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FOODBORNE ILLNESS IN AUSTRALIA:
ANNUAL INCIDENCE CIRCA 2010
Foodborne illness in Australia: Annual incidence circa 2010
Authors: Martyn Kirk, Kathryn Glass, Laura Ford, Kathryn Brown and Gillian Hall,
National Centre for Epidemiology and Population Health, Australian National University.
Prepared for the Australian Government Department of Health, New South Wales Food
Authority and Food Standards Australia New Zealand by the National Centre for
Epidemiology and Population Health, Australian National University.
Online ISBN: 978-1-74186-170-9
Publications approval number: 10768
Copyright Commonwealth of Australia 2014
This work is copyright. You may download, display, print and reproduce the whole or part of this work in unaltered form for your own personal use or, if you are part of an organisation, for internal use within your organisation, but only if you or your organisation do not use the reproduction for any commercial purpose and retain this copyright notice and all disclaimer notices as part of that reproduction. Apart from rights to use as permitted by the Copyright Act 1968 or allowed by this copyright notice, all other rights are reserved and you are not allowed to reproduce the whole or any part of this work in any way (electronic or otherwise) without first being given the specific written permission from the Commonwealth to do so. Requests and inquiries concerning reproduction and rights are to be sent to the Communication Branch, Department of Health, GPO Box 9848, Canberra ACT 2601, or via e-mail to [email protected].
Foodborne illness in Australia circa 2010i
GLOSSARY AND ACRONYMS
Asymptomatic
GBS
CI
CrI
DALY
Delphi process
Foodborne illness
HUS
IBS
ICD-10-AM
IID2
Incidence
Monte Carlo simulation
Notifiable
NGSI
NGSII
A state where a person who is infected does not show any symptoms.Guillain-Barré syndrome – a disorder where the body’s immune system attacks the peripheral nervous system and may be the result of a preceding infectious event.Confidence interval – represents a range of values that act as good estimates for an unknown parameter using a frequentist distribution. Credible interval – represents a range of values where the most likely estimate might lie using a posterior probability distribution. It may be interpreted similar to confidence intervals.Disability adjusted life year – a metric to describe the burden of disease that takes into account the morbidity and mortality of a condition.A method for structuring a group communication process so that the process is effective in allowing a group of individuals, as a whole, to deal with a complex problem.Any illness resulting from the consumption of contaminated food, pathogenic bacteria, viruses or parasites that contaminate food.Haemolytic uraemic syndrome – a disorder where blood cells are destroyed injuring the kidneys; often occurring following infection with toxin-producing bacteria.Irritable bowel syndrome – chronic abdominal pain, bloating, constipation and diarrhoea; often triggered as the result of bacterial gastroenteritis.International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification.The second longitudinal study of infectious intestinal disease in the United Kingdom by Tam et al.1
The rates at which new cases occur in a population in a specific time period.
A computerised mathematical technique that performs risk analysis by building models of possible results by substituting a probability distribution for any factor that has inherent uncertainty and producing distributions of possible outcome values.
An infection that doctors, laboratories, or other health professionals must report or notify to health departments for the purpose of prevention and control.National Gastroenteritis Survey I – a nationally representative cross-sectional survey conducted in Australia in 2001–2002.National Gastroenteritis Survey II – a nationally representative cross-sectional survey conducted in Australia in 2008–2009.
Foodborne illness in Australia circa 2010ii
NNDSS
OzFoodNet
OzFoodNet outbreak register
Prevalence
ReA
STEC
WQS
National Notifiable Diseases Surveillance System – the national system of surveillance for infectious diseases in Australia.
An Australian network for enhanced surveillance of foodborne disease established by the Australian Government Department of Health in 2000 with Australia’s state and territory health authorities. OzFoodNet investigates multi-jurisdictional outbreaks of disease, provides understanding of the causes and incidence of foodborne disease in the community, and provides evidence for policy formulation.A register of data on outbreaks of gastrointestinal and foodborne diseases that was established in 2000.
The proportion of the population that has a condition at a given point in time, including new and chronic cases of disease.Reactive arthritis – arthritis following bacterial infection; previously known as Reiter’s syndrome.Shiga toxin-producing Escherichia coli – strains of E. coli producing Shiga toxins, which may result in haemolytic uraemic syndrome.Water Quality Study – A randomised controlled trial conducted in Melbourne in 1997–1999 to examine whether reticulated water meeting national guidelines resulted in gastroenteritis by Hellard et al.2
Foodborne illness in Australia circa 2010iii
EXECUTIVE SUMMARYFoodborne illness causes significant morbidity and occasional mortality in Australia.
Reports of disease outbreaks linked to contaminated food are common and may result in
damage to specific food businesses, related food businesses and whole industries. Lost
productivity, impacts on lifestyle and medical expenses from foodborne illness can result in a
substantial burden for Australia. The costs of foodborne illness highlight the need to improve
efforts to prevent disease and strengthen food safety.
Understanding the epidemiology of diseases that occur as a result of contaminated food
is complicated, as there are many different agents that can cause illness. While the majority
of foodborne pathogens cause gastroenteritis, there are some that result in different illnesses,
such as meningitis and hepatitis. Only a fraction of cases of foodborne illness are reported to
health departments and investigated, and for many diseases it is not mandatory for doctors
and laboratories to report cases to health departments for investigation. It is necessary to use
novel methods and analyse multiple datasets to estimate incidence and outcome of foodborne
illness, including notifiable disease reports, laboratory data, outbreak surveillance reports,
expert opinion and the literature.
This report estimates the number of cases of illness and common sequelae acquired in
Australia from contaminated food, circa 2010. It also estimates the number of hospitalisations
and deaths due to foodborne illnesses and sequelae. Uncertainty in the data is accounted for
by reporting 90% Credible intervals (CrI) and comparing estimates using different sources of
data and over time.
Circa 2010, there were an estimated annual 4.1 million (90% CrI: 2.3–6.4 million)
cases of foodborne gastroenteritis acquired in Australia, along with 5,140 (90% CrI: 3,530–
7,980) cases of non-gastrointestinal illness and 35,840 (90% CrI: 25,000–54,000) cases of
sequelae. Norovirus, pathogenic Escherichia coli, Campylobacter spp. and non-typhoidal
Salmonella spp. were the most common known causes of foodborne gastroenteritis, although
approximately 80% of illnesses are of unknown pathogens. Approximately 25% (90% CrI:
13%–42%) of the 15.9 million episodes of gastroenteritis that occur in Australia were
estimated to be transmitted by contaminated food. This equates to an average of
approximately one episode of foodborne gastroenteritis every five years per person. Data on
the number of hospitalisations and deaths represent the occurrence of serious foodborne
illness. Including gastroenteritis, non-gastroenteritis and sequelae, there were an estimated
Foodborne illness in Australia circa 2010iv
annual 31,920 (90% CrI: 29,500–35,500) hospitalisations due to foodborne illness and 86
(90% CrI: 70–105) deaths due to foodborne illness circa 2010.
A main aim of this study was to compare if foodborne illness incidence had increased
over time. In this study, similar methods of assessment were applied to data from circa 2000,
which showed that the rate of foodborne gastroenteritis had not changed significantly over
time. Two key estimates were the total number of gastroenteritis episodes each year, and the
proportion considered foodborne. In circa 2010, it was estimated that 25% of all episodes of
gastroenteritis were foodborne. By applying this proportion of episodes due to food to the
incidence of gastroenteritis circa 2000, there were an estimated 4.3 million (90% CrI: 2.2–7.3
million) episodes of foodborne gastroenteritis circa 2000, although credible intervals overlap
with 2010. Taking into account changes in population size, applying these equivalent
methods suggests a 17% decrease in the rate of foodborne gastroenteritis between 2000 and
2010, with considerable overlap of the 90% credible intervals.
For certain specific foodborne illnesses data were compared from 2000 and 2010 to
examine whether they are increasing or decreasing. For example, foodborne salmonellosis
was estimated to have increased from 28,000 annual infections circa 2000 to 39,600 annual
infections circa 2010; a rate increase of 24% from 1,500 cases per million to 1,850 cases per
million annually. Similarly, for foodborne campylobacteriosis there were 139,000 infections
circa 2000, rising to 179,000 infections circa 2010; representing a 13% increase in incidence
from 7,400 cases per million to 8,400 cases per million annually. Illnesses from hepatitis A
decreased from 245 cases circa 2000 to 40 cases circa 2010, representing a rate decrease of
85% from 13 cases per million to two cases per million annually.
The findings of this study are similar to recent estimates in the United States of
America (USA), Canada and other countries. These estimates take into account
improvements in understanding foodborne illnesses and the agents responsible, improved
information sources, and advances made in methodological approaches. Where possible,
contemporary Australian data collected at or around 2010 were used.
The results of this study will improve the understanding of the epidemiology of specific
pathogens and foodborne causes of gastroenteritis in Australia. This will assist in prioritizing
foodborne illness for intervention. It is important that Australian governments and industry
work together to reduce the incidence of preventable foodborne illness and educate
consumers about good hygiene and food safety strategies.
Foodborne illness in Australia circa 2010v
ACKNOWLEDGEMENTSThe Australian Government Department of Health, New South Wales Food Authority
and Food Standards Australia New Zealand would like to thank the many people who helped
with the conduct of this study and the production of this report. This report was authored by
Dr Martyn Kirk, Dr Kathryn Glass, Ms Laura Ford, Dr Kathryn Brown and Dr Gillian Hall of
the National Centre for Epidemiology and Population Health, under contract to the
commissioning agencies. The Communicable Diseases Network Australia provided data
from the National Notifiable Diseases Surveillance System on notifications of infectious
causes of gastroenteritis. State and territory health departments, public health laboratories
and OzFoodNet epidemiologists also provided data used in this study. The commissioning
agencies and the authors thank Drs Martha Sinclair and Karin Leder for providing Water
Quality Study data for this study. This work was funded by the Australian Government
Department of Health, New South Wales Food Authority and Food Standards Australia New
Zealand.
Foodborne illness in Australia circa 2010vi
INTRODUCTIONIn 2000, it was estimated that every Australian would experience an episode of
gastroenteritis due to food every four years.3 The economic costs of foodborne illness are
substantial from medical practitioner visits, antibiotic prescriptions and days of work lost
each year. The total cost of foodborne illness in Australia is largely attributable to
productivity and lifestyle costs, premature mortality and health care services.4 This, largely
preventable, burden of foodborne illness to the Australian community highlights the need to
continue to improve food safety in Australia.
This report presents an updated estimate of the incidence of foodborne illness in
Australia, circa 2010. It is important to regularly update estimates for foodborne illnesses,
due to improvements in methods of estimation and changing incidence of disease. There are
many different influences on the incidence of foodborne illness, including:
new regulatory measures aimed at preventing infections;
changing agricultural and manufacturing practices;
trends in the way food is prepared, along with consumers’ food choices and changes in eating patterns;
international distribution of food;
emergence of new pathogens; and
identification of new and emerging strains of common infectious agents, such as those that are resistant to antibiotics.
In order to ensure that the estimate of the incidence of foodborne illness in Australia is
internationally comparable, the approach first used by Mead et al.5 and subsequently by
Scallan et al.6 in the USA has been adopted. This approach entails determining the total
amount of gastroenteritis in the country and secondly the proportion of gastroenteritis that is
foodborne. The product of these two estimates gives the total number of cases of foodborne
gastroenteritis. However, a slightly different list of pathogens to those used in the study by
Scallan et al.6 has been selected, including pathogens and illnesses of particular relevance to
Australia. Additionally, this report estimates the incidence of sequelae occurring as a delayed
reaction to episodes of acute gastroenteritis, such as Reactive arthritis (ReA), irritable bowel
syndrome (IBS), haemolytic uraemic syndrome (HUS) and Guillain-Barré syndrome (GBS).
There are many challenges associated with quantifying the true incidence of foodborne
illness in the community. A wide variety of different pathogens produce symptoms of
gastroenteritis. Often clinical cases of gastroenteritis are assessed as ‘presumed infectious’
Foodborne illness in Australia circa 2010Page 1
and a pathogen is never identified, either because a stool test is not performed, the stool test
fails to identify a known pathogen, or because the pathogen is totally unknown. Scallan et al.7
estimated that in the USA, 38.4 million (90% CrI: 19.8–61.3 million) episodes, or 80% of
domestically acquired foodborne illness were caused by unspecified agents. Only a few
decades ago, pathogens such as Campylobacter spp., Shiga toxin-producing Escherichia coli
(STEC), and norovirus were completely unknown.3
One of the most uncertain areas is estimating what proportion of illnesses is due to
contaminated food. Most of the pathogens included in this study may be transmitted to
humans through contaminated foods or water, as well as from other infected persons, from
the environment or from animals. To account for this, expert elicitation was used to estimate
the proportion of illness thought to result from contaminated food.
Most cases of gastroenteritis are mild and self-limiting and most people do not visit a
doctor or submit a specimen for testing. In Australia, state and territory health departments
forward reports of gastrointestinal diseases such as campylobacteriosis, cryptosporidiosis,
salmonellosis, STEC and shigellosis to the National Notifiable Diseases Surveillance System
(NNDSS). However, if people suffering gastroenteritis do not visit a doctor or submit a
specimen for testing their illness will not be recorded in either NNDSS or in state
surveillance systems.
Not all foodborne illnesses are notifiable to health departments, for example
campylobacteriosis is not notifiable in New South Wales (NSW), norovirus, which is one of
the most common causes of gastroenteritis in the developed world, is not notifiable to the
NNDSS and poisoning by ciguatera is only recorded in Queensland. Surveillance data
represents only a small fraction of the total incidence and multipliers are devised to adjust for
underreporting and incomplete population coverage in the Australian surveillance system as
well as for infections acquired overseas. The uncertainty in estimates must be taken into
consideration. The approach used in the current study was to quantify each component of
uncertainty with a plausible probability distribution. Simulations of these distributions were
then used to generate an interval that contains the credible estimates of the number of
foodborne cases of gastroenteritis.
In this report, improvements in the understanding of foodborne illness and the agents
responsible since circa 2000 are taken into account. Specifically, these estimates circa 2010
are based upon new data from:
a review of the literature to determine the best methodological approaches to estimating foodborne illness;
Foodborne illness in Australia circa 2010Page 2
a nationally representative gastroenteritis survey conducted in 2008–9;
updated estimates of underdiagnosis and underreporting of infectious foodborne illnesses to surveillance;
an expert consultation to determine which pathogens and potentially foodborne toxins are currently considered to be of most concern;
an expert elicitation to estimate the proportion of several key pathogens that are transmitted by contaminated food;
NNDSS, state surveillance and the OzFoodNet outbreak register;
hospitalisation separations from each state and territory in Australia; and
mortality statistics for all of Australia from the Australian Bureau of Statistics (ABS).
Although there are many challenges in estimating foodborne illness, the estimates are
valuable for development of public health policy. These estimates provide big-picture
information on the safety of food in Australia and help to evaluate national intervention and
control strategies. In addition, the updated foodborne illness estimates provide a rational basis
for undertaking additional costing studies. This report provides an updated picture on the
incidence of foodborne illness in Australia circa 2010.
AIMSThe aims of this study were to:
1. estimate the incidence, hospitalisations and deaths due to domestically acquired foodborne illness in Australia circa 2010, and
2. examine whether there have been changes to the incidence of foodborne illness over time.
Foodborne illness in Australia circa 2010Page 3
COMPARING FOODBORNE ILLNESS INTERNATIONALLYEstimation of the incidence of foodborne illness has proven important for public health
policy, both in Australia and internationally.3,8,9 Studies to estimate incidence require complex
methods due to the need to synthesize information from various sources, on many different
pathogens and agents, and the need to consider many outcomes of illness. The literature was
examined to review national studies in other countries that estimated the incidence of
foodborne illness due to different pathogens. This included the previous Australian study
estimating foodborne illness incidence circa 2000.10 The report reviews methods used to
estimate the incidence of foodborne illnesses from nine key papers, which were the:
USA assessment of 28 different pathogens or agents in 19995
USA assessment of 30 different pathogens or agents in 20116,7
France assessment of 24 pathogens or agents published in 200511
Australia circa 2000 assessment of 25 pathogens, agents or sequelae, published in 20053,10
United Kingdom (UK) assessment of 24 pathogens or agents, published in 200212
Greece assessment of 15 pathogens or agents, published in 201113
Netherlands assessment of 18 pathogens, agents or sequelae, published in 2012 14
New Zealand assessment of 10 pathogens or agents, published in 200015
Jordan assessment of four pathogens or agents, published in 2009.16
Recent national burden of foodborne illness estimates are shown in Table 1 for
Greece,13 UK,12 France,11 Australia,3 New Zealand,15 USA,6,7 Canada,17 and the Netherlands.14
It should be noted when directly compare these figures that the definition of ‘foodborne
illness’ varies considerably across studies. Excluding the Australian figures, the incidence
rates per million inhabitants per year ranged widely across countries from 4,500 cases in
France to 369,305 cases in Greece. Hospitalisation rates per million inhabitants per year
ranged from 173 in France to 905 in Greece, and deaths per million inhabitants per year
ranged from three in Greece to 12 in France. When the incidence rates are compared across
all countries with estimates, Greece has the highest incidence, followed by Australia, USA,
New Zealand, the Netherlands, UK, and lastly France. In these studies, a similar pattern was
seen for the rate of hospitalisations across countries, and deaths ranged from two to 12
foodborne deaths per million inhabitants (Table 1).
Foodborne illness in Australia circa 2010Page 4
While these studies have commonalities in the overarching approach to estimating the
burden of foodborne illness, some countries assess ‘all types’ of foodborne illness, while
others are more limited in scope. For example, France and New Zealand did not include
unknown pathogens. Not taking unknown pathogens into account results in an underestimate
of the total burden on society. Similarly, not all studies included sequelae or non-
gastrointestinal illnesses in their estimates. In addition, there are significant variations in the
study designs used, the sampling methodologies and case definitions. The authors of these
studies relied on surveillance data, hospital statistics, survey data, previously published
studies, population based cohort studies, expert elicitation studies, reports of outbreak
investigations, and community incidence data from cross-sectional surveys. It is important to
recognise that differences in the sources of data and methods used impacts on results making
it difficult to interpret comparisons between countries and across time.
A landmark paper regarding national foodborne illness estimation is the study from the
USA by Mead et al.5 This paper has been cited an extraordinary number of times and the
methodology replicated by other countries. The method used by Mead and colleagues to
estimate the burden of infectious illnesses transmitted by food was to estimate the total
burden of illnesses due to infections that could potentially be transmitted by food, and then
the proportion of these thought to be foodborne. The burden of illness in the Mead study
included total incidence, hospitalisations and deaths.5
The Mead methodology5 involves:
a) estimating the burden of all gastroenteritis potentially due to foodborne infection;
b) estimating the burden of gastroenteritis due to specific pathogens potentially transmitted by food;
c) estimating the burden of gastroenteritis due to unknown pathogens potentially transmitted by food;
d) estimating the burden of non-gastroenteritis illnesses due to specific pathogens potentially transmitted by food;
e) estimating the proportion of illness burden that is due to transmission of pathogens by food; and
f) accounting for uncertainty.
Foodborne illness in Australia circa 2010Page 5
Country -Author
Years of study
Scope of Study All illnesses(estimate per million
population)
Hospitalisations (estimate per
million population)
Deaths (estimate
per million popn)
Disability-adjust life
years
Unknown pathogens included
Greece -Gkogka13
1996–2006 Total foodborne illness, includes sequelae
369,305(95% CrI: 68,283–910,608)
905(499–1,340)
3(2.0–4.8)
896(470–1,461)
Yes
UK - Adak12 1992–2000 Acute gastroenteritis and listeriosis
26,161 406 9 NA Yes
France -Vaillant11
1997–2000 Total foodborne illness 4,500 173–304 4–12 NA No
Australia - Abelson4
1996–2000 Non-gastroenteritis including sequelae
2,449 156 2 NA No
Australia -Hall3
1996–2000 Acute gastroenteritis 281,250(95% CrI: 208,333–359,375)
766(594–922)
4(2–6) NA Yes
New Zealand - Lake15
2000–2005 Acute gastroenteritis & sequelae
128,421(95% CrI: 34,801–330,075)
NA NA 632(344–1,066)
No
USA - Scallan7
2000–2008 Acute gastroenteritis: unknown pathogens
128,404(90% CrI: 66,318–204,670)
240(33–526)
6(1–11)
NA Yes
USA -Scallan6
2000–2008 Acute gastroenteritis and non-gastroenteritis: known pathogens
31,438(90% CrI: 22,074–42,475)
187(132–253)
5(2–8)
NA No
Canada - Thomas17
2000–2010 Total foodborne illness 126,500(90% CrI: 97,953–158,066)
NA NA NA Yes
Netherlands – Havelaar14
2008–2009 Total foodborne illness, includes sequelae
41,000 NA 5 NA Yes
Table 1: National level estimates of the annual burden of foodborne illness in different countries, 2000–2012.* Adapted from Gkogka et al.13
Foodborne illness in Australia circa 2010Page 6
*The different definitions of foodborne illness, study designs, and sampling methodologies used in these studies have not been taken into account in this comparison. NA – Not available
Foodborne illness in Australia circa 2010Page 7
Since 1999, there have been some significant modifications and improvements in each
of the steps used by Mead et al.5 More recently, disability-adjusted life years (DALYS),
general practitioner (GP) visits, medication, and costings have also been used to estimate the
burden of total gastroenteritis, as well as foodborne illness incidence, hospitalisations and
deaths. Also, new studies have been published, providing more information on incidence of
certain pathogens. Cohort and cross-sectional studies are being used to inform estimates of
the level of underreporting of illness to surveillance systems and large cohort studies
estimating the incidence of total gastroenteritis have become invaluable to this work. In order
to estimate the proportion of illness that is due to contaminated food, recent studies have used
more formal studies of ‘expert opinion’ that have become increasingly rigorous in approach
over time, and outbreak studies have become more available. Simulation techniques are now
used to model uncertainty with varying levels of sophistication.
In the last estimation of the incidence of foodborne illness in Australia, circa 2000,3,10,18
the rate of foodborne acute gastroenteritis was 281,250 (95% CrI: 208,333–359,375) per
million inhabitants per year. The number of illnesses resulting from non-gastroenteritis
foodborne illnesses (invasive listeriosis, toxoplasmosis and hepatitis A), and from their
sequelae (HUS, IBS, GBS and ReA) was estimated to be 2,449 cases per million population
annually.4
More recently, the second study of infectious intestinal disease in the community (IID2
study) estimated that around 25% of people in the UK suffer from an episode of Infectious
Intestinal Disease (IID) each year and that norovirus, sapovirus, rotavirus and Campylobacter
spp. were the most commonly identified pathogens.1 The IID2 study will form an important
basis for future estimates of foodborne illness burden in England and Wales.
Scallan et al.6,7 recently updated estimates of foodborne illness incidence for the USA.
The authors estimated the number of foodborne illnesses, hospitalisations and deaths caused
by 31 domestically acquired pathogens, using surveillance data for the years 2000–2008, and
used data from surveys, hospital records and death certificates to estimate domestically
acquired foodborne illnesses, hospitalisations and deaths caused by unspecified agents.
There were an estimated 31,438 (90% CrI: 22,074–42,475) cases of acute gastroenteritis and
acute non-gastroenteritis caused by known pathogens, 187 (90% CrI: 132–253)
hospitalisations and five deaths (90% CrI: 2–8), per million inhabitants.6 Eighty per cent of
foodborne illness was due to unspecified agents,7 which was similar to the 73% estimated by
Hall et al.3 for Australia circa 2000. In the USA, norovirus was found to account for the most
illnesses (58%) and other important pathogens were non-typhoidal Salmonella spp. (11%),
Foodborne illness in Australia circa 2010Page 8
Clostridium perfringens (10%) and Campylobacter spp. (9%) circa 2000. Pathogens resulting
in the most hospitalisations were non-typhoidal Salmonella spp. (35%), norovirus (26%),
Campylobacter spp. (15%) and Toxoplasma gondii (8%). Non-typhoidal Salmonella spp.
were also responsible for the most deaths (28%), closely followed by T. gondii (24%),
Listeria monocytogenes (19%) and norovirus (11%). The incidence rate for unspecified
agents was estimated at 128,404 (90% CrI: 66,318–204,670) cases, 240 (90% CrI: 33–526)
hospitalisations, and six (90% CrI: 1–11) deaths per million per year in the USA.
The main types of outcome measures of disease burden used in the estimation of
foodborne illnesses internationally have been:
Incidence of new illnesses, hospitalisations, and deaths in a certain time period and population. For some longer lasting illnesses, prevalence may be relevant.
DALYs.
General practitioner (GP) visits, medication use, days of lost productivity.
The direct and indirect costs due to a disease in a set time period. Most of the outcomes above are required to conduct an economic assessment and are combined with raw costing information.
