Multiple modes of data sharing can facilitate secondary use of sensitive health data for research
Bibliographic Data
| ID | 21880345 |
|---|---|
| Authors | Tsaone Tamuhla (0000-0001-5966-9153, University of the Western Cape), Eddie T Lulamba (University of the Western Cape), Themba Mutemaringa (0000-0003-2857-7197, Western Cape Department of Health), Nicki Tiffin (0000-0001-5083-2735, University of the Western Cape, corresponding author) |
| Year | 2023 |
| Volume | 8 |
| Issue | 10 |
| Pages | e013092 |
| Publication date | 2023-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | BMJ Global Health (JOURNAL) |
| Journal identifiers | ISSN: 2059-7908 • E-ISSN: 2059-7908 |
| Publisher | BMJ (PUBLISHER • GB) |
| DOI | 10.1136/bmjgh-2023-013092 |
| PMID | 37802544 |
| OpenAlex | W4387439160 |
| Language | EN |
| Citations received | 1 |
| References cited | 44 |
Evidence-based healthcare relies on health data from diverse sources to inform decision-making across different domains, including disease prevention, aetiology, diagnostics, therapeutics and prognosis. Increasing volumes of highly granular data provide opportunities to leverage the evidence base, with growing recognition that health data are highly sensitive and onward research use may create privacy issues for individuals providing data. Concerns are heightened for data without explicit informed consent for secondary research use. Additionally, researchers—especially from under-resourced environments and the global South—may wish to participate in onward analysis of resources they collected or retain oversight of onward use to ensure ethical constraints are respected. Different data-sharing approaches may be adopted according to data sensitivity and secondary use restrictions, moving beyond the traditional Open Access model of unidirectional data transfer from generator to secondary user. We describe collaborative data sharing, facilitating research by combining datasets and undertaking meta-analysis involving collaborating partners; federated data analysis, where partners undertake synchronous, harmonised analyses on their independent datasets and then combine their results in a coauthored report, and trusted research environments where data are analysed in a controlled environment and only aggregate results are exported. We review how deidentification and anonymisation methods, including data perturbation, can reduce risks specifically associated with health data secondary use. In addition, we present an innovative modularised approach for building data sharing agreements incorporating a more nuanced approach to data sharing to protect privacy, and provide a framework for building the agreements for each of these data-sharing scenarios
Data access · Data science · Data sharing · Database · Health care · Health data · Internet privacy · Open data · Political science · World Wide Web · Artificial Intelligence in Healthcare and Education · Computer Science · Ethics in Clinical Research · Medicine · Privacy-Preserving Technologies in Data
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| Citations per year | 0.5 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |