Screening, assessment and diagnosis in the eating disorders : findings from a rapid review

Emma Bryant, Karen Spielman, Anvi Le, National Eating Disorder Research Consortium, Janet Conti, Nasim Foroughi, Phillipa Hay, Deborah Mitchison, Evelyn Smith, Stephen Touyz, Sarah Maguire

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

Background: Limited screening practices, minimal eating disorder training in the healthcare professions, and barriers related to help-seeking contribute to persistent low rates of eating disorder detection, significant unmet treatment need, and appreciable associated disease burden. The current review sought to broadly summarise the literature and identify gaps relating to the screening, assessment, and diagnosis of eating disorders within Western healthcare systems. Methods: This paper forms part of a Rapid Review series scoping the evidence base for the field of eating disorders, conducted to inform the Australian National Eating Disorders Research and Translation Strategy 2021–2031, funded and released by the Australian Government. ScienceDirect, PubMed and Ovid/Medline were searched for studies published between 2009 and mid 2021 in English. High-level evidence such as meta-analyses, large population studies and Randomised Control Trials were prioritised through purposive sampling. Data from selected studies relating to Screening, Assessment and Diagnosis of eating disorders were synthesised and are disseminated in the current review. Results: Eighty seven studies were identified, 38% relating to screening and 62% to assessment and diagnosis. The majority of screening studies were conducted in university student samples, showing high prevalence but only modest improvements in help-seeking in those studies that followed up post-screen. In healthcare settings, clinicians continue to have difficulty identifying eating disorder presentations, particularly Binge Eating Disorder, Other Specified Feeding or Eating Disorders, and sub-threshold eating disorders. This is preceded by inadequate and frequently homogenous screening mechanisms and exacerbated by considerable personal and health-system barriers, including self-stigma and lack of resourcing. While all groups are at risk of delayed or no diagnosis, those at particular risk include LGBTQ+ and gender diverse individuals, individuals living in larger bodies, and males. Conclusions: A majority of individuals with eating disorders remain undiagnosed and untreated despite a high prevalence of these conditions and increased advocacy in recent years. Research into improving detection and clinician diagnostic skill is extremely limited. Innovative empirical research is strongly recommended to address significant individual and health-system barriers currently preventing appropriate and timely intervention for many.
Original languageEnglish
Article number78
Number of pages16
JournalJournal of Eating Disorders
Volume10
Issue number1
DOIs
Publication statusPublished - 2022

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© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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