Maltreatment in childhood substantially increases the risk of adult depression and anxiety in prospective cohort studies: systematic review, meta-analysis, and proportional attributable fractions
Bibliographic record
Abstract
BACKGROUND: Literature supports a strong relationship between childhood maltreatment and mental illness but most studies reviewed are cross-sectional and/or use recall to assess maltreatment and are thus prone to temporality and recall bias. Research on the potential prospective impact of maltreatment reduction on the incidence of psychiatric disorders is scarce. METHOD: Electronic databases and grey literature from 1990 to 2014 were searched for English-language cohort studies with criteria for depression and/or anxiety and non-recall measurement of childhood maltreatment. Systematic review with meta-analysis synthesized the results. Study quality, heterogeneity, and publication bias were examined. Initial screening of titles and abstracts resulted in 199 papers being reviewed. Eight high-quality articles met eligibility criteria. Population attributable fractions (PAFs) estimated potential preventive impact. RESULTS: The pooled odds ratio (OR) between any type of maltreatment and depression was 2.03 [95% confidence interval (CI) 1.37-3.01] and 2.70 (95% CI 2.10-3.47) for anxiety. For specific types of maltreatment and depression or anxiety disorders, the ORs were: physical abuse (OR 2.00, 95% CI 1.25-3.19), sexual abuse (OR 2.66, 95% CI 1.88-3.75), and neglect (OR 1.74, 95% CI 1.35-2.23). PAFs suggest that over one-half of global depression and anxiety cases are potentially attributable to self-reported childhood maltreatment. A 10-25% reduction in maltreatment could potentially prevent 31.4-80.3 million depression and anxiety cases worldwide. CONCLUSION: This review provides robust evidence of childhood maltreatment increasing the risk for depression and anxiety, and reinforces the need for effective programs and policies to reduce its occurrence.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.052 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".