Incidence, hospitalisations, sequelae and deaths are all useful outcome measures and
some studies have shown that DALYs also give another useful perspective. However DALYs
do not take into account differences in socioeconomic and cultural circumstances between
individuals and require subjective value judgements on how to weight or discount for age of
onset, disability weights, and future losses.19 Questions remain about the ability of different
countries, including Australia, to provide high quality, detailed data and to compare disability
weights across countries. Refer to Technical Appendix 1 for further comparisons.
Foodborne illness in Australia circa 2010Page 9
METHODS This report provides estimates of community incidence, hospitalisations, and deaths for
23 pathogens and four sequelae following illness with these pathogens for Australia circa
2010. These pathogens (listed in Table 2) were chosen after review of key papers, and
through consultations with OzFoodNet staff, communicable disease experts, microbiologists
and food safety specialists around Australia. Pathogens that are considered rare in Australia,
or are mostly acquired overseas were excluded.
The fundamental approach to this study builds on methods that were used in Australia
circa 2000, and that have also been used in international estimation efforts in the USA and
the Netherlands.3,6,14 This approach uses estimates of incidence, hospitalisations or deaths to
generate estimates of the yearly incidence of foodborne illness that are domestically acquired
and allows for variability in yearly estimates as well as other sources of uncertainty as
described below.
PATHOGENS INCLUDED
In February 2012, foodborne disease epidemiologists and food safety specialists were
consulted about what pathogens to include in an estimation of foodborne illness in Australia
circa 2010. There was consensus amongst experts to include similar pathogens to the
previous estimate circa 2000. Experts recommended that the ‘Assessment of foodborne
illness, Australia circa 2010’ include 19 pathogens, two chemical agents and four sequelae of
foodborne illness (Table 2). In addition to these recommended pathogens, three viruses
(adenovirus, astrovirus and sapovirus) were included as they had been associated with a
reasonable proportion of all gastroenteritis episodes in recent international studies.1 Finally, a
category of gastroenteritis due to unspecified agents was included.
Foodborne illness in Australia circa 2010Page 10
Table 2: Pathogens, agents and conditions recommended by experts for inclusion in Australian estimation of foodborne illness circa 2010*
Viruses Bacteria Protozoa Chemicals SequelaeRotavirusNorovirusHepatitis A
Bacillus cereusShigellaStaphylococcus aureusVibro parahaemolyticusShiga-toxin producing E. coli
Other pathogenic E. coliCampylobacter spp.Clostridium perfringensListeria monocytogenesSalmonella, non-typhoidalSalmonella TyphiYersinia enterocolitica
Toxoplasma gondiiCryptosporidium spp.Giardia lamblia
ScombrotoxicosisCiguatera
Guillain-Barré syndromeIrritable bowel syndromeReactive arthritisHaemolytic uraemic syndrome
*In addition to these recommended pathogens, the circa 2010 study included three viruses (adenovirus, astrovirus and sapovirus) and a category of gastroenteritis due to unspecified agents.
DATA SOURCES
The main sources of data used in this study are listed in Box 1. Estimates of incidence
relied on notifiable surveillance data at the national and state level, other surveillance data
available through the OzFoodNet outbreak register, cross-sectional data from the National
Gastroenteritis Survey II (NGSII) and cohort study data from the water quality study
(WQS).2,20 Estimates of severe illness were made using hospitalisation and mortality data.
Further details of the use of data sets for this study are provided in Technical
Appendices 3 and 7.
Foodborne illness in Australia circa 2010Page 11
Box 1: Australian datasets used to assess foodborne Illness, hospitalisations and deaths circa 2010
State and territory surveillance: In all Australian jurisdictions, doctors and pathology laboratories are required by legislation to report patients with diagnoses of certain infections to a state or territory health department. Basic information is then stored on each case-patient. The specific illnesses that are reportable vary slightly between each jurisdiction; however data elements and case definitions for many illnesses are largely standardised across Australia. The numbers of infections or foodborne syndromes where illnesses were not nationally notifiable were used from state or territory surveillance, as well as the proportion of infections acquired from overseas travel from infections where illnesses were not nationally notifiable.
National Notifiable Diseases Surveillance System (NNDSS): NNDSS was established in 1990 and is managed by the Australian Government Department of Health through the Communicable Diseases Network Australia. State and territory health departments collect notifications of communicable disease under their respective public health legislation. Under the National Health Security Agreement, states and territories forward de-identified notification data on the nationally agreed set of 65 communicable disease to the Australian Government Department of Health for the purposes of national communicable disease surveillance, although not all 65 diseases are notifiable in each jurisdiction. The national numbers of infections or syndromes from NNDSS from 2006–2010, which are nationally notifiable and potentially due to food, were used to assess the incidence of foodborne illness.
OzFoodNet outbreak register: In 2000, the Australian Government established OzFoodNet—a national network of epidemiologists in state and territory health departments to enhance national surveillance of foodborne illness. OzFoodNet epidemiologists collect enhanced surveillance data on a variety of foodborne illnesses. The OzFoodNet outbreak register aggregates data on outbreaks of disease into a national dataset. A cleaned version of the outbreak register for the years 2006 to 2009 was used for those pathogens that occur in outbreaks, especially those that are not nationally notified.
National Gastroenteritis Surveys I & II (NGSI & NGSII): OzFoodNet and the National Centre for Epidemiology and Population Health (NCEPH), funded by the Australian Government Department of Health and New South Wales Food Authority, conducted national cross-sectional surveys to estimate the national incidence of gastroenteritis meeting a specific case definition. The surveys used computer assisted telephone interviews. NGSI was conducted in 2001–2, while NGSII was conducted in 2008–9.
Water Quality Study (WQS): The WQS was conducted by the Cooperative Research Centre for Water Quality and Treatment between 1997 and 1999 in the suburbs of Melbourne. This randomised-controlled trial included 600 families and examined the relationship between water quality and gastroenteritis. For further information on the WQS (refer to <http://www.med.monash.edu.au/epidemiology/infdis/waterqstudy.html>. After applying an age adjustment, the Water Quality Study was used to determine the proportion of infectious agents causing diarrhoea in community gastroenteritis.
Hospitalisation data: Principal and additional hospital diagnoses for the pathogens of interest were obtained for all states and territories. Data from Tasmania, Australian Capital Territory, Western Australia, South Australia, and Northern Territory was for 2006–2010, data from New South Wales was for 2009 and 2010, and data from Victoria was for the 2009–2010 financial year.
Foodborne illness in Australia circa 2010Page 12
Box 1: Australian datasets used to assess foodborne Illness, hospitalisations and deaths circa 2010 (continued)
Australian Bureau of Statistics mortality data: The ABS supplied aggregated data on deaths registered in Australia from 2001 to 2010, by age group and sex, where the underlying or multiple cause of death was from an illness that could potentially be foodborne.
Other: The assessment of foodborne illness also relied on data from expert elicitations as well as published reports in the literature. The expert elicitations took place in 2005 and 2009 and included eleven participants comprising of two public health physicians, two microbiologists, one food safety officer, two public health veterinarians, three foodborne disease epidemiologists and one research scientist.
INCIDENCE
TOTAL INCIDENCE OF GASTROENTERITISTo estimate the annual incidence of gastroenteritis in Australia, OzFoodNet, in
conjunction with the National Centre for Epidemiology and Population Health (NCEPH), and
funded by the Australian Government Department of Health and New South Wales Food
Authority, conducted the NGSII over a 12-month period between February 2008 and January
2009. This was a cross sectional study using a nationally-representative telephone survey to
improve the estimates of the incidence of gastroenteritis and assess whether there were
significant changes in incidence from the previous National Gastroenteritis Survey (NGSI)
conducted in 2001–2. Gastroenteritis was defined as experiencing ≥3 loose stools and/or ≥2
vomits in a 24 hour period, or if the person had concomitant respiratory symptoms,
gastroenteritis was defined as ≥4 loose stools and/or ≥3 vomits in a 24 hour period. NGSII
provided an incidence estimate of 0.74 (95%CI 0.64–0.84) episodes of gastroenteritis per
person per year, or 15.9 million cases each year in Australia (Technical Appendix 2).
INCIDENCE BY PATHOGENThree main approaches were used to calculate the incidence of illness due to each
pathogen or agent:
1. Notifiable surveillance approach using data from NNDSS or state-based notification systems;
2. Pathogen fraction approach using data from the NGSII together with cohort studies such as the WQS2,20 conducted in Melbourne between 1997 and 1999;
3. Other surveillance approach using data from the OzFoodNet outbreak register, or from hospitalisations.
Approaches 1 and 3 both consist of scaling up from surveillance data to estimates in the
community by use of underreporting and outbreak multipliers. Approach 2 can be thought of
Foodborne illness in Australia circa 2010Page 13
as a top-down approach; it attributes a fraction of all gastrointestinal illness to the given
pathogen.
These three approaches were considered to form a hierarchy, with the notifiable
surveillance approach used by preference, followed by the pathogen fraction approach, and
the other surveillance approach where other data sources were not available. Where possible,
more than one approach was used for each pathogen to provide a secondary estimate used as
a tool to validate the methods.
In the final calculations, the notifiable surveillance approach was used for 11
pathogens, the pathogen fraction approach for 6, and the other surveillance approach for five
pathogens or agents (Technical Appendix 4 and Technical Appendix 11). An additional
approach (Technical Appendix 6) based on USA seroprevalence data was applied to
toxoplasmosis,21 owing to a lack of Australian data.
MULTIPLIERS AND UNCERTAINTYWithin each approach, multipliers were applied to adjust for factors such as the age
profile of cohort studies, and the proportion of the population covered by surveillance. The
study by Hall et al.22 was used as a basis for estimating underreporting multipliers for
moderate illnesses and bloody diarrhoea, and the underreporting factor for serious illnesses
was estimated as one reported illness for every two illnesses that occur in the community, as
in Mead et al.5 In addition, an outbreak multiplier was applied to further adjust for estimates
made using other surveillance data. More details of the underreporting multipliers are
provided in Technical Appendix 5.
Two other key multipliers were the ‘domestically acquired multiplier’ and the
‘foodborne multiplier’. The domestically acquired multiplier adjusted total incidence data to
exclude infections acquired overseas. For many pathogens, this multiplier was estimated
using NNDSS data by state and territory. Other pathogens with short duration of illness were
assumed to be 100% domestically acquired.
Foodborne multipliers were estimated for nine pathogens using expert elicitation data
using a Delphi approach from 2009 and for a further nine pathogens using a Delphi approach
from 2005. All illness due to the seafood toxins was assumed to be caused by food. More
details on the estimation of the proportion of illness that is foodborne are given in Technical
Appendix 5.
Uncertainty in estimates of incidence and in the value of key multipliers was included
by means of distributions. The main distributions used were empirical distributions and
Foodborne illness in Australia circa 2010Page 14
program evaluation review technique (PERT) distributions. Empirical distributions were used
for counts of cases detected by year through notifiable or other surveillance systems. PERT
distributions are based on the beta distribution, and are commonly used for expert elicitation
and risk assessment studies. This distribution was used widely in the analysis as it allows for
asymmetric distributions, and can be easily produced from many data sources including
expert elicitation data.
INCIDENCE OF SEQUELAEThis report estimates the incidence of sequelae illnesses for GBS, HUS, IBS and ReA
using data from Australian and international literature, as well as from NNDSS. Where
available, data from large cohort studies, Australian surveillance studies, and meta-analyses
were used in conjunction with adjusted notification data from preceding foodborne
gastrointestinal infections. More details on the literature and methods used to estimate the
number of cases of foodborne illness sequelae can be found in Technical Appendix 8.
HOSPITALISATIONS AND DEATHS FROM FOODBORNE ILLNESSYearly hospitalisations were estimated from state and territory hospitalisation data
(ICD-10-AM codes) and deaths from the ABS mortality data (ICD-10 codes) for the 24
potentially foodborne illnesses in Table 2, as well as unknown gastroenteritis, listed as either
principal or additional diagnosis. Hospitalisation data were provided over the period 2006 to
2010 for most states and territories, and this was used to calculate national estimates for
2006–2010. A large number of hospitalisations and deaths due to gastrointestinal illness do
not identify a causal pathogen, and these were categorised as ‘gastroenteritis of unknown
aetiology’ (refer to Technical Appendix 7 for ICD-10-AM codes used).
Monte Carlo simulations were used to adjust for travel associated cases, and to estimate
the proportion of hospitalisations and deaths that were foodborne. Similar multipliers were
used to adjust raw hospitalisation and mortality data as for incidence data, as described in
Technical Appendix 7. More details on methods to estimate sequelae hospitalisations and
deaths can be found in Technical Appendix 9.
CHANGES IN METHODS TO CALCULATE INCIDENCE FROM 2000
There were several changes in the methods used to calculate incidence and hospitalisations
since the estimates circa 2000, including updated underreporting multipliers22 and a more
rigorous expert elicitation.23 These changes make direct comparison of the circa 2010
Foodborne illness in Australia circa 2010Page 15
findings with those of the circa 2000 estimates potentially misleading (Box 2). To estimate
changes over time, circa 2000 estimates were recalculated with original data but using
identical methods to those used to calculate the circa 2010 estimates. The circa 2000
incidence rate for foodborne gastroenteritis and key pathogens was then calculated using
population statistics from the ABS from 1996 to 2000.24 Only pathogens for which there were
surveillance data from both time periods were included in this analysis. More details on the
new methods to calculate incidence from 2000 can be found in Technical Appendix 10.
Box 2: Change in methods to estimate foodborne illness in Australia since circa 2000
Domestically acquired multiplier: In 2000, this was estimated using Victorian data only. In 2010, data from all jurisdictions were obtained to inform the estimate of the proportion of cases that were acquired in Australia. Pathogen-specific changes are provided in Table T5.1 of Technical Appendix 5.
Under-reporting multiplier: A study published by Hall et al. (2008) produced more accurate, data-driven estimates of the under-reporting multipliers for Salmonella, Campylobacter, and STEC. These changes have a considerable impact on the circa 2010 estimates – for instance the multiplier for Salmonella decreased from 15 to seven following the use of this study.
Foodborne multiplier: An additional expert elicitation process allowed us to update this multiplier to include assessments of uncertainty from experts. Table T5.2 of Technical Appendix 5 provides all of the foodborne multipliers by pathogen and their data source. Changes in the foodborne multipliers have a considerable effect on the overall proportion of gastroenteritis that is attributed to food.
Outbreak multiplier: Given that illnesses with short duration are less likely to be confirmed, the outbreak multiplier was estimated based on the total number of ill patients in confirmed outbreaks, using Salmonella as the reference pathogen. This multiplier was estimated using NNDSS data and the OzFoodNet outbreak register, which are more nationally representative than the Victorian state outbreak and surveillance data used in 2000.
Choice of approach: As in 2000, there were three main approaches to estimate incidence of pathogens using notifiable surveillance data, cohort data, and outbreak data. However, this time two methods for each pathogen were used to test the sensitivity of the results to the choice of method, and to help identify the most appropriate method. Although only the results of the primary method are reported, the secondary approach is listed in the pathogen sheets in Technical Appendix 11. In some cases, such as Cryptosporidium spp., the choice of primary method changed from 2000 to 2010.
Water Quality Study: In 2000, the rates from the WQS were used but in 2010 rates were adjusted to account for the sample selection of families with children. This is an improvement as the age structure of the sample for the WQS does not accurately reflect the age structure of the Australian population. As gastroenteritis is age related, adjustment gives a better estimate for the whole population. For rotavirus, WQS data were adjusted for changes over time, based on published information.
Hospitalisation data: In this assessment the numbers of hospitalisations for both principal and additional diagnoses was acquired, rather than only principal diagnosis as in 2000. The 2000 estimate included a multiplier to estimate total hospitalisations while circa 2010 data included all hospitalisations. A multiplier was applied in both 2000 and 2010 to account for underreporting.
Foodborne illness in Australia circa 2010Page 16
RESULTS
INCIDENCE OF GASTROENTERITIS DUE TO FOOD
Based on data collected from 2006–2010, each year, an estimated 4.1 million (90% CrI:
2.3–6.4 million) cases of foodborne gastroenteritis occur in Australia, equating to 0.19 cases
per person per year. Foodborne gastroenteritis was considered to include any infectious
gastroenteritis caused by eating food, including food contaminated just before eating.3 An
estimated 25% (90% CrI: 13–42%) of 18 known gastrointestinal pathogens were transmitted
by contaminated food (Table 3). Bacterial pathogens had the highest foodborne proportion
with 36% estimated to be transmitted by contaminated food, compared to 16% for viruses,
and 11% for parasites.
Of the total 4.1 million cases of foodborne gastroenteritis annually, about 0.8 million
(20%) were estimated to be due to one of the 18 known pathogens. The remaining 3.3
million cases (80%) were due to unknown or unidentified pathogens. The key known
pathogens in terms of incidence were: pathogenic E. coli, norovirus, Campylobacter spp., and
non-typhoidal Salmonella spp. Together these four pathogens make up 93% of foodborne
episodes due to the 18 known pathogens.
INCIDENCE OF OTHER FOODBORNE ILLNESS
In addition to foodborne gastroenteritis, circa 2010, contaminated food was estimated to
cause around 5,100 cases of non-gastrointestinal illness each year in Australia (Table 4). T.
gondii was the most common cause of non-gastrointestinal illness due to food. The
percentage of these illnesses that were estimated to be transmitted by contaminated food
ranged from 12% for hepatitis A up to 100% for the fish-associated diseases
scombrotoxicosis and ciguatera.
INCIDENCE OF SEQUELAE FOLLOWING FOODBORNE GASTROENTERITIS
Circa 2010, contaminated food was estimated to cause around 35,840 episodes of
sequelae following acute gastroenteritis each year in Australia (Table 5). This represents a
rate of 1,620 sequel illnesses per million population resulting from foodborne illness. IBS
was the most common sequelae, causing an estimated 19,500 episodes each year; followed by
ReA resulting in 16,200 episodes each year.
Foodborne illness in Australia circa 2010Page 17
PathogenMedian number of domestically acquired
episodes of gastroenteritis (90% CrI)Median percentage
foodborne(90% CrI)
Median number of domestically acquired episodes of gastroenteritis (90%
CrI)Bacillus cereus 3,350 (900–10,100) 100% (98–100) 3,350 (900–10,100)Campylobacter spp. 234,000 (147,000–374,000) 77% (62–89) 179,000 (108,500–290,000)Clostridium perfringens 16,500 (2,600–53,400) 98% (86–100) 16,100 (2,550–50,600)STEC 4,300 (2,050–9,500) 56% (32–83) 2,350 (950–5,850)Other pathogenic E. coli 1,100,000 (833,000–1,450,000) 23% (8–55) 255,000 (85,800–632,000)
Salmonella, non-typhoidal
56,200 (31,900–101,000) 72% (53–86) 39,600 (21,200–73,400)
Salmonella Typhi 20 (8–45) 75% (2–97) 15 (5–30)Shigella 3,000 (1,650–5,400) 12% (5–23) 350 (150–850)Staphylococcus aureus 1,300 (200–7,050) 100% (95–100) 1,300 (200–7,000)Vibrio parahaemolyticus 60 (15–170) 75% (5–96) 40 (10–120)Yersinia enterocolitica 1,500 (900–2,500) 84% (28–94) 1,150 (650–1,950)Adenovirus 88,400 (28,800–205,000) 2% (1–3) 1,650 (500–4,650)Astrovirus 67,100 (20,900–155,000) 2% (1–3) 1,300 (350–3,400)Norovirus 1,550,000 (1,220,000–1,940,000) 18% (5–35) 276,000 (78,100–563,000)Rotavirus 44,800 (18,500–90,800) 2% (1–3) 850 (300–2,000)Sapovirus 81,600 (63,400–102,000) 18% (5–35) 15,000 (7,450–24,300)Cryptosporidium spp. 17,900 (8,150–39,800) 10% (1–27) 1,700 (150–6,100)Giardia lamblia 32,800 (19,800–56,400) 6% (1–50) 3,700 (800–10,600)Subtotal 3,090,000 (2,810,000–3,900,000) 25% (13–42) 798,000 (528,000–1,310,000)Unknown aetiology 12,800,000 (10,500,000–14,500,000) 25% (13–42) 3,310,000 (1,800,000–
5,152,000)Total 15,900,000 (13,700,000–18,000,000) 25% (13–42) 4,110,000 (2,330,000–6,390,000)
Table 3: Estimated annual number of episodes of domestically acquired foodborne gastroenteritis caused by pathogens in Australia, circa 2010
Foodborne illness in Australia circa 2010Page 18
Table 4: Estimated annual number of episodes of acute foodborne illness due to pathogens or syndromes that do not result in gastroenteritis in a typical year in Australia, circa 2010
Pathogen/acute illness Median percentage foodborne(90% CrI)
Median number of domestically acquired foodborne illnesses
(90% CrI)
Hepatitis A 12% (5–24) 40 (10–100)Listeria monocytogenes 98% (90–100) 150 (50–200)Toxoplasma gondii 31% (4–74) 3,750 (1,400–7,150)Ciguatera 100% (100–100) 150 (40–300)Scombrotoxicosis 100% (100–100) 1,050 (0–2,450)Total 40% (25–59) 5,140 (3,530–7,980)
Sequelae Foodborne agents
Median percentage foodborne(90% CrI)
Median number of domestically acquired foodborne illnesses
(90% CrI)
Guillain-Barré syndrome
Campylobacter spp. 25% (10–43)
70 (30–150)
Haemolytic uraemic syndrome
Shiga-toxin producing E. coli
33% (17–53)
70 (25–200)
Irritable bowel syndrome
Campylobacter spp., non-typhoidal Salmonella spp., Shigella
13% (8–20)
19,500 (12,500–30,700)
Reactive arthritis Campylobacter spp., non-typhoidal Salmonella spp., Shigella, Yersinia enterocolitica
48% (36–62)
16,200 (8,750–30,400)
Total 35,840 (25,000–54,000)
Table 5: Estimated annual incidence of sequelae following foodborne gastroenteritis in a typical year in Australia, circa 2010
CHANGES IN INCIDENCE FROM 2000
A direct comparison of incidence estimates with those circa 2000 is complicated by
changes in methods between the two studies. Points of difference and method-related issues
are described in Box 2 in the Methods section.
Foodborne illness in Australia circa 2010Page 19
FOODBORNE GASTROENTERITISThe proportion of gastroenteritis that was estimated to be foodborne circa 2010 was
25%, compared to 32% in 2000. These changes are largely driven by changes in the estimates
of the foodborne multiplier for other (non-STEC) pathogenic Escherichia coli and norovirus,
as ‘other pathogenic E. coli’ is assumed to be 23% foodborne compared to 50% foodborne in
2000 and norovirus is assumed to be 18% foodborne compared to 25% foodborne in 2000.
These lower proportions represent better informed estimates by experts rather than true
changes in the proportion of disease transmitted by food. By applying the 2010 estimate of
25% foodborne to the results of the NGSI study that was conducted in 2001, there are an
estimated 4.3 million (90% CrI: 2.2–7.3 million) episodes of foodborne gastroenteritis circa
2000. When adjusted for changes in population size over this period, this represents a 17%
decrease in the rate of foodborne gastroenteritis between 2000 and 2010 (rate ratio 0.83, 90%
CrI: 0.4–1.8), although credible intervals overlap (Table 6).
SALMONELLA AND CAMPYLOBACTERComparing estimates of non-typhoidal Salmonella spp. and Campylobacter spp. with
those circa 2000 requires further adjustment for considerable changes in methods, such as the
halving of the underreporting multiplier for non-typhoidal Salmonella spp. Recalculation of
the 2000 estimates using these new methods gives a revised estimate of 28,000 (90% CrI:
15,000–50,000) yearly cases of foodborne gastroenteritis due to non-typhoidal Salmonella
spp. and 139,000 (90% CrI: 82,500–227,000) yearly cases of foodborne gastroenteritis due to
Campylobacter spp. That is, the 2010 point estimates represent around 11,000 more cases of
foodborne non-typhoidal Salmonella spp. and 40,000 more cases of foodborne
Campylobacter spp.; a rate increase of 24% for non-typhoidal Salmonella spp. and 13% for
Campylobacter spp. when taking into account population changes (Table 6). Credible
intervals for all incidence rate ratios include uncertainty derived from incidence multipliers,
and were considerably wider than intervals for ratios derived from raw surveillance data.
