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Record W4404468000 · doi:10.1159/000542392

Adverse Childhood Experiences Are Associated with Mental Health Problems Later in Life: An Umbrella Review of Systematic Review and Meta-Analysis

2024· review· en· W4404468000 on OpenAlexaboutno aff
Biruk Beletew Abate, Ashenafi Kibret Sendekie, Abebe Merchaw, Gebremeskel Kibret Abebe, Molla Azmeraw, Addis Wondmagegn Alamaw, Alemu Birara Zemariam, Tegene Atamenta Kitaw, Amare Kassaw, Tilahun Wodaynew, Ayelign Mengesha Kassie, Gizachew Yilak, Mulat Awoke Kassa

Bibliographic record

VenueNeuropsychobiology · 2024
Typereview
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisMental healthContext (archaeology)ScopusSubgroup analysisSystematic reviewMedicinePublication biasMEDLINEPsychiatryPsychologyGerontologyInternal medicineGeography

Abstract

fetched live from OpenAlex

INTRODUCTION: Evidence suggested a link between early adversity and mental health problems. However, it is unclear how much adverse childhood experiences (ACEs) contribute to mental health problems because researchers have produced inconsistent findings. Therefore, the objective of this umbrella review was to combine the contradictory data regarding the effect of ACEs on the development of mental health problems later in life in the global context. METHODS: PubMed, Embase, Scopus, Web of Sciences, Cochrane Database of Systematic Reviews, Scopus, and Google Scholar which reported the effect of ACEs on the development of mental health problems was searched. The quality of the included studies was assessed using the Assessment of Multiple Systematic Reviews (AMSTAR). A weighted inverse variance random-effects model was applied to find the pooled estimates. The subgroup analysis, heterogeneity, publication bias, and sensitivity analysis were also assessed. RESULTS: Forty-three SRM with 14,707,614 study participants were included. The pooled effect of ACEs on the development of mental health problems later in life in the global context is found to be (AOR = 1.66 [1.46, 1.87]). Subgroup analysis based on country revealed (AOR = 1.67 [1.23, 2.11]) in UK, (AOR = 0.61 [0.41, 0.81]) in Canada, (AOR = 1.55 [1.40, 1.69]) in Brazil, (AOR = 5.65 [4.12, 7.18]) in Ethiopia, (AOR = 1.92 [1.45, 2.38]) in USA, (AOR = 2.30 [1.89, 2.72]) in Australia, and (AOR = 1.66 [1.46, 1.87]) in Ireland. While subgroup analysis based on types of adverse childhood adverse experience: domestic violence (AOR = 4.13 [1.96, 6.30]), maltreatment (AOR = 1.5 [0.79, 2.21]), physical abuse (AOR = 1.56 [1.43, 1.63]), sexual abuse (AOR = 2.07 [1.63, 2.51]), child abuse (AOR = 5.66 [4.12, 7.18]), parental mental health problem (AOR = 1.73 [1.39, 2.08]), bullying (AOR = 1.99 [1.69, 2.29], neglect (AOR = 2.11 [1.53, 2.69]), and parental divorce (AOR = 1.66 [1.46, 1.87]). Based on the type of mental health problem, the pooled effect size is 1.87 (1.45, 2.30) for depression and 1.67 (1.22, 2.13) for anxiety. CONCLUSION: This umbrella review revealed that ACE is significantly associated (with 66% increased risk) with anxiety and depression later in life in a global context. This association is most noticeable when one is subjected to domestic violence, maltreatment, physical abuse, sexual abuse, child abuse, parental mental health problems, bullying, neglect, and parental divorce. Childhood periods are a critical window of opportunity for reducing the risk of developing mental illness in the future and for implementing intervention measures. Preventing childhood maltreatment and addressing psychiatric risk factors can prevent psychopathology. Longitudinal studies are needed to optimize healthcare responses to ACEs. Increased awareness and public health interventions are needed to prevent childhood adversity and prevent mental problems among these victims. To optimize healthcare responses to unfavorable outcomes of childhood adversities, longitudinal and intervention research findings, more public health initiatives, and awareness are required.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.465
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0130.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.109
GPT teacher head0.390
Teacher spread0.280 · 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".

Quick stats

Citations45
Published2024
Admission routes1
Has abstractyes

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