NR 704 Appropriate Application of Epidemiological Terms DQ

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NR 704 Appropriate Application of Epidemiological Terms DQ

NR 704 Appropriate Application of Epidemiological Terms DQ



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Relative Risk of Breast Cancer: The National Cancer Institute estimates that women have an average lifetime risk of 13.2% (one in eight) of being diagnosed with breast cancer at some time in their lives. The chance that a woman will never develop breast cancer is 86.8% (seven in eight). Having one first-degree relative with breast cancer approximately doubles a woman’s risk of developing cancer (compared to women having no first-degree relatives with breast cancer). Having two first-degree relatives with breast cancer increases risk fivefold. What is the probability or risk of developing breast cancer in both of the scenarios? What is the relative risk? What is the attributable risk? Discuss the importance (or lack of) of these terms in describing a patient’s risk of breast cancer.

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Week 3: Applying Epidemiological or Biostatistical Terminology

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Which epidemiological or biostatistical terminology best supports the magnitude of the problem for your proposed evidence-based promotion class project.

Epidemiology: a tool for the
assessment of risk
Ursula J. Blumenthal, Jay M. Fleisher,
Steve A. Esrey and Anne Peasey
The purpose of this chapter is to introduce and demonstrate the use of a key tool
for the assessment of risk. The word epidemiology is derived from Greek and its
literal interpretation is ‘studies upon people’. A more usual definition, however,
is the scientific study of disease patterns among populations in time and space.
This chapter introduces some of the techniques used in epidemiological studies
and illustrates their uses in the evaluation or setting of microbiological
guidelines for recreational water, wastewater reuse and drinking water.
Modern epidemiological techniques developed largely as a result of outbreak
investigations of infectious disease during the nineteenth century.
136 Water Quality: Guidelines, Standards and Health
Environmental epidemiology, however, has a long history dating back to Roman
and Greek times when early physicians perceived links between certain
environmental features and ill health.
John Snow’s study of cholera in London and its relationship to water supply
(Snow 1855) is widely considered to be the first epidemiological study (Baker et
al. 1999). Mapping cases of cholera, Snow was able to establish that cases of
illness were clustered in the streets close to the Broad Street pump, with
comparatively few cases occurring in the vicinity of other local pumps.
Epidemiological investigations can provide strong evidence linking exposure
to the incidence of infection or disease in a population. They can provide
estimates of the magnitude of risk related to a particular level of exposure or
dose and so can be used in the evaluation of appropriate microbiological quality
guideline levels or standards. Epidemiological methods can quantify the
probability that observed relationships occurred by chance factors and they also
have the potential to control for other risk factors and/or confounders of the
outcome illness being studied. Epidemiological studies used for the evaluation
or setting of guidelines must be of high quality, so that there is confidence in the
validity of the results.
The following sections outline the basic elements of epidemiological studies
(including comments on features that are important for high quality studies), the
different types of epidemiological study, and the use of epidemiology in
guideline setting, with case studies of the use of epidemiology in recreational
water, drinking water and wastewater reuse settings.
The basic elements of an epidemiological study can be characterised as follows:
• formulation of the study question or hypothesis
• selection of study populations and study samples
• selection of indicators of exposure
• measurement of exposure and disease
• analysis of the relationship between exposure and disease
• evaluation of the role of bias
• evaluation of the role of chance.
These elements will be considered here in a simplified format. Readers are
referred to epidemiology textbooks for consideration of the factors in more
detail (Beaglehole et al. 1993; Friis and Sellers 1996; Hennekens and Buring
Epidemiology: a tool for the assessment of risk 137
1987; Rothman and Greenland 1998). The case studies include examples of the
elements described here.
7.2.1 Formulation of the study question or hypothesis
The study question must be formulated so that it can be tested using statistical
methods. For example:
• Exposure to wastewater (meeting the WHO guidelines) compared
with no exposure to wastewater does not increase the rate of
Ascaris infection.
The null hypothesis (which implies there is no relationship between
postulated cause and effect) states that observed differences are due to sampling
errors (i.e. to chance). Stated in the null form, the propositions are refutable and
can be assessed using statistical tests (see section 7.2.6).
7.2.2 Selection of study populations
A study population exposed (to the factor of interest) and a control population
(not exposed to the factor of interest) need to be selected (except in a
prospective cohort study where a single cohort is studied and analysis is on
exposure status). A sample from the exposed and control populations needs to
be selected to be as similar as possible in all factors other than the factor of
interest e.g. socio-economic status, and other risk factors for the disease
outcome of interest. Since samples are never totally similar, we need to record
possible confounding factors and control for them in the analysis (see below).
For enteric infections arising from exposure to contaminated water, such factors
would include sanitation, personal hygiene, drinking-water supply, food
hygiene, and travel. It is important that both exposure and disease can be
measured as accurately as possible in the chosen populations. For example, in
studies on drinking water, the drinking water source (and therefore the quality)
for each household needs to be known accurately. In most studies, a sample will
be selected from a larger population exposed to the factor of interest, using a
sampling frame. This needs to be done so that it is representative of the larger
population – difficulties here can arise due to selection bias and inadequate
sample size (see also sections 7.2.6. and 7.2.7). The choices of study population
will depend on the type of epidemiological study selected (see section 7.3).
138 Water Quality: Guidelines, Standards and Health
7.2.3 Selection of indicators of exposure
The quality of the water to which the population is exposed needs to be
measured. The use of indicators of contamination are preferred to
measurements of pathogenic organisms in the water due to the low numbers
of pathogenic organisms present, the difficulties in detecting them and the
expense involved (see Chapter 13). Indicators should be selected that are
appropriate to the water being studied e.g. thermotolerant coliforms or E.coli
are used in assessing the quality of drinking water whereas these are less
suitable for assessing the quality of coastal recreational waters where
enterococci and faecal streptococci are generally preferred. Where the
density of an indicator does not accurately reflect the relative density of the
underlying pathogenic organism, then it is not a valid indicator organism.
This is a particular concern when bacterial indicators are used to indicate the
presence of both bacterial and viral pathogens, as treatment methods are
often less effective against viruses. This has led to concern about the
adequacy of the zero faecal coliform guideline for drinking water quality
(Payment et al. 1991).
7.2.4 Measurements of exposure and disease status
In the study population measurements of exposure and disease status need to be
made while minimising the various types of error that can occur. Where errors
occur, this is called information bias and results in misclassification (see below).
For exposure to occur, an individual must have contact with water of a given
quality. It is preferable to measure exposure at an individual level, but in many
studies, exposure status is measured at a group level, which can give rise to
misclassification of exposure for the individual. For example, in studies of the
effects of aerosol exposure from wastewater irrigation in Israel, exposure status
was assigned at the kibbutz level and no differences in individual exposure
status were measured. However, the effect of exposure was assessed separately
for children and agricultural workers and for the general population, so allowing
for some differences in exposure between sub-groups (Fattal et al. 1986; Shuval
et al. 1989). Where the misclassification does not depend on disease status, then
this is called non-differential misclassification, and the bias would be towards
the null, making it more difficult to detect true associations between exposure
and disease. This is important in studies assessing the validity of specific
microbiological quality guideline levels, as a study may fail to show an effect of
exposure to the guideline level whereas a true effect may exist. Recent studies of
recreational water exposure and wastewater reuse have put a lot of effort into
avoiding misclassification of exposure (see section 7.5). Differential
Epidemiology: a tool for the assessment of risk 139
misclassification can either overestimate or underestimate the effect of exposure
on disease. One source of misclassification of exposure results from the limited
precision of current techniques for the enumeration of indicator organisms
(Fleisher and McFadden 1980). This has not been taken into account in most
epidemiological and experimental studies of the health impact of contaminated
recreational water, drinking water or treated wastewater.
