Children with autism spectrum disorder (ASD) have significantly higher prevalence and caries severity compared to the average population. Knowledge about the oral health indices of children with this mental disorder is key to designing efficient plans of intervention. This paper reports the results of a study on the oral health status of children with ASD in central Italy. This is the first study of this type in Italy. The sample consists of 229 autistic children aged between 5 and 14 years, attending the Unit of Special Needs Policlinico Umberto I in Rome. Each patient received an intraoral examination to investigate decayed, missing, and filled teeth as well as periodontal status. Information on demographic attributes, dietary habits, medical history, and child’s cooperativeness at the first visit was also recorded. Of the participants, 79.26% presented signs of gingivitis and about 90% of them had plaque. Caries prevalence was 66.38%. The average of the total number of decayed, missing, and filled teeth in the permanent and primary dentition was 2.91. Among the factors considered, only dietary habits and the periodontal indices showed statistically significant association with caries prevalence and caries severity. Despite the selection bias, that prevents us to interpret the results presented as epidemiological evidence, our study suggests that children with ASD in central Italy represent a population at risk.
1 October 2002 in International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Statistically defensible methods for disclosure limitation allow users to make inferences about parameters in a model similar to those that would be possible using the original unreleased data. We present a new perturbation method for protecting confidential continuous microdata ? Random Orthogonal Matrix Masking (ROMM) which preserves the sufficient statistics for multivariate normal distributions, and thus is statistically defensible. ROMM encompasses all methods that preserve these statistics and can be restricted to provide 'small' perturbations. We contrast ROMM with other microdata perturbation methods and we discuss methods for evaluating it from the perspective of the tradeoff between disclosure risk and data utility.