.

The 11,702 samples were collected at 4 federal and 85 provincial abattoirs. The numbers

recorded from the CSIP tags attached to the slaughtered animals indicated that the sampled sheep

originated from 3,233 farms. 7 scrapie positive samples were identified by testing. Each positive

animal originated from a different farm representing three provinces: Alberta (1), Ontario (2) ,

and Quebec (4). The sheep-level and farm level prevalence estimates for each province are

shown in Table 2.1 and Table 2.2 respectively. Sheep level prevalence estimates ranged from 0%

(no classical scrapie case found) to 0.083% (Ontario), whereas farm level ranged from 0% to

0.50% (Quebec). The lower bounds of the Agresti-Coull interval were negative for some of the

provinces; those values should be assumed to be 0 since prevalence cannot be negative. The

Agresti-Coull intervals are wider than the Wilson interval except for the province of NL.

However, among all the provinces, the CI value by the two methods, both lower and upper

bounds, differed by less than 2%.

The χ2-test result, for proportional representativeness of samples from different provinces,

was significant (P-value = 0.007). This indicates a significant difference in the sample

distribution and the sheep distribution at provincial level. Therefore the national scrapie

prevalence was estimated using a stratified estimator based on a weighted average of the

provincial estimates.

At the individual sheep level (Table 2.1), the non-stratified prevalence of scrapie for

Canada is 0.06% with the Wilson CI (0.03%, 0.12%) and the Agresti-Coull CI (0.03%, 0.13%) at

95% confidence. The stratified prevalence of scrapie at the sheep level for Canada (Equation 2.1)

56

was estimated to be 0.06% with stratified 95% Wilson confidence interval for the prevalence

estimate being CI95%(psheep)=(0.02%, 0.20%).

At the farm level (Table 2.2), the non-stratified prevalence of scrapie for Canada is 0.22%

with the Wilson CI (0.11%, 0.45%) and the Agresti-Coull CI (0.10%, 0.46%) at 95% confidence.

The stratified prevalence of scrapie at the farm level for Canada (Equation 2.1) was estimated to

be 0.15%, with stratified 95% Wilson interval for the prevalence estimate being CI95%(pfarm)=

(0.06%, 0.66%).

2.4 Discussion

The objective of this study was to obtain an accurate estimate with 95% confidence

interval for the prevalence of scrapie among sheep in Canada. It was planned to sample 15,000

sheep nationwide. This number was based on previous data collected from national surveillance

activities conducted in 2006 and 2007. Past data showed a prevalence of approximately 1 scrapie

case per 1000 sheep (Penny Greenwood, CFIA, personal communication). This current study

found a lower prevalence than expected. Therefore, even though the required sample size was

not achieved, the power of the study was sufficient to provide an accurate estimate, since the

assumption was based on a prevalence of 0.1%, which is larger than the actual value of 0.06%.

Since the sample populations were not proportional to the actual sheep populations in

each province, the prevalence was estimated using both non-stratified and stratified estimation

methods at the national level. When comparing the stratified with non-stratified prevalence

results, the CIs are shorter without stratification for both sheep level and farm level. If a good

stratification predictor is chosen, the variance should be smaller after stratification (Cochran,

1977). For this study, each province was classified as a stratum because the prevalence was

expected to be different between them. The stratified CI ranges are 86% and 75% wider than

non-stratified intervals for sheep level and farm level, respectively. This indicates that “province”

57

is not an effective stratification variable. In other words, the prevalence of scrapie is not

significantly different among the provinces; it is considered unnecessary to stratify. Therefore,

the non-stratified prevalence is reported as the final result. For future sampling, “age” or “breed”

might be used as stratification variables; this information, however, was not consistently

available for this study and is difficult to collect. Age can be roughly determined by examination

of the eruption of the incisors (Hongu et al., 2004). Accurate breed identification of purebred

animals requires considerable knowledge and experience. Additionally, many sheep are cross-

bred and not phenotypically uniform even within cross-breed types. However, notation of colour

of the face (black-faced versus white-faced breeds) is used by the US when stratifying sampling

and could be easily done by CFIA inspectors at the abattoir (USDA et al, 2013).

Most studies on scrapie conducted in European countries and the US showed a higher

prevalence than the results found in this study. Active surveillance conducted from 2002 to 2006

in 20 European countries showed 6 countries did not have scrapie detected; for those that had

disease detected, the prevalence of classical scrapie in healthy slaughtered animals varied from

0.003% in Switzerland in 2004 to 0.28% in Northern Ireland in 2005, with Cyprus observing the

highest prevalence of 15.09% in 2004 (Fediaevsky, 2008). The same study reported the

prevalence in fallen stock to have varied from 0.02% in Norway in 2006 to 2.2% in Slovenia in

2005, with Cyprus observing the highest prevalence of 24.56% in 2003. A study in France

estimated the prevalence of classical scrapie to be 0.44% (Vergne et al, 2012). In Great Britain,

the prevalence of classical scrapie declined from 0.6-0.7% in 2003 to 0.3-0.4% in 2007,

approximately a 40% decrease, as a result of various control schemes (Gubbins and McIntyre,

2009). The European Commission’s annual report for 2011 from all its 27 members states shows

an overall prevalence of 0.048% in the adult sheep population slaughtered for human

consumption, and a prevalence of 0.20% in other sheep populations which were not intended for

58

human consumption (mainly fallen stock) (The European Commission, 2012). In the United

States, the active surveillance studies conducted at the abattoir level estimated prevalence in cull

sheep at 0.20% from October 1, 2001, to September 30, 2002 (United States Department of

Agriculture, 2004). In a more recent study conducted in the US from October 1, 2011 to

September 30, 2012 (United States Department of Agriculture et al, 2012), 8 sheep were found

scrapie positive among 43,228 animals collected from the abattoirs, of which 83% were sheep.

The prevalence estimate was 0.022%, approximately one-third of the prevalence found in this

study.

This study, which focused on the seemingly healthy sheep population sampled at

abattoirs, showed a prevalence value lower than most other countries and is closest to the two

most recent estimates in the US (0.2% and 0.022%) and in the European Union (EU) (0.048%)

obtained through similar surveillance systems. These studies all had samples collected mainly

from abattoirs.

Various studies have demonstrated that the prevalence of scrapie among sheep sampled at

abattoir is lower than in dead stock (The European Commission, 2012). This study did not

consider dead stock samples, but future studies are recommended to consider this animal group.

Samples with either no CSIP ID or no sampling location information were excluded from

statistical analysis in this study. This was to assure that all samples were traceable to the

province of origin, also were collected through active surveillance in abattoirs rather than from

other sources. However, these 567 excluded samples, approximately 5% of the total, tested all

negative; thus the national scrapie prevalence was slightly over-estimated.

This study identified scrapie positive animals originating from three Canadian provinces:

Alberta, Ontario and Quebec. As shown in Table 1.1 in Chapter 1, passive surveillance from

1984 to 2011 had indicated scrapie cases in these three provinces with Ontario and Quebec

59

consistently having the most scrapie cases. Therefore, it was expected to similarly find this in

this study. However, although Saskatchewan, Manitoba and the Atlantic Provinces had reported

scrapie cases in the past, no case was found in those provinces during this study. This result

might be the effect of effective national scrapie control and eradication programs. Additionally,

this might result from too small sample sizes achieved in those provinces. Although every

mature sheep slaughtered in Saskatchewan and Manitoba abattoirs during the two years of this

study was sampled, the number was less than 1,700 sheep, so likely too few sheep were tested to

identify presence of infection.

Both lymphoid tissues and obex tissues were sampled. However, in 1,589 samples

(13.6%) only one type of tissue was available for testing; the second sample was either not

available or damaged and thus unfit for diagnostic testing. The results from these samples were

included in the analysis. In the future, sampling and sample storage procedure needs to be

improved to avoid these losses. In some infected individuals, scrapie prion PrPSc accumulates

more in the obex than in lymphoid tissues (Andreoletti et al, 2000); test sensitivity may be

compromised if only one type of sample is tested.

The objective of this study was to estimate the confidence intervals of scrapie in each

individual province as well as at the national level. Scrapie is considered a rare disease around

the world, with the exception of Cyprus. The typical confidence interval estimation method, i.e.

the Wald CI, is inappropriate when the prevalence is close to zero. Therefore, alternative CI

estimation methods were considered here. Of these, the Wilson score and Agresti-Coull intervals

were chosen to estimate the CIs for provincial and non-stratified national prevalence. Some of

the Agresti-Coull intervals showed negative values as the lower boundary, as expected. The

negative values can be adjusted to zero since prevalence cannot be negative. The Wilson interval

showed narrower ranges than the Agresti-Coull interval in most situations, except for the

60

province of NL due to few samples, indicating the Agresti-Coull method is more conservative

than the Wilson method.

2.5 Conclusion

This study has estimated both individual level and farm level scrapie prevalence for the

healthy sheep population at slaughter in Canada with sufficient accuracy and reliability. This

study provides information for regulatory veterinarians in charge of zoosanitary measures in

Canada, and for scrapie researchers in general. Since the samples were taken at abattoirs only,

future scrapie surveillance activities should include dead stock sampling to provide further

insight of the scrapie distribution in the Canadian sheep population.

The active surveillance design used for the study was feasible and effective. However,

the samples were not geographically representative. The Atlantic Provinces did not have many

samples taken but have previously reported scrapie cases. There seems to be a need for the CFIA

to sample more sheep from those underrepresented provinces to get a more accurate scrapie

prevalence estimate.

In order to achieve geographical representation, animals can be identified at the farm

level in a targeted region and then be followed to slaughter. This way, the animals are being

tested for scrapie regardless of the slaughter location. The Radio Frequency Identification (RFID)

tags would be helpful in this situation and improve the Canadian Sheep surveillance system. The

dissemination and requirements of RFID tags are still in progress with the exception of Quebec

where the RFID tags are mandatory (Canadian Food Inspection Agency, 2012c).

In order to monitor and assess the effect of scrapie control and eradication efforts in

Canada, continuing sampling of the surveillance project is required to track changes in

prevalence. For the next step of this project, more accurate information is needed on the origin of

the sheep in order to understand the geographic distribution of scrapie. Furthermore, better

61

geographic representation needs to be achieved, as some provinces were under-represented.

Future surveillance activities should be targeted to under-sampled areas.

2.6 Acknowledgements

I would like to acknowledge the data compiling of Heather Brown at the Canadian Food

Inspection Agency (CFIA) and Hernan Ortegon at the Alberta Agriculture and Rural

Development (AARD). I would like to further acknowledge Bimal Chhetri from the Department

of Population Medicine, University of Guelph, for helping with coding in R, and Herbert Tang

from the Department of Applied Mathematics, University of Waterloo, for assistance with

estimating the Stratified Wilson confidence intervals. In addition, I would like to thank the

Canadian Sheep Federation for funding this study.

62

2.7 References:

Agresti, A., Coull, B.A., 1998. Approximate is better than “exact” for interval estimation of

binomial proportions. The American Statistician, 52, 119-126.

Andreoletti, O., Berthon, P., Marc, D., Sarradin, P., Grosclaude, J., van Keulen, L., Schelcher, F.,

Elsen, J-M., Lantier, F., 2000. Early accumulation of PrPSc in gut-associated lymphoid

and nervous tissues of susceptible sheep from a Romanov flock with natural scrapie.

Journal of General Virology 81,3115-3126.

