HUMAN ADVERSE HEALTH OUTCOMES

In the fields of toxicology and environmental health related to chemical hazards, a big focus of the hazard identification step in risk assessment is to determine whether there is enough evidence to consider a chemical substance to be the cause of an adverse health effect like cancer. However, in microbial risk assessment, the hazard is usually already identified as being capable of causing human illness prior to the initiation of the risk assessment. Microbial pathogens are often isolated from the individuals) who exhibit(s) the adverse health effect(s), which can provide positive evidence for a cause-and-effect relationship. This is because for microbial hazards, the cause-and-effect relationship can often be measured over short periods of time (acute illness), and the likelihood of an adverse effect exhibited in a population to be associated with a pathogen/food combination can be relatively large. Because the link between pathogen and disease is well established, it does not require detailed evaluation, but the information is collected and presented to provide insight and a framework of reference around the assessment (Lammerding & Fazil, 2000).

A microbial pathogen infection is the process by which a microorganism multiplies or grows in a host (Haas et al., 2014). Infection can result in either an asymptomatic state, where no illness is observed, or symptomatic disease (clinical illness). The capacity of a microbial pathogen to cause disease is known as virulence (Haas et al., 2014). Once infection begins after exposure to a pathogen, there are various possible adverse health outcomes, including acute illness, chronic illness or death. The type of adverse health outcome is dependent not only on the pathogen

(determined by pathogenicity and virulence) but also on the host (affected by factors such as the immune system and age).

Assessing the public health significance of a hazard requires characterizing the symptoms, severities and likelihoods of outcomes that can be caused by a given pathogen. Different acute manifestations and chronic sequelae of illness can result from the same pathogen in different individuals, and similar adverse health effects can result from different pathogens. For instance, Salmonella, E. coli 0157:H7 and Campylobacter can all lead to diarrhea as an acute illness. However, chronic outcomes, such as reactive arthritis (from Salmonella) and Guillain-Barre syndrome (from Campylobacter) may occur in some individuals. Examples of longterm sequelae associated with major foodborne pathogens are shown in Table 10.2 (adapted from Batz et al., 2013). Some of these symptoms may particularly affect sensitive populations, which depending on the pathogen, includes pregnant women, the elderly, infants and immunocompromised individuals (Haas et al., 2014).

One of the goals of hazard identification is to define and describe these adverse health outcomes for the microorganism of interest, including a description of the pathogenesis of the microorganism and the consequences of the disease given a sensitive population.

THE POPULATION BURDEN OF DISEASE

One of the key goals of hazard identification is to assess - at least qualitatively and if possible quantitatively - the magnitude and scope of the burden of disease on the population that can be attributed to the pathogens in question in the foods in question. Whereas quantitative microbial risk assessments are a "bottom-up" approach that uses predictive microbiological modeling to estimate the burden of illness due to a pathogen- product pathway, the hazard identification phase often uses "top down" approaches that are based on analyzing data on human illness.

Public Health Surveillance

The primary data source for assessing the magnitude and scope of disease burden is national public health surveillance systems, which facilitate the ongoing, systematic collection, management, analysis and interpretation of health data (Thacker et al., 2012). Published summaries of these data, often at the national or regional level, can be critical to hazard identification efforts.

Table 10.2 Examples of Long-Term Sequelae for Major Foodborne Pathogens

Campylobacter

Chronic diarrhea, Guillain-Barre syndrome (peripheral neuropathies resulting in muscle weakness, ascending paralysis, respiratory failure, permanent paraplegia and walking difficulties), irritable bowel syndrome, inflammatory bowel disease, reactive arthritis

Cryptosporidium

Chronic diarrhea, reactive arthritis, cognitive, developmental and fitness deficits in small children

Escherichia coli 0157:H7

HUS (hemolytic-uremic syndrome), chronic kidney disease, renal failure and other post-HUS sequelae (e.g., hypertension, pancreatitis, seizures, hemiplegia), irritable bowel syndrome, dyspepsia, reactive arthritis

