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Record W4411319573 · doi:10.1111/cea.70096

Prevalence of Mental Health Symptoms in Patients With Atopic Dermatitis: A Systematic Review and Meta‐Analysis

2025· review· en· W4411319573 on OpenAlexaff
Daniel Rayner, David Gou, Jennifer Lin, Grace Xiong, Alessandra Giglia, Lola Irelewuyi, Shreya Bera

Bibliographic record

VenueClinical & Experimental Allergy · 2025
Typereview
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsMcMaster UniversityWestern University
Fundersnot available
KeywordsAtopic dermatitisMedicineMeta-analysisDermatologyMEDLINEMental healthPsychiatryInternal medicine

Abstract

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Atopic dermatitis (AD) represents a significant challenge to patients through uncontrolled symptoms, including itch, oedema, xerosis, crusting, and oozing [1, 2]. In large part due to mental health comorbidities, AD has the highest disease burden among skin diseases, leading to poor sleep, impedance of daily activities, and decreased quality of life [3, 4]. A recent meta-analysis found the prevalence of depression and anxiety to be 20%–24% and 11%–14% in patients with AD, respectively [5]. However, the prevalence of patients with AD who have subthreshold psychiatric symptoms remains uncertain [5]. This systematic review and meta-analysis aimed to evaluate the prevalence of depressive symptoms, anxiety symptoms, and sleep disturbances in patients with AD. This review was registered on PROSPERO (CRD42024566580) and was reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 statement. We systematically searched MEDLINE, Embase, PsycInfo, and CINAHL up to July 5, 2024, and hand-searched the reference lists of included studies. Pairs of reviewers independently screened studies in two stages to identify studies reporting on depressive symptoms, anxiety symptoms, or sleep disturbances in patients with AD. We extracted data related to study design, participant characteristics, and outcome prevalences at the earliest timepoint and lowest threshold. We conducted DerSimonian-Laird random-effects meta-analyses of proportions using the metan function in STATA (v18) and logit-transformed prevalences before pooling. We explored sources of heterogeneity through pre-specified subgroup analysis and meta-regression, quantified heterogeneity using Cochran's Q test and the I2 statistic, assessed study risk of bias using Hoy et al.'s validated tool for studies of prevalence [6], identified publication bias using funnel plots and Egger's tests, and evaluated the certainty of the evidence using the GRADE approach. We screened 11,381 citations, assessed 377 full-texts for eligibility, and included 93 studies. These studies included 488,473 patients with AD, with a median mean age of 37.0 years and a median of 55.3% females. From 66 studies (n = 474,598), the pooled prevalence of depressive symptoms in patients with AD was 29.6% (95% CI 25.9%–33.5%, low certainty) with significant heterogeneity (I2 = 99.0%, p < 0.001; Table 1). From 43 studies (n = 21,730), the prevalence of anxiety symptoms was 36.7% (95% CI 30.4%–43.6%, low certainty) with significant heterogeneity (I2 = 98.7%, p < 0.001; Table 1). From 30 studies (n = 13,451), the prevalence of sleep disturbances was 65.2% (95% CI 56.5%–72.9%, low certainty; Table 1) with significant heterogeneity (I2 = 98.7%, p < 0.001). Anxiety symptoms, depressive symptoms, and sleep disturbances were more prevalent in patients from the South-East Asian region (anxiety symptoms 48.1%, 95% CI 37.6%–58.9%; depressive symptoms 56.8%, 95% CI 45.9%–67.0%; sleep disturbances 86.7%, 95% CI 74.7%–93.5%) and less prevalent in the Eastern Mediterranean region (anxiety symptoms 21.4%, 95% CI 12.7%–33.8%, subgroup p-value < 0.001; depressive symptoms 10.7%, 95% CI 5.0%–21.5%, subgroup p-value = 0.024; sleep disturbances 32.6%, 95% CI 24.0%–42.6%, subgroup p-value < 0.001). Additionally, there were differences in prevalence based on the tools and thresholds used to assess mental health symptoms. The most common screening tools used were the Hospital Anxiety and Depression Scale for depressive symptoms (28 [42%] studies; most common threshold ≥ 8, 20 studies, prevalence 30.0%, 95% CI 24.5%–36.2%) and anxiety symptoms (27 [63%] studies; most common threshold ≥ 8, 19 studies, prevalence 42.4%, 95% CI 36.1%–48.9%), and the Pittsburgh Sleep Quality Index for sleep disturbances (7 [23%] studies; most common threshold ≥ 5, 3 studies, prevalence 79.0%, 95% CI 70.5%–85.6%). Meta-regression analyses for age, sex, or AD severity revealed no significant associations. Publication bias was detected in studies reporting on depressive symptoms (Egger's test p = 0.001) and was not detected for other outcomes. Additional information about study methods and findings is available at https://osf.io/wnbjf/. 29.6 (25.9–33.5) Low Due to serious inconsistency and the detection of publication biasa 36.7 (30.4–43.6) Low Due to very serious inconsistencyb 65.2 (56.5–72.9) Low Due to very serious inconsistencyb Our review has several notable strengths. We performed a comprehensive literature search and did not exclude based on language or publication status. We also performed a priori subgroup analysis and meta-regression to investigate potential sources of heterogeneity. However, our findings are also limited by the quality of the available evidence. We observed significant heterogeneity across all outcomes, particularly in the variation in screening tools and thresholds employed between studies, which limits the generalizability of our pooled estimates. Notably, we found that AD severity did not predict the prevalence of mental health symptoms, which conflicts with prior evidence [7]. This suggests that existing interventions tailored towards improving traditional AD-related health outcomes, including overall AD severity and itch severity, may not sufficiently address patients' mental health needs, underscoring the potential need for interventions specifically targeting anxiety and depression in patients with AD. This systematic review and meta-analysis of 93 studies identified the high prevalence of depressive symptoms, anxiety symptoms, and sleep disturbances in patients with AD. Our findings, combined with the lack of mental health and patient well-being discussions during clinical encounters [8], highlight the need for clinicians to actively consider the psychological effects of AD and to integrate patient education and psychological interventions into treatment plans. Further research on prevention, detection, and management strategies is needed to address the mental health needs of patients with AD. Daniel G. Rayner: conceptualisation; methodology; formal analysis; investigation; writing – original draft; visualisation; supervision; project administration. David Gou: validation; formal analysis; investigation; writing – original draft; visualisation; supervision; project administration. Jennifer Lin: investigation; writing – review and editing. Grace Xiong: investigation; writing – review and editing. Alessandra Giglia: investigation; writing – review and editing. Lola Irelewuyi: investigation; writing – review and editing. Jason Jianxin Zhao: investigation; writing – review and editing. Shreya Bera: investigation; writing – review and editing. The authors have nothing to report. The authors declare no conflicts of interest. The data that support the findings of this review are publicly available in OSF at https://osf.io/wnbjf/.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0100.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.407
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes1
Has abstractyes

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