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From: TSS ()
Subject: Factors determining the potential for vCJD via surgical instruments
Date: August 2, 2006 at 6:39 am PST

Factors determining the potential for

onward transmission of variant

CreutzfeldtJakob disease via

surgical instruments

Tini Garske1,*, Hester J. T. Ward2, Paul Clarke1, Robert G. Will2

and Azra C. Ghani1

1Department of Epidemiology and Population Health,

London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK

2National CreutzfeldtJakob Disease Surveillance Unit, Western General Hospital,

Edinburgh EH4 2XU, UK

While the number of variant CreutzfeldtJakob disease (vCJD) cases continues to decline,

concern has been raised that transmission could occur directly from one person to another

through routes including the transfer of blood and shared use of surgical instruments. Here

we firstly present data on the surgical procedures undertaken on vCJD patients prior to onset

of clinical symptoms, which supports the hypothesis that cases via this route are possible. We

then apply a mathematical framework to assess the potential for self-sustaining epidemics via

surgical procedures. Data from hospital episode statistics on the rates of high- and mediumrisk

procedures in the UK were used to estimate model parameters, and sensitivity to other

unknown parameters about surgically transmitted vCJD was assessed. Our results

demonstrate that a key uncertainty determining the scale of an epidemic and whether it is

self-sustaining is the number of times a single instrument is re-used, alongside the infectivity

of contaminated instruments and the effectiveness of cleaning. A survey into the frequency of

re-use of surgical instruments would help reduce these uncertainties.

Keywords: variant CreutzfeldtJakob disease; self-sustaining epidemic;

mathematical model; epidemiology

1. INTRODUCTION

To date it is believed that the majority of clinical cases

of variant CreutzfeldtJakob disease (vCJD) in the UK

have been caused by consumption of Bovine spongiform

encephalopathy (BSE)-infected beef (Bruce et al. 1997;

Hill et al. 1997; Scott et al. 1999). Despite high

estimates of the number of BSE-infected cattle that

entered the food supply (in the order of 34 million

animals (Donnelly et al. 2002)), the number of cases of

vCJD has remained low with 161 cases to the end of

2005, and the annual incidence steadily decreasing since

2000. Estimates for the total scale of the epidemic

through this route have reduced over time and now lie

in the low hundreds (Clarke & Ghani 2005).

A survey of appendix and tonsil tissues (Hilton et al.

2004) estimated a much higher prevalence of infection in

the population than suggested by the clinical cases. This

finding is best explained by the hypothesis that a large

proportion (84.4%) of infections are sub-clinical (i.e. will

never go on to develop symptoms within their lifetime;

Clarke & Ghani 2005). Evidence for a sub-clinical vCJD

state has been found in animal studies (Hill et al. 2000;

Hill & Collinge 2003; Bishop et al. 2006). Infection is

detectable throughout the CNS and lymphatic system in

patients with clinical vCJD (Wadsworth et al. 2001). In

addition, the abnormal formof the prion protein (PrPSc)

has been detected in the spleen of a patient who died

from other causes (Peden et al. 2004) and in the

appendix of a vCJD case removed 3 years prior to

their death (Hilton et al. 1998). Given that infectious

PrPSc is detectable in patients showing no clinical

symptoms, it is possible that it could be transmitted

through surgical procedures. Surgical instruments are

decontaminated routinely before use on another patient.

However, research suggests that PrP binds strongly to

stainless steel and that current sterilisation procedures

are unlikely to be effective because of the high

temperatures required to deactivate PrPSc (Flechsig

et al. 2001; Yan et al. 2004). Furthermore, a survey of

decontamination practices has shown that past practices

fell short of the expected standard in many hospitals

(Estates 2000). Although steps have been taken to

improve the situation (Estates 2001), it is likely that the

reality of decontamination is still less than perfect and

hence that residual infectivity will remain.

J. R. Soc. Interface

doi:10.1098/rsif.2006.0142

Published online

*Author for correspondence (tini.garske@lshtm.ac.uk).

Received 22 May 2006

Accepted 21 June 2006 1 q 2006 The Royal Society

In this paper, we firstly present results from an

ongoing case-control study of risk factors for vCJD in

relation to previous surgical procedures. We then

extend the mathematical framework developed to

explore the potential for self-sustaining epidemics via

blood transfusions (Clarke et al. submitted) to one

appropriate for high- and medium-risk surgical

procedures in the UK. Data from hospital episode

statistics (HES; www.hesonline.nhs.uk) are used to

parameterize the rate and age distribution of high- and

medium-risk procedures, and the model is used to assess

the current risks and uncertainties associated with an

epidemic transmitted by this route.

