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1. Barriers to Working With National Health Service England’s Open Data

Author

Bacon, Seb and Goldacre, Ben

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

Open data is information made freely available to third parties in structured formats without restrictive licensing conditions, permitting commercial and noncommercial organizations to innovate. In the context of National Health Service (NHS) data, this is intended to improve patient outcomes and efficiency. EBM DataLab is a research group with a focus on online tools which turn our research findings into actionable monthly outputs. We regularly import and process more than 15 different NHS open datasets to deliver OpenPrescribing.net, one of the most high-impact use cases for NHS England’s open data, with over 15,000 unique users each month. In this paper, we have described the many breaches of best practices around NHS open data that we have encountered. Examples include datasets that repeatedly change location without warning or forwarding; datasets that are needlessly behind a “CAPTCHA” and so cannot be automatically downloaded; longitudinal datasets that change their structure without warning or documentation; near-duplicate datasets with unexplained differences; datasets that are impossible to locate, and thus may or may not exist; poor or absent documentation; and withholding of data for dubious reasons. We propose new open ways of working that will support better analytics for all users of the NHS. These include better curation, better documentation, and systems for better dialogue with technical teams.

Published
2020
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2. Consistency, completeness and external validity of ethnicity recording in NHS primary care records: a cohort study in 25 million patients’ records at source using OpenSAFELY

Author

Andrews, Colm D., Mathur, Rohini, Massey, Jon, Park, Robin, Curtis, Helen J., Hopcroft, Lisa, Mehrkar, Amir, Bacon, Seb, Hickman, George, Smith, Rebecca, Evans, David, Ward, Tom, Davy, Simon, Inglesby, Peter, Dillingham, Iain, Maude, Steven, O’Dwyer, Thomas, Butler-Cole, Ben F. C., Bridges, Lucy, Bates, Chris, Parry, John, Hester, Frank, Harper, Sam, Cockburn, Jonathan, Goldacre, Ben, MacKenna, Brian, Tomlinson, Laurie A., Walker, Alex J., and Hulme, William J.

Published
2024
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3. Changes in opioid prescribing during the COVID-19 pandemic in England: an interrupted time-series analysis in the OpenSAFELY-TPP cohort

Author

Bridges, Lucy, Butler-Cole, Benjamin FC, Davy, Simon, Dillingham, Iain, Evans, David, Fisher, Louis, Green, Amelia, Hart, Liam, Hickman, George, Maude, Steven, O'Dwyer, Thomas, Smith, Rebecca M, Stokes, Pete, Ward, Tom, Cockburn, Johnathan, Harper, Sam, Hester, Frank, Parry, John, Schaffer, Andrea L, Andrews, Colm D, Brown, Andrew D, Croker, Richard, Hulme, William J, Nab, Linda, Quinlan, Jane, Speed, Victoria, Wood, Christopher, Wiedemann, Milan, Massey, Jon, Inglesby, Peter, Bacon, Seb C J, Mehrkar, Amir, Bates, Chris, Goldacre, Ben, Walker, Alex J, and MacKenna, Brian

Published
2024
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4. Implementing Germ Defence digital behaviour change intervention via all primary care practices in England to reduce respiratory infections during the COVID-19 pandemic: an efficient cluster randomised controlled trial using the OpenSAFELY platform

Author

Ainsworth, Ben, Horwood, Jeremy, Walter, Scott R., Miller, Sascha, Chalder, Melanie, De Vocht, Frank, Denison-Day, James, Elwenspoek, Martha M. C., Curtis, Helen J., Bates, Chris, Mehrkar, Amir, Bacon, Seb, Goldacre, Ben, Craggs, Pippa, Amlôt, Richard, Francis, Nick, Little, Paul, Macleod, John, Moore, Michael, Morton, Kate, Rice, Cathy, Sterne, Jonathan, Stuart, Beth, Towler, Lauren, Willcox, Merlin L., and Yardley, Lucy

Published
2023
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5. Gout incidence and management during the COVID-19 pandemic in England, UK: a nationwide observational study using OpenSAFELY

Author

Russell, Mark D, Massey, Jon, Roddy, Edward, MacKenna, Brian, Bacon, Seb, Goldacre, Ben, Andrews, Colm D, Hickman, George, Mehrkar, Amir, Mahto, Arti, Rutherford, Andrew I, Patel, Samir, Adas, Maryam A, Alveyn, Edward, Nagra, Deepak, Bechman, Katie, Ledingham, Joanna M, Hudson, Joanna, Norton, Sam, Cope, Andrew P, and Galloway, James B

Published
2023
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6. Risk of severe COVID-19 outcomes associated with immune-mediated inflammatory diseases and immune-modifying therapies: a nationwide cohort study in the OpenSAFELY platform

Author

MacKenna, Brian, Kennedy, Nicholas A, Mehrkar, Amir, Rowan, Anna, Galloway, James, Matthewman, Julian, Mansfield, Kathryn E, Bechman, Katie, Yates, Mark, Brown, Jeremy, Schultze, Anna, Norton, Sam, Walker, Alex J, Morton, Caroline E, Harrison, David, Bhaskaran, Krishnan, Rentsch, Christopher T, Williamson, Elizabeth, Croker, Richard, Bacon, Seb, Hickman, George, Ward, Tom, Davy, Simon, Green, Amelia, Fisher, Louis, Hulme, William, Bates, Chris, Curtis, Helen J, Tazare, John, Eggo, Rosalind M, Evans, David, Inglesby, Peter, Cockburn, Jonathan, McDonald, Helen I, Tomlinson, Laurie A, Mathur, Rohini, Wong, Angel Y S, Forbes, Harriet, Parry, John, Hester, Frank, Harper, Sam, Douglas, Ian J, Smeeth, Liam, Lees, Charlie W, Evans, Stephen J W, Goldacre, Ben, Smith, Catherine H, and Langan, Sinéad M

Published
2022
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7. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Author

