Adverse childhood experiences: a meta‐analysis of prevalence and moderators among half a million adults in 206 studies
Bibliographic record
Abstract
Exposure to adverse childhood experiences (ACEs), including maltreatment and family dysfunction, is a major contributor to the global burden of disease and disability. With a large body of international literature on ACEs having emerged over the past 25 years, it is timely to now synthetize the available evidence to estimate the global prevalence of ACEs and, through a series of moderator analyses, determine which populations are at higher risk. We searched studies published between January 1, 1998 and August 5, 2021 in Medline, PsycINFO and Embase. Study inclusion criteria were using the 8- or 10-item ACE Questionnaire (±2 items), reporting the prevalence of ACEs in population samples of adults, and being published in English. The review protocol was registered with PROSPERO (CRD42022348429). In total, 206 studies (208 sample estimates) from 22 countries, with 546,458 adult participants, were included. The pooled prevalence of the five levels of ACEs was: 39.9% (95% CI: 29.8-49.2) for no ACE; 22.4% (95% CI: 14.1-30.6) for one ACE; 13.0% (95% CI: 6.5-19.8) for two ACEs; 8.7% (95% CI: 3.4-14.5) for three ACEs, and 16.1% (95% CI: 8.9-23.5) for four or more ACEs. In subsequent moderation analyses, there was strong evidence that the prevalence of 4+ ACEs was higher in populations with a history of a mental health condition (47.5%; 95% CI: 34.4-60.7) and with substance abuse or addiction (55.2%; 95% CI: 45.5-64.8), as well as in individuals from low-income households (40.5%; 95% CI: 32.9-48.4) and unhoused individuals (59.7%; 95% CI: 56.8-62.4). There was also good evidence that the prevalence of 4+ ACEs was larger in minoritized racial/ethnic groups, particularly when comparing study estimates in populations identifying as Indigenous/Native American (40.8%; 95% CI: 23.1-59.8) to those identifying as White (12.1%; 95% CI: 10.2-14.2) and Asian (5.6%; 95% CI: 2.4-10.2). Thus, ACEs are common in the general population, but there are disparities in their prevalence. They are among the principal antecedent threats to individual well-being and, as such, constitute a pressing social issue globally. Both prevention strategies and downstream interventions are needed to reduce the prevalence and mitigate the severity of the effects of ACEs and thereby reduce their deleterious health consequences on future generations.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.052 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".