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A Fair Test of the IAPT LTC/MUS Pathfinders

A Fair Test of the IAPT LTC/MUS Pathfinders. IAPT data GBU analysis?. Professor Simon Jones Ms Eleni Theodorou. Objectives:. To : Share our outline analysis plan What data to we have for the evaluation? What is Good, Bad, or Ugly? . Subgroups of interest.

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A Fair Test of the IAPT LTC/MUS Pathfinders

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  1. A Fair Test of the IAPT LTC/MUS Pathfinders IAPT data GBU analysis? Professor Simon Jones Ms Eleni Theodorou

  2. Objectives: To : • Share our outline analysis plan • What data to we have for the evaluation? • What is Good, Bad, or Ugly?

  3. Subgroups of interest • Question: How can we identify people in the • various groups? • Data sources • Fields and Codes • GBU analysis

  4. Data Analysis for LTC & MUS service users For each of the subgroups • Socio-Demographics profile • Common mental health problems and other comorbidities • describe duration and type of intervention • outcomes of intervention • explore and quantify relations between independent/predictor variables • Question: Are there any other analysis which you would find useful?

  5. Outcome scales • Personal Health Questionnaire (PHQ9) • Generalised Anxiety Disorder (GAD) score • Work and Social Adjustment Scale Score (W&SAS) Question: Which outcome is the most appropriate for MUS/LTC? • GBU analysis

  6. Data Validation Study Validate the self-reported co-morbidities from IAPT dataset a against CHOICE (NIHR Programme Grant) data set based on routinely collected primary and secondary healthcare data sets (i.e. Doncaster and Newham). Primary care services Acute/Secondary Services Outpatient activity In-patient activity Accident and Emergency activity • Use of medication • Psychotropic • Non-psychotropic • Referral rate

  7. Economic analysis To ascertain the cost-effectiveness of the service in improving the outcomes of the different patient sub-groups • Convert PHQ-9 into QALYs • See Brazier, Yang , Tsuchiya , Rowen (2010) A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures, Eur J Health Econ. • To validate the map between PHQ-9 and EQ-5D we will also seek to identify a dataset where both PHQ-9 and EQ-5D are measured and observed and can be regressed along with interaction and polynomial effects to account for non-linearity and other confounding variables.

  8. IAPT Highlight Report

  9. IAPT MDS - Personal and demographic details

  10. IAPT MDS - Disability

  11. IAPT MDS - Referral details

  12. IAPT MDS- Appointment details

  13. Thanks for listening Simon Jones simonjones@surrey.ac.uk Acknowledgements: XXX

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