2. SURGICAL PROCEDURES UNDERTAKEN

ON VARIANT CREUTZFELDTJAKOB

DISEASE CASES

Data on previous surgical procedures are routinely

collected for all vCJD cases at initial interview with a

close relative of the patient (Ward et al. 2006).

Furthermore, after death of the patient, medical records

are obtained from their GP. According to these medical

records, in total 130 patients have undergone a surgical

procedure, with 119 patients having undergone a total of

335 surgical procedures prior to the onset of clinical

symptoms. Since individual-level data on surgical

procedures in the general population are not available,

it is difficult to determine whether this rate is higher than

average. However, no evidence has been found in the

ongoing case-control study that the rate of surgical

procedures is higher among cases than in age- and sexmatched

controls (Ward et al. 2006).

Figure 1a shows the distribution of when surgical

procedures were undertaken in relation to the onset of

clinical symptoms. While a large proportion (45%) of

procedures were undertaken over 10 years prior to onset

(when the patients, if infected, could be expected to

have low levels of infectivity and hence pose little risk

for onward transmission), a substantial number of

procedures were undertaken close to clinical onset. A

minority of patients underwent multiple procedures

(figure 1b). Overall the majority of procedures were

classified as medium-risk for vCJD transmission, with

only 19 classified as high-risk. For details of the

classification of procedures see 3 below. These data

therefore suggest that, while the overall rate of highrisk

procedures is low, there remains an important

potential risk of transmission via surgical instruments.

3. CURRENT RATES OF OPERATIONS AND

CLASSIFICATIONS IN RELATION TO THE

RISK OF VARIANT CREUTZFELDTJAKOB

DISEASE TRANSMISSION

The distribution of infective prion protein PrPSc is

uneven across the human body so that surgical

operations on different body parts pose different risks

for transmission. A classification of tissues according to

the level of infectivity (into high, medium and none;

WHO 2003) was translated into a classification of

surgical procedures according to OPCS-4 codes (HSMO

1990; see tables 1 and 2).

The total number of operations performed in NHS

hospitals in England from 1990 to 2004 stratified by the

age of the patient at operation were obtained from HES

(see figure 2). These numbers were scaled up by a factor of

1.2 to account for the difference in population size between

England and Great Britain and by a further factor of 1.15

to account for 15% of operations that are undertaken in

the private sector (Economics & Division 2001).

4. MODEL STRUCTURE

The model used here builds on previous models used to

describe vCJD transmission (Ghani et al. 2003;

Clarke & Ghani 2005). The population is described in

(a)

0

20

40

60

80

100

120

140

160

1 or

less

1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 10 10 or

more

number of years prior to onset of clinical symptoms

number of operations

(b)

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 10 11 12 13

number of operations per patient prior to onset

number of patients

Figure 1. The number of operations performed prior to

the onset of clinical symptoms on 130 vCJD cases. (a) By the

number of years prior to onset of clinical symptoms in the

case; (b) frequency of multiple operations.

Table 1. List of high-infectivity tissues and corresponding

OPCS-4 operation codes.

tissue corresponding OPCS-4 codes

brain AA: tissue of brain

AB: ventricle of brain and subarachnoid

space

optic nerve AC: cranial nerves

dura mater AD: meninges of brain

spinal cord AE: spinal cord and other contents of

spinal canal (only A44A48)

pituitary gland BA: pituitary and pineal glands

(without B06)

retina CH: retina and other parts of eye (only

C82C84)

2 vCJD transmission via surgery T. Garske and others

J. R. Soc. Interface

a deterministic compartmental model, stratified by

birth cohort c. A flowchart for one cohort of the model

is depicted in figure 3. The population is subdivided

into susceptibles Xc, primary infected (by beef consumption)

Y1 c and secondary infected (by surgery)

Y2 c . Infection can either be pre-clinical, with individuals

going on to develop clinical disease at the end of the

incubation period, or sub-clinical, meaning that individuals

never develop clinical disease. The first part of

the incubation period is assumed to be non-infectious,

whereas the later stages of the incubation period are

infectious at a constant level. It shall be assumed that

the distribution of the non-infectious period for those

with sub-clinical infection is identical to that for the

pre-clinically infected.