Benestad, S. L., Arsac, J., Goldmann, W., Noremark, M., 2008. Atypical/Nor98 scrapie:

properties of the agent, genetics, and epidemiology. Vet. Res., 39(4), 19.

Boomsma, A., 2006. Confidence Intervals for a Binomial Proportion. Department of Statistics &

Measurement Theory, University of Groningen, The Netherlands.

Canadian Food Inspection Agency (CFIA), 2012a. Fact Sheet – Scrapie.

http://www.inspection.gc.ca/animals/terrestrial-animals/diseases/reportable/scrapie/fact-

sheet/eng/1356131973857/1356132310673

Canadian Food Inspection Agency (CFIA), 2012b. Flocks infected with scrapie in Canada in

2011. Retrieved Mar 2012 from: http://www.inspection.gc.ca/animals/terrestrial-

animals/diseases/reportable/2011/flocks-infected-in-

2011/eng/1329729421107/1329729572094

Canadian Food Inspection Agency (CFIA), 2012c. Canadian sheep identification program.

http://www.inspection.gc.ca/animals/terrestrial-animals/traceability/sheep-

identification/eng/1328852777479/1328852957523

Canadian Sheep Federation (CSF), 2011. Myth: Allowing sheep to transit through Canada from

the northern US states to Alaska puts the Canadian industry at a disadvantage. Retrieved

from: http://www.cansheep.ca/User/Docs/POV%20Summer%20Edition%202011.pdf

Cochran, W. G., 1977. Sampling Techniques, 3rd Edition. John Wiley & Sons. Inc., USA, pp99.

The European Commision, 2012. Report on the monitoring of ruminants for the presence of

transmissible spongiform encephalopathies (TSEs) in the EU in 2011. Retrieved from:

http://ec.europa.eu/food/food/biosafety/tse_bse/monitoring_annual_reports_en.htm

Fediaevsky, A., Tongue, S. C., Nöremark, M., Calavas, D., Ru, G., Hopp, P., 2008. A descriptive

study of the prevalence of atypical and classical scrapie in sheep in 20 European

countries. Veterinary Research, 4:19.

Foster J.D., Dickinson, A.G., 1989. Age at death from natural scrapie in a flock of Suffolk sheep.

Vet. Rec. 125, 415-417.

Gregoire, T. G., Valentine, H. T., 2008. Sampling Strategies for Natural Resources and The

Environment. Boca Raton, Florida, USA, Taylor & Francis Group, LLC, pp127-164.

Gubbins, S., McIntyre, K.M., 2009. Prevalence of sheep infected with classical scrapie in Great

Britian, 1993-2007. Epidemiol Infect., 136(6): 787-91.

Hongo A, Zhang J, Toukura Y, Akimoto M., 2004. Changes in incisor dentition of sheep

influence biting force. Grass Forage Sci, 59:293–297.

Hunter, N., Cairns, D., 1998. Scrapie-free Merino and Poll Dorset sheep from Australia and New

Zealand have normal frequencies of scrapie-susceptible PrP genotypes. J. Gen. Virol., 79

(Pt 8):2079-82.

Kim, K., 2007. The Social Construction of Disease: From Scrapie to Prion. Routledge, Taylor &

Francis Group, New York, NY, USA, pp. 107-126.

Levy, P. S., Lemeshow, S., 2008. Sampling of Populations: Methods and Applications (Forth

Edition). John Wiley & Sons, Inc., Hoboken, NJ, USA, pp. 121-139.

63

McV Messam, L.L., Branscum, A.J., Collins, M.T. Gardner, I.A., 2008. Frequentist and

Bayesian approaches to prevalence estimation using examples from Johne’s disease.

Anim. Health Res. Rev., 9(1), 1-23.

Newcombe, R. G., 2013. Confidence Intervals for Proportions and Related Measures of Effect

Size. Taylor & Francis Group, LLC, Boca Raton, FL, USA, pp55-75.

Petrie, A., Watson, P., 2006. Statistics for Veterinary and Animal Science (second edition).

Blackwell, Oxford, pp. 12-53.

R Development Core Team, 2012. R: A language and environment for statistical computing. R

Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL

http://www.r-project.org

Statistics Canada, retrieved May 2013. Sheep inventories, by province.

http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/prim52a-eng.htm

Yan, X., Su, X. G., 2010. Statified Wilson and Newcombe Confidence Intervals for Multiple

Binomial Proportions. Statistics in Biopharmaceutical Research, 2:3, 329-335.

United States Department of Agriculture (USDA), 2004. Phase II: Scrapie: Ovine Slaughter

Surveillance Study 2002-2003. USDA:APHIS:VS,CEAH, National Animal Health

Monitoring System, Fort Collins, CO., #N419.0104. Retrieved from:

http://www.aphis.usda.gov/animal_health/animal_diseases/scrapie/downloads/sossphase2

.pdf

United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service

(APHIS), Veterinary Services Centers for Epidemiology and Animal Health (VSCEAH),

National Surveillance Unit (NSU), Fort Collins, CO., 2010. National Scrapie

Surveillance Plan. Retrieved from:

http://www.aphis.usda.gov/animal_health/animal_diseases/scrapie/downloads/national_s

crapie_surv_plan.pdf

United States Department of Agriculture (USDA), Animal Plant Health Inspection Service,

Veterinary Services (APHIS), National Center for Animal Health Programs, Ruminant

Health Programs, 2012. National Scrapie Eradication Program, Fiscal Year 2012 Report,

October 1, 2011 to September 30, 2012. Retrieved July 2013 from:

http://www.aphis.usda.gov/animal_health/animal_diseases/scrapie/downloads/yearly_rep

ort.pdf

Vergne, T., Calavas, D., Cazeau, G., Durand, B., Dufour, B., Grosbois, V., 2012. A Bayesian

zero-truncated approach for analysing capture-recapture count data from classical scrapie

surveillance in France. Prev Vet Med., 105 (1-2): 127-135.

Woolhouse, M.E.J., Matthews, L., Coen, P., Stringer, S.M., Foster, J.D., Hunter, N., 1999.

Population dynamics of scrapie in a sheep flock. Philosophical Transactions: Biological

Sciences: 354(No. 1384): 751-756.

Woolhouse, M.E.J., Coen, P., Matthews, L., Foster, J.D., Elsen, J., Lewis, R.M., Haydon, D.T.,

Hunter, N., 2001. A centuries-long epidemic of scrapie in British sheep? Trends

Microbiol., 9(2): 67-70.

64

Table 2.1 Prevalence estimation at sheep-level of classical scrapie in 10 provinces of Canada (sample collected from November 2010 to

December 2012).

Province Sample

Size (nh)

Scrapie

case (r)

Mature

Sheep (Nh)

Maximum

likelihood

estimator

(ph)

Agresti-Coull

shrinkage estimator

(Pψ)

Wilson CI1 for Ph Agresti-Coull CI1 for Ph

Alberta (AB) 1358 1 98200 0.000736 0.002203 0.000038 0.004159 -0.000312 0.004601

British

Columbia (BC) 932 0 27700 0.000000 0.002137 0.000000 0.004105 -0.000847 0.004952

Manitoba (MB) 365 0 29400 0.000000 0.005420 0.000000 0.010415 -0.002138 0.012553

New Brunswick

(NB) 64 0 4700 0.000000 0.029412 0.000000 0.056624 -0.011156 0.067780

Newfoundland

and Labrador

(NL) 1 0 1200 0.000000 0.400000 0.000000 0.948707 -0.039050 0.832501

Nova Scotia

(NS) 329 0 12500 0.000000 0.006006 0.000000 0.011541 -0.002367 0.013908

Ontario (ON) 2412 2 194200 0.000830 0.001656 0.000227 0.003018 0.000018 0.003228

Prince Edward

Island (PE) 39 0 4100 0.000000 0.046512 0.000000 0.089667 -0.017133 0.106800

Quebec (QC) 5035 4 146800 0.000794 0.001191 0.000309 0.002041 0.000229 0.002121

Saskatchewan

(SK) 1167 0 62100 0.000000 0.001708 0.000000 0.003270 -0.000675 0.003945

Canada without

stratification 11702 7 580900 0.000598 0.000769 0.000290 0.001234 0.000262 0.001262

Canada with

stratification 11702 7 580900 0.000602 NA2 0.000224 0.001984 NA2 NA2

Note:

1. CI: confidence intervals

2. NA: there is no Agresti-Coull method for stratified samples

65

Table 2.2 Prevalence estimation at farm-level of classical scrapie in 10 provinces of Canada (sample collected from November 2010 to

December 2012).

Province

Farm

sampled

(nh)

Scrapie

case (r)

Farm in

Province

(Nh)

Maximum

likelihood

estimator

(ph)

Agresti-Coull

shrinkage estimator

(Pψ)

Wilson CI1 for Ph Agresti-Coull CI1 for Ph

Alberta (AB) 462 1 1747 0.002169 0.006452 0.000111 0.012157 -0.000898 0.013438

British

Columbia (BC) 303 0 1587 0.000000 0.006515 0.000000 0.012519 -0.002565 0.015084

Manitoba (MB) 153 0 521 0.000000 0.012739 0.000000 0.024493 -0.004966 0.029459

New Brunswick

(NB) 33 0 137 0.000000 0.054054 0.000000 0.104270 -0.019647 0.123917

Newfoundland

and Labrador

(NL)

1 0 47 0.000000 0.400000 0.000000 0.948707 -0.039050 0.832501

Nova Scotia

(NS) 120 0 293 0.000000 0.016129 0.000000 0.031019 -0.006254 0.037273

Ontario (ON) 964 2 3569 0.002075 0.004132 0.000569 0.007533 0.000049 0.008053

Prince Edward

Island (PE) 18 0 72 0.000000 0.090909 0.000000 0.175879 -0.030832 0.206711

Quebec (QC) 798 4 1166 0.005013 0.007481 0.001951 0.012817 0.001458 0.013310

Saskatchewan

(SK) 381 0 2488 0.000000 0.005195 0.000000 0.009982 0.009982 0.012032

Canada without

stratification 3233 7 11627 0.002166 0.002781 0.001050 0.004464 0.000950 0.004563

Canada with

stratification 3233 7 11627 0.001465 NA2 0.000602 0.006570 NA2 NA2

Note:

1. CI: confidence intervals

2. NA: there is no Agresti-Coull method for stratified samples

66

Chapter 3: Choropleth mapping the sampling intensity of a Canadian scrapie prevalence

study

Abstract

Scrapie is a fatal neurodegenerative disease in sheep and goats. To control scrapie in

Canada, the Canadian Food Inspection Agency (CFIA) has been sampling healthy sheep at

slaughter since 2005. To assure geographic representativeness, the sampling intensity by region

should be monitored. Sheep samples collected from November 2010 to December 2012 were

used to investigate the geographic distribution of sampling intensity with respect to the farm of

origin and number of sheep farms in the region.

A total of 11,702 sheep samples were traced back to their farm of origin. These samples

came from 3,233 farms or 233 Census Division (CD) regions, representing all 10 provinces of

Canada.

