Assessing waterborne risks: an introduction

Gunther F. Craun, Rebecca L. Calderon and Timothy J. Wade

ABSTRACT

Gunther F. Craun (corresponding author)

Gunther F. Craun & Associates,

101 West Frederick Street,

Suite 207, Staunton, VA 24401,

USA

E-mail: gfcraun@verizon.net

Rebecca L. Calderon

Timothy J. Wade

National Health & Environmental Effects Research

Laboratory, Office of Research & Development,

US Environmental Protection Agency,

Research Triangle Park, NC 27711,

USA

Information in this paper can help readers evaluate the results of epidemiologic studies of

waterborne disease risks. It is important that readers understand the various epidemiologic study

designs, their strengths and limitations, and potential biases. Terminology used by

epidemiologists to describe disease risks can be confusing. Thus, readers should not only

evaluate the adequacy of the information to estimate waterborne risks but should also

understand how the risk was estimated. For example, one author’s definition of attributable risk

may be quite different from another author’s in terms of the population to which the risk may

apply and how it should be interpreted.

Key words | attributable risk, bias, endemic disease, epidemic disease, epidemiologic studies,

gastroenteritis, outbreaks, waterborne disease

INTRODUCTION

Sanitary practices for the disposal of sewage, source water

protection, and the filtration and chlorination of drinking

water have dramatically decreased waterborne disease risks

in the United States, especially for typhoid fever mortality

(Figure 1). In fact, the treatment of drinking water has been

acclaimed as one of the major public health achievements

of the 20th century (NRC 1999; 2004). Nevertheless,

outbreaks associated with contaminated drinking water

still occur in the United States, and a substantial fraction of

waterborne illness may not be reported (Hauschild & Bryan

1980; Bennett et al. 1987).

An annual average of 17 drinking water outbreaks was

reported during 1991–2002—only slightly less than the

annual average of 23 outbreaks reported during 1920–30.

These outbreaks were associated with inadequately treated

water systems and distribution system contamination. In

some outbreaks, water systems had not exceeded water

quality regulations. In addition to outbreaks, public health

officials have now become more aware of the importance of

non-outbreak waterborne risks. Payment et al. (1991; 1997)

found that increased gastrointestinal illness was associated

with the use of tap water in a major Canadian municipal

water system that met current microbiological water quality

standards and reported no outbreaks.

WATERBORNE ASSOCIATIONS

Although acute and chronic exposures to various chemicals

in water can cause illness, microbiologically contaminated

water is the concern of the authors of papers in this special

issue of the Journal of Water and Health. Infectious

waterborne diseases are usually caused by exposure to

enteric pathogens that are transmitted by the “fecal–oral”

pathway. Occasionally the pathogens may be in urine (e.g.

Leptospira). Waterborne pathogens are excreted by infected

persons and in many instances by wild or domestic animals.

Although the principal exposure to waterborne pathogens is ingestion through contaminated drinking water or

food and hand-to-mouth activity, dermal contact or inhalation of contaminated aerosols may also be important. Illness

This paper is in the public domain: verbatim copying and redistribution of this paper are

permitted in all media for any purpose, provided this notice is preserved along with the

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acknowledge the source as J. Wat. Health 4(Suppl. 2), 3–18.

doi: 10.2166/wh.2006.015

3 Journal of Water and Health | 04.Suppl 2 | 2006
is manifested primarily in the intestinal tract and generally

referred to as an “enteric disease”. The most common

symptom is diarrhea (i.e. frequent, loose stools). In assessing

waterborne risks, investigators have primarily considered

illness based on symptoms—acute gastrointestinal illness

(AGI). Some of the published studies considered illnesses in

which specific pathogens were identified. AGI or “enteric

diseases” are caused by multiple infectious agents as well as

non-infectious agents and other conditions such as drugs,

chemicals, metals, toxins, food intolerance, dietary changes,

and food and alcohol abuse. Badger et al. (1956) found that,

of the 1466 cases of gastrointestinal symptoms reported

among families in Cleveland, 4% were due to dietary

indiscretion, 4% to coughing and gagging, 3% to medication, and 1% to emotional causes.

Mead et al. (1999) estimated that 211 million episodes of

AGI occurred in the United States in 1997; 38.3 million

cases (18%) were caused by known foodborne pathogens.

Of the 38.3 million cases due to foodborne pathogens, 14%

were estimated to be due to bacteria, 6% to parasites, and

80% to viruses (Table 1). This estimate does not include

cases caused by: (1) chemical agents and pathogens such as

Entamoeba histolytica, Aeromonas, parvoviruses, and

adenoviruses, which may also cause enteric symptoms,

and (2) Listeria, Toxoplasma, and hepatitis A virus, which

generally cause non-gastrointestinal illness.

Many, but not all, of the foodborne agents considered in

Table 1 can also be waterborne, but the proportion

of waterborne illnesses caused by various waterborne

pathogens may be quite different. Although not capturing

information on sporadic and endemic waterborne disease,

reports of drinking-water AGI outbreaks associated with

infectious agents can help identify the important waterborne etiologies. Of the waterborne outbreaks reported

during 1971–2002, 54% had an unknown etiology. Of the

remaining 46%, approximately 17% were due to bacteria,

23% to parasites, and only 6% to viruses. In another paper

in this special issue, Craun et al. (2006) discuss the causes of

waterborne outbreaks in the United States. Additional

information about waterborne pathogens is also available

in several recent publications (AWWA 1999; Hunter et al.

2003; Cotruvo et al. 2004).

Infection and illness

If exposed to a waterborne pathogen, a human or animal

host may become infected. The pathogen can multiply or

pass through its life cycle within the host, and the host may

excrete pathogens into the environment and become

infectious to others. Illness refers to the symptomatic

manifestation of an infection. The severity of illness

can range from self-diagnosed, mild AGI to death

(Figure 2). Other measures of severity include the duration

of symptoms, impact on daily activity, and the cost of

physician visits or hospitalizations (Rice et al. 2006). Chronic

sequelae may occur. For example, bacterial infections due to

several waterborne pathogens may act as triggers in

susceptible persons for reactive arthritis, Reiter’s syndrome,

and ankylosing spondylitis (CAST 1994). Hemolytic uremia

syndrome has been associated with Escherichia coli

O157:H7 infection, and Campylobacter can be a precipitating factor for Guillain–Barre syndrome (CAST 1994).

Infection without illness is also important to consider.

Although not exhibiting illness symptoms, asymptomatic

persons may be sources of continuing infection and illnesses

in the community. For some pathogens frequent low-level

exposures from infected persons or other sources may

confer protective immunity from illness; however, not all

pathogens can confer protective immunity, and for some

pathogens, the immunity may be short-lived. Epidemiologists use the term “herd immunity” to refer to the immunity

of a population group or community and their resistance

Figure 1 | Typhoid fever mortality and water chlorination.

4 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
Table 1 | Estimated annual cases of acute gastroenteritis caused by known foodborne pathogens, United Statesa

Disease or agent Estimated total cases Estimated total percent

BACTERIAL

Bacillus cereus 27,360 0.07

Botulism, foodborne 58 0.00

Brucella spp. 1554 0.00

Campylobacter spp. 2453,926 6.40

Clostridium perfringens 248,520 0.65

Escherichia coli O157:H7 73,480 0.19

E. coli, non-O157:H7 36,740 0.10

E. coli, enterotoxigenic 79,420 0.21

E. coli, other diarrheogenic 79,420 0.21

Salmonella typhi b 824 0.00

Salmonella, nontyphoidal 1412,498 3.69

Shigella spp. 448,240 1.17

Staphylococcus food poisoning 185,060 0.48

Streptococcus, foodborne 50,920 0.13

Vibrio cholerae, toxigenic 54 0.00

V. vulnificus 94 0.00

Vibrio, other 7880 0.02

Yersinia enterocolitica 96,368 0.25

Subtotal 5202,416 13.58

PARASITIC

Cryptosporidium parvum 300,000 0.78

Cyclospora cayetanensis 16,264 0.04

Giardia lamblia 2000,000 5.22

Trichinella spiralis 52 0.00

Subtotal 2316,316 6.04

VIRAL

Norwalk-like viruses 23,000,000 60.02

Rotavirus 3900,000 10.18

Astrovirus 3900,000 10.18

Subtotal 30,800,000 80.38

Grand total 38,318,732 100.00

a

Adapted from Mead et al. (1999).

b

Greater than 70% of cases acquired abroad.

5 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
to the invasion and spread of an infectious agent (Last 1995).

A large proportion of asymptomatic infections in a

population may reflect the ability of a pathogen to confer

immunity. For some pathogens, asymptomatic infection can

be studied by serological or other clinical tests (Casemore

2006).

Waterborne transmission

Confirming the waterborne transmission of disease in a

single patient is extremely difficult and in most cases

practically impossible, since few pathogens are transmitted

only by waterborne exposure. Dracunculiasis can only be

transmitted by contaminated water, but this disease is not

found in the United States. Single-case waterborne outbreaks that have been reported in the United States include

laboratory-confirmed primary amebic meningoencephalitis

associated with whole body water and chemical poisonings

if water quality data indicate contamination by the chemical

(Blackburn et al. 2004). For other waterborne illness, at

least two cases must be reported for an epidemiologist to

evaluate a potential common exposure and determine a

mode of transmission.

In waterborne outbreak investigations, epidemiologists

usually consider the primary mode of transmission (i.e.

drinking water), but secondary infection can also

occur, especially through person-to-person transmission.

In waterborne outbreaks caused by E. coli O157:H7 and

Cryptosporidium, transmission to familial, institutional, or

other contacts by a primary case has been confirmed

epidemiologically. Secondary transmission of cryptosporidiosis associated with the 1993 Milwaukee waterborne

outbreak was estimated at 5% among residents of all ages

(MacKenzie et al. 1995) and 40% among the elderly

(Naumova et al. 2003). Cordell & Addiss (1994) reported

secondary cryptosporidiosis transmission rates of 12–22%

from infected children to their household members and

caregivers. When secondary transmission is not considered,

the impact of a waterborne pathogen may be underestimated (Eisenberg et al. 2004).

The epidemiologic triad

Epidemiologists use disease models to help understand the

cause and development of diseases (Rockett, 1994). A simple

model is the epidemiologic triad (Figure 3). Although the

pathogen, environment, and host coexist independently,

disease occurs through their interaction. The presence of a

pathogen is necessary for exposure to occur, but usually the

pathogen is not a sufficient cause of the illnesses. With few

exceptions, cofactors or host characteristics play an

important role in the development and severity of illness.

Thus, to assess waterborne illness risks, we need to

understand pathogen-host-environmental interactions.

Waterborne pathogens (e.g. Campylobacter, Leptospira,

and E. coli O1:57:H7) may have significant animal

reservoirs in addition to human sources of contamination.

