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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".