Predicting population dental disease experience at a small area level using census and health service data

M Tickle, E Kay, H Worthington, A Blinkhorn
2000 Journal of public health  
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
more » ... nary series of bivariate linear regression analyses were undertaken at ward level with the mean number of decayed, missing or ®lled 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 ®tting 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 signi®cant (p < 0.001) bivariate linear relationship with ward dmft. The Jarman deprivation score gave the highest R 2 value (0.45), but the Townsend index (R 2 0.43) and the single Census variable'percentage of households with no car' (R 2 0.42) gave very similar results. The health and lifestyle indicators also showed highly signi®cant (p < 0.001) linear relationships with dmft. The R 2 values were generally much lower than the deprivationrelated Census variables, with the exception of the percentage of residents who smoked (R 2 0.42). None of the health or lifestyle variables was included in the ®nal dental disadvantage model. This model explained 51 per cent of the variability of ward dmft. Conclusions 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. 14 Morris R, Carstairs V. Which deprivation? A comparison of selected deprivation indices. J Publ Hlth Med 1991; 13: 318±326. 15 Gordon D. Census based deprivation indices: their weighting and validation. J Epidemiol Commun Hlth 1995; 49(Suppl 2): S39±S44. 16 Dolk H, Mertens B, Kleinschmidt I, et al. A standardisation approach to PREDICTING DENTAL CARIES
doi:10.1093/pubmed/22.3.368 pmid:11077912 fatcat:4eyu2vqn3bg2xmso5ianybjb3m