Leveraging Community Health Workers to Improve Access to Maternity Care in Rural Sub-Saharan Africa
Notice bibliographique
Résumé
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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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
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Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».