Mind the data gaps: Comparing the quality of data sources for maternal health services in Cameroon
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Notice bibliographique
Résumé
Numerous sources of routine data exist but there is limited information on how they relate or complement each other to improve data availability and the quality of data collected. This paper compares data coverage and completeness on selected maternal health service indicators between (1) a performance-based financing(PBF) database, (2) the national health information system, and (3) health facility registers in selected districts in Cameroon. Data on antenatal care, skilled birth delivery and family planning were collected from 2010 to 2020 in three purposively selected districts (Buea, Limbe and Tiko) in the southwest region of Cameroon. The coverage and completeness of data from the performance-based financing database, the district health information system (dhis2, a national system) and health facility registers were compared. Data sources for the performance-based financing database and the district health information system are based on data generated from health facilities. Among the 90 health facilities in the three districts, 13 (14.5 %) facilities could not be accessed due to ongoing political conflict. Therefore, data were collected from 77 health facilities. Of the 77 facilities, half were public, a third private, and the remainder para-public (13 %) or confessional (5 %). Approximately seven registers at each health facility included data on maternal and child health. Problems of these data included incomplete coverage, misplacement of records, and incomplete data in the records identified. There was inconsistency across all sources. dhis2 collected antenatal care only for the first and fourth visits and PBF collected data for any antenatal care visits without specifying the visit number and health facility collected data for all antenatal care visits. The introduction of dhis2 and PBF programs has strengthened the availability of data in electronic format. Generally, we noted important gaps and heterogeneity in data reporting as well as incomplete data across health sectors and districts. There is need to transform the way data are collected at health facilities and there is also need for capacity building and better data governance to improve data quality and use. This will ensure that reliable, consistent, accurate, and actionable data are available to inform policy towards achieving Universal Health Coverage.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle