# 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.