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Dekel D, Marchant A, Smith T, Morgan H, Tombs S, Khanom A, Ingham K, John A. #BeSeen: understanding young people's views of the motivation and impacts of sharing self-harm imagery online and use of their social media data for research-a UK participatory arts-led qualitative study. BMJ Open 2024; 14:e076981. [PMID: 39043594 PMCID: PMC11268025 DOI: 10.1136/bmjopen-2023-076981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 06/26/2024] [Indexed: 07/25/2024] Open
Abstract
OBJECTIVES This study explored the views of young people from diverse backgrounds, with or without a history of self-harm, on the motivation and impacts of sharing self-harm imagery online and the use of their social media data for mental health research. DESIGN Thematic analysis of 27 semi-structured one-to-one interviews. SETTING Two workshops were conducted in 2021. PARTICIPANTS We recruited 27 study participants aged 16-24 (60% male). Sixteen (59%) participants were refugee and asylum seekers (RAS). RESULTS Two main themes were generated: (1) Online imagery of self-harm captured perceived motivations for sharing such images, the potential impacts on others and possible need of censorship. This theme was characterised by mixed attitudes towards motivations for sharing, with some perceiving this as attention seeking, while others thought of it as help seeking or sharing of pain. Overall, participants agreed that images of self-harm can be triggering and should include trigger warnings. (2) Data sharing for mental health and self-harm research captured views on the use of social media posts and images for research purposes, and levels of trust in public and private organisations. It outlined positive views on their data being shared for research for public benefit, but highlighted issues of consent. The two most trusted organisations to hold and conduct research were the National Health Service and Universities. Participants from the RAS group were more inclined to agree to their data being used and had higher levels of trust in government. CONCLUSION Young people care about their privacy and use of their data even when it is publicly available. Coproduction with young people of resources to support understanding and develop innovative solutions to gaining informed consent for data sharing and research for public benefit is required. Young people from excluded communities, post-immigration RAS and males should be purposively involved in future social media research.
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Affiliation(s)
- Dana Dekel
- Medical School, Swansea University, Swansea, UK
| | | | - Todd Smith
- Medical School, Swansea University, Swansea, UK
- Public Health Wales NHS Trust, Cardiff, UK
| | | | | | | | - Karen Ingham
- Medical School, Swansea University, Swansea, UK
- Independent Artist, Swansea, UK
| | - Ann John
- Medical School, Swansea University, Swansea, UK
- Public Health Wales NHS Trust, Cardiff, UK
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2
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Tazare J, Henderson AD, Morley J, Blake HA, McDonald HI, Williamson EJ, Strongman H. NHS national data opt-outs: trends and potential consequences for health data research. BJGP Open 2024:BJGPO.2024.0020. [PMID: 38438199 DOI: 10.3399/bjgpo.2024.0020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/23/2024] [Accepted: 02/23/2024] [Indexed: 03/06/2024] Open
Abstract
BACKGROUND The English NHS data opt-out allows people to prevent use of their health data for purposes other than direct care. In 2021, the number of opt-outs increased in response to government-led proposals to create a centralised pseudonymised primary care record database. AIM To describe the potential impact of NHS national data opt-outs in 2021 on health data research. DESIGN & SETTING We conducted a descriptive analysis of opt-outs using publicly available data and the potential consequences on research are discussed. METHOD Trends in opt-outs in England were described by age, sex, and region. Using a hypothetical study, we explored statistical and epidemiological implications of opt-outs. RESULTS During the lead up to a key government-led deadline for registering opt-outs (from 31 May 2021-30 June 2021), 1 339 862 national data opt-outs were recorded; increasing the percentage of opt-outs in England from 2.77% to 4.97% of the population. Among females, percentage opt-outs increased by 83% (from 3.02% to 5.53%) compared with 76% in males (from 2.51% to 4.41%). Across age groups, the highest relative increase was among people aged 40-49 years, which rose from 2.89% to 6.04%. Considerable geographical variation was not clearly related to deprivation. Key research consequences of opt-outs include reductions in sample size and unpredictable distortion of observed measures of the frequency of health events or associations between these events. CONCLUSION Opt-out rates varied by age, sex, and place. The impact of this and variation by other characteristics on research is not quantifiable. Potential effects of opt-outs on research and consequences for health policies based on this research must be considered when creating future opt-out solutions.
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Affiliation(s)
- John Tazare
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Alasdair D Henderson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jessica Morley
- Oxford Internet Institute, University of Oxford, Oxford, United Kingdom
| | - Helen A Blake
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Applied Health Research, Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Helen I McDonald
- Department of Life Sciences, University of Bath, Bath, United Kingdom
- Department of Infectious Disease Epidemiology (International Health), London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Elizabeth J Williamson
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Helen Strongman
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
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3
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Richter G, Krawczak M. How to Elucidate Consent-Free Research Use of Medical Data: A Case for "Health Data Literacy". JMIR Med Inform 2024; 12:e51350. [PMID: 38889087 PMCID: PMC11196244 DOI: 10.2196/51350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/19/2024] [Accepted: 04/21/2024] [Indexed: 06/20/2024] Open
Abstract
Unlabelled The extensive utilization of personal health data is one of the key success factors of modern medical research. Obtaining consent to the use of such data during clinical care, however, bears the risk of low and unequal approval rates and risk of consequent methodological problems in the scientific use of the data. In view of these shortcomings, and of the proven willingness of people to contribute to medical research by sharing personal health data, the paradigm of informed consent needs to be reconsidered. The European General Data Protection Regulation gives the European member states considerable leeway with regard to permitting the research use of health data without consent. Following this approach would however require alternative offers of information that compensate for the lack of direct communication with experts during medical care. We therefore introduce the concept of "health data literacy," defined as the capacity to find, understand, and evaluate information about the risks and benefits of the research use of personal health data and to act accordingly. Specifically, health data literacy includes basic knowledge about the goals and methods of data-rich medical research and about the possibilities and limits of data protection. Although the responsibility for developing the necessary resources lies primarily with those directly involved in data-rich medical research, improving health data literacy should ultimately be of concern to everyone interested in the success of this type of research.
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Affiliation(s)
- Gesine Richter
- Institute of Experimental Medicine, Division of Biomedical Ethics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
- German Center for Lung Research (DZL), Airway Research Center North (ARCN), Borstel, Germany
| | - Michael Krawczak
- Institute of Medical Informatics und Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
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4
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Evans W, Meslin EM, Kai J, Qureshi N. Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care. J Pers Med 2024; 14:418. [PMID: 38673045 PMCID: PMC11051552 DOI: 10.3390/jpm14040418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/27/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024] Open
Abstract
Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.
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Affiliation(s)
- William Evans
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
| | - Eric M. Meslin
- PHG Foundation, Cambridge University, Cambridge CB1 8RN, UK;
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Joe Kai
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
| | - Nadeem Qureshi
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
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DelPozo-Banos M, Stewart R, John A. Machine learning in mental health and its relationship with epidemiological practice. Front Psychiatry 2024; 15:1347100. [PMID: 38528983 PMCID: PMC10961376 DOI: 10.3389/fpsyt.2024.1347100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 02/22/2024] [Indexed: 03/27/2024] Open
Affiliation(s)
| | - Robert Stewart
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
- South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Ann John
- Swansea University Medical School, Swansea, United Kingdom
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6
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Healey J, Davey V, Liddle J, O’Rourke G, Hanratty B, Beresford B. UK homecare providers' views about, and experiences of, digitalisation: A national survey. Digit Health 2024; 10:20552076241255477. [PMID: 38784052 PMCID: PMC11113022 DOI: 10.1177/20552076241255477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
Objective Using digital systems to support the management and delivery of social care is a priority for UK governments. This study explored progress towards, and experiences of, digitalisation in the homecare sector and providers' views on contributing client data to a national policy/research dataset. Methods Over 150 UK homecare providers completed an on-line survey (October-December 2022). The survey was hosted on Qualtrics and comprised fixed- and free-text response questions. The recruited sample aligned with the profile of UK homecare providers in terms of use of digital systems, organisation type and size. Results Almost all respondents (95.5%) were using digital systems, in part or exclusively, to support care delivery. However, many (42.7%) reported a desire to further digitalise or a dissatisfaction with existing systems. Findings highlight the time and work involved in choosing a a software system, with the decision regarded as relatively high risk. Over 50 different software systems were being used across the sample. Most respondents (72.5%) supported the creation of a national dataset on homecare users. However, support and recompense are likely to needed to secure buy-in from what is a predominantly private sector context. Conclusions Findings suggest a complex and changing situation, with numerous different digital systems being used and the sector at different stages of digitalisation. The high-pressure, low margin context of UK homecare appeared to be exerting an influence on progress towards digitalisation. Evaluations of government strategies to stimulate and support digitalisation in this diverse and predominantly private sector context will be valuable.
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Affiliation(s)
- Jan Healey
- Social Policy Research Unit, School for Business and Society, University of York, York, UK
| | - Vanessa Davey
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Jennifer Liddle
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Gareth O’Rourke
- Social Policy Research Unit, School for Business and Society, University of York, York, UK
| | - Barbara Hanratty
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Bryony Beresford
- Social Policy Research Unit, School for Business and Society, University of York, York, UK
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7
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Watson E, Fletcher-Watson S, Kirkham EJ. Views on sharing mental health data for research purposes: qualitative analysis of interviews with people with mental illness. BMC Med Ethics 2023; 24:99. [PMID: 37964278 PMCID: PMC10648337 DOI: 10.1186/s12910-023-00961-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 09/24/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Improving the ways in which routinely-collected mental health data are shared could facilitate substantial advances in research and treatment. However, this process should only be undertaken in partnership with those who provide such data. Despite relatively widespread investigation of public perspectives on health data sharing more generally, there is a lack of research on the views of people with mental illness. METHODS Twelve people with lived experience of mental illness took part in semi-structured interviews via online video software. Participants had experience of a broad range of mental health conditions including anxiety, depression, schizophrenia, eating disorders and addiction. Interview questions sought to establish how participants felt about the use of routinely-collected health data for research purposes, covering different types of health data, what health data should be used for, and any concerns around its use. RESULTS Thematic analysis identified four overarching themes: benefits of sharing mental health data, concerns about sharing mental health data, safeguards, and data types. Participants were clear that health data sharing should facilitate improved scientific knowledge and better treatments for mental illness. There were concerns that data misuse could become another way in which individuals and society discriminate against people with mental illness, for example through insurance premiums or employment decisions. Despite this there was a generally positive attitude to sharing mental health data as long as appropriate safeguards were in place. CONCLUSIONS There was notable strength of feeling across participants that more should be done to reduce the suffering caused by mental illness, and that this could be partly facilitated by well-managed sharing of health data. The mental health research community could build on this generally positive attitude to mental health data sharing by following rigorous best practice tailored to the specific concerns of people with mental illness.
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Affiliation(s)
- Emily Watson
- University of Edinburgh Medical School, Edinburgh, UK
| | | | - Elizabeth Joy Kirkham
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
- Clinical Psychology, School of Health in Social Science, University of Edinburgh, Edinburgh, UK.
- Medical School, Teviot Place, Edinburgh, EH8 9AG, UK.
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8
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Woodman RJ, Mangoni AA. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future. Aging Clin Exp Res 2023; 35:2363-2397. [PMID: 37682491 PMCID: PMC10627901 DOI: 10.1007/s40520-023-02552-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023]
Abstract
The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.
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Affiliation(s)
- Richard J Woodman
- Centre of Epidemiology and Biostatistics, College of Medicine and Public Health, Flinders University, GPO Box 2100, Adelaide, SA, 5001, Australia.