Giardia lamblia

Chronic diarrhea, irritable bowel syndrome, inflammatory bowel disease, reactive arthritis, severe malabsorption leading to cognitive, developmental and fitness deficits in small children

Listeria monocytogenes

Chronic neurologic manifestations (e.g., cranial nerve palsies, epilepsy, impaired executive and cognitive function, memory loss, vision and hearing loss), congenital neurologic sequelae (cerebral palsy, epilepsy, hearing loss, cognitive deficits, chronic lung disease)

Norovirus

Irritable bowel syndrome

Salmonella enterica (nontyphoidal)

Chronic diarrhea, irritable bowel syndrome, dyspepsia, inflammatory bowel disease, reactive arthritis

Shigella

HUS, chronic kidney disease, renal failure and other post-HUS sequelae (e.g., hypertension, pancreatitis, seizures, hemiplegia), irritable bowel syndrome, dyspepsia, inflammatory bowel disease, reactive arthritis

Toxoplasma gondii

Myocarditis, encephalitis, meningitis, irreversible ocular impairment, congenital toxoplasmosis (neurological manifestations including hearing impairment, cognitive deficits, learning disabilities, epilepsy, palsies and growth retardation), psychiatric sequelae (schizophrenia, depression)

Vibrio vulnificus

Sepsis, secondary lesions with necrotizing fasciitis or vasculitis necessitating debridement or amputation, altered mental status, hypotension, pneumonia, endometritis

Yersinia enterocolitica

Appendicitis-like mesenteric lymphadenitis, chronic diarrhea, autoimmune thyroid disease (Graves' disease), reactive arthritis

Adapted from Batz et al. (2013)

There are many kinds of surveillance systems, but for the purposes of foodborne disease, the three most important distinctions are between passive surveillance, active surveillance and outbreak surveillance. Passive surveillance systems rely on hospitals, clinics or other health care units to report cases of illness to the relevant health jurisdiction (Nsubuga et al., 2006). In many countries, there are lists of "nationally notifiable" diseases with mandatory reporting requirements and associated surveillance programs. In active surveillance, staff members monitor or solicit reporting from health care providers or laboratories and may follow up with individual patients, which vastly increases the timeliness, accuracy and completeness of data. Because this is so resource-intensive, however, these systems often use a sentinel surveillance model with limited population coverage (Ford et al., 2015). The rapid detection and investigation of foodborne disease outbreaks, usually defined as the occurrence of two or more cases of a similar illness resulting from consumption of the same food, is a goal of public health. Data from these investigations form the basis of outbreak surveillance. Unlike other surveillance systems, outbreak surveillance routinely links cases of human illness to contaminated food vehicles, contributing factors, and the settings in which food was prepared and consumed.

In the United States, passive surveillance for some foodborne pathogens is conducted by the National Notifiable Disease Surveillance System (NNDSS), which collects data on over 100 diseases and conditions (Adams et al., 2017). Based on these data, CDC publishes annual summaries, which include tables listing the number of cases and rates of each condition by region, month, age, gender, race and ethnicity, as well as maps and figures to provide additional context (e.g., Adams et al., 2015; Adams et al., 2017; Adams et al., 2016). In contrast, the Foodborne Diseases Active Surveillance Network, or FoodNet, is active surveillance; it monitors the incidence of laboratory-confirmed illnesses caused by nine foodborne pathogens across 10 catchment areas covering about 15% of the U.S. population (Scallan et al., 2011). FoodNet publishes cases of infection, incidence and trends on an annual basis (Marder et al., 2017; CDC, 2017a). Surveillance for foodborne outbreaks in the United States is provided by CDC's National Outbreak Reporting System (NORS).

CDC publishes annual summaries of reported outbreaks(Gould et al., 2013; CDC, 2017b), and makes these data searchable and downloadable online (CDC, 2013).

