Racial Disparities in Medication Use During Pregnancy: Results from the NISAMI Cohort
Bibliographic record
Abstract
Purpose: This study aimed to evaluate racial disparities in medication use and associated factors among pregnant women receiving prenatal care at Brazilian Unified Health System primary care health units in the northeast region. Patients and Methods: A total of 1058 pregnant women in the NISAMI Cohort were interviewed between June 2012 and February 2014. Medicines used during pregnancy were classified according to the Anatomical Therapeutic Chemical (ATC) classification system and ANVISA pregnancy risk categories. Prevalence ratios (crude and adjusted) and 95% confidence intervals (CIs) were estimated using Poisson regression with robust error variance. All analyses were stratified by race (Asian, black, brown/mixed, Brazilian indigenous, and white). Results: Approximately 84% of the pregnant women used at least one medication, with a lower proportion among white women. The most reported medications were antianemic preparations (71.08%; 95% CI 68.27-73.72%), analgesics (21.74%; 95% CI 19.36-24.32%), and drugs for functional gastrointestinal disorders (18.81%; 95% CI 16.57-21.28%). Approximately 29% of women took potentially risky medications during pregnancy, with a higher prevalence among Asian and white women. Factors associated with medication use during pregnancy include a greater number of prenatal consultations, higher education levels, health problems, and smoking. In addition, maternal age above 25 years, smoking status, and two or more previous pregnancies were associated with potentially risky medication use during pregnancy. Conclusion: A high prevalence of medication use during pregnancy was found; however, this prevalence was lower among white women. Nonetheless, black and brown women used antianemic preparations less frequently. This finding suggests that race is a factor of inequity in prenatal care, demanding public policies to mitigate it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".