Some pathogens live and multiply in the water environment

Deaths

Persons who are

hospitalized

Persons who visit physician or

health care facility

Persons who exhibit symptoms

Person is exposed to pathogen in water

and becomes infected

Figure 2 | The burden of illness pyramid.

HOST

AGENT ENVIRONMENT

Figure 3 | Host, agent, environment relationship.

6 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
while others survive for varying time periods. Some survive

well in cold waters; others survive better in warm waters.

The water environment must not only support survival of

the pathogen, but the pathogen must also be available in a

sufficient concentration to infect the human host. Once

infected, host susceptibility becomes an important issue.

Host susceptibility can be affected by personal characteristics such as age, occupation, income, education, nutrition, immune status, behavior, and genetic traits. Persons

who are more likely to experience symptomatic and severe

illness include the very young and the elderly, undernourished individuals, and people with compromised immunity

due to underlying diseases (e.g. AIDS) and medical treatments (e.g. organ transplant or cancer treatment). Because

susceptibility can vary both within a community and between

communities, information about the importance of waterborne transmission of some pathogens and the risk of

infection and disease cannot necessarily be extrapolated

from one country or community to another.

The occurrence and distribution of waterborne pathogens in both the environment and the human populations

can change as socio-economic changes occur. Social,

physical, or biological environments (e.g. personal behavior,

cultural practices, hygiene and sanitation practices, climate,

and reservoirs of infection) may affect exposure possibilities.

Rapid population growth can accelerate changes in prevailing pathogen populations and provide closer contact

among animal and human hosts. The frequent movement of

humans and animals over long distances from one environment and community to another offers ideal opportunities

for new strains of pathogens to find environments and hosts

in which they can survive.

Causal pathways can be complex, and these complexities are illustrated in a model diagram developed by

Eisenberg et al. (2002). The model describes the relationship

of drinking water, medication, and immune status in HIVassociated diarrhea (Figure 4). The most straightforward

pathway is: an increased prevalence of diarrhea results in a

concern for drinking water quality which in turn may cause

persons to seek additional water treatment or bottled water.

However, diarrhea prevalence may be increased due to the

side effects of medication use, and although water quality

may be improved, the prevalence of diarrhea may not

necessarily be decreased. CD4þT lymphocyte counts influence a physician’s decision to use antiretroviral medication,

and medication may decrease the prevalence of diarrhea. A

low CD4þ count may also increase a person’s susceptibility

to infection by an enteric pathogen.

DETERMINING DISEASE RISKS

The risk of contracting an illness may be expressed as the

probability of infection or illness during a defined time period

or that may be attributed to an exposure (e.g. tap water). The

risk may be an individual- or population-based risk. Various

epidemiologic study designs are conducted to provide

quantitative risk estimates, and these are briefly described

here. Epidemiologic studies of waterborne risks and their

findings are discussed in other papers of this special issue

(Calderon & Craun 2006; Colford et al. 2006; Craun &

Calderon 2006; Roy et al. 2006). Risk assessment approaches,

which rely on modeling the occurrence, exposure, and

transmission of known waterborne pathogens, can also be

Concern about water

quality

Water treatment (e.g., boiling,

filtering, bottled water)

Diarrhea

Medication (CD4 count influences

use of antiretrovirals; side effect is

diarrhea)

CD4 count (low count

increases susceptibility

to enteric pathogens)

Figure 4 | Causal model diagram HIV-infected patients and diarrhea (adapted from Eisenberg et al. 2002).

7 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
conducted (ILSI 2000). For example, quantitative microbial

risk assessments have made use of dose–response information from human volunteer and other studies to estimate

risks of infection or illness associated with concentrations of

several waterborne pathogens. A framework for structuring

risk assessments and the various exposure scenarios for

waterborne pathogens are discussed in greater detail in this

special issue (Soller 2006).

Disease occurrence

Epidemiologists use several measures of disease frequency

to ascertain illness risk in populations. Two frequently used

measures are prevalence and incidence. Prevalence, the

proportion of people who have a specific disease, condition,

or infection at any specified time includes both new and

existing cases (Last 1995). Incidence measures the new cases

that occur in a population and is usually expressed as a rate

during a defined time period (e.g. cases per person-year).

Cumulative incidence, the proportion of healthy persons

who move to the disease state, is a measure of individual

rather than population risk (Kleinbaum et al. 1982; Ahlbom

& Norell 1990). Prevalence and incidence measures are

related. Under most conditions, prevalence is directly

proportional to incidence (Kleinbaum et al. 1982). A general

rule of thumb is that prevalence is equal to incidence times

the duration of the disease. A meaningful measure of either

prevalence or incidence requires the accurate compilation

of the conditions (e.g. cases of illness) of interest and an

estimate of the susceptible or at-risk population from which

the cases arise.

Epidemic or outbreak disease

Epidemic disease is a clear increase in illness or other

health-related events above that which is normally

expected (Figure 5). The time period and geographic area

in which cases occur must be specified (Last 1995). An

increased number of illnesses may occur during a short time

period ranging from a few hours (e.g. food intoxications) to

several days or weeks, or the increase can continue for

months or years (e.g. AIDs or a cholera epidemic). There is

no generally accepted number or percentage increase of

cases that may describe an epidemic. It is what public

health officials consider, usually based on previous surveillance, to be greater than expected for a specific disease or

symptoms for that area. The increase can be relatively small.

For example, a few sporadic cases of smallpox anywhere in

the world would likely be characterized as an epidemic,

since even a single case is more than would be expected.

Epidemic disease does not have a geographic restriction

nor is it limited to traditional geopolitical boundaries.

Epidemics can occur in a few city blocks, encompass an

entire municipality or country, or cross international

boundaries (i.e. a pandemic). It is also possible for one

population in a geographic area to experience an outbreak

while another does not. For example in 1996, a waterborne

outbreak of Cryptosporidium in Canada caused symptomatic illness only among young children in the town and

visitors. Although adult residents did not experience an

increased incidence of illness, an increased serological

response among these residents suggested that they had

been exposed (Frost et al. 2000).

The recognition of an outbreak will vary based on the

sensitivity of a surveillance system to detect disease or

infection. Langmuir (1963) defined surveillance as “the

continued watchfulness over the distribution and trends of

incidence through the systematic collection, consolidation

and evaluation of morbidity and mortality reports and other

relevant data.” Disease surveillance systems typically focus

on specific diseases (e.g. cryptosporidiosis), but AGI may

also be of interest. Standard epidemiologic information (e.g.

person, place, and time) about the cases is collected so that

Time

Cases of illness

Epidemic or Outbreak

Endemic and Sporadic

Undetected outbreak

Outbreak detection level

Figure 5 | Example of epidemic, endemic, and sporadic illness.

8 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
an increased occurrence of illness can be recognized.

Officials may conduct active surveillance, relying on a

formal surveillance network of health care providers and/or

clinical laboratories to report cases (Frost et al. 2003).

Periodic contact is maintained to encourage reporting.

Officials may also passively wait for physicians to report a

sudden increase in patients presenting with gastrointestinal

symptoms or for clinical laboratories to report an increase

in positive stools for a specific microorganism (Frost et al.

2003). The key characteristics of a surveillance system

include the timely interpretation of the information and an

action plan to respond to increased illnesses. The CDC has

proposed guidelines for evaluating disease surveillance

systems (CDC 2001).

In the United States, cases of some 40 or more

infectious diseases, some of which may be transmitted by

water, are voluntarily reported to the CDC. Requirements

for reporting are based on legislation or administrative rule

in the state or city; clinical laboratories may also be required

to report the identification of positive cultures and

immunodiagnostic tests for selected infections. Individual

cases of AGI are not usually required to be reported, but

the reporting of extraordinary occurrence or clustering of

AGI cases may be required by the state or city.

Endemic disease

In contrast to epidemic, endemic refers to the persistent low to

moderate level or the usual ongoing occurrence of illness in a

given population or geographic area (Figure 5). Endemic can

apply to a wide range of health outcomes, case definitions, and

severity measures; asymptomatic infection may also be

assessed. There is no generally accepted incidence or

prevalence of AGI that is considered usual. What may be the

usual occurrence in one population or geographic area may be

unusual in another. The time period is an important factor

when studying AGI occurrence, as there is often a seasonal

change in the number of illnesses, with peaks of illness

occurring in the late summer to early fall and the late winter to

early spring (Monto & Koopman 1980). This seasonal

component may vary depending on local factors.

In the United States, incidence rates of infectious AGI

have been estimated to be less than one illness per adult person

per year and two illnesses per person per year for children

under 10 years of age (Hodges et al. 1956; Monto & Koopman

1980). Elsewhere in this special issue, Roy et al. (2006) report

current estimates from the Foodborne Diseases Active

Surveillance Network (FoodNet) and other sources.

Hyper-endemic and sporadic disease

A persistently high level of illness occurrence during a

specific period is referred to as hyper-endemic; an irregular

pattern of occurrence is called sporadic (Figure 5). Over a

long period of time, the number of cases may be similar

between endemic and sporadic disease, but when shorter

time periods are considered, sporadic cases may appear

infrequently or in a random-like fashion. Seasonal fluctuations of AGI may result in a sustained increase that is

considered hyper-endemic. Last (1995) notes that hyperendemic illness should affect all age groups equally;

however, we feel this would be an unreasonable condition

to impose for infectious diseases where factors such as the

host’s immune status may affect an age-group’s susceptibility to symptomatic illness.

EPIDEMIOLOGIC STUDY DESIGNS

Many epidemiologists have discussed how endemic and

epidemic waterborne disease associations should be interpreted given the potential biases that may affect study

findings (Fewtrell & Bartram 2001; Craun et al. 2001, Craun

& Frost 2002; Frost et al. 2003; Hunter et al. 2003). We

provide a brief discussion here. Various epidemiology

textbooks, as well as review articles on epidemiologic

methods, can be consulted for a more comprehensive

coverage of study designs and how to evaluate epidemiologic associations (Hill 1965; MacMahon & Pugh 1970;

Lilienfeld 1976; Kleinbaum et al. 1982; Rothman 1986;

Hennekens & Buring 1987; Monson 1990; Beaglehole et al.

1993; Rockett 1994; Gordis 2000; Craun et al. 2003).

Epidemiologic studies fall into two general categories:

(1) experimental studies in which investigators control the

conditions of exposure in the study, and (2) observational

studies in which investigators study populations with

selected diseases or health outcomes under exposure

conditions as they exist (Table 2).

9 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
Experimental studies

Randomized, controlled trials or clinical studies can provide

some of the strongest evidence that a given exposure or risk

factor “causes” a health outcome. These studies involve

ethical concerns, are expensive, and are challenging to

conduct because the exposure must be controlled and

participants must be randomly assigned to an exposure.

Dose– response studies where individuals are given known

doses of a microorganism of interest have been conducted.