Abbasizanjani, Hoda, Ahmed, Nida, Ahmed, Badar, Akbari, Ashley, Akinoso-Imran, Abdul Qadr, Allara, Elias, Allery, Freya, Angelantonio, Emanuele Di, Ashworth, Mark, Ayyar-Gupta, Vandana, Babu-Narayan, Sonya, Bacon, Seb, Ball, Steve, Banerjee, Ami, Barber, Mark, Barrett, Jessica, Bennie, Marion, Berry, Colin, Beveridge, Jennifer, Birney, Ewan, Bojanić, Lana, Bolton, Thomas, Bone, Anna, Boyle, Jon, Braithwaite, Tasanee, Bray, Ben, Briffa, Norman, Brind, David, Brown, Katherine, Buch, Maya, Canoy, Dexter, Caputo, Massimo, Carragher, Raymond, Carson, Alan, Cezard, Genevieve, Chang, Jen-Yu Amy, Cheema, Kate, Chin, Richard, Chudasama, Yogini, Cooper, Jennifer, Copland, Emma, Crallan, Rebecca, Cripps, Rachel, Cromwell, David, Curcin, Vasa, Curry, Gwenetta, Dale, Caroline, Danesh, John, Das-Munshi, Jayati, Dashtban, Ashkan, Davies, Alun, Davies, Joanna, Davies, Gareth, Davies, Neil, Day, Joshua, Delmestri, Antonella, Denaxas, Spiros, Denholm, Rachel, Dennis, John, Denniston, Alastair, Deo, Salil, Dhillon, Baljean, Docherty, Annemarie, Dong, Tim, Douiri, Abdel, Downs, Johnny, Dregan, Alexandru, Ellins, Elizabeth A, Elwenspoek, Martha, Falck, Fabian, Falter, Florian, Fan, Yat Yi, Firth, Joseph, Fraser, Lorna, Friebel, Rocco, Gavrieli, Amir, Gerstung, Moritz, Gilbert, Ruth, Gillies, Clare, Glickman, Myer, Goldacre, Ben, Goldacre, Raph, Greaves, Felix, Green, Mark, Grieco, Luca, Griffiths, Rowena, Gurdasani, Deepti, Halcox, Julian, Hall, Nick, Hama, Tuankasfee, Handy, Alex, Hansell, Anna, Hardelid, Pia, Hardy, Flavien, Harris, Daniel, Harrison, Camille, Harron, Katie, Hassaine, Abdelaali, Hassan, Lamiece, Healey, Russell, Hemingway, Harry, Henderson, Angela, Herz, Naomi, Heyl, Johannes, Hidajat, Mira, Higginson, Irene, Hinchliffe, Rosie, Hippisley-Cox, Julia, Ho, Frederick, Hocaoglu, Mevhibe, Hollings, Sam, Horne, Elsie, Hughes, David, Humberstone, Ben, Inouye, Mike, Ip, Samantha, Islam, Nazrul, Jackson, Caroline, Jenkins, David, Jiang, Xiyun, Johnson, Shane, Kadam, Umesh, Kallis, Costas, Karim, Zainab, Kasan, Jake, Katsoulis, Michalis, Kavanagh, Kim, Kee, Frank, Keene, Spencer, Kent, Seamus, Khalid, Sara, Khawaja, Anthony, Khunti, Kamlesh, Killick, Richard, Kinnear, Deborah, Knight, Rochelle, Kolamunnage-Dona, Ruwanthi, Kontopantelis, Evan, Kurdi, Amanj, Lacey, Ben, Lai, Alvina, Lambarth, Andrew, Larzjan, Milad Nazarzadeh, Lawler, Deborah, Lawrence, Thomas, Lawson, Claire, Li, Qiuju, Li, Ken, Llinares, Miguel Bernabeu, Lorgelly, Paula, Lowe, Deborah, Lyons, Jane, Lyons, Ronan, Machado, Pedro, Macleod, Mary Joan, Macleod, John, Malgapo, Evaleen, Mamas, Mamas, Mamouei, Mohammad, Manohar, Sinduja, Mapeta, Rutendo, Martelli, Javiera Leniz, Martos, David Moreno, Mateen, Bilal, McCarthy, Aoife, Melville, Craig, Milton, Rebecca, Mizani, Mehrdad, Moncusi, Marta Pineda, Morales, Daniel, Mordi, Ify, Morrice, Lynn, Morris, Carole, Morris, Eva, Mu, Yi, Mueller, Tanja, Murdock, Lars, Nafilyan, Vahé, Nicholson, George, Nikiphorou, Elena, Nolan, John, Norris, Tom, Norris, Ruth, North, Laura, North, Teri-Louise, O'Connell, Dan, Oliver, Dominic, Oluyase, Adejoke, Olvera-Barrios, Abraham, Omigie, Efosa, Onida, Sarah, Padmanabhan, Sandosh, Palmer, Tom, Pasea, Laura, Patel, Riyaz, Payne, Rupert, Pell, Jill, Petitjean, Carmen, Pherwani, Arun, Pickrell, Owen, Pierotti, Livia, Pirmohamed, Munir, Priedon, Rouven, Prieto-Alhambra, Dani, Proudfoot, Alastair, Quinn, Terry, Quint, Jennifer, Raffetti, Elena, Rahimi, Kazem, Rao, Shishir, Razieh, Cameron, Roberts, Brian, Rogers, Caroline, Rossdale, Jennifer, Salim, Safa, Samani, Nilesh, Sattar, Naveed, Schnier, Christian, Schwartz, Roy, Selby, David, Seminog, Olena, Shabnam, Sharmin, Shah, Ajay, Shelton, Jon, Sheppard, James, Sinha, Shubhra, Skrypak, Mirek, Slapkova, Martina, Sleeman, Katherine, Smith, Craig, Sofat, Reecha, Sosenko, Filip, Sperrin, Matthew, Steeg, Sarah, Sterne, Jonathan, Stoica, Serban, Sudell, Maria, Sudlow, Cathie, Sun, Luanluan, Suseeladevi, Arun Karthikeyan, Sweeting, Michael, Sydes, Matt, Takhar, Rohan, Tang, Howard, Thygesen, Johan, Tilston, George, Tochel, Claire, Toit, Clea du, Tomlinson, Christopher, Toms, Renin, Torabi, Fatemeh, Torralbo, Ana, Townson, Julia, Tufail, Adnan, Tungamirai, Tapiwa, Varma, Susheel, Vollmer, Sebastian, Walker, Venexia, Wang, Tianxiao, Wang, Huan, Warwick, Alasdair, Watkinson, Ruth, Watson, Harry, Whiteley, William, Whittaker, Hannah, Wilde, Harry, Wilkinson, Tim, Williams, Gareth, Williams, Michelle, Williams, Richard, Withnell, Eloise, Wolfe, Charles, Wood, Angela, Wright, Lucy, Wu, Honghan, Wu, Jinge, Wu, Jianhua, Yates, Tom, Zaccardi, Francesco, Zhang, Haoting, Zhang, Huayu, Zuccolo, Luisa, Thygesen, Johan H, Mizani, Mehrdad A, Banerjee, Amitava, Lai, Alvina G, Li, Kezhi, Mateen, Bilal A, Sterne, Jonathan A C, Pagel, Christina, and Whiteley, William N

