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1. Public Health in the Information Age: Recognizing the Infosphere as a Social Determinant of Health

Author

Morley, Jessica, Cowls, Josh, Taddeo, Mariarosaria, and Floridi, Luciano

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

Since 2016, social media companies and news providers have come under pressure to tackle the spread of political mis- and disinformation (MDI) online. However, despite evidence that online health MDI (on the web, on social media, and within mobile apps) also has negative real-world effects, there has been a lack of comparable action by either online service providers or state-sponsored public health bodies. We argue that this is problematic and seek to answer three questions: why has so little been done to control the flow of, and exposure to, health MDI online; how might more robust action be justified; and what specific, newly justified actions are needed to curb the flow of, and exposure to, online health MDI? In answering these questions, we show that four ethical concerns—related to paternalism, autonomy, freedom of speech, and pluralism—are partly responsible for the lack of intervention. We then suggest that these concerns can be overcome by relying on four arguments: (1) education is necessary but insufficient to curb the circulation of health MDI, (2) there is precedent for state control of internet content in other domains, (3) network dynamics adversely affect the spread of accurate health information, and (4) justice is best served by protecting those susceptible to inaccurate health information. These arguments provide a strong case for classifying the quality of the infosphere as a social determinant of health, thus making its protection a public health responsibility. In addition, they offer a strong justification for working to overcome the ethical concerns associated with state-led intervention in the infosphere to protect public health.

Published
2020
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2. A Robust Governance for the AI Act: AI Office, AI Board, Scientific Panel, and National Authorities

Author

Novelli, Claudio, Hacker, Philipp, Morley, Jessica, Trondal, Jarle, and Floridi, Luciano

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence
Abstract

Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU's new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative model of governance, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfilment of the legislation. Taken together, the article explores how the AIA may be implemented by national and EU institutional bodies, encompassing longstanding bodies, such as the European Commission, and those newly established under the AIA, such as the AI Office. It investigates their roles across supranational and national levels, emphasizing how EU regulations influence institutional structures and operations. These regulations may not only directly dictate the structural design of institutions but also indirectly request administrative capacities needed to enforce the AIA., Comment: European Journal of Risk Regulation, 2024

Published
2024
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6. Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations

Author

Mokander, Jakob, Morley, Jessica, Taddeo, Mariarosaria, and Floridi, Luciano

Subjects
Computer Science - Computers and Society, Computer Science - Software Engineering
Abstract

Important decisions that impact human lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems (ADMS) can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social benefits of automation. In this article, we consider the feasibility and efficacy of ethics-based auditing (EBA) as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity's present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS., Comment: Artificial Intelligence, Auditing, Automated Decision-Making, Ethics, Governance

Published
2021
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7. Trends in weight gain recorded in English primary care before and during the Coronavirus-19 pandemic: An observational cohort study using the OpenSAFELY platform

Author

Samuel, Miriam, Park, Robin Y., Eastwood, Sophie V., Eto, Fabiola, Morton, Caroline E., Stow, Daniel, Bacon, Sebastian, Mehrkar, Amir, Morley, Jessica, Dillingham, Iain, Inglesby, Peter, Hulme, William J., Khunti, Kamlesh, Mathur, Rohini, Valabhji, Jonathan, MacKenna, Brian, and Finer, Sarah

Subjects
Control, Social aspects, Complications and side effects, Health aspects, Causes of, Forecasts and trends, Market trend/market analysis, Weight gain -- Forecasts and trends -- Health aspects -- Causes of, Epidemics -- Complications and side effects -- Social aspects -- Control -- United Kingdom, COVID-19 -- Social aspects -- Control -- Complications and side effects
Abstract

