Effectiveness of home-based records on maternal, newborn and child health outcomes: A systematic review and meta-analysis
Bibliographic record
Abstract
Home-based records (HBRs) may improve the health of pregnant women, new mothers and their children, and support health care systems. We assessed the effectiveness of HBRs on maternal, newborn and child health reporting, care seeking and self-care practice, mortality, morbidity and women's empowerment in low-, middle- and high-income countries. We conducted a systematic search in MEDLINE, EMBASE, CENTRAL, Health Systems Evidence, CINAHL, HTA database, NHS EED, and DARE from 1950 to 2017. We also searched the WHO, CDC, ECDC, JICA and UNAIDS. We included randomised controlled trials, prospective controlled trials, and cost-effectiveness studies. We used the Cochrane Risk of Bias tool to appraise studies. We extracted and analyzed data for outcomes including maternal, newborn and child health, and women's empowerment. We synthesized and presented data using GRADE Evidence Profiles. We included 14 studies out of 16,419 identified articles. HBRs improved antenatal care and reduced likelihood of pregnancy complications; improved patient-provider communication and enhanced women's feelings of control and empowerment; and improved rates of vaccination among children (OR: 2·39, 95% CI: 1.45-3·92) and mothers (OR 1·98 95% CI:1·29-3·04). A three-year follow-up shows that HBRs reduced risk of cognitive delay in children (p = 0.007). HBRs used during the life cycle of women and children in Indonesia showed benefits for continuity of care. There were no significant effects on healthy pregnancy behaviors such as smoking and consumption of alcohol during pregnancy. There were no statistically significant effects on newborn health outcomes. We did not identify any formal studies on cost or economic evaluation. HBRs show modest but important health effects for women and children. These effects with minimal-to-no harms, multiplied across a population, could play an important role in reducing health inequities in maternal, newborn, and child health.
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 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".