Skip to main content

Main menu

  • HOME
  • ONLINE FIRST
  • CURRENT ISSUE
  • ALL ISSUES
  • AUTHORS & REVIEWERS
  • SUBSCRIBE
  • BJGP LIFE
  • MORE
    • About BJGP
    • Conference
    • Advertising
    • eLetters
    • Alerts
    • Video
    • Audio
    • Librarian information
    • Resilience
    • COVID-19 Clinical Solutions
  • RCGP
    • BJGP for RCGP members
    • BJGP Open
    • RCGP eLearning
    • InnovAIT Journal
    • Jobs and careers

User menu

  • Subscriptions
  • Alerts
  • Log in

Search

  • Advanced search
British Journal of General Practice
Intended for Healthcare Professionals
  • RCGP
    • BJGP for RCGP members
    • BJGP Open
    • RCGP eLearning
    • InnovAIT Journal
    • Jobs and careers
  • Subscriptions
  • Alerts
  • Log in
  • Follow bjgp on Twitter
  • Visit bjgp on Facebook
  • Blog
  • Listen to BJGP podcast
  • Subscribe BJGP on YouTube
Intended for Healthcare Professionals
British Journal of General Practice

Advanced Search

  • HOME
  • ONLINE FIRST
  • CURRENT ISSUE
  • ALL ISSUES
  • AUTHORS & REVIEWERS
  • SUBSCRIBE
  • BJGP LIFE
  • MORE
    • About BJGP
    • Conference
    • Advertising
    • eLetters
    • Alerts
    • Video
    • Audio
    • Librarian information
    • Resilience
    • COVID-19 Clinical Solutions
Editorials

Predicting and preventing relapse of depression in primary care

Andrew S Moriarty, Joanne Castleton, Simon Gilbody, Dean McMillan, Shehzad Ali, Richard D Riley and Carolyn A Chew-Graham
British Journal of General Practice 2020; 70 (691): 54-55. DOI: https://doi.org/10.3399/bjgp20X707753
Andrew S Moriarty
Department of Health Sciences; Hull York Medical School, University of York, York.
Roles: National Institute for Health Research Doctoral Research Fellow
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joanne Castleton
Patient Representative.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Simon Gilbody
Department of Health Sciences; Hull York Medical School, University of York, York.
Roles: Director of the Mental Health and Addictions Research Group
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dean McMillan
Department of Health Sciences; Hull York Medical School, University of York, York.
Roles: Professor of Clinical Psychology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shehzad Ali
Department of Health Sciences; Associate Professor, Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
Roles: Visiting Senior Research Fellow
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Richard D Riley
School of Primary, Community and Social Care, Keele University, Keele, UK.
Roles: Professor of Biostatistics
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carolyn A Chew-Graham
School of Primary, Community and Social Care, Keele University, Keele.
Roles: Professor of General Practice Research
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info
  • eLetters
  • PDF
Loading

INTRODUCTION

Depression is now the leading cause of disability worldwide.1 The majority of people with depression are managed in primary care.2 There has been a shift in the understanding of depression as a discrete or episodic illness to being considered a long-term relapsing-remitting condition with possibly incomplete recovery between episodes for some patients. The literature draws a distinction between relapse (the re-emergence of depressive symptoms following some level of remission, but preceding full recovery) and recurrence (the onset of a new episode of depression following recovery), recurrence rates being lower than relapse rates.3 This dichotomy may be more important to researchers and clinicians than it is to patients, who are likely to be less concerned with terminology and more concerned by the risk of ‘becoming unwell again’ and what can be done to reduce this risk.

After treatment of the first episode of depression, approximately half of all patients will relapse, and this risk increases for every subsequent episode (70% and 90% after a second and third episode respectively).4 A recent study of a cohort of patients who had received psychosocial treatment through the Improving Access to Psychological Therapies (IAPT) service in England showed that, of those who relapse, the majority (79%) do so within the first 6 months.5

There is also evidence to suggest that the severity of depression and resistance to treatment increases with each successive episode,6 so there are potential benefits of providing on-going care following remission, perhaps after the first episode, to prevent relapse and improve overall disease trajectory. This editorial examines the current evidence around relapse prevention in primary care before discussing the case for improved risk-stratification of patients and the implications that this would have for clinical practice.

CAN RELAPSE BE PREVENTED?

