Multiple modes of data sharing can facilitate secondary use of sensitive health data for research

Abstract

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.

Keywords: Health policies and all other topics; Public Health.

Figure 1. Direct sharing. Unidirectional transfer of resources from generator to consumer. The consumer performs data analysis and generates the research output.

Figure 2. Collaborative meta-analysis. Generators combine their resources and do a joint meta-analysis on the combined dataset. The generators do a joint analysis and generate a collaborative research output.

Figure 3. Federated analysis. Researchers independently conduct the same analysis on their own datasets and then combine their analysis outputs. Only the independently generated analysis results are combined in a joint research output.

Figure 4. Trusted Research Environment. Researchers register for an account that allows them access to a dataset on a secure platform where they can run analyses and generate outputs, but can only download and take away the outputs of the analyses without copying, downloading or retaining the raw data. Researchers generate independent analyses and research outputs from a common data source.