Impact of Mobile Health (mHealth) Use by Community Health Workers on the Utilization of Maternity Care in Rural Malawi: A Time Series Analysis
Bibliographic record
Abstract
Purpose: Maternal mortality in Malawi is high, with low coverage of maternity care being a contributing factor. To improve maternal health coverage, an Android-based, integrated mobile health (mHealth) app called YendaNafe was introduced to community health workers (CHWs) in the Neno district, rural Malawi. This study evaluates the impact of this app on the uptake of antenatal care (ANC), facility-based births, and postnatal care (PNC), compared to a reference period where CHWs provided the same services without mHealth, using the interrupted time series analysis. Patients and Methods: Using aggregated monthly data and segmented quasi-Poisson regression models, we compared the effects of mHealth on selected maternal health outcomes. The models were adjusted for the COVID-19 pandemic, the occurrence of cyclones, and a cholera epidemic. We analyzed data from six eligible health facilities and their respective catchment areas in which CHWs were using YendaNafe, and compared 12 months before and 12 months after its introduction. Results: The use of YendaNafe was associated with a 22% immediate increase in facility-based births (aIRR 1.22, 95% CI 1.12-1.33, p<0.001) but not an immediate increase in new ANC visits (aIRR 1.02,95% CI 0.90-1.14, p=0.77), ANC in the first trimester (aIRR 1.17, 95% CI 0.95-1.45 p=0.13), or PNC visits (aIRR 1.03, 95% CI 0.79-1.36, p=0.81). For long-term effect, YendaNafe was associated with an increase in new ANC visits (aIRR 1.04, 95% CI 1.01-1.07, p <0.01) and ANC in the first trimester (aIRR 1.03,95% CI 1.00-1.07 p=0.046), but not facility-based births (aIRR 1.01, 95% CI 0.99-1.03, p=0.46) or PNC (aIRR 0.97 95% CI 0.93-1.01, p=0.14). Conclusion: mHealth shows potential of increasing utilization of new ANC visits, ANC in the first trimester and facility-based births. Further research is needed to understand why mHealth did not have an effect on PNC.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".