Incidence Rate

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Incidence Rate
Local smoothing with spatial filters
• Spatial filters are
useful for mapping
dense point patterns
• Filtered data are
produced by
superimposing a grid
onto a study area
Disease cases
Healthy individuals
Local smoothing with spatial filters
Population: 8
2 cases
Disease cases
Healthy individuals
• Spatial filters are
useful for mapping
dense point patterns
• Filtered data are
produced by
superimposing a grid
onto a study area
• A circle is drawn
around each grid point
and the number of
disease cases
calculated.
• The total population
within the circle is also
calculated.
Local smoothing with spatial filters
Disease cases
Healthy individuals
• For simplicity’s sake,
the population in each
circle shown here is
very low.
• In reality, the circles
would cover much
bigger populations.
• Circles can be drawn
around the other grid
points too…
• ..and the total
population and number
of disease cases in
each circle counted.
• Circles are allowed to
overlap
Local smoothing with spatial filters
• The disease rate for
each grid point (centre
of each circle) can be
calculated from the no.
of cases and population.
• The rate for each grid
point (centre of each
circle) can then be
calculated.
Incidence Rate:
0 – 5%
5 – 10%
10 - 20%
Other local smoothing techniques 1 – using population
• There are many
variations on this basic
idea.
• Here are just 2
examples.
• EXAMPLE 1: instead of
having circles of
constant radius…
• …circles may have
constant population
instead.
These circles
all contain the
same total
population of 8
Disease cases
Healthy individuals
Other local smoothing techniques 2 – working with areas
• EXAMPLE 2: Filters can
be used with areal data
as well as with point.
• A polygon-based map of
disease rates can be
converted to grid format
and displayed as a map.
• Filters are especially
useful where the
population at risk in each
area is small.
Incidence Rate:
0 – 5%
5 – 10%
10 - 20%
Other local smoothing techniques 2 – working with areas
Population and cases are summed from 4 polygons within circle
• A circle is drawn around
each grid point.
• Polygons with centroids
lying within the circle
are identified.
• Disease cases and total
population are
calculated for these
polygons
Incidence Rate:
0 – 5%
5 – 10%
10 - 20%
Other local smoothing techniques 2 – working with areas
• Once again, the disease
rate for each grid point
can be calculated from
the no. of cases and
population.
• The rate for each grid
point can then be
mapped.
Incidence Rate:
0 – 5%
5 – 10%
Original polygon boundaries
10 - 20%
Summary: local smoothing with spatial filters
• Local smoothing can make maps of dense point patterns easier
to understand
• The technique can also reduce ‘small numbers’ problems.
• The technique involves superimposing a grid on a study area
• Local average disease rates are then calculated for each grid
square.
• The grid of local average disease rates can then be displayed as
a map.
• Local smoothing can be used with polygon-based data as well as
with points.

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