There are few studies looking at relapse prevention strategies specifically in a primary care setting;7 the vast majority of studies looking at relapse have been undertaken in secondary care. During the development of the most recent update to the Depression Guideline, the National Institute for Health and Care Excellence (NICE) recommends that work be done to identify individuals at increased risk of relapse and provide relapse prevention strategies for these individuals.8

Current relapse prevention interventions recommended by NICE are a minimum of 2 years treatment with antidepressant medication for patients who have had two or more episodes of depression; high-intensity, mindfulness-based cognitive therapy (MBCT) for patients who have had three episodes or more of depression; and high-intensity individual cognitive behavioural therapy (CBT) for patients who have relapsed despite antidepressant medication.9 In more severe cases, patients are usually referred for specialist treatment where relapse prevention interventions can include further high-intensity psychological treatment and lithium augmentation of antidepressant medication. There is some evidence that acute treatment with electroconvulsive therapy (ECT) and an antidepressant is more effective at preventing relapse rather than antidepressant medication alone, although the NICE Guideline Committee recognised that the evidence for this was of low quality.8

The availability and supply of psychological treatments as recommended by NICE is inadequate at present and it is possible that these interventions do not constitute realistic treatment options in the real-world NHS.10 Evidence for their effectiveness and cost-effectiveness in a primary care setting is also lacking.8 Lessons need to be learned from trials of primary care- based relapse prevention interventions and novel feasible, scalable interventions are likely to be required to ensure effective implementation and improved outcomes for patients. More research is needed to better understand relapse prevention of depression in primary care to guide optimal allocation of interventions in practice.

CAN RELAPSE BE PREDICTED?

If relapse and remission of depression could be reliably predicted at the individual patient-level, then resources can be better targeted towards relapse prevention of depression and support precision medicine, such as tailoring of intervention decisions conditional on an individual predicted risk and response to treatment.11 This process requires prognosis research; specifically, the identification of prognostic factors and the development, validation, and impact evaluation of prognostic models for outcome risk prediction. Prognosis is ‘the forecast of future outcomes for people with a particular disease or health condition.’ 11

A recent systematic review identified several prognostic factors associated with increased risk of relapse and recurrence in depression including: childhood adversity; recurrent depression; presence of residual symptoms; comorbid anxiety; rumination; neuroticism; and age of onset of depression.12 In the UK, NICE currently highlights only a small number of these (in particular, number of previous depressive episodes and presence of residual depression symptoms) to guide prognostication in people with depression.9

Primary care is not yet at the point where practitioners can reliably predict outcomes for a given patient with depression based on their demographic, clinical, and disease- level characteristics. Single prognostic factors are seldom sufficient to effectively aid risk-stratification at the individual level. Rather, individualised outcome prediction is better shaped by using multiple prognostic factors in combination, in the form of a multivariable prognostic model.11 Such risk prediction tools are increasingly recommended by policymakers and, in general practice, can be successfully built into IT systems.11

A robust clinical tool to risk-stratify patients and then target relapse prevention interventions to those at increased risk would be of significant benefit to patients, healthcare professionals, and the NHS as a whole.

IMPLICATIONS FOR PATIENTS AND PRACTICE

Improving risk-stratification and the allocation of relapse prevention interventions in primary care will involve discussion with patients about the risk of relapse and, for some patients, the framing of depression as a potentially chronic, on-going illness rather than something that can be ‘cured’. Do patients want to have these discussions and is relapse something that concerns people with a lived experience of depression? Are such discussions required for all patients following a first episode of depression? How do clinicians decide when to adopt a chronic disease model of depression management and for which people aiming towards a more definitive treatment might be appropriate? Patient expectations and understanding may affect outcomes and so these are important questions to consider.

The majority of existing research addressing patient preferences has been in the context of discussions around antidepressants, with fear of relapse recognised as a barrier to patients discontinuing antidepressant medication13 and some patients confusing relapse with discontinuation symptoms.14 Research has also shown that patients may not have full confidence in the GPs’ ability to discuss discontinuation of antidepressants due to a perceived lack of knowledge and time.15 Interestingly, GPs felt that they did have sufficient knowledge to manage continuation therapy and would be more inclined to continue antidepressant medication in patients with a history of relapse.15 They did agree, however, that time constraints and a lack of evidence-based guidance on long- term depression management resulted in some patients being sub-optimally managed.15