Published
2022
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9. 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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11. P086 Trends in the Incidence and Management of Gout in England: A Nationwide Study using the OpenSAFELY Platform

Author

Russell, Mark D, primary, Massey, Jon, additional, Roddy, Edward, additional, MacKenna, Brian, additional, Bacon, Seb, additional, Goldacre, Ben, additional, Andrews, Colm D, additional, Hickman, George, additional, Mehrkar, Amir, additional, Mahto, Arti, additional, Rutherford, Andrew I, additional, Patel, Samir, additional, Adas, Maryam A, additional, Alveyn, Edward, additional, Nagra, Deepak, additional, Bechman, Katie, additional, Ledingham, Jo M, additional, Hudson, Joanna, additional, Norton, Sam, additional, Cope, Andrew, additional, and Galloway, James, additional

Published
2024
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12. Effect of pre-exposure use of hydroxychloroquine on COVID-19 mortality: a population-based cohort study in patients with rheumatoid arthritis or systemic lupus erythematosus using the OpenSAFELY platform

Author

Rentsch, Christopher T, DeVito, Nicholas J, MacKenna, Brian, Morton, Caroline E, Bhaskaran, Krishnan, Brown, Jeremy P, Schultze, Anna, Hulme, William J, Croker, Richard, Walker, Alex J, Williamson, Elizabeth J, Bates, Chris, Bacon, Seb, Mehrkar, Amir, Curtis, Helen J, Evans, David, Wing, Kevin, Inglesby, Peter, Mathur, Rohini, Drysdale, Henry, Wong, Angel Y S, McDonald, Helen I, Cockburn, Jonathan, Forbes, Harriet, Parry, John, Hester, Frank, Harper, Sam, Smeeth, Liam, Douglas, Ian J, Dixon, William G, Evans, Stephen J W, Tomlinson, Laurie, and Goldacre, Ben

Published
2021
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13. Risk of COVID-19-related death among patients with chronic obstructive pulmonary disease or asthma prescribed inhaled corticosteroids: an observational cohort study using the OpenSAFELY platform

Author

Schultze, Anna, Walker, Alex J, MacKenna, Brian, Morton, Caroline E, Bhaskaran, Krishnan, Brown, Jeremy P, Rentsch, Christopher T, Williamson, Elizabeth, Drysdale, Henry, Croker, Richard, Bacon, Seb, Hulme, William, Bates, Chris, Curtis, Helen J, Mehrkar, Amir, Evans, David, Inglesby, Peter, Cockburn, Jonathan, McDonald, Helen I, Tomlinson, Laurie, Mathur, Rohini, Wing, Kevin, Wong, Angel Y S, Forbes, Harriet, Parry, John, Hester, Frank, Harper, Sam, Evans, Stephen J W, Quint, Jennifer, Smeeth, Liam, Douglas, Ian J, and Goldacre, Ben

Published
2020
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14. Impact of COVID-19 on mental illness in vaccinated and unvaccinated people: a population-based cohort study in OpenSAFELY

Author

Walker, Venexia, primary, Patalay, Praveetha, additional, Cuitin Coronado, Jose Ignacio, additional, Denholm, Rachel, additional, Forbes, Harriet, additional, Stafford, Jean, additional, Moltrecht, Bettina, additional, Palmer, Tom, additional, Walker, Alex, additional, Thompson, Ellen J, additional, Taylor, Kurt, additional, Cezard, Genevieve, additional, Horne, Elsie MF, additional, Wei, Yinghui, additional, Al Arab, Marwa, additional, Knight, Rochelle, additional, Fisher, Louis, additional, Massey, Jon, additional, Davy, Simon, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Goldacre, Ben, additional, Wood, Angela, additional, Chaturvedi, Nishi, additional, Macleod, John, additional, John, Ann, additional, and Sterne, Jonathan AC, additional

Published
2023
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15. Factors associated with COVID-19-related death using OpenSAFELY

Author

Williamson, Elizabeth J., Walker, Alex J., Bhaskaran, Krishnan, Bacon, Seb, Bates, Chris, Morton, Caroline E., Curtis, Helen J., Mehrkar, Amir, Evans, David, Inglesby, Peter, Cockburn, Jonathan, McDonald, Helen I., MacKenna, Brian, Tomlinson, Laurie, Douglas, Ian J., Rentsch, Christopher T., Mathur, Rohini, Wong, Angel Y. S., Grieve, Richard, Harrison, David, Forbes, Harriet, Schultze, Anna, Croker, Richard, Parry, John, Hester, Frank, Harper, Sam, Perera, Rafael, Evans, Stephen J. W., Smeeth, Liam, and Goldacre, Ben

Published
2020
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16. During the COVID‐19 pandemic 20 000 prostate cancer diagnoses were missed in England.

Author

Lemanska, Agnieszka, Andrews, Colm, Fisher, Louis, Bacon, Seb, Mehrkar, Amir, Inglesby, Peter, Davy, Simon, Goldacre, Ben, MacKenna, Brian, Walker, Alex J., Rentsch, Christopher T., Curtis, Helen J., Morton, Caroline E., Morley, Jessica, Hickman, George, Bates, Chris, Croker, Richard, Evans, David, Ward, Tom, and Cockburn, Jonathan

Subjects
COVID-19 pandemic, CANCER diagnosis, PROSTATE cancer, PROSTATE cancer patients, OLDER men
Abstract

