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Record W4413258329 · doi:10.2196/70182

The Impact of a Mobile Money–Based Intervention on Maternal and Neonatal Health Outcomes in Madagascar: Cluster-Randomized Controlled Trial

2025· article· en· W4413258329 on OpenAlexvenueno aff
Lisa Bogler, Bítia Vieira, Harizaka Emmanuel Andriamasy, Zavaniarivo Rampanjato, Sebastián Vollmer, Till Bärnighausen, Andriamampianina Ralisimalala, J Emmrich, Samuel Knauß

Bibliographic record

VenueJMIR Public Health and Surveillance · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersBerlin Institute of HealthBundesministerium für Bildung und Forschung
KeywordsRandomized controlled trialCluster (spacecraft)Intervention (counseling)MedicineCluster randomised controlled trialEnvironmental healthComputer scienceNursingSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Financial barriers to accessing obstetric care persist in many low-resource settings. With increasing use of mobile phones, mobile money services appear as a promising tool to address this concern. Maternal health care is particularly suitable for a savings program using mobile money due to the predictable timing and costs of delivery. The mobile money-based Mobile Maternal Health Wallet (MMHW) intervention aimed to ease the burden of out-of-pocket expenses related to maternal health care by providing an accessible savings tool. OBJECTIVE: This study aimed to assess the impact of the MMHW on maternal and neonatal health outcomes. METHODS: We used a stratified cluster-randomized trial to assess the impact of the MMHW on maternal and neonatal health outcomes in the Analamanga region of Madagascar. All 63 eligible public sector primary care health facilities (Centres de Santé de Base [CSBs]) within 6 strata were randomized to either receive the intervention or not. We estimated intention-to-treat effects and contamination-adjusted effects following an instrumental variable approach. The primary outcomes included (1) delivery at a health facility, (2) antenatal care visits, and (3) total health care expenditure. Between March 2022 and December 2022, a total of 6483 women who had been pregnant between July 2020 and December 2021 were surveyed. RESULTS: Among women in catchment areas of treated CSBs, 38.79% (1297/3344) had heard of the MMHW, and 37.42% (485/1296) of them registered for the tool. There was considerable variation in uptake across treated CSBs. Descriptively, women in the catchment areas of treated CSBs were more likely to deliver in a facility and had more antenatal care visits and higher total health expenditures compared to women in control CSB catchment areas in the intention-to-treat and contamination-adjusted analyses. However, none of the effects were statistically significant. CONCLUSIONS: While this study did not identify a statistically significant impact, the estimated contamination-adjusted effects suggest that the MMHW has potential to improve access to maternal care for women who are receptive to such a mobile money-based savings tool. Estimated population-level effects were much smaller, and this study was underpowered to detect such effects due to lower-than-anticipated uptake of the intervention. TRIAL REGISTRATION: German Clinical Trials Register DRKS00014928; https://www.drks.de/search/de/trial/DRKS00014928. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-021-05694-8.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.022
GPT teacher head0.427
Teacher spread0.405 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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