7.2.5 Analysis of the relationship between exposure and
The basic measures of disease frequency in each population are described by
using the prevalence rate (which is the proportion of the population that has the
disease at a specific point in time) or the incidence rate (the number of new
cases of disease per unit of person-time). Measuring the difference between
disease frequencies in the exposed and control populations is usually done using
a relative measure. The relative risk (RR) estimates the magnitude of an
association between exposure and disease. It indicates the likelihood of
developing the disease in the exposed group relative to those who are not
exposed. If the disease is rare the odds ratio will approximate to the relative risk.
The odds ratio (OR) is the ratio of the odds of exposure among the cases
(numbers exposed divided by numbers not exposed) to the odds in favour of
exposure among the controls. Where multivariate analysis is carried out (a
technique that allows an assessment of the association between exposure and
disease, while taking account of other risk factors that may be confounding
factors) the odds ratios is the relative measure normally calculated. In many
studies, the effect of different levels or doses of exposure will be calculated in
order to see if there is a dose–response relationship. Response is defined as the
proportion of the exposed group that develops a specific effect in comparison to
the control group. Such information is very important in the setting of guideline
levels where the guideline can be set at the level at which a response first
occurs, or can be set at a level that is deemed ‘acceptable’ (see Chapter 10).
7.2.6 Evaluation of the role of chance
This involves two components. The first is hypothesis testing, or performing a
test of statistical significance to determine the probability that chance can
explain the observed results. The role of chance is assessed by calculating the Pvalue – if this is low, it is unlikely that the observed results would have been
caused by chance alone, and if it is high, it is more likely that they are due to
chance. Although arbitrary in nature, it is usual to choose either 0.05 (5%) or
140 Water Quality: Guidelines, Standards and Health
0.01 (1%) as significance values for testing the null hypothesis. The P-value
reflects both the size of the sample and the magnitude of the effect, e.g., Pvalues can be above the level of significance where the sample is too small to
detect a significant effect. The second component is the estimation of the
confidence interval. This indicates the range within which the true estimate of
effect is likely to lie (with a certain degree of assurance) thus reflecting the
precision of the point estimate of effect. This will be calculated for the chosen
measure of effect, and is normally presented as, for example, the relative risk
and the 95% confidence intervals.
7.2.7 Evaluation of the role of bias
Bias is any systematic error that results in an incorrect estimate of the
association between exposure and disease. The main types of bias include
selection bias, information bias, recall bias, and confounding. The case studies
(outlined in Section 7.5) give examples of studies where particular attention has
been paid to reducing bias.
Selection bias occurs when inclusion of study subjects on the basis of either
exposure or disease is somehow related to the disease or exposure being studied.
In a recent study of the risks of enteric disease from consumption of vegetables
irrigated with partially treated wastewater (Blumenthal et al. 1996) problems
were faced in determining a suitable control population. This was due to
selection bias, as the other strong risk factors for enteric disease were more
prevalent in the only nearby area where fresh water was used for irrigation of
vegetables. In this case, the exposed population alone was studied, and
individuals with low exposure (infrequent consumption of raw vegetables)
compared with individual with higher exposure levels: tests were also done for a
dose–response relationship.
Information bias occurs when there are systematic differences in the way data
on exposure or outcome are obtained from the different study groups. Recall
bias occurs when the reporting of disease status is different depending on the
exposure status (or vice versa, in a case-control study). There was potential for
recall bias in the cross-sectional study of the effect of wastewater reuse on
diarrhoeal disease in Mexico (Blumenthal et al. 2001a), where individuals
exposed to untreated wastewater may have recalled episodes of diarrhoea more
accurately than individuals exposed to partially-treated wastewater. Interviewer
bias occurs where interviewers are aware of the exposure status of individuals
and may probe for answers on disease status differentially between exposure
groups. In cohort studies, where individuals leave the study or are otherwise lost
to follow-up, there can be bias if those lost are different in status to those who
Epidemiology: a tool for the assessment of risk 141
remain. These types of bias can generally be dealt with by careful design and
conduct of a study.