An index is proposed which measures for each CD the combined information available at

both farm and sheep level, to indicate where insufficient sampling may have occurred, increasing

the risk of missed cases. This index ranges from 0 to 1, where a smaller value indicates lower

sampling intensity and thus less accurate information. After dividing the distribution of the index

values from 283 CDs within 10 provinces of Canada into quartiles, a choropleth map was

constructed using a four colour scale: indices less than 0.2, between 0.2 and 0.3, between 0.3 and

0.4, and between 0.4 and 1. CDs with indices less than 0.1 are outlined on the map to emphasize

the need of intensified sampling. Those CDs cluster in the West Coast, the southern aspect of the

border between British Columbia and Alberta, southern Manitoba, northern Ontario and portions

of the Atlantic Provinces, indicates that sheep farms in those areas are under-sampled in the

current national scrapie surveillance program.

67

3.1 Introduction

Scrapie is a fatal and infectious neurological disease that occurs in sheep and goats. It is a

type of transmissible spongiform encephalopathy (TSE), or prion disease. Since 1945, scrapie

has been a reportable disease in Canada (Canadian Food Inspection Agency, 2012b). Despite its

long history (Detwiler and Baylis, 2003), scrapie prevalence is lower than 1% around the world

(The European Commission, 2012; United States Department of Agriculture et al, 2012), with

Australia and New Zealand being recognized as scrapie-free (Animal and Plant Health

Inspection Service, 2004; Animal Health Australia, 2009; Ministry for Primary Industries, 2014).

In Canada, the prevalence of scrapie is estimated at a level of about 1 case per 1,700 sheep by

abattoir surveillance over a two-year period (Chapter 2); no scrapie cases were found in goats for

the same sampling period (Leung, Berke, Ortegon, Brown, Menzies, 2013, in preparation).

However, scrapie is a public health concern and it has caused economic loss for farmers.

Once a scrapie case is detected, strict eradication measures are applied to the infected

sheep flock or goat herd. As is usual for infectious diseases, scrapie is expected to cluster on

affected farms as well as farms acquiring sheep or goats from affected farms. Even in the

situation whereby all infected animals had been euthanized in a region, prion diseases had been

found reoccurring several years later (Wiggins, 2009). Therefore, identifying the risk

geographically is critical to disease prevention and control.

The goal of this study is to advise the Canadian Food Inspection Agency (CFIA), the

facilitator of the Canadian Scrapie Eradication Program, regarding the geographic representation

of sheep scrapie samples collected during past surveillance activities, and thus to plan future

sampling. Since 2003, sheep have been required to be tagged using a tag compatible with the

mandatory Canadian Sheep Identification Program (CSIP), which allows for samples to be traced

68

to the farm of origin. Conversely, goat samples are excluded in this study because of the

difficulty of tracing the origin of goats due to lack of a mandatory national goat identification

program.

Specific objectives are (i) to develop an index that measures the intensity of sheep scrapie

sampling at both the farm and sheep level by region, i.e. Census Division (CD), (ii) to identify

those CDs with a low sampling information index, and (iii) to map the sampling information

indexes categorized by quartiles so as to identify areas which should be targeted for scrapie

sampling in the future.

3.2 Materials and methods

3.2.1 Study design

The sheep sample data used in this study was collected through active surveillance

conducted by the CFIA from November 2010 to December 2012. Samples were taken from both

federal and provincial abattoirs across the 10 provinces of Canada. No samples were collected

from the territories of Yukon, Nunavut or the North West Territories as the sheep data from

those provinces are not available and their estimated sheep populations are negligible (Koizumi

et al., 2011).

During the two year sampling period, the CFIA had collected data from 12,368 adult

sheep (adult status determined by presence of permanent incisors) to be used in this study. For

the purpose of this study, only sheep sampled from abattoirs were used; samples from other

locations (e.g. government agencies, farms) were excluded to ensure the samples were obtained

strictly through active rather than passive surveillance. Only sheep with CSIP tags, which

enables CFIA agents to trace their farm of origin, were included in the analysis.

69

Postal codes were obtained for the farm of origin of each sample; these were then linked

to CDs which represent larger areas but are not necessarily inclusive to postal code areas

(Statistics Canada, 2012a). The number of sheep farms in each CD was obtained through the

2011 Census of Agriculture (Statistics Canada, 2011a).

3.2.2 Sampling procedures

Samples were taken from 4 federally licensed abattoirs and 85 provincially licensed

abattoirs in all 10 provinces of Canada. The CFIA coordinated the sampling process and its

inspectors collected samples at abattoirs in all provinces except Alberta, where the Alberta

Agriculture and Rural Development (AARD) fulfilled this function.

Four federally licensed abattoirs across Canada slaughtered sheep during the study period:

one in Ontario, two in Quebec, and one in Alberta. Federally licensed abattoirs are staffed full

time with CFIA inspectors. Every mature sheep slaughtered in these abattoirs was sampled

(Heather Brown, CFIA, Scrapie Prevalence Study Sampling Plan, personal communication,

2011).

The methodology for sampling adult sheep slaughtered at provincially licensed abattoirs

varied by province. Provincially licensed abattoirs in British Columbia, Saskatchewan, and

Manitoba are staffed full-time with CFIA inspectors, and all mature sheep slaughtered in those

abattoirs were sampled. In Ontario and Quebec, the abattoirs were sampled by multi-stage

sampling. Only the abattoirs which slaughter more than 50 sheep per year were included in the

sampling frame. Those abattoirs were visited on a random basis, proportional to the size of

throughput of the abattoirs. All sheep slaughtered during those visits were sampled. In New

Brunswick, Prince Edward Island and Newfoundland and Labrador, the abattoirs were

conveniently sampled; CFIA inspectors sampled adult sheep whenever the abattoirs were visited.

70

In Nova Scotia, almost all of the sheep slaughtered were sampled because the provincial

inspectors agreed to collect samples on behalf of the CFIA. In Alberta, the provincial abattoirs

were conveniently sampled with the exception of the two largest abattoirs at which every mature

sheep was sampled, approximately 80% of all mature sheep slaughtered in Alberta.

3.2.3 Data management

The CSIP ID number of each sheep sampled at slaughter was recorded by the inspectors.

This number was linked to the postal code of the farm of origin, as well as the farm’s Public

Account Number and entered into an electronic database. For each sample, the result from the

Bio-Rad ELISA test was recorded and if positive, the results from the Western blot (WB) and

Immunohistochemistry (IHC) tests and determination of type of scrapie, i.e. atypical or classical

form of scrapie was also recorded. A confidentiality agreement between the CFIA and

researchers at the University of Guelph allowed for data sharing. The accuracy of data entry was

reviewed and necessary corrections were made in agreement with the responsible CFIA

supervising inspector, before data analysis began. The corrected data were then statistically

analyzed using R 3.1.0 (R Core Team, 2014). The R software add-on package “maptools” and

“rgdal” were used to draw a choropleth map.

The CSIP number recorded from the ear tag on sheep was used to determine the address

of the purchaser of the tag, assumed to be the address of the farm of origin of the sheep. The

address was not provided to researchers but rather the CFIA agent provided only the postal code

of the farms to protect the privacy of the farmers. The spatial unit of analysis chosen was Census

Divisions (CDs). CD refers to provincially legislated areas (such as county, municipalité

régionale de comté and regional district) or their equivalents (Statistics Canada, 2012a), which

make up the provinces or territories. Postal code areas are considered too small to be targeted in

71

future eradication programs and are lacking necessary census information. However, postal code

allowed for determination of the CD of the farms from which sheep were sampled.

The sheep farm distributions in each CD region were obtained from the 2011 census by

Statistics Canada (Statistics Canada, 2011a). The data used were from the “crop categorization”

which declares a farm as a sheep farm based on any sheep being present on the farm at the time

of the census, rather than the “industrial categorization” which is based on the major farming

activity.

Postal codes of the farms were converted to CD using a software routine in SAS

(Copyright © 2002-2003 by SAS Institute Inc., Cary, NC, USA) with the help of the Data

Resource Centre (DRC), University of Guelph. The conversion files in the SAS program were

last updated in 2013. Because the postal codes are updated every year and the study was

conducted from 2010 to 2012, the conversion did not work on all the postal codes. Therefore,

some “postal code to CD” conversions were provided by the CFIA using exact addresses.

The boundary file of Canada was obtained from Statistics Canada (Stats Canada, 2011b).

The boundary file contains the boundaries of 283 CDs for the 10 provinces of Canada.

3.2.4 Sampling information index

To determine how intensively the various CD's were sampled, an index was developed.

This index is to describe the risk of detecting a scrapie case in a particular area, i.e. CD. In

epidemiology, “risk” usually refers to the probability that a particular outcome (e.g. developing a

disease) will occur over a given period of time (Last, 2001). However for this study, the term

“risk” is the probability of finding a scrapie case by sampling adult sheep at an abattoir given the

CD of origin of the sheep. Specifically, the risk of having undetected scrapie cases was assumed

to be higher in areas in which fewer sheep and farms were sampled as compared to areas where

72

more sheep and farms were sampled. Thus the risk of having an undetected scrapie case in a CD

is considered higher the less information was collected in the past. This relates to the fraction of

farms sampled as well as the fraction (or number) of animals sampled in a CD.

The sheep level sampling proportion (the number of sheep sampled divided by the total

number of sheep) in each CD area was not available due to confidentiality reasons where the

number of sheep in certain regions cannot be revealed to the public. Also, the number of sheep

changes constantly and the sheep number being reported to Statistics Canada may not correspond

to the sampling period. Effectively this is down-weighting the sheep information for all but one

area, i.e. where the maximum was observed.

The farm level sampling proportion can be calculated using the number of farms with

sheep that were sampled divided by the number of total farms reported by the census. However,

some CDs had more farms sampled than the number reported by the census thus resulting in a

fraction greater than one. In such situations, the number of farms reported for that CD was

changed to the number of sampled farms to assure the fraction of sampled farms per CD is

always between 0 and 1. However, the sampling proportion calculation would need further

adjustments for geographic comparisons due to variations in regional sample sizes.

To assess the intensity of sampling of farms and sheep in a given CD, an index is used

which combines information from both farm level and individual level sampling proportions.

The index is partially based on the Freeman-Tukey Transformation (Cressie, 1993):

√

√

(Equation 3.1)

where Zi is the transformed attribute of region i, mi and ni are the number of samples and the

total population in region i respectively, and i is the region index ranging from 1 to the total

number of regions in the study area (N=283 for this study).

73

For the purpose of this study, the Freeman-Tukey Transformation has been modified.

Two farms were added in a CD to the total number of farms to be consistent with the Agresi-

Coull estimation applied in the prevalence study (Chapter 2 of this thesis). The modified index is

calculated as the following:

[(√

√

) ] (

)

(Equation 3.2)

where, Ii is the index estimator for CD i, fi is the number of farms sampled in CD i, Fi is the total

number of farms in this CD, ni is the number of sheep sampled in CD i, M is the maximum

number of sheep sampled in any CD, wf is a weight assigned to farm level sampling proportion,

and ws is a weight assigned to individual level sampling proportion. The weights wf and ws range

between 0 and 1 with the condition wf + ws = 1, and are introduced to assure that the index ranges

between 0 and 1 and is interpretable as a probability or level of information available regarding

sampling intensity at the farm and animal level. Furthermore, these weights can be changed to

increase the importance of information at sheep level or at the farm level, which can depend on

the suspected or measured level of clustering within farms. If clustering among sheep from the

same farm is strong, then sampling more sheep is unnecessary as the additional information is

mostly redundant and thus should be down weighted, i.e. ws should be small. Here no preference

is given, as no prior knowledge about the clustering of scrapie is available and all cases found in

this study were from different farms. Thus ws = wf = 0.5 was chosen.