Foodborne illness in Australia circa 2010Page 20
Table 6: Comparison of annual incidence estimates and rates from revised circa 2000 and circa 2010 of foodborne gastroenteritis, key pathogens, and two sequelae using current assumptions, Australia
Pathogen/Illness Incidence circa 2000(90% CrI)
Rate per million population circa 2000(90% CrI)
Incidence circa 2010
(90% CrI)
Rate per million population circa 2010(90% CrI)
Rate ratio
(90% CrI)Foodborne
gastroenteritis4.3 million
(2.2–7.3 million)
224,000(116,000–374,000)
4.1 million(2.3–6.4 million)
186,000(105,000–289,000)
0.83(0.4–1.8)
Campylobacter spp. 139,000(82,500–227,000)
7,400(4500–12,200)
179,000(108,500–290,000)
8,400(5,050–13,650)
1.13(0.5–2.3)
Salmonella spp., non-typhoidal
28,000(15,000–50,000)
1,500(800–2,700)
39,600(21,200–73,400)
1,850(1,000–3,350)
1.24(0.5–2.8)
Salmonella Typhi 9(3–21)
0.5(0–1)
15(5–30)
0.6(0–1)
1.2(0.5–2.6)
Shigella 515(175–1,300)
28(9–70)
350(150–850)
16(6–40)
0.57(0.15–2.25)
Hepatitis A 245(65–725)
13(3–40)
40(10–100)
2(1–5)
0.15(0.06–0.4)
Listeria monocytogenes
125(70–185)
7(4–10)
150(50–200)
7(3–10)
1(0.4–1.9)
Giardia lamblia 2,600(565–7,400)
140(30–405)
3,700(800–10,600)
175(35–490)
1.25(0.2–7.5)
Guillain-Barré syndrome
50(25–100)
2.8(1–6)
70(30–150)
3.1(2–6)
1.13(0.5–3.6)
Irritable bowel syndrome
14,800(9,500–23,500)
850(550–1350)
19,500(12,500–30,700)
915(570–1,440)
1.07(0.5–2)
Foodborne illness in Australia circa 2010Page 21
OTHER PATHOGENSComparing estimates of other key pathogens shows that the number of hepatitis A and
Shigella cases has decreased since 2000, with rate decreases of 85% and 43% respectively
(Table 6). The rate of L. monocytogenes has remained the same, and the rates of Salmonella
Typhi and Giardia lamblia increased 20% and 25% respectively (Table 6). Comparing
estimates of two sequelae shows an increase in the rate of both GBS and IBS from 2000 to
2010 (Table 6). While estimates of Staphylococcus aureus circa 2010 are lower than those
estimated circa 2000, this may be a consequence of changes in methods for outbreak data, or
the considerable variation in yearly cases associated with outbreaks. The estimates for S.
aureus and Cryptosporidium for circa 2000 could not be recalculated using new methods as
source data has changed, and the new source of data was not available circa 2000. No new
sources of data were available for ‘other pathogenic E. coli’, and the other viral pathogens, so
that meaningful comparison of the two time periods is not possible.
SEVERE FOODBORNE ILLNESS
HOSPITALISATIONSEach year, there were an estimated 30,600 hospitalisations with foodborne
gastrointestinal illness as the principal or additional diagnosis (Table 7). Of these, 5,900
were due to known pathogens each year, with Campylobacter spp. (3,200 hospitalisations)
and non-typhoidal Salmonella spp. (2,100 hospitalisations) the main causes. The remaining
24,700 hospitalisations were due to foodborne gastroenteritis of unknown aetiology.
Further to this, there were an estimated 240 hospitalisations for acute foodborne illness
that were not gastrointestinal, with L. monocytogenes as a key cause. Sequelae resulting from
gastrointestinal illness were estimated to cause 1,080 hospitalisations each year, as a
secondary outcome from illness due to foodborne pathogens.
DEATHSThere were an estimated 60 (90% CrI: 45–75) deaths each year due to foodborne
gastroenteritis (Table 7). Of these, non-typhoidal Salmonella spp. was the most commonly
identified pathogen, causing an estimated 15 foodborne deaths each year. Gastroenteritis of
unknown aetiology was a principal or additional cause of death in an estimated 39 people per
year.
Foodborne illness in Australia circa 2010Page 22
Non-gastrointestinal foodborne illness caused an additional 16 deaths each year, with
most of these attributed to L. monocytogenes. Finally, 10 deaths each year were due to
sequelae following foodborne illness with pathogens such as STEC, non-typhoidal
Salmonella spp., Campylobacter spp., Shigella, and Yersinia enterocolitica. The leading
cause of death from foodborne sequelae was GBS following campylobacteriosis, which
resulted in an estimated six deaths each year.
CHANGES IN SEVERE ILLNESS FROM CIRCA 2000
There were relatively few methodological changes associated with estimates of
hospitalisations and deaths due to foodborne pathogens allowing for direct comparison
between 2000 and 2010 for many pathogens (refer to Hall et al3,10 for circa 2000 estimates).
Estimates of hospitalisations for both non-typhoidal Salmonella spp. and
Campylobacter spp. increased from 2000 to 2010, with a rise in around 1,000 hospitalisations
for each pathogen. In particular, the estimated hospitalisations for non-typhoidal Salmonella
spp. approximately doubled in the ten year period from an estimated 1,060 hospitalisations
circa 2000 to an estimated 2,100 hospitalisations in circa 2010.
As was noted for incidence, estimates of hospitalisations for Staphylococcus aureus, as
well as for Vibrio parahaemolyticus, declined since 2000, although numbers for each are
small and diagnosis in hospital settings is likely to be poor. The decline in hospitalisations for
rotavirus was less marked than that for incidence, but does show a decrease from an
estimated 70 foodborne hospitalisations per year in 2000 to 50 each year in 2010. The
increase in estimated hospitalisations for foodborne norovirus from four per year in 2000 to
150 per year in 2010 reflects increased testing and diagnosis of this pathogen.
Foodborne illness in Australia circa 2010Page 23
Table 7: Estimated annual number of hospitalisations and deaths caused by domestically acquired foodborne pathogens in Australia, circa 2010Pathogen/Illness ICD-10-AM code Median number of
hospitalisations(90% CrI)
Median number of deaths (90% CrI)
Gastrointestinal foodborne illnessBacillus cereus A05.4 25 (4–45) 0 (0–0)Campylobacter spp. A04.5 3,200 (2,100–4,500) 3 (2–4)Clostridium perfringens A05.2 0 (0–2) 1 (0–1)STEC A04.3 7 (2–15) 0 (0–0)Other pathogenic E. coli A04.0, A04.1, A04.4 20 (6–50) 0 (0–1)Salmonella, non-typhoidal A02.0–A02.9 2,100 (1,300–3,000) 15 (8–20)Salmonella Typhi A01.0 15 (6–35) 0 (0–0)Shigella A03 25 (9–50) 0 (0–0)Staphylococcus aureus A05.0 10 (7–20) 0 (0–0)Vibrio parahaemolyticus A05.3 1 (0–1) 0 (0–0)Yersinia enterocolitica A04.6 35 (10–65) 1 (0–1)Adenovirus A08.2 15 (8–25) 0 (0–0)Astrovirus n/a not applicable not applicableNorovirus A08.1 150 (35–350) 1 (0–2)Rotavirus A08.0 50 (30–100) 0 (0–0)Sapovirus n/a not applicable not applicableCryptosporidium spp. A07.2 40 (6–100) 0 (0–0)Giardia lamblia A07.1 100 (25–300) 0 (0–0)Subtotal 5,900 (4,700–7,500) 21 (14–26)Unknown aetiology A08.4, A09, A09.0, A09.9 24,700 (22,600–27,800) 39 (27–54)Total (foodborne gastroenteritis)
30,600 (28,000–34,000) 60 (45–75)
Non-gastrointestinal foodborne illnessHepatitis A B15.9 20 (6–50) 0 (0–2)Listeria monocytogenes A32 150 (100–250) 15 (9–20)Toxoplasma gondii B58 30 (10–60) 1 (0–2)Ciguatera T61.0 25 (10–40) 0 (0–0)Scombrotoxicosis T61.1 8 (5–10) 0 (0–0)Total (non-gastrointestinal) 240 (180–350) 16 (10–21)Sequelae resulting from foodborne illnessGuillain-Barré syndrome G61.0 70 (30–150) 6 (2–10)Haemolytic uraemic syndrome D59.3 70 (25–200) 2 (1–3)Irritable bowel syndrome K58.0, K58.9 915 (550–1,400) 2 (1–2)Reactive arthritis M02.1, M02.3, M02.8,
M03.225 (20–40) 0 (0–0)
Total (sequelae) 1,080 (700–1,600) 10 (5–14)
DISCUSSION
Foodborne illness in Australia circa 2010Page 24
There are an estimated 4.1 million (90% CrI: 2.3–6.4 million) cases of gastroenteritis
attributed to contaminated food in Australia each year. This equates to each Australian
experiencing an episode of foodborne gastroenteritis approximately every five years. While
foodborne gastroenteritis is often not serious, it results in considerable costs to society
through medical costs and days of work lost. Approximately one in five people with
gastroenteritis seek medical attention, which could mean that foodborne illness results in as
many as a million visits to a doctor annually.18
Acute non-gastroenteritis illnesses and sequelae due to contaminated food are also a
significant contributor to the incidence of foodborne illness in Australia. While there are
notably fewer cases from these illnesses than from foodborne gastroenteritis, they cause more
serious symptoms and are of longer duration. In this study, sequelae responsible for the
highest rates of illness included IBS and ReA, which are not fatal, but cause disabling
symptoms.25,26 It is important to recognise that the current study estimates yearly incident
cases only, and does not measure the long-term burden of these sequelae.
It was concerning to identify that foodborne Campylobacter spp. were responsible for
80% of the sequelae cases estimated in this study, as well as approximately 179,000 cases of
gastroenteritis every year. The rate of infection due to Campylobacter spp. has increased over
the last 10 years and is higher than many other developed nations. For example, the
Australian Campylobacter spp. rate is approximately 10 times higher than the USA,27 double
that of the Netherlands14 and slightly higher than that of the UK.1 In New Zealand, source
attribution and policy regulation led to interventions that effectively lowered Campylobacter
spp. rates and sequelae rates as well. Interventions introduced in 2006 in New Zealand,
including Campylobacter spp. performance targets at primary processing and the promotion
of freezing all fresh poultry meat, which by 2008 resulted in the rate of Campylobacter spp.
notifications decreasing by 54% and a fall in GBS hospitalisations by 13%.28,29 If Australia
were able to introduce successful interventions and experience the same decreases in
infection as New Zealand, the rate of foodborne Campylobacter spp. cases in the Australian
community would be expected to drop from approximately 8,400 to 3,864 cases per million,
leading to a decline in the incidence of sequelae from 1,620 to 870 cases per million per year.
In addition to Campylobacter spp., norovirus, ‘other pathogenic E. coli’, and non-
typhoidal Salmonella spp. were the main contributors to the incidence of foodborne illness.
Reducing the number of cases of these illnesses in the community would reduce the societal
burden of foodborne gastroenteritis. Meats, particularly chicken meat, and eggs have been
shown to be important food sources for infections due to Campylobacter spp.,non-typhoidal
Foodborne illness in Australia circa 2010Page 25
Salmonella spp.,30-33 and in Australia, fish is the source of seafood toxins responsible for
ciguatera and scombrotoxicosis.34 Generating robust attribution data to identify food sources
for specific pathogens, as well as contributing factors throughout the production and supply
chain may help target food safety policies and interventions.
Similar foodborne illness estimation studies have been conducted in the USA,5,6 UK,12
Canada,17 and the Netherlands.14 The proportion of gastroenteritis estimated to be due to
foodborne transmission in the current study (25%) is remarkably similar to the UK estimate
(26.2%) and the most recent USA estimate (25.8%), but lower than that of the Netherlands
(39%). Although the Canadian study does not report an overall proportion foodborne,
analysis of their results put it around 20%. In addition, the proportion of foodborne
gastroenteritis due to unknown aetiology that was estimated in the USA (80%) is the same
proportion that was estimated in this study. The overall estimates of the proportion that is
foodborne depend on the selection of pathogens for inclusion, the incidence of common
pathogens, and the estimated proportion that are foodborne.
While this study builds on an earlier Australian study estimating the incidence of
foodborne illness circa 2000, there have been a number of methodological improvements
since then. Data from the OzFoodNet outbreak register were used for pathogens that are not
included in the national surveillance system. National data from NNDSS were used to
determine the proportion of cases that were associated with travel. Hospitalisation data from
all jurisdictions and death data from the ABS were used to estimate the incidence of severe
illness. Use of national data takes account of variation in foodborne illness patterns by state
and territory, and provides more representative national estimates. Use of both principal and
additional diagnosis data from hospitals more accurately captures hospitalisation patterns by
pathogen.
A new expert elicitation undertaken in 2009 for this estimation effort was incorporated,
further improving data quality.23,35 The proportion of foodborne transmission was generally
estimated to be lower and uncertainty bounds wider, compared to the estimates found in the
Delphi process in 2005. This may be a reflection of a general perception that environmental
sources of infection have been somewhat neglected and that health departments have a
primary focus on ‘foodborne disease’.23,35 These lower foodborne proportions translated into
fewer illnesses, hospitalisations, and deaths being attributed to food, compared to the circa
2000 study.
In estimating community incidence, underreporting multipliers were used to adjust for
the proportion of people who were infected with a pathogen or agent but did not seek
Foodborne illness in Australia circa 2010Page 26
treatment or submit specimens for testing. Following a 2008 study by Hall et al.,22 new
multipliers were used for non-typhoidal Salmonella spp., Campylobacter spp., and STEC.
The non-typhoidal Salmonella spp. underreporting multiplier of 7 (95% CI: 4–14) was
extrapolated to all other moderate illnesses, besides Campylobacter spp. These new
underreporting multipliers are smaller than the multiplier of 15 (CrI: 5–25) that was used in
2005.3 This is one of the main reasons that direct comparison between the estimates circa
2000 and circa 2010 can be misleading. However, application of these new multipliers to
data circa 2000 has validated that there have been increases in the incidence of illness due to
Campylobacter spp. and non-typhoidal Salmonella spp. between 2000 and 2010.
An underreporting multiplier for serious illnesses and the underdiagnosis multiplier for
hospitalisations and deaths of two cases for every case reported was used,3,10 which is
consistent with studies by Mead et al.5 and Scallan et al.6 The use of this multiplier for
hospitalisations and deaths was supported by comparing data from the OzFoodNet outbreak
register to hospital and ABS deaths data, which suggested that a multiplier of at least two was
necessary to account for underdiagnosis. There are limited data on pathogen-specific
underdiagnosis and further studies are required to thoroughly validate this multiplier and
assess whether there are pathogen-specific differences in underdiagnosis of severe illness.
Another improvement in this study was the higher level of detail and research that went
into the estimates for sequelae, including extensive literature searches and the addition of a
bacterial multiplier in estimates of the foodborne proportion. Many of the international
estimation efforts have not undertaken the task of estimating foodborne sequelae.5-7,12 The
current estimation work revealed the additional burden from foodborne pathogens, such as
Campylobacter spp., non-typhoidal Salmonella spp., and STEC, that is not seen when just
looking at the incidence of acute illness. For instance, while foodborne Campylobacter spp.
infection is directly responsible for three (90% CrI: 2–4) deaths each year, it also causes a
further six (90% CrI: 2–10) deaths from GBS and is potentially responsible for deaths from
other sequelae. Overall, Campylobacter spp. was responsible for around 80% of sequelae
estimated in this study, highlighting the importance of control measures to reduce both acute
and chronic illness from contaminated food.
To validate the estimates of incidence, a primary and additional estimation method
were used depending on the available data for each pathogen. When the additional approach
estimates were similar to the primary approach, this validated the estimate and gave more
confidence in the data. When the two approaches produced different estimates, this allowed
for a closer examination of the data to determine which source was more reliable and should
Foodborne illness in Australia circa 2010Page 27
be used as the primary method. Using these two approaches gives us confidence that
estimates were created using the best available data for each pathogen. One consequence of
this was a reduction in estimates of incidence for Cryptosporidium spp. and G. lamblia in
2010 when compared with 2000, which reflect a methodological shift from the use of the
1997–1999 WQS data for these pathogens in 2000, to the use of notification data for these
pathogens in 2010. Estimates circa 2010 used the Australian NNDSS for Cryptosporidium
spp., and state data for Victoria for G. lamblia.
By applying the current estimation methods to the data from 1996–2000 (which were
used for the circa 2000 report), there was an increase in estimates of foodborne
Campylobacter spp. and non-typhoidal Salmonella spp. annual incidence by about 40,000
and 11,000 cases respectively from circa 2000 to circa 2010. This estimated increase is
supported by the fact that notifications for both pathogens have been increasing over the 10
year period and higher numbers of hospitalisations were observed in 2010 when compared
with 2000. In addition, the circa 2010 estimates for GBS and other sequelae increased over
this time. These increases are concerning and highlight the importance of government and
industry partnerships to reduce the incidence of specific foodborne pathogens,28,29 as well as
the promotion of food safety education for consumers.
There was a clear decline in estimates for rotavirus in 2010 compared to 2000 reflecting
the success of the immunisation program, which began in 2006, in reducing illness in young
children.36 Similarly, estimated foodborne illness due to hepatitis A declined from 245
foodborne cases per year circa 2000 to 40 foodborne cases per year in 2010, reflecting
improved public health control of this pathogen, such as vaccination programs in Indigenous
communities and other interventions.37 While these vaccination programs were not targeting
foodborne transmission of disease, these declines show the systemic effects that public health
interventions can have and the benefits for food safety due to reduced infection pressure.
Overall foodborne gastroenteritis in Australia from the NGSII decreased when
compared to the NGSI, which consequently reduced the estimate of overall foodborne
gastroenteritis. The reduction in incidence of gastroenteritis may be due to a range of factors,
including biases in the study methodology as the response rate decreased over time, or a true
decrease in incidence of pathogens causing gastroenteritis, such as rotavirus.36
STUDY LIMITATIONS
Foodborne illness in Australia circa 2010Page 28
In a complex study of this type, there are many gaps in data availability and each data
source had strengths and weaknesses. While NNDSS and the OzFoodNet outbreak register
were nationally representative, there may have been differences in the way that jurisdictions
reported or coded their data, and some outbreaks may not always be captured in the
OzFoodNet outbreak register, such as those due to illnesses like hepatitis A and listeriosis,
unless a source is identified.
In contrast, there may have been outbreaks or unusual increases in disease that occurred
during the study period. In 2009, there was a large outbreak of hepatitis A associated with
semi-dried tomatoes across Australia.38 Similarly, outbreaks of salmonellosis and other agents
were routine occurrences.39 It was decided that data would not be adjusted in the estimation
to account for outbreaks or investigated clusters, as these events represent the real situation of
foodborne illness in Australia and form an important part of the burden of disease.
Data from the WQS2,20 were used for pathogens that were not nationally notifiable or
had limited outbreak data (Technical Appendix 3). This study is the best of its kind in
Australia, but is now over 15 years old. The study centred on families with children in the
Melbourne area, and as such may not represent all foodborne illness in Australia. Where
WQS data were used, changes over time and the differing age structure to the general
population were adjusted for. Participation and stool submission in the study may also be an
issue: only about one third of gastroenteritis cases submitted a stool sample, not all ‘known’
pathogens were tested for, and only 17% of stools that were examined had a pathogen
identified.2,20
One aspect of using WQS data is that the estimate of the incidence of ‘other pathogenic
E. coli’ is very high. The WQS reported a high recovery rate from participants’ stool samples
of enteropathogenic E. coli, at least some of which had atypical virulence characteristics.40
Apart from notifiable disease data on Shiga toxin-producing E. coli, there were no other data
available on pathogenic E. coli from other sources. The public health significance of
enteropathogenic E. coli is uncertain, as little is known about sources of infection in the
community. Consequently, the range of uncertainty around the estimates of incidence of
‘other pathogenic E. coli’ is very wide. Given these concerns – and the lack of a second
dataset for comparison – these estimates should be viewed with caution. It is also worth
noting that hospitalisation rates for ‘other pathogenic E. coli’ were low, as this hospitalisation
code is a generic category and laboratories are unlikely to test for pathogenic E. coli as a
cause of diarrhoea.
Foodborne illness in Australia circa 2010Page 29
While the study by Hall et al.22 provided a more appropriate underreporting multiplier
for specific pathogens than was previously available, both the underreporting multiplier and
the outbreak multiplier in the circa 2010 estimates were largely based on non-typhoidal
Salmonella spp. Although pathogens responsible for moderate illness may act similarly to
non-typhoidal Salmonella spp., in reality, multipliers are likely to be illness specific. Ideally,
underreporting and outbreak multipliers would be specific to each pathogen.
Another limitation was the amount of research and data available for the proportion of
diarrhoea that was due to astrovirus, adenovirus, and sapovirus. While they may cause
gastroenteritis commonly, they are rarely tested for and laboratory tests are probably
unreliable. In addition, sapovirus is a newly identified virus and hospitalisations and deaths
could not be estimated as there was no ICD-10-AM code for the illness. The total envelope of
disease resulting from these three pathogens estimated circa 2010 is similar to that estimated
for adenovirus and astrovirus circa 2000, however the data to inform these estimates were
sparse.
This study attempted to account for many weaknesses in the data through the estimation
of uncertainty. In this study, the uncertainty bounds were expanded from 95% credible
intervals to 90% credible intervals. The main reason for doing this was to ensure consistency
with international efforts.6 Wider credible intervals reflected greater uncertainty. When these
intervals were examined, it was identified that over half the uncertainty arose from the
distribution for the foodborne multiplier, with distributions for the underreporting and
pathogen fraction multipliers the next most important sources of uncertainty.
The implication of this is that a massive change in the incidence of foodborne
pathogens would need to be observed in order to see a significant difference in the rate ratio
over time. When comparing rates from 2010 to 2000, only the incidence of hepatitis A was
significantly lower, representing a seven-fold decrease. Studies targeted at high incidence
pathogens—norovirus and ‘other pathogenic E. coli’ in particular—would help to reduce this
uncertainty and improve understanding for disease prevention. Given the significant
uncertainty in estimation methods, there is a need to improve the ability to detect less-
dramatic changes in disease.
POLICY IMPLICATIONS AND FURTHER RESEARCH
Foodborne illness in Australia circa 2010Page 30
Foodborne illness estimates are an important tool for public health policy as they
elucidate the societal burden of foodborne illness, and allow economic assessment to
determine the direct costs of illness from contaminated food. A costing report of the circa
2000 estimates suggested that foodborne illness in Australia costs AUD$125 billion dollars
annually.4 Updating this figure for the circa 2010 estimates is important for understanding the
direct costs of foodborne illness. In addition to the direct costs incurred, foodborne illness is
also a significant public health issue due to the potential costs that food contamination and
food recalls may have on industry. Outbreaks and sporadic illness occur from a variety of
food commodities and many different industries and even whole economies can be affected.
A recent USA study found that most foodborne illnesses from outbreaks can be
attributed to food commodities that constitute a major portion of the diet in the USA.41 As
these types of commodities are frequently consumed, food contamination often leads to
massive recalls, along with industry wide damage. These high costs demonstrate the public
health importance of reducing foodborne illness and provide an opportunity for regulatory
policy to reduce these common illnesses. The study by Painter et al.41 attributes outbreaks to
food commodities in the USA. Similar studies in Australia to identify the food commodities
responsible for the greatest amount of illness, as well as for contributing factors through the
production and supply chain, could complement these estimates of incidence, hospitalisation
and death and provide increased support for targeted interventions in the food industry.
There are several areas where further research is needed to strengthen the estimates and
improve public health policy. Foodborne illness estimate and burden studies can assist with
identifying vulnerable populations. For this, it is important to consider specific sub-studies to
examine the specific foodborne burden affecting sub-populations, such as the young or the
elderly. For these groups, certain pathogens have higher incidence and the ameliorable risk
factors will be different. Another area that is needed to improve the understanding of
foodborne illness in Australia is better validation of hospitalisations admission records and
death certificates for potentially foodborne agents.
Importantly, this report highlights the benefits of a large prospective community cohort
study that follows participants for reports of symptoms of diarrhoea and/or vomiting, similar
to the IID2 study in the UK1 to better identify rates of infectious intestinal disease rates in the
community due to specific pathogens or agents. For pathogens that are not covered by the
national surveillance system and not commonly seen in outbreaks (such as G. lamblia and
‘other pathogenic E. coli’), this report had to rely on the Melbourne WQS2 which is now 15
years old, and has other limitations mentioned previously. A large prospective cohort study
Foodborne illness in Australia circa 2010Page 31
would improve current estimates for infectious gastroenteritis and aid in determining the
burden and causes of specific pathogens in the Australian community, along with
identification of preventable risk factors.
Finally, it would be important to repeat this estimation exercise circa 2020 to examine
changes in disease incidence over time. Ideally, there would be a well-articulated plan to
improve the information base for this assessment.
CONCLUSIONThere are an estimated 4.1 million (90% CrI: 2.3–6.4 million) cases of foodborne
gastroenteritis occurring each year, along with 5,140 (90% CrI: 3,530–7,980) cases of non-
gastrointestinal foodborne illness and 35,840 (90% CrI: 25,000–54,000) cases of sequelae.
The majority of foodborne illness occurs as gastroenteritis, but non-gastrointestinal illness
and sequelae are also important due to the fact that they result in hospitalisations and
occasional deaths. Over time, the incidence of foodborne gastroenteritis decreased slightly,
while salmonellosis and campylobacteriosis had increased. There is a need to improve the
evidence-base in Australia to improve the understanding of foodborne illness by conducting
research into specific pathogens and the overall causes of gastroenteritis through a large-scale
cohort study. The results of this study should assist policy makers to advocate for improved
regulation and control of foodborne illness.