Confounding occurs when the relationship between the exposure and disease
is attributable (partly or wholly) to the effect of another risk factor, i.e. the
confounder. It happens when the other risk factor is an independent risk factor
for the disease and is also associated with the exposure. It can result in an overor underestimate of the relationship between exposure and disease. For example,

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personal hygiene is a potential confounder of the association between drinking
water quality and gastro-intestinal disease status. Risk factors that could
potentially act as confounders must be measured during the study and controlled
for using statistical analysis (e.g. logistic regression analysis can be used to
adjust the measure of association between exposure and disease for the effect of
the other risks factors). Many epidemiological studies of water-related infections
before the mid-1980s did not adequately control for confounding.
Essentially there are three broad types of epidemiological study design:
• descriptive studies
• analytical or observational studies
• experimental or intervention studies.
These will be outlined, in turn, in the following sections.
7.3.1 Descriptive studies
These examine the distribution of disease and possible determinants of disease
in a defined population, and can often lead to suggestions of important risk or
protective factors. They aim to identify changes in morbidity and/or mortality in
time or to compare the incidence or prevalence of disease in different
geographical areas or between groups of individuals with different
characteristics. Descriptive studies generally use routinely collected health data,
such as infectious disease notifications, and are cheap and quick to carry out. A
series of descriptive studies of Ascaris lumbricoides infection in Jerusalem have
shed light on the role of wastewater irrigation of vegetable and salad crops in the
transmission of Ascaris infection (Shuval et al. 1985, 1986). Analysis of stool
samples taken in a hospital in western Jerusalem between 1935 and 1947
showed that 35% were positive for Ascaris infection, whereas analysis of
samples taken between 1949 and 1960 indicated that only 1% were positive –
142 Water Quality: Guidelines, Standards and Health
the decrease was related by the authors to the partitioning of the city and the
cessation in the supply of wastewater irrigated vegetables from valleys to the
east of Jerusalem. Further descriptive studies indicated that the prevalence of
Ascaris increased again when the city was reunited and the supply of
wastewater-irrigated vegetables reintroduced, and decreased again when
wastewater irrigation of vegetables was stopped. Descriptive studies are useful
in generating hypotheses about the causes of certain disease patterns, but are not
useful for testing hypotheses concerning the effect of particular exposures on
particular disease outcomes.
7.3.2 Analytical studies
These are planned investigations designed to test specific hypotheses, and can
be categorised into four groups:
• ecological
• cross-sectional studies
• cohort studies
• case-control studies. Ecological (or correlational) studies
These examine associations between exposures and health outcomes using
groups of people, rather than individuals, and often use surrogate measures
of exposure, e.g. place and time of residence. Such a study would compare
an aggregate measure of exposure (such as average exposure or the
proportion of the population exposed) with an aggregate measure of health
outcome in the same population. They are sometimes included under
descriptive studies (e.g. in the US). In Thailand, for example, the seasonal
variation in the reported incidence of acute diarrhoea in selected areas was
examined in relation to rainfall and temperature records for the same areas
(Pinfold et al. 1995). The authors found that the incidence of diarrhoea
appeared to be inversely related to a sharp seasonal decrease in temperature.
Rainfall did not appear to have a direct effect on the relative incidence of
acute diarrhoea. The lack of ability to link individual exposure to individual
disease risk and to control for possible confounders are major disadvantages
of this approach and severely limit its usefulness in many settings,
especially where the exposure changes over time and space and where there
are many risk factors for the disease outcome of interest.
Epidemiology: a tool for the assessment of risk 143 Cross-sectional studies
In a cross-sectional study exposure and health status are ascertained
simultaneously on one occasion, and prevalence rates (or incidence over a
limited recent time) in groups varying in exposure are compared. Careful
measurement and statistical control of confounding variables is important to
assess the effect of other risk factors for the outcome on observed prevalence.
