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Environmental Health
Lecture 2
Cluster Investigation
Dr. Bartlett and Dr. Geary Olsen
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Elizabeth Lyons – MSU Graduate
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Environmental Health:
Cluster Investigation
 A lot
of what health departments
do is respond to citizen
complaints regarding clusters.
Usually cancer clusters.
 They also participate in longterm studies.
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Environmental Health:
Cluster Investigation

Somebody calls the health department:
 Three kids at our school have
childhood leukemia
 Your vet clinic is right next to the
playground, and we can smell pesticide
coming from your clinic.
 We think the pesticide fumes are
causing our kids to get leukemia.
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Cluster Investigation Epidemiology

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Cluster - A number of persons, animals, or things
gathered or situated close together.
 A cluster is a closely grouped series of events or
cases of a disease, with well-defined distribution
patterns, in relation to time or place (or both).
 Time cluster, space cluster, time-space cluster
Random = happening by chance.
P= probability. A P-value of .01 would mean that
the probability of a event occurring by chance
would be 1 in 100. (NO!)
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Cluster Investigation Epidemiology

If the null hypothesis is true, then P = .01
represents the probability that a difference
as extreme as that observed (or more
extreme) would occur (just by chance).

With regards to clusters of disease,
a P-value or Significance Level is
meaningful and interpretable as a
probability statement only if the
observations were drawn at random
from a defined population.
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Time-Space Cluster investigation

Why it is hard to use P values to
determine if you have a cluster.

The problem is that sometimes clusters
happen “naturally” just by chance.
 Is your cluster due to something
causing an increase rate of disease?
 Or is it one of these “chance” clusters?
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What can cause a cluster?
 “Statistical”
Clusters: - Given
enough time and enough potential
groupings, eventually there will be
subsets of the data (a particular town,
month, farm, sector, county, etc.) that
may (by chance alone) have a higher rate
of a disease than the entire population.
 Illinois Subdivision
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A posteriori vs. A priori
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“After the fact” vs. “before the fact”
What is the probability that, through the eons of
time, I would be standing here before you with
two arms, two eyes, 10 fingers and only one
nose?
 The probability is 1.000…. (It has already
happened!) But that’s not the point.
The question was posed after observing the data.
If I had 8 fingers, I would have asked a different
question.
When the question is posed based on what you
observe in the data, then p values from statistical
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tests of association are no longer valid.
Random is a process,
not a result.
 Consider
the tables of random
numbers.
 Look at about 100 of them
until you see one that somehow
doesn’t look random.
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Statistical test of association
 Chi-square
goodness of fit test
 Expect 10% of the numbers to be “7”
 110 number in the cluster
 Would expect 11 to be “7”
 Observed 25 to be “7”
 P = .01 – but is it valid?
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Texas sharpshooter
Texas sharpshooter who shoots at the side
of a barn and then draws a bull’s eye
around the bullet hole.
 If you define a cluster (draw the bull’s
eye) based on what you observe in the
data (the bullet hole), then statistical tests
can not be used to confirm the existence
of the cluster (time-space association with
a particular risk factor).
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
Why are you studying “7” disease?
Why are you studying it here?
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“Statistical” Clusters:
Given the large number of diseases and
risk factors (employment, organizations,
housing location, etc), some will appear to
be associated with disease just by chance
alone.
 “Given enough time, it is probable for the
improbable to happen.”
Albert Einstein
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Other causes for clusters
 Biological clusters - are clusters of
disease which have a biological basis.
 This is what we are looking for!
 Confounding (and time-space clusters)
 Legionnaire’s Disease in Michigan
 Reporting Bias (and time-space clusters)
 Rabies hysteria or apathy
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Example: www.RUSick2.msu.edu

For foodborne outbreaks:
 Statistical Clusters:
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Given enough food items and enough
time, clusters of foodborne disease will
occur which do not have a biological basis
and do not represent common-source
foodborne outbreaks.
Pranksters or malicious intent
 Confounding
 Strawberries every spring
 Restaurant next to “an event”
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Cluster Investigation
 Cancer clusters- these cannot be
investigated like acute infectious disease
clusters.
 1. Long and indefinite incubation/induction
period for the disease.
 2. Routes of the cancer causing agents are
usually through the environment not through
personal contact, or consumption of food or
beverage.
 “Hot pursuit” case control studies usually done
in acute infectious disease outbreaks are not
useful in cancer cluster analysis.
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Cluster Investigation
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The following list of characteristics can help to
identify a situation where a case-control study
or a multicommunity investigation might be
useful.
1.
There must be at least five cases to a cluster
and they must have a high relative risk (RR).
1. What is high? 2.0? 10.0?
2.
A unique and well known etiological agent is
known to be the cause and the
pathophysiologic mechanism for that agent is
known.
3.
The agent is in the environment and can be
measured there.
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4. The agent is persistent in the
infected/exposed people but rare in normal
populations and its unique physiologic
response in the exposed can be measured.
5. There is a heterogeneity of exposure (range
from high to low) within the neighborhood so
effects can be easily measured.
6. The route of exposure can be easily recalled.
7. Multi-community studies can be done by
looking at (otherwise) similar exposed and
unexposed communities.
8. Endemic space cluster, not a space-time
cluster that exists for a while then vanishes.
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Cluster Investigation
Minnesota Cluster Analysis Track Record
1. Information and education- 95%
 2. Public initiated surveys- 4%
 3. Validation, evaluation, feasibility
and education- 1%
 4. In-depth study- <1%
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Cluster Investigation
Evaluation of false positive reports is the
price we must pay in order to identify the
true biological cluster.
 Examples:
 The first few AIDS cases
 Foodborne outbreaks of E.coli 0157:H7
 Most foodborne outbreaks
 (Note: These are all infectious agents!)
 Bitter Harvest (PBB)
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