Author(s): Miriam Samuel 1,*, Robin Y. Park 2, Sophie V. Eastwood 3, Fabiola Eto 1, Caroline E. Morton 1,2, Daniel Stow 1, Sebastian Bacon 2, Amir Mehrkar 2, Jessica Morley [...], Background Obesity and rapid weight gain are established risk factors for noncommunicable diseases and have emerged as independent risk factors for severe disease following Coronavirus Disease 2019 (COVID-19) infection. Restrictions imposed to reduce COVID-19 transmission resulted in profound societal changes that impacted many health behaviours, including physical activity and nutrition, associated with rate of weight gain. We investigated which clinical and sociodemographic characteristics were associated with rapid weight gain and the greatest acceleration in rate of weight gain during the pandemic among adults registered with an English National Health Service (NHS) general practitioner (GP) during the COVID-19 pandemic. Methods and findings With the approval of NHS England, we used the OpenSAFELY platform inside TPP to conduct an observational cohort study of routinely collected electronic healthcare records. We investigated changes in body mass index (BMI) values recorded in English primary care between March 2015 and March 2022. We extracted data on 17,742,365 adults aged 18 to 90 years old (50.1% female, 76.1% white British) registered with an English primary care practice. We estimated individual rates of weight gain before ([delta]-prepandemic) and during ([delta]-pandemic) the pandemic and identified individuals with rapid weight gain (>0.5 kg/m.sup.2 /year) in each period. We also estimated the change in rate of weight gain between the prepandemic and pandemic period ([delta]-change = [delta]-pandemic-[delta]-prepandemic) and defined extreme accelerators as the 10% of individuals with the greatest increase in their rate of weight gain ([delta]-change [greater than or equal to]1.84 kg/m.sup.2 /year) between these periods. We estimated associations with these outcomes using multivariable logistic regression adjusted for age, sex, index of multiple deprivation (IMD), and ethnicity. P-values were generated in regression models. The median BMI of our study population was 27.8 kg/m.sup.2, interquartile range (IQR) [24.3, 32.1] in 2019 (March 2019 to February 2020) and 28.0 kg/m.sup.2, IQR [24.4, 32.6] in 2021. Rapid pandemic weight gain was associated with sex, age, and IMD. Male sex (male versus female: adjusted odds ratio (aOR) 0.76, 95% confidence interval (95% CI) [0.76, 0.76], p < 0.001), older age (e.g., 50 to 59 years versus 18 to 29 years: aOR 0.60, 95% CI [0.60, 0.61], p < 0.001]); and living in less deprived areas (least-deprived-IMD-quintile versus most-deprived: aOR 0.77, 95% CI [0.77, 0.78] p < 0.001) reduced the odds of rapid weight gain. Compared to white British individuals, all other ethnicities had lower odds of rapid pandemic weight gain (e.g., Indian versus white British: aOR 0.69, 95% CI [0.68, 0.70], p < 0.001). Long-term conditions (LTCs) increased the odds, with mental health conditions having the greatest effect (e.g., depression (aOR 1.18, 95% CI [1.17, 1.18], p < 0.001)). Similar characteristics increased odds of extreme acceleration in the rate of weight gain between the prepandemic and pandemic periods. However, changes in healthcare activity during the pandemic may have introduced new bias to the data. Conclusions We found female sex, younger age, deprivation, white British ethnicity, and mental health conditions were associated with rapid pandemic weight gain and extreme acceleration in rate of weight gain between the prepandemic and pandemic periods. Our findings highlight the need to incorporate sociodemographic, physical, and mental health characteristics when formulating research, policies, and interventions targeting BMI in the period of post pandemic service restoration and in future pandemic planning.

Published
2024
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8. Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis

Author

Walker, Alex, Green, Amelia, Mehrkar, Amir, Schaffer, Andrea, Brown, Andrew, Goldacre, Ben, Butler-Cole, Ben, MacKenna, Brian, Morton, Caroline, Walters, Caroline, Stables, Catherine, Cunningham, Christine, Wood, Christopher, Andrews, Colm, Evans, David, Hickman, George, Curtis, Helen, Drysdale, Henry, Dillingham, Iain, Morley, Jessica, Massey, Jon, Nab, Linda, Hopcroft, Lisa, Fisher, Louis, Bridges, Lucy, Wiedemann, Milan, DeVito, Nicholas, Macdonald, Orla, Inglesby, Peter, Smith, Rebecca, Croker, Richard, Park, Robin, Higgins, Rose, Bacon, Sebastian, Davy, Simon, Maude, Steven, O'Dwyer, Thomas, Ward, Tom, Speed, Victoria, Hulme, William, Hart, Liam, Stokes, Pete, Bhaskaran, Krishnan, Costello, Ruth, Cowling, Thomas, Douglas, Ian, Eggo, Rosalind, Evans, Stephen, Forbes, Harriet, Grieve, Richard, Grint, Daniel, Herrett, Emily, Langan, Sinead, Mahalingasivam, Viyaasan, Mansfield, Kathryn, Mathur, Rohini, McDonald, Helen, Parker, Edward, Rentsch, Christopher, Schultze, Anna, Smeeth, Liam, Tazare, John, Tomlinson, Laurie, Walker, Jemma, Williamson, Elizabeth, Wing, Kevin, Wong, Angel, Zheng, Bang, Bates, Christopher, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, O'Hanlon, Shaun, Eavis, Alex, Jarvis, Richard, Avramov, Dima, Griffiths, Paul, Fowles, Aaron, Parkes, Nasreen, Perera, Rafael, Harrison, David, Khunti, Kamlesh, Sterne, Jonathan, Quint, Jennifer, Henderson, Alasdair D., Butler-Cole, Ben FC., Tomlinson, Laurie A., Marks, Michael, Jit, Mark, Briggs, Andrew, Lin, Liang-Yu, Carlile, Oliver, Bates, Chris, Bacon, Sebastian CJ., Dennison, William A., Costello, Ruth E., Wei, Yinghui, Walker, Alex J., and Eggo, Rosalind M.

Published
2024
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9. Towards a framework for evaluating the safety, acceptability and efficacy of AI systems for health: an initial synthesis

Author

Morley, Jessica, Morton, Caroline, Karpathakis, Kassandra, Taddeo, Mariarosaria, and Floridi, Luciano

Subjects
Computer Science - Artificial Intelligence
Abstract

The potential presented by Artificial Intelligence (AI) for healthcare has long been recognised by the technical community. More recently, this potential has been recognised by policymakers, resulting in considerable public and private investment in the development of AI for healthcare across the globe. Despite this, excepting limited success stories, real-world implementation of AI systems into front-line healthcare has been limited. There are numerous reasons for this, but a main contributory factor is the lack of internationally accepted, or formalised, regulatory standards to assess AI safety and impact and effectiveness. This is a well-recognised problem with numerous ongoing research and policy projects to overcome it. Our intention here is to contribute to this problem-solving effort by seeking to set out a minimally viable framework for evaluating the safety, acceptability and efficacy of AI systems for healthcare. We do this by conducting a systematic search across Scopus, PubMed and Google Scholar to identify all the relevant literature published between January 1970 and November 2020 related to the evaluation of: output performance; efficacy; and real-world use of AI systems, and synthesising the key themes according to the stages of evaluation: pre-clinical (theoretical phase); exploratory phase; definitive phase; and post-market surveillance phase (monitoring). The result is a framework to guide AI system developers, policymakers, and regulators through a sufficient evaluation of an AI system designed for use in healthcare.

Published
2021

10. Impact of long COVID on health-related quality-of-life: an OpenSAFELY population cohort study using patient-reported outcome measures (OpenPROMPT)

Author

Walker, Alex, Green, Amelia, Mehrkar, Amir, Schaffer, Andrea, Brown, Andrew, Goldacre, Ben, Butler-Cole, Ben, MacKenna, Brian, Morton, Caroline, Walters, Caroline, Stables, Catherine, Cunningham, Christine, Wood, Christopher, Andrews, Colm, Evans, David, Hickman, George, Curtis, Helen, Drysdale, Henry, Dillingham, Iain, Morley, Jessica, Massey, Jon, Nab, Linda, Hopcroft, Lisa, Fisher, Louis, Bridges, Lucy, Wiedemann, Milan, DeVito, Nicholas, Macdonald, Orla, Inglesby, Peter, Smith, Rebecca, Croker, Richard, Park, Robin, Higgins, Rose, Bacon, Sebastian, Davy, Simon, Maude, Steven, O'Dwyer, Thomas, Ward, Tom, Speed, Victoria, Hulme, William, Hart, Liam, Stokes, Pete, Bhaskaran, Krishnan, Costello, Ruth, Cowling, Thomas, Douglas, Ian, Eggo, Rosalind, Evans, Stephen, Forbes, Harriet, Grieve, Richard, Grint, Daniel, Herrett, Emily, Langan, Sinead, Mahalingasivam, Viyaasan, Mansfield, Kathryn, Mathur, Rohini, McDonald, Helen, Parker, Edward, Rentsch, Christopher, Schultze, Anna, Smeeth, Liam, Tazare, John, Tomlinson, Laurie, Walker, Jemma, Williamson, Elizabeth, Wing, Kevin, Wong, Angel, Zheng, Bang, Bates, Christopher, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, O'Hanlon, Shaun, Eavis, Alex, Jarvis, Richard, Avramov, Dima, Griffiths, Paul, Fowles, Aaron, Parkes, Nasreen, Perera, Rafael, Harrison, David, Khunti, Kamlesh, Sterne, Jonathan, Quint, Jennifer, Carlile, Oliver, Briggs, Andrew, Henderson, Alasdair D., Butler-Cole, Ben F.C., Tomlinson, Laurie A., Marks, Michael, Jit, Mark, Lin, Liang-Yu, Bates, Chris, Bacon, Sebastian C.J., Dennison, William A., Costello, Ruth E., Walker, Alex J., and Eggo, Rosalind M.

Published
2024
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12. Ethics as a service: a pragmatic operationalisation of AI Ethics

Author

Morley, Jessica, Elhalal, Anat, Garcia, Francesca, Kinsey, Libby, Mokander, Jakob, and Floridi, Luciano

Subjects
Computer Science - Computers and Society
Abstract

As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the what and the how of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible (and thus vulnerable to ethics washing) or too strict (unresponsive to context). This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed Ethics as a Service., Comment: 21 pages, first draft

Published
2021

14. Clinical and health inequality risk factors for non-COVID-related sepsis during the global COVID-19 pandemic: a national case-control and cohort study

Author

Walker, Alex J., MacKenna, Brian, Inglesby, Peter, Goldacre, Ben, Curtis, Helen J., Morley, Jessica, Mehrkar, Amir, Bacon, Sebastian C.J., Hickman, George, Croker, Richard, Evans, David, Ward, Tom, DeVito, Nicholas J., Fisher, Louis, Green, Amelia C.A., Massey, Jon, Smith, Rebecca M., Hulme, William J., Davy, Simon, Andrews, Colm D., Hopcroft, Lisa E.M., Dillingham, Iain, Higgins, Rose, Cunningham, Christine, Wiedemann, Milan, Nab, Linda, Maude, Steven, Macdonald, Orla, Butler-Cole, Ben F.C., O'Dwyer, Thomas, Stables, Catherine L., Wood, Christopher, Brown, Andrew D., Speed, Victoria, Bridges, Lucy, Schaffer, Andrea L., Walters, Caroline E., Bates, Christopher, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, Zhong, Xiaomin, Ashiru-Oredope, Diane, Pate, Alexander, Martin, Glen P., Sharma, Anita, Dark, Paul, Felton, Tim, Lake, Claire, Hand, Kieran, Bladon, Sian, Cunningham, Neil, Gilham, Ellie, Brown, Colin S., Mirfenderesky, Mariyam, Palin, Victoria, and van Staa, Tjeerd Pieter

Published
2023
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15. Online information of vaccines: information quality is an ethical responsibility of search engines

Author

Ghezzi, Pietro, Bannister, Peter G, Casino, Gonzalo, Catalani, Alessia, Goldman, Michel, Morley, Jessica, Neunez, Marie, Prados, Andreu, Taddeo, Mariarosaria, Vanzolini, Tania, and Floridi, Luciano

Subjects
Computer Science - Social and Information Networks, Computer Science - Information Retrieval
Abstract

The fact that internet companies may record our personal data and track our online behavior for commercial or political purpose has emphasized aspects related to online privacy. This has also led to the development of search engines that promise no tracking and privacy. Search engines also have a major role in spreading low-quality health information such as that of anti-vaccine websites. This study investigates the relationship between search engines' approach to privacy and the scientific quality of the information they return. We analyzed the first 30 webpages returned searching 'vaccines autism' in English, Spanish, Italian and French. The results show that alternative search engines (Duckduckgo, Ecosia, Qwant, Swisscows and Mojeek) may return more anti-vaccine pages (10 to 53 percent) than Google.com (zero). Some localized versions of Google, however, returned more anti-vaccine webpages (up to 10 percent) than Google.com. Our study suggests that designing a search engine that is privacy savvy and avoids issues with filter bubbles that can result from user tracking is necessary but insufficient; instead, mechanisms should be developed to test search engines from the perspective of information quality (particularly for health-related webpages), before they can be deemed trustworthy providers of public health information., Comment: 18 pages, 3 figures, 2 tables

Published
2019

16. From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

Author

Morley, Jessica, Floridi, Luciano, Kinsey, Libby, and Elhalal, Anat

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The debate about the ethical implications of Artificial Intelligence dates from the 1960s. However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such debate has primarily focused on principles - the what of AI ethics - rather than on practices, the how. Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Therefore, our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs., Comment: 15 pages, links to typology available on the web

Published
2019

18. Ethnic differences in the indirect effects of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: a population-based, observational cohort study using the OpenSAFELY platform

Author

Chaturvedi, Nishi, Park, Chloe, Carnemolla, Alisia, Williams, Dylan, Knueppel, Anika, Boyd, Andy, Turner, Emma L., Evans, Katharine M., Thomas, Richard, Berman, Samantha, McLachlan, Stela, Crane, Matthew, Whitehorn, Rebecca, Oakley, Jacqui, Foster, Diane, Woodward, Hannah, Campbell, Kirsteen C., Timpson, Nicholas, Kwong, Alex, Soares, Ana Goncalves, Griffith, Gareth, Toms, Renin, Jones, Louise, Annie, Herbert, Mitchell, Ruth, Palmer, Tom, Sterne, Jonathan, Walker, Venexia, Huntley, Lizzie, Fox, Laura, Denholm, Rachel, Knight, Rochelle, Northstone, Kate, Kanagaratnam, Arun, Horne, Elsie, Forbes, Harriet, North, Teri, Taylor, Kurt, Arab, Marwa A.L., Walker, Scott, Coronado, Jose I.C., Karthikeyan, Arun S., Ploubidis, George, Moltrecht, Bettina, Booth, Charlotte, Parsons, Sam, Wielgoszewska, Bozena, Bridger-Staatz, Charis, Steves, Claire, Thompson, Ellen, Garcia, Paz, Cheetham, Nathan, Bowyer, Ruth, Freydin, Maxim, Roberts, Amy, Goldacre, Ben, Walker, Alex, Morley, Jess, Hulme, William, Nab, Linda, Fisher, Louis, MacKenna, Brian, Andrews, Colm, Curtis, Helen, Hopcroft, Lisa, Green, Amelia, Patalay, Praveetha, Maddock, Jane, Patel, Kishan, Stafford, Jean, Jacques, Wels, Tilling, Kate, Macleod, John, McElroy, Eoin, Shah, Anoop, Silverwood, Richard, Denaxas, Spiros, Flaig, Robin, McCartney, Daniel, Campbell, Archie, Tomlinson, Laurie, Tazare, John, Zheng, Bang, Smeeth, Liam, Herrett, Emily, Cowling, Thomas, Mansfield, Kate, Costello, Ruth E., Wang, Kevin, Mansfield, Kathryn, Mahalingasivam, Viyaasan, Douglas, Ian, Langan, Sinead, Brophy, Sinead, Parker, Michael, Kennedy, Jonathan, McEachan, Rosie, Wright, John, Willan, Kathryn, Badrick, Ellena, Santorelli, Gillian, Yang, Tiffany, Hou, Bo, Steptoe, Andrew, Giorgio, Di Gessa, Zhu, Jingmin, Zaninotto, Paola, Wood, Angela, Cezard, Genevieve, Ip, Samantha, Bolton, Tom, Sampri, Alexia, Rafeti, Elena, Almaghrabi, Fatima, Sheikh, Aziz, Shah, Syed A., Katikireddi, Vittal, Shaw, Richard, Hamilton, Olivia, Green, Michael, Kromydas, Theocharis, Kopasker, Daniel, Greaves, Felix, Willans, Robert, Glen, Fiona, Sharp, Steve, Hughes, Alun, Wong, Andrew, Howes, Lee Hamill, Rapala, Alicja, Nigrelli, Lidia, McArdle, Fintan, Beckford, Chelsea, Raman, Betty, Dobson, Richard, Folarin, Amos, Stewart, Callum, Ranjan, Yatharth, Carpentieri, Jd, Sheard, Laura, Fang, Chao, Baz, Sarah, Gibson, Andy, Kellas, John, Neubauer, Stefan, Piechnik, Stefan, Lukaschuk, Elena, Saunders, Laura C., Wild, James M., Smith, Stephen, Jezzard, Peter, Tunnicliffe, Elizabeth, Sanders, Zeena-Britt, Finnigan, Lucy, Ferreira, Vanessa, Green, Mark, Rhead, Rebecca, Kibble, Milla, Wei, Yinghui, Lemanska, Agnieszka, Perez-Reche, Francisco, Piehlmaier, Dominik, Teece, Lucy, Parker, Edward, Walker, Alex J., Inglesby, Peter, Curtis, Helen J., Morton, Caroline E., Morley, Jessica, Mehrkar, Amir, Bacon, Sebastian C.J., Hickman, George, Croker, Richard, Evans, David, Ward, Tom, DeVito, Nicholas J., Green, Amelia C.A., Massey, Jon, Smith, Rebecca M., Hulme, William J., Davy, Simon, Andrews, Colm D., Hopcroft, Lisa E.M., Drysdale, Henry, Dillingham, Iain, Park, Robin Y., Higgins, Rose, Cunningham, Christine, Wiedemann, Milan, Maude, Steven, Macdonald, Orla, Butler-Cole, Ben F.C., O'Dwyer, Thomas, Stables, Catherine L., Wood, Christopher, Brown, Andrew D., Speed, Victoria, Bridges, Lucy, Schaffer, Andrea L., Walters, Caroline E., Rentsch, Christopher T., Bhaskaran, Krishnan, Schultze, Anna, Williamson, Elizabeth J., McDonald, Helen I., Tomlinson, Laurie A., Mathur, Rohini, Eggo, Rosalind M., Wing, Kevin, Wong, Angel Y.S., Grieve, Richard, Grint, Daniel J., Mansfield, Kathryn E., Douglas, Ian J., Evans, Stephen J.W., Walker, Jemma L., Cowling, Thomas E., Herrett, Emily L., Parker, Edward P.K., Bates, Christopher, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, O'Hanlon, Shaun, Eavis, Alex, Jarvis, Richard, Avramov, Dima, Griffiths, Paul, Fowles, Aaron, Parkes, Nasreen, Nicholson, Brian, Perera, Rafael, Harrison, David, Khunti, Kamlesh, Sterne, Jonathan AC., Quint, Jennifer, Henderson, Alasdair D., Carreira, Helena, Bidulka, Patrick, Warren-Gash, Charlotte, Hayes, Joseph F., Quint, Jennifer K., Katikireddi, Srinivasa Vittal, and Langan, Sinéad M.

Published
2023
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19. Patient and public involvement within epidemiological studies of long COVID in the UK

Author

Routen, Ash, O’Mahoney, Lauren, Aiyegbusi, Olalekan Lee, Alder, Yvonne, Banerjee, Amitava, Buckland, Lewis, Brightling, Chris, Calvert, Melanie, Camaradou, Jenny, Chaturvedi, Nishi, Chong, Amy, Dalrymple, Emma, Eggo, Rosalind M., Elliott, Paul, Evans, Rachael A., Gibson, Andy, Haroon, Shamil, Herrett, Emily, Houchen-Wolloff, Linzy, Hughes, Sarah E., Jeyes, Flic, Matthews, Karen, McMullan, Christel, Morley, Jessica, Shafran, Roz, Smith, Nikki, Stanton, David, Stephenson, Terence, Sterne, Jonathan, Turner, Grace M., Ward, Helen, and Khunti, Kamlesh

Published
2023
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20. Changes in COVID-19-related mortality across key demographic and clinical subgroups in England from 2020 to 2022: a retrospective cohort study using the OpenSAFELY platform

Author

Nab, Linda, Parker, Edward P K, Andrews, Colm D, Hulme, William J, Fisher, Louis, Morley, Jessica, Mehrkar, Amir, MacKenna, Brian, Inglesby, Peter, Morton, Caroline E, Bacon, Sebastian C J, Hickman, George, Evans, David, Ward, Tom, Smith, Rebecca M, Davy, Simon, Dillingham, Iain, Maude, Steven, Butler-Cole, Ben F C, O’Dwyer, Thomas, Stables, Catherine L, Bridges, Lucy, Bates, Christopher, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, Zheng, Bang, Williamson, Elizabeth J, Eggo, Rosalind M, Evans, Stephen J W, Goldacre, Ben, Tomlinson, Laurie A, and Walker, Alex J

Published
2023
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24. Introduction

Author

Cowls, Josh, Morley, Jessica, Floridi, Luciano, Series Editor, Taddeo, Mariarosaria, Series Editor, Cowls, Josh, editor, and Morley, Jessica, editor

Published
2021
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26. Describing the population experiencing COVID-19 vaccine breakthrough following second vaccination in England: a cohort study from OpenSAFELY

Author

Green, Amelia, Curtis, Helen, Hulme, William, Williamson, Elizabeth, McDonald, Helen, Bhaskaran, Krishnan, Rentsch, Christopher, Schultze, Anna, MacKenna, Brian, Mahalingasivam, Viyaasan, Tomlinson, Laurie, Walker, Alex, Fisher, Louis, Massey, Jon, Andrews, Colm, Hopcroft, Lisa, Morton, Caroline, Croker, Richard, Morley, Jessica, Mehrkar, Amir, Bacon, Seb, Evans, David, Inglesby, Peter, Hickman, George, Ward, Tom, Davy, Simon, Mathur, Rohini, Tazare, John, Eggo, Rosalind, Wing, Kevin, Wong, Angel, Forbes, Harriet, Bates, Chris, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, Douglas, Ian, Evans, Stephen, Smeeth, Liam, and Goldacre, Ben

Published
2022
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27. Comparison of methods for predicting COVID-19-related death in the general population using the OpenSAFELY platform

Author

Williamson, Elizabeth J., Tazare, John, Bhaskaran, Krishnan, McDonald, Helen I., Walker, Alex J., Tomlinson, Laurie, Wing, Kevin, Bacon, Sebastian, Bates, Chris, Curtis, Helen J., Forbes, Harriet J., Minassian, Caroline, Morton, Caroline E., Nightingale, Emily, Mehrkar, Amir, Evans, David, Nicholson, Brian D., Leon, David A., Inglesby, Peter, MacKenna, Brian, Davies, Nicholas G., DeVito, Nicholas J., Drysdale, Henry, Cockburn, Jonathan, Hulme, William J., Morley, Jessica, Douglas, Ian, Rentsch, Christopher T., Mathur, Rohini, Wong, Angel, Schultze, Anna, Croker, Richard, Parry, John, Hester, Frank, Harper, Sam, Grieve, Richard, Harrison, David A., Steyerberg, Ewout W., Eggo, Rosalind M., Diaz-Ordaz, Karla, Keogh, Ruth, Evans, Stephen J. W., Smeeth, Liam, and Goldacre, Ben

Published
2022
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28. The Chinese Approach to Artificial Intelligence: An Analysis of Policy, Ethics, and Regulation

Author

Roberts, Huw, Cowls, Josh, Morley, Jessica, Taddeo, Mariarosaria, Wang, Vincent, Floridi, Luciano, Taddeo, Mariarosaria, Editor-in-Chief, Allo, Patrick, Editorial Board Member, Baker, Lynne, Advisory Editor, Cohen, Stewart, Advisory Editor, Bogdan, Radu, Advisory Editor, David, Marian, Advisory Editor, Fischer, John, Advisory Editor, Lehrer, Keith, Advisory Editor, Meyerson, Denise, Advisory Editor, Recanati, Francois, Advisory Editor, Sainsbury, Mark, Advisory Editor, Smith, Barry, Advisory Editor, Smith, Nicholas, Advisory Editor, Zagzebski, Linda, Advisory Editor, and Floridi, Luciano, editor

Published
2021
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29. The Ethics of Algorithms: Key Problems and Solutions

Author

Tsamados, Andreas, Aggarwal, Nikita, Cowls, Josh, Morley, Jessica, Roberts, Huw, Taddeo, Mariarosaria, Floridi, Luciano, Taddeo, Mariarosaria, Editor-in-Chief, Allo, Patrick, Editorial Board Member, Baker, Lynne, Advisory Editor, Cohen, Stewart, Advisory Editor, Bogdan, Radu, Advisory Editor, David, Marian, Advisory Editor, Fischer, John, Advisory Editor, Lehrer, Keith, Advisory Editor, Meyerson, Denise, Advisory Editor, Recanati, Francois, Advisory Editor, Sainsbury, Mark, Advisory Editor, Smith, Barry, Advisory Editor, Smith, Nicholas, Advisory Editor, Zagzebski, Linda, Advisory Editor, and Floridi, Luciano, editor

Published
2021
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30. The Ethics of AI in Health Care: A Mapping Review

Author

Morley, Jessica, Machado, Caio C. V., Burr, Christopher, Cowls, Josh, Joshi, Indra, Taddeo, Mariarosaria, Floridi, Luciano, Taddeo, Mariarosaria, Editor-in-Chief, Allo, Patrick, Editorial Board Member, Baker, Lynne, Advisory Editor, Cohen, Stewart, Advisory Editor, Bogdan, Radu, Advisory Editor, David, Marian, Advisory Editor, Fischer, John, Advisory Editor, Lehrer, Keith, Advisory Editor, Meyerson, Denise, Advisory Editor, Recanati, Francois, Advisory Editor, Sainsbury, Mark, Advisory Editor, Smith, Barry, Advisory Editor, Smith, Nicholas, Advisory Editor, Zagzebski, Linda, Advisory Editor, and Floridi, Luciano, editor

Published
2021
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31. From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

Author

Morley, Jessica, Floridi, Luciano, Kinsey, Libby, Elhalal, Anat, Taddeo, Mariarosaria, Editor-in-Chief, Allo, Patrick, Editorial Board Member, Baker, Lynne, Advisory Editor, Cohen, Stewart, Advisory Editor, Bogdan, Radu, Advisory Editor, David, Marian, Advisory Editor, Fischer, John, Advisory Editor, Lehrer, Keith, Advisory Editor, Meyerson, Denise, Advisory Editor, Recanati, Francois, Advisory Editor, Sainsbury, Mark, Advisory Editor, Smith, Barry, Advisory Editor, Smith, Nicholas, Advisory Editor, Zagzebski, Linda, Advisory Editor, and Floridi, Luciano, editor

Published
2021
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36. Weight trends among adults with diabetes or hypertension during the COVID-19 pandemic: an observational study using OpenSAFELY.

Author

Samuel, Miriam, Park, Robin Y, Eastwood, Sophie V, Eto, Fabiola, Morton, Caroline E, Stow, Daniel, Bacon, Sebastian, Goldacre, Ben, Mehrkar, Amir, Morley, Jessica, Dillingham, Iain, Inglesby, Peter, Hulme, William J, Khunti, Kamlesh, Mathur, Rohini, Valabhji, Jonathan, MacKenna, Brian, and Finer, Sarah

Subjects
COVID-19 pandemic, TYPE 2 diabetes, WEIGHT gain, BODY mass index, PRIMARY health care
Abstract

Background: COVID-19 pandemic restrictions may have influenced behaviours related to weight. Aim: To describe patterns of weight change among adults living in England with type 2 diabetes (T2D) and/or hypertension during the pandemic. Design and setting: An observational cohort study using the routinely collected health data of approximately 40% of adults living in England, accessed through the OpenSAFELY service inside TPP. Method: Clinical and sociodemographic characteristics associated with rapid weight gain (>0.5 kg/m2/year) were investigated using multivariable logistic regression. Results: Data were extracted on adults with T2D (n = 1 231 455, 43.9% female, and 76.0% White British) or hypertension (n = 3 558 405, 49.7% female, and 84.3% White British). Adults with T2D lost weight overall (median δ = −0.1 kg/m2/year [interquartile range {IQR} −0.7–0.4]). However, rapid weight gain was common (20.7%) and associated with the following: sex (male versus female: adjusted odds ratio [aOR] 0.78 [95% confidence interval {CI} = 0.77 to 0.79]); age (older age reduced odds, for example, aged 60–69 years versus 18–29 years: aOR 0.66 [95% CI = 0.61 to 0.71]); deprivation (least deprived Index of Multiple Deprivation [IMD] quintile versus most deprived IMD quintile: aOR 0.87 [95% CI = 0.85 to 0.89]); White ethnicity (Black versus White: aOR 0.95 [95% CI = 0.92 to 0.98]); mental health conditions (for example, depression: aOR 1.13 [95% CI = 1.12 to 1.15]); and diabetes treatment (non-insulin treatment versus no pharmacological treatment: aOR 0.68 [95% CI = 0.67 to 0.69]). Adults with hypertension maintained stable weight overall (median δ = 0.0 kg/m2/year [IQR −0.6–0.5]); however, rapid weight gain was common (24.7%) and associated with similar characteristics as in T2D. Conclusion: Among adults living in England with T2D and/or hypertension, rapid pandemic weight gain was more common among females, younger adults, those living in more deprived areas, and those with mental health conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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37. NHS national data opt-outs: trends and potential consequences for health data research.

Author

Tazare, John, Henderson, Alasdair D., Morley, Jessica, Blake, Helen A., McDonald, Helen I., Williamson, Elizabeth J., and Strongman, Helen

Subjects
DATABASES, MEDICAL information storage & retrieval systems, NATIONAL health services, MEDICAL care research, DATA security, RESEARCH funding, PRIMARY health care, SEX distribution, HEALTH policy, AGE distribution, POPULATION geography, RESEARCH methodology
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. [ABSTRACT FROM AUTHOR]

Published
2024
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39. OpenSAFELY: A platform for analysing electronic health records designed for reproducible research

Author

Nab, Linda, primary, Schaffer, Andrea L., additional, Hulme, William, additional, DeVito, Nicholas J., additional, Dillingham, Iain, additional, Wiedemann, Milan, additional, Andrews, Colm D., additional, Curtis, Helen, additional, Fisher, Louis, additional, Green, Amelia, additional, Massey, Jon, additional, Walters, Caroline E., additional, Higgins, Rose, additional, Cunningham, Christine, additional, Morley, Jessica, additional, Mehrkar, Amir, additional, Hart, Liam, additional, Davy, Simon, additional, Evans, David, additional, Hickman, George, additional, Inglesby, Peter, additional, Morton, Caroline E., additional, Smith, Rebecca M., additional, Ward, Tom, additional, O'Dwyer, Thomas, additional, Maude, Steven, additional, Bridges, Lucy, additional, Butler‐Cole, Ben F. C., additional, Stables, Catherine L., additional, Stokes, Pete, additional, Bates, Chris, additional, Cockburn, Jonny, additional, Hester, Frank, additional, Parry, John, additional, Bhaskaran, Krishnan, additional, Schultze, Anna, additional, Rentsch, Christopher T., additional, Mathur, Rohini, additional, Tomlinson, Laurie A., additional, Williamson, Elizabeth J., additional, Smeeth, Liam, additional, Walker, Alex, additional, Bacon, Sebastian, additional, MacKenna, Brian, additional, and Goldacre, Ben, additional

Published
2024
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41. Regulation by Design:Features, Practices, Limitations, and Governance Implications

Author

Prifti, Kostina, Morley, Jessica, Novelli, Claudio, Floridi, Luciano, Prifti, Kostina, Morley, Jessica, Novelli, Claudio, and Floridi, Luciano

Abstract

Regulation by design (RBD) is a growing research field that explores, develops, and criticises the regulative function of design. In this article, we provide a qualitative thematic synthesis of the existing literature. The aim is to explore and analyse RBD’s core features, practices, limitations, and related governance implications. To fulfil this aim, we examine the extant literature on RBD in the context of digital technologies. We start by identifying and structuring the core features of RBD, namely the goals, regulators, regulatees, methods, and technologies. Building on that structure, we distinguish among three types of RBD practices: compliance by design, value creation by design, and optimisation by design. We then explore the challenges and limitations of RBD practices, which stem from risks associated with compliance by design, contextual limitations, or methodological uncertainty. Finally, we examine the governance implications of RBD and outline possible future directions of the research field and its practices.

Published
2024

48. Effectiveness of mRNA COVID-19 Vaccines as First Booster Doses in England: An Observational Study in OpenSAFELY-TPP.

Author

Horne, Elsie M. F., Hulme, William J., Parker, Edward P. K., Keogh, Ruth H., Williamson, Elizabeth J., Walker, Venexia M., Palmer, Tom M., Denholm, Rachel, Knight, Rochelle, Curtis, Helen J., Walker, Alex J., Andrews, Colm D., Mehrkar, Amir, Morley, Jessica, MacKenna, Brian, Bacon, Sebastian C. J., Goldacre, Ben, Hernán, Miguel A., and Sterne, Jonathan A. C.

Abstract

Background: The UK delivered its first “booster” COVID-19 vaccine doses in September 2021, initially to individuals at high risk of severe disease, then to all adults. The BNT162b2 Pfizer-BioNTech vaccine was used initially, then also Moderna mRNA-1273. Methods: With the approval of the National Health Service England, we used routine clinical data to estimate the effectiveness of boosting with BNT162b2 or mRNA-1273 compared with no boosting in eligible adults who had received two primary course vaccine doses. We matched each booster recipient with an unboosted control on factors relating to booster priority status and prior COVID-19 immunization. We adjusted for additional factors in Cox models, estimating hazard ratios up to 182 days (6 months) following booster dose. We estimated hazard ratios overall and within the following periods: 1–14, 15–42, 43–69, 70–97, 98–126, 127–152, and 155–182 days. Outcomes included a positive SARS-CoV-2 test, COVID-19 hospitalization, COVID-19 death, non-COVID-19 death, and fracture. Results: We matched 8,198,643 booster recipients with unboosted controls. Adjusted hazard ratios over 6-month follow-up were: positive SARS-CoV-2 test 0.75 (0.74, 0.75); COVID-19 hospitalization 0.30 (0.29, 0.31); COVID-19 death 0.11 (0.10, 0.14); non- COVID-19 death 0.22 (0.21, 0.23); and fracture 0.77 (0.75, 0.78). Estimated effectiveness of booster vaccines against severe COVID- 19-related outcomes peaked during the first 3 months following the booster dose. By 6 months, the cumulative incidence of positive SARS-CoV-2 test was higher in boosted than unboosted individuals. Conclusions: We estimate that COVID-19 booster vaccination, compared with no booster vaccination, provided substantial protection against COVID-19 hospitalization and COVID-19 death but only limited protection against positive SARS-CoV-2 test. Lower rates of fracture in boosted than unboosted individuals may suggest unmeasured confounding. Observational studies should report estimated vaccine effectiveness against nontarget and negative control outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Weight trends amongst adults with diabetes or hypertension during the COVID-19 pandemic: an observational study using OpenSAFELY

Author

Samuel, Miriam, primary, Park, Robin Y, additional, Eastwood, Sophie V, additional, Eto, Fabiola, additional, Morton, Caroline, additional, Stow, Daniel, additional, Bacon, Sebastian, additional, Goldacre, Ben, additional, Mehrkar, Amir, additional, Morley, Jessica, additional, Dillingham, Iain, additional, Inglesby, Peter, additional, Hulme, William J, additional, Khunti, Kamlesh, additional, Mathur, Rohini, additional, Valabhji, Jonathan, additional, MacKenna, Brian, additional, and Finer, Sarah, additional

Published
2023
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