Journal of Public Health Medicine 22:368-374 (2000)
© 2000 Faculty of Public Health Medicine of the Royal Colleges of Physicians of the United Kingdom
Predicting population dental disease experience at a small area level using census and health service data
M Tickle0,z
E Kay1
H Worthington1
A Blinkhorn1
0 Manchester Health Authority, Gateway House, Piccadilly South, Manchester M60 7LP, UK
1 University of Manchester, Manchester M15 6FH, UK
z Corresponding author
E-mail: Martin.Tickle@mchester-ha.nwest.nhs.uk
Background.Information on the dental disease patterns of child populations is required at a small area level. At present, this can be provided only by expensive whole population surveys. The aim of this study was to evaluate the ability of Census data combined with health service information to provide estimates of population dental disease experience at the small area level.
Method.Clinical dental data were collected from a large cross-sectional survey of 5-year-old children. A preliminary series of bivariate linear regression analyses were undertaken at ward level with the mean number of decayed, missing or filled teeth per child (dmft) as the dependent variable, and the Census and health service and lifestyle variables suspected of having a strong relationship with dmft as independent variables. This was followed by fitting a multiple linear regression model using a stepwise procedure to include independent variables that explain most of the variability in the dependent variable dmft.
Results.All deprivation indicators derived from the Census showed a highly significant (p<0.001) bivariate linear relationship with ward dmft. The Jarman deprivation score gave the highest R2 value (0.45), but the Townsend index (R2=0.43) and the single Census variable 'percentage of households with no car' (R2=0.42) gave very similar results. The health and lifestyle indicators also showed highly significant (p<0.001) linear relationships with dmft. The R2 values were generally much lower than the deprivation-related Census variables, with the exception of the percentage of residents who smoked (R2=0.42). None of the health or lifestyle variables was included in the final dental disadvantage model. This model explained 51 per cent of the variability of ward dmft.
Conclusion.The results demonstrate the strong relationship between dental decay and deprivation, and all of the commonly used measures of deprivation exhibited a similar performance. For this population of young children health and health services shelf data did not improve on the ability of deprivation-related Census variables to predict population dental caries experience at a small area level.
Keywords: dental caries, prediction, deprivation
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