Best Practices for Ethical Sharing of Individual-Level Health Research Data From Low- and Middle-Income Settings
Year: 2015
Type: review
Abstract: Sharing individual-level data from clinical and public health research is increasingly being seen as a core requirement for effective and efficient biomedical research. This article discusses the results of a systematic review and multisite qualitative study of key stakeholders' perspectives on best practices in ethical data sharing in low- and middle-income settings. Our research suggests that for data sharing to be effective and sustainable, multiple social and ethical requirements need to be met. An effective model of data sharing will be one in which considered judgments will need to be made about how best to achieve scientific progress, minimize risks of harm, promote fairness and reciprocity, and build and sustain trust.
Institutions: University of Oxford, Mahidol University, University of KwaZulu-Natal, Kenya Medical Research Institute, KEMRI-Wellcome Trust Research Programme, Oxford University Clinical Research Unit, Society for Nutrition Education and Health Action, Children's Hospital 1, University College London, Thammasat University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,650
Funders: Wellcome Trust, Medical Research Council
Awards: Viet Nam Major Overseas Programme Core Grant Renewal 2010-2015., 'The Global Health Bioethics Network: a programme to carry out ethics research and build ethics capacity across the Wellcome Trust's Major Overseas Programmes.', 'The Global Health Bioethics Network: a programme to carry out ethics research and build ethics capacity across the Wellcome Trust's Major Overseas Programmes.', Community resource centres to improve the health of women and children in Mumbai slums: a cluster randomized controlled trial of a complex intervention., University of Oxford Big Data Institute: Development & dissemination of efficient analysis methods for large, complex, heterogeneous clinical datasets, Wellcome Trust Bloomsbury Centre for Global Health Research.
OpenAlex ID: W2114312861