Increased prevalence of childhood adversity in comorbid posttraumatic stress disorder and substance use disorders compared to either disorder alone
Bibliographic record
Abstract
Background. Prior findings highly implicate childhood adversity (CA) as a risk factor for the emergence of comorbid posttraumatic stress disorder (PTSD) and substance use disorder (SUD). However, a general estimated CA prevalence among individuals with comorbid PTSD+SUD is unknown, limiting the extent to which the field should consider the impact of CA in comorbid PTSD+SUD research and treatment. Objective. We conduct a systematic review and meta-analysis to compare CA prevalence in samples of comorbid PTSD+SUD and PTSD or SUD alone.Methods. A systematic review of PTSD, CA, and SUD literature was conducted using online databases. A meta-analysis for binary outcomes was fitted to three models comparing CA prevalence in comorbid PTSD+SUD to all comparators, PTSD only, and SUD only.Results. Seven studies were included and estimates for CA prevalence were higher, on average, among individuals with comorbid PTSD+SUD (26-73%) compared to PTSD alone (4-58%) and SUD alone (7-45%). A meta-analysis of four studies indicated individuals with comorbid PTSD+SUD were 19% more likely (RR=1.19, 95% CI=1.13;1.26) to have experienced CA compared to individuals with PTSD only and 24% (RR=1.24, 95% CI=1.19;1.29) compared to individuals with SUD only. Conclusions. Higher rates of CA among this population will inform research study design and clinical targets during treatment for individuals with comorbid PTSD+SUD. More research is needed to establish a global prevalence rate for individuals with comorbid PTSD+SUD.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.014 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".