Studies have also assessed intervention effects of water

treatment at the individual or household level (Colford et al.

2006), and health outcomes have been evaluated for

populations in which a community water treatment process

has changed (Calderon & Craun 2006). Community-intervention studies are considered quasi-experimental because

they take advantage of a natural experiment. Since participants are not randomly assigned to a water treatment

regimen, we have classified community-intervention studies

as observational rather than experimental (Table 2).

Household-intervention trial

In this type of study, persons are randomly selected and

placed into two groups: one that will receive an experimental

treatment or intervention and the other that will not.

Randomization tends to produce comparability between

the two groups with respect to other factors that might affect

the health outcome being studied. The greatest objectivity is

provided when the three primary groups involved in the study

(i.e. the subjects, investigators, and statisticians analyzing the

data) are unaware of the subject’s allocation to a particular

treatment or intervention. When this is achieved, the trial is

said to be triple-blinded. The major advantage of a randomized, blinded trial is that the design precludes many sources of

systematic error that may be associated with observational

studies and tends to minimize the potential for confounding

(Tables 3 and 4). However, microbial drinking water

contaminants have multiple routes of exposure, and it may

be difficult to separate primary (e.g. waterborne) from

secondary (e.g. food, person-to-person) transmission. Other

difficulties include separating household and non-household

waterborne exposures, participant dropout, and noncompliance with study procedures. Also, it may not be possible to

generalize the results to other populations due to differences

in water system vulnerabilities or differences in the population studied. Because of their cost, randomized controlled

trials are generally considered for environmental exposures

only when a well-defined hypothesis has been identified and

it is ethically feasible.

Observational studies

Observational epidemiologic studies can be descriptive or

analytical (Table 2). Descriptive epidemiology is primarily

Table 2 | Types of epidemiological studiesa

Observational

Experimental Descriptive Analytical

1. Dose– response studies 1. Disease surveillance or surveys 1.Cross-sectional

2. Randomized controlled trials 2. Ecologic 2. Longitudinal

Household-intervention Cohort or follow-up

Drug effectiveness Case-control

Community-intervention

Time-series

a

Adapted from Monson (1990).

10 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
used to summarize disease information, assess geographical

or temporal patterns of disease, and develop hypotheses

about disease etiologies. Ecologic studies, also called

geographical, correlation, group, or aggregate studies, are

descriptive and are used primarily to explore relationships

between available health statistics and population characteristics or environmental and water quality measures.

Because the results of ecologic studies may be difficult to

interpret, investigators usually conduct them to help

develop hypotheses for further evaluation with analytical

studies (Greenland & Robins 1994a, b; Piantadosi 1994;

Poole 1994). The major limitation is the so-called ecologic

bias in which the observed association fails to accurately

represent the biologic effect at the individual level. Ecologic

studies, however, can be useful to study environmental

exposures, especially when group exposure measures reflect

individual exposures (Walter 1991).

Analytical studies that test specific hypotheses provide a

quantitative estimate of the risk and help epidemiologists

assess causality (Monson 1990). Information about disease,

exposures, and important behaviors or characteristics is

obtained for each study participant. The studies are either

longitudinal or cross-sectional. The longitudinal study

allows a time sequence to be inferred between an exposure

and disease; it can be either prospective, retrospective, or a

combination of the two approaches. A cohort or follow-up

study begins with an exposure or characteristic of interest

and evaluates disease consequences of the exposure or

characteristic. A case-control study begins with a disease or

health condition of interest and evaluates previous

exposures and risk factors of interest. A cross-sectional

study provides a snapshot of the status of a target

population with regard to the exposure and/or health

outcome at a specific point in time (Eisenberg et al. 2004).

Because exposure and disease are measured at the same

point in time, cross-sectional studies are most useful for

studying diseases with a short latency or incubation period.

The incubation period is the time interval between initial

contact with the pathogen and the first appearance of

symptoms associated with infection. Hepatitis A may have

an incubation period of 30 days or more. Cryptosporidium

Table 3 | Assessing bias for reported associations

Lack of random error (precision) Lack of systematic rrror (validity)

Study size and statistical power Misclassification bias

Selection bias

Recall bias

Confounding

Table 4 | Systematic error or bias in epidemiological studiesa

Selection bias A bias resulting from systematic differences in characteristics between those who are selected for study

and those who are not. Comparable criteria should be used to select study participants.

Information bias A bias resulting from flaws in measuring exposure or disease, especially the use of noncomparable methods

to collect information that results in different quality (accuracy) of information between the groups

being compared. This can be due to interviewers’ subconscious or conscious gathering of information.

Recall bias refers to differences in the accuracy or completeness of information provided by

study participants due to their memory of past events or experiences. Misclassification bias

is the erroneous classification of a study participant into either a disease or exposure category.

Confounding A situation where the measure of the effect of an exposure on disease risk is distorted because of the

association of the exposure with other factor(s) that influence the disease. Effect modification refers

to a change in the magnitude of the effect and should not be confused with confounding.

Analytic bias A bias resulting from the way the data are analyzed. An example would be a distortion in the shape of an

exposure– response trend produced by poor categorization of a continuously measured exposure.

a

Adapted from Craun et al. (2001).

11 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
and Giardia may have an incubation period of seven or

more days. However, most waterborne pathogens have a

much shorter incubation period. FoodNet is a crosssectional survey of the occurrence of AGI and possible

foodborne exposures (Roy et al. 2006).