| | - Arduino A Mangoni
- Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, SA, Australia
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Zhang J, Morley J, Gallifant J, Oddy C, Teo JT, Ashrafian H, Delaney B, Darzi A. Mapping and evaluating national data flows: transparency, privacy, and guiding infrastructural transformation. Lancet Digit Health 2023; 5:e737-e748. [PMID: 37775190 DOI: 10.1016/s2589-7500(23)00157-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/07/2023] [Accepted: 08/02/2023] [Indexed: 10/01/2023]
Abstract
The importance of big health data is recognised worldwide. Most UK National Health Service (NHS) care interactions are recorded in electronic health records, resulting in an unmatched potential for population-level datasets. However, policy reviews have highlighted challenges from a complex data-sharing landscape relating to transparency, privacy, and analysis capabilities. In response, we used public information sources to map all electronic patient data flows across England, from providers to more than 460 subsequent academic, commercial, and public data consumers. Although NHS data support a global research ecosystem, we found that multistage data flow chains limit transparency and risk public trust, most data interactions do not fulfil recommended best practices for safe data access, and existing infrastructure produces aggregation of duplicate data assets, thus limiting diversity of data and added value to end users. We provide recommendations to support data infrastructure transformation and have produced a website (https://DataInsights.uk) to promote transparency and showcase NHS data assets.
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Affiliation(s)
- Joe Zhang
- Institute of Global Health Innovation, Imperial College London, London, UK; Department of Critical Care, Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Jess Morley
- Oxford Internet Institute, University of Oxford, Oxford, UK
| | - Jack Gallifant
- Department of Intensive Care, Imperial College Healthcare NHS Trust, London, UK; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Chris Oddy
- Department of Anaesthesia, Critical Care and Pain, St George's Healthcare NHS Trust, London, UK
| | - James T Teo
- London Medical Imaging and AI Centre, Guy's and St Thomas' NHS Foundation Trust, London, UK; Department of Neurology, King's College Hospital NHS Foundation Trust, London, UK
| | - Hutan Ashrafian
- Institute of Global Health Innovation, Imperial College London, London, UK; Leeds University Business School, Leeds, UK
| | - Brendan Delaney
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Ara Darzi
- Institute of Global Health Innovation, Imperial College London, London, UK
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10
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Kerasidou A, Kerasidou CX. Data-driven research and healthcare: public trust, data governance and the NHS. BMC Med Ethics 2023; 24:51. [PMID: 37452393 PMCID: PMC10349411 DOI: 10.1186/s12910-023-00922-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023] Open
Abstract
It is widely acknowledged that trust plays an important role for the acceptability of data sharing practices in research and healthcare, and for the adoption of new health technologies such as AI. Yet there is reported distrust in this domain. Although in the UK, the NHS is one of the most trusted public institutions, public trust does not appear to accompany its data sharing practices for research and innovation, specifically with the private sector, that have been introduced in recent years. In this paper, we examine the question of, what is it about sharing NHS data for research and innovation with for-profit companies that challenges public trust? To address this question, we draw from political theory to provide an account of public trust that helps better understand the relationship between the public and the NHS within a democratic context, as well as, the kind of obligations and expectations that govern this relationship. Then we examine whether the way in which the NHS is managing patient data and its collaboration with the private sector fit under this trust-based relationship. We argue that the datafication of healthcare and the broader 'health and wealth' agenda adopted by consecutive UK governments represent a major shift in the institutional character of the NHS, which brings into question the meaning of public good the NHS is expected to provide, challenging public trust. We conclude by suggesting that to address the problem of public trust, a theoretical and empirical examination of the benefits but also the costs associated with this shift needs to take place, as well as an open conversation at public level to determine what values should be promoted by a public institution like the NHS.
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Affiliation(s)
- Angeliki Kerasidou
- Ethox Centre, Oxford Population Health (Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery Oxford, University of Oxford, Oxford, UK.