Foodborne illness in Australia circa 2010Page 32
TECHNICAL APPENDIX 1: FURTHER COMPARING FOODBORNE ILLNESS INTERNATIONALLY
INCIDENCE OF INFECTIOUS GASTROENTERITIS
The estimate of total infectious gastroenteritis incidence in the community may be
made using either a prospective cohort study or a retrospective cross sectional study with
recall of episodes of gastroenteritis in the previous weeks. These studies are usually run over
one year to capture any seasonal effects. The prospective cohort study design requires
participants to record symptoms relevant to gastroenteritis in a diary during the study period.
The retrospective cross sectional study design usually involves surveying people by
telephone with regard to their symptoms of gastroenteritis in the recent past.
It is important to note that the two different study designs may not give the same
estimates of population incidence of disease. Retrospective cross sectional studies have
tended to provide higher estimates of gastroenteritis rates than do prospective studies. Cross
sectional studies gave incidence close to one case per person per year in the USA, Canada
and Australia,18,42,43 while prospective cohort studies have resulted in incidence considerably
less than one case per person per year at 0.19 and 0.27 episodes per person-year in two UK
studies,1,44 and 0.28 in the Netherlands.45
In the second UK study, the IID2 study in 2008, both a population based cohort study
and a population based cross sectional study were run deliberately to compare incidence from
the two study designs using the same case definition.1 In the IID2 study, using 7-day recall in
the retrospective telephone study, the incidence was 1,530 cases per 1000 person-years,
which was five times higher than the rate estimated in the prospective cohort study (274 cases
per 1000 person-years). When they used 28-day recall in the telephone study, the incidence
was estimated at 533 cases per 1000 person-years, which was twice as high as the rate
estimated in the prospective cohort study. It has been proposed that retrospective studies tend
to result in higher estimates of morbidity due to ‘recall errors’.44,46 If the recall period is short
and the illness severe, then the event is more likely to be memorable and over-reported;
whereas if the recall period is long, the illness mild and less memorable, it is more likely to
be underreported or even forgotten.
While prospective studies do not tend to have the same issue with recall errors, they
can, however, suffer from the effects of reporting fatigue, which occurs when participants
lose interest in reporting their symptoms, possibly leading to an underestimation of the
Foodborne illness in Australia circa 2010Page 33
disease rate.2,44,47,48 A decline in the reported rate of highly credible gastroenteritis was
observed in two Australian studies as the study progressed.2,48 Hellard et al.2 suggest that one
way to avoid this phenomenon may be to use a shorter observation period with a larger
number of participants.
Although more expensive and longer than retrospective cross sectional studies, cohort
studies generally include specimen collection that allows for direct estimation of the
incidence of some specific pathogens. Hellard et al.2 used a randomised clinical trial with a
cohort of study participants to give incidence rates to 16 known enteric pathogens.
Besides study design, the case definition of gastroenteritis can greatly influence
incidence estimates, as there is no standardised international case definition. When four
different case definitions were applied to a given country, the result was four different
incidence estimates for that country.49 This strongly suggests that valid comparisons cannot
be made between studies that use different case definitions. Incidence of illness and cost
estimates will also be influenced by different case definitions because they rely on estimates
of incidence or number of cases. In the study by Majowicz et al.,49 the different case
definitions were also shown to affect gender specific values and age distributions, the
observed duration of illness (by 20%), and the proportion of cases seeking medical care and
submitting stool samples for testing. The use of a standard symptom-based case definition by
investigators would enable much better inter-country comparisons and global estimates of
gastroenteritis incidence.
A further complication in defining an appropriate case definition for gastroenteritis
arises with regard to respiratory symptoms. When a person presents with concurrent
respiratory symptoms and symptoms of diarrhoea or vomiting, they could be due to
respiratory infections, gastrointestinal infections, or both; however this distinction is rarely
made.50 Analysis of population based studies in Australia, Canada and the USA has shown
that respiratory symptoms occur frequently in persons with acute gastrointestinal symptoms
(diarrhoea, vomiting or both), specifically 29% in Australia, 42% in Canada and 47% in
USA.50 Hence, if the case definition of acute gastroenteritis is adjusted to remove cases with
respiratory symptoms, there is a substantial decrease in the estimates of incidence of acute
gastroenteritis. Therefore, when estimating the incidence of foodborne illness from
population studies, it is important to give consideration to cases with gastrointestinal
symptoms possibly arising from respiratory infections and to make appropriate adjustments.
To date, no international consensus has been reached on whether all or some of such cases
should be excluded from analyses.
Foodborne illness in Australia circa 2010Page 34
In the previous estimation of the incidence of gastroenteritis in Australia, Hall et al.3
used a case definition which referred to moderate-to-severe illness, with ≥3 loose stools or ≥2
episodes of vomiting in a 24 hour period in the previous four weeks. Patients with
concomitant respiratory symptoms were excluded unless they had more severe symptoms of
diarrhoea or vomiting, specifically, ≥4 loose stools or ≥3 episodes of vomiting, in a 24 hour
period in the previous four weeks. In the Mead study5 respiratory symptoms were not
excluded, but an adjustment was made to reduce the number of cases from a population based
cross sectional study by 25% based on data from other studies to account for this.51,52 The
case definition in the more recent study by Scallan et al.6 excluded persons with respiratory
symptoms, which effectively halved the incidence rate of gastroenteritis.
PATHOGENS ASSESSED IN AUSTRALIA CIRCA 2000
There are in excess of 200 different pathogens and agents that may be transmitted by
contaminated food and cause gastroenteritis or other syndromes and conditions, such as
hepatitis and septicaemia.5 Foodborne pathogens include viruses, such as norovirus and
hepatitis A; bacteria, such as non-typhoidal Salmonella spp., Campylobacter spp. and
toxigenic E. coli; along with toxins, such as ciguatoxins and histamines.6,8 The Australian
study by Hall et al.10 included 16 infectious agents causing gastroenteritis, five pathogens or
agents causing acute non-gastrointestinal illness, and four syndromes representing sequelae
of acute foodborne infections. The key rationale for covering different pathogens in this study
included: high incidence, severe outcomes of infection or exposure, availability of data,
strong relationships with specific food vehicles, and expert opinion on importance to food
safety.
ESTIMATING INCIDENCE OF SPECIFIC PATHOGENS
The variation in methods used to estimate incidence of illness due to specific pathogens
is largely data driven. Different methods have been used in different countries and across
different pathogens, depending on what data are available. In many cases there are no
‘definitive’ data that give an absolute value of incidence, and varying adjustments and
assumptions are usually required to estimate the incidence in the population. Data sources
include cohort studies and surveys with laboratory testing of stool or other relevant
specimens to identify pathogens, surveys with collection of human sera to identify antibodies
to particular agents, surveillance systems of individual cases of illness due to various
pathogens, or surveillance of outbreaks and data on hospitalisations and deaths. If there is
Foodborne illness in Australia circa 2010Page 35
more than one data source available then this allows for validation across different methods
of estimation.
Population based prospective cohort studies are used to assess the incidence of all
gastroenteritis and they also allow for stool specimens to be tested from those participants
exhibiting relevant symptoms. This means the pathogens can be identified allowing
estimation of the incidence of specific pathogens. This has given very useful results that have
been used in burden studies in the Netherlands,14,45 in the first study of infectious intestinal
disease (IID1) and in the IID2 study in the UK.1,44 The identification of known pathogens in
specimens from population based cohort studies is often not complete, generally being found
in around 30–50% of specimens. Numbers of cases also tend to be small and confidence
intervals may be wide.
Active or mandatory laboratory based surveillance data maintained by health
departments or other organisations are often used in the absence of cohort data and,
depending on quality, are generally preferentially used above some other data sources. In
countries like the USA and Australia, there may be more than one surveillance system
operating, where there is surveillance to a national notifiable diseases system, and an
enhanced surveillance system focussed on foodborne illness where reporting of cases is
actively followed up, such as FoodNet.5,6,53 The number of cases reported to laboratory based
surveillance is usually less than the number of cases in the community and multiplicative
factors are used to adjust the numbers of reported cases to reach a more realistic estimate of
incidence in the community. Multiplicative factors include adjustment factors to account for
coverage of the population under surveillance (necessary when only a proportion of the
population may be under enhanced surveillance), to adjust for the fact that only a proportion
of cases go to the doctor and of these, only a proportion have a stool test ordered. Of these
not all will have a positive test result depending on the sensitivity of the laboratory test, and
further adjustment may be needed to account for incomplete reporting of positive tests to the
surveillance system. Where only outbreak surveillance data are available, a further
multiplicative factor is required to adjust from the number of cases reported in outbreaks to
the number of individual cases that would have been reported if the pathogen were under
individual surveillance.
ACCOUNTING FOR UNDERREPORTING AND UNDERDIAGNOSIS
The underreporting of illnesses to surveillance is widely recognized and strategies to
deal with this range from simply noting the issue to deriving quantitative factors using
Foodborne illness in Australia circa 2010Page 36
varying levels of data and sophistication. The calculation of community incidence from
laboratory based surveillance data requires multipliers that account for the fact that not all
cases in the community are reported. For this to happen, a person must have symptoms, visit
a doctor, have a stool sample taken, the laboratory test has to be positive and the result
reported to surveillance. Each of these steps can be assigned a probability based on the
proportion of people or tests that proceed to the next step. The overall proportion of
individuals recorded by surveillance is the product of all these component probabilities and
the multiplier is the inverse of that proportion. The sequence of steps is commonly known as
the reporting pyramid. The corresponding multiplicative factors comprise the components
‘population factor’, ‘doctor visit factor’, ‘stool test factor’, ‘laboratory sensitivity factor’,
‘reporting to surveillance factor’ and ‘outbreak factor’. In different studies these factors may
have been estimated from ‘opinion’ or on more substantial evidence from various data
sources.
In recent studies, more attention has been paid to trying to estimate these multipliers in
a more pathogen-specific and evidence-based way and including measures of uncertainty. In
the study by Mead et al.,5 multipliers were based on limited evidence and some multipliers
were simply based on the authors’ opinions. In the more recent USA study,6,7 considerable
effort was put into improving multipliers through the use of data to derive estimates and the
modelling of uncertainty. An underreporting multiplier, comprised of a ‘population factor’,
‘reporting to surveillance factor’ and ‘outbreak factor’; and an underdiagnosis factor,
comprised ‘doctor visit factor’, ‘stool test factor’, and ‘laboratory sensitivity factor’ were
used to account for underreporting. The data for the multiplier that adjusts for passive to
active surveillance were taken from pathogens that have both passive and active surveillance,
including Cryptosporidium spp., STEC, L. monocytogenes and non-typhoidal Salmonella
spp. To estimate the underdiagnosis factor, three population surveys of gastroenteritis were
used to estimate the proportion of people who had bloody diarrhoea and sought medical care
(35%), the proportion who then submitted a stool sample (36%), and the proportion of people
who had non bloody diarrhoea and sought medical advice and then submitted a stool sample
(18% and 19% respectively). For severe invasive illness it was assumed that 100% of cases
would seek medical care and 80–100% would have a specimen test. A factor to account for
the percentage of laboratories that tested for a particular pathogen was derived from FoodNet
and other laboratory surveys and gave pathogen-specific factors of 25% to 100% and factors
to account for sensitivity of the test of 28% to 100%. For the five pathogens with only
outbreak data, the underdiagnosis factor was assumed to be the same as for non-typhoidal
Foodborne illness in Australia circa 2010Page 37
Salmonella spp. The outbreak factor to adjust for outbreak data to active surveillance was
derived from ratios from pathogens with both active surveillance and outbreak surveillance.
In Hall et al.,3 most of the pathogen-specific incidence rates were based on methods
using either direct estimation from cohort data from a study in Melbourne, Victoria,2,20 or
surveillance data adjusted for underreporting using multipliers. The multipliers used to adjust
for underreporting cases in the community to individual surveillance were: 15 (CrI: 5–25) for
moderate illnesses, nine (CrI: 1–17) for bloody diarrhoea, and two (CrI: 1–3) for serious
illnesses. The outbreak factor was estimated from cases infected with non-typhoidal
Salmonella spp. in Victorian data that were reported to outbreak surveillance compared with
the number of cases reported to individual surveillance, a factor of about 14. A method
similar to that used by Voetsch et al.54 was used to calculate the underreporting factor of 15
for moderate illness using data from an unpublished case control study of non-tyhoidal
Salmonella spp. cases in the Hunter region and surveillance data (1997–2000). Comparison
of the incidence of cases of non-tyhoidal Salmonella spp. notified to the estimated incidence
of non-tyhoidal Salmonella spp. from the Melbourne water quality study (WQS)2 also
informed this factor of 15.10 The ‘serious illness factor’ of two was based on the precedent set
by the Mead et al.5 study.
In 2008, new underreporting factors for Australia were estimated together with
uncertainty for non-tyhoidal Salmonella spp. at seven (95% CrI: 4–16), for Campylobacter
spp. at 10 (95% CrI: 7–22), and for STEC at eight (95% CrI: 3–75).22 The multipliers resulted
in estimates of community incidence of these pathogens that were lower than the estimates
used in the last Australian foodborne illness incidence study.3 It is notable that the recent
Australian underreporting factor estimates are similar to the recent UK estimates from the
IID2 cohort study where underascertainment ratios for non-tyhoidal Salmonella spp. and
STEC were nine and seven respectively.1 The underascertianment ratio of five for
Campylobacter spp. in the IID2 study was lower than the Australian underreporting estimate
of 10.
ESTIMATING HOSPITALISATIONS AND DEATHS
Although the incidence of hospitalisations and deaths are important indicators of the
severity of foodborne gastroenteritis, it is not easy to estimate them accurately. This is in
large part because most routine surveillance systems underreport mortality from infectious
diseases. In addition, very little information is collected on illness outcome by pathogen-
specific surveillance.
Foodborne illness in Australia circa 2010Page 38
National data on outpatient visits resulting in hospitalisation, hospital discharges and
death certificates generally underestimate the pathogen-specific incidence. This is because
pathogen-specific diagnoses need to be recorded, health care providers need to order
appropriate diagnostic tests, and coding must be accurate. Scallan et al.6 used surveillance
data to determine the proportion of persons that were hospitalised and the proportion that
died, and then applied these proportions to the estimated number of laboratory confirmed
illnesses. The number of hospitalisations and deaths was then doubled to account for
underdiagnosis of those with unconfirmed illnesses.
Mead et al.5 also accounted for underreporting by doubling the number of
hospitalisations and deaths among reported cases. Multiple cause of death data from the
National Vital Statistics System where acute gastroenteritis was listed as the underlying or a
contributing cause, were used to estimate the number of deaths. Acute gastroenteritis was
defined by the following ICD-10 diagnostic codes: A00.9–A08.5 (infectious gastroenteritis of
known cause), A09 (diarrhoea and gastroenteritis of presumed infectious origin), and K52.9
(non-infectious gastroenteritis and colitis, unspecified). A04.7 (enterocolitis due to
Clostridium difficile) and A05.1 (botulism) were excluded. Mead et al.5 also excluded
diagnoses of infectious enteritis associated with respiratory symptoms or a diagnosis of
influenza (ICD-9-CM code 487).
In Australia, Hall et al.3 used data from the National Hospital Morbidity Database to
estimate the total number of hospitalisations and deaths for each of the pathogens under
investigation. In the Netherlands, de Wit et al.45 used linear regression to combine laboratory
data and hospital registration data to obtain estimates for incidence of hospitalisations for
rotavirus infection, only taking into account those with microbiologically confirmed rotavirus
infection. In Canada, Ruzante et al.55 estimated the number of hospitalised cases and the
fatalities from four years of data from the Canadian Institute for Health Information Hospital
Morbidity Database (HMDB) and the Canadian Vital Statistics-Death Database for five
enteric pathogens and three sequelae. Vaillant et al.,11 Mead et al.5 and Adak et al.12 applied
pathogen-specific case fatality ratios, from selected outbreaks and studies, to the estimated
number of cases. Their estimates do not relate to all directly coded deaths, which may have
led to overestimation of the number of deaths attributable to foodborne infection.
ESTIMATING PROPORTION OF ILLNESSES THAT ARE FOODBORNE
A key component of all studies examining the incidence and burden of foodborne
illness is the estimation of the proportion of disease due to food for different pathogens or
Foodborne illness in Australia circa 2010Page 39
agents.56 This is important, as for many agents, disease may be acquired from exposure to a
range of different sources, including contaminated food or water, infected animals or humans
and contact with contaminated environments.57 Many enteric diseases are faecal-oral spread
and may be transmitted via a range of different modes of transmission.8
Estimating the proportion of cases of disease due to different modes of transmission is
difficult due to the paucity of data and is a weak point of studies assessing foodborne illness
incidence.6,57 Public health investigators have used several different methods for assessing the
fraction of disease that may be due to contaminated foods. Source attribution studies, which
specifically examine the route of transmission, may be focused on different points in the food
chain.57 Different approaches to studying source attribution include: microbiological
approaches, epidemiological approaches, intervention studies, and expert elicitation. All
national-level studies examining the incidence of foodborne illness have incorporated some
element of source attribution to arrive at estimates of disease incidence. Investigators have
used a range of these different approaches (Table T1.1).
An estimate of the proportion of transmission of known pathogens that is due to food is
critical for estimation of the incidence of foodborne illnesses of unknown aetiology, which
comprises the majority of the total burden. Even when using the most sensitive diagnostic
screening tests, a recognised pathogen is recovered from only 30–40% of specimens.1 Only
the UK, USA, Greek and Australian studies estimating the incidence of foodborne illness
have taken gastroenteritis of unknown aetiology into account, following the approach adopted
by the Mead study.3,5,7,12,13
Many foodborne illness incidence studies, including the circa 2000 estimates for
Australia, have relied on expert elicitation to attribute illnesses to foodborne spread, due to
the paucity of other sources of data on the mode of transmission. There have been several
methodological improvements over the years to the expert elicitation framework, including
providing experts with prior information from systematic reviews of the literature and
modelling uncertainty in expert opinions using Monte Carlo simulation. This technique is
fairly widespread in risk analysis studies and involves simulating distributions of components
used in the calculations rather than using point estimates. The end product is a final
distribution that provides a central point estimate and a credible interval.
However, there have been expert elicitations where the findings have proven unusual or
contradictory.58 One Canadian study that used cluster analysis to examine opinions from
experts from different backgrounds found that for L. monocytogenes, one group of experts
had a mean of 8% of human infections were foodborne, compared to another cluster
Foodborne illness in Australia circa 2010Page 40
estimating 84% of human infections were foodborne.58 These differences may result from the
type of expert selected, or from the prior information given to participants in these structured
elicitations.
Expert elicitation should be regarded as a structured way to obtain a consensus opinion,
based on evaluation of all available data. It cannot be expected to provide an unbiased
estimate of the relative importance of different transmission routes. An advantage of expert
elicitation is that it allows for information from different sources to be combined in a single
analytical framework.
ESTIMATING UNCERTAINTY
There are many sources of uncertainty in national incidence of disease studies
including: uncertainty in the proportion of illness caused by specified or unknown pathogens,
uncertainty in the proportion of cases that can be attributed to foodborne transmission,
uncertainty in the level of underreporting of cases of illness, inconsistency in the quality of
incidence data, and value choices made in the DALY formula. Furthermore, norovirus and
other viruses are suspected of being responsible for a significant proportion of the ill-defined
intestinal infections and hence have a major impact on overall estimates, but are poorly
captured by routine surveillance.
It is important to try to express uncertainty quantitatively in order that reported
estimates and intervals convey the message that foodborne illness estimations rely on a
number of assumptions, and on data that are often variable, incomplete or minimal. While the
study by Mead et al.5 did not attempt to quantify uncertainty, a number of other more recent
studies have done so. The main method used to account for uncertainty is through Monte
Carlo simulation.
The study that has the most extensive descriptions of the techniques to account for
uncertainty used is the recent USA estimation of foodborne illness by Scallan et al.6,7 In this
study the rationale for the type of distributions selected is given, and the resultant final
estimates are given as a mean and 90% credible interval. This study provides the most
thorough reasoning for the approach used and also sufficient detail in appendices to replicate
their methods.
Foodborne illness in Australia circa 2010Page 41
Table T1.1: Proportion of illnesses attributed to food for different pathogens from national studies. Note that not all pathogens or agents considered in these studies have been included in the comparison table. Adapted from Havelaar et al.59
Characteristics of national studies
Mead5 Adak12 Vaillant11 Hall3 Havelaar59 Scallan6 Gkogka13 Thomas17
Country USA UK France Australia Netherlands USA Greece CanadaPeriod 1990s 1992–2000 1997–2000 2000 2006 2000–2008 1996–2006 2000–2010Data sources* E/O/R O E/O/R/CC/L E/O/R/L E E/O/R/L L E/O/R/LTravel-related cases Included Excluded Included Excluded Included Excluded Included ExcludedCampylobacter spp. 80% 80% 80% 75% 42% 50% 55% 68%Shiga toxin-producing E. coli
50% 65% 51% NA
O157 85% 63% NA NA 40% 68% NA NANon-O157 85% NA NA NA 42% 82% NA NA
Listeria monocytogenes 99% 99% 99% 98% 69% 99% 99% 84%Salmonella spp., non-typhoidal
95% 92% 95% 87% 55% 94% 95% 80%
Bacillus cereus toxin 100% 100% 100% 100% 90% 100% NA 100%Clostridium perfringens 100% 94% 100% 100% 91% 100% NA 100%Staphylococcus aureus 100% 96% 100% 100% 87% 100% NA 100%Hepatitis A virus 50% NA 5% 10% 11% 7% 8% 7%Norovirus 40% 11% 14% 25% 17% 26% NA 31%Rotavirus 1% 3% NA 2% 13% <1% NA 1%Cryptosporidium spp. 10% 6% NA 10% 12% 8% 5.6% 9%Giardia lamblia 10% 10% NA 5% 13% 7% 10% 7%Toxoplasma gondii 50% NA 50% 35% 56% 50% 50% 50%*Data sources: E=Expert opinion; O=Outbreak reports; R=Reported cases; CC=Case control studies; L=literature searchNA – Not applicable
Foodborne illness in Australia circa 2010Page 42
TECHNICAL APPENDIX 2: NATIONAL GASTROENTERITIS SURVEY IIThe National Gastroenteritis Survey II (NGSII) was conducted by the Australian
Government Department of Health, the New South Wales Food Authority and the National
Centre for Epidemiology and Population Health (NCEPH) in 2008–2009. The main aim of
the survey was to estimate the incidence and public health impact of gastroenteritis in
Australia.
For the NGSII, a stratified sample was collected in each state and territory, with a larger
sample collected from New South Wales and the Australian Capital Territory. During the 12
months of the study, the response rate for the telephone-based survey was 49.1%
(7590/15456), which represents the proportion of successful interviews for all eligible
residential households contacted. Overall, 50.9% (7867/15456) of eligible households
refused to participate in the survey. The lowest response rate was 47.3% for Victoria and the
highest was 54.0% for Tasmania. There were a total of 7,590 interviews conducted. Twelve
respondents did not provide an age during the interview and were excluded from analysis, as
data were unable to be weighted. Data were weighted to account for the two-stage sampling
design for jurisdiction and household and data were post-stratified by age category and sex
using 2006 census data to make data more representative of the Australian population.
A total of 555/7578 (7.3%) respondents reported experiencing diarrhoea or vomiting in
the previous four weeks, equating to 25 million episodes of vomiting or diarrhoea each year
in Australia, compared to 29 million cases in the 2001 National Gastroenteritis Survey
(NGSI) (Table T2.1). The primary definition of gastroenteritis was met by 341 cases, which
equates to 15.9 million cases of gastroenteritis in Australia in 2008–09, compared to 18.9
million cases of gastroenteritis in the NGSI. The weighted incidence of gastroenteritis
meeting the case definition in the NGSII was 0.74 (95% confidence interval [CI]: 0.64–0.84)
episodes per person per year.