Objectives: To investigate the effect of the COVID‐19 pandemic on prostate cancer incidence, prevalence, and mortality in England. Patients and Methods: With the approval of NHS England and using the OpenSAFELY‐TPP dataset of 24 million patients, we undertook a cohort study of men diagnosed with prostate cancer. We visualised monthly rates in prostate cancer incidence, prevalence, and mortality per 100 000 adult men from January 2015 to July 2023. To assess the effect of the pandemic, we used generalised linear models and the pre‐pandemic data to predict the expected rates from March 2020 as if the pandemic had not occurred. The 95% confidence intervals (CIs) of the predicted values were used to estimate the significance of the difference between the predicted and observed rates. Results: In 2020, there was a drop in recorded incidence by 4772 (31%) cases (15 550 vs 20 322; 95% CI 19 241–21 403). In 2021, the incidence started to recover, and the drop was 3148 cases (18%, 17 950 vs 21 098; 95% CI 19 740–22 456). By 2022, the incidence returned to the levels that would be expected. During the pandemic, the age at diagnosis shifted towards older men. In 2020, the average age was 71.6 (95% CI 71.5–71.8) years, in 2021 it was 71.8 (95% CI 71.7–72.0) years as compared to 71.3 (95% CI 71.1–71.4) years in 2019. Conclusions: Given that our dataset represents 40% of the population, we estimate that proportionally the pandemic led to 20 000 missed prostate cancer diagnoses in England alone. The increase in incidence recorded in 2023 was not enough to account for the missed cases. The prevalence of prostate cancer remained lower throughout the pandemic than expected. As the recovery efforts continue, healthcare should focus on finding the men who were affected. The research should focus on investigating the potential harms to men diagnosed at older age. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Eleven key measures for monitoring general practice clinical activity during COVID-19: A retrospective cohort study using 48 million adults’ primary care records in England through OpenSAFELY

Author

Fisher, Louis, primary, Curtis, Helen J, additional, Croker, Richard, additional, Wiedemann, Milan, additional, Speed, Victoria, additional, Wood, Christopher, additional, Brown, Andrew, additional, Hopcroft, Lisa EM, additional, Higgins, Rose, additional, Massey, Jon, additional, Inglesby, Peter, additional, Morton, Caroline E, additional, Walker, Alex J, additional, Morley, Jessica, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Hickman, George, additional, Macdonald, Orla, additional, Lewis, Tom, additional, Wood, Marion, additional, Myers, Martin, additional, Samuel, Miriam, additional, Conibere, Robin, additional, Baqir, Wasim, additional, Sood, Harpreet, additional, Drury, Charles, additional, Collison, Kiren, additional, Bates, Chris, additional, Evans, David, additional, Dillingham, Iain, additional, Ward, Tom, additional, Davy, Simon, additional, Smith, Rebecca M, additional, Hulme, William, additional, Green, Amelia, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, Cockburn, Jonathan, additional, O'Hanlon, Shaun, additional, Eavis, Alex, additional, Jarvis, Richard, additional, Avramov, Dima, additional, Griffiths, Paul, additional, Fowles, Aaron, additional, Parkes, Nasreen, additional, MacKenna, Brian, additional, and Goldacre, Ben, additional

Published
2023
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18. Implementing Germ Defence digital behaviour change intervention via all primary care practices in England to reduce respiratory infections during the COVID-19 pandemic: an efficient cluster randomised controlled trial using the OpenSAFELY platform.

Author

Ainsworth, Ben, primary, Horwood, Jeremy, additional, Walter, Scott R, additional, Miller, Sascha, additional, Chalder, Melanie, additional, De Vocht, Frank, additional, Denison-Day, James, additional, Elwenspoek, Martha, additional, Curtis, Helen, additional, Bates, Chris, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Goldacre, Ben, additional, Collaborative, The OpenSAFELY, additional, Craggs, Pippa, additional, Amlot, Richard, additional, Francis, Nick, additional, Little, Paul, additional, MacLeod, John, additional, Moore, Michael, additional, Morton, Kate, additional, Rice, Cathy, additional, Sterne, Jonathan, additional, Stuart, Beth, additional, Towler, Lauren, additional, Willcox, Merlin, additional, and Yardley, Lucy, additional

Published
2023
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19. Challenges in Estimating the Effectiveness of COVID-19 Vaccination Using Observational Data

Author

Hulme, William J., primary, Williamson, Elizabeth, additional, Horne, Elsie M.F., additional, Green, Amelia, additional, McDonald, Helen I., additional, Walker, Alex J., additional, Curtis, Helen J., additional, Morton, Caroline E., additional, MacKenna, Brian, additional, Croker, Richard, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Evans, David, additional, Inglesby, Peter, additional, Davy, Simon, additional, Bhaskaran, Krishnan, additional, Schultze, Anna, additional, Rentsch, Christopher T., additional, Tomlinson, Laurie, additional, Douglas, Ian J., additional, Evans, Stephen J.W., additional, Smeeth, Liam, additional, Palmer, Tom, additional, Goldacre, Ben, additional, Hernán, Miguel A., additional, and Sterne, Jonathan A.C., additional

Published
2023
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21. Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY

Author

Walker, Alex, primary, MacKenna, Brian, additional, Inglesby, Peter, additional, Rentsch, Christopher, additional, Curtis, Helen, additional, Morton, Caroline, additional, Morley, Jessica, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Hickman, George, additional, Bates, Chris, additional, Croker, Richard, additional, Evans, David, additional, Ward, Tom, additional, Cockburn, Jonathan, additional, Davy, Simon, additional, Bhaskaran, Krishnan, additional, Schultze, Anna, additional, Williamson, Elizabeth, additional, Hulme, William, additional, McDonald, Helen, additional, Tomlinson, Laurie, additional, Mathur, Rohini, additional, Eggo, Rosalind, additional, Wing, Kevin, additional, Wong, Angel, additional, Forbes, Harriet, additional, Tazare, John, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, OHanlon, Shaun, additional, Eavis, Alex, additional, Jarvis, Richard, additional, Avramov, Dima, additional, Griffiths, Paul, additional, Fowles, Aaron, additional, Parkes, Nasreen, additional, Douglas, Ian, additional, Evans, Stephen, additional, Smeeth, Liam, additional, and Goldacre, Ben, additional

Published
2021
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22. Protocol for an OpenSAFELY cohort study collecting patient-reported outcome measures using the TPP Airmid smartphone application and linked big data to quantify the health and economic costs of long COVID (OpenPROMPT)

