This population-based study will follow the current cohort of live (n=225) cases with Rett syndrome in Australia for a further five years. New cases ascertained during the study period will also be included. It will describe the natural history of Rett syndrome and assess its impact on resource utilization and the economic and social burden on families and community in comparison with Down syndrome and a normal comparison group. Baseline data on communication, mobility, symptoms and classification have been gathered on the cohort since 1993. In 2000 data were collected on functional ability in daily living, behavior, hand function, medical conditions, and use of health and education services. Mutation data, collected on 80 percent of cases will be continued. A questionnaire has been developed, piloted and will be used to collect data on function, health and well-being of the Rett syndrome subjects and family in 2002, 2004 and 2006. Data will be gathered for the Down syndrome comparison group in 2003. Participants will respond via paper-based or through secure on-line formats. Optical scanning or on-line data capture will be used for data entry. In 2003 and 2005 clinical assessments or clinical file review will provide EEG, ECG, blood parameters, bone densitometry and autonomic nervous system data. A video protocol developed in 2001 to record functional ability will be extended to include gross motor and oral motor function, hand apraxia, gait assessment and language function. Serial videos collected in 2003 and again in 2005 will enable us to monitor changes over time and the effect of therapy or surgery. Yearly telephone interviews to families will record anthropometric data, current medication usage and update previously collected family tree data. Every two years a validated questionnaire to identify epilepsy type will be included. Resource data to determine the direct, indirect and opportunity costs associated with Rett syndrome will be compared with a normal and Down syndrome comparison group. Data analysis will use multiple regression models to examine effects of different variables on child and family level outcomes and discriminant analysis, recursive partitioning and machine learning to identify genotype/phenotype associations at the individual child level. Feedback to families on study progress will be given through the study Web site.
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