3.2.5 Choropleth mapping

A disease map is usually constructed to show the spatial distribution of the disease and

identify any spatial patterns. The three basic types of disease maps are dot maps, choropleth

maps and isopleth maps. Choropleth maps visualize the spatial distribution of a regional attribute

(e.g. regional prevalence). Generally the attribute is categorized and its value range is

74

represented by a few (i.e. up to a maximum of 7) distinct gray or colour scales (Cressie, 1993,

Berke, 2001).

A Choropleth map was constructed in this study to compare the sampling intensity in the

administrative region type chosen, i.e., CDs. The choropleth map reveals areas of Canada that

lack information obtained by scrapie surveillance at abattoir. This map was based on the

Azimuthal equidistant projection in which all points on the map are at proportionately correct

distances from the center point.

3.3 Results

From November 2010 through to December 2012, a total of 13,057 diagnostic test

records were collected. Of those, 11,702 records were included in the statistical analysis. Of the

1,355 records excluded from analysis, 689 were not from sheep, 567 belonged to sheep with

missing or unreadable CSIP ID tags. Further records were excluded due to other reasons such as

repeated entries or non-abattoir sampling.

The final 11,702 samples were taken from 4 federal abattoirs and 85 provincial abattoirs

from all 10 provinces of Canada. The CSIP ear tags indicated that the sampled sheep came from

3,233 farms, or 1,957 postal code areas. After the postal codes were linked to CDs, the samples

represent 233 CD areas.

According to Statistics Canada’s 2011 Census of Agriculture, there were a total of 283

CDs across Canada excluding territories, of which 263 reported having one or more sheep farms.

However, of the 20 CDs reporting no sheep farms, 4 recorded samples taken. Another 19 CDs

had more farms sampled than were reported in that CD (Appendix C). The 23 CDs where more

farms were sampled than reported were assigned a 100% farm level sampling proportion. The 16

75

CDs which have no reported sheep farms and had no samples taken from were considered having

0% farm level sampling proportion, and were outlined in the map (Figure 3.1).

The sheep level sampling proportion for a CD area was calculated by dividing the

number of sheep sampled in this CD by the maximum number of sheep sampled in any CD,

which was 615 sheep and was observed for CD #2409 in Quebec (Appendix C).

The exact proportions of farms sampled in each of the 283 CDs and their corresponding

index values are shown in Appendix C. The CDs are listed in ascending order using the index

value. Rankings according to the farm level sampling proportion alone are shown in a separate

column.

Figure 3.1 is a choropleth map of the available sampling information according to the

newly developed index. The three territories of Canada (Yukon, Northwest Territories, Nunavut)

that were not sampled in this study are not shown on the map. The 283 CDs that make up the 10

provinces of Canada are classified approximately into quartiles: index values of less than 0.2,

between 0.2 and 0.3, between 0.3 and 0.4, and between 0.40 and 1. These quartiles are colour

coded for visual clarity, with those areas with higher index values being represented with darker

colours so as to reflect the higher level of sampling intensity. The approximation is due to the

index values that were rounded to two decimal places.

The choropleth map of Canada was divided into four close-up views (Figure 3.2, Figure

3.3, Figure 3.4, Figure 3.5) in order to show more detail. CDs with higher index values are

mostly located in Alberta (Figure 3.2), Saskatchewan (Figure 3.3), Ontario (Figure 3.4), Quebec

and Nova Scotia (Figure 3.5).

Three groups of CDs are outlined on the choropleth map to further illustrate the areas

with lower sampling intensity (Figure 3.1): CDs with no samples collected are outlined in purple;

76

CDs with no recorded sheep farms are outlined in blue. CDs with an index value of less than 0.1

are outlined in black. The CDs that are outlined by a black borderline are those CDs that are

proposed for intensified sampling in the future. From these figures, the CDs with low sampling

intensity can be seen to cluster on the West Coast (Figure 3.2), the southern aspect of the border

between British Columbia and Alberta (Figure 3.2), southern Manitoba (Figure 3.3), northern

Ontario (Figure 3.4) and portions of the Atlantic Provinces (Figure 3.5).

3.4 Discussion

This study presents a choropleth map of the sampling intensity of scrapie surveillance of

sheep farms in Canada at the farm and animal level and allows for geographic comparison. The

map was constructed based on an index proposed here as a new methodological approach. CDs

were chosen as the appropriate administrative regions for analysis and ultimately mapping,

because this level is considered as appropriate for the management of scrapie by the CFIA. The

next higher administrative level is at the provincial level and is too heterogeneous for targeted

disease control initiatives. On the other hand, the next lower administrative level (i.e. the more

than 2300 Census Consolidated Subdivisions) likely would result in tedious and inefficient

disease control activities.

Several CDs had more farms sampled than were reported according to census records and

this may be due to various reasons. It may be that the census information is out-dated; samples

were taken from 2010 to 2012 and census data was obtained during the early spring of 2011. The

sheep farms might have closed or switched to another type of farming during the two year

sampling duration, while the 2011 Census of Agriculture was only based on a period of time in

2011. It may also be that the farm owners’ addresses as registered with a sheep’s ID tag and

recorded by the Canadian Cattle Identification Agency (CCIA), or the Agri-Traçabilité Québec

77

(ATQ) in Quebec Province, does not match a farm location but rather the owners’ civic address

if they do not live full-time on the farm. Additionally, mailing addresses are not necessarily the

same as the farm’s locations. This is true in areas of Canada where mail is delivered to a post

office in a near-by town and not delivered directly to the farm. This could result in the farms

being categorized in the wrong CD. While it was possible to adjust the index for those CDs

where farms sampled exceeded those reported (see section 3.2.4), it cannot be determined if CDs

with low sampling information were classified as such because there were actually fewer farms

in the CD than reported in the 2011 census.

The new sampling information index proposed for this study ranges from 0 to 1 where

higher values indicate higher sampling intensity and information level or lower “risk” of

detecting scrapie in future studies. It combines two sources of information: 1) the farm level

sampling fraction per CD using modified Freeman-Tukey Transformation and 2) sheep level

sampling fraction using the number of sheep sampled in the same CD divided by the maximum

number of sheep sampled in any CD. The Freeman-Tukey transformation is a variance-

stabilizing transformation that removes the dependence of the variance on the mean of the

transformed proportion. It corrects for overdispersion and shows more stability than the original

data (Cressie, 1993). Even though logarithm and square root transformations can also be used to

make the variance homogeneous between CDs, the two different numerators of the Freeman-

Tukey transformation distinguish the zero counts between CDs with different farm numbers

which is the denominator. The modification of this transformation was done in order to restrict

the index within the boundaries between 0 and 1, and also be in consistency with Agresti-Coull

estimation applied in the scrapie prevalence study which used the same data. The sheep level

sampling proportion did not need this correction because a fixed number, i.e. the maximum

78

number of sheep sampled in one CD, was used in the denominator, the variance does not vary

between CDs.

This index allows flexibility in choosing how much weight is given to sheep and farm

level information. A single sheep sampled per farm is generally less informative than a sample of

2 or more sheep per farm. However, scrapie is an infectious disease and thus is expected to

cluster on farms leading to redundant information from repeated farm samples. Even though the

prevalence study (Chapter 2 of this thesis) did not reveal evidence for clustering (all 7 scrapie

cases came from 7 different farms), scrapie as an infectious disease is expected to cluster on

farms. However, the trace-out information of the confirmed scrapie cases found in the prevalence

study was not available.

Depending on the strength of clustering one can choose to emphasize farm level or sheep

level information, by choosing respective weights (i.e. ws and wf). If strong clustering is

expected, the farm level sampling proportion can be given more weight; conversely, if no

clustering on the farms is expected, sheep level sampling proportions can be given more weight.

Furthermore, the information at sheep level could be weighted in a nonlinear way via

application of certain power functions, e.g., when sheep level information is subjected to a

square root transformation then increasing sheep sample sizes per farm will have a limited effect

on the overall information index.

A choropleth map was constructed to visualize the geographic distribution of the

sampling intensities across Canada. Clusters of high risk or low information areas can be

identified from this map. The CFIA agents, however, can also inspect the data table in Appendix

C, which lists the exact values and full information of CDs. This allows them to adjust decision

79

making process by defining the high risk areas according to the ranking of the index or the farm

level sampling proportion alone; thus plan the next scrapie surveillance.

3.5 Conclusion

A sampling information index was proposed in order to monitor and map the information

gathered from a disease surveillance system for classical scrapie in the Canadian sheep industry.

This index combines information from both farm level and sheep level sampling proportion

which is a better scheme than just using only farm level or sheep level sampling proportion.

Application of the method results in a recommendation for the Canadian scrapie surveillance

system to intensify its sampling activities in certain Census Districts (CDs) out of 10 provinces

in Canada, and allows to further plan a future eradication program. This study provides insight

into the sampling intensity across the country and allows identification of underrepresented

areas, which are considered at higher risk for future scrapie observations. Specific ranking and

mapping of the index provides a means to prioritize future surveillance activities. According to

the choropleth map, the West Coast regions, the southern border regions between British

Columbia and Alberta, Northern Ontario and the Atlantic Provinces are the areas in need for

intensified sampling.

3.6 Acknowledgements

I would like to acknowledge the data compiling of Heather Brown at the Canadian Food

Inspection Agency (CFIA) and Hernan Ortegon at Alberta Agriculture and Rural Development

(AARD). I would like to further acknowledge the Data Resource Center (DRC) of the University

of Guelph library for providing consistent support of geospatial data. In addition, I would like to

thank the Canadian Sheep Federation for funding this study.

80

3.7 References

Animal and Plant Health Inspection Service, 2004. Scrapie. Retrieved Mar 2012 from:

http://www.aphis.usda.gov/animal_health/animal_diseases/scrapie/downloads/fs_ahscrap

ie.pdf

Animal Health Australia, 2009. Scrapie. Retrieved Mar 2014 from:

http://nahis.animalhealthaustralia.com.au/pmwiki/pmwiki.php?N=Factsheet.111-

2?Skin=factsheet

Berke, O., 2001. Choropleth mapping of regional count data of Echinococcus

multilocularis among red foxes in Lower Saxony, Germany. Prev Vet Med., 52 (2), 119-

31.

Canadian Food Inspection Agency (CFIA), 2012a. Flocks infected with scrapie in Canada in

2011. Retrieved Mar 2012 from: http://www.inspection.gc.ca/animals/terrestrial-

animals/diseases/reportable/2011/flocks-infected-in-

2011/eng/1329729421107/1329729572094

Canadian Food Inspection Agency (CFIA), 2012b. Fact Sheet – Scrapie.

Http://www.inspection.gc.ca/animals/terrestrial-animals/diseases/reportable/scrapie/fact-

sheet/eng/1356131973857/1356132310673

Canadian Food Inspection Agency (CFIA), 2012c. Scrapie - What to expect if your animals may

be infected. http://www.inspection.gc.ca/animals/terrestrial-

animals/diseases/reportable/scrapie/if-your-animals-may-be-

infected/eng/1355963623752/1355963789207

Cressie, N., 1993. Statistics for Spatial Data, Rev. Edition. Wiley, New York.

Detwiler, L.A., Baylis, M., 2003. The epidemiology of scrapie. Rev Sci Tech., 22(1),121-43.