This approach has been used to assess the effects of wastewater reuse for
irrigation. In India, the prevalence of intestinal parasitic infections was assessed
in agricultural workers working on farms which were flood-irrigated with
wastewater and compared with a control population where agricultural workers
practised irrigation with clean water (Krishnamoorthi et al. 1973 cited in Shuval
et al. 1986). Stool samples were examined for Ancylostoma duodenale
(hookworm), Ascaris lumbricoides (roundworm) and Trichuris trichiura
(whipworm). The exposed population had at least a two-fold excess of
hookworm and Ascaris infection as compared to the control population. The
usefulness of this study and other past cross-sectional studies has been limited
by its failure to control for confounding variables and to document the type and
extent of exposure of potentially exposed persons (Blum and Feachem 1985). A
cross-sectional study can only provide information on the association between
an exposure and disease, and the temporal relationship between exposure and
disease cannot be established. Other problems include the need for large sample
sizes (for infections where prevalence is low), and potential bias due to exposure
and disease misclassification. However, the advantages are that such studies are
relatively cheap and can provide meaningful results where exposure and
confounding factors are measured carefully. Cohort studies
In a cohort study the population under investigation consists of individuals who
are at risk of developing a specific disease or health outcome. These individuals
will then be observed for a period of time in order to measure the frequency of
occurrence of the disease among those exposed to the suspected causal agent as
compared to those not exposed. This type of approach has been used to examine
the health effects of recreational water use (Balarajan et al. 1991; Cabelli et al.
1983). Typically, individuals are recruited immediately before or after
participation in some form of recreational water exposure, with controls drawn
from a population at the same location not participating in the water-based
activity. During the follow-up period, data are acquired on the symptoms
experienced by the two cohorts using questionnaire interviews. The quality of
the recreational water is defined through sampling on the day of exposure. The
144 Water Quality: Guidelines, Standards and Health
exposure data are often combined to produce a daily mean value for the full
group of bathers using a particular water on any one day. The problem with this
approach is that the aggregation of exposure and subsequent assignment of the
same exposure to many people produces a large degree of non-differential
misclassification bias, which biases the measure of association. Cohort studies
are useful for the study of relatively common outcomes and for the study of
relatively rare exposures e.g. risks from occupational exposure to wastewater
(Shuval et al. 1989). Careful classification of exposures and outcomes is needed,
as is the measurement and control for confounding factors. The disadvantages
are that the studies are often complex and difficult to manage, the time span is
often at least a year (to take into account seasonality of disease incidence) and
the studies can therefore be expensive. A wastewater reuse cohort study is
outlined in Section 7.5.2. Case-control studies
Case-control studies examine the association between exposure and a health
outcome by comparing individuals already ill with the disease of interest (i.e.
cases) and a control group who are a sample of the same population from which
the cases were identified. Gorter et al. (1991) used a case-control study design
to examine the effects of water supply and sanitation on diarrhoeal disease in
Nicaragua. They compared over 1200 children with diarrhoea with a similar
number of controls (children of a similar age with illnesses other than
diarrhoea). They found a statistically significant association between water
availability and diarrhoea morbidity. Children from homes with water supplies
over 500 metres from the house had incidence rates of diarrhoea 34% higher
than those of children from houses with their own water supply. This
relationship remained significant after controlling for confounding factors. The
advantages of case-control studies are that they require smaller sample sizes,
fewer resources, require less time and less money, and sometimes are the only
way to study rare diseases. The difficulties are in appropriate study design to
minimise bias, including the selection of appropriate controls and the control of
confounding variables and minimising recall bias. Regarding wastewater reuse
and recreational water reuse, the potential for misclassification of exposure is
higher within a case-control design than in other types of study due to recall
bias. They are therefore of less value than other designs in evaluating risks
related to exposure to water of varying qualities.