DEMOGRAPHIC FEATURES AND SEASONALITY
In the NGSII, children under five years old reported the most gastroenteritis and
people over the age of 65 years old were nine times less likely to report gastroenteritis than
people aged less than five years old. Only people aged between 20–29 years old were similar
to children under the age of five years old in regards to their experience of gastroenteritis
(Figure T2.1)
Foodborne Illness in Australia Circa 2010Page 43
Table T2.1: Weighted number of cases of gastroenteritis and incidence meeting various case definitions in Australia in one year, NGSI & NGSII
Definition NGSII-2008 million cases
per year (95% CI)
NGSII-2008 cases per person per year (95%
CI)
NGSI-2001β
million cases per year (95% CI)
NGSI-2001β
cases per person per year (95%
CI)Gastroenteritis meeting case definitionα
15.9 (13.7–18.0) 0.74 (0.64–0.84) 18.9 (16.6–21.1) 0.97 (0.86–1.09)
Any diarrhoea or vomitingγ
24.8 (22.2–27.4) 1.16 (1.04–1.28) 29.0 (26.3–31.7) 1.49 (1.36–1.63)
Diarrhoea onlyδ
14.1 (12.1–16.1) 0.66 (0.57–0.76) 17.2 (15.1–19.3) 0.89 (0.78–1.00)
α–Case definition of ≥3 loose stools and/or ≥2 vomits in 24 hours that was not due to a known non-infectious cause. If respiratory symptoms were also present, a case was required to experience ≥4 loose stools and/or ≥3 vomits in 24 hours and no non-infectious cause. γ–Includes any person reporting symptoms of diarrhoea or vomiting and does not exclude cases due to non-infectious causes.δ–Includes any person reporting ≥3 loose stools in 24 hours.β–Data for NGSI-2001 analysed using same data weighting procedure as NGSII-2008 .
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0-4 yo 5-9 yo 10-19 yo 20-29 yo 30-39 yo 40-49 yo 50-59 yo 60-64 yo 65+
Age category (years)
Epi
sode
s pe
r per
son
per y
ear
Females Males
Figure T2.1: Weighted annual incidence of gastroenteritis by age group and sex in Australia, NGSII
People living in rural areas or on farms were significantly less likely to experience
gastroenteritis (adjusted odds ratio 0.57; 95% CI 0.35–0.93). There was no significant
difference in the proportion of respondents reporting gastroenteritis by Indigenous status
(Table T2.2). There was also no significant difference in the proportion of respondents
Foodborne illness in Australia circa 2010Page 44
reporting gastroenteritis by sex, state or territory, level of education or income, size of
household, or whether a person had health insurance or not.
Table T2.2 Four week period prevalence of gastroenteritis by demographic features in Australia, NGSII
Characteristic No. surveyed
Prevalence in previous four weeks (No. with gastroenteritis)
Prevalence in previous four weeks (Crude %)
Prevalence in previous four weeks [Weighted % (95%CI)]
SexFemale 4554 199 4.4 5.2 (4.3–6.2)Male 3024 142 4.7 6.2 (5.0–7.5)Age category (years)0–4 249 42 16.9 12.1 (7.5–16.8)5–9 249 16 6.4 6.3 (3.0–9.7)10–19 497 20 4.0 5.6 (3.0–8.2)20–29 507 47 9.3 9.1 (6.1–12)30–39 802 53 6.6 6.1 (4.1–8.1)40–49 1064 54 5.1 6.1 (4.2–8.0)50–59 1374 52 3.8 3.5 (0–8.2)60–64 755 25 3.3 2.7 (0–6.0)65+ 2081 32 1.5 1.2 (0–5.9)Geographical distribution by state and territoryNSW/ACT 2367 113 4.8 6.1 (4.9–7.3)NT 869 53 6.1 7.2 (5.1–9.2)Qld 869 42 4.8 6.5 (4.5–8.5)SA 870 33 3.8 5.1 (3.3–7.0)Tas. 868 38 4.4 5.2 (3.4–7.0)Vic. 867 34 3.9 5.2 (3.3–7.1)WA 868 28 3.2 4.5 (2.6–6.3)Indigenous statusNon-indigenous 7406 330 4.5 5.7 (4.9–6.5)Indigenous 161 10 6.2 5.1 (0.8–9.4)Highest level of education in householdPrimary 106 4 3.8 5.4 (0–13.3)Years 7–10 1714 62 3.6 5.4 (3.7–7.2)Years 11–12 1425 56 3.9 4.9 (3.3–6.6)Apprenticeship 562 29 5.2 6.8 (3.6–10)Diploma/certificate 1298 55 4.2 5.2 (3.4–6.9)University 2222 124 5.6 6.2 (4.8–7.7)Household income≤$25000 1641 46 2.8 4.4 (2.7–6.2)$25,000 to <50,000 1399 62 4.4 6.1 (4.2–8.0)$50,000 to <100,000 2024 108 5.3 6.3 4.9–7.8)≥$100,000 1452 81 5.6 5.8 (4.1–7.5)Unknown income 1062 44 4.1 5.0 (3.2–6.9)Health insurance status
Foodborne illness in Australia circa 2010Page 45
Characteristic No. surveyed
Prevalence in previous four weeks (No. with gastroenteritis)
Prevalence in previous four weeks (Crude %)
Prevalence in previous four weeks [Weighted % (95%CI)]
Health insurance 4307 187 4.3 5.2 (4.2–6.1)No insurance 3183 151 4.7 6.4 (5.1–7.6)Unknown insurance 88 3 3.4 6.0 (0–13.1)Urban or rural locationUrban/town 6411 304 4.7 6.1 (5.2–7.0)Rural/remote 1138 36 3.2 3.1 (1.7–4.5)Number of people in household1 person 1736 47 2.7 3.1 (1.9–4.3)2 people 2768 100 3.6 5.6 (4.1–7.0)3 people 1064 66 6.2 6.4 (4.4–8.4)4 people 1226 80 6.5 6.8 (4.9–8.6)5 people 519 36 6.9 7.7 (4.8–10.5)6 or more people 231 12 5.2 3.2 (0.7–5.7)
SYMPTOMS AND DURATION
The most common symptoms amongst cases were diarrhoea (87%), followed by loss
of appetite (75%), nausea (66%) and stomach cramps (63%) (Table T2.3). Approximately
one in four cases reported upper respiratory symptoms of sore throat, cough or runny nose.
These cases fulfilled the stricter criteria of at least four loose stools or three vomits within a
24 hour period. Blood in stools was reported by 3.5%. The majority of cases had vomiting
and/or diarrhoea for one or two days, and 6.5% of cases experienced symptoms for at least a
weak. The estimated median duration of gastroenteritis was two days (interquartile range 1–3
days) and a mean of 3.5 days (95% CI: 2.9–4.1). Cases who experienced vomiting either in
isolation or in combination with diarrhoea tended to have a shorter duration of symptoms.
HEALTHCARE SEEKING BEHAVIOUR
Twenty-eight per cent of respondents who reported gastroenteritis sought some kind of
health professional advice or treatment for their symptoms (Table T2.4). A total of 94 cases
visited at least one healthcare facility for their illness, which would equate to 4.77 million
(95% CI 3.55–5.99 million) visits each year, with 2.71 million (95% CI: 1.8–3.62 million) of
these visits to a doctor.
Cases with gastroenteritis lasting two or more days were more likely to see a doctor
compared to those who were ill for one to two days. The presence of vomiting and ear ache
were significant predictors of cases going to see a doctor, as well as indigenous status (odds
Foodborne illness in Australia circa 2010Page 46
ratio (OR) 7.02, 95% CI: 1.38–35.8). People with stomach cramps were less likely to seek
medical attention.
Of the cases that had diarrhoea only and saw a doctor, approximately 24% were asked
to submit a stool specimen. Of those who were asked to submit a stool sample, 11 out of 12
cases subsequently submitted a specimen. Duration of illness of five days or more was
associated with higher likelihood of a case submitting a specimen (OR 4.4, 95% CI: 1.96–
9.87).
Around 37% of cases reported taking at least one medication to treat or relieve
symptoms. This equates to about 5.5 million people taking medication for gastroenteritis
each year. Approximately 6% of those taking medications were prescribed antibiotics, which
after weighted analysis equates to an estimated 520,944 (95% CI: 54,699–987,189) courses
of antibiotics that are prescribed in Australia for treatment each year.
Table T2.3: Unweighted and weighted proportion of cases with gastroenteritis reporting different symptoms in Australia, NGSII (n=341)
Symptom No. reporting
(unweighted)
Crude % (unweighted)
Missing (unweighted)
Weighted % (95% CI)
Diarrhoea 298 87.4 0 84.1 (78.7–89.5)Bloody stools 12 3.5 6 2.9 (0.8–4.9)Loss of appetite 256 75.1 2 78.4 (72.7–84.1)Nausea 225 66.0 6 66.7 (59.9–73.5)Stomach cramps 216 63.3 9 64.5 (57.7–71.4)Headache 142 41.6 13 45.3 (38.1–52.6)Fever or chills 151 44.3 6 50.0 (42.9–57.1)Vomiting 161 47.2 0 49.8 (42.7–56.8)Muscle/body aches 135 39.6 16 43.4 (36.3–50.6)Respiratory symptoms 87 25.5 3 29.2 (22.6–35.9)Stiff neck 51 15.0 19 14.7 (9.8–19.6)Ear ache 22 6.5 1 7.0 (3.7–10.3)
Table T2.4: Number of gastroenteritis cases seeking health careα in Australia, NGSII (n=94)
Health facility Number visiting %Emergency/casualty 12 3.5Doctor’s surgery/health clinic 53 15.5Pharmacy 49 14.4Other 9 2.6α Some cases may have sought care from more than one health facility.
Foodborne illness in Australia circa 2010Page 47
MISSED WORK OR ACTIVITIES
Gastroenteritis had a considerable impact on cases’ work, school and recreational
activities in the survey, with 65% reporting that their illness interfered for a median of one
day (range 1–30). Extrapolation from the data indicates that about 1.19 million days were
lost from paid work each month, with 71% of instances where a person missed paid work due
to the person being ill themselves and 29% due to another person having to care for a person
with gastroenteritis.
Foodborne illness in Australia circa 2010Page 48
TECHNICAL APPENDIX 3: DATA SOURCES
DATA SOURCES USED TO CALCULATE COMMUNITY INCIDENCE
Here, the data sources used to calculate community incidence of the 23 pathogens
included in this assessment are described.
DATA SOURCES
There were three main sources of data: notifiable surveillance at the national or state
and territory level, other surveillance through the OzFoodNet outbreak register, and estimates
of incidence using the Australian gastroenteritis study60 together with cohort studies such as
the Water Quality Study (WQS).2,20 The data source and estimation approach used for each
pathogen is explained in Table T3.1.
Table T3.1: Data sources and estimation approach used for each pathogen or syndrome
Pathogen or syndrome Data source Estimation approachCampylobacterSalmonella, non-typhoidalSalmonella TyphiShigellaCryptosporidiumHepatitis AListeria monocytogenes
National Notifiable Diseases Surveillance System (NNDSS)
Notifiable surveillance approach
Giardia lambliaShiga toxin-producing Escherichia coli (STEC)Vibrio parahaemolyticusYersinia enterocolitica
State and territory surveillanceNotifiable surveillance
approach
Other pathogenic E. coliAdenovirusAstrovirusNorovirusRotavirusSapovirus
Water Quality Study 2,20
andNational Gastroenteritis Survey
II 60
Pathogen fraction approach
Bacillus cereusClostridium perfringensStaphylococcus aureusCiguateraScombrotoxicosis
OzFoodNet outbreak registerOther surveillance
approach
Foodborne illness in Australia circa 2010Page 49
Pathogen or syndrome Data source Estimation approachToxoplasma gondii USA seroprevalence study 21 Special calculations
NOTIFIABLE SURVEILLANCE: NNDSS AND STATE AND TERRITORY NOTIFICATIONS
The National Notifiable Diseases Surveillance System (NNDSS) provides national data
for diseases that are notifiable in Australia, such as salmonellosis, shigellosis and
cryptosporidiosis. Some diseases are notifiable in some states and territories, but not in
others; for example, campylobacteriosis is not notifiable in New South Wales, but is
notifiable in all other states and territories. In these cases, notification data were used for the
available states and territories and include a population adjustment multiplier to estimate
national notification rates (Technical appendix 4). In each case, the total number of
confirmed notifications is used for all available years over the period 2006–2010.
Additionally, further data were requested through Communicable Diseases Network
Australia (CDNA), who own the NNDSS data, to determine the proportion of cases that were
acquired in Australia. Details of the use of these data are described below under the section
‘Domestically acquired multiplier’ (Technical Appendix 4).
OTHER SURVEILLANCE: OZFOODNET OUTBREAK REGISTER
The OzFoodNet outbreak register includes all outbreaks of foodborne gastroenteritis
identified from 2000 and this study used data over the period 2006–2008. The register
provides data on the number of individuals ill in each outbreak, the pathogen identified, and
the total number of individuals with laboratory confirmed illness in each outbreak.
NATIONAL GASTROENTERITIS SURVEY II 2008
The NGSII was a nationally representative telephone survey conducted February 2008
to January 2009 to estimate gastroenteritis incidence in Australia. It provides age-specific
rates of gastroenteritis in the community (Technical Appendix 2).
RESEARCH STUDIES
Australian and international cohort studies were used to assess the proportion of
gastroenteritis that was due to specific pathogens. A key study was the 1997 WQS, which
was a double-blinded, randomised, controlled trial of families conducted in Melbourne,
Australia, between September 1997 and February 1999.2,20 Six hundred families were
Foodborne illness in Australia circa 2010Page 50
allocated to receive either real or sham water treatment units installed in their houses and
study participants reported any gastroenteritis symptoms weekly. The study provides testing
data on 795 faecal specimens identifying pathogens causing gastroenteritis, and these data
were used to calculate a pathogen fraction multiplier for included pathogens. As there was no
significant difference in incidence of gastroenteritis in control and experimental families, the
study found that waterborne pathogens do not play a major role in gastroenteritis in
Melbourne.2
Foodborne illness in Australia circa 2010Page 51
TECHNICAL APPENDIX 4: APPROACHES FOR CALCULATING COMMUNITY INCIDENCE
MAIN APPROACHES
Three main approaches were used for calculating the incidence of illness in the
community. Where possible, a primary and secondary approach were applied to each
pathogen to provide a cross-check of the results. The three approaches were based on the
source of the data as:
1. Notifiable surveillance approach using data from NNDSS or state and territory notifications;
2. Pathogen fraction approach using data from the NGSII together with cohort studies, such as the WQS;
3. Other surveillance approach using data from the OzFoodNet outbreak register, or from hospitalisations.
These approaches were considered to form a hierarchy, with the notifiable surveillance
approach used by preference, and outbreak data used only when other sources were not
available. For each approach, the final estimate is produced from a statistical model that
incorporated uncertainty in case numbers and in multipliers using probability distributions.
That is, at each stage of the calculation, the estimate was represented by a probability
distribution, and the final estimates and credible intervals were computed from this
distribution.
In each approach, input data arises from specific data sources (discussed above), or
from multipliers that are listed below. Three main input distribution types were used:
empirical, pert, and lognormal.
EMPIRICAL DISTRIBUTION
Source distributions on the number of cases are typically represented by an empirical or
discrete distribution driven by the data. For example, if the number of cases notified to
NNDSS for the years 2006–2010 were 15416, 16980, 15539, 16075 and 16967, this would be
represented as a discrete distribution with 20% of the probability mass at 15416, 20% of the
probability mass at 16980, and so on. This use of empirical distributions for such data was
used previously by Scallan et al.,6 and avoids assumptions about the expected shape of this
distribution.
Foodborne illness in Australia circa 2010Page 52
PERT DISTRIBUTION
The PERT distribution is widely used for expert elicitation and risk assessment studies.
It is based on the beta distribution, and within @Risk (risk analysis software), can be
specified either using a minimum, maximum and modal value, or by three percentile points,
such as a median value and 95% credible intervals. This distribution is used widely in this
analysis, as it allows for asymmetric distributions, and can be easily produced from many
data sources including expert elicitation data.
LOGNORMAL DISTRIBUTION
When re-calculating underreporting multipliers, the PERT distribution did not
adequately capture the shape of these multiplier data. A lognormal distribution was adopted
instead, as the distribution providing the best fit as measured by @Risk, and demonstrating
an improved fit on the normal distribution used previously.22
Foodborne illness in Australia circa 2010Page 53
TECHNICAL APPENDIX 5: MULTIPLIERS
METHODS USED TO CALCULATE COMMUNITY INCIDENCE
MULTIPLIERS
Approaches to calculating foodborne illness used key multipliers to either scale up
(surveillance approaches) from detected cases to the full community incidence or scale down
(pathogen fraction approach) from the envelope of all gastroenteritis to the proportion that
was due to specific pathogens. Here, the multipliers used in this approach and the data that
were used to derive these multipliers are described.
POPULATION ADJUSTMENT MULTIPLIER
This multiplier was used where notifiable surveillance data were not available for all
states and territories in Australia, and was necessary to scale up the number of infections
according to the proportion of the population covered by surveillance. For example,
campylobacteriosis is notifiable in all states and territories except New South Wales. In this
example, the total number of cases was adjusted for the remaining states and territories by a
population adjustment multiplier of 1.5 to approximate the total number of cases that would
be expected if all states and territories undertook notifiable surveillance of
campylobacteriosis.
DOMESTICALLY ACQUIRED MULTIPLIER
For some pathogens, a proportion of cases acquired their infections overseas. As data
from the WQS used for the pathogen fraction calculations were centred on families, it was
assumed all these incident cases were domestically acquired. For Campylobacter spp.,
Cryptosporidium spp., hepatitis A, L. monocytogenes, non-tyhoidal Salmonella spp.,
Salmonella Typhi, Shigella, and STEC, the domestically acquired multiplier was calculated
from NNDSS data on the proportion of cases that acquired their infection within Australia.
These data contained a number of missing entries, varying by pathogen, state or territory and
year, with the most complete data for Victoria and Western Australia. Four methods were
considered for adjusting for this missing data:
1. Extrapolate travel patterns from Western Australia to the Northern Territory and travel patterns from Victoria to all other states and territories;
Foodborne illness in Australia circa 2010Page 54
2. Extrapolate travel patterns from Western Australia to both the Northern Territory and Queensland, and travel patterns from Victoria to all other states and territories;
3. Discard all missing data and calculate the proportion of cases acquired in Australia for the existing data only;
4. Assume all unidentified cases are domestically acquired.
Method 1 was adopted as the primary approach, and the other methods were used as a
comparison and to identify a range for the multiplier. Specifically, the median estimate was
made using all five years of data combined, while the minimum and maximum value reflects
the largest and smallest proportion estimated by all four methods over each year of 2006–
2010. Table T5.1 presents the resulting parameters for the PERT distribution, including
median value, minimum and maximum, together with the estimations used circa 2000. For
Cryptosporidium spp., non-tyhoidal Salmonella spp., and Shigella, estimates on the full data
over 2006–2010 using methods 1 and 3 were reassuringly similar, while the expanded ranges
reflect the yearly variability and sensitivity to missing data. Larger differences are seen for
hepatitis A, Salmonella Typhi, and STEC. There were very few missing data for hepatitis A
and Salmonella Typhi which raises confidence in these estimates. Only zero to two overseas
acquired cases of STEC were recorded per year, and this is reflected in the higher estimate of
domestically acquired infection for this pathogen. This multiplier was also used for
calculations of hospitalisations and deaths for ‘other pathogenic E. coli’.
Estimates for the domestically acquired multiplier for G. lamblia were made using
Victorian data over 2006–2009,61-64 using the total proportion to derive the median and the
variability over years to give a range. Domestically acquired multipliers for
V. parahaemolyticus and Y. enterocolitica were calculated from Western Australian data in a
similar manner using OzFoodNet Annual Reports from 2006–2010. Given the higher rate of
overseas acquired infections in Western Australia as compared with other jurisdictions, the
proportion from overseas was reduced for other states and territories using a multiplier of
0.72 based on data for non-tyhoidal Salmonella spp. Even with this adjustment, the
multiplier for V. parahaemolyticus is much lower than that used in the USA suggesting a
greater proportion of overseas-acquired cases in Australia;6 more information on the
behaviour of this pathogen in states and territories outside Western Australia would be
valuable to confirm these results.
Foodborne illness in Australia circa 2010Page 55
Finally, it was assumed that all cases of adenovirus, Bacillus cereus, ciguatera,
C. perfringens, L. monocytogenes, norovirus, rotavirus, scombrotoxicosis, Staphylococcus
aureus, and T. gondii were acquired in Australia. Domestically acquired multipliers were not
needed for the remaining pathogens (astrovirus and sapovirus) for which incidence was
calculated using the pathogen fraction approach, and that do not have specific codes to
calculate hospitalisations and deaths.
Table T5.1: Circa 2010 estimates and ranges for the proportion of infections that were domestically acquired compared to the previously published estimates circa 2000, where available
Pathogen Estimate circa 2010 (range)
Estimate circa 2000 65
Adenovirus 100% (100% – 100%)Bacillus cereus 100% (100% – 100%)Campylobacter spp. 97% (91% – 99%) 96%Ciguatera 100% (100% – 100%)Clostridium perfringens 100% (100% – 100%)Cryptosporidium spp. 97% (92% – 99%)Giardia lamblia 85% (84% – 89%)Hepatitis A 58% (42% – 77%)Listeria monocytogenes 100% (100% – 100%)Norovirus 100% (100% – 100%)Other pathogenic E. coli 99% (93% – 100%)Rotavirus 100% (100% – 100%)Salmonella, non-typhoidal spp.
85% (70% – 95%) 92%
Salmonella Typhi 11% (2% – 25%)Scombrotoxicosis 100% (100% – 100%)Shigella 70% (45% – 84%) 60%Staphylococcus aureus 100% (100% – 100%)Shiga toxin-producing E.coli
99% (93% – 100%) 79%
Toxoplasma gondii 100% (100% – 100%)Vibrio parahaemolyticus 18% (0% – 33%)Yersinia enterocolitica 90% (80% – 100%) 98%
Foodborne illness in Australia circa 2010Page 56
UNDERREPORTING MULTIPLIER
Only a fraction of community cases visit a health professional, have a sample taken and
have their illness recorded in surveillance data. Using data from a 2008 paper by Hall et al.,22
underreporting multipliers were estimated based on lognormal distributions of seven (95%
CrI: 4–14) for non-tyhoidal Salmonella spp., 10 (95% CrI: 6.5–18.5) for Campylobacter spp.,
and eight (95% CrI: 3–18.5) for STEC. Where underreporting multipliers were needed for
other pathogens, the non-tyhoidal Salmonella spp. multiplier was applied except in the case
of pathogens leading to very severe illness (hepatitis A, L. monocytogenes, and
Salmonella Typhi) where the underreporting multiplier was assumed to be two (95% CrI: 1–
3). Details of the choice of multiplier for each pathogen are provided in Technical Appendix
11.
FOODBORNE MULTIPLIER
For most pathogens, the proportion of illness that is foodborne was estimated using data
from Delphi based expert elicitations. For nine pathogens, a 2009 elicitation was used, and
for another eight, a similar 2005 elicitation was used.23,35 The 2009 elicitation was informed
by systematic reviews for each pathogen that included scientific literature, reports and
surveillance data. The foodborne multiplier for sapovirus was extrapolated from elicited
norovirus estimates, and used best judgement assumptions for three additional viruses and the
two marine biotoxins. Refer to table T5.2 for a listing of pathogens, multipliers and the data
source for each. A comparison of these estimates with those used in prior studies is provided
elsewhere.23 Expert elicitation data from 2009 included a best estimate and 90% CrI for
Campylobacter spp., C. perfringens, STEC, ‘other pathogenic E. coli’, non-tyhoidal
Salmonella spp., Shigella, norovirus, hepatitis A, and L. monocytogenes. A PERT distribution
was fitted to each expert’s assessment, fitting the best estimate as the median and setting the
90% CrI where possible. In a few cases, a PERT distribution could not be fitted in this way,
and the best estimate had to be adjusted to be the mode of the distribution (if the median point
was too close to an upper bound or lower bound), or an interval bound had to be adjusted to
be a min or max if the PERT distribution led to values outside the interval 0 to 1. A
combined empirical distribution was calculated by computing the point-wise mean value of
individual uncertainty distribution for each expert. The median, 5th and 95th percentiles of
Foodborne illness in Australia circa 2010Page 57
this empirical distribution were then used to describe a final PERT distribution that was input
into the relevant @Risk spreadsheet.
The 2005 questionnaire provided a best estimate from participants. To include uncertainty in
this estimate, a 90% credible interval was generated about each estimate, assuming an upper
bound 10 percentage points higher and a lower bound 10 percentage points lower. For
example, an estimate of 30% foodborne was modelled as a PERT distribution with median as
0.3, 95% bound 0.4, and 5% bound 0.2. The exception to this was where estimates were too
close to zero (or one) for this method. In these cases, symmetric estimates half the distance
from zero (or one) were assumed. That is, an estimate of 5% foodborne was modelled as a
PERT with median as 0.05, 5% bound as 0.025 and 95% bound as 0.075. The combined
distribution was calculated as for the expert elicitation data. The 2005 expert elicitation did
not achieve consensus for some pathogens; in particular, best estimates ranged from 2%–95%
for S. Typhi, 5%–100% for V. parahaemolyticus, and 33%–90% for Y. enterocolitica. Given
the variability arising from these expert data, the sensitivity of the results to the choice of
distribution was tested by simulating the full empirical distribution of the foodborne
multiplier for each of these pathogens, and comparing estimates of foodborne illness with
those using the PERT distribution. In general, median estimates were little changed, but
credible intervals were a little wider under the empirical distribution. The largest change was
for Y. enterocolitica, where the estimate of domestically acquired foodborne illness was
1,150 (650–1,950) using a PERT distribution and 1,100 (350–2,050) using empirical data.
