Dengue Infection During Pregnancy and Adverse Birth Outcomes: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Dengue is a rapidly spreading mosquito-borne viral disease, posing significant public health challenges in tropical and subtropical regions. This systematic review and meta-analysis aimed to evaluate the relationship between maternal dengue virus infection and adverse birth outcomes. A literature search was conducted in PubMed, Embase, and web of science databases until April 2024. Observational studies examining the association between laboratory-confirmed maternal dengue infection and adverse birth outcomes such as preterm birth, low birth weight (LBW), small for gestational age (SGA), stillbirth, and postpartum haemorrhage were included. Data were extracted, and risk of bias was assessed using the Newcastle-Ottawa Scale. Random-effects meta-analysis models were used to pool data in R software (V 4.3). Twenty studies met the inclusion criteria. The pooled prevalence of preterm birth among dengue-affected pregnancies was 18.3% (95% CI: 12.6%-25.8%), with an OR of 1.21 (95% CI: 0.78-1.89). For LBW, the pooled prevalence was 17.1% (95% CI: 10.4%-26.6%), with an OR of 1.00 (95% CI: 0.69-1.41). SGA had a pooled prevalence of 11.2% (95% CI: 2.7%-36.9%) and an OR of 0.93 (95% CI: 0.41-2.14). The prevalence of stillbirth was 3.3% (95% CI: 1.6%-6.8%), with significant associations found in some studies (RR: 2.67; 95% CI: 1.09-6.57). Postpartum haemorrhage had an OR of 1.97 (95% CI: 0.53-2.69). While maternal dengue infection was associated with a higher prevalence of preterm birth and LBW, the associations were not statistically significant. Significant associations were observed for stillbirth in specific studies. Further research with standardized methodologies is needed to clarify these relationships and identify potential mechanisms.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".