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Types of data: areas
Health data may be held
as counts
of cases and total
population for
specified areas. This
preserves
confidentiality.
17
12
28
45
19
Sometimes point
centroids
may be used instead of
polygon boundaries. Such
count data are also called
group data.
Types of data: points
Instead of counts for
areas, sometimes
data are available for
individuals.
Sometimes locations are
only available for
individuals diagnosed
with a disease (cases)….
Types of data: points
Instead of counts for areas,
sometimes data are
available for individuals.
Sometimes locations are
only available for
individuals diagnosed with
a disease (cases)….
…but there may also be
locations for individuals
who have not been
diagnosed withthe disease
(controls), e.g. children
from the same age group
as controls from birth
register.
Looking at all these data,
is there clustering?
Is there evidence of
clustering of health
events anywhere
in the study area?
This is a global test
for clustering – the
tendency for cases
to aggregate together
across the whole area
Clusters
A greater than
expected number of
health events (real or
perceived) in space,
time or both is a
Cluster.
Clusters
A possible cluster?
A greater than
expected number of
health events (real or
perceived) in space,
time or both is a
Cluster.
Local cluster tests
identify particular
clusters (‘hot spots’)
within the study area.
Interpreting clusters:
chance
1 in 60
25 in 2000
20 in 2500
17 in 3000
There seems to be a high
proportion of cases in one
region, but this is based
on low numbers and is
unreliable: this is
commonly referred to as
the small number
problem.
There may also be
composition effects –
diseases affecting specific
population sub-groups
should be measured
against appropriate
denominators.
Interpreting clusters:
relationship to underlying
population distribution
Here, there appear to be
two clusters, but this
reflects the underlying
population distribution.
Interpreting clusters:
data quality issues
Data problems may
also include problems
geo-referencing
locations for
individuals, or
identifying appropriate
controls.
Clusters
This health centre coded mortality
correctly.
Data problems may
also include problems
geo-referencing
locations for
individuals, or
identifying appropriate
controls.
This health centre did not!
Interpreting clusters:
contextual effects
In this example, the
cluster in the shaded
region may be due to
factors that affect only
that region, such as
failure of a water
chlorination plant, for
example.
Clusters: collective
effects
In this instance, the
cluster results from an
infectious disease
contagion process. For
example, measles may
spread among children
playing in the same
neighbourhood.