Similar surveillance systems are in place elsewhere in the world and are used to produce government reports on the incidence and risk factors associated with foodborne disease. In the European Union (EU), for example, member states are mandated to publish annual reports describing surveillance of zoonotic infections in humans and animals as well as the occurrence of zoonotic agents in food and feeding stuffs (DTU Food, 2017). These reports are based on data from active, passive and outbreak surveillance and are available online (EFSA, 2018). Based on these and other data, the European Centre for Disease Prevention and Control (ECDC) and the European Food Safety Authority (EFSA) coordinate annual summary reports for the EU on the trends and sources of zoonoses, zoonotic agents and foodborne outbreaks for 37 countries on over two dozen pathogens (EFSA and ECDC, 2017). Similarly, Australia's OzFoodNet publishes annual reports on eight foodborne pathogens (Ozfoodnet Working Group, 2015). Canada's FoodNet program also publishes annual reports based on active surveillance of human illness due to 10 foodborne pathogens as well as results of sampling programs in retail food, animals and water sources (PHAC,2017). Japan's National Institute of Infectious Diseases publishes data and reports on notifiable diseases and foodborne outbreaks on a regular basis (NIID, 2018).

Estimating the Burden of Foodborne Disease

Disease surveillance systems provide insight into the numbers of cases of illness that have been reported to authorities, but these illnesses reflect only a small slice of the overall burden of disease in the population. Most illnesses go unreported (Figure 10.1). Although circumstances vary by disease severity, few who become ill with acute gastroenteritis symptoms

Outcome tree of underestimation of disease burden based on reported cases of illness. Adapted from Gibbons et al. (2014)

Figure 10.1 Outcome tree of underestimation of disease burden based on reported cases of illness. Adapted from Gibbons et al. (2014).

seek medical care, and only a fraction of those cases that seek medical care are asked by physicians to provide specimens for identification of the causative organism by a clinical laboratory. In turn, laboratories may fail to identify the causative organism. Clinics and laboratories may also fail to report lab-confirmed cases to local, state or federal authorities, particularly under passive surveillance.

To estimate the true incidence of foodborne diseases for a particular pathogen, the number of reported cases can be multiplied by factors to account for under-ascertainment of community infections by health care providers, under-diagnosis within health care settings, and underreporting to surveillance systems (Gibbons et al., 2014). Of these estimates, some percentage may be assumed or estimated to be due to foodborne exposure.

Epidemiological studies are also used to measure the incidence of disease when disease surveillance is either unavailable or deemed insufficient for the need. For example, in the United Kingdom, a prospective cohort study approach is used to estimate the burden of foodborne disease (Tam et al., 2012). In Japan, the burden of foodborne campylobacteriosis is based on extrapolating from national hospital patient surveys (Kumagai et al., 2015). Norovirus is rarely estimated based on surveillance; rather, in the United States, it is often based on an estimate of the overall burden of acute gastroenteritis, based on U.S. health survey data, multiplied by a fraction assumed to be caused by norovirus (Scallan et al., 2011).

Burden of foodborne disease estimates have been developed for a number of countries, including Australia (Kirk et al., 2014), Canada (Thomas et al., 2013), France (Vaillant et al., 2005), Japan (Kumagai et al., 2015), the Netherlands (Mangen et al., 2015), New Zealand (Lake et al., 2010), South Korea (Park et al., 2015), the United Kingdom (Tam et al., 2012) and the United States (Scallan et al., 2011). Pilot studies have also been conducted in Albania, Thailand and Uganda (Lake et al., 2015). Most of these studies also estimate the number of annual hospitalizations and deaths due to foodborne illness.

In a very large study, the World Health Organization estimated the global burden of foodborne diseases, including rates of foodborne illnesses, hospitalizations and deaths, for six regions, as shown in Table 10.3 (Kirk et al., 2015). These estimates are based on data synthesis from national studies, systematic reviews of published literature, and primary data collection efforts.