The European Commission, 2012. Report on the monitoring of ruminants for the presence of

transmissible spongiform encephalopathies (tses) in the EU in 2011. Retrieved from:

http://ec.europa.eu/food/food/biosafety/tse_bse/monitoring_annual_reports_en.htm

Koizumi, C. L., J. Carey, M. Branigan, K. Callaghan, Yukon Environment, 2011. Status of

Dall’s Sheep (Ovis dalli dalli) in the Northern Richardson Mountains. Retrieved from:

http://www.env.gov.yk.ca/publications-maps/documents/dalls_sheep_richardson_status_report_2011.pdf

Last JM, ed. A dictionary of epidemiology. 4th edition, 2001. New York: Oxford University

Press.

Ministry of Primary Industries, 2014. NEW ZEALAND’S POSITION WITH REGARD TO

SCRAPIE. Retrieved Mar 2014 from: http://www.biosecurity.govt.nz/files/pests/tse/nz-

position-scrapie.pdf

Wiggins, R.C., 2009. Prion Stability and Infectivity in the Environment. Neurochemical

Research, 34(1): 158-168.

R Core Team (2014). R: A language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna, Austria. URL: http://www.R-project.org/.

Statistics Canada, 2011a. Census of Agriculture, 2011 [Canada]: Farm Data and Farm Operator

Data, Initial Release [Excel files]. Accessed via ODESI, August 21, 2013.

Http://odesidownload.scholarsportal.info/documentation/AGRIC/AGCENSUS/2011/DO

CS/farm_data_tables_2011.html

Statistics Canada, 2011b. Census Division - Cartographic Boundary Files (CD-CBF), 2011

Census. Accessed via Scholars geoportal, August 21, 2013.

81

Http://geo1.scholarsportal.info/#r/search/_queries@=CENSUS%20DIVISION;&fields@

=;&sort=relevance&limit=entitled

Statistics Canada, 2012a. Census division (CD). Retrieved Sep 2013 from:

http://www12.statcan.gc.ca/census-recensement/2011/ref/dict/geo008-eng.cfm

Statistics Canada, 2012b. 2011 CENSUS OF AGRICULTURE QUESTIONNAIRE. Retrieved

Dec 2013 from: http://www.statcan.gc.ca/ca-ra2011/201108/q11-eng.htm.

United States Department of Agriculture (USDA), Animal Plant Health Inspection Service,

Veterinary Services (APHIS), National Center for Animal Health Programs, Ruminant

Health Programs, 2012. National Scrapie Eradication Program, Fiscal Year 2012 Report,

October 1, 2011 to September 30, 2012. Retrieved July 2013 from:

http://www.aphis.usda.gov/animal_health/animal_diseases/scrapie/downloads/yearly_rep

ort.pdf

82

Figure 3.1 Choropleth map of Canadian provinces based on the sampling information index of scrapie study, using Azimuthal equidistant projection:

CDs having no samples collected from are outlined in purple; CDs having no sheep farms recorded are outlined in blue. CDs with an index less than

0.1 are outlined in black1.

Note:

1. CDs having no samples collected (purple) were outlined first. CDs with index<0.1(black) were outlined later. CDs fall in both categories are

shown as black.

83

Figure 3.2 Enlargement of Figure 3.1: Choropleth map of sampling information index for British Columbia and Alberta. CDs having no samples

collected from are outlined in purple; CDs having no sheep farms recording are outlined in blue. CDs with an index less than 0.1 are outlined in

black1.

Note:

1. CDs having no samples collected (purple) were outlined first. CDs with index<0.1(black) were outlined later. CDs fall in both categories are

shown as black.

84

Figure 3.3 Enlargement of Figure 3.1: Choropleth map of sampling information index for Saskatchewan and Manitoba. CDs having no samples

collected from are outlined in purple; CDs having no sheep farms recording are outlined in blue. CDs with an index less than 0.1 are outlined in

black1.

Note:

1. CDs having no samples collected (purple) were outlined first. CDs with index<0.1(black) were outlined later. CDs fall in both categories are

shown as black.

85

Figure 3.4 Enlargement of Figure 3.1: Choropleth map of sampling information index for Ontario. CDs haveing no samples collected from are

outlined in purple; CDs having no sheep farms recording are outlined in blue. CDs with an index less than 0.1 are outlined in black1.

Note:

1. CDs having no samples collected (purple) were outlined first. CDs with index<0.1(black) were outlined later. CDs fall in both categories are

shown as black.

86

Figure 3.5 Enlargement of Figure 3.1: Choropleth map of sampling information index for Quebec and the Atlantic Provinces. CDs having no samples

collected from are outlined in purple; CDs having no sheep farms recording are outlined in blue. CDs with an index less than 0.1 are outlined in

black1.

Note:

1. CDs having no samples collected (purple) were outlined first. CDs with index<0.1(black) were outlined later. CDs fall in both categories are

shown as black.

87

Chapter 4: General Summary and Conclusion

4.1 Motivations for conducting scrapie surveillance

Scrapie is an economically important infectious prion disease of goats and sheep, and like

other transmissible spongiform encephalopathy (TSE) diseases it is fatal and cannot be cured. It

has been a reportable disease in Canada since 1945, but is not considered to be a zoonotic disease

(CFIA, 2012a). Scrapie has a long incubation period ranging from one to several years and is

difficult to detect in the pre-clinical state (Detwiler, 1992). The clinical signs mostly show in

animals of ages two and older, but the infection can be detected much earlier than the time of

onset of clinical signs (O’Rourke et al., 2002; Dennis et al., 2009). Once a scrapie case is

detected, its control requires rapid and accurate identification of the animal’s farm of origin and

any other farms it had lived on due to the infectious nature of the disease. Following the

identification of related flocks and herds the scrapie status of these populations is assessed.

Scrapie control and eradication is based on a systematic surveillance program and includes an

animal and farm ID system which allows for rapid traceability of livestock. Examples of these

are in place in the UK (Department for Environment Food and Rural Affairs, 2010) and Quebec

(CFIA, 2012b).

Several countries have initiated national scrapie eradication plans, e.g. the United

Kingdom (UK) (Department for Environment Food and Rural Affairs, 2010) and the United

States (US) (Animal and Plant Health Inspection Service, 2014). Canada established the

Canadian Sheep Identification Program (CSIP) in 2004 (CFIA, 2012) and the Canadian

surveillance program started in May 2005. New funding and strong endorsement were received

in 2010 from Agriculture and Agri-Food Canada (Scrapie Canada, 2013). Because the Canadian

scrapie surveillance program has not kept pace with other countries and its scrapie status has not

88

been adequately described, the US has not fully lifted the trading restrictions placed on importing

Canadian small ruminants, which was started upon the confirmation of the first bovine

spongiform encephalopathy (BSE) case in 2003 (CSF, 2011). Even though animals less than one

year old are allowed to be exported to the US for slaughter, the border has remained closed to

sheep and goat breeding stock (CSF, 2011). Hence, scrapie surveillance needs to be enhanced

and improved in order to facilitate regulatory change allowing the border to be more open. This

will help to reduce the great economic burden on small ruminant producers and the Canadian

government.

The ultimate goal of scrapie surveillance is to eradicate scrapie from affected flocks and

herds and therefore Canada. The national eradication of scrapie has been determined to be a goal

by the Scrapie Eradication Steering Committee, an appointed committee made up of producers,

industry groups, academia and government agencies (Scrapie Canada, 2014).

This study was a part of the national scrapie surveillance program, and its goal was to

estimate the national scrapie prevalence in sheep and assess the geographic distribution of

available sample information to inform future scrapie surveillance and possibly eradication. To

be precise, only classical (but not atypical) scrapie in adult sheep of 12 month and older (but not

in goats) was considered.

4.2 Review of the results

The animal samples were mainly collected at slaughter facilities, but also farms, auction

markets, animal diagnostic laboratories, and dead stock facilities (CFIA, 2012a). A total of

13,057 samples were collected through active surveillance in abattoirs from November 2010 to

December 2012. Of these, 1,355 samples were excluded from the analysis, because some

89

samples belonged to species other than sheep, some had missing animal ID information or other

recording errors.

The study has found classical scrapie to be rare among sheep in Canada. A total of 7

cases of classical scrapie were detected from 11,702 traceable sheep, or 3,233 sheep farms. Both

stratified and non-stratified prevalence estimation by province were conducted and compared.

Stratification by province did not improve the estimates, implying that there is no significant

difference between provinces. Therefore, the non-stratified results are presented as the national

scrapie prevalence. The national sheep-level prevalence is estimated at a level of 0.06%, or about

1 scrapie case per 1,700 sheep, with a 95% Wilson confidence interval ranging from 0.03% to

0.12%. The national farm-level prevalence is estimated at a level of 0.22%, or about 1 scrapie

case farm per 500 farms, with a 95% Wilson confidence interval ranging from 0.11% to 0.45%.

A choropleth map was constructed to assess the geographic distribution of available

sample information and thus identify the areas which need intensified sampling in the future. The

map has four colour scales which are based on the sampling information index proposed

specifically for this study. The sampling information index is composed of both farm level and

sheep level sampling proportion which describes the sampling intensity of the national scrapie

surveillance. According to the choropleth map, certain CDs at the West Coast, the southern

border between British Columbia and Alberta, southern Manitoba, northern Ontario and the

Atlantic Provinces are shown to have small index, indicating those areas are underrepresented in

the current national scrapie surveillance program.

4.3 Implications of the study

The study examines both the individual level and farm level prevalence of scrapie in the

Canadian sheep population. Several advanced methods to estimate the prevalence and respective

90

confidence intervals (CIs) of a rare event, such as scrapie in Canada, were reviewed. Scrapie has

been diagnosed in sheep and goats worldwide with prevalence estimates lower than 1% (Hunter

and Cairns, 1998; The European Commission, 2012; United States Department of Agriculture et

al, 2012). The individual level prevalence found in this study is within the range reported by

other countries. A national farm level prevalence estimate as reported here for Canada seems to

have never been established for other countries. This finding should give the Canadian sheep

producers and the government confidence in scrapie control because it is lower than the

prevalence estimated in 2008, which was about 1 case per 1000 sheep (Olaf Berke, Scrapie

among sheep in Canada-Sample size considerations for prevalence estimation, unpublished

report to the CFIA and CSF, University of Guelph, 2008). This apparent decrease is a sign that

the national scrapie surveillance has been effective in reducing scrapie cases. The surveillance

program needs to continue in order for Canada to reach scrapie free status.

According to the World Organization for Animal Health (OIE), a country or zone can be

considered scrapie free when a representative and sufficient number of sheep and goats over 18

months of age (sample size assuming 0.1% prevalence) are tested annually with no case found

for at least seven years (World Organization for Animal Health—OIE, 2013). According to this

standard, Canada needs to improve its scrapie surveillance with regards to following: 1) need to

have a traceable ID system for goats in order to adequately sample goats; 2) the sample needs to

be collected from not only the abattoirs (active surveillance) but also fallen stock (FT) and dead

in transit (DIT) (passive surveillance); 3) the current CSIP system needs to be improved to be

reliably and completely traceable.

This study does not present information on the prevalence of scrapie in goats. According

to the OIE standard of scrapie free status, both sheep and goats must be sampled. The goats

91

being collected by the CFIA was studied in another project and no scrapie cases were found

(Leung, Berke, Ortegon, Brown, Menzies, 2013, in preparation). However, fewer goat samples

were collected than required to give an appropriate estimate of ≤ 0.1%. Additionally, a

mandatory national traceable goat identification system should be in place before the sampling

collection in order for researchers to identify farm of origin, something that is currently lacking.