Case-control study

A case-control design is often used when investigators want

to determine the association of a health outcome with

multiple rather than a single exposure factor (Craun &

Calderon 2006). These studies are also called case-referent

or case-comparison studies (Miettinen 1974). Issues that

should be considered in the ascertainment of cases or

conditions include the diagnostic criteria for defining a

case, population source and selection of incident or

prevalent cases and appropriate controls. Persons with

the disease or outcome may be selected from a defined

geographical area, hospital(s), clinic(s), or a cohort. A

comparison group of persons (controls) in which the

condition or disease is absent is also selected, preferably

at random from the same population from which the cases

were selected. The frequency of existing or past attributes

and exposures thought to be relevant in the development of

the disease are determined for all participants and

compared among cases and controls. Information about

the relevant past exposures or behaviors (e.g. drinking tap

water, swimming activities) may be obtained by interview.

Clinical specimens may be collected to help define the

disease or condition. Environmental exposure information

can be obtained from historical records, or if appropriate,

current exposures can be assessed by analyzing environmental samples from the surrounding environment or the

study participants’ micro-environment. Case-control

studies are subject to several sources of systematic error

(Tables 3 and 4), especially recall bias. A major advantage

of the case-control design is that it is ideal for looking at

relatively rare illnesses.

Cohort study

This design compares disease rates among groups of

persons who have differing exposures (Craun & Calderon

2006). Cohort studies of infectious diseases can be

prospective (i.e. current exposure information is recorded

and the follow-up period occurs afterward) or retrospective

(i.e. the cohort is based on a historical exposure period).

Disease incidence is determined during the follow-up

period for the exposed and unexposed groups. An advantage of this study is that several health-related outcomes or

diseases can be studied. The cost, however, is usually greater

than the cost of a case-control study.

Other study designs

Epidemiologic studies such as community-intervention and

longitudinal time-series studies (Craun & Calderon 2006)

incorporate elements of the basic designs previously

described. A community-intervention study may be considered when water utilities change their water source or

drinking water treatment such as adding filtration. Timeseries studies can evaluate how changes in water quality

may affect AGI.

Quantifying the exposure –disease relationship

Measures of association

Several measures can convey information about risk. The

appropriate measure depends on the study design and the

way the data were collected. Analytical studies can provide

a direct estimate of individual risk, and the incidence of

illness among the unexposed and exposed can be directly

compared. The basic measures are the risk or rate difference

(RD), incidence rate ratio (IRR), cumulative incidence ratio

(CIR) or odds ratio (OR). The RD is a measure of the

absolute difference between two measures of incidence (e.g.

incidence rate for the exposed minus the incidence rate for

the unexposed in a cohort study) and includes units (e.g.

person-years). The IRR and CIR are relative measures of

incidence. The IRR, CIR and OR are sometimes referred to

as measures of the relative risk (RR), and if certain specific

conditions are met, all of these measures will be similar.

Readers should note that the RD is sometimes referred to as

the attributable risk (AR), and thus, they should consider

how the AR was computed (Table 5).

A reported RR of unity (1.0) indicates no association;

any other value signifies either an increased or decreased

risk. Because participants in a case-control study are

12 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
selected according to their disease status, the exposure odds

ratio (OR) is determined. As defined by Last (1995), “The

exposure-odds ratio for a set of case-control data is the ratio

of the odds in favor of exposure among the cases to the odds

in favor of exposure among non-cases.” The OR may be

defined differently in a cross-sectional study when the “odds

of disease” are of interest (Last 1995). If the rate of disease is

rare in the general population, the OR is considered to be a

Table 5 | Several types of relative and attributable risk measures (Rothman 1986; Beaglehole et al. 1993; Rockett 1994; Last 1995; Sahai & Khurshid 1996)

Risk measure Calculation Description

Rate ratio; risk ratio;

relative risk

Ie / Iu A relative measure; ratio of the rate of disease among

the exposed to the rate among unexposed

Excess relative risk (Ie 2 Iu) / Ie 1⁄4 (Ie/Iu) 2 1 Another relative effect measure

Attributable risk; rate difference;

risk difference (exposed)

Ie 2 Iu An absolute measure; number of cases or incidence

of disease among the exposed that can be attributed

to the exposure; sometimes called attributable

risk (exposed) or absolute risk

Risk difference (population) Ip 2 Iu Number of cases or incidence of disease in the entire

population minus cases or incidence in the unexposed.

Attributable proportion

(exposed) or attributable

risk percent

(Ie 2 Iu) / Ie or (RR 2 1) / RR Proportion of the disease among the exposed population that

is attributable to the exposure; may also be called:

attributable risk (exposed), attributable fraction (exposed),

or etiologic fraction (exposed); multiplied

by 100 to be expressed as a % (e.g. AR%)

Attributable proportion

(population) (see also

population attributable

risk percent)

(Ip 2 Iu) / Ip Excess risk in the population; proportion of the disease

in the entire population (exposed and unexposed) that is

attributable to the exposure and that could be reduced

if exposure were eliminated; may also be called:

attributable fraction (population) or etiologic fraction

(population)

Population attributable risk (Ip 2 Iu) or (Ie 2 Iu)(Pe) Excess risk in the population

Population attributable risk

percent (see also attributable

proportion (population))

[100(Ip 2 Iu)] / Ip or

[100(Ie 2 Iu)(Pe)] / Ip

Excess risk in the population; proportion of the disease in the

entire population that is attributable to the exposure;

expressed as %

p 100 [(Pe)(RR 2 1)] /

[1 þ Pe(RR 2 1)]

p In case-control studies, PAR% can be calculated if the

proportion of persons in control group can be used as an

estimate of Pe and if the odds ratio (OR) can be used as an

estimate for RR

Prevented fraction (Iu 2 Ie) / Iu or 1 2 RR When the exposure is preventative, proportion of potential

cases prevented by exposure or intervention

Preventable fraction (population) (Ip 2 Ie) / Ip When the exposure is preventative, proportion of disease in

the population that would be prevented if the whole

population were exposed to factor or the intervention

Ie 1⁄4 incidence of disease in the exposed.