Another consideration is whether the results of risk predictions can be used and shared in a clear and helpful manner and result in improved outcomes or lower costs when applied. To be useful in practice, prognostic models must include unambiguous prognostic factors, address a common and important problem, and have face validity (doctors must trust a model to guide their practice rather than their own experience).11 It is possible that a statistical prediction tool aligns too closely with a biomedical model of depression that does not fully describe the course of depression in many patients. It may be that, for some patients, practitioners should be aiming to ‘minimise relapse’ or ‘prolong remission’ rather than to set the unrealistic goal of preventing relapse altogether. At the same time the authors recognise that, due to limitations imposed by the healthcare system, GPs must gather and synthesise information to aid clinical decision-making in a relatively short amount of time and a prognostic model could facilitate the identification and stratification of these different risk groups. The views and preferences of patients, healthcare professionals, and commissioners certainly need to be explored more comprehensively.

The authors hope that this editorial will encourage GPs to reflect on how relapse is currently discussed in consultations with a patient with depression. The authors highlight the need for further research into risk-stratification and more effective relapse prevention for people with depression managed in primary care.

Notes

Provenance

Commissioned; externally peer reviewed.

Competing interests

This report is independent research supported by the National Institute for Health Research (NIHR Doctoral Research Fellowship, Dr Andrew Moriarty, DRF-2018-11-ST2-044). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.

  • © British Journal of General Practice 2020

REFERENCES

  1. 1.↵
    1. World Health Organization (WHO)
    (2017) Depression and other common mental disorders Global health estimates, https://www.who.int/mental_health/management/depression/prevalence_global_health_estimates/en (accessed 16 Dec 2019).
  2. 2.↵
    1. Rait G,
    2. Walters K,
    3. Griffin M,
    4. et al.
    (2009) Recent trends in the incidence of recorded depression in primary care. Br J Psychiatry 195(6):520–524.
    OpenUrlAbstract/FREE Full Text
  3. 3.↵
    1. Beshai S,
    2. Dobson KS,
    3. Bockting CLH,
    4. Quigley L
    (2011) Relapse and recurrence prevention in depression: current research and future prospects. Clin Psychol Rev 31(8):1349–1360.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Kupfer DJ
    (1991) Long-term treatment of depression. J Clin Psychiatry 52(Suppl):28–34.
    OpenUrlPubMed
  5. 5.↵
    1. Ali S,
    2. Rhodes L,
    3. Moreea O,
    4. et al.
    (2017) How durable is the effect of low intensity CBT for depression and anxiety? Remission and relapse in a longitudinal cohort study. Behav Res Ther 94:1–8.
    OpenUrl
  6. 6.↵
    1. Kendler KS,
    2. Thornton LM,
    3. Gardner CO
    (2000) Stressful life events and previous episodes in the etiology of major depression in women: an evaluation of the ‘kindling’ hypothesis. Am J Psychiatry 157(8):1243–1251.
    OpenUrlCrossRefPubMed
  7. 7.↵
    1. Gili M,
    2. Vicens C,
    3. Roca M,
    4. et al.
    (2015) Interventions for preventing relapse or recurrence of depression in primary health care settings: a systematic review. Prev Med 76(Suppl):S16–S21.
    OpenUrl
  8. 8.↵
    1. National Institute for Health and Care Excellence
    (2017) Depression in adults: treatment and management. Full guideline, Methods, evidence and recommendations, https://www.nice.org.uk/guidance/GID-CGWAVE0725/documents/draft-guideline (accessed 16 Dec 2019).
  9. 9.↵
    1. NICE
    (2018) Depression in adults: recognition and management CG90, http://www.nice.org.uk/guidance/cg90 (accessed 16 Dec 2019).
  10. 10.↵
    1. Mental Health Taskforce
    (2016) The five year forward view for mental health, https://www.england.nhs.uk/wp-content/uploads/2016/02/Mental-Health-Taskforce-FYFV-final.pdf (accessed 16 Dec 2019).
  11. 11.↵
    1. Riley RD,
    2. van der Windt DA,
    3. Croft P,
    4. Moons KGM
    (2019) Prognosis research in healthcare: concepts, methods, and impact (Oxford University Press, Oxford), 1st edn.
  12. 12.↵
    1. Buckman JEJ,
    2. Underwood A,
    3. Clarke K,
    4. et al.
    (2018) Risk factors for relapse and recurrence of depression in adults and how they operate: a four-phase systematic review and meta- synthesis. Clin Psychol Rev 64:13–38.
    OpenUrl
  13. 13.↵
    1. Maund E,
    2. Dewar-Haggart R,
    3. Williams S,
    4. et al.
    (2019) Barriers and facilitators to discontinuing antidepressant use: a systematic review and thematic synthesis. J Affect Disord 245:38–62.
    OpenUrl
  14. 14.↵
    1. Leydon GM,
    2. Rodgers L,
    3. Kendrick T
    (2007) A qualitative study of patient views on discontinuing long- term selective serotonin reuptake inhibitors. Fam Pract 24(6):570–575.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Bosman RC,
    2. Huijbregts KM,
    3. Verhaak PF,
    4. et al.
    (2016) Long-term antidepressant use: a qualitative study on perspectives of patients and GPs in primary care. Br J Gen Pract, https://doi.org/10.3399/bjgp16X686641.
View Abstract
Back to top
Previous ArticleNext Article

