Interventions to improve birth outcomes of pregnant women living in low- and middle-income countries: a systematic review and network meta-analysis
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> Improving the health of pregnant women is important to prevent adverse birth outcomes, such as preterm birth and low birthweight. We evaluated the comparative effectiveness of interventions under the domains of micronutrient, balanced energy protein, deworming, maternal education, and water sanitation and hygiene (WASH) for their effects on these adverse birth outcomes. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> For this network meta-analysis, we searched for randomized clinical trials (RCTs) of interventions provided to pregnant women in low- and middle-income countries (LMICs). We searched for reports published until September 17, 2019 and hand-searched bibliographies of existing reviews. We extracted data from eligible studies for study characteristics, interventions, participants’ characteristics at baseline, and birth outcomes. We compared effects on preterm birth (<37 gestational week), low birthweight (LBW; <2500 g), and birthweight (continuous) using studies conducted in LMICs. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> Our network meta-analyses were based on 101 RCTs (132 papers) pertaining to 206,531 participants. Several micronutrients and balanced energy food supplement interventions demonstrated effectiveness over standard-of-care. For instance, versus standard-of-care, micronutrient supplements for pregnant women, such as iron and calcium, decreased risks of preterm birth (iron: RR=0.70, 95% credible interval [Crl] 0.47, 1.01; calcium: RR=0.76, 95%Crl 0.56, 0.99). Daily intake of 1500kcal of local food decreased the risks of preterm birth (RR=0.36, 95%Crl 0.16, 0.77) and LBW (RR=0.17, 95%Crl 0.09, 0.29), respectively when compared to standard-of-care. Educational and deworming interventions did not show improvements in birth outcomes, and no WASH intervention trials reported on these adverse birth outcomes. </ns4:p> <ns4:p> <ns4:bold>Conclusion:</ns4:bold> We found several pregnancy interventions that improve birth outcomes. However, most clinical trials have only evaluated interventions under a single domain (e.g. micronutrients) even though the causes of adverse birth outcomes are multi-faceted. There is a need to combine interventions that of different domains as packages and test for their effectiveness. </ns4:p> <ns4:p> <ns4:bold>Registration:</ns4:bold> PROSPERO <ns4:ext-link xmlns:ns3="http://www.w3.org/1999/xlink" ext-link-type="uri" ns3:href="https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=110446">CRD42018110446</ns4:ext-link> ; registered on 17 October 2018. </ns4:p>
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".