Ip 1⁄4 incidence of disease in the entire population.

Iu 1⁄4 incidence of disease in the unexposed.

Pe 1⁄4 proportion of exposed persons in population.

13 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
good estimate of the RR (Cornfield & Haenszel 1960). If the

cases are incident cases rather than old or prevalent cases,

the OR is equivalent to the RR (Miettinen 1976). Even if

neither of these conditions applies to the study, Monson

(1990) notes that the OR can still be interpreted similar to

the RR. However, if a disease is not rare, the OR can

overestimate (if RR . 1) or underestimate (if RR , 1) the

IRR or the CIR (Kleinbaum et al. 1982).

Ecologic studies can be designed to estimate an IRR

(Craun & Calderon 2006) or a standardized mortality or

morbidity ratio (SMR). The SMR is the ratio of the observed

number of cases to the number of expected cases if the

population under study were unexposed. An SMR above 1

indicates an increased risk for the exposed group. For

example, a SMR of 1.35 indicates that there were 35% more

cases in the exposed group compared to the unexposed

group. An SMR is often multiplied by 100 for interpretation

purposes; in the previous example, the SMR would then be

reported as 135.

Epidemiologists have used several terms, including

population attributable risk (PAR) or the PAR percent

(PAR%), to describe the incidence or proportion of a

disease or other outcome in a population that can be

attributed to the exposure or risk factor in question.

Alternatively used terms include etiologic fraction (Miettinen 1974), attributable proportion (Rothman 1986), and

attributable fraction, all of which are similar computations

(Table 5). Epidemiologists may even use a different

terminology to describe the PAR or they may estimate a

different measure (Table 5). In general, all of these measures

provide an estimate of the amount by which a particular

disease rate (e.g. AGI) might be reduced if the specified

exposure were removed (MacMahon & Pugh 1970).

However, there are important distinctions among the

various measures. Some measures refer to the removal of

the exposure among only the exposed members of the

population; others refer to the removal of the exposure

among the general population of both exposed and

unexposed persons. In addition, the use of different

terminology to describe the same computation can cause

confusion. So as not to draw erroneous inferences when

comparing statistics across different studies, readers should

fully understand these risk measures, their computation,

and the terminology used to describe them.

In defining the PAR, we quote from two epidemiology

texts. Beaglehole et al. (1993) define the PAR as “a measure

of the excess rate of disease in a total study population

which is attributable to an exposure... and could be

removed if the exposure were avoided completely.” Hennekens & Buring (1987) define the “population attributable

risk percent expresses the proportion of disease in the study

population that is attributable to the exposure and thus

could be eliminated if the exposure were eliminated.” In

contrast to the PAR and PAR%, the attributable risk (AR),

AR%, or AR (exposed) is the incidence or proportion of

disease only among the exposed members of the population

that can be attributed to the exposure. This distinction is

important, because usually the incidence of disease in the

exposed persons that are being studied will be greater than

or at least equal to the incidence of disease in the entire

study population of both exposed and unexposed persons.

The AR is often reported instead of the PAR. Epidemiologists may refer to the AR computation as the AR (exposed),

risk difference (exposed), absolute risk, etiologic fraction

(exposed), attributable proportion (exposed), or attributable

fraction (exposed). When the exposure is beneficial,

epidemiologists may compute the prevented fraction,

which applies to only the exposed population or the

preventable fraction (population), which applies to both

the exposed and unexposed members of the population. To

add further confusion, the various terminologies may be

used without specifying the population. For example, risk

difference (RD) may be used rather than RD (population) or

RD (exposed), and it may be difficult to know which is being

reported. Readers should carefully evaluate the reported

measure to ensure they understand the population to which

they apply. Hopefully, sufficient information is provided in

the study results for the reader to calculate the risk measure.

From a public health perspective, estimation of the PAR

is most useful when there is consensus about causality of

the association and that the exposure is amenable to

intervention (Hill 1965; Rothman 1986; Last 1995; Rockhill

et al. 1998). Sufficient information is available to infer

causality for known pathogens that might be important

causes of waterborne disease in the United States. Information from clinical and microbiological studies is supported by waterborne outbreak data, and there is general

knowledge of biology of the illness and the microorganism.

14 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
To achieve the anticipated benefits suggested by the

PAR, we should have some knowledge about the specific

exposure and how we can eliminate it. An important

concern is whether the estimates of PAR for endemic

waterborne illness should be extrapolated to an entire

population or only to select populations based on their

exposure (e.g. type of water source and/or water treatment)

or other factors (e.g. age, susceptibility). For example, a PAR

that has been estimated for a conventionally filtered river

water source may not be relevant for populations that use

unfiltered water from protected reservoirs or populations

that use groundwaters.

Interpreting epidemiologic results

Results from a relatively large number of studies in various

geographical areas and using different designs allow for a

more definitive estimate of the magnitude of an association.

However, the design, precision, and validity of each

individual study should be evaluated before attempting to

interpret the results of a group of studies that assess similar

risks and exposures (Table 3).