| | - Charalampia Xaroula Kerasidou
- Ethox Centre, Oxford Population Health (Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery Oxford, University of Oxford, Oxford, UK
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11
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Cardinal RN, Moore A, Burchell M, Lewis JR. De-identified Bayesian personal identity matching for privacy-preserving record linkage despite errors: development and validation. BMC Med Inform Decis Mak 2023; 23:85. [PMID: 37147600 PMCID: PMC10163749 DOI: 10.1186/s12911-023-02176-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 04/21/2023] [Indexed: 05/07/2023] Open
Abstract
BACKGROUND Epidemiological research may require linkage of information from multiple organizations. This can bring two problems: (1) the information governance desirability of linkage without sharing direct identifiers, and (2) a requirement to link databases without a common person-unique identifier. METHODS We develop a Bayesian matching technique to solve both. We provide an open-source software implementation capable of de-identified probabilistic matching despite discrepancies, via fuzzy representations and complete mismatches, plus de-identified deterministic matching if required. We validate the technique by testing linkage between multiple medical records systems in a UK National Health Service Trust, examining the effects of decision thresholds on linkage accuracy. We report demographic factors associated with correct linkage. RESULTS The system supports dates of birth (DOBs), forenames, surnames, three-state gender, and UK postcodes. Fuzzy representations are supported for all except gender, and there is support for additional transformations, such as accent misrepresentation, variation for multi-part surnames, and name re-ordering. Calculated log odds predicted a proband's presence in the sample database with an area under the receiver operating curve of 0.997-0.999 for non-self database comparisons. Log odds were converted to a decision via a consideration threshold θ and a leader advantage threshold δ. Defaults were chosen to penalize misidentification 20-fold versus linkage failure. By default, complete DOB mismatches were disallowed for computational efficiency. At these settings, for non-self database comparisons, the mean probability of a proband being correctly declared to be in the sample was 0.965 (range 0.931-0.994), and the misidentification rate was 0.00249 (range 0.00123-0.00429). Correct linkage was positively associated with male gender, Black or mixed ethnicity, and the presence of diagnostic codes for severe mental illnesses or other mental disorders, and negatively associated with birth year, unknown ethnicity, residential area deprivation, and presence of a pseudopostcode (e.g. indicating homelessness). Accuracy rates would be improved further if person-unique identifiers were also used, as supported by the software. Our two largest databases were linked in 44 min via an interpreted programming language. CONCLUSIONS Fully de-identified matching with high accuracy is feasible without a person-unique identifier and appropriate software is freely available.
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Affiliation(s)
- Rudolf N. Cardinal
- Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Clifford Allbutt Building, Bay 13, Cambridge, CB2 0AH UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Fulbourn Hospital, Cambridge, CB21 5EF UK
| | - Anna Moore
- Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Clifford Allbutt Building, Bay 13, Cambridge, CB2 0AH UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Fulbourn Hospital, Cambridge, CB21 5EF UK
| | - Martin Burchell
- Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Clifford Allbutt Building, Bay 13, Cambridge, CB2 0AH UK
| | - Jonathan R. Lewis
- Cambridgeshire & Peterborough NHS Foundation Trust, Fulbourn Hospital, Cambridge, CB21 5EF UK
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12
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Astle DE, Moore A, Marryat L, Viding E, Mansfield KL, Fazel M, Pierce M, Abel KM, Green J, John A, Broome MR, Upthegrove R, Bould H, Minnis H, Gajwani R, Groom MJ, Hollis C, Liddle E, Sayal K, Berry V, Collishaw S, Dawes H, Cortese S, Violato M, Pollard J, MacCabe JH, Blakemore SJ, Simonoff E, Watkins E, Hiller RM, Townsend E, Armour C, Geddes JR, Thompson L, Schwannauer M, Nicholls D, Hotopf M, Downs J, Rahman A, Sharma AN, Ford TJ. We need timely access to mental health data: implications of the Goldacre review. Lancet Psychiatry 2023; 10:242-244. [PMID: 36931773 DOI: 10.1016/s2215-0366(23)00030-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/12/2023] [Accepted: 01/16/2023] [Indexed: 03/17/2023]
Affiliation(s)
- Duncan E Astle
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 1TN, UK.
