Using routine health data and intermittent community surveys to assess the impact of maternal and neonatal health interventions in low‐income countries: A systematic review
Bibliographic record
Abstract
BACKGROUND: There is a need to provide increased evidence on effective interventions to reduce maternal and neonatal mortality in low- and middle-income countries (LMICs). OBJECTIVES: To summarize the breadth of knowledge on using routine data (Routine Health Information Systems [RHIS] and Intermittent Community Surveys [ICS]) for well-designed maternal and neonatal health evaluations in LMICs. SEARCH STRATEGY: We searched reports and articles published in Embase, Medline, and Google scholar. Selection criteria Studies were considered for inclusion if they were carried out in LMICs, using RHIS or ICS data with experimental or quasi-experimental design. DATA COLLECTION AND ANALYSIS: A form was used to collect information on indicators used for interventions' impact assessment. Descriptive statistics and multiple correspondence analyses were then performed. MAIN RESULTS: Of the 1201 publications identified, 46 studies met the inclusion criteria. Most of these were using RHIS data (n=40), mainly extracted from health facility registers (n=34), and non-controlled before and after design (n=30). The indicators, which were mostly reported, were related to the use of healthcare services (n=36) and maternal/neonatal health outcomes (n=31). Few studies used ICS data (n=6) or indicators of severity (n=2). CONCLUSION: RHIS and ICS data should be increasingly used for impact studies on maternal and neonatal health in LMICs.
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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 | 0.031 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".