On the other hand, scrapie has been found in goats only in 2007 and 2013 during the past decade,

indicating its rareness in goats (Canadian National Goat Federation—CNGF, 2014). This low

apparent prevalence may not reflect the true state of nature. In December 2013, several goats

from an Ontario dairy goat herd was confirmed scrapie positive by the CFIA (Canadian National

Goat Federation—CNGF, 2014). Previous cases of goat scrapie were all goats residing in

infected sheep flocks. CFIA has not released information stating if this recent positive case was

associated with sheep flocks, but it has certainly further raised the awareness and needs of goats

surveillance in Canada.

In order to have an accurate estimation, OIE requires scrapie surveillance activities

collect animal samples representative of populations of healthy slaughtered animals as well as

dead stock which includes fallen stock (FT) on farm (includes those euthanized) or dead in

transit (DIT) (World Organization for Animal Health—OIE, 2013). For the purpose of this study,

only samples collected from healthy animals slaughtered at inspected abattoirs were used in the

statistical analysis. CFIA had also collected 158 samples in 2011 from other sources, such as

veterinarian submission and sheep markets. Risk from those populations may be different than

from animals sampled at abattoirs so it is recommended to include these animals in future

studies. Studies in other countries which included dead stock had found the prevalence of scrapie

92

is higher in this animal group (The European Commission, 2012). This higher prevalence is

expected because the abattoir samples are from a seemingly healthy population.

Currently, the CSIP (Scrapie Canada, 2012) has a mandatory requirement for producers

to tag the sheep with the pink metal tag, and is in transition to a more reliable and traceable

system, i.e. radio frequency identification (RFID) tags, which would record all the animal

movement (CSF, 2012; CSF, 2014). The national ID system for goats is currently voluntary. The

small ruminant industry needs to have the mandatory traceable ID system in place as soon as

possible in order for the surveillance to proceed.

4.4 Strengths and limitations

Various countries have reported extremely low scrapie prevalence. Therefore, advanced

methods for rare diseases were necessary for proper analysis and thus reviewed in this study. The

methods can be applied in other rare disease studies, such as BSE.

Given that 1,355 samples were excluded in the final analysis because of missing

information, and that all of these samples were scrapie-negative, the prevalence of scrapie is

likely over-estimated. This conservative estimate suggests the true prevalence in the abattoirs

samples should be less than or equal to the estimated value.

The benefit of constructing a disease map, compared to a data table, is that the

geographical distribution of the manually divided groups can be identified and it is easy to find

clustering of the similar groups. In this study, a choropleth map was constructed in order to

compare the sampling intensity in different administrative regions, i.e., CDs. In order to have

comparable values between CDs, a variance stabilizing index was proposed. This sampling

information index measures the combined information from both farm level and sheep level

sampling proportion to have a comprehensive estimation of the available samples. The farm level

93

sampling proportion is calculated based on the Freeman-Tukey transformation, which corrects

the overdispersion caused by the different number and size of farms in each CD. The sheep level

portion does not need this correction.

The choropleth map based on the sampling information index is able to show

geographically how close low ranking CDs are to each other. This clustering would suggest that

nearby abattoirs could be targeted by the CFIA for additional sampling. For those CDs have low

index and are isolated from other CDs in this group, CFIA should try to obtain samples from the

farms directly. In this situation, biopsy testing by sampling the recto-anal mucosa associated

lymphoid tissues (RAMALT) might be more appropriate to avoid the waiting time of sending the

animals to abattoirs, even though Bio-rad test has a higher sensitivity (Dennis et al., 2009).

However, the map is a snapshot of the information for the sampling period and the results

will change when new data are sampled and new census records become available. The industry

is changing as the number and the size of the farms fluctuate. Therefore, continuing surveillance

is needed to keep track of the scrapie status in Canada.

In order to speed up the process of scrapie eradication, producers are encouraged to

participate in the Voluntary Scrapie Flock Certification Program (VSFCP) in which the flocks

are under strict monitoring by the CFIA of infection in on-farm deaths (Scrapie Canada, 2012).

This program should continually recruit small ruminant producers since only a small portion of

farms have participated.

The results presented here do not include information from the VSFCP data: the

information was not released. As well, the “trace out” scrapie investigations, i.e. the scrapie

status of the positive cases’ farm of origin and its trading farms, were not looked into for this

study. Future studies are recommended to include that information.

94

4.5 Conclusion

In conclusion, the results of this research provide benchmarking information about the

prevalence of scrapie in Canada. This study informs a future scrapie eradication program for

Canada with the goal to remove trade restrictions and regain access to the world market.

Information about the baseline prevalence allows estimating the necessary sample size, i.e. how

many animals will need to be tested for future surveillance activities. Furthermore, this study

provides a spatial distribution of the available sampling information and this should be used as a

decision making tool to determine where to sample animals. The result should give the producers

and the Canadian government some confidence in controlling scrapie and encouraging the

implantation of a scrapie eradication plan. The eradication of scrapie will increase Canada’s

competitiveness on the international small ruminants markets and improve consumer satisfaction

among Canadians.

95

4.6 References

Animal and Plant Health Inspection Service (APHIS), 2014. National Scrapie Eradication

Program. Retrieved from:

http://www.aphis.usda.gov/wps/portal/aphis/ourfocus/animalhealth?1dmy&urile=wcm%

3apath%3a%2Faphis_content_library%2Fsa_our_focus%2Fsa_animal_health%2Fsa_ani

mal_disease_information%2Fsa_sheep_goat_health%2Fsa_scrapie%2Fct_scrapie_home

Canadian Food Inspection Agency (CFIA), 2012a. Fact sheet –

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

animals/diseases/reportable/scrapie/fact-sheet/eng/1356131973857/1356132310673

Canadian Food Inspection Agency (CFIA), 2012b. Canadian sheep identification program.

http://www.inspection.gc.ca/animals/terrestrial-animals/traceability/sheep-

identification/eng/1328852777479/1328852957523

Canadian National Goat Federation—CNGF, 2014. Industry news: Goat in Ontario tests positive

for scrapie. Retrieved from:

http://www.cangoats.com/index.php?pageid=526&noticeid=120

Canadian Sheep Federation (CSF), 2011. Myth: Allowing sheep to transit through Canada from

the northern US states to Alaska puts the Canadian industry at a disadvantage. Retrieved

from: http://www.cansheep.ca/User/Docs/POV%20Summer%20Edition%202011.pdf

Canadian Sheep Federation (CSF), retrieved Aug 2012. Key Milestones for Mandatory RFID.

http://www.cansheep.ca/cms/en/key_milestones.aspx

Canadian Sheep Federation (CSF), retrieved May 2014. Update on the Sheep Industry’s Progress

Towards Mandatory RFID Tags.

http://cansheep.ca/User/Docs/Update%20RFID%20tags.pdf.

Dennis, M.M., Thomsen, B.V., Marshall, K.L., Hall, S.M., Wagner, B.A., Salman, M.D.,

Norden, D.K., Gaise,r C, Sutton, D.L., 2009. Evaluation of immunohistochemical

detection of prion protein in rectoanal mucosa-associated lymphoid tissue for diagnosis

of scrapie in sheep. Am J Vet Res 70:63-72.

Department for Environment Food and Rural Affairs (DEFRA), 2010. Archive: BSE: Other

TSEs - National Scrapie Plan for Great Britain. Retrieved from:

http://archive.defra.gov.uk/foodfarm/farmanimal/diseases/atoz/bse/othertses/scrapie/nsp.h

tm

Detwiler, L.A., 1992. Scrapie. Rev. Sci. Tech. Off. Int. Epiz. 11, 491-537.

The European Commission, 2012. Report on the monitoring of ruminants for the presence of

transmissible spongiform encephalopathies (tses) in the EU in 2011. Retrieved from:

http://ec.europa.eu/food/food/biosafety/tse_bse/monitoring_annual_reports_en.htm

Hongo A, Zhang J, Toukura Y, Akimoto M., 2004. Changes in incisor dentition of sheep

influence biting force. Grass Forage Sci, 59:293–297.

Hunter, N., Cairns, D., 1998. Scrapie-free Merino and Poll Dorset sheep from Australia and New

Zealand have normal frequencies of scrapie-susceptible PrP genotypes. J. Gen. Virol., 79

(Pt 8):2079-82.

96

Scrapie Canada, retrieved Aug 2012. Voluntary scrapie flock certification program,

http://www.scrapiecanada.ca/certification.html

Scrapie Canada, retrieved Jul 2013. Welcome to Scrapie Canada.

http://www.scrapiecanada.ca/home.html

Scrapie Canada, retrieved Apr 2014. Strategic planning for scrapie eradication. Retrieved from:

http://www.scrapiecanada.ca/eradication.html

United States Department of Agriculture (USDA), 2004. Phase II: Scrapie: Ovine Slaughter

Surveillance Study 2002-2003. USDA:APHIS:VS,CEAH, National Animal Health

Monitoring System, Fort Collins, CO., #N419.0104. Retrieved from:

http://www.aphis.usda.gov/animal_health/animal_diseases/scrapie/downloads/sossphase2

.pdf

United States Department of Agriculture (USDA), Animal Plant Health Inspection Service,

Veterinary Services (APHIS), National Center for Animal Health Programs, Ruminant

Health Programs, 2012. National Scrapie Eradication Program, Fiscal Year 2012 Report,

October 1, 2011 to September 30, 2012. Retrieved July 2013 from:

http://www.aphis.usda.gov/animal_health/animal_diseases/scrapie/downloads/yearly_rep

ort.pdf

World Organization for Animal Health—OIE, 2013. Terrestrial Animal Health Code. Chapter

14.9. Scrapie.

http://www.oie.int/fileadmin/Home/eng/Health_standards/tahc/2010/en_chapitre_1.14.9.

htm

97

Appendix A: R-code for the Stratified Wilson Confidence Interval

Strat.Wilson.CI<-function(x,n,conf)

{

p<-x/n

k<-length(p)

#Step 1

conf.level <-conf

alpha <- 1 - conf.level

k<-length(x)

m<-1-(alpha^(1/k))

zy<-qnorm(1-((1-m)/2));zy

wnum<-c()

for(i in 1:k)

{

wnum[i]<-(((1+(zy^2)/n[i])^2)/(((p[i]*(1 -

p[i]))/n[i])+(zy^2/(4*n[i]^2))))

}

wdenom<-sum(wnum)

w<-wnum/wdenom

w # these are w0i

#step 2

z1<-c()

z2<-c()

for(i in 1:k)

98

{

z1[i]<-w[i]^2*((p[i]*(1 - p[i]))/n[i])

z2[i] <-w[i]*sqrt((p[i]*(1 - p[i]))/n[i])

}

znum<- sqrt(sum(z1))

zdenom <-sum(z2)

nalpha<-1-(0.5*alpha)

zy<-(znum/zdenom)*qnorm(1-((1-nalpha)/2))# These are z0y

#Step 3

wnum<-c()

for(i in 1:k)

{

wnum[i]<-(((1+(zy^2)/n[i])^2)/(((p[i]*(1 -

p[i]))/n[i])+(zy^2/(4*n[i]^2))))

}

wdenom<-sum(wnum)

w2<-wnum/wdenom

w2 # these are w0i #these are w_hat_i's.

