Associations between child maltreatment and hospital admissions for alcohol and other substance use‐related disorders up to 40 years of age: Results from the Childhood Adversity and Lifetime Morbidity study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Evidence on the associations between child maltreatment (CM), alcohol use disorders (AUDs) and other substance use disorders (SUDs) comes largely from retrospective studies. These rely on self-reported data, which may be impacted by recall bias. Using prospective CM reports to statutory agencies, we measured associations between CM notifications and inpatient admissions for AUDs and SUDs up to 40 years of age. DESIGN, SETTING AND PARTICIPANTS: Observational study linking administrative health data from Queensland, Australia to prospective birth cohort data comprising both agency-reported and substantiated notifications of CM. MEASUREMENTS: Outcomes were inpatient admissions for AUDs and SUDs based on ICD-10-Australian modification (AM)-coded primary diagnoses. Unadjusted and adjusted logistic regression analyses were undertaken. FINDINGS: Ten per cent (n = 609) of the cohort had a history of agency-reported or substantiated CM notifications before age 15. These individuals had higher adjusted odds of being admitted for AUDs and SUDs. For AUDs, the adjusted odds of inpatient admission were 2.86 [95% confidence interval (CI) = 1.73-4.74] greater where there was any previous agency-reported CM and 3.38 (95% CI = 1.94-5.89) greater where there was any previous substantiated CM. For SUDs, the adjusted odds of inpatient admission were 3.34 (95% CI = 2.42-4.61) greater where there was any previous agency-reported CM and 2.98 (95% CI = 2.04-4.36) greater where there was any previous substantiated CM. CONCLUSIONS: People with a history of child maltreatment appear to have significantly higher odds of inpatient admissions for alcohol use disorders and other substance use disorders up to 40 years of age compared to people with no history of child maltreatment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".