Disparities in Screening and Treatment Patterns for Depression and Anxiety During Pregnancy: An Integrative Review
Bibliographic record
Abstract
INTRODUCTION: Symptoms of untreated depression and anxiety during pregnancy are associated with serious adverse effects for the pregnant person, birth outcomes, and child development. However, pregnant persons are less likely to be screened and treated compared with nonpregnant people. In this systematic review, we aimed to explore individual, provider, and systems factors that impact screening, identification, and treatment patterns for depression and anxiety during pregnancy. METHODS: Studies were eligible for inclusion if they were conducted within the United States and published in English between January 2012 and January 2023. Each study included analysis that compared rates of screening, identification, or treatment engagement and explicitly discussed disparities or health equity in marginalized groups. Fifteen articles met full inclusion criteria. RESULTS: Results demonstrated variation in the screening, identification, and treatment of depression and anxiety during pregnancy among diverse groups of patients. Screening rates ranged from 51.3% in Puerto Rico to 90.7% in Alaska. Among specific clinical populations, rates were as low as 2.0%. Fewer than half of patients were referred to treatment when indicated by screening or diagnoses. Patient characteristics such as age, race, ethnicity, socioeconomic and health factors, mental health history, and obesity were associated with variation in the rates of screening, diagnoses, or treatment engagement. Language factors were the most common factor associated with lower rates of screening and treatment access. DISCUSSION: Results suggest that many pregnant people are being overlooked and lack appropriate referrals or resources to access treatment. Results are consistent with previous findings that role confusion and lack of time, provider training, and interest contribute to low rates of screening and treatment. Future research must focus on system level factors to address perceived barriers to screening and treating depression and anxiety during pregnancy in a systematic and equitable way.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".