#Step4

zgamma1<-c()

zgamma2<-c()

zgamma1;zgamma2

for(i in 1:k)

{

99

zgamma1[i]<-w2[i]^2*((p[i]*(1 - p[i]))/n[i])

zgamma2[i] <-w2[i]*sqrt((p[i]*(1 - p[i]))/n[i])

}

zgammanum<- sqrt(sum(zgamma1))

zgammadenom <-sum(zgamma2)

alpha <- 1 - conf.level

nalpha<-1-(0.5*alpha)

zy1<-(zgammanum/zgammadenom)*qnorm(1-((1-nalpha)/2))# These

are z0y

#step5

#The updated normal percentile zy is used to compute (Li ,

Ui ) and the updated weights w_hat_i's are used to

construct

#the stratified confidence interval (L, U).

L1<-c();L2<-c()

U1<-c();U2<-c()

for (i in 1:k)

{

L1[i]= (p[i]+(zy1^2/(2*n[i])))/(1+(zy1^2/n[i]))

L2[i]=(zy1/(1+(zy1^2/n[i])))*sqrt(((p[i]*(1 -

p[i]))/n[i]) + zy1^2/(4*(n[i]^2)))

U1[i]= (p[i]+(zy1^2/(2*n[i])))/(1+(zy1^2/n[i]))

U2[i]=(zy1/(1+(zy1^2/n[i])))*sqrt(((p[i]*(1 -

p[i]))/n[i]) + zy1^2/(4*(n[i]^2)))

}

100

Li=w2*(L1-L2)

Ui=w2*(U1+U2)

L=sum(Li)

U=sum(Ui)

Wilson.CI <-cbind(L,U)#the stratified confidence interval

(L, U)

Wilson.CI

}

#Now the function has been defined, calculate the

Stratified Wilson CI#

x<-c(1,0,0,0,0,0,2,0,4,0)

n<-c(1358,932,365,64,1,329,2412,39,5035,1167)

Strat.Wilson.CI(x,n,0.95)

#L U#

#[1,] 0.0002242659 0.001983768#

101

Appendix B: Matlab code for the Stratified Wilson Confidence Interval

% Input

r=[1 0 0 0 0 0 2 0 4 0];

n=[1370 932 365 64 1 329 2411 39 5038 1167];

p=r./n;

% Find the number of populations

k=length(n);

% Set the confidence level

conf_level = 0.95;

alpha = 1 - conf_level;

% Constants

% alpha

h = 1-(0.5*alpha);

z_alpha = norminv(1-((1-h)/2));

% gamma

m=1-(alpha^(1/k));

z_initial=norminv(1-((1-m)/2),0,1);

z_gamma = z_initial;

% Output

num_iterations=3;

W = zeros(num_iterations,k);

Z = zeros(num_iterations,1);

L = zeros(num_iterations,1);

U = zeros(num_iterations,1);

A = zeros(num_iterations,1);

102

for qq=1:num_iterations

for ii=1:k

%calculate denominator of wi

w_denom = 0;

for jj=1:k

w_denom=w_denom+(1+z_gamma^2/n(jj))^2/(p(jj)*(1-

p(jj))/n(jj)+z_gamma^2/4/n(jj)^2);

end

%calculate numerator of wi

w_num =(1+z_gamma^2/n(ii))^2/(p(ii)*(1-

p(ii))/n(ii)+z_gamma^2/4/n(ii)^2);

%calculate wi

W(qq,ii)= w_num/w_denom;

end

% update z_gamma

z_num = 0;

for ii=1:k

z_num = z_num + W(qq,ii)^2*p(ii)*(1-p(ii))/n(ii);

end

z_gamma_num=sqrt(z_num);

z_denom=0;

for ii=1:k

z_denom = z_denom + W(qq,ii)*sqrt(p(ii)*(1-

p(ii))/n(ii));

end

z_gamma=z_gamma_num/z_denom*z_alpha;

103

%save to output

Z(qq)=z_gamma;

% Calculate L & U

for ii=1:k

first_part=(p(ii)+z_gamma^2/2/n(ii))/(1+z_gamma^2/n(ii));

second_part=z_gamma/(1+z_gamma^2/n(ii))*sqrt(p(ii)*(1-

p(ii))/n(ii)+z_gamma^2/4/n(ii)^2);

L(qq)=L(qq)+W(qq,ii)*(first_part-second_part);

U(qq)=U(qq)+W(qq,ii)*(first_part+second_part);

%calculate the point estimate

A(qq)= A(qq)+W(qq,ii)*(first_part);

