The Prevalence of Postpartum Depression among Women with Substance Use, an Abuse History, or Chronic Illness: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Although much is known about risk factors for postpartum depression (PPD), many potentially important clinical variables have still not been investigated. In this systematic literature review, we examine the published evidence for the prevalence of PPD among three populations of women commonly seen by providers of perinatal care: women who use substances, women with current or past experiences of abuse, and women with chronic illness. METHODS: We searched Medline, CINAHL, EMBASE, PsycINFO, and the Cochrane Library from their start dates through to August 1, 2008, using keywords relevant to depression and each of the three target clinical populations. All published, peer-reviewed papers in English or French were included in the review if a standardized assessment of depression between 3 and 52 weeks postpartum was used and if either the prevalence of PPD in the target population or a comparison of depression scores between the target population and a control group were reported. RESULTS: Seventeen papers were included in the review. There were high rates of PPD among substance-using women and those with current or past experiences of abuse. However, little evidence was found to suggest an increased risk for depression among women with chronic illness. CONCLUSIONS: Few eligible studies were identified for each clinical population of interest. Despite limitations of the studies reviewed, the results indicate that both substance use and current or past experiences of abuse are associated with increased risk for PPD. Targeted clinical interventions for these women may be beneficial.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".