A Systematic Review and Meta-Analysis of Maternal Dengue Infection and Adverse Pregnancy Outcomes
Bibliographic record
Abstract
Dengue virus (DENV) infection is endemic in many regions with high fertility rates and poses a significant public health concern during pregnancy. This systematic review and meta-analysis aimed to evaluate the association between maternal dengue infection and adverse pregnancy outcomes. A comprehensive search of PubMed, Embase, and Web of Science was conducted up to July 11, 2025. Eligible studies were comparative observational in design, with laboratory-confirmed dengue infection in pregnant women and at least one fetal or neonatal outcome reported. Outcomes of interest included stillbirth rate (SBR), miscarriage rate (MBR), preterm birth rate (PBR), low birth weight (LBW), small for gestational age (SGA), neonatal death (ND), and postpartum hemorrhage (PPH). Random-effects models were employed to calculate pooled odds ratios (OR), and the risk of bias was assessed using the Newcastle-Ottawa Scale. A total of 35 studies encompassing approximately 65,000 pregnancies from 14 countries were included. Dengue infection during pregnancy was significantly associated with increased odds of stillbirth (OR 2.70; 95% CI: 1.44-5.10), miscarriage (OR 3.51; 95% CI: 1.15-10.77), and ND (OR 3.03; 95% CI: 1.17-7.83). Associations with preterm birth, LBW, and SGA were inconsistent but appeared stronger in cases of severe or first-trimester infections. These findings underscore the need for targeted clinical and public health interventions in dengue-endemic regions to mitigate maternal and perinatal risks. This review is registered with PROSPERO (CRD420251102253).
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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".