Severity and Longitudinal Course of Depression, Anxiety and Post-Traumatic Stress in Paediatric and Young Adult Cancer Patients: A Systematic Review and Meta-Analysis

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background: A diagnosis of cancer and treatment may constitute a highly traumatic period for paediatric cancer patients (PYACPs). However, no review has comprehensively analysed how the mental health of PYACPs is acutely affected and the longitudinal course. Methods: This systematic review followed PRISMA guidelines. Comprehensive searches of databases were conducted to identify studies of depression, anxiety and post-traumatic stress symptoms in PYACPs. Random effects meta-analyses were used for the primary analysis. Results: From 4898 records, 13 studies were included. Acutely after diagnosis, depressive and anxiety symptoms were significantly elevated in PYACPs. Depressive symptoms only significantly decreased after 12 months (standardised mean difference, SMD = −0.88; 95% CI: −0.92, −0.84). This downward trajectory persisted to 18 months (SMD = −1.862; 95% CI: −1.29, −1.09). Anxiety symptoms similarly only decreased after 12 (SMD = −0.34; 95% CI: −0.42, −0.27) up to 18 months (SMD = −0.49; 95% CI: −0.60, −0.39) after the cancer diagnosis. Post-traumatic stress symptoms showed protracted elevations throughout follow-up. Overall, significant predictors of poorer psychological outcomes included unhealthy family functioning, concomitant depression or anxiety, poor cancer prognosis or experiencing cancer and treatment-related side effects. Conclusions: While depression and anxiety may improve over time with a favourable environment, post-traumatic stress may have a protracted course. Timely identification and psycho-oncological intervention are critical.

Related collections

Most cited references 61

Record: found
Abstract: found
Article: found

Is Open Access

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Matthew J L Page, Joanne E McKenzie, Patrick Bossuyt … (2025)

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

0 comments Cited 14869 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

Bias in meta-analysis detected by a simple, graphical test

Jos I. M. Egger, G D Smith, M. Schneider … (1997)

0 comments Cited 7186 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Quantifying heterogeneity in a meta-analysis.

Julian P T Higgins, Simon G Thompson (2002)

The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.