Previous studies have found that the incubation

period distribution for pre-clinical vCJD infections is

well approximated by a G-distribution. To model

G-distributed incubation periods, the infected population

is partitioned into F consecutive compartments

with exponentially distributed waiting times in each.

More precisely, a pre-clinically infected individual must

pass through each of the F stages before dying,

becoming infectious after fmin stages. Similarly, a subclinically

infected individual must pass through fmin

stages before becoming infectious. The purpose of the F

stages is not to mimic an underlying aetiological

process, but to ensure the incubation period is

G-distributed. Also, the waiting time in the noninfectious

and infectious parts of the incubation periods

follow G-distributions, with means summing to the

mean of the total incubation period.

For pre-clinical infection, the onset of clinical

symptoms, which inevitably leads to death, occurs

upon leaving the last incubation stage. The time span

from onset of symptoms to death (approx. 1 year) is

short compared to the incubation period (approx. 11

years), and any surgical operations undertaken on a

patient known to have vCJD would follow the strictest

standards for hygiene, minimizing any transmission

after the onset of symptoms. Therefore, patients are

removed from the model with the onset of symptoms,

and death is effectively assumed to coincide with the

onset of clinical disease. In contrast, the sub-clinically

infected never leave the last incubation stage, and

finally die from causes unrelated to vCJD.

For birth cohort c, which is composed of all

individuals born in year c, the differential equations

governing the dynamics of the susceptible population

are

d

dt

XctZBcdaKXct޽lc;1tClc;2tCma; 4:1

with the delta-distribution d($) (defined as d(x)Z0 if

xs0 with a singularity at xZ0, and N

KNdxdxZ1).

Age aZtKc, and for t!c, Xc(t)Z0. Bc is the number of

births into cohort c (for simplicity assumed to happen

at the beginning of year c), and ma is an age-dependent

death rate of causes other than vCJD. lc,1 and lc,2 are

the time- and age-dependent rates of infection for

primary and secondary infection, respectively.

For the primary and secondary infection, we have

d

dt

Ysub;i c;0 tZuilc;itXctKgiCmaYsub;i c;0 t;

4:2 d

dt

Ypre;i c;0 tZ1Kuilc;itXct

KgiCmaYpre;i c;0 t; 4:3

d

dt

Ysub;i c;f tZgiYsub;i c;fK1 KgiCmaYsub;i c;f ; 4:4

d

dt

Ypre;i c;f tZgiYpre;i c;fK1 KgiCmaYpre;i c;f ; 4:5

d

dt

Ysub;i c;F tZgiYsub;i c;FK1 KmaYsub;i c;F ; 4:6

with fZ1 . FK1 and iZ1 for primary and iZ2 for

secondary infection, where ui is the probability of subclinical

infection, and gi is the rate of progression

through the incubation stages.

The cumulative number of deaths from primary and

secondary infection, respectively, is given by

d

dt

DitZgiXc

Yi c;FK1t: 4:7

The parameters lc,1, u1, g1 and F are fixed at the

maximum-likelihood values obtained by fitting a

survival model to the clinical cases of vCJD observed

to the end of 2005 assuming that all but one (suspected

to have been infected through a blood transfusion)

occurred through consumption of infected beef (Ghani

et al. 2003; Clarke & Ghani 2005). At the beginning of

the epidemic (taken to be in 1980), the whole

population is in the susceptible classes, subdivided by

Table 2. List of medium-infectivity tissues and corresponding

OPCS-4 operation codes.

tissue corresponding OPCS-4 code

peripheral nerves AF: peripheral nerves

thymus & adrenall BC: other endocrine glands

cornea CE: conjunctiva and cornea (only

C45C51)

lung EF: lung and mediastinum (only

E53E59)

gingival tissue F20: operations on gingiva

tonsil F34, F36: excision of & other

operations on tonsil

salivary gland FE: salivary apparatus (only

F44F48)

oesophagus GA: oesophagus including hiatus

hernia

stomach GB: stomach pylorus & gen uppr

gastrinstl tract endoscop

duodenum GC: duodenum

jejunum GD: jejunum

ileum GE: ileum

large intestine H: lower digestive tract

liver JA: liver

pancreas JD: pancreas

spleen JE: spleen

blood vessels L: arteries and veins

kidney MA: kidney

lymph nodes TG: lymphatic and other soft tissue

(only T85T88)

bone marrow W34: graft of bone marrow

vCJD transmission via surgery T. Garske and others 3

J. R. Soc. Interface

birth cohorts. The population size, age distribution and

survival distribution are estimated from census data

(Ghani et al. 2003; Clarke & Ghani 2005), details can be

found in appendix A. In the absence of data, we make

the conservative assumption that survival is not

affected by having had a surgical procedure.