Author

Herrett, Emily, primary, Tomlin, Keith, additional, Lin, Liang-Yu, additional, Tomlinson, Laurie A, additional, Jit, Mark, additional, Briggs, Andrew, additional, Marks, Michael, additional, Sandmann, Frank, additional, Parry, John, additional, Bates, Christopher, additional, Morley, Jessica, additional, Bacon, Seb, additional, Butler-Cole, Benjamin, additional, Mahalingasivam, Viyaasan, additional, Dennison, Alan, additional, Smith, Deb, additional, Gabriel, Ethan, additional, Mehrkar, Amir, additional, Goldacre, Ben, additional, Smeeth, Liam, additional, and Eggo, Rosalind M M, additional

Published
2023
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23. Eleven key measures for monitoring general practice clinical activity during COVID-19 using federated analytics on 48 million adults’ primary care records through OpenSAFELY

Author

Fisher, Louis, primary, Curtis, Helen J., additional, Croker, Richard, additional, Wiedemann, Milan, additional, Speed, Victoria, additional, Wood, Christopher, additional, Brown, Andrew, additional, Hopcroft, Lisa EM, additional, Higgins, Rose, additional, Massey, Jon, additional, Inglesby, Peter, additional, Morton, Caroline E., additional, Walker, Alex J., additional, Morley, Jessica, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Hickman, George, additional, Macdonald, Orla, additional, Lewis, Tom, additional, Wood, Marion, additional, Myers, Martin, additional, Samuel, Miriam, additional, Conibere, Robin, additional, Baqir, Wasim, additional, Sood, Harpreet, additional, Drury, Charles, additional, Collison, Kiren, additional, Bates, Chris, additional, Evans, David, additional, Dillingham, Iain, additional, Ward, Tom, additional, Davy, Simon, additional, Smith, Rebecca M., additional, Hulme, William, additional, Green, Amelia, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, Cockburn, Jonathan, additional, O’Hanlon, Shaun, additional, Eavis, Alex, additional, Jarvis, Richard, additional, Avramov, Dima, additional, Griffiths, Paul, additional, Fowles, Aaron, additional, Parkes, Nasreen, additional, MacKenna, Brian, additional, and Goldacre, Ben, additional

Published
2022
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24. Data-driven identification of unusual prescribing behaviour: an analysis and interactive data tool using six months of primary care data from 6500 practices in England

Author

Hopcroft, Lisa EM, primary, Massey, Jon, additional, Curtis, Helen, additional, MacKenna, Brian, additional, Croker, Richard, additional, Macdonald, Orla, additional, Evans, David, additional, Inglesby, Peter, additional, Bacon, Seb, additional, O’Dwyer, Tom, additional, Goldacre, Ben, additional, and Walker, Alex J, additional

Published
2022
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26. Patient Characteristics Associated with Repeat Antibiotic Prescribing Pre- and during the COVID-19 Pandemic: A Retrospective Nationwide Cohort Study of >19 Million Primary Care Records Using the OpenSAFELY Platform.

Author

Orlek, Alex, Harvey, Eleanor, Fisher, Louis, Mehrkar, Amir, Bacon, Seb, Goldacre, Ben, MacKenna, Brian, and Ashiru-Oredope, Diane

Subjects
COVID-19 pandemic, ANTIBIOTICS, COMORBIDITY, URINARY tract infections, PHARMACISTS
Abstract

COVID-19 pandemic-related pressures on primary care may have driven the inappropriate continuation of antibiotic prescriptions. Yet, prescribing modality (repeat/non-repeat) has not previously been investigated in a pandemic context. With the approval of NHS England, we conducted a retrospective cohort study of >19 million English primary care patient records using the OpenSAFELY-TPP analytics platform. We analysed repeat/non-repeat prescribing frequency in monthly patient cohorts between January 2020 and 2022. In-depth analysis was conducted on January 2020 ("pre-pandemic") and January 2021 ("pandemic") cohorts (with a particular focus on repeat prescribing). Per-patient prescribing and clinical conditions were determined by searching primary care records using clinical codelists. Prescriptions in a 6-month lookback period were used to delineate repeat prescribing (≥3 prescriptions) and non-repeat prescribing (1–2 prescriptions). Associations between demographics (e.g., age, sex, ethnicity) and prescribing were explored using unadjusted risk ratios. The frequency of clinical conditions among prescribed patients was examined. Antibiotic prescribing declined from May 2020; non-repeat prescribing declined more strongly than repeat prescribing (maximum declines −26% vs. −11%, respectively). Older patients were at a higher risk of prescribing (especially repeat prescribing). Comorbidities were more common among repeat- vs. non-repeat-prescribed patients. In the pandemic cohort, the most common clinical conditions linked to repeat prescribing were COPD comorbidity and urinary tract infection. Our findings inform the ongoing development of stewardship interventions in England, targeting patient groups wherein there is a high prevalence of repeat prescribing. [ABSTRACT FROM AUTHOR]

Published
2023
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28. Describing the population experiencing COVID-19 vaccine breakthrough following second vaccination in England: a cohort study from OpenSAFELY

Author

OpenSAFELY Collaborative, 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

Abstract

BACKGROUND: While the vaccines against COVID-19 are highly effective, COVID-19 vaccine breakthrough is possible despite being fully vaccinated. With SARS-CoV-2 variants still circulating, describing the characteristics of individuals who have experienced COVID-19 vaccine breakthroughs could be hugely important in helping to determine who may be at greatest risk. METHODS: With the approval of NHS England, we conducted a retrospective cohort study using routine clinical data from the OpenSAFELY-TPP database of fully vaccinated individuals, linked to secondary care and death registry data and described the characteristics of those experiencing COVID-19 vaccine breakthroughs. RESULTS: As of 1st November 2021, a total of 15,501,550 individuals were identified as being fully vaccinated against COVID-19, with a median follow-up time of 149 days (IQR: ​107-179). From within this population, a total of 579,780 (

Published
2022

29. Association between household composition and severe COVID-19 outcomes in older people by ethnicity: an observational cohort study using the OpenSAFELY platform

Author

Wing, Kevin, primary, Grint, Daniel J, additional, Mathur, Rohini, additional, Gibbs, Hamish P, additional, Hickman, George, additional, Nightingale, Emily, additional, Schultze, Anna, additional, Forbes, Harriet, additional, Nafilyan, Vahé, additional, Bhaskaran, Krishnan, additional, Williamson, Elizabeth, additional, House, Thomas, additional, Pellis, Lorenzo, additional, Herrett, Emily, additional, Gautam, Nileesa, additional, Curtis, Helen J, additional, Rentsch, Christopher T, additional, Wong, Angel Y S, additional, MacKenna, Brian, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Douglas, Ian J, additional, Evans, Stephen J W, additional, Tomlinson, Laurie, additional, Goldacre, Ben, additional, and Eggo, Rosalind M, additional