| | - Anna Moore
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK
| | - Louise Marryat
- School of Health Sciences, University of Dundee, Dundee, UK
| | - Essi Viding
- Division of Psychology and Language Sciences, University College London, London, UK
| | | | - Mina Fazel
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Matthias Pierce
- Division of Psychology and Mental Health, University of Manchester, Manchester, UK
| | - Kathryn M Abel
- Division of Psychology and Mental Health, University of Manchester, Manchester, UK; Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Jonathan Green
- Division of Psychology and Mental Health, University of Manchester, Manchester, UK
| | - Ann John
- Population Data Science, Swansea University, Swansea, UK
| | - Matthew R Broome
- Institute for Mental Health, University of Birmingham, Birmingham, UK; Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK; Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Helen Bould
- Population Health Science, Centre for Academic Mental Health and Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Helen Minnis
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Ruchika Gajwani
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Madeleine J Groom
- Academic Unit of Mental Health & Clinical Neurosciences School of Medicine, University of Nottingham, Nottingham, UK; Centre for ADHD & Neurodevelopmental Disorders Across the Lifespan, University of Nottingham, Nottingham, UK
| | - Chris Hollis
- NIHR MindTech MIC & NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Elizabeth Liddle
- Centre for ADHD & Neurodevelopmental Disorders Across the Lifespan, University of Nottingham, Nottingham, UK
| | - Kapil Sayal
- Academic Unit of Mental Health & Clinical Neurosciences School of Medicine, University of Nottingham, Nottingham, UK; Centre for ADHD & Neurodevelopmental Disorders Across the Lifespan, University of Nottingham, Nottingham, UK
| | - Vashti Berry
- Children & Young People's Mental Health Research Collaboration, University of Exeter, Exeter, UK
| | - Stephan Collishaw
- Wolfson Centre for Young People's Mental Health, Cardiff University, Cardiff, UK
| | - Helen Dawes
- NIHR Exeter Biomedical Research Centre, University of Exeter, Exeter, UK
| | - Samuele Cortese
- Centre for Innovation in Mental Health, University of Southampton, Southampton, UK; Solent NHS Trust, Southampton, UK
| | - Mara Violato
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jack Pollard
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - James H MacCabe
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | | | - Emily Simonoff
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | | | - Rachel M Hiller
- Division of Psychology and Language Sciences, University College London, London, UK; Anna Freud National Centre for Children and Families, University College London, London, UK
| | - Ellen Townsend
- School of Psychology, University of Nottingham, Nottingham, UK
| | - Cherie Armour
- School of Psychology, Queen's University Belfast, Belfast, UK
| | - John R Geddes
- NIHR Oxford Health Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Lucy Thompson
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Matthias Schwannauer
- Centre for Applied Developmental Psychology, University of Edinburgh, Edinburgh, UK
| | - Dasha Nicholls
- Division of Psychiatry, Imperial College London, London, UK
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Johnny Downs
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Atif Rahman
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Aditya Narain Sharma
- Translational and Clinical Research Institute, University of Newcastle, Newcastle, UK; Cumbria Northumberland Tyne and Wear NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Tamsin J Ford
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK
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13
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Savic Kallesoe SA, Rabbani T, Gill EE, Brinkman F, Griffiths EJ, Zawati M, Liu H, Palmour N, Joly Y, Hsiao WWL. Canadians' opinions towards COVID-19 data-sharing: a national cross-sectional survey. BMJ Open 2023; 13:e066418. [PMID: 36750286 PMCID: PMC9905784 DOI: 10.1136/bmjopen-2022-066418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
OBJECTIVES COVID-19 research has significantly contributed to pandemic response and the enhancement of public health capacity. COVID-19 data collected by provincial/territorial health authorities in Canada are valuable for research advancement yet not readily available to the public, including researchers. To inform developments in public health data-sharing in Canada, we explored Canadians' opinions of public health authorities sharing deidentified individual-level COVID-19 data publicly. DESIGN/SETTING/INTERVENTIONS/OUTCOMES A national cross-sectional survey was administered in Canada in March 2022, assessing Canadians' opinions on publicly sharing COVID-19 datatypes. Market research firm Léger was employed for recruitment and data collection. PARTICIPANTS Anyone greater than or equal to 18 years and currently living in Canada. RESULTS 4981 participants completed the survey with a 92.3% response rate. 79.7% were supportive of provincial/territorial authorities publicly sharing deidentified COVID-19 data, while 20.3% were hesitant/averse/unsure. Datatypes most supported for being shared publicly were symptoms (83.0% in support), geographical region (82.6%) and COVID-19 vaccination status (81.7%). Datatypes with the most aversion were employment sector (27.4% averse), postal area (26.7%) and international travel history (19.7%). Generally supportive Canadians were characterised as being ≥50 years, with higher education, and being vaccinated against COVID-19 at least once. Vaccination status was the most influential predictor of data-sharing opinion, with respondents who were ever vaccinated being 4.20 times more likely (95% CI 3.21 to 5.48, p=0.000) to be generally supportive of data-sharing than those unvaccinated. CONCLUSIONS These findings suggest that the Canadian public is generally favourable to deidentified data-sharing. Identifying factors that are likely to improve attitudes towards data-sharing are useful to stakeholders involved in data-sharing initiatives, such as public health agencies, in informing the development of public health communication and data-sharing policies. As Canada progresses through the COVID-19 pandemic, and with limited testing and reporting of COVID-19 data, it is essential to improve deidentified data-sharing given the public's general support for these efforts.