4.1. Transmission via surgery

To incorporate surgical transmission, additionally we

need to track clean and contaminated surgical instruments,

which act as vectors for transmission. We

assume that surgical instruments are kept in sets, and

that the number of sets of surgical instruments n is

constant over time. The proportion of sets that are

contaminated is denoted by x(t) and the proportion of

clean sets by 1Kx(t). With this, the rate of infection for

secondary transmission, lc,2, is given by

lc;2tZbhitsJstKc

Nt

Nct

xt; 4:8

where bhi is the transmission coefficient for transmission

from surgical instruments to humans, ts is the rate of

surgical procedures per person, Js(a) is the age

distribution of surgical procedures (i.e. the probability

that the patient is age a conditioned on the event of

surgery), Nc(t) is the number of people alive in cohort c

at time t and NtZPcNct is the population size at

time t. The factor N(t)/Nc(t) is used to adjust the age

group specific procedure rate Js(a) in equation (4.8) for

application to a specific birth cohort (note that

generally, age groups are wide and made up of several

yearly birth cohorts), under the assumption that events

occurring in an age group are allocated to each birth

cohort in direct proportion to its surviving population.

Clean instruments are contaminated with a contamination

rate c, which clearly depends on the frequency of

operations and the prevalence of infection in the human

population. As the rate of operations per set of

instruments is tsN(t)/n, we have

ctZbihts

Nt

n Xc

JstKc

Nct

Ninf;ct; 4:9

with the transmission coefficient from humans to

instruments bih and the number of infectious people in

cohort c given by

Ninf;ctZX2

iZ1 X

FK1

fZfmin

Ysub;i c;f tCYpre;i c;f t  "

CYsub;i c;F t#: 4:10

This assumes that both sub- and pre-clinically

infected patients are on average infectious for the

last proportion of their incubation period given by

rZ(FKfmin)/F.

The number of operations for which contaminated

instruments stay infectious is assumed to follow an

exponential distribution with average d. Instruments

are replaced at a rate of 1/d per operation, therefore the

0

1000

2000

3000

4000

5000

6000

04

59

1014

1519

20 24

25 29

30 34

35 39

40 44

45 49

50 54

55 59

60 64

65 69

70 74

75 79

80 84

85 89

90 120

age

annual count of operations

with high infectivity

0

20000

40000

60000

80000

100000

120000

140000

160000

annual count of operations

with medium infectivity

high medium

Figure 2. Age-distribution of the annual number of operations performed in England stratified by the high- and medium-risk

classification.

Xc

Y (sub, 1)

c, noninfectious Y (sub, 1)

c, infectious

Y (pre, 1)

c, noninfectious Y (pre, 1)

c, infectious D(1)

Y (sub, 2)

c, noninfectious Y (sub, 2)

c, infectious

Y (pre, 2)

c, noninfectious Y (pre, 2)

c, infectious D(2)

Figure 3. Flowchart of cohort c of the population: susceptibles

Xc and primary/secondary infected Y1=2 c . During the first

part of the incubation period, pre- as well as sub-clinical

individuals are non-infectious, in the later stages of incubation,

they are infectious. D(1/2) are the cumulative deaths

from primary/secondary infection.

4 vCJD transmission via surgery T. Garske and others

J. R. Soc. Interface

rate at which contaminated instruments are replaced

by clean instruments is given by

stZts

Nt

n

1

d

: 4:11

With this, the proportion of contaminated instruments

is given by

d

dt

xtZct1KxtKstxt: 4:12

The frequency of operations and age profiles Js(a)

were fixed at the values presented in figure 2 with tsZ

1.13!10K3 operations per person per year for high-risk

procedures, and tsZ3.41!10K2 operations per person

per year for medium-risk procedures. The total number

of sets in use n was parameterized via the frequency

with which a set of instruments is used, n and the total

number of operation per year, tsN, as nZtsN/n.