Published
2022
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30. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Author

Thygesen, Johan H, primary, Tomlinson, Christopher, additional, Hollings, Sam, additional, Mizani, Mehrdad A, additional, Handy, Alex, additional, Akbari, Ashley, additional, Banerjee, Amitava, additional, Cooper, Jennifer, additional, Lai, Alvina G, additional, Li, Kezhi, additional, Mateen, Bilal A, additional, Sattar, Naveed, additional, Sofat, Reecha, additional, Torralbo, Ana, additional, Wu, Honghan, additional, Wood, Angela, additional, Sterne, Jonathan A C, additional, Pagel, Christina, additional, Whiteley, William N, additional, Sudlow, Cathie, additional, Hemingway, Harry, additional, Denaxas, Spiros, additional, Abbasizanjani, Hoda, additional, Ahmed, Nida, additional, Ahmed, Badar, additional, Akinoso-Imran, Abdul Qadr, additional, Allara, Elias, additional, Allery, Freya, additional, Angelantonio, Emanuele Di, additional, Ashworth, Mark, additional, Ayyar-Gupta, Vandana, additional, Babu-Narayan, Sonya, additional, Bacon, Seb, additional, Ball, Steve, additional, Banerjee, Ami, additional, Barber, Mark, additional, Barrett, Jessica, additional, Bennie, Marion, additional, Berry, Colin, additional, Beveridge, Jennifer, additional, Birney, Ewan, additional, Bojanić, Lana, additional, Bolton, Thomas, additional, Bone, Anna, additional, Boyle, Jon, additional, Braithwaite, Tasanee, additional, Bray, Ben, additional, Briffa, Norman, additional, Brind, David, additional, Brown, Katherine, additional, Buch, Maya, additional, Canoy, Dexter, additional, Caputo, Massimo, additional, Carragher, Raymond, additional, Carson, Alan, additional, Cezard, Genevieve, additional, Chang, Jen-Yu Amy, additional, Cheema, Kate, additional, Chin, Richard, additional, Chudasama, Yogini, additional, Copland, Emma, additional, Crallan, Rebecca, additional, Cripps, Rachel, additional, Cromwell, David, additional, Curcin, Vasa, additional, Curry, Gwenetta, additional, Dale, Caroline, additional, Danesh, John, additional, Das-Munshi, Jayati, additional, Dashtban, Ashkan, additional, Davies, Alun, additional, Davies, Joanna, additional, Davies, Gareth, additional, Davies, Neil, additional, Day, Joshua, additional, Delmestri, Antonella, additional, Denholm, Rachel, additional, Dennis, John, additional, Denniston, Alastair, additional, Deo, Salil, additional, Dhillon, Baljean, additional, Docherty, Annemarie, additional, Dong, Tim, additional, Douiri, Abdel, additional, Downs, Johnny, additional, Dregan, Alexandru, additional, Ellins, Elizabeth A, additional, Elwenspoek, Martha, additional, Falck, Fabian, additional, Falter, Florian, additional, Fan, Yat Yi, additional, Firth, Joseph, additional, Fraser, Lorna, additional, Friebel, Rocco, additional, Gavrieli, Amir, additional, Gerstung, Moritz, additional, Gilbert, Ruth, additional, Gillies, Clare, additional, Glickman, Myer, additional, Goldacre, Ben, additional, Goldacre, Raph, additional, Greaves, Felix, additional, Green, Mark, additional, Grieco, Luca, additional, Griffiths, Rowena, additional, Gurdasani, Deepti, additional, Halcox, Julian, additional, Hall, Nick, additional, Hama, Tuankasfee, additional, Hansell, Anna, additional, Hardelid, Pia, additional, Hardy, Flavien, additional, Harris, Daniel, additional, Harrison, Camille, additional, Harron, Katie, additional, Hassaine, Abdelaali, additional, Hassan, Lamiece, additional, Healey, Russell, additional, Henderson, Angela, additional, Herz, Naomi, additional, Heyl, Johannes, additional, Hidajat, Mira, additional, Higginson, Irene, additional, Hinchliffe, Rosie, additional, Hippisley-Cox, Julia, additional, Ho, Frederick, additional, Hocaoglu, Mevhibe, additional, Horne, Elsie, additional, Hughes, David, additional, Humberstone, Ben, additional, Inouye, Mike, additional, Ip, Samantha, additional, Islam, Nazrul, additional, Jackson, Caroline, additional, Jenkins, David, additional, Jiang, Xiyun, additional, Johnson, Shane, additional, Kadam, Umesh, additional, Kallis, Costas, additional, Karim, Zainab, additional, Kasan, Jake, additional, Katsoulis, Michalis, additional, Kavanagh, Kim, additional, Kee, Frank, additional, Keene, Spencer, additional, Kent, Seamus, additional, Khalid, Sara, additional, Khawaja, Anthony, additional, Khunti, Kamlesh, additional, Killick, Richard, additional, Kinnear, Deborah, additional, Knight, Rochelle, additional, Kolamunnage-Dona, Ruwanthi, additional, Kontopantelis, Evan, additional, Kurdi, Amanj, additional, Lacey, Ben, additional, Lai, Alvina, additional, Lambarth, Andrew, additional, Larzjan, Milad Nazarzadeh, additional, Lawler, Deborah, additional, Lawrence, Thomas, additional, Lawson, Claire, additional, Li, Qiuju, additional, Li, Ken, additional, Llinares, Miguel Bernabeu, additional, Lorgelly, Paula, additional, Lowe, Deborah, additional, Lyons, Jane, additional, Lyons, Ronan, additional, Machado, Pedro, additional, Macleod, Mary Joan, additional, Macleod, John, additional, Malgapo, Evaleen, additional, Mamas, Mamas, additional, Mamouei, Mohammad, additional, Manohar, Sinduja, additional, Mapeta, Rutendo, additional, Martelli, Javiera Leniz, additional, Martos, David Moreno, additional, Mateen, Bilal, additional, McCarthy, Aoife, additional, Melville, Craig, additional, Milton, Rebecca, additional, Mizani, Mehrdad, additional, Moncusi, Marta Pineda, additional, Morales, Daniel, additional, Mordi, Ify, additional, Morrice, Lynn, additional, Morris, Carole, additional, Morris, Eva, additional, Mu, Yi, additional, Mueller, Tanja, additional, Murdock, Lars, additional, Nafilyan, Vahé, additional, Nicholson, George, additional, Nikiphorou, Elena, additional, Nolan, John, additional, Norris, Tom, additional, Norris, Ruth, additional, North, Laura, additional, North, Teri-Louise, additional, O'Connell, Dan, additional, Oliver, Dominic, additional, Oluyase, Adejoke, additional, Olvera-Barrios, Abraham, additional, Omigie, Efosa, additional, Onida, Sarah, additional, Padmanabhan, Sandosh, additional, Palmer, Tom, additional, Pasea, Laura, additional, Patel, Riyaz, additional, Payne, Rupert, additional, Pell, Jill, additional, Petitjean, Carmen, additional, Pherwani, Arun, additional, Pickrell, Owen, additional, Pierotti, Livia, additional, Pirmohamed, Munir, additional, Priedon, Rouven, additional, Prieto-Alhambra, Dani, additional, Proudfoot, Alastair, additional, Quinn, Terry, additional, Quint, Jennifer, additional, Raffetti, Elena, additional, Rahimi, Kazem, additional, Rao, Shishir, additional, Razieh, Cameron, additional, Roberts, Brian, additional, Rogers, Caroline, additional, Rossdale, Jennifer, additional, Salim, Safa, additional, Samani, Nilesh, additional, Schnier, Christian, additional, Schwartz, Roy, additional, Selby, David, additional, Seminog, Olena, additional, Shabnam, Sharmin, additional, Shah, Ajay, additional, Shelton, Jon, additional, Sheppard, James, additional, Sinha, Shubhra, additional, Skrypak, Mirek, additional, Slapkova, Martina, additional, Sleeman, Katherine, additional, Smith, Craig, additional, Sosenko, Filip, additional, Sperrin, Matthew, additional, Steeg, Sarah, additional, Sterne, Jonathan, additional, Stoica, Serban, additional, Sudell, Maria, additional, Sun, Luanluan, additional, Suseeladevi, Arun Karthikeyan, additional, Sweeting, Michael, additional, Sydes, Matt, additional, Takhar, Rohan, additional, Tang, Howard, additional, Thygesen, Johan, additional, Tilston, George, additional, Tochel, Claire, additional, Toit, Clea du, additional, Toms, Renin, additional, Torabi, Fatemeh, additional, Townson, Julia, additional, Tufail, Adnan, additional, Tungamirai, Tapiwa, additional, Varma, Susheel, additional, Vollmer, Sebastian, additional, Walker, Venexia, additional, Wang, Tianxiao, additional, Wang, Huan, additional, Warwick, Alasdair, additional, Watkinson, Ruth, additional, Watson, Harry, additional, Whiteley, William, additional, Whittaker, Hannah, additional, Wilde, Harry, additional, Wilkinson, Tim, additional, Williams, Gareth, additional, Williams, Michelle, additional, Williams, Richard, additional, Withnell, Eloise, additional, Wolfe, Charles, additional, Wright, Lucy, additional, Wu, Jinge, additional, Wu, Jianhua, additional, Yates, Tom, additional, Zaccardi, Francesco, additional, Zhang, Haoting, additional, Zhang, Huayu, additional, and Zuccolo, Luisa, additional

