Landscape analysis of pregnancy exposure registries in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Drug and vaccine safety information relevant to pregnant individuals is typically insufficient, especially so for persons living in low- and middle-income countries (LMICs). Pregnancy exposure registries (PERs) and similar systems are used to monitor medical products safety. A better understanding of the landscape of PERs in LMICs can support medicines regulatory system strengthening and preparation for new vaccine and drug introductions. OBJECTIVES: To identify PERs and related health data collection platforms in LMICs that systematically record pregnancy exposures to medical products and pregnancy outcomes to inform how future efforts, such as new vaccine introductions and treatment programmes, can better support maternal populations in these countries. DESIGN: Scoping review based on methodology outlined in the Joanna Briggs Institute manual for scoping reviews. DATA SOURCES: Electronic search of Medline/PubMed, Embase, CINAHL and Global Index Medicus in June 2022, and key informants via online survey in July 2022 and interviews. ELIGIBILITY CRITERIA: Eligible resources included registries, surveillance systems and databases that collect information on exposures to medical products during pregnancy and on subsequent maternal, perinatal and neonatal outcomes in populations located entirely or partially in LMICs. Eligible records were published from January 2000 through June 2022. DATA EXTRACTION AND SYNTHESIS: Search results were screened and data extracted using a standardised form by two independent reviewers. Instances of discordance were resolved by a third reviewer. Identified systems were categorised by resource type. RESULTS: A total of 7515 records from electronic searches were screened, with 396 selected for full-text review and 47 additional records obtained from other sources. From these, 45 data collection systems located in African, Asian and Latin American LMICs were identified, with 36 currently in operation. These resources were grouped into six categories based on structure and approach and summarised according to key features, strengths and weaknesses. CONCLUSIONS: This scoping review identified several resources in LMICs dedicated to drug and vaccine safety in pregnancy, but findings indicate that more investment will be needed to ensure such efforts are widespread and sustainable. Understanding the current landscape of such resources in these settings is an important step towards improving safe, world-wide access to life-saving interventions for pregnant populations. TRIAL REGISTRATION NUMBER: The protocol for this review has been registered with Open Science Framework (https://doi.org/10.17605/OSF.IO/FU5AT).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 teacher head, 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".