Leveraging Community Health Workers to Improve Access to Maternity Care in Rural Sub-Saharan Africa
Bibliographic record
Abstract
Progress towards reducing maternal mortality has stalled, with 80% of the countries off course to achieve the 2030 Sustainable Development Goals targets. Therefore, urgent interventions need to be implemented to accelerate the reduction of maternal mortality. Interventions to increase the utilization of antenatal care (ANC), facility-based births, and postnatal care (PNC) are needed. However, utilization of ANC, facility births, and PNC is low in many settings, and targets for these outcomes may not be achieved by 2030. This thesis discusses two community health worker (CHW) interventions (proactive CHW home visits and mobile Health (mHealth)) that hold great potential in improving ANC, facility-based births, and PNC. It answers the question: what is the impact of proactive home visits and mHealth use by CHWs on improving ANC, facility-based births, and PNC in limited resource contexts like sub-Saharan Africa and Malawi? In Neno, a rural district in Malawi, a CHW program supported by a local non-governmental organization, Partners In Health, has been operating since 2007. Initially supporting HIV and tuberculosis patients referred to them from the facilities, the CHW program was switched to a “household-based” approach, where CHWs were assigned to households, made proactive home visits at least once a month to identify women suspected to be pregnant, referred and/ or accompanied women to care, and provided support throughout ANC, birth, and PNC in 2015. Findings showed that new ANC visits increased by 18%, ANC attendance in the first trimester increased by 200%, four or more ANC visits increased by 37%, and facility-based births increased by 20%. This intervention did not change PNC visits. There has been an increase in the use of mHealth to support health delivery. However, limited evidence exists on the use of mHealth by CHWs on maternal health outcomes. A systematic review of published studies showed that most studies (89%, eight out of nine studies that reported on facility-based births as an outcome) improved the uptake of facility-based births using mHealth. About 43% of the studies (three out of seven studies that reported on ANC as an outcome) showed that mHealth increased uptake of ANC. Although few studies evaluated PNC (four studies), three studies (75%) showed that mHealth increased the utilization of ANC. The qualitative findings of this review showed that many studies explored technology-related facilitators influencing the adoption by CHWs, such as providing free equipment, supplies, and internet connectivity. Common technological barriers reported included connectivity, power, and mHealth maintenance challenges. Factors outside of mHealth also influenced CHWs' use of mHealth. These factors included perception of CHWs by communities, trust, relationships, literacy, incentives, and salaries, availability of training, refresher training, on-the-job mentorship, and supervision. Based on the systematic review's lessons, an evaluation of a locally adapted mHealth app, YendaNafe, implemented in the Neno district between 2019 and 2022, was conducted. CHWs used YendaNafe during home visits to encourage women to utilize maternity care. Findings showed that YendaNafe reduced CHW workload and improved trust. The barriers and facilitators were similar to the findings of the systematic review. Quantitative evaluation showed that YendaNafe immediately increased facility-based births (22%) but not ANC and PNC. mHealth showed a long-term increase in new ANC (4% month-to-month increase), and ANC in the first trimester (3% month-to-month increase) but not facility-based birth and PNC. This thesis's findings showed that proactive CHW home visits and mHealth use by CHWs were associated with an increase in the utilization of ANC and facility-based births. Policymakers and implementers can consider proposing a workflow review of CHWs, especially the addition of proactive home visits and mHealth, to optimize the work of CHWs and improve utilization of ANC, facility-based births, and 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".