7.3.3 Experimental or intervention studies
These differ from the observational techniques outlined above in that the
investigators determine who will be exposed. A key part of the experimental
Epidemiology: a tool for the assessment of risk 145
design consists of randomising a single cohort into two groups. The process of
randomisation attempts to ensure the same distribution of various intraindividual traits and potential confounders between study groups so that they are
as comparable as possible. One group is then assigned to exposure to the factor
under study; the other group is the control and the health outcomes for the
groups are compared. Randomisation of subjects is important to minimise the
potential for confounding or selection bias. In terms of determining causality
this type of study is generally considered to be the most powerful. It is
equivalent to the randomised controlled trial used in testing the impact of drugs
and other medical interventions. Its use in examining environmental exposures
has been limited because of ethical concerns, since many exposures of interest
are potentially detrimental. A notable exception is provided by the first case
study in this chapter (section 7.5.1), which presents the study design and results
of four randomised trials assessing the risk of bathing in marine waters
contaminated with domestic sewage (Fleisher et al. 1996; Kay et al. 1994). In
the third case study (in section 7.5.3), intervention trials are described which
have recently been used in evaluating the current guidelines for drinking water
quality. These have compared persons drinking ordinary tap water with those
drinking water that has been ‘treated’ in the home, using reverse-osmosis filters
or UV light (Hellard et al. 2000; Payment et al. 1991). This type of design is not
applicable in the study of wastewater treatment and reuse where the intervention
is at a community not an individual level, and it is not possible to assign
wastewater treatment plants randomly to a number of different communities
(due to costs and practical issues).
There are several different approaches that can be taken to the use of
epidemiological studies in the setting or evaluation of microbiological
guidelines for drinking water, recreational water or wastewater:
• Measure the relationship between exposure and disease for a range
of levels of indicator organisms to get a dose–response curve. Set
an acceptable level of risk and then find the microbiological level
related to that level of risk (using the dose–response curve). This
method has been used for proposing recreational water guidelines
(see section 7.5.1 and Chapter 2).
146 Water Quality: Guidelines, Standards and Health
• Measure the relationship between exposure and disease for water at
the current guideline level, and possibly for water above or below
the guideline level. Examples of this approach can be provided by
both drinking water and wastewater reuse studies. The studies in
the drinking-water case study (section 7.5.3) assessed the
relationship between exposure and disease for water that met the
current drinking-water guideline limits. The studies outlined in the
wastewater case study section (section 7.5.2) assessed the
relationship between exposure and disease for wastewater meeting
the WHO guideline levels (WHO 1989).
• Use the results of several studies where the relationship between
exposure and disease has been assessed for water of different
qualities, and estimate the level at which no effect would be found.
This method was used informally to propose a new faecal coliform
guideline to protect agricultural workers involved in wastewater
reuse (Blumenthal et al. 2000b). Ideally a meta-analysis, such as
that conducted by Esrey et al. (1985, 1991) would be conducted to
combine the results of several studies.
Three case studies, using different approaches and epidemiological methods, are
outlined in the following sections. The recreational water studies have been used
to inform standards development, while the wastewater reuse and drinkingwater studies are likely to inform future development.
7.5.1 Recreational water case study
Four separate study locations around England and Wales (UK) were used
(Fleisher et al. 1996; Kay et al. 1994). The study locations were sufficiently
distant from one another so that site-specific differences in the risk of bathingassociated illness could be assessed. All the study locations met European
Community (EC) mandatory bacteriological marine bathing-water quality
criteria as well as US EPA bathing-water criteria for marine waters. A
randomised controlled trial design was used in order to minimise selection bias
and control for intra-individual differences in susceptibility, immune status and
so on between study groups. Equally importantly, the risk of non-differential
misclassification of exposure was minimised by assigning precise measures of
exposure to each individual bather (studies by Cabelli et al. (1993) were
seriously affected by bias of this type). Healthy volunteers aged 18 or over were
randomised into two groups

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