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Predicting patient engagement in IAPT services: a statistical analysis of electronic health records
  1. Alice Davis1,
  2. Theresa Smith1,
  3. Jenny Talbot1,2,
  4. Chris Eldridge1,2,
  5. David Betts1,2
  1. 1 Department of Mathematical Sciences, University of Bath, Bath, UK
  2. 2 Mayden, Bath, UK
  1. Correspondence to Dr Alice Davis, Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK; A.Davis{at}


Background Across England, 12% of all improving access to psychological therapy (IAPT) appointments are missed, and on average around 40% of first appointments are not attended, varying significantly around the country. In order to intervene effectively, it is important to target the patients who are most likely to miss their appointments.

Objective This research aims to develop and test a model to predict whether an IAPT patient will attend their first appointment.

Methods Data from 19 adult IAPT services were analysed in this research. A multiple logistic regression was used at an individual service level to identify which patient, appointment and referral characteristics are associated with attendance. These variables were then used in a generalised linear mixed effects model (GLMM). We allow random effects in the GLMM for variables where we observe high service to service heterogeneity in the estimated effects from service specific logistic regressions.

Findings We find that patients who self-refer are more likely to attend their appointments with an OR of 1.04. The older a patient is, the fewer the number of previous referrals and consenting to receiving a reminder short message service are also found to increase the likelihood of attendance with ORs of 1.02, 1.10, 1.04, respectively.

Conclusions Our model is expected to help IAPT services identify which patients are not likely to attend their appointments by highlighting key characteristics that affect attendance.

Clinical implications This analysis will help to identify methods IAPT services could use to increase their attendance rates.

  • depression and mood disorders
  • anxiety disorders

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  • Contributors AD designed and performed the modelling, analysed the data and drafted the manuscript with support from TS and JT. CE devised the project, with support from TS. All authors discussed the results and commented on the final manuscript.

  • Funding This research was funded by Innovate UK as part of a Knowledge Transfer Partnership between the University of Bath and Mayden. We are grateful to the IAPT services included in the analysis for their support and use of their data.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval Ethical approval for the secondary analysis of IAPT data was obtained from the Wales NHS Research Ethics Committee, IRAS project 255 364.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement No data are available. This study uses anonymised patient data collected from the electronic patient record system iaptus, shared in accordance with the terms agreed upon between Mayden (the data processor and developer of iaptus) and the psychological therapy services (data controllers). No individual anonymised participant data will be shared and related documents will not be made available.