Impact of a Mobile Money–Based Conditional Cash Transfer Intervention on Health Care Utilization in Southern Madagascar: Mixed-Methods Study
Bibliographic record
Abstract
Background: Mobile money-based cash transfer interventions are becoming increasingly utilized, especially in humanitarian settings. southern Madagascar faced a humanitarian emergency in 2021-2022, when the second wave of the COVID-19 pandemic and a severe famine affected the fragile region simultaneously. Objective: This mixed-methods study aims to analyze the impact and factors influencing the success of a mobile money-based conditional cash transfer intervention for health care utilization at 4 primary and 11 secondary facilities in Madagascar. Methods: We obtained quantitative data from 11 facility registers, detailing patient numbers per month, categorized into maternity care, surgical care, pediatric care, outpatient care, and inpatient care. An interrupted time series analysis, without a control group, was conducted using the end of the intervention in July 2022 as the cut off point. For qualitative data, 64 in-depth interviews were conducted with health care providers, NGO staff, policymakers, beneficiaries, and nonbeneficiaries of the intervention, and was interpreted by 4 independent researchers using reflexive thematic analysis to identify facilitators and barriers to implementation. Results: The interrupted time series analysis showed a significant negative impact on health care utilization, indicating a reduction in health care-seeking behavior after the end of the cash transfer intervention. The effect was stronger in the slope change of patient numbers per month (defined as P<.05), which significantly decreased in 39 of 55 (70%) models compared to the step change at the end of the intervention, which showed a significant but lower change (P <.05) in 40% (22/55) of models. The changes were most pronounced in surgical and pediatric care. The key factors that influenced the success of the implementation were grouped across three levels. At the community level, outreach conducted to inform potential beneficiaries about the project by community health workers and using the radio was a decisive factor for success. At participating facilities, high intrinsic staff motivation and strong digital literacy among facility staff positively influenced the intervention. Confusion regarding previous activities by the same implementing NGO and perceptions of unfair bonus payments for health care providers included in the project negatively affected the intervention. Finally, at the NGO-level, the staff present at each facility and the speed and efficiency of administrative processes during the intervention were decisive factors that influenced the intervention. Conclusions: The conditional cash transfer intervention was overarchingly successful in increasing health care utilization in southern Madagascar in a humanitarian setting. However, this success was conditional on key implementation factors at the community, facility, and NGO levels. In the future, similar interventions should proactively consider the key factors identified in this study to optimize the impact.
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 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".