In this issue

British Journal of General Practice: 70 (691)
British Journal of General Practice
Vol. 70, Issue 691
February 2020
  • Table of Contents
  • Index by author
Download PDF
Article Alerts
Or,
sign in or create an account with your email address
Email Article

Thank you for recommending British Journal of General Practice.

NOTE: We only request your email address so that the person to whom you are recommending the page knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Predicting and preventing relapse of depression in primary care
(Your Name) has forwarded a page to you from British Journal of General Practice
(Your Name) thought you would like to see this page from British Journal of General Practice.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Predicting and preventing relapse of depression in primary care
Andrew S Moriarty, Joanne Castleton, Simon Gilbody, Dean McMillan, Shehzad Ali, Richard D Riley, Carolyn A Chew-Graham
British Journal of General Practice 2020; 70 (691): 54-55. DOI: 10.3399/bjgp20X707753

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero

Share
Predicting and preventing relapse of depression in primary care
Andrew S Moriarty, Joanne Castleton, Simon Gilbody, Dean McMillan, Shehzad Ali, Richard D Riley, Carolyn A Chew-Graham
British Journal of General Practice 2020; 70 (691): 54-55. DOI: 10.3399/bjgp20X707753
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
  • Mendeley logo Mendeley

Jump to section

  • Top
  • Article
    • INTRODUCTION
    • CAN RELAPSE BE PREVENTED?
    • CAN RELAPSE BE PREDICTED?
    • IMPLICATIONS FOR PATIENTS AND PRACTICE
    • Notes
    • REFERENCES
  • Info
  • eLetters
  • PDF

More in this TOC Section

  • Continuity of GP care: using personal lists in general practice
  • Creating space for gut feelings in the diagnosis of cancer in primary care
  • GP workforce crisis: what can we do now?
Show more Editorials

Related Articles

Cited By...

Intended for Healthcare Professionals

BJGP Life

BJGP Open

 

@BJGPjournal's Likes on Twitter

 
 

British Journal of General Practice

NAVIGATE

  • Home
  • Current Issue
  • All Issues
  • Online First
  • Authors & reviewers

RCGP

  • BJGP for RCGP members
  • BJGP Open
  • RCGP eLearning
  • InnovAiT Journal
  • Jobs and careers

MY ACCOUNT

  • RCGP members' login
  • Subscriber login
  • Activate subscription
  • Terms and conditions

NEWS AND UPDATES

  • About BJGP
  • Alerts
  • RSS feeds
  • Facebook
  • Twitter

AUTHORS & REVIEWERS

  • Submit an article
  • Writing for BJGP: research
  • Writing for BJGP: other sections
  • BJGP editorial process & policies
  • BJGP ethical guidelines
  • Peer review for BJGP

CUSTOMER SERVICES

  • Advertising
  • Contact subscription agent
  • Copyright
  • Librarian information

CONTRIBUTE

  • BJGP Life
  • eLetters
  • Feedback

CONTACT US

BJGP Journal Office
RCGP
30 Euston Square
London NW1 2FB
Tel: +44 (0)20 3188 7400
Email: journal@rcgp.org.uk

British Journal of General Practice is an editorially-independent publication of the Royal College of General Practitioners
© 2022 British Journal of General Practice

Print ISSN: 0960-1643
Online ISSN: 1478-5242