Technology-Based Innovative Health Care Solutions for Improving Maternal and Child Health Outcomes in Low- and Middle-Income Countries: Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
Background Technology-based health care interventions (TBIs) are being considered as a promising solution to effectively strengthen maternal and child health (MCH) service uptake in resource-limited settings. Objective This study aimed to identify the most effective TBIs that could achieve the best functional MCH outcomes in low- and middle-income countries (LMICs). Methods A comprehensive search was performed in January 2021. In all, 2 independent researchers identified randomized controlled trials (RCTs) implemented in LMICs using the Population, Intervention, Comparison, Outcomes framework: Population: healthy pregnant women; Intervention: TBIs; Comparison: usual care or non-TBIs; and Outcomes: MCH outcomes. We estimated the direct, indirect, and relative effects, with their certainty based on the Grading of Recommendations, Assessments, Development, and Evaluations approach, for a wide range of MCH outcomes. Results In total, 30 trials with 70,807 participants were included, and 80% had low risk of bias. Our network meta-analysis (NMA) estimates indicated that TBIs, particularly SMS text messaging or phone call with mobile voucher interventions, were likely to be effective in improving MCH outcomes. The 1-way communication intervention was likely to be the most effective for the uptake of ≥4 antenatal care visits (relative risk [RR] 1.81, 95% CI 1.33-2.45; moderate certainty), facility delivery (RR 1.45, 95% CI 1.10-1.91; moderate certainty), early breastfeeding initiation (RR 1.18, 95% CI 1.02-1.37; moderate certainty), and caesarean delivery (RR 0.87, 95% CI 0.84-0.91; low certainty) outcomes; however, 2-way communication intervention was likely to be the most effective for the skilled birth attendance (RR 1.36, 95% CI 1.14-1.63; low certainty), maternal (RR 2.04, 95% CI 1.05-3.96; very low certainty) and infant’s postnatal care use (RR 1.55, 95% CI 1.19-2.04; low certainty), exclusive breastfeeding practice (RR 1.53, 95% CI 1.14-2.05; moderate certainty), and perinatal death (RR 0.51, 95% CI 0.32-0.83; low certainty) outcomes. There was no substantial inconsistency between direct and indirect evidence, but small study effects were detected in the NMA. Conclusions Different forms of TBIs have a possibility to improve MCH outcomes in LMICs and can be integrated into the existing health systems based on their priorities. This study suggests the implementation of large-scale, well-designed RCTs in low-income countries due to the limited number of RCTs in the NMA. Trial Registration PROSPERO CRD42021239185; https://tinyurl.com/yuvmp35x
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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 | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.040 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".