» Articles » PMID: 34095536

Exploring Barriers and Solutions in Advancing Cross-centre Population Data Science

Overview
Specialty Public Health
Date 2021 Jun 7
PMID 34095536
Citations 3
Authors
Affiliations
Soon will be listed here.
Abstract

Introduction: It is widely acknowledged that population health and administrative data, especially when linked at the individual level, hold great value for research. Cross-centre working between data centres providing access to such data has the potential to further increase this value by effectively expanding the data available for research. However, there is limited published information on how to address the challenges and achieve success. The aim of this paper is to explore perceived barriers and solutions to inform developments in cross-centre working across data centres.

Methods: We carried out a narrative literature review on data sharing and cross centre working. We used a mixed methods approach to assess the opinions of members of the public on cross-centre data sharing, and the views and experiences of among data centre staff connected with the UK Farr Institute for Health Informatics Research.

Results: The literature review uncovered a myriad of practical and cultural issues. Our engagement with a public group suggested that cross-centre working involving anonymised data being moved between established centres is considered acceptable. The main themes emerging from discussions with data centre staff were dedicated resourcing, practical issues, information governance and culture.

Conclusion: In seeking to advance cross-centre working between data centres, we conclude that there is a need for dedicated resourcing, indicators to recognise data reuse, collaboration to solve common issues, and balancing necessary barrier removal with incentivisation. This will require on-going commitment, engagement and an academic culture change.

Citing Articles

Identifying researcher learning needs to develop online training for UK researchers working with administrative data: CENTRIC training.

Lugg-Widger F, Munnery K, Townson J, Trubey R, Robling M Int J Popul Data Sci. 2022; 7(1):1712.

PMID: 35310556 PMC: 8900594. DOI: 10.23889/ijpds.v6i1.1712.


Considerations for an integrated population health databank in Africa: lessons from global best practices.

Igumbor J, Bosire E, Vicente-Crespo M, Igumbor E, Olalekan U, Chirwa T Wellcome Open Res. 2022; 6:214.

PMID: 35224211 PMC: 8844538. DOI: 10.12688/wellcomeopenres.17000.1.


The SPOR-Canadian Data Platform: a national initiative to facilitate data rich multi-jurisdictional research.

Dahl L, Katz A, McGrail K, Diverty B, Ethier J, Gavin F Int J Popul Data Sci. 2021; 5(1):1374.

PMID: 34007883 PMC: 8104066. DOI: 10.23889/ijpds.v5i1.1374.

References
1.
Groome P, McBride M, Jiang L, Kendell C, Decker K, Grunfeld E . Lessons Learned: It Takes a Village to Understand Inter-Sectoral Care Using Administrative Data across Jurisdictions. Int J Popul Data Sci. 2020; 3(3):440. PMC: 7299469. DOI: 10.23889/ijpds.v3i3.440. View

2.
Butler A, Smith M, Jones W, Adair C, Vigod S, Lesage A . Multi-province epidemiological research using linked administrative data: a case study from Canada. Int J Popul Data Sci. 2020; 3(3):443. PMC: 7299461. DOI: 10.23889/ijpds.v3i3.443. View

3.
Smith R, Roberts I . Time for sharing data to become routine: the seven excuses for not doing so are all invalid. F1000Res. 2016; 5:781. PMC: 4909097. DOI: 10.12688/f1000research.8422.1. View

4.
Piwowar H, Becich M, Bilofsky H, Crowley R . Towards a data sharing culture: recommendations for leadership from academic health centers. PLoS Med. 2008; 5(9):e183. PMC: 2528049. DOI: 10.1371/journal.pmed.0050183. View

5.
Song J, Elliot E, Morris A, Kerssens J, Akbari A, Ellwood-Thompson S . A case study in distributed team science in research using electronic health records. Int J Popul Data Sci. 2021; 3(3):442. PMC: 8142956. DOI: 10.23889/ijpds.v3i3.442. View