Using microdata to analyse spatial trends in tooth decay

Having integerised the weightings from the spatial microsimulation model and validated the output, it was then possible to calculate other aggregate statistics for the city of Sheffield, and study a number of trends in the data. The results showed that the city had a mean tooth decay score of 1.047, with 40.1% of the city’s residents experiencing some sort of decay. Within the 345 LSOAs of Sheffield, the mean tooth decay score ranged

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from 0.77 (LSOA E01008043 – an area near Nether Edge to the south-west of the city centre) to 1.69 (LSOA E01007878 – an area between Park Hill and Manor to the east of the city centre). The east-west divide between these two LSOAs is mirrored by the geographical divide between the highest and lowest scoring areas for mean tooth decay scores at the city level (Figure 29). The locations of the highest and lowest scoring LSOAs is presented in Figure 30. These were mapped using Quantum Geographic Information Systems (QGIS - version 2.8 – Wien, Development Team, 2015) and the British National Grid coordinate system. Sadly, the pattern shown in Figure 29 is not surprising when the spatial patterning of the city is considered in a wider social and historical context (Thomas et al, 2009).

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Figure 30 – Location of the highest (purple) and lowest (white) scoring LSOAs

As can be seen in Figure 29, LSOAs in central, northern and eastern areas of Sheffield tended to have higher levels of decay than more westerly LSOAs. In a general sense this divide within the city is historical, and can be traced back to the positioning of heavy industry in Sheffield during its heyday. This was mainly located in the east of Sheffield, particularly along the Don Valley, while more affluent residents were located in the west of the city, away from the smoke and nearer to the Peak District. This divide is well recognised, and has manifest itself across a wide variety of other indicators including health, poverty and wealth, education, housing and crime (Thomas et al, 2009).

Positive associations were also found between deprivation scores per LSOA (IMD, 2015) and mean tooth decay scores from the spatial microdata through the use of linear regression, with an R2 statistic of 0.8467. While use of the 2010 IMD would have been conceptually more favourable (being closer in date to the collection of the Census data, as well as the ADHS), this was not possible due to boundary changes at the LSOA level in Sheffield between the 2001 and 2011 Census’. The 2010 IMD was based on the 2001 LSOA boundaries, thus the later IMD data from 2015 was the most recent compatible source. While not as convenient chronologically, it still demonstrates an alarming pattern of inequality within the city.

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The unequal distribution of tooth decay in the city is further demonstrated by the human cartogram displayed in Figure 31. Human cartograms can only be used to display counts, rather than proportions, percentages or rates, so the total number of decayed teeth in each LSOA was used to create this figure. Human cartograms aim to represent geographical regions in proportion to their population, or some other property of these areas (Gastner and Newman, 2004). In this case the maps are representing the counts for tooth decay in each LSOA, and emphasising, proportionally, those LSOAs with the highest counts. This is interesting when comparing the cartogram with the standard geographical map of Sheffield, as they differ greatly. The west of the city has decreased in size proportionally, with the LSOAs in the east of the city being far more prominent.

Figure 31 – Human cartogram of tooth decay counts by LSOA in Sheffield

This can be seen in Figure 32, where the LSOAs in the east with higher tooth decay scores have been highlighted in yellow in an exploratory example. It should be stated that values of ‘higher’ and ‘lower’ are somewhat subjective and purely for

demonstration purposes, rather than being based on exact calculations. From this it can be seen that these LSOAs now take up a far larger proportion of the total area in Sheffield compared to the standard geographical map presented in Figure 29. This distorted image demonstrates the nature of the divide in the city with regard to the

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number of decayed teeth, emphasising the spatial clustering of higher levels of tooth decay in areas of east Sheffield. This is another example of the possible use of data from a spatial microsimulation model, as well as an example of a novel spatial technique to highlight inequalities and the uneven distribution of variables. These cartograms were created using the ScapeToad open source software tool (ScapeToad, 2008).

Figure 32 – Human cartogram of tooth decay counts by LSOA in Sheffield, with higher scores highlighted in yellow

Patterns other than the spatial distribution of tooth decay in the city can also be studied. Thanks to data kindly provided by Kate Jones (National Consultant in Dental Public Health at Public Health England) on the location of all dental services in Sheffield, it was possible to map this along with the tooth decay scores (Figure 33).

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Figure 33 – Tooth decay in Sheffield with dentist locations

From a visual analysis of the map it can be seen that dental surgeries tended to be located in areas not experiencing the highest levels of tooth decay. The areas

characterised by the darkest purple shading (i.e. the highest levels of tooth decay) would seem to be poorly served by these services. This would be in line with the inverse care law theory (Hart, 1971), although of course a visual inspection is never going to be a thorough enough way to evaluate this in a policy context. There are more technically accurate ways to judge the spatial distribution of such locations, and this section is merely demonstrating the types of analysis that could be undertaken in a dental context once spatial microdata on a given subject is available. One of the more popular

techniques in the early days of spatial analysis in Dental Public Health was the use of Voronoi polygons (or Thiessen polygons – Bradley et al, 1978) to delineate areas that were closest to each point, relative to all other points. This has been applied to the map in Figure 33, and is seen below in Figure 34.

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Figure 34 – Voronoi polygons applied to dentist locations in Sheffield

A visual inspection of the Voronoi polygons created in Figure 34 shows that the areas around the LSOAs with higher levels of tooth decay tend to be larger, indicating fewer dentists in these areas, with those that are present having to ‘cover’ a larger area. Again this is more of a visual inspection, and there are specific allocation tools that may help in exact calculations (Horner et al, 2007), but again this demonstrates the type of analysis that is now possible with this data.