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Study protocol: Generation Victoria (GenV) special care nursery registry

Authors :
Jing Wang
Yanhong Hu
Lana Collins
Anna Fedyukova
Varnika Aggarwal
Fiona Mensah
Jeanie Cheong
Melissa Wake
None
Source :
International Journal of Population Data Science, Vol 8, Iss 1 (2023)
Publication Year :
2023
Publisher :
Swansea University, 2023.

Abstract

Introduction Newborn babies who require admission for specialist care can experience immediate and sometimes lasting impacts. For babies admitted to special care nurseries (SCN), there is no dataset comparable to that of the Australian and New Zealand Neonatal Network (ANZNN), which has helped improve the quality and consistency of neonatal intensive care through standardised data collection. Objectives We aim to establish a proof-of-concept, Victoria-wide registry of babies admitted to SCN, embedded within the whole-of-Victoria Generation Victoria (GenV) cohort. Methods This prototype registry is a depth sub-cohort nested within GenV, targeting all babies born in Victoria from Oct-2021 to Oct-2023. Infants admitted to SCN are eligible. The minimum dataset will be harmonised with ANZNN for common constructs but also include SCN-only items, and will cover maternal, antenatal, newborn, respiratory/respiratory support, cardiac, infection, nutrition, feeding, cerebral and other items. As well as the dataset, this protocol outlines the anticipated cohort, timeline for this registry, and how this will serve as a resource for longitudinal research through its integration with the GenV longitudinal cohort and linked datasets. Conclusion The registry will provide the opportunity to better understand the health and future outcomes of the large and growing cohort of children that require specialist care after birth. The data would generate translatable evidence and could lay the groundwork for a stand-alone ongoing clinical quality registry post-GenV.

Details

Language :
English
ISSN :
23994908
Volume :
8
Issue :
1
Database :
Directory of Open Access Journals
Journal :
International Journal of Population Data Science
Publication Type :
Academic Journal
Accession number :
edsdoj.8f586e176db94039a4fef6c4d0146ed5
Document Type :
article
Full Text :
https://doi.org/10.23889/ijpds.v8i1.2139