OUTBREAK MULTIPLIER
For pathogens not captured by notifiable surveillance or by cohort studies, data from
outbreaks were used in the other surveillance approach. Only a fraction of cases are
associated with outbreaks. The outbreak multiplier adjusted for this to estimate the total
number of cases that would be captured if notifiable surveillance was in place for that
pathogen. Many of the pathogens for which this method was used have a short duration of
illness, and thus low rates of laboratory confirmation. To adjust for this, the multiplier was
calculated based on total number of ill (but not necessarily lab confirmed) cases associated
with a confirmed outbreak (where laboratory confirmation of at least one case or of a food
source has been occurred). Non-typhoidal Salmonella was chosen as the reference pathogen
for the outbreak multiplier as it has the most complete data. The outbreak multiplier was
calculated as the ratio of the number of ill cases in outbreaks of non-tyhoidal Salmonella spp.
Foodborne illness in Australia circa 2010Page 58
to the total number of laboratory confirmed domestically acquired cases of non-tyhoidal
Salmonella spp. in the NNDSS for the same year. For example, in 2008 there were 8, 316
laboratory confirmed cases of non-tyhoidal Salmonella spp. in NNDSS, of which 85%
(range: 70–90) were assumed to be acquired in Australia. The total number of ill cases
associated with non-tyhoidal Salmonella spp. outbreaks in 2008 was 524, giving an outbreak
multiplier of around 13.5 for this year. Extending this approach to calculate multipliers for
each year from 2006–2008, and for data for all years combined, an outbreak multiplier of 14,
with range 5–20 is estimated.
Table T5.2: Estimates of the foodborne multiplier with 90% credible interval using PERT distributions for each of the 23 pathogens used in the circa 2010 study*Pathogen/Illness Foodborne multiplier with
90% CrIData source 23,35
Adenovirus 0.02 (0.01–0.03) Assumption
Astrovirus 0.02 (0.01–0.03) Assumption
Bacillus cereus 1.00 (0.98–1.00) 2005 EE as PERT
Campylobacter spp. 0.77 (0.62–0.89) 2009 EE as PERT
Ciguatera 1.00 (1.00–1.00) Assumption
Clostridium perfringens 0.98 (0.86–1.0) 2009 EE as PERT
Cryptosporidium spp. 0.10 (0.01–0.27) 2005 EE as PERT
Other pathogenic E. coli 0.23 (0.08–0.55) 2009 EE as PERT
Giardia lamblia 0.06 (0.01–0.50) 2005 EE as PERT
Hepatitis A 0.12 (0.05–0.24) 2009 EE as PERT
Listeria monocytogenes 0.98 (0.90–1.00) 2009 EE as PERT
Norovirus 0.18 (0.05– 0.35) 2009 EE as PERT
Rotavirus 0.02 (0.01–0.03) Assumption
Salmonella, non-typhoidal 0.72 (0.53–0.86) 2009 EE as PERT
Salmonella Typhi 0.75 (0.02–0.97) 2005 EE as PERT
Sapovirus 0.18 (0.05–0.35) Norovirus multiplier
Scombrotoxicosis 1.00 (1.00–1.00) Assumption
Shigella 0.12 (0.05–0.23) 2009 EE as PERT
Foodborne illness in Australia circa 2010Page 59
Pathogen/Illness Foodborne multiplier with 90% CrI
Data source 23,35
Staphylococcus aureus 1.00 (0.95–1.00) 2005 EE as PERT
Shiga toxin-producing E. coli 0.56 (0.32–0.83) 2009 EE as PERT
Toxoplasma gondii 0.31 (0.04–0.74) 2005 EE as PERT
Vibrio parahaemolyticus 0.75 (0.05–0.96) 2005 EE as PERT
Yersinia enterocolitica 0.84 (0.28–0.94) 2005 EE as PERT
*EE = Expert elicitation
GASTROENTERITIS MULTIPLIER
For pathogens captured by cohort studies such as the WQS,2,20 a proportion of all
gastroenteritis cases was attributed to that pathogen using the pathogen fraction approach.
The first step of this approach was to determine the total incidence of gastroenteritis. To do
this, the NGSII study was used to estimate the total number of gastroenteritis episodes per
person per year, weighted by the Australian population. This estimate served to provide a
gastroenteritis multiplier, which was then multiplied by the total Australian population for the
years 2006–2010 to give the estimated number of cases of gastroenteritis for each year. The
gastroenteritis multiplier was modelled as an alternative PERT distribution with median 0.74
and 95% confidence interval (0.64–0.84), based on the estimates and uncertainty intervals
estimated by the NGSII study.
PATHOGEN FRACTION MULTIPLIER
The pathogen fraction multiplier attributed a proportion of the total number of
gastroenteritis episodes to particular pathogens. The primary data source for this was the
WQS.2,20 While data from the UK infectious intestinal disease (IID2) study1 was also used as
a comparator, the WQS gave the most reliable picture of the incidence of illness due to
different pathogens in Australia. The data from the study were age-adjusted (using age
ranges 0–4, 5–14, 15+ years) to the Australian population (circa 2010) take account of the
higher numbers of children in the WQS. For example, the raw data for adenovirus in the
WQS was nine positive samples from a total of 713 samples taken from participants with a
highly credible episode of gastroenteritis. However, eight of those positives were from
participants aged 0–4 years old, an age group over sampled in the study. Using data on the
Foodborne illness in Australia circa 2010Page 60
incidence of gastroenteritis by age from the NSGII study, and the Australian population as a
reference, age-adjusted estimates were calculated for each pathogen based on the WQS data.
For example, for adenovirus, an estimate of four samples positive for adenovirus from 713
gastroenteritis episodes was derived. This resulted in a pathogen fraction multiplier of 0.0056
(95% CI: 0.0015–0.0143), which was then modelled in @Risk using an alternative PERT
distribution. Note that the pathogen sheets provided in Technical Appendix 11 provide the
age adjusted estimates for each pathogen, so will differ slightly from studies reporting
findings of the WQS.
Finally, no Australian cohort study that gave estimates of prevalence of astrovirus or
sapovirus for all age groups could be found. Instead, the pathogen fraction multiplier from
the WQS for adenovirus and norovirus, together with cohort data from children66,67 was used
to calculate multipliers relating astrovirus to adenovirus, and sapovirus to norovirus.
Although the use of children only in this approach is not ideal, it allowed the use of
Australian data. An alternative approach using data from the UK IID2 study1 was also
considered, but was found to lead to unexpectedly high estimates for astrovirus and sapovirus
that were not consistent with estimates for other viral pathogens estimated used data from the
WQS. These differences perhaps arise from differences in the gastroenteritis case definitions
in the UK IID2 study compared to the NGSII study conducted in Australia. The incidence of
gastroenteritis in the NGSII study was almost fourfold higher than the IID2, which may
indicate large differences in case definitions.
TIME TREND MULTIPLIER
The WQS was undertaken before the addition of a rotavirus vaccine to the
nationalvaccination schedule in 2007. In calculating rotavirus incidence circa 2010, a time
trend multiplier was included to adjust for the reduction in rotavirus in 2010 compared with
pre-vaccination levels. In calculating this multiplier, data from a study of rotavirus
hospitalisations by age before and after the introduction of the national vaccination program
was used.36 By comparing age-specific hospitalisation rates in 2010 with that prior to
vaccination, a time-trend multiplier of 0.34 (95% CI: 0.32–0.36) was estimated in order to
adjust for the decline in rotavirus notifications following vaccination.
Foodborne illness in Australia circa 2010Page 61
TECHNICAL APPENDIX 6: OTHER METHODS TO ESTIMATE INCIDENCE
METHODS TO CALCULATE COMMUNITY INCIDENCE
TOXOPLASMOSIS – SPECIAL CALCULATIONS
The calculations for toxoplasmosis differed from all other methods, as seroprevalence
studies were used to estimate yearly incident cases assuming a constant force of infection
with age.21 While there is an Australian study of toxoplasmosis,68 the sample size was too
small to rely on for this estimate. In adopting this USA study rather than European studies
(refer to Pappas et al.69 for a systematic review), comparability is ensured with the circa 2000
estimates, and a conservative approach is taken to estimating Australian incidence of
toxoplasmosis. This incidence estimate was then adjusted by a “proportion symptomatic”
multiplier of 15% (90% CrI:11–21) in line with the approach by Hall et al. circa 200010 and
that of Scallan et al.6
UNKNOWN PATHOGENS
The NGSII survey of gastroenteritis conducted in 2008–2009 was used to estimate the
total envelope of domestically acquired gastrointestinal illness, and so calculate the incidence
of unknown pathogens by subtracting the incidence of domestically acquired gastrointestinal
pathogens from that of the survey. Credible intervals for unknown pathogens were estimated
using @Risk, assuming all cases in the NGSII were domestically acquired. The foodborne
multiplier for all known pathogens of 25% (90% CrI: 15–39) was calculated as a weighted
average of the foodborne multiplier for each pathogen, weighted by the number of
domestically acquired cases of each pathogen. Although this value is remarkably similar to
that estimated by Scallan et al., it is worth noting that it is based entirely on Australian expert
elicitation data, together with incidence calculations using Australian data, and so is entirely
independent of that study. Examination of the two studies will identify differences in many
components of the calculations. The foodborne multiplier was applied to unknown pathogens
to estimate the total number of domestically acquired foodborne illness due to unknown
pathogens, again using @Risk for credible intervals.
Foodborne illness in Australia circa 2010Page 62
TECHNICAL APPENDIX 7: HOSPITALISATIONS AND DEATHS METHODS
DATA SOURCES
Hospitalisation data from all Australian states and territories for 2006–2010 (where
available), and deaths data from the ABS were obtained, using ICD-10 codes for deaths and
ICD-10-AM codes for hospitalisations as in Table T7.1. Both astrovirus and sapovirus were
excluded from this analysis as lacking appropriate codes. Patients were included as a
hospitalisation if the appropriate code was included as the principal or an additional
diagnosis. Table T7.2 shows the percentage of all hospital diagnoses that were listed as the
principal diagnosis for each pathogen for 2010 (the year with most complete data). In the
circa 2000 estimates,3,10 only data on principal diagnoses were used, with a multiplier of two
(credible interval 1–3) for all pathogens to model both principal and additional diagnoses. It
is clear form Table T7.2 that diagnosis patterns vary considerably by pathogen, so that use of
both principal and additional diagnosis provides a more complete picture of hospitalisations.
Since only one year of hospitalisation data for Victoria and two years for New South
Wales were available, it was necessary to extrapolate from these data to the remaining years
to derive a distribution of the number of hospitalisations across all states and territories,
which was modelled as an empirical distribution. In most cases, it was assumed the same
number of hospitalisations each year, but some pathogens required further adjustment due to
evident outbreak trends. For example, an outbreak of hepatitis A associated with sundried
tomatoes coincided with the one year of hospitalisation data for Victoria. A ratio of
hospitalisations in South Australia to Victoria was used to estimate Victorian hospitalisations
for the missing years. As vaccination against rotavirus resulted in a decrease in incidence,
hospitalisations, and deaths, post universal vaccination data, from 2008–2010 only, were used
to estimate hospitalisations circa 2010.
ESTIMATION
To calculate estimates of hospitalisations and deaths, a statistical model was used that
incorporated uncertainty in case numbers and in multipliers using probability distributions.
That is, at each stage of the calculation, the estimate was represented by a probability
distribution, and the final estimates and credible intervals were computed from this
distribution. Input data was obtained from specific data sources (discussed above) or from
Foodborne illness in Australia circa 2010Page 63
multipliers that are described below. A fuller description of these probability distributions is
provided in the methods section for incidence.
MULTIPLIERS
UNDERDIAGNOSIS MULTIPLIER
Recorded hospitalisations and deaths associated with each pathogen reflect only those
individuals that have been tested and confirmed for the pathogen. Following previous
studies, this was adjusted for using an underdiagnosis multiplier of two,5 including a
distribution for the multiplier with range 1–3 as in Hall et al. and Scallan et al.3,6 The
appropriateness of the multiplier for hospitalisations was confirmed as follows. Firstly, the
OzFoodNet outbreak register was used to calculate the proportion of all ill cases associated
with an outbreak that were hospitalised. This proportion was then compared to the ratio of
incidence to hospitalisations both with and without the underdiagnosis multiplier. Although
there was some variability by pathogen, overall, 3% of ill cases from 14 pathogens or
illnesses in the OzFoodNet outbreak register were hospitalised. In contrast, the ratio of all
incident cases to all hospitalised cases was around 0.01 when the underdiagnosis multiplier
was included (and 0.005 otherwise). Although outbreak cases may be more severe than all
incident cases (on average), and under–ascertainment of cases or under-recording of
hospitalizations may have biased the validation of the multiplier, these results suggest that an
underdiagnosis multiplier was appropriate. Further work would assist in better quantifying
this multiplier.
DOMESTICALLY ACQUIRED MULTIPLIER
This multiplier adjusted for the proportion of cases that acquired infection in Australia,
and was adopted from the method for incidence. More details of the data and methods behind
this multiplier are provided in the methods section for calculating incidence.
FOODBORNE MULTIPLIER
This multiplier adjusted for the proportion of illness that is foodborne using expert
elicitation data, and was used for incidence, hospitalisations and deaths. Details are provided
in the methods section for incidence.
Foodborne illness in Australia circa 2010Page 64
Table T7.1: Mortality and hospitalisation codes for each pathogen
Pathogen or illness Mortality ICD-10 Code and description
ICD-10-AM
Adenovirus A08.2: Adenoviral enteritis A08.2: Adenoviral enteritisBacillus cereus A05.4: Foodborne Bacillus cereus
intoxicationA05.4: Foodborne Bacillus cereus intoxication
Campylobacter spp. A04.5: Campylobacter enteritis A04.5: Campylobacter enteritisCiguatera T61.0: Ciguatera fish poisoning T61.0: Ciguatera fish poisoningClostridium perfringens
A05.2: Foodborne Clostridium perfringens intoxication
A05.2: Foodborne Clostridium perfringens [Clostridium welchii] intoxication
Cryptosporidium spp. A07.2: Cryptosporidiosis A07.2: CryptosporidiosisGuillain-Barré syndrome
G61.0: Guillain-Barré syndrome G61.0: Guillain-Barré syndrome
Giardia lamblia A07.1: Giardiasis [lambliasis] A07.1: Giardiasis [lambliasis]Hepatitis A* B15: Acute hepatitis A B15.9: Hepatitis A without hepatic
comaHaemolytic uraemic syndrome
D59.3: Haemolytic uraemic syndrome D59.3: Haemolytic uraemic syndrome
Irritable bowel syndrome
K58: Irritable bowel syndrome K58.0: Irritable bowel with diarrhoeaK58.9: Irritable bowel without diarrhoea
Listeria monocytogenes
A32: Listeriosis A32.0–A32.9: Listeriosis
Norovirus A08.1: Acute gastroenteropathy due to Norwalk agent
A08.1: Acute gastroenteropathy due to Norwalk agent
Other pathogenic E. coli
A04.0: Enteropathogenic Escherichia coli infectionA04.1: Enterotoxigenic Escherichia coli infectionA04.2: Enteroinvasive Escherichia coli infectionA04.4: Other intestinal Escherichia coli infection
A04.0: Enteropathogenic Escherichia coli infectionA04.1: Enterotoxigenic Escherichia coli infectionA04.2: Enteroinvasive Escherichia coli infectionA04.4: Other intestinal Escherichia coli infections
Reactive arthritis* M02.1: Postdysenteric arthropathyM02.8: Other reactive arthropathies
M02.1: Postdysenteric arthropathy, multiple sitesM02.3: Reiter’s disease, multiple sitesM02.8: Other reactive arthropathies, multiple sitesM03.2: Other postinfectious arthropathies in diseases classified
Foodborne illness in Australia circa 2010Page 65
Pathogen or illness Mortality ICD-10 Code and description
ICD-10-AM
elsewhere, multiple sitesRotavirus A08.0: Rotaviral enteritis A08.0: Rotaviral enteritisSalmonella, non-typhoidal*
A02: Other Salmonella infections A02.0–A02.9: Salmonellosis
Salmonella Typhi A01: Typhoid and paratyphoid fevers A01: Typhoid feverScombrotoxicosis T61.1: Scombroid fish poisoning T61.6: Scombroid fish poisoningShigella A03: Shigellosis A03.0–A03.9: ShigellosisStaphylococcus aureus
A5.0: Foodborne staphylococcal intoxication
A05.0: Foodborne staphylococcal intoxication
Shiga toxin-producing E. coli
A04.3: Enterohaemorrhagic Escherichia coli infection
A04.3: Enterohaemorrhagic Escherichia coli infection
Toxoplasma gondii B58: Toxoplasmosis B58.0–B58.9: ToxoplasmosisVibrio parahaemolyticus
A05.3: Foodborne Vibrio parahaemolyticus intoxication
A05.3: Foodborne Vibrio parahaemolyticus intoxication
Yersinia enterocolitica
A04.6: Enteritis due to Yersinia enterocolitica
A04.6: Enteritis due to Yersinia enterocolitica
Other* A04.8: Other specified bacterial intestinal infectionA04.9: Bacterial intestinal infection unspecifiedA05.8: Other specified bacterial foodborne intoxicationsA05.9: Bacterial foodborne intoxication unspecifiedA07.8: Other specified protozoal intestinal diseasesA07.9: Protozoal intestinal disease, unspecifiedA08.3: Other viral enteritisA08.4: Viral intestinal infection, unspecifiedA09: Diarrhoea and gastroenteritis of presumed infectious originT61.2 Other fish and shellfish poisoningT61.8 Toxic effect of other seafoodT61.9 Toxic effect of unspecified seafoodT62: Toxic effect of other noxious substances eaten as foodT64: Toxic effect of aflatoxin and other mycotoxin food contaminants
A08.4: Viral intestinal infection, unspecifiedA09: Diarrhoea and gastroenteritis of presumed infectious originA09.0: Other gastroenteritis and colitis of infectious originA09.9: Other gastroenteritis and colitis of unspecified origin
Foodborne illness in Australia circa 2010Page 66
*Pathogens are starred if there are differences between the mortality codes (ICD-10) and the hospitalisation codes (ICD-10-AM)
Table T7.2: The percentage of all hospital diagnoses that were listed as principal for each pathogen, based on 2010 data for all states and territories
Pathogen or illness Percentage of all diagnoses listed as principal
Adenovirus 82%
Bacillus cereus 75%
Campylobacter spp. 79%
Ciguatera 83%
Clostridium perfringens 100%
Cryptosporidium spp. 59%
Other pathogenic E. coli 59%
Giardia lamblia 34%
Guillain-Barré syndrome 71%
Irritable bowel syndrome 69%
Haemolytic uraemic syndrome 30%
Hepatitis A 77%
Listeria monocytogenes 48%
Norovirus 37%
Reactive arthritis 50%
Rotavirus 77%
Salmonella, non-typhoidal 77%
Salmonella Typhi 93%
Scombrotoxicosis 100%
Shigella 76%
Staphylococcus aureus 100%
Shiga toxin-producing E. coli 59%
Toxoplasma gondii 39%
Foodborne illness in Australia circa 2010Page 67
Pathogen or illness Percentage of all diagnoses listed as principal
Vibrio parahaemolyticus 50%
Yersinia enterocolitica 64%
HOSPITALISATIONS AND DEATHS DUE TO UNKNOWN PATHOGENS
A large proportion of hospitalisations and deaths did not identify the source of infection
(refer to “other” codes in Table T7.1). These data were adjusted and reported as follows for
hospitalisations, with a similar approach used for deaths. Firstly, the total number of
hospitalisations due to unknown pathogens was calculated from the appropriate codes.
Hospitalisations that were attributed to known pathogens according to the underdiagnosis
multiplier described above were then subtracted from this number. That is, where total
numbers of known gastrointestinal pathogens were increased to adjust for underdiagnosis,
this increase was subtracted from the total unknown gastrointestinal pathogens. A
domestically acquired multiplier of one was assumed for unknown pathogens, but adjusted
for the foodborne multiplier using an average of known pathogens, weighted by the number
of hospitalisations for each pathogen. For hospitalisation data, this gave a foodborne
multiplier of 44% (90% CrI: 38–50), and for death data, a foodborne multiplier of 51% (90%
CrI: 36–71). Although Scallan et al.6 do not report their weighted foodborne multipliers for
hospitalisations and deaths, analysis of their tables suggest their values are 24% for
hospitalisations and 52% for deaths. As noted in Technical Appendix 2, the calculations here
are entirely independent; the Australian hospitalisation estimate is considerably higher
although the estimate for deaths shows good agreement.
Foodborne illness in Australia circa 2010Page 68
TECHNICAL APPENDIX 8: METHODS FOR SEQUELAE INCIDENCE
To calculate community incidence of the four sequelae included in this assessment,
namely Guillain-Barré syndrome (GBS), haemolytic uraemic syndrome (HUS), irritable
bowel syndrome (IBS), and reactive arthritis (ReA), data were used from notifiable
surveillance to estimate incidence of the causal pathogens, and adjusted using a sequelae
multiplier modelled as a PERT distribution.
The approach to estimating sequelae-specific multiplier was to conduct a detailed
search of studies in the literature, and base the circa 2010 estimates on these studies, using
Australian studies and systematic reviews where possible. In literature searches, attention
was confined to peer-reviewed studies published in English between 1995 and 2012. Specific
keywords were used for each sequel to search PubMed. All types of studies were included;
titles and abstracts were reviewed, and full text articles were obtained for all relevant studies.
GUILLAIN-BARRÉ SYNDROME
Community incidence of Campylobacter spp. was estimated using NNDSS data,
adjusted for non-notification in New South Wales as described in Technical Appendix 5.
Incidence of GBS was estimated by further adjusting the distributions for Campylobacter
spp. by a sequelae multiplier estimated from values in published studies. Search terms for
GBS included: “Guillain-Barré syndrome”, “incidence”, “Campylobacter” and “Australia”,
with each selected paper including at least one search term. Four papers were identified that
estimated the proportion of Campylobacter spp. cases that led to GBS, with the one by Baker
et al.29 being excluded because it was only a study of hospitalised cases (Table T8.1). These
studies were used to estimate a range for the sequelae multiplier of 0.000192 to 0.000945,
with a midpoint value of 0.000304.
Table T8.1: References used to estimate the sequelae multiplier for Guillain-Barré syndrome – that is the proportion of Campylobacter spp. cases that result in GBS
Reference Study years Country # GBS cases /campylobacteriosis patients
Baker et al.29 1995–2008 New Zealand 35 / 8,448 (0.4%)Tam et al.70 1991–2001 UK 3 / 15,587 (0.019%)McCarthy & Giesecke71 1987–1995 Sweden 9 / 29,563 (0.03%)Allos72 1964–1996* Global/USA 1/1058 cases (0.0945%)* Years of reviewed studies
Foodborne illness in Australia circa 2010Page 69
HAEMOLYTIC URAEMIC SYNDROME
Community incidence of STEC infection was estimated using South Australian
notifications, with multipliers to extend to the national population as described in Technical
Appendix 5. Incidence of HUS was estimated by applying a ‘sequelae multiplier’ estimated
from published studies to STEC. Search terms for HUS included: “Haemolytic uraemic
syndrome”, “HUS”, “incidence”, “STEC” and “E. coli”, with each selected paper including at
least one search term. Several published studies reported that 3–7% of sporadic STEC
infections resulted in HUS.73-76 Australian studies supported this range. Vally et al.77
examined South Australian surveillance data for a 13 year period where among the 460 cases
of STEC notified, there were 14 reported cases of HUS, resulting in an estimate of 3.0%
(95% CrI: 1.7–5.1) of STEC cases developing into HUS.
IRRITABLE BOWEL SYNDROME
Community incidence of Campylobacter spp., non-tyhoidal Salmonella spp., and
Shigella infections were estimated using NNDSS data as described in Technical Appendices
4 and 5. Incidence of IBS was estimated by further multiplying each incidence distribution
by a ‘sequelae multiplier’ estimated from published studies. Search terms for IBS included:
“Irritable bowel syndrome”, “IBS”, “Post-infection irritable bowel syndrome”, “PI-IBS”,
“incidence”, “Australia’, “Campylobacter”, “Salmonella” and “Shigella” with each selected
paper including at least one search term. The sequelae multiplier for IBS was based on a
meta-analysis conducted in the Netherlands by Haagsma et al.,78 who estimated that
approximately 8.8% (90% CI: 7.2–10.4%) of bacterial cases of gastroenteritis go on to
develop IBS 10 to 12 months after infection.
REACTIVE ARTHRITIS
Community incidence of Campylobacter spp., non-tyhoidal Salmonella spp., Shigella,
and Y. enterocolitica infections were estimated using NNDSS data (Technical Appendices 4
and 5). Incidence of ReA was estimated by multiplying incidence distributions for each of
these by sequelae multipliers for each of these four pathogens. Search terms for ReA included
“reactive arthritis”, “ReA”, “incidence”, “Australia”, “Campylobacter”, “Salmonella”,
“Shigella”, and “Yersinia”. Table T8.2 lists studies used to estimate the sequelae multipliers
following infection with each pathogen. From these studies sequelae multipliers of 0.07
(range: 0.028–0.16) for Campylobacter spp., 0.085 (range: 0–0.26) for non-tyhoidal
Foodborne illness in Australia circa 2010Page 70
Salmonella spp., 0.097 (range: 0.012–0.098) for Shigella, and 0.12 (range: 0–0.231) for
Y. enterocolitica were adopted.
Table T8.2: References used to estimate the sequelae multiplier for reactive arthritis (ReA) for each of four pathogens: Campylobacter spp., non-tyhoidal Salmonella spp., Shigella, and Yersinia enterocoliticaReference Study years Country ReA cases /Gastroenteritis casesReA cases/Campylobacter casesSchonberg-Norio et al.79 2002 Finland 8/201 (4.0%)Doorduyn et al.80 2005 The Netherlands 20/434 (4.6%)Townes et al.81 2002–2004 USA 302/2384 (12.7%)Schiellerup et al.82 2002–2003 Denmark 131/1003 (13.1%)Pope et al.83 1966–2006 Europe 1 – 5%Hannu et al.84 2000 Finland 9/350 (2.6%)Rees et al.85 1998–1999 USA 9/324 (2.8%)Hannu et al.86 1997–1998 Finland 45/609 (7.4%)Locht & Krogfelt87 1997–1999 Denmark 27/173 (15.6%)ReA cases / non-tyhoidal Salmonella casesArnedo-Pena et al.88 2005 Spain 16/155 (10.3%)Doorduyn et al.80 2005 The Netherlands 8/181 (4.4%)Townes et al.81 2002–2004 USA 204/1356 (15.0%)Schiellerup et al.82 2002–2003 Denmark 104/619 (16.8%)Lee et al.89 1999 Australia 38/261 (14.6%)Rees et al.85 1998–1999 USA 2/100 (2.0%)Buxton et al.90 1999–2000 Canada 17/66 (25.7%)Hannu et al.91 1999 Finland 5/63 (7.9%)Locht et al.92 1999 Denmark 17/91 (18.7%)Rudwaleit et al.93 1998 Germany 0/286 (0%) (Children only)Dworkin et al.94 1994 USA 63/217 (29.0%)McColl et al.95 1997 Australia 13/312 (4.2%) & 6/112 (5.3%)Urfer et al.96 1993 Switzerland 1/156 (0.6%)Mattila et al.97 1994 Finland 22/191 (11.5%)Thomson et al.98 1984–1989 Canada 27/423 (6.4%)ReA cases /Shigella casesTownes et al.81 2002–2004 USA 29/298 (9.7%)Schiellerup et al.82 2002–2003 Denmark 10/102 (9.8%)Rees et al.85 1998–1999 USA 1/81 (1.2%)
Foodborne illness in Australia circa 2010Page 71
Reference Study years Country ReA cases /Gastroenteritis casesReA cases /Yersiniosis casesHuovinen et al.99 2006 Finland 6/61 (9.8%)Townes et al.81 2002–2004 USA 5/35 (14.3%)Schiellerup et al.82 2002–2003 Denmark 21/91 (23.1%)Hannu et al.100 1998 Finland 4/33 (12.1%)
Foodborne illness in Australia circa 2010Page 72
TECHNICAL APPENDIX 9: METHODS TO ESTIMATE HOSPITALISATIONS AND DEATHS DUE TO SEQUELAE
Hospitalisation data for 2006–2010 from all Australian states and territories for IBS and
ReA, and death data for all four sequelae from the ABS was obtained, using ICD-10 codes
for deaths and ICD-10-AM codes for hospitalisations as described in Table T7.1, and with
details of the percentage of hospitalisations that were the principal diagnosis in Table T7.2
(Technical Appendix 7). Additional and adjusted multipliers are described below. All
estimated incident foodborne GBS and HUS cases were considered hospitalised, so were not
modelled. Multipliers were still needed for GBS and HUS to estimate deaths.
MULTIPLIERS
FOODBORNE MULTIPLIER
This multiplier adjusts for proportion of illnesses that were acquired from food, and
was calculated to estimate hospitalisations and deaths due to sequelae, as it was not necessary
to estimate sequelae incidence since antecedent bacterial gastroenteritis cases were already
adjusted by a foodborne multiplier. Sequelae can arise from a source other than a bacterial
pathogen, from a bacterial pathogen that was not foodborne, or from a foodborne pathogen.
Only this latter category was considered a foodborne source. The proportion foodborne
multiplier is the simulated product of the bacterial multiplier and the weighted foodborne
multiplier and is shown for each sequel in Table T9.1.The approach calculating the
proportion foodborne multiplier for each sequel is described as follows:
Table T9.1: Foodborne multipliers for sequelae hospitalisations and deaths estimation
Sequelae Foodborne multiplierGuillain-Barré syndrome 0.25 (90% CrI: 0.1–0.43)Haemolytic uraemic syndrome 0.33 (90% CrI: 0.17–0.53)Irritable bowel syndrome 0.13 (90% CrI: 0.08–0.20)Reactive arthritis 0.48 (90% CrI: 0.36–0.62)
GUILLAIN-BARRÉ SYNDROME
As not all GBS hospitalisations and deaths are from Campylobacter spp., a bacterial
multiplier was determined from published studies and multiplied with a foodborne proportion
Foodborne illness in Australia circa 2010Page 73
to find the proportion of hospitalisations and deaths from foodborne Campylobacter spp. A
systematic review by Poropatich et al.,101 which found that 31% (range 4.8%–72%) of GBS
cases arise from Campylobacter spp. for the bacterial multiplier, was used as this was the
most recent systematic review. Another systematic review by McGrogan et al.102 reviewed 63
published papers between 1980 and 2009, and estimated that between 6% and 26% of cases
of GBS were due to a prior gastrointestinal infection. However the authors did not look at
Campylobacter spp. specifically. Applying the bacterial multiplier from Poropatich et al.101
together with the Campylobacter spp. foodborne multiplier described in Technical Appendix
5 gave a foodborne multiplier for GBS of 0.25 (90% CrI: 0.11–0.43).
HAEMOLYTIC URAEMIC SYNDROME
A bacterial multiplier for HUS was first identified from published studies. Table T9.2
presents the percentage of cases of HUS that arise from STEC for four different papers,
including a global systematic review. From this, it was assumed that 61% (range 30%–85%)
of cases of HUS arise from STEC, modelled as a PERT distribution. Multiplying this
bacterial multiplier with the STEC foodborne multiplier described in Technical Appendix 5
led to a foodborne multiplier for HUS of 0.33 (90% CrI: 0.18–0.54).
Table T9.2: The percentage of haemolytic uraemic syndrome (HUS) that arise from STEC
Reference Study years Country Study type Percentage of HUS attributable to STEC
Walker et al.103 1980–2011 Global Systematic review 61% (range 30 – 85%)Askar et al.104 2011 Germany Surveillance 58%Elliot et al.105 1994–1998 Australia Surveillance 51%van de Kar et al.106 1989–1993 The Netherlands Case control 78%
IRRITABLE BOWEL SYNDROME:
The proportion of cases of IBS that arise from one of Campylobacter spp., non-tyhoidal
Salmonella spp., or Shigella spp. was estimated based on the proportion of IBS considered to
be post-infections in the literature. Table T9.3 presents four papers that estimate this
proportion. From this, it was assumed 17% of IBS to be triggered by a gastrointestinal
infection, with a range of 7–33%. This bacterial multiplier was modelled as a PERT
distribution. A foodborne multiplier for the combined three pathogens of 73% (90% CrI:
64%–82%) was calculated as a weighted average of the foodborne multipliers for each
Foodborne illness in Australia circa 2010Page 74
pathogen, weighted by the total number of IBS cases for each pathogen. Multiplied together,
this gave a foodborne multiplier for irritable bowel syndrome of 13% (90% CrI: 8%–20%).
Table T9.3: The percentage of irritable bowel syndrome (IBS) that arises from Campylobacter spp., non-tyhoidal Salmonella spp., or Shigella
Reference Publication year
Country Study type Number of post infectious IBS
cases/IBS cases
% of IBS that is post-
infectiousChaudhary & Truelove107
1962 UK Epidemiological report
34/130 26.2%
Spiller & Garsed108
2009 Global Review - 6%–17%
Haagsma et al.78
2010 The Netherlands
Meta-analysis and estimation
- 17%
Schwille-Kiuntke et al.109
2013 Global Review - 7%–33%
REACTIVE ARTHRITIS
In a review of ReA, Hannu110 compiled population-based studies on the annual
incidence of ReA – both from enteric and urogenital infection. Using this compilation, this
report calculated the proportion of ReA due to enteric infection by dividing the enteric
incidence by the total incidence found in each study (Table T9.4). The midpoint and range of
the proportions from these studies was used for the bacterial multiplier. Therefore, it was
assumed a median of 66.7% of ReA is due to an enteric infection, with a range of 50%–
94.7%. The proportion foodborne was adjusted using a weighted average of the foodborne
multipliers for Campylobacter spp., non-tyhoidal Salmonella spp., Shigella spp., and
Y. enterocolitica, weighted by the total number of ReA cases for each pathogen. This gave a
foodborne multiplier of 72% (90% CrI: 60–82%). Multiplied by the above alternate PERT
distribution of median 66.7% (range 50%–94.7%), gave a foodborne multiplier for reactive
arthritis of 48% (90% CrI: 36%–61%).
Foodborne illness in Australia circa 2010Page 75
Table T9.4: Proportion of reactive arthritis (ReA) attributable to enteric infection, adapted from the table of annual incidence of reactive arthritis based on population studies in Hannu110
Reference Country Publication year
Enteric incidence
per 100,000
population
Urogenital incidence
per 100,000
population
Total incidence
per 100,000
population
Proportion of ReA due to
enteric infection[enteric/total
incidence (%)]Isomaki et al.111
Finland 1978 14 13 27 14/27 (51.9%)
Kvien et al.112
Norway 1994 5 5 10 5/10 (50%)
Savolainen et al.113
Finland 2003 7 3 10 7/10 (70%)
Soderlin et al.114
Sweden 2003 18 1 19 18/19 (94.7%)
Townes115 USA 2010 0.6–3.1 NA NA NAHanova et al.116
Czech Republic
2010 6 3 ~9 6/9 (66.7%)
NA – Not applicable
DOMESTICALLY ACQUIRED MULTIPLIER
This multiplier adjusts for the proportion of cases that were acquired infection in
Australia. The choice of domestically acquire multiplier for each sequel is shown in Table
T9.5, with the approach for selecting the multiplier described for each sequelae illness below.
GUILLAIN-BARRÉ SYNDROME
The domestically acquired multiplier for Campylobacter spp. of 0.97 (range: 0.91–
0.99), as described elsewhere was adopted.
HAEMOLYTIC URAEMIC SYNDROME
Given the relatively small numbers of notified cases of HUS, the domestically acquired
multiplier for STEC of 0.99 (range: 0.93–1.0) was adopted. A comparison of available travel
data for HUS was in agreement with this assumption.
Foodborne illness in Australia circa 2010Page 76
IRRITABLE BOWEL SYNDROME
A combined domestically acquired multiplier for Campylobacter spp., non-tyhoidal
Salmonella spp., and Shigella of 91% (90% CrI: 88%–94%) was calculated as a weighted
average of the domestically acquired multipliers for each pathogen, weighted by the total
number of IBS cases for each pathogen.
REACTIVE ARTHRITIS
A combined domestically acquired multiplier for Campylobacter spp., non-tyhoidal
Salmonella spp., Shigella, and Y. enterocolitica of 91% (90% CrI: 86%–95%) was calculated
as a weighted average of the domestically acquired multipliers for each pathogen, weighted
by the total number of ReA cases for each pathogen.
Table T9.5: Domestically acquired multipliers for sequelae hospitalisations and deaths estimation
Sequelae Domestically acquired multiplierGuillain-Barré syndrome 0.97 (range: 0.91–0.99)Haemolytic uraemic syndrome 0.99 (range: 0.93–1.0)Irritable bowel syndrome 0.91 (90% CrI: 0.88–0.94)Reactive arthritis 0.91 (90% CrI: 0.86–0.95)
Foodborne illness in Australia circa 2010Page 77
TECHNICAL APPENDIX 10: COMPARISON WITH ESTIMATES CIRCA 2000
COMPARISON OF INCIDENCE FOR FOODBORNE GASTROENTERITIS AND KEY PATHOGENS
As methods and data sources have changed since the circa 2000 estimation effort,
incidence estimates were recalculated for total foodborne gastroenteritis and for key
pathogens and two sequelae, using the same methods and data sources as were used for the
calculation of the circa 2010 estimates. The envelope of foodborne gastroenteritis for 2000
was estimated using the NGSI and the 2010 foodborne proportion of 25%. NNDSS data
from 1996 to 2000 and latest pathogen-specific foodborne proportions were used to
recalculate estimates for Campylobacter spp., non-typhoidal Salmonella spp., Salmonella
Typhi, hepatitis A, L. monocytogenes, GBS, IBS, and Victorian state surveillance data from
1996 to 2000 for the G. lamblia estimate. Incidence rates for 2000 were determined by using
the recalculated circa 2000 estimates and population numbers from the ABS from 1996 to
2000.24
Foodborne illness in Australia circa 2010Page 78
TECHNICAL APPENDIX 11: PATHOGEN AND ILLNESS SHEETS
ADENOVIRUS
Primary data: Water Quality Study Alternate data: IID2
Model input, source & comments Distribution Data for model inputReported illness:Gastroenteritis multiplier – based on the NGSII
Pathogen fraction multiplier – based on age adjusted WQS of an estimated four positive isolates per 713 specimens (Hellard et al.2)
Alternate PERT
Alternate PERT
2.5%, median, 97.5% values:0.64, 0.74, 0.84
2.5%, median, 97.5% values:0.0015, 0.0056, 0.0143
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year (2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier:All isolations in the WQS were domestically acquired
N/A
Time trend multiplier:No time trend
N/A
Underreporting:WQS is community surveillance
N/A
Total illness:Population at risk*gastroenteritis multiplier*pathogen fraction multiplier*time trend multiplier
Outcome 5%, medial, 95% values:28800, 88400, 205000
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:1300, 4150, 9675
Foodborne multiplier:Assumed to be the same as rotavirus
Alternate PERT
5%, median, 95% values:0.01, 0.02, 0.03
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:500, 1650, 4650
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:25, 80, 215
Foodborne illness in Australia circa 2010Page 79
ASTROVIRUS
Primary data: Water Quality Study Alternate data:
Model input, source & comments Distribution Data for model inputReported illness:Gastroenteritis multiplier – based on the NGSII
Pathogen fraction multiplier – based on age adjusted WQS of an estimated four positive isolates of adenovirus per 713 specimens (Hellard et al.2)
Pathogen comparison multiplier – Kirkwood multiplier67 comparing adenovirus to astrovirus
Alternate PERT
Alternate PERT
Constant
2.5%, median, 97.5% values:0.64, 0.74, 0.84
2.5%, median, 97.5% values:0.0015, 0.0056, 0.0143
0.76
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year (2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier:All isolations in the WQS were domestically acquired
N/A
Time trend multiplier:No time trend
N/A
Underreporting:WQS is community surveillance
N/A
Total illness:Population at risk*gastroenteritis multiplier*pathogen fraction multiplier*time trend multiplier
Outcome 5%, median, 95% values:20900, 67100, 15500
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:1000, 3150, 7250
Foodborne multiplier:Assumed to be the same as rotavirus
Alternate PERT
5%, median, 95% values:0.01, 0.02, 0.03
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:350, 1300, 3400
Foodborne illness in Australia circa 2010Page 80
Model input, source & comments Distribution Data for model inputRate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:20, 60, 160
BACILLUS CEREUS
Primary data: Outbreak Alternate data:
Model input, source & comments Distribution Data for model input
Reported illness:The number of B. cereus outbreak-associated illnesses reported to OzFoodNet 2006–2008.
Empirical By year (2006–2008):14, 35, 75
Population adjustment:
Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year (2006–2008):20697880, 21015936, 21384427
Domestically acquired multiplier:Assumed to be 100% domestically acquired due to the short incubation period
PERT Minimum, modal, maximum values:1, 1 ,1
Underreporting:
Outbreak multiplier used to adjust from outbreak to surveillance (O-S)
Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al.22
PERT
Log normal
Minimum, modal, maximum values:5, 14, 20
Mean, standard deviation:7.44, 2.38
Total illness:Outbreak cases*Underreporting(O-S)(S-C)*Proportion travel-related
Outcome 5%, median, 95% values:900, 3350, 10100
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:40, 150, 485
Foodborne multiplier:Based on 2005 expert elicitation
PERT Minimum, modal, maximum values:0.98, 1, 1
Foodborne illness in Australia circa 2010Page 81
Model input, source & comments Distribution Data for model input
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:900, 3350, 10100
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:40, 150, 485
CAMPYLOBACTER SPP.
Primary data: NNDSS Alternate data: Water Quality Study
Model input, source & comments Distribution Data for model inputReported illness:NNDSS dataAvailable from: (http://www9.health.gov.au/cda/source/rpt_4.cfm)(Accessed on 12/11/13)
Empirical By year(1996–2000):12169, 11984, 12647, 12373, 13676(2006–2010):15416, 16980, 15539, 16075, 16967
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Correction factor: Hall et al.22
NSW Population - June quarterBy year (1996–2000 & 2006–2010):6176461, 6246267, 6305799, 6375103, 6446558 & 6816087, 6885204, 6975891, 7069707, 71449281/(1-NSW Pop/AUS Pop)
Empirical
Constant
By year(1996–2000):18310714, 18517564, 18711271, 18925855, 19153380(2006–2010):20697880, 21015936, 21384427, 21778845, 220653171.5
Domestically acquired multiplier:NNDSS travel data
PERT Minimum, modal, maximum values:0.91, 0.97, 0.99
Underreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)Campylobacter spp. multiplier adapted from Hall et al.22
Log normal Mean, standard deviation:10.45, 2.98
Total illness circa 2010:Reported cases (NNDSS)*correction factor*travel adjustment* underreporting(S-C)
Outcome 5%, median, 95% values:147000, 234000, 374000
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:6850, 10950, 17415
Foodborne multiplier: Alternate 5%, median, 95% values:
Foodborne illness in Australia circa 2010Page 82
Model input, source & comments Distribution Data for model inputExpert elicitation study 2009 PERT 0.62, 0.77, 0.89Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:108500, 179000, 290000 (circa 2010)82500, 139000, 227000 (circa 2000)
Rate of foodborne illness per million: circa 2010 andcirca 2000
Outcome 5%, median, 9% values:5050, 8400, 13650 (circa 2010)4500, 7400, 12200 (circa 2000)
CIGUATERA
Primary data: Queensland notifications Alternate data: Outbreak
Model input, source & comments Distribution Data for model input
Reported illness:The number of ciguatera notifications reported in Queensland in OzFoodNet Queensland Annual Reports 2006–2010
Empirical By year (2006–2010):26, 18, 14, 7, 30
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Correction factor:Qld Population and NT populationAustralian population/Qld and NT population
Empirical
Empirical
By year (2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
1.05
Domestically acquired multiplier:Assumed to be 100% domestically acquired
PERT Minimum, modal, maximum values:1, 1 ,1
Underreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al.22
Log Normal Mean, standard deviation:7.44, 2.38
Total illness:Reported cases (Qld notifications)*Population adjustment*Underreporting(O-S)(S-C)*Proportion travel-related
Outcome 5%, median, 95% values:40, 150, 300
Foodborne illness in Australia circa 2010Page 83
Model input, source & comments Distribution Data for model input
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:2, 7, 14
Foodborne multiplier:Assumed to be 100% foodborne
PERT Minimum, modal, maximum values:1, 1, 1
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:40, 150, 300
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:2, 7, 14
CLOSTRIDIUM PERFRINGENS
Primary Data: Outbreak Alternate Data: Water Quality Study
Model input, source & comments Distribution Data for model inputReported illness:The number of C. perfringens outbreak-associated illnesses reported to OzFoodNet 2006–2008.
Empirical By year (2006–2008):183, 44, 383
Population adjustment:
Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year (2006–2008):20697880, 21015936, 21384427
Domestically acquired multiplier:Assumed to be 100% domestically acquired due to the short incubation period
PERT Minimum, modal, maximum values:1, 1, 1
Underreporting:
Outbreak multiplier used to adjust from outbreak to surveillance (O-S)
Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al. 22
PERT
Log normal
Minimum, modal, maximum values:5, 14, 20
Mean, standard deviation:7.44, 2.38
Foodborne illness in Australia circa 2010Page 84
Model input, source & comments Distribution Data for model inputTotal illness:Outbreak cases*Underreporting(O-S)(S-C)*Proportion travel-related
Outcome 5%, median, 95% values:2600, 16500, 53400
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:135, 785, 2465
Foodborne Multiplier:Expert elicitation study 2009
PERT Minimum, modal, maximum values:0.86, 0.98, 1
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:2550, 16100, 50600
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:130, 765, 2350
CRYPTOSPORIDIUM SPP.
Primary data: NNDSS Alternate data: Water Quality Study
Model input, source & comments Distribution Data for model input
Reported illness:NNDSS dataAvailable from: (http://www9.health.gov.au/cda/source/rpt_4.cfm)(Accessed on 28/06/12)
Empirical By year (2006–2010):3201, 2809, 2004, 4624, 1479
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year (2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier:NNDSS travel data
PERT Minimum, modal, maximum values:0.92, 0.97, 0.99
Underreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al.22
Log normal Mean, standard deviation:7.44, 2.38
Foodborne illness in Australia circa 2010Page 85
Model input, source & comments Distribution Data for model input
Total illness:Reported cases (NNDSS)*travel adjustment*underreporting(S-C)
Outcome 5%, medial, 95% values:8150, 17900, 39800
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:365, 850, 1860
Foodborne multiplier:Based on 2005 Delphi survey
Alternate PERT
5%, median, 95% values:0.01, 0.1, 0.27
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:150, 1700, 6100
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:7, 80, 320
GIARDIA LAMBLIA
Primary Data: Victoria notifications Alternate Data: Water Quality Study
Model input, source & comments Distribution Data for model inputReported illness:Victorian state notifications from: O’Grady and Tallis;117 Brown et al.61-64
Giardiasis became a non-notifiable disease in Victoria in 2010
Empirical By year(1996–2000):1085, 1060, 999, 921, 866(2006–2009):1192, 1382, 1434, 1433
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Correction factor:Victorian population - June quarterBy year (1996–2000 & 2006–2009):4534984, 4569297, 4606970, 4652462, 4704065& 5126540, 5204607, 5293088, 5395137Australian population/Victorian population
Empirical
Constant
By year(1996–2000):18310714, 18517564, 18711271, 18925855, 19153380(2006–2009):20697880, 21015936, 21384427, 21778845
4.03
Domestically acquired multiplier:Victorian notification data
PERT Minimum, modal, maximum values:0.84, 0.85, 0.89
Foodborne illness in Australia circa 2010Page 86
Model input, source & comments Distribution Data for model inputUnderreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al.22
Log Normal Mean, standard deviation:7.44, 2.38
Total illness:Reported cases (Victorian notifications)*correction factor*travel adjustment*underreporting(S-C)
Outcome 5%, median, 95% values:19800, 32800, 56400
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:920, 1560, 2665
Foodborne multiplier:Based on Delphi 2005
PERT Minimum, modal, maximum values:0.01, 0.06, 0.5
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:800, 3700, 10600 (circa 2010)565, 2600, 7400 (circa 2000)
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:35, 175, 490 (circa 2010)30, 140, 405 (circa 2000)
GUILLAIN-BARRÉ SYNDROME (GBS)
Primary data: NNDSS and literature Alternate Data: Hospitalisations and literature
Foodborne illness in Australia circa 2010Page 87
Model input, source & comments Distribution Data for model input
Reported illness:As GBS is not notified, sequelae multiplier, or the proportion of foodborne Campylobacter spp. cases that develop GBS, was used to determine the number of foodborne GBS cases. This proportion was a midpoint between estimates from the literature reported in McCarthy and Giesecke,71 Tam et al.,70 and Allos.72 Refer to Technical Appendix 8 for further explanation.
Antecedent bacterial gastroenteritis cases: estimated number of foodborne Campylobacter spp. cases.