4.2. Calculation of the basic reproductive

number R0

If we assume that the total number of infecteds remains

small compared to the population size, we can follow

the approach by Diekmann et al. (1990) to obtain

an analytical expression for the basic reproduction

number R0:

R20

ZbihbhidtsXA

aZ0

NaX

AKa

uZ0

JsaCu

NaCu

SaCuja

$ u2 mu; aKeKg2u X

fminK1

qZ0

gq

2

q!

kqu; a " # (

C1Ku2 eKg2uX

FK1

rZ0

gr

2

r! kr u; a "

KeKg2u X

fminK1

qZ0

gq

2

q!

lrCqu; a!#); 4:13

with

mu;aZ

SaCuC1jaCuK1

ln SaCuC1jaCu

if SaCuC1jaCu!1;

1 if SaCuC1jaCuZ1;

8>

<>

: 4:14

kr u;aZuC1r eKg2SaCuC1jaCuKurKrkrK1u;a

ln SaCuC1jaCuKg2

4:15

and

lr u;aZuC1reK2g2SaCuC1jaCuKurKrlrK1u;a

ln SaCuC1jaCuK2g2

:

4:16

Here, A is the maximal age that can be reached in the

model population, Na is the number of people of age a

and S(a0ja) is the survival probability to age a0

conditioned on survival to a. The conditional survival

probability is related to the annual death rate at age a,

ma, by S(aC1ja)Z1Kma. A derivation of this equation

can be found in appendix B.

4.3. Numerical simulations

Extensive sampling of parameter space was undertaken

using Latin Hypercube sampling, varying most of the

input parameters for which no data was available, as

detailed in table 3. Scenarios were accepted if they were

consistent at the 95% level with having observed no

deaths from surgical routes to the end of 2005 (i.e. less

than 3.3 expected deaths via this route). About 62% of

scenarios for high-infectivity procedures and 8% of

scenarios for medium-infectivity procedures were

accepted. Simulations were run in batches of 1000 and

10 000 for high- and medium-infectivity procedures,

respectively, and the results shown in figure 6 represent

a typical batch.

5. THE POTENTIAL FOR A SELF-SUSTAINING

EPIDEMIC

Inspection of the model equations and simulations

reveals three factors influencing the potential for a selfsustaining

epidemic of vCJD via surgical instruments:

the infectivity of contaminated instruments, bihbhi, the

average number of times an instrument is used (and the

subsequent decay in infectivity which may be enhanced

by cleaning), d, and the number of operations undergone

in different age-groups, J(a). Similarly to the

transmission via blood transfusion (Clarke et al. submitted),

the overall frequency of surgical procedures ts

rather than the age profile Js(a) appears to dominate

the potential for a self-sustaining epidemic.

The infectivity of contaminated instruments is

unknown but in the worst-case scenario can be no

greater than one, which occurs if use of a contaminated

instrument always results in infection. Thus, for a given

frequency of operations, the major unknown determining

the potential for a self-sustaining epidemic is the

average number of times an instrument is used. It is

difficult to put an upper bound on this value; the results

presented here consider values up to 100 but it could

Table 3. List of key model parameters for which no data was

available, and range considered.

parameter

and

description range

u2 probability of sub-clinical

infection

01

g2 annual rate of progression

through the incubation

stages for secondary

infection (fixed at the

primary infection value)

0.877

rpre proportion of incubation

period that is non-infectious

01

bihbhi combined transmission parameter 01

d average number of operations

for which an instrument stays

infectious after initial

contamination

1100

n annual frequency with which

a set of instruments is used

150

vCJD transmission via surgery T. Garske and others 5

J. R. Soc. Interface

plausibly be higher. It is not known whether infectivity

declines over time or remains constant after initial

infection (e.g. if protein is not removed during the first

cleaning cycle, does it become more strongly baked onto

the instrument?). Here, constant infectivity is assumed,

and therefore our results correspond to worst-case

scenarios.

Under the assumption that the cases observed to

date have not been infected via surgery, our scenarios

give values of the reproductive number R0, ranging

from well below 1 (the value required for a selfsustaining

epidemic) to approximately 2 for high-risk

procedures, and over 10 for medium-risk procedures.

Figure 4 shows the relationship between the average

number of procedures in which an instrument is used, d,

the product of the probability of transmission from an

infected person to the instrument, bih, and from a

contaminated instrument to a susceptible person, bih

(which we term the combined transmission parameter)

and the reproductive number for high- and mediumrisk

surgical procedures, R0. Self-sustaining epidemics

can occur via this route at plausible values for d. For

example, for high-risk procedures the average number

of procedures per instrument need only be greater than

35 for high values of the combined transmission

parameter while for lower values an instrument may

need to be used up to 100 times.