Random error

The likelihood that an observed association is due to random

error is assessed by the level of statistical significance (“p”

value) or the confidence interval (C.I.). As a measure of the

stability of the risk measure, the “p-value indicates the

likelihood that, if the study were repeated a number of

times, a (risk) as large as or larger than that obtained would

occur, given no true association between exposure and

disease,” assuming the data are unbiased (Monson 1990). P

values were designed to be used in rigorous experiments and

their application to epidemiologic and other non-experimental data is a source of debate. The C.I., usually reported in

analytical studies and a variation of the p value, provides a

range of possible values consistent with the risk estimate at a

certain level of confidence. For example, suppose the

following is reported in a study: RR 1⁄4 1.8 (95% C.I. 1⁄4 1.6–

2.0). This suggests an 80% increased relative risk of disease

and that the estimate is relatively precise. One way to

practically interpret this C.I. is: we are 95% confident that

the true RR is between 1.6 and 2.0. An RR 1⁄4 1.8 (95%

C.I. 1⁄4 0.6–14.5) would indicate that the likely range of values

for the risk estimate includes the null value of 1.0 and the

point estimate is not precise (the C.I. suggests a wide range of

values). A technically more accurate interpretation would

consider that the C.I. reflects the likely distribution of

observed risk estimates in repeated samples. In the above

example, the C.I. could be interpreted to mean that over many

repeated samples, 95% of the observed risk estimates would

fall within the interval (0.6–14.5).

The p value and the C.I. are measures of the stability of a

reported risk, but both are dependent on sample size as well

as the observed effects (Monson 1990). Monson sees little

utility to the computation of a measure of stability for very

small studies or for studies with biased data. It should also

be remembered that random error or chance can never be

completely ruled out, and similarly, studies may fail to

observe an effect which is truly significant.

Systematic error and confounding

Possible bias or confounding in each study and the

consistency of the results among various studies should be

evaluated. The importance of assessing and controlling

bias and confounding has been extensively discussed

(Murphy 1990; Last 1995). Bias refers to any trend in

the collection, analysis, interpretation, publication, or

review of data that can lead to distortion in an estimate

of effect. As used by epidemiologists, bias does not carry

an imputation of prejudice or the investigator’s desire for

a particular outcome and thus differs from the conventional usage which refers to a partisan point of view (Last

1995). Last (1995) defines confounding and more than

26 specific biases. We have classified these into four

major groups which are briefly described in Table 4

(Craun et al. 2001).

Magnitude of risk measures

Historically, a small RR or OR was considered by many

epidemiologists to indicate that the observed association

was suspect because of possible uncontrolled confounding

(Table 6). More recently, a substantial number of environmental epidemiologic studies have found relatively small

associations, and public health officials have begun to look

15 G. F. Craun et al. | Assessing waterborne risks: an introduction Journal of Water and Health | 04.Suppl 2 | 2006
at a small RR or OR as having implications on a population

basis rather than on an individual level. If a large proportion

of the population is exposed and the outcome of interest is

relatively common, environmental exposures with weak

associations can have a substantive impact. Thus, for

smaller associations, epidemiologists should thoroughly

evaluate the possibility that the association is affected by

uncontrolled confounding. Techniques to identify confounding factors include causal diagrams (Greenland 1999)

and modeling approaches such as a change in estimate or

backward elimination procedure (Rothman & Greenland

1998). On the other hand, a very large RR or OR is unlikely

to be completely explained by an unidentified or uncontrolled confounding factor. The magnitude of a RR,

however, has no bearing on the possibility that an

association is due to bias. RRs for continuous quantitative

exposures cannot be evaluated by the simplified descriptions in Table 6 because the magnitude then becomes scale

dependent (e.g. RR per 1 unit increase of turbidity; 10 unit

increase; or 100 unit increase).

CONCLUDING REMARKS

This special issue of the Journal of Water and Health is

devoted exclusively to a discussion of waterborne risks in

the United States and other developed countries. This paper

provides an introduction to epidemiologic methods for

assessing waterborne disease risks and identifies several

important considerations when evaluating the current

information about waterborne risks. Readers should consider the following questions when reading the subsequent

papers in this special issue:

† Is sufficient information available to estimate the

magnitude (e.g. PAR) of an increased risk of endemic

AGI or a specific disease (e.g. cryptosporidiosis) that

may be associated with drinking water systems in the

United States or another developed country?

† If so, can the risk be generalized to the national

population? Are certain populations (e.g. the elderly) at

greater or less risk?

† What factors (e.g. water source and treatment processes)

may modify the risk?

† What are the uncertainties associated with estimating the

magnitude of waterborne disease risks? How can these

uncertainties be addressed?

† How can waterborne outbreak information help inform

the endemic waterborne risk estimate?

† How can microbial risk assessments help inform the

estimate?

† Is it sufficient to estimate the number of cases or should

the severity of the cases, the full disease burden, be

considered?

ACKNOWLEDGEMENTS

We thank Sharon Roy, Centers for Disease Control,

Atlanta, GA; Patricia Murphy, National Center for Environmental Assessment, US Environmental Protection Agency,

Edison, NJ; and Frantisek Kozisek, National Institute of

Public Health, Prague, Czech Republic, for their review and

helpful comments in the preparation of this paper.

DISCLAIMER

The views expressed in this paper are those of the individual

authors and do not necessarily reflect the views and policies

of the US Environmental Protection Agency. The paper has

been subject to the Agency’s peer review and approved for

publication.

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