Numbers of illnesses, hospitalizations and deaths are useful summary measures but do not fully account for differences in symptoms

Table 10.3 World Health Organization Estimates of the Median Rates of Foodborne Illness per 100,000 Persons for Select Pathogens, by Region, in the Year 2010 (Kirk et al., 2015)

African

Region

Region of the Americas

Eastern

Mediterranean

Region

European

Region

South-East Asian Region

Western Pacific Region

Global

Campylobacter

2,221

1,389

1,873

522

1,152

876

1,390

Cryptosporid iu m

205

114

346

21

78

32

125

Entamoeba histolytica

796

212

737

0

256

229

407

E. coli, Shiga-toxin producing

5

16

65

18

19

3

17

E. coli,

cntcropathogcnic

454

189

430

8

594

166

346

E. coli, enterotoxigenic

982

1,281

4,971

6

1,075

555

1,257

Ciardia

809

309

670

54

159

354

410

Hepatitis A

232

12

237

11

494

51

199

Listeria monocytogenes

0.1

0.3

0.1

0.2

0.1

0.2

0.2

Norovirus

1,749

2,491

2,276

1,652

841

1Д)5

1,814

Salmonella enterica, non-typhoidal

896

1002

1610

186

906

898

1,140

Salmonella typhi

108

10

73

1

250

77

110

Shigella

523

278

627

3

1084

689

741

Vibrio cholerae

43

0.02

9

0.03

17

0.2

11

Total-'

10,304

7,937

16,865

2,506

8,068

6,491

8,369

a Total median estimates include diarrheal and enteric invasive diseases caused by agents not shown in this table.

or severities or reflect the long-term health sequelae often associated with foodborne illness. Integrated measures of disease burden, such as Disability Adjusted Life Years (DALYs), Quality Adjusted Life Years (QALYs) and monetary costs of illness, are used to quantify and combine these various health impacts into a single number (Mangen et al., 2010). Many estimates of foodborne disease burden have published total and per-case estimates using these measures (Havelaar et al., 2015; Hoffmann et al., 2012; Kumagai et al., 2015; Mangen et al., 2015; Minor et al., 2015).

Attributing Illnesses to Foods

Unfortunately, national estimates of the burden of foodborne disease do not often answer the question of the role of specific foods in these illnesses. Additional studies are needed to quantify the percentage of foodborne illnesses caused by a specific pathogen that can be attributed to a food or category of foods (Batz et al., 2005).

The most common approaches used for foodborne illness source attribution include mathematical modeling studies that compare microbial subtypes isolated from human patients with those obtained from food and animal sources (De Knegt et al., 2015; Sheppard et al., 2009); prospective epidemiological studies such as case-control studies, some of which incorporate microbial subtyping and additional modeling (Macdonald et al., 2015; Rosner et al., 2017); analysis of aggregated foodborne outbreak data (Greig & Ravel, 2009; Painter et al., 2013); and structured elicitations of expert judgment to fill data gaps or synthesize data from multiple sources (Hald et al., 2016; Hoffmann et al., 2007).

For example, Figure 10.2 shows the estimated percentage of E. coli 0157 cases in the United States that were attributed to categories of foods based on statistical modeling of foodborne outbreaks that occurred between 1998 and 2013, with 90% credibility intervals (IFSAC, 2017). The number of annual illnesses estimated due to E. coli 0157 using data from Scallan et al. (2011) can be multiplied by these percentages to estimate the number of illnesses due to a pathogen-product pathway.

Estimated percentage of E

Figure 10.2 Estimated percentage of E. coli 0157 illnesses (with 90% credibility intervals) for 2013, in descending order, attributed to each of 17 food categories, based on statistical modeling of foodborne outbreaks from 1998 to 2013. Adapted from IFSAC (2017).

 
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