end

end

W

Z

L

104

Appendix C: Scrapie surveillance sampling data ranking by index values

CD1 Farmexist2 Farmsam3 Ratio4 Sheepno5 Index Rankratio6 ProCod7 Pro

1 34 0 0.00 0 0.0417 1 59 BC

2 29 0 0.00 0 0.0449 1 59 BC

3 18 0 0.00 0 0.0559 1 59 BC

4 13 0 0.00 0 0.0645 1 46 MB

5 12 0 0.00 0 0.0668 1 35 ON

6 7 0 0.00 0 0.0833 1 10 NL

7 6 0 0.00 0 0.0884 1 10 NL

8 6 0 0.00 0 0.0884 1 24 QC

9 5 0 0.00 0 0.0945 1 12 NS

10 5 0 0.00 0 0.0945 1 24 QC

11 5 0 0.00 0 0.0945 1 48 AB

12 4 0 0.00 0 0.1021 1 10 NL

13 4 0 0.00 0 0.1021 1 12 NS

14 4 0 0.00 0 0.1021 1 13 NB

15 4 0 0.00 0 0.1021 1 24 QC

16 4 0 0.00 0 0.1021 1 24 QC

17 4 0 0.00 0 0.1021 1 35 ON

18 28 1 0.04 1 0.1126 51 10 NL

19 24 1 0.04 1 0.1212 52 35 ON

20 2 0 0.00 0 0.1250 1 24 QC

21 2 0 0.00 0 0.1250 1 24 QC

22 2 0 0.00 0 0.1250 1 24 QC

23 2 0 0.00 0 0.1250 1 46 MB

24 2 0 0.00 0 0.1250 1 59 BC

25 2 0 0.00 0 0.1250 1 59 BC

26 23 1 0.04 3 0.1253 53 35 ON

27 35 2 0.06 3 0.1334 54 59 BC

28 1 0 0.00 0 0.1443 1 10 NL

29 1 0 0.00 0 0.1443 1 10 NL

30 1 0 0.00 0 0.1443 1 12 NS

31 1 0 0.00 0 0.1443 1 13 NB

32 1 0 0.00 0 0.1443 1 13 NB

33 1 0 0.00 0 0.1443 1 24 QC

34 1 0 0.00 0 0.1443 1 24 QC

35 1 0 0.00 0 0.1443 1 24 QC

36 1 0 0.00 0 0.1443 1 24 QC

37 1 0 0.00 0 0.1443 1 46 MB

38 1 0 0.00 0 0.1443 1 59 BC

39 41 3 0.07 5 0.1479 55 59 BC

40 25 2 0.08 2 0.1557 56 46 MB

41 53 5 0.09 8 0.1659 60 35 ON

42 12 1 0.08 1 0.1675 57 35 ON

105

CD1 Farmexist2 Farmsam3 Ratio4 Sheepno5 Index Rankratio6 ProCod7 Pro

43 12 1 0.08 6 0.1715 57 35 ON

44 38 4 0.11 5 0.1736 64 48 AB

45 0 0 0.00 0 0.1768 1 10 NL

46 0 0 0.00 0 0.1768 1 10 NL

47 0 0 0.00 0 0.1768 1 10 NL

48 0 0 0.00 0 0.1768 1 10 NL

49 0 0 0.00 0 0.1768 1 10 NL

50 0 0 0.00 0 0.1768 1 13 NB

51 0 0 0.00 0 0.1768 1 24 QC

52 0 0 0.00 0 0.1768 1 24 QC

53 0 0 0.00 0 0.1768 1 24 QC

54 0 0 0.00 0 0.1768 1 35 ON

55 0 0 0.00 0 0.1768 1 46 MB

56 0 0 0.00 0 0.1768 1 46 MB

57 0 0 0.00 0 0.1768 1 47 SK

58 0 0 0.00 0 0.1768 1 48 AB

59 0 0 0.00 0 0.1768 1 59 BC

60 0 0 0.00 0 0.1768 1 59 BC

61 10 1 0.10 1 0.1819 61 24 QC

62 10 1 0.10 1 0.1819 61 59 BC

63 10 1 0.10 2 0.1827 61 13 NB

64 51 6 0.12 11 0.1855 66 35 ON

65 75 9 0.12 15 0.1889 67 59 BC

66 30 4 0.13 4 0.1934 72 35 ON

67 84 11 0.13 14 0.1952 71 35 ON

68 35 5 0.14 5 0.1992 74 48 AB

69 8 1 0.13 1 0.2010 68 13 NB

70 8 1 0.13 1 0.2010 68 59 BC

71 32 4 0.13 23 0.2030 68 59 BC

72 14 2 0.14 3 0.2052 74 35 ON

73 46 7 0.15 7 0.2053 81 35 ON

74 33 5 0.15 6 0.2057 80 46 MB

75 14 2 0.14 4 0.2060 74 13 NB

76 12 1 0.08 50 0.2073 57 24 QC

77 7 1 0.14 1 0.2132 74 13 NB

78 7 1 0.14 2 0.2140 74 46 MB

79 32 5 0.16 13 0.2144 83 35 ON

80 19 3 0.16 8 0.2150 85 59 BC

81 36 6 0.17 7 0.2151 88 13 NB

82 13 2 0.15 7 0.2156 82 59 BC

83 40 6 0.15 24 0.2184 79 11 PE

84 12 2 0.17 3 0.2202 88 24 QC

85 12 2 0.17 4 0.2210 88 13 NB

86 34 6 0.18 8 0.2218 97 35 ON

106

CD1 Farmexist2 Farmsam3 Ratio4 Sheepno5 Index Rankratio6 ProCod7 Pro

87 29 5 0.17 11 0.2227 96 35 ON

88 83 13 0.16 28 0.2232 84 35 ON

89 55 9 0.16 22 0.2237 87 59 BC

90 11 2 0.18 2 0.2283 98 12 NS

91 6 1 0.17 2 0.2287 88 59 BC

92 6 1 0.17 3 0.2295 88 24 QC

93 53 6 0.11 71 0.2310 65 46 MB

94 31 5 0.16 31 0.2322 86 48 AB

95 76 14 0.18 20 0.2332 100 48 AB

96 11 2 0.18 10 0.2348 98 12 NS

97 6 1 0.17 12 0.2368 88 24 QC

98 40 8 0.20 12 0.2373 101 46 MB

99 168 28 0.17 48 0.2443 88 59 BC

100 5 1 0.20 1 0.2462 101 13 NB

101 136 23 0.17 49 0.2469 95 59 BC

102 5 1 0.20 2 0.2471 101 24 QC

103 89 18 0.20 28 0.2494 105 35 ON

104 9 2 0.22 2 0.2500 114 46 MB

105 22 5 0.23 8 0.2507 116 35 ON

106 191 27 0.14 80 0.2543 73 59 BC

107 86 18 0.21 33 0.2574 107 35 ON

108 72 16 0.22 26 0.2588 114 35 ON

109 16 4 0.25 5 0.2608 126 12 NS

110 95 21 0.22 30 0.2610 113 35 ON

111 75 16 0.21 35 0.2614 109 59 BC

112 64 15 0.23 23 0.2628 119 35 ON

113 12 3 0.25 7 0.2643 126 24 QC

114 33 8 0.24 22 0.2678 122 35 ON

115 61 13 0.21 43 0.2682 108 47 SK

116 51 12 0.24 29 0.2687 120 35 ON

117 4 1 0.25 1 0.2702 126 12 NS

118 4 1 0.25 2 0.2710 126 12 NS

119 101 23 0.23 39 0.2717 118 35 ON

120 4 1 0.25 3 0.2718 126 24 QC

121 138 30 0.22 47 0.2724 112 35 ON

122 15 4 0.27 11 0.2736 140 24 QC

123 16 4 0.25 26 0.2779 126 35 ON

124 110 27 0.25 37 0.2789 124 35 ON

125 47 13 0.28 18 0.2797 141 47 SK

126 24 6 0.25 31 0.2799 126 24 QC

127 14 4 0.29 10 0.2815 143 59 BC

128 129 26 0.20 71 0.2835 104 59 BC

129 69 17 0.25 42 0.2841 125 59 BC

130 83 21 0.25 42 0.2871 135 48 AB

107

CD1 Farmexist2 Farmsam3 Ratio4 Sheepno5 Index Rankratio6 ProCod7 Pro

131 12 3 0.25 36 0.2879 126 24 QC

132 10 3 0.30 9 0.2886 151 24 QC

133 16 5 0.31 6 0.2890 157 12 NS

134 27 8 0.30 17 0.2892 150 46 MB

135 80 21 0.26 39 0.2893 138 59 BC

136 33 10 0.30 19 0.2932 154 46 MB

137 24 7 0.29 25 0.2940 146 24 QC

138 119 29 0.24 57 0.2942 123 35 ON

139 29 9 0.31 19 0.2967 156 47 SK

140 15 5 0.33 6 0.2977 162 11 PE

141 120 26 0.22 80 0.2990 111 35 ON

142 6 2 0.33 2 0.2991 162 12 NS

143 44 13 0.30 33 0.3006 149 12 NS

144 15 5 0.33 12 0.3026 162 24 QC

145 52 16 0.31 31 0.3041 155 46 MB

146 81 23 0.28 45 0.3042 142 35 ON

147 3 1 0.33 3 0.3049 162 13 NB

148 26 9 0.35 12 0.3062 176 46 MB

149 98 25 0.26 66 0.3074 136 59 BC

150 3 1 0.33 7 0.3081 162 24 QC

151 190 39 0.21 101 0.3095 106 48 AB

152 6 2 0.33 15 0.3096 162 24 QC

153 50 16 0.32 32 0.3104 159 35 ON

154 12 4 0.33 21 0.3108 162 24 QC

155 75 22 0.29 48 0.3111 147 35 ON

156 30 9 0.30 46 0.3141 151 24 QC

157 8 3 0.38 5 0.3153 184 35 ON

158 191 45 0.24 89 0.3158 121 48 AB

159 16 6 0.38 9 0.3163 184 24 QC

160 56 19 0.34 38 0.3233 172 48 AB

161 233 53 0.23 107 0.3261 117 48 AB

162 11 4 0.36 25 0.3261 180 24 QC

163 27 10 0.37 26 0.3273 182 46 MB

164 97 25 0.26 90 0.3282 137 35 ON

165 72 19 0.26 86 0.3283 139 35 ON

166 26 10 0.38 21 0.3288 186 46 MB

167 27 10 0.37 28 0.3289 182 13 NB

168 17 7 0.41 9 0.3300 191 11 PE

169 10 4 0.40 19 0.3349 190 24 QC

170 59 17 0.29 80 0.3350 144 47 SK

171 31 12 0.39 30 0.3368 187 12 NS

172 182 39 0.21 129 0.3372 110 48 AB

173 46 18 0.39 29 0.3373 188 47 SK

174 60 21 0.35 50 0.3375 177 47 SK

108

CD1 Farmexist2 Farmsam3 Ratio4 Sheepno5 Index Rankratio6 ProCod7 Pro

175 112 33 0.29 82 0.3389 148 35 ON

176 123 37 0.30 79 0.3392 153 35 ON

177 93 32 0.34 57 0.3403 175 47 SK

178 82 27 0.33 65 0.3406 160 47 SK

179 208 52 0.25 113 0.3425 126 35 ON

180 35 15 0.43 18 0.3427 198 48 AB

181 9 4 0.44 10 0.3433 203 24 QC

182 29 12 0.41 26 0.3438 194 47 SK

183 90 26 0.29 92 0.3446 145 48 AB

184 31 13 0.42 27 0.3467 196 46 MB

185 16 7 0.44 24 0.3515 202 24 QC

186 27 12 0.44 23 0.3527 203 46 MB

187 17 8 0.47 13 0.3541 208 12 NS

188 23 10 0.43 30 0.3551 200 48 AB

189 17 7 0.41 40 0.3552 191 24 QC

190 2 1 0.50 2 0.3552 212 12 NS

191 2 1 0.50 3 0.3560 212 12 NS

192 89 30 0.34 85 0.3602 170 47 SK

193 39 17 0.44 38 0.3616 201 47 SK

194 8 4 0.50 12 0.3633 212 24 QC

195 16 8 0.50 17 0.3674 212 46 MB

196 16 8 0.50 17 0.3674 212 46 MB

197 52 22 0.42 54 0.3697 197 46 MB

198 81 32 0.40 71 0.3725 189 48 AB

199 23 11 0.48 35 0.3746 210 24 QC

200 11 6 0.55 8 0.3746 221 24 QC

201 11 6 0.55 9 0.3754 221 24 QC

202 167 57 0.34 106 0.3787 174 48 AB

203 11 6 0.55 14 0.3795 221 24 QC

204 17 8 0.47 47 0.3818 208 24 QC

205 121 43 0.36 106 0.3847 178 35 ON

206 18 10 0.56 16 0.3848 224 24 QC

207 5 3 0.60 6 0.3875 231 46 MB

208 9 5 0.56 21 0.3880 224 24 QC

209 58 21 0.36 112 0.3929 179 59 BC

210 12 7 0.58 16 0.3929 228 24 QC

211 3 2 0.67 2 0.3994 238 24 QC

212 3 2 0.67 3 0.4002 238 24 QC

213 51 21 0.41 101 0.4036 191 35 ON

214 49 22 0.45 85 0.4045 205 47 SK

215 6 4 0.67 4 0.4050 238 24 QC

216 8 5 0.63 19 0.4067 236 24 QC

217 72 30 0.42 103 0.4069 195 35 ON

218 35 19 0.54 54 0.4119 220 12 NS

109

CD1 Farmexist2 Farmsam3 Ratio4 Sheepno5 Index Rankratio6 ProCod7 Pro

219 48 24 0.50 76 0.4153 212 35 ON

220 23 13 0.57 51 0.4165 226 24 QC

221 26 12 0.46 97 0.4190 207 24 QC

222 42 24 0.57 53 0.4205 227 24 QC

223 39 20 0.51 80 0.4230 218 12 NS

224 42 25 0.60 53 0.4281 230 47 SK

225 29 17 0.59 57 0.4283 229 24 QC

226 191 63 0.33 179 0.4331 161 35 ON

227 85 29 0.34 175 0.4351 173 48 AB

228 13 9 0.69 30 0.4365 241 24 QC

229 5 4 0.80 4 0.4381 246 13 NB

230 10 6 0.60 69 0.4407 231 24 QC

231 33 20 0.61 65 0.4411 235 47 SK

232 10 6 0.60 71 0.4423 231 24 QC

233 154 52 0.34 194 0.4487 171 35 ON

234 25 15 0.60 81 0.4520 231 24 QC

235 64 34 0.53 108 0.4521 219 47 SK

236 1 1 1.00 2 0.4558 256 24 QC

237 11 9 0.82 25 0.4657 249 24 QC

238 14 11 0.79 35 0.4666 245 24 QC

239 83 38 0.46 160 0.4686 206 47 SK

240 45 29 0.64 86 0.4703 237 12 NS

241 185 68 0.37 217 0.4799 181 35 ON

242 2 2 1.00 17 0.4803 256 24 QC

243 13 11 0.85 37 0.4837 251 24 QC

244 4 4 1.00 9 0.4855 256 13 NB

245 16 14 0.88 31 0.4873 253 24 QC

246 26 18 0.69 98 0.4936 241 24 QC

247 20 16 0.80 63 0.4946 246 24 QC

248 23 16 0.70 103 0.4984 243 24 QC

249 0 1 1.00 1 0.5008 256 24 QC

250 0 1 1.00 1 0.5008 256 35 ON

251 1 2 1.00 2 0.5016 256 24 QC

252 67 33 0.49 186 0.5022 211 47 SK

253 0 1 1.00 3 0.5024 256 24 QC

254 0 3 1.00 5 0.5041 256 24 QC

255 18 15 0.83 66 0.5055 250 24 QC

256 3 4 1.00 11 0.5089 256 24 QC

257 4 5 1.00 13 0.5106 256 24 QC

258 3 4 1.00 13 0.5106 256 24 QC

259 189 60 0.32 293 0.5204 158 59 BC

260 4 6 1.00 26 0.5211 256 24 QC

261 8 10 1.00 33 0.5268 256 24 QC

262 10 10 1.00 49 0.5292 256 24 QC

110

CD1 Farmexist2 Farmsam3 Ratio4 Sheepno5 Index Rankratio6 ProCod7 Pro

263 3 6 1.00 36 0.5293 256 24 QC

264 146 49 0.34 299 0.5332 169 48 AB

265 262 113 0.43 252 0.5333 199 35 ON

266 5 7 1.00 52 0.5423 256 24 QC

267 16 13 0.81 132 0.5531 248 24 QC

268 15 17 1.00 72 0.5585 256 24 QC

269 13 16 1.00 78 0.5634 256 24 QC

270 14 16 1.00 85 0.5691 256 24 QC

271 45 34 0.76 168 0.5696 244 24 QC

272 14 19 1.00 86 0.5699 256 24 QC

273 13 11 0.85 146 0.5723 251 24 QC

274 14 21 1.00 109 0.5886 256 24 QC

275 12 16 1.00 109 0.5886 256 24 QC

276 8 14 1.00 118 0.5959 256 24 QC

277 17 15 0.88 167 0.6000 255 24 QC

278 4 8 1.00 138 0.6122 256 24 QC

279 40 35 0.88 253 0.6710 253 24 QC

280 22 22 1.00 258 0.7045 256 24 QC

281 22 23 1.00 280 0.7276 256 24 QC

282 24 29 1.00 360 0.7927 256 24 QC

283 44 50 1.00 615 1.0000 256 24 QC

Note:

1. Actual CD id are not revealed here due to confidentiality issues

2. Farmexist: the number of farms in the CD according to the 2011 Census of Aguriculture

3. Farmsam: the number of farms in the CD that had samples taken from

4. Ratio: farm level sampling proportion. If ratio>1, manually changed to ratio=1.

5. Sheepno: the number of sheep collected in the CD

6. Rankratio: the ranking of ratio. Equal values are all assigned the minimum rank.

7. ProCod: the province code corresponding to the name of the province which is used in all the

Canadian Census.