Assessment of the potential of hospital birth records to estimate the number of births: A case study of Germiston and Nkomazi Local Municipalities
Notice bibliographique
Résumé
The advantage of a well-developed health information system is the significant role played by records produced by such a system beyond recording medical history of individuals.They are the foundation for birth registrations which when fully complete is an important tool for acquiring data necessary for planning and monitoring child and maternal health in a country.This study aimed to investigate the potential of hospital birth records to estimate the number of births in the country and supplement birth registrations data.Data was abstracted from public facilities where births occur in two municipalities; Germiston in Gauteng and Nkomazi in Mpumalanga for the period 2014 to 2016.Modified version of the BORN Data Quality Framework (BORN-DQF) of the Ontario Agency for Health Protection and Promotion (2016) was used to assess the contents and quality of hospital birth records.Four dimensions of framework were employed to test the relevance, usability, comparability and accuracy of the data.Hospital records provided evidence of their potential as source of birth data, and for providing detailed information on maternal and child health conditions at birth currently unavailable in the birth register data.However, challenges observed in relation to lack of adherence to documented record management policies and guidelines particularly at lower levels of care, cast doubt on accessibility of these records for research purposes.For a number of key data items, data was moderately complete with space for improvement.Marked differences were found in quality of recording between the two study areas.Poor quality of data for indicators associated with health of the mother (parity and gestational age) and health of the child (birthweight) may be attributable to lack of awareness of the importance of capturing these data by hospital personnel as these are important indicators for health facilities.Linked hospital records and birth register data rates obtained point to limited common data items from both sources and questionable quality of reporting as main weaknesses.An assessment of the level of agreement between hospital records and birth register data undertaken showed high agreement and sensitivity for a number of variables, pointing to high quality of matched data.Exception was made for death, fewer numbers available for this data item suggested pervasive misreporting in hospital records.This indicates inability of hospital records in their current state http://etd.uwc.ac.za/ ii to provide solution to underreporting of child deaths observed in vital registrations data.The challenge is to impress on facilities managers the importance of completing data items important to monitor their own performance and for other stakeholders and to appreciate benefits of birth and other vital data generated within facilities.For hospital records to be the source of improvements for birth register, quality of recording must improve, this will enable birth register data to serve a wider range of stakeholders including researchers.Interventions and various interim measures such as service level agreements and memoranda of understanding between key role players to improve efficiency of the system are encouraged, however long term legislative reforms needed to improve efficiency of the system must be prioritised by key entities involved for the benefit of the country.A broader study incorporating births in private health facilities will provide clarity on the influence of socio economic status on matching rates observed in this study.
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Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».