Published
2022
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31. OpenSAFELY: Representativeness of Electronic Health Record platform OpenSAFELY-TPP data compared to the population of England

Author

Andrews, Colm D, primary, Schultze, Anna, additional, Curtis, Helen J, additional, Hulme, William J, additional, Tazare, John, additional, Evans, Stephen JW, additional, Mehrkhar, Amir, additional, Bacon, Seb, additional, Hickman, George, additional, Bates, Chris, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, Cockburn, Jonathan, additional, Evans, David, additional, Ward, Tom, additional, Davey, Simon, additional, Inglesby, Peter, additional, Goldacre, Ben, additional, MacKenna, Brian, additional, Tomlinson, Laurie, additional, and Walker, Alex J, additional

Published
2022
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32. Effectiveness of BNT162b2 booster doses in England: an observational study in OpenSAFELY-TPP

Author

Hulme, William J, primary, Williamson, Elizabeth J, additional, Horne, Elsie, additional, Green, Amelia, additional, Nab, Linda, additional, Keogh, Ruth, additional, Parker, Edward PK, additional, Walker, Venexia, additional, Palmer, Tom, additional, Curtis, Helen, additional, Wiedemann, Milan, additional, Cunningham, Christine, additional, Walker, Alex J, additional, Fisher, Louis, additional, MacKenna, Brian, additional, Rentsch, Christopher T, additional, Schultze, Anna, additional, Bhaskaran, Krishnan, additional, Tazare, John, additional, Tomlinson, Laurie, additional, McDonald, Helen I, additional, Morton, Caroline E, additional, Croker, Richard, additional, Andrews, Colm, additional, Parks, Robin, additional, Hopcroft, Lisa, additional, Massey, Jon, additional, Morley, Jessica, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Evans, Dave, additional, Inglesby, Peter, additional, Hickman, George, additional, Davy, Simon, additional, Dillingham, Iain, additional, Ward, Tom, additional, Mahalingasivam, Viyasaan, additional, Zheng, Bang, additional, Douglas, Ian J, additional, Evans, Stephen JW, additional, Bates, Chris, additional, Sterne, Jonathan AC, additional, Hernán, Miguel A, additional, and Goldacre, Ben, additional

Published
2022
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33. Changes in English medication safety indicators throughout the COVID-19 pandemic: a federated analysis of 57 million patients’ primary care records in situ using OpenSAFELY

Author

Fisher, Louis, primary, Hopcroft, Lisa E. M., additional, Rodgers, Sarah, additional, Barrett, James, additional, Oliver, Kerry, additional, Avery, Anthony J., additional, Evans, Dai, additional, Curtis, Helen, additional, Croker, Richard, additional, Macdonald, Orla, additional, Morley, Jessica, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Davy, Simon, additional, Dillingham, Iain, additional, Evans, David, additional, Hickman, George, additional, Inglesby, Peter, additional, Morton, Caroline E., additional, Smith, Becky, additional, Ward, Tom, additional, Hulme, William, additional, Green, Amelia, additional, Massey, Jon, additional, Walker, Alex J., additional, Bates, Chris, additional, Cockburn, Jonathan, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, O’Hanlon, Shaun, additional, Eavis, Alex, additional, Jarvis, Richard, additional, Avramov, Dima, additional, Griffiths, Paul, additional, Fowles, Aaron, additional, Parkes, Nasreen, additional, Goldacre, Ben, additional, and MacKenna, Brian, additional

Published
2022
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34. Using data science to identify unusual treatment choices in England: illustrative findings of uncommon antipsychotics Pericyazine and Promazine

Author

MacKenna, Brian, primary, Curtis, Helen J, additional, Walker, Alex J, additional, Croker, Richard, additional, Macdonald, Orla, additional, Evans, Stephen JW, additional, Inglesby, Peter, additional, Evans, Dave, additional, Morley, Jessica, additional, Bacon, Seb CJ, additional, and Goldacre, Ben, additional

Published
2022
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35. Potentially inappropriate prescribing of DOACs to people with mechanical heart valves: A federated analysis of 57.9 million patients' primary care records in situ using OpenSAFELY

Author

Fisher, Louis, primary, Speed, Victoria, additional, Curtis, Helen J., additional, Rentsch, Christopher T., additional, Wong, Angel Y.S., additional, Schultze, Anna, additional, Massey, Jon, additional, Inglesby, Peter, additional, Morton, Caroline E., additional, Wood, Marion, additional, Walker, Alex J., additional, Morley, Jessica, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Hickman, George, additional, Bates, Chris, additional, Croker, Richard, additional, Evans, David, additional, Ward, Tom, additional, Cockburn, Jonathan, additional, Davy, Simon, additional, Bhaskaran, Krishnan, additional, Smith, Becky, additional, Williamson, Elizabeth, additional, Hulme, William, additional, Green, Amelia, additional, Eggo, Rosalind M., additional, Forbes, Harriet, additional, Tazare, John, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, Meadows, Jonathan, additional, O'Hanlon, Shaun, additional, Eavis, Alex, additional, Jarvis, Richard, additional, Avramov, Dima, additional, Griffiths, Paul, additional, Fowles, Aaron, additional, Parkes, Nasreen, additional, Douglas, Ian J., additional, Evans, Stephen J.W., additional, Smeeth, Liam, additional, MacKenna, Brian, additional, Tomlinson, Laurie, additional, and Goldacre, Ben, additional

Published
2022
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36. Mortality among Care Home Residents in England during the first and second waves of the COVID-19 pandemic: an observational study of 4.3 million adults over the age of 65

Author

Schultze, Anna, primary, Nightingale, Emily, additional, Evans, David, additional, Hulme, William, additional, Rosello, Alicia, additional, Bates, Chris, additional, Cockburn, Jonathan, additional, MacKenna, Brian, additional, Curtis, Helen J, additional, Morton, Caroline E, additional, Croker, Richard, additional, Bacon, Seb, additional, McDonald, Helen I, additional, Rentsch, Christopher T, additional, Bhaskaran, Krishnan, additional, Mathur, Rohini, additional, Tomlinson, Laurie A, additional, Williamson, Elizabeth J, additional, Forbes, Harriet, additional, Tazare, John, additional, Grint, Daniel, additional, Walker, Alex J, additional, Inglesby, Peter, additional, DeVito, Nicholas J, additional, Mehrkar, Amir, additional, Hickman, George, additional, Davy, Simon, additional, Ward, Tom, additional, Fisher, Louis, additional, Green, Amelia CA, additional, Wing, Kevin, additional, Wong, Angel YS, additional, McManus, Robert, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, Evans, Stephen JW, additional, Douglas, Ian J, additional, Smeeth, Liam, additional, Eggo, Rosalind M, additional, Goldacre, Ben, additional, and Leon, David A, additional

Published
2022
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37. OpenSAFELY: factors associated with COVID-19 death in 17 million patients

Author

Williamson, Elizabeth J, Walker, Alex J, Bhaskaran, Krishnan, Bacon, Seb, Bates, Chris, Morton, Caroline E, Curtis, Helen J, Mehrkar, Amir, Evans, David, Inglesby, Peter, Cockburn, Jonathan, McDonald, Helen I, MacKenna, Brian, Tomlinson, Laurie, Douglas, Ian J, Rentsch, Christopher T, Mathur, Rohini, Wong, Angel YS, Grieve, Richard, Harrison, David, Forbes, Harriet, Schultze, Anna, Croker, Richard, Parry, John, Hester, Frank, Harper, Sam, Perera, Rafael, Evans, Stephen JW, Smeeth, Liam, and Goldacre, Ben

Subjects
SARS-CoV-2, Pneumonia, Viral, COVID-19, Article, Asthma, deprivation, Betacoronavirus, death, risk factors, ethnicity, informatics, Humans, Coronavirus Infections, Pandemics
Abstract

Summary COVID-19 has rapidly impacted on mortality worldwide.1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.

Published
2020

38. Association between oral anticoagulants and COVID-19-related outcomes: a population-based cohort study

Author

Wong, Angel Ys, Tomlinson, Laurie, Brown, Jeremy P, Elson, William, Walker, Alex J, Schultze, Anna, Morton, Caroline E, Evans, David, Inglesby, Peter, MacKenna, Brian, Bhaskaran, Krishnan, Rentsch, Christopher T, Powell, Emma, Williamson, Elizabeth, Croker, Richard, Bacon, Seb, Hulme, William, Bates, Chris, Curtis, Helen J, Mehrkar, Amir, Cockburn, Jonathan, McDonald, Helen I, Mathur, Rohini, Wing, Kevin, Forbes, Harriet, Eggo, Rosalind M, Evans, Stephen Jw, Smeeth, Liam, Goldacre, Ben, Douglas, Ian J, and (The OpenSAFELY Collaborative)

Abstract

BACKGROUND: Early evidence has shown that anticoagulant reduces the risk of thrombotic events in those infected with COVID-19. However, evidence of the role of routinely prescribed oral anticoagulants (OACs) in COVID-19 outcomes is limited. AIM: To investigate the association between OACs and COVID-19 outcomes in those with atrial fibrillation and a CