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A Diverse Autism Registry for Etiologic and Effectiveness Studies Prevalance and Demographic Characteristics CROEN
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A Diverse Autism Registry for Etiologic and Effectiveness Studies Prevalance and Demographic Characteristics CROEN

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Mental Health

Mental Health


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  • 406 cases to be validated at KPNC
  • Transcript

    • 1. A Diverse Autism Registry for Etiologic and Effectiveness Studies: Prevalence andDemographic Characteristics
    • 2. Background• Prevalence of Autism Spectrum Disorders (ASD) is rising• Current estimate: 1 in 88 children• Little is known about causes• Very limited evidence-base on treatments• Locating, characterizing, and enrolling sufficiently large and representative ASD patient samples is a major limitation
    • 3. Objective• Create a large, comprehensive, and dynamic ASD registry to enable rapid identification and enrollment of patients into large-scale studies investigating treatment interventions as well as pharmacogenomic and etiologic hypotheses
    • 4. Participating MHRN sites• KPNC - Division of Research• KPSC - Dept. of Research and Evaluation• KPNW - Center for Health Research• KPGA - Center for Health Research, Southeast• Harvard Pilgrim Health Care
    • 5. Specific Aims1. Refine and validate case-finding algorithms to identify children with ASD from EMR and health claims databases.2. Harmonize existing data on children and adolescents with ASD into an ASD registry database.3. Conduct a web-based survey of parents of children affected by ASD to obtain information that is not available in health plan databases.4. Obtain and store biosamples from registry participants and their family members for future studies
    • 6. Diagnostic Validation Process• Records sampled based on: - Gender (M/F) - Age of Child (1-4, 5-11, 12-17) - Specialty of Provider (specialist/generalist) - Number of diagnoses (1 dx/2+ dx)• ASD diagnoses are validated using structured record review followed by expert review• Develop algorithm based on findings to assign reliability score to all ASD diagnoses in EMR
    • 7. Harmonize existing data• Birth Certificates• Department of Developmental Services• Standardized assessments – ADOS (Autism Diagnostic Observation Scale) – CBCL (Child Behavior Checklist) – Mullen (cognitive measure) – Vineland (adaptive measure)
    • 8. Web-based Survey• What are the different treatment approaches used by children with ASD?• What are the treatment burdens (financial, time) for families of children with ASD?• What is the decision process used by families to select therapies for their children?• What are parental perceptions of the efficacy of different treatments• To what extent do families access and use recommended treatments for ASD and how is this related to perceptions of burden and efficacy?
    • 9. Survey Content• Diagnosis• Satisfaction with care• Services and treatments• Caregiver Strain Questionnaire• Pediatric Quality of Life Inventory (Peds QL)• The effect of child’s ASD on parent’s career• Demographics• Educational resources for family• Willingness to participate in future research• Saliva/Blood samples for future research
    • 10. Treatments and Services• Provided by a medical or other professional• Complementary and alternative medical (CAM) treatments• Prescription medications• Vitamins/herbs/supplements• Provided at school• Provided at home
    • 11. Preliminary Results Prevalence• 0-17 year olds• Data recorded in EMR from 1995-2010• Health plan members as of December 2010 – Overall Prevalence: 1.2% Total Total Site 1 Site 2 Site 3 Site 4 Site 5 Children ASD Cases 2,049,442 23,811 1.5% 1.0% 1.6% 1.1% 0.86%
    • 12. Age Distribution: ASD PopulationAge Site 1 Site 2 Site 3 Site 4 Site 50-4 7.26% 11.4% 10.1% 12.0% 9.95%5-9 30.3% 34.3% 34.8% 35.6% 37.3%10-14 40.7% 34.9% 37.1% 36.0% 35.9%15-17 21.7% 19.3% 18.0% 16.4% 16.9%
    • 13. Sex Distribution: ASD Population Overall male to female ratio: 4.29 (range: 3.71-5.11)Sex Site 1 Site 2 Site 3 Site 4 Site 5Male 82.1% 81.3% 78.8% 81.4% 83.6%Female 17.9% 18.7% 21.2% 18.6% 16.4%
    • 14. Summary• Large and diverse patient population• Extensive information in electronic medical records• Survey data• Genetic material• Ideal environment for studying variation in care, comparing effectiveness and cost of treatments across practice environments, and studying dissemination of information and health policies related to autism
    • 15. Acknowledgements• KPNC • KPGA – Lisa Croen – Ashli Owen-Smith – Vincent Yau – Robert Davis – Marta Lutsky – Janet Cummings – Yinge Qian • Harvard Pilgrim• KPNW – Jeanne Madden – Frances Lynch – Matthew Lakoma – Kathy Pearson• KPSC – Karen Coleman – Virginia Quinn – Karen Schenk

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