Sequelae multiplier
Outcome
PERT
5%, median, 95% values:108500, 179000, 290000 (circa 2010)82500, 13900, 227000 (circa 2000)
Minimum, modal, maximum values:0.000192, 0.00034, 0.000945
Total foodborne illness:Foodborne Campylobacter spp. cases*Proportion of cases that develop GBS
Outcome 5%, median, 95% values:30, 75, 150 (circa 2010)25, 50, 100 (circa 2000)
Rate of foodborne illness from Campylobacter spp. per million:circa 2010 and circa 2000
Outcome 5%, median, 95% values:2, 3.1, 6 (circa 2010)1, 2.8, 6 (circa 2000)
HAEMOLYTIC URAEMIC SYNDROME (HUS)
Primary data: South Australian STEC surveillance and literature Alternate data: Hospitalisations
Foodborne illness in Australia circa 2010Page 88
Model input, source & comments Distribution Data for model inputReported illness:As HUS is a sequel to STEC, the proportion of foodborne STEC cases that develop HUS was used to determine the number of foodborne HUS cases. This proportion (sequelae multiplier is from Vally et al.77 Refer to Technical Appendix 8 for further explanation.
Antecedent bacterial gastroenteritis cases: estimated number of foodborne STEC cases
Sequelae multiplier
Outcome
PERT
5%, median, 95% values:950, 2350, 5850 (circa 2010)550, 1900, 5000 (circa 2000)
Minimum, modal, maximum values:0.017, 0.03, 0.051
Total foodborne illness:Foodborne STEC cases*Proportion of cases that develop HUS
Outcome 5%, median, 95% values:25, 70, 200
Rate of foodborne illness from STEC per million:circa 2010
Outcome 5%, median, 95% values:1, 3.3, 9
Foodborne illness in Australia circa 2010Page 89
HEPATITIS A
Primary data: NNDSS Alternate data:
Model input, source & comments Distribution Data for model input
Reported illness:NNDSS dataAvailable from: (http://www9.health.gov.au/cda/source/rpt_4.cfm) (Accessed on 15/05/12)
Empirical By year(1996–2000):2058, 3032, 2466, 1551, 809(2006–2010):281, 166, 277, 564, 267
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year(1996–2000):18310714, 18517564, 18711271, 18925855, 19153380
(2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier:NNDSS travel data
PERT Minimum, modal, maximum values:0.42, 0.58, 0.77
Underreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)
Alternate PERT
2.5%, median, 97.5% values:1, 2, 3
Total illness:Reported cases (NNDSS)*travel adjustment*underreporting(S-C)
Outcome 5%, median, 95% values:150, 300, 800
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:7, 15, 35
Foodborne multiplier:Expert elicitation study 2009
Alternate PERT
5%, median, 95% values:0.05, 0.12, 0.24
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:10, 40, 100 (circa 2010)65, 245, 725 (circa 2000)
Foodborne illness in Australia circa 2010Page 90
Model input, source & comments Distribution Data for model input
Rate of foodborne illness per million: 2010 andcirca 2000
Outcome 5%, median, 95% values:1, 2, 5 (circa 2010)3, 13, 40 (circa 2000)
IRRITABLE BOWEL SYNDROME (IBS)
Primary data: NNDSS and literature Alternate data: Literature
Model input, source & comments Distribution Data for model input
Reported illness:As IBS is not notified, the proportion of foodborne Campylobacter spp., Salmonella spp., and Shigella cases that develop IBS was calculated to determine the number of foodborne IBS cases. Refer to Technical Appendix 8 for further explanation
Antecedent bacterial gastroenteritis cases: Estimated number of foodborne Campylobacter spp. cases
Estimated number of foodborne non-typhoidal Salmonella spp. cases
Estimated number of foodborne Shigella spp. cases
Sequelae multiplier
Outcome
Outcome
Outcome
Alternate PERT
5%, median, 95% values108500, 179000, 290000 (circa 2010)82500, 139000, 227000 (circa 2000)
5%, median, 95% values21200, 39600, 73400 (circa 2010)15000, 28000, 50000 (circa 2000)
5%, median, 95% values150, 350, 850 (circa 2010)175, 515, 1300 (circa 2000)
2.5%, median, 97.5% values:0.072, 0.088, 0.104
Total foodborne illness:Foodborne bacterial gastroenteritis cases from 3 pathogens*Proportion of these cases that develop IBS
Outcome 2.5%, median, 97.5% values:12500, 19500, 30700 (circa 2010)9500, 14800, 23500 (circa 2000)
Foodborne illness in Australia circa 2010Page 91
Model input, source & comments Distribution Data for model input
Rate of foodborne illness per million: circa 2010 Outcome 2.5%, median, 97.5% values:570, 915, 1440 (circa 2010)550, 850, 1350 (circa 2000)
LISTERIA MONOCYTOGENES
Primary data: NNDSS Alternate data: Outbreak
Model input, source & comments Distribution Data for model input
Reported illness:NNDSS dataAvailable from: (http://www9.health.gov.au/cda/source/rpt_4.cfm)(Accessed on 12/11/13)
Empirical By year(1996–2000):66, 74, 53, 63, 67
(2006–2010):61, 50, 68, 92, 71
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year(1996–2000):18310714, 18517564, 18711271, 18925855, 19153380
(2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier:Assumed to be 100% as the majority of travellers are not at high risk
PERT Minimum, modal, maximum values:1, 1, 1
Underreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)
Alternate PERT
2.5%, medial, 97.5% values:1, 2, 3
Total illness:Reported cases (NNDSS)*travel adjustment*underreporting(S-C)
Outcome 5%, median, 95% values:50, 150, 200
Foodborne illness in Australia circa 2010Page 92
Model input, source & comments Distribution Data for model input
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:3, 7, 75
Foodborne multiplier:Expert elicitation study 2009
PERT Minimum, modal, maximum values:0.9, 0.98, 1
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:50, 150, 200 (circa 2010)70, 125, 185 (circa 2000)
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:3, 7, 75 (circa 2010)4, 7, 10 (circa 2000)
NOROVIRUS
Primary data: Water Quality Study Alternate data: Outbreak
Model input, source & comments Distribution Data for model inputReported illness:Gastroenteritis multiplier – based on the NGSII
Pathogen fraction multiplier – based on age adjusted WQS of an estimated 69 positive isolates per 703 specimens (Sinclair et al.20)
Alternate PERT
Alternate PERT
2.5%, median, 97.5% values:0.64, 0.74, 0.84
2.5%, median, 97.5% values:0.0772, 0.0982, 0.1226
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year (2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier:All isolations in the WQS were domestically acquired
N/A
Time trend multiplier:No time trend
N/A
Underreporting:WQS is community surveillance
N/A
Foodborne illness in Australia circa 2010Page 93
Model input, source & comments Distribution Data for model inputTotal illness:Population at risk*gastroenteritis multiplier*pathogen fraction multiplier*time trend multiplier
Outcome 5%, median, 95% values:1220000, 1550000, 1940000
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:57100, 72500, 90550
Foodborne multiplier:Expert elicitation study 2009
Alternate PERT
5%, median, 95% values:0.05, 0.18, 0.35
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:78100, 276000, 563000
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:3620, 12920, 26300
OTHER PATHOGENIC ESCHERICHIA COLI
Primary data: Water Quality Study Alternate data: IID2
Model input, source & comments Distribution Data for model inputReported illness:Gastroenteritis multiplier – based on the NGSII
Pathogen fraction multiplier – based on age adjusted WQS of an estimated 50 positive isolates per 713 specimens (Hellard et al.2)
Alternate PERT
Alternate PERT
2.5%, median, 97.5% values:0.64, 0.74, 0.84
2.5%, median, 97.5% values:0.0525, 0.074, 0.0914
Population adjustment:
Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year (2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier:All isolations in the WQS were domestically acquired
N/A
Time trend multiplier:No time trend
N/A
Underreporting:WQS is community surveillance
N/A
Foodborne illness in Australia circa 2010Page 94
Model input, source & comments Distribution Data for model inputTotal illness:Population at risk*gastroenteritis multiplier*pathogen fraction multiplier*time trend multiplier
Outcome 5%, median, 95% values:833000, 1100000, 1450000
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:39150, 51350, 67550
Foodborne multiplier:Expert elicitation study 2009
Alternate PERT
5%, median, 95% values:0.08, 0.23, 0.55
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:85800, 255000, 632000
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:4100, 11600, 29700
REACTIVE ARTHRITIS (REA)
Primary data: NNDSS and literature Alternate data:
Model input, source & comments Distribution Data for model input
Reported illness:As ReA is not notified, the proportion of foodborne Campylobacter spp., Salmonella spp., Shigella, and Y. enterocolitica cases that develop ReA was calculated to determine the number of foodborne ReA cases. The proportion for each of the four pathogens was calculated from the literature. Refer to Technical Appendix 8 for further explanation
Antecedent bacterial gastroenteritis cases:Estimated number of foodborne Campylobacter spp. cases
Estimated number of foodborne non-typhoidal Salmonella spp. cases
Estimated number of foodborne Shigella spp. cases
Estimated number of foodborne Yersinia enterocolitica cases
Outcome
Outcome
Outcome
Outcome
5%, median, 95% values:108500, 179000, 290000 (circa 2010)82500, 139000, 227000 (circa 2000)
5%, median, 95% values:21200, 39600, 73400 (circa 2010)15000, 28000, 50000 (circa 2000)
5%, median, 95% values:150, 350, 850 (circa 2010)175, 515, 1300 (circa 2000)
5%, median, 95% values:650, 1150, 1950 (circa 2010)300, 800, 1650 (circa 2000)
Foodborne illness in Australia circa 2010Page 95
Model input, source & comments Distribution Data for model input
Sequelae multiplier for Campylobacter spp.
Sequelae multiplier for non-typhoidal Salmonella spp.
Sequelae multiplier for Shigella spp.
Sequelae multiplier for Yersinia enterocolitica
Alternate PERT
Alternate PERT
PERT
Alternate PERT
Minimum, medial, maximum values:0.028, 0.07, 0.16
Minimum, medial, maximum values:0, 0.085, 0.26
Minimum, modal, maximum values:0.012, 0.097, 0.098
Minimum, medial, maximum values:0, 0.12, 0.231
Total foodborne illness:Foodborne bacterial gastroenteritis cases from pathogens*Proportion of cases that develop ReA
Outcome 5%, median, 95% values:8750, 16200, 30400
Rate of foodborne illness per million:circa 2010
Outcome 5%, median, 95% values:415, 765, 1375
ROTAVIRUS
Primary data: Water Quality Study Alternate data: IID2
Model input, source & comments Distribution Data for model inputReported illness:Gastroenteritis multiplier – based on the NGSII
Pathogen fraction multiplier – based on age adjusted WQS of an estimated 6 positive isolates per 713 specimens (Hellard et al.2)
Alternate PERT
Alternate PERT
2.5%, median, 97.5% values:0.64, 0.74, 0.84
2.5%, median, 97.5% values:0.0031, 0.0084, 0.0182
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year (2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier:All isolations in the WQS were domestically acquired
N/A
Time trend multiplier:Based on Dey et al.36
Alternate PERT
2.5%, median, 97.5% values:0.318, 0.338, 0.359
Foodborne illness in Australia circa 2010Page 96
Model input, source & comments Distribution Data for model inputUnderreporting:WQS is community surveillance
N/A
Total illness:Population at risk*gastroenteritis multiplier*pathogen fraction multiplier*time trend multiplier
Outcome 5%, median, 95% values:18500, 44800, 90800
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:875, 2100, 4260
Foodborne multiplier:Expert elicitation study 2009
Alternate PERT
5%, median, 95% values:0.01, 0.02, 0.03
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:300, 850, 2000
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:15, 40, 95
SALMONELLA SPP. , NON-TYPHOIDAL
Primary data: NNDSS Alternate data: Water Quality Study
Model input, source & comments Distribution Data for model inputReported illness:NNDSS dataAvailable from: (http://www9.health.gov.au/cda/source/rpt_4.cfm) (Accessed on 11/12/13)
Empirical By year(1996–2000)5744, 6955, 7513, 7008, 6187
(2006–2010)8241, 9502, 8316, 9524, 11928
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year(1996–2000):18310714, 18517564, 18711271, 18925855, 19153380
(2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier:NNDSS travel data
PERT Minimum, modal, maximum values:0.7, 0.85, 0.95
Foodborne illness in Australia circa 2010Page 97
Model input, source & comments Distribution Data for model inputUnderreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al. 22
Log normal Mean, standard deviation:7.44, 2.38
Total illness circa 2010:Reported cases (NNDSS)*travel adjustment*underreporting(S-C)
Outcome 5%, median, 95% values:31900, 56200, 101000
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:1515, 2650, 4650
Foodborne multiplier:Expert elicitation study 2009
Alternate PERT
5%, median, 95% values:0.53, 0.72, 0.86
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:21200, 39600, 73400 (circa 2010)15000, 28000, 50000 (circa 2000)
Rate of foodborne illness per million: circa 2010 and circa 2000
Outcome 5%, median, 95% values:1000, 1850, 3350 (circa 2010)800, 1500, 2700 (circa 2000)
SALMONELLA TYPHI
Primary data: NNDSS Alternate data:
Model input, source & comments Distribution Data for model inputReported illness:NNDSS dataAvailable from: (http://www9.health.gov.au/cda/source/rpt_4.cfm)(Accessed on 28/06/12)
Empirical By year(1996–2000):72, 72, 57, 63, 58
(2006–2010):77, 90, 105, 115, 95
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year(1996–2000):18310714, 18517564, 18711271, 18925855, 19153380
(2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Foodborne illness in Australia circa 2010Page 98
Model input, source & comments Distribution Data for model inputDomestically acquired multiplier:NNDSS travel data
PERT Minimum, modal, maximum values:0.02, 0.11, 0.25
Underreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)Multiplier of two for serious illnesses
Alternate PERT
2.5%, median, 97.5% values:1, 2, 3
Total illness:Reported cases (NNDSS)*travel adjustment*underreporting(S-C)
Outcome 5%, median, 95% values:8, 20, 45
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:0, 1, 2
Foodborne multiplier:Based on Delphi 2005
PERT Minimum, modal, maximum values:0.02, 0.75, 0.97
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:5, 15, 30 (circa 2010)3, 9, 21 (circa 2000)
Rate of foodborne illness per million: circa 2010 and circa 2000
Outcome 5%, median, 95% values:0, 0.6, 1 (circa 2010)0, 0.5, 1 (circa 2000)
SAPOVIRUS
Primary data: Water Quality Study Alternate data: IID2
Model input, source & comments Distribution Data for model inputReported illness:Gastroenteritis multiplier – based on the NGSII
Pathogen fraction multiplier – based on age adjusted WQS findings for norovirus of an estimated 69 isolates per 703 specimens (Sinclair et al.20
Pathogen comparison multiplier – Kirkwood multiplier67 comparing norovirus to sapovirus
Alternate PERT
Alternate PERT
Constant
2.5%, median, 97.5% values:0.64, 0.74, 0.84
2.5%, median, 97.5% values:0.0772, 0.0982, 0.1226
0.5
Foodborne illness in Australia circa 2010Page 99
Model input, source & comments Distribution Data for model inputPopulation adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year (2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier:All isolations in the WQS were domestically acquired
N/A
Time trend multiplier:No time trend
N/A
Underreporting:WQS is community surveillance
N/A
Total illness:Population at risk*gastroenteritis multiplier*pathogen fraction multiplier*pathogen comparison multiplier*time trend multiplier
Outcome 5%, medial, 95% values:63400, 81600, 102000
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:3000, 3800, 4800
Foodborne multiplier:Assumed to be the same as norovirus
PERT Minimum, modal, maximum values:0.05, 0.18, 0.35
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:7450, 15000, 24300
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:350, 700, 1150
SCOMBROTOXICOSIS
Primary data: Outbreak Alternate data:
Model input, source & comments Distribution Data for model inputReported illness:The number of scombrotoxicosis outbreak-associated illnesses reported to OzFoodNet 2006–2008.
Empirical By year (2006–2008)12, 17, 0
Foodborne illness in Australia circa 2010Page 100
Model input, source & comments Distribution Data for model inputPopulation adjustment:
Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year (2006–2008):20697880, 21015936, 21384427
Domestically acquired multiplier:Assumed to be 100% domestically acquired due to the short incubation period
PERT Minimum, modal, maximum values:1, 1 ,1
Underreporting:
Outbreak multiplier used to adjust from outbreak to surveillance (O-S)
Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al.22
PERT
Log Normal
Minimum, modal, maximum values:5, 14, 20
Mean, standard deviation:7.44, 2.38
Total illness:Outbreak cases*Underreporting (O-S)(S-C)*Proportion travel-related
Outcome 5%, median, 95% values:0, 1050, 2450
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:0, 50, 115
Foodborne multiplier:Assumed to be 100% foodborne
PERT Minimum, modal, maximum values:1, 1, 1
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:0, 1050, 2450
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:0, 50, 115
SHIGELLA
Primary data: NNDSS Alternate data:
Model input, source & comments Distribution Data for model input
Reported illness:NNDSS dataAvailable from: (http://www9.health.gov.au/cda/source/rp
Empirical By year(1996–2000):660, 802, 580, 534, 488
(2006–2010):
Foodborne illness in Australia circa 2010Page 101
Model input, source & comments Distribution Data for model input
t_4.cfm )(Accessed on 12/11/13) 545, 597, 828, 618, 550
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical By year(1996–2000):18310714, 18517564, 18711271, 18925855, 19153380
(2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
Domestically acquired multiplier: NNDSS travel data
PERT Minimum, modal, maximum values:0.45, 0.7, 0.84
Underreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al.22
Log normal Mean, standard deviation:7.44, 2.38
Total illness:Reported cases (NNDSS)*travel adjustment*underreporting(S-C)
Outcome 5%, median, 95% values:1650, 3000, 5400
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:75, 140, 260
Foodborne multiplier:Expert elicitation study 2009
Alternate PERT
5%, median, 95% values:0.05, 0.12, 0.23
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:150, 350, 850 (circa 2010)175, 515, 1300 (circa 2000)
Rate of foodborne illness per million: circa 2010 and circa 2000
Outcome 5%, median, 95% values:6, 16, 40 (circa 2010)9, 28, 70 (circa 2000)
STAPHYLOCOCCUS AUREUS
Primary data: Outbreak Alternate data:
Foodborne illness in Australia circa 2010Page 102
Model input, source & comments Distribution Data for model inputReported illness:The number of S. aureus outbreak-associated illnesses reported to OzFoodNet 2006–2008.
Empirical By year (2006–2008)3, 14, 50
Population adjustment:
Australian resident population 2006–2008 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument)(Accessed on 16/8/12)
Empirical By year (2006–2008):20697880, 21015936, 21384427
Domestically acquired multiplier:Assumed to be 100% domestically acquired due to short incubation period
PERT Minimum, modal, maximum values:1, 1, 1
Underreporting:
Outbreak multiplier used to adjust from outbreak to surveillance (O-S)
Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al. 22
PERT
Log normal
Minimum, modal, maximum values:5, 14, 20
Mean, standard deviation:7.44, 2.38
Total illness:Outbreak cases*Underreporting(O-S)(S-C)*Proportion travel-related
Outcome 5%, median, 95% values:200, 1300, 7050
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:9, 60, 350
Foodborne multiplier:Based on Delphi 2005
PERT Minimum, modal, maximum values:0.95, 1, 1
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:200, 1300, 7000
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:9, 60, 350
SHIGA TOXIN-PRODUCING ESCHERICHIA COLI
Primary data: South Australia surveillance Alternate data: NNDSS
Foodborne illness in Australia circa 2010Page 103
Model input, source & comments Distribution Data for model inputReported illness:South Australian state STEC surveillance from the study by Vally et al.77
Empirical By year (2006–2010)35, 40, 39, 62, 32
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Correction factor:SA population - June quarterBy year (2006–2010):1567888, 1582559, 1597343, 1614375, 1629434Australian population/SA population
Empirical By year (2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
13.4
Domestically acquired multiplier:NNDSS travel data
PERT Minimum, modal, maximum values:0.93, 0.99, 1
Underreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)STEC multiplier adapted from Hall et al.22
Log normal Mean, standard deviation:8.83, 3.7
Total illness:Reported cases (SA surveillance)*correction factor*travel adjustment*underreporting(S-C)
Outcome 5%, median, 95% values:2050, 4300, 9500
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:100, 200, 450
Foodborne multiplier:Expert elicitation study 2009
Alternate PERT
5%, median, 95% values:0.32, 0.56, 0.83
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:950, 2350, 5850
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:45, 110, 260
TOXOPLASMA GONDII
Primary data: Seroprevalence estimate Alternate data:
Foodborne illness in Australia circa 2010Page 104
Model input, source & comments Distribution Data for model input
Reported illness:USA seroprevalence data21 extrapolated to the Australian population for 2010 by age group
Empirical 0–4: 57095–9: 574910–19: 1074420–29: 1172830–39: 1080940–49: 1037750–59: 890360–69: 652170–79: 371380+: 2342
Total: 76095
Population adjustment:
Australian resident population 2010 June quarter by age group - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Empirical 0–4: 14416795–9: 135221110–19: 285205020–29: 324034730–39: 310822440–49: 310587750–59: 277351160–69: 211415870–79: 125311480+: 824146
Domestically acquired multiplier:Assumed to be 100% domestically acquired
PERT Minimum, modal, maximum values:1, 1, 1
Proportion symptomatic:Scallan et al.6 and Abelson et al.4
PERT Minimum, modal, maximum values:0.11, 0.15, 0.21
Total illness:Estimated yearly cases*travel adjustment*proportion symptomatic
Outcome 5%, median, 95% values:8350, 11400, 16000
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:380, 515, 760
Foodborne multiplier:Based on Delphi 2005
PERT Minimum, modal, maximum values:0.04, 0.31, 0.74
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:1400, 3750, 7150
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:65, 170, 325
VIBRIO PARAHAEMOLYTICUS
Primary data: Western Australia notifications Alternate data:
Foodborne illness in Australia circa 2010Page 105
Model input, source & comments Distribution Data for model inputReported illness:WA notifications(http://www.public.health.wa.gov.au/cproot/4195/2/12172_DiseaseWAtch.pdf)
Empirical By year (2006–2010):3, 9, 7, 9, 10
Population adjustment:Australian resident population 2006–2010 June quarter - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (Accessed on 16/8/12)
Correction factor:WA population - June quarterBy year (2006–2009):2059381, 2113841, 2178577, 2246659, 2296129Australian population/WA Population
Empirical
Constant
By year (2006–2009):20697880, 21015936, 21384427, 21778845
9.61
Domestically acquired multiplier:OzFoodNet WA Annual Reports 2006–2010
PERT Minimum, modal, maximum values:0, 0.18, 0.33
Underreporting:
Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al.22
Log normal Mean, standard deviation:7.44, 2.38
Total illness:Reported cases (WA surveillance)*correction factor*travel adjustment*underreporting(S-C)
Outcome 5%, median, 95% values:15, 60, 170
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:1, 3, 8
Foodborne multiplier:Based on Delphi 2005
PERT Minimum, modal, maximum values:0.05, 0.75, 0.96
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:10, 40, 120
Rate of foodborne illness per million: circa 2010
Outcome 5%, median, 95% values:0, 2, 6
YERSINIA ENTEROCOLITICA
Primary data: State and territory notifications Alternate data:
Foodborne illness in Australia circa 2010Page 106
Model input, source & comments Distribution Data for model input
Reported illness:State notifications from Queensland, SA, WA, and NT extrapolated from state data to the Australian population to determine the expected number of notifications if all states were reporting
Empirical By year (2006–2010)214, 249, 326, 242
Population adjustment:Australian resident population 2006–2010 June quarter
Queensland resident population (June quarter)
SA resident population (June quarter)
WA resident population (June quarter)
NT Population (June quarter) - ABS(http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/3101.0Dec%202011?OpenDocument) (16/8/12)Notifications / estimated Australian population
Empirical By year (2006–2010):20697880, 21015936, 21384427, 21778845, 22065317
By year (2006–2010):4090908, 4177089, 4270091, 4365426, 4424158By year (2006–2010):1567888, 1582559, 1597343, 1614375, 1629434By year (2006–2010):2059381, 2113841, 2178577, 2246659, 2296129By year (2006–2010)210627, 215021, 220935, 226841, 230315
Domestically acquired multiplier:OzFoodNet WA Annual Reports 2006-2010
PERT Minimum, modal, maximum values:0.8, 0.9, 1
Underreporting:Multiplier used to adjust for underreporting from surveillance to community (S-C)Non-typhoidal Salmonella multiplier adapted from Hall et al.22
Log normal Mean, standard deviation:7.44, 2.38
Total illness:Reported cases(Extrapolated state notifications)*travel adjustment*underreporting(S-C)
Outcome 5%, median, 95% values:900, 1500, 2500
Rate of total illness per million:circa 2010
Outcome 5%, median, 95% values:40, 70, 115
Foodborne multiplier:Based on Delphi 2005
PERT Minimum, modal, maximum values:0.28, 0.84, 0.94
Total foodborne illness:Total illness*Foodborne multiplier
Outcome 5%, median, 95% values:650, 1150, 1950
Rate of foodborne illness per million:circa 2010
Outcome 5%, median, 95% values:30, 50, 90
Foodborne illness in Australia circa 2010Page 107
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