The main difference between the high- and mediumrisk

procedures stems from the difference in the

frequency of operations, with medium-risk procedures

occurring approximately 30 times more frequently than

high-risk procedures, and in the plausible values for the

combined transmission parameter (although it is not

possible at this stage to attempt to quantify how much

lower this would be). Thus if medium-risk procedures

still result in a considerable infection risk, instruments

used only 48 times could plausibly result in a selfsustaining

epidemic.

A final uncertainty in the potential for self-sustaining

transmission is the probability of sub-clinical

infection and how infectious these and asymptomatic

individuals are. For simplicity, the model assumes that

both pre- and sub-clinically infecteds have a constant

infectivity in the later stages of the incubation period.

Figure 5 shows the relationship between the infectious

proportion of the incubation period r, the probability of

0 10 20 30 40 50 60 70 80 90 100

0 1 2 3 4 5 6 7 8 9 10

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

average number of procedures per instrument

combined transmission parameter

(a)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

average number of procedures per instrument

combined transmission parameter

(b)

00.25 0.250.50 0.500.75 0.751.00 1.001.25 1.251.50

1.501.75 1.752.00 2.002.25 2.252.50 2.502.75 2.753.00

Figure 4. The figure shows how the basic reproductive number R0 given in equation (4.13) depends on the combined transmission

parameter, bihbhi and the average number of procedures an instrument is used in before it is discarded, d. (a) For high-risk

procedures and (b) for medium-risk procedures. For this figure we assume that pre- and sub-clinically infected individuals are

infectious throughout their incubation period and that 40% of secondary infections are sub-clinical.

6 vCJD transmission via surgery T. Garske and others

J. R. Soc. Interface

sub-clinical infection u2 and the reproductive number

R0 for high-risk operations. Higher values of R0 are

obtained if the probability of sub-clinical infection is

high; such scenarios result in infected individuals living

longer and hence being more likely to transmit

infection. In contrast, asymptomatic pre-clinical infections

play a relatively minor role in onward transmission

under such scenarios.

6. POTENTIAL SCALE OF AN EPIDEMIC

Figure 6 shows the relationship between the reproductive

number, the probability sub-clinical infection and

the potential numbers of cases between 2006 and 2021.

Data shown correspond to results obtained from1000 and

10 000 different scenarios for high-risk and medium-risk

operations, respectively, where those scenarios predicting

more than 3.3 deaths to the end of 2005 were

discarded as they are not compatible with having

observed no cases via this route. This left 621 scenarios

for high-infectivity procedures and 828 scenarios for

medium-infectivity procedures.

In this range of scenarios cases in the next 15 years

range up to approximately 200 for high-infectivity

procedures and up to 1000 for medium-infectivity

procedures. While the higher scenarios assume an

perhaps unrealistically high probability of sub-clinical

infection, the scenarios were only run using values of d,

the average number of times an instrument is re-used,

up to 100. Thus it is possible to generate a much larger

range of epidemics if this constraint is relaxed and

hence at present it is not possible to provide any

guidance on future case numbers through this route.

7. DISCUSSION

Given the frequency of high- and medium-risk surgical

procedures undertaken in the UK, a range of plausible

scenarios suggest that surgical procedures could provide

a potential route for a self-sustaining epidemic of

vCJD. The main factors driving such an epidemic are

the intrinsic ability of this route to transmit infection

(both from infected individual to the instrument and

from a contaminated instrument to an individual

undergoing surgery with this instrument) and the

average number of times a single instrument is re-used.

Experimental studies have demonstrated that current

sterilization procedures undertaken in hospitals are

insufficient to totally remove all infectious PrPSc (Yan

et al. 2004; Jackson et al. 2005). However, translating

such studies into measurable levels of infectivity in

terms of human infectious doses will remain difficult.

Thus it will be difficult to assess the potential impact of

reducing infectivity on the scale of an epidemic. Our

results demonstrate that the level of infectivity for

different procedures is particularly important because

it appears twice in the expression for the reproductive

number: firstly, as the infectivity from infected person

to instrument (which is difficult to reduce) and

secondly, from contaminated instrument to susceptible

individual (which can be reduced through improved

cleaning).

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

probability of sub-clinical secondary infection

infectious proportion of the incubation period

0.220.24

0.200.22

0.180.20

0.160.18

0.140.16

0.120.14

0.100.12

0.080.10

0.060.08

0.040.06

0.020.04

00.02

Figure 5. Basic reproductive number R0 dependent on the probability of sub-clinical infection u2 and the infectious proportion of

the incubation period rpre for high-risk operations. Parameter values used were the combined transmission parameter bihbhiZ1,

the average number of re-uses per instrument dZ1 and the rate of progression through the incubation stages g2Z0.877 per year.

vCJD transmission via surgery T. Garske and others 7

J. R. Soc. Interface

The other unknown factor driving the potential for

an epidemic is the average number of times an

instrument is re-used. Currently, there are no data

available to guide our choice for this parameter. Of

course, if instruments are used only once then this

effectively removes all potential for onwards transmission.

Such a policy was introduced in Great Britain

for tonsillectomies in 2001, but was later stopped in

England because of concerns over the safety of the

single-use instruments. However, as tonsillectomies

form only a very small percentage of the total

operations performed each year, such a policy is

unlikely to have an impact at the population level. A

first step to reducing the current uncertainty in the

potential for self-sustaining transmission via surgery

would be to survey the frequency with which different

instruments are used, particularly those used on highinfectivity

procedures. Also, tracking of surgical instruments

should be improved, so that, at the very least,

instruments are not re-used once the infection status of

a patient is known.

The results presented here are based on a number of

simplifying assumptions which are made in the absence

of data but which may also be important in determining

the potential for onward transmission: first, we have

assumed that the risk of becoming infected during a

surgical procedure depends only on the overall prevalence

of infection on surgical instruments. Cohorting of

instruments clearly occurs (e.g. an instrument used for

eye surgery will subsequently likely be used again for

eye surgery). This type of cohorting can increase the

risk of a self-sustaining epidemic within the very highrisk

procedures but is likely to reduce the overall spread

of infection (Anderson & May 1991).

Second, we have not incorporated multiple

operations per patient, which again could reduce the

overall spread of infection, but increase the risk for

patients that frequently undergo surgical procedures.

Third, we have neglected the effect of reduced

survival after surgery due to the lack of quantitative

data. Survival is however likely to be substantially

reduced, particularly for some procedures of the highrisk

group such as neurosurgery. Incorporating this

would lead to a reduction in transmission.

And finally, we have assumed that no cases so far

have been infected via surgery. If there was even one

case infected via surgery, this would increase our

estimates substantially.

Despite these simplifications, our results clearly

demonstrate that surgical procedures provide a

potential route for self-sustaining vCJD transmission

from human-to-human. The further strong association

between surgical procedures and blood transfusion

(with approximately half of all transfusions occurring

during surgery, Wells et al. (2002)) will increase the

probability of such an event. Data on these overlaps as

well as some simple measures of surgical instrument use

are therefore a high priority so that models can be

further refined to guide potential public health

interventions.

We are grateful to James Ironside and David Hilton for

providing data on the survey of lymphoreticular tissues. We

also thank Simon Cousens and Peter Smith for helpful

comments. This work was supported by the Department of

Health. The views expressed in this publication are those of

the authors and not necessarily those of the Department

of Health.

APPENDIX A. SURVIVAL PROBABILITY

The annual numbers of births Bc and the age-dependent

death-rate ma have been estimated from census data

(c)

0.0001

0.001

0.01

0.1

1

10

100

1000

0.01 0.1 1 10

0.01 0.1 1 10

100

basic reproductive number R0

predicted number of

secondary deaths between

2006 and 2021

(d)

0.0001

0.001

0.01

0.1

1

10

100

1000

(a)

0.001

0.01

0.1

1

10

100

1000

predicted number of

secondary deaths between

2006 and 2021

(b)

0.001

0.01

0.1

1

10

100

1000

0 0.2 0.4 0.6 0.8 1.0

0 0.2 0.4 0.6 0.8 1.0

probability of subclinical secondary infection

Figure 6. Predicted number of deaths fromsecondary infection between 2006 and 2021, dependent on the basic reproductive number

R0 and the probability of sub-clinical infection u2 for high-risk procedures (a and b) and medium-risk procedures (c and d ).

8 vCJD transmission via surgery T. Garske and others

J. R. Soc. Interface

(see Ghani et al. (2003); Clarke & Ghani (2005)). A plot

of the probability of survival S(a) is shown in figure 7.

The conditional survival probabilities used in appendix

B are given as S(aja0)ZS(a)/S(a0), whereas the death

rate at age a can be obtained as maZKd

da Sa.

APPENDIX B. CALCULATION OF THE BASIC

REPRODUCTION NUMBER R0

It is assumed that any potential vCJD epidemic is much

smaller than the population size, and then the method

from Diekmann et al. (1990) can be used for calculating

R0.

R0 is given as the leading eigenvalue of the next

generation matrix

KSZ

0 Nphi

npih 0 !; B 1

where Nphi is the expected number of humans infected

by one infected set of surgical instruments, whereas

npih is the expected number of sets of surgical

instruments that are infected by one human.

The calculation of Nphi is fairly easy: once a set is

infected, it has on average d operations left before it is

cleaned/discarded, and thus the expected number of

humans infected by it is

Nphi Zbhid: B 2

The expected number of sets that are infected by one

human depends on the age a at which the human

himself became infected, and thus we have

npih ZXA

aZ0

Na

N

npia ; B 3

where npia is the expected number of sets that are

infected by one human who became infected at age a.

This is given by the infectivity P(t;i, a) of humans

(infected at age a) towards instruments, at time t after

infection as

npia Z

N

0

Pt; i; adt: B 4

We have

Pt; i; aZat; aPt; a; B 5

where

at; aZbihtsJsaCt

N

NaCt

Gfmin t; B 6

is the rate of infection of sets of instruments by one

infected person infected at age a, time t after infection.

Gfmin tZ1KexpKg2tPfminK1

rZ0 g2tr=r! is the cumulative

distribution function of the non-infectious period

after infection, which is a G-distribution, and it ensures

that individuals can only transmit disease after having

passed the non-infectious period.

P(t;a) is the survival probability of an infected

person from age a to aCt, given as

Pt; aZu2C1Ku21KGFt

$SaCujaSaCuC1jaCutKu;

where uZbtc. Here, GFtZ1KexpKctPFK1

rZ0 ctr=r!

is the cumulative distribution function of the incubation

period distribution, and it determines the additional risk

of dying from disease for the pre-clinically infected.

Thus,

npia Z

N

0

at; aPt; adt; B 7

ZbihtsN

N

0

JsaCt

NaCt

Gfmin t

$u2C1Ku21KGt

$SaCujaSaCuC1jaCutKudt;

B 8

ZbihtsNX

AKa

uZ0

JsaCu

NaCu

SaCuja

SaCuC1jaCuu

$ u2

uC1

u

Gfmin tSaCuC1jaCutdt 24

C1Ku2

uC1

u

Gfmin t1KGt

$SaCuC1jaCutdt#:

B 9

Integration by parts gives

npiaZbihtsNX

AKa

uZ0

JsaCu

NaCu

SaCuja

$ u2 mu;aKeKg2u X

fminK1

qZ0

gq

2

q!

kqu;a " # (

C1Ku2 eKg2uX

FK1

rZ0

gr

2

r! kr u;a "

KeKg2u X

fminK1

qZ0

gq

2

q!

lrCqu;a!#); B10

with m(u, a), kr(u, a) and lr(u, a) from equations (4.14),

(4.15) and (4.16), respectively. For R0 we have

R20

ZNphi$npihZNphi$XA

aZ0

Na

N

npia : B11

This yields the basic reproduction number given in

equation (4.13).

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100

age

probability of survival

Figure 7. Probability of survival dependent on age.

vCJD transmission via surgery T. Garske and others 9

J. R. Soc. Interface

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10 vCJD transmission via surgery T. Garske and others

J. R. Soc. Interface

http://www.journals.royalsoc.ac.uk/media/7hxdahxwvm0wxxrrvbfk/contributions/8/0/v/8/80v81437446nu2m5.pdf

NOT TO FORGET THE SPORADIC CJDs ;

Transmission of Creutzfeldt-Jakob disease to a chimpanzee by electrodes contaminated during neurosurgery.

Gibbs CJ Jr, Asher DM, Kobrine A, Amyx HL, Sulima MP, Gajdusek DC.

Laboratory of Central Nervous System Studies, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892.

Stereotactic multicontact electrodes used to probe the cerebral cortex of a middle aged woman with progressive dementia were previously implicated in the accidental transmission of Creutzfeldt-Jakob disease (CJD) to two younger patients. The diagnoses of CJD have been confirmed for all three cases. More than two years after their last use in humans, after three cleanings and repeated sterilisation in ethanol and formaldehyde vapour, the electrodes were implanted in the cortex of a chimpanzee. Eighteen months later the animal became ill with CJD. This finding serves to re-emphasise the potential danger posed by reuse of instruments contaminated with the agents of spongiform encephalopathies, even after scrupulous attempts to clean them.

PMID: 8006664 [PubMed - indexed for MEDLINE]

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=8006664&dopt=Abstract
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