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Affiliation(s)
- Sarah A Savic Kallesoe
- Simon Fraser University Faculty of Health Sciences, Burnaby, British Columbia, Canada
- Department of Public Health and Primary Care, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Tian Rabbani
- Simon Fraser University Faculty of Health Sciences, Burnaby, British Columbia, Canada
- School of Kinesiology, The University of British Columbia Faculty of Education, Vancouver, British Columbia, Canada
| | - Erin E Gill
- Department of Molecular Biology and Biochemistry, Simon Fraser University Faculty of Sciences, Burnaby, British Columbia, Canada
| | - Fiona Brinkman
- Department of Molecular Biology and Biochemistry, Simon Fraser University Faculty of Sciences, Burnaby, British Columbia, Canada
| | - Emma J Griffiths
- Simon Fraser University Faculty of Health Sciences, Burnaby, British Columbia, Canada
| | - Ma'n Zawati
- Department of Human Genetics, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada
| | - Hanshi Liu
- Department of Human Genetics, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada
| | - Nicole Palmour
- Department of Human Genetics, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada
| | - Yann Joly
- Department of Human Genetics, McGill University Faculty of Medicine and Health Sciences, Montreal, Québec, Canada
| | - William W L Hsiao
- Simon Fraser University Faculty of Health Sciences, Burnaby, British Columbia, Canada
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14
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Dove ES, Reed-Berendt R, Pareek M. "Data makes the story come to life:" understanding the ethical and legal implications of Big Data research involving ethnic minority healthcare workers in the United Kingdom-a qualitative study. BMC Med Ethics 2022; 23:136. [PMID: 36527096 PMCID: PMC9756740 DOI: 10.1186/s12910-022-00875-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
The aim of UK-REACH ("The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers") is to understand if, how, and why healthcare workers (HCWs) in the United Kingdom (UK) from ethnic minority groups are at increased risk of poor outcomes from COVID-19. In this article, we present findings from the ethical and legal stream of the study, which undertook qualitative research seeking to understand and address legal, ethical, and social acceptability issues around data protection, privacy, and information governance associated with the linkage of HCWs' registration data and healthcare data. We interviewed 22 key opinion leaders in healthcare and health research from across the UK in two-to-one semi-structured interviews. Transcripts were coded using qualitative thematic analysis. Participants told us that a significant aspect of Big Data research in public health is varying drivers of mistrust-of the research itself, research staff and funders, and broader concerns of mistrust within participant communities, particularly in the context of COVID-19 and those situated in more marginalised community settings. However, despite the challenges, participants also identified ways in which legally compliant and ethically informed approaches to research can be crafted to mitigate or overcome mistrust and establish greater confidence in Big Data public health research. Overall, our research indicates that a "Big Data Ethics by Design" approach to research in this area can help assure (1) that meaningful community and participant engagement is taking place and that extant challenges are addressed, and (2) that any new challenges or hitherto unknown unknowns can be rapidly and properly considered to ensure potential (but material) harms are identified and minimised where necessary. Our findings indicate such an approach, in turn, will help drive better scientific breakthroughs that translate into medical innovations and effective public health interventions, which benefit the publics studied, including those who are often marginalised in research.
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Affiliation(s)
- Edward S. Dove
- grid.4305.20000 0004 1936 7988Edinburgh Law School, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL UK
| | - Ruby Reed-Berendt
- grid.4305.20000 0004 1936 7988Edinburgh Law School, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL UK
| | - Manish Pareek
- grid.9918.90000 0004 1936 8411Department of Respiratory Sciences, University of Leicester, Leicester, UK ,grid.269014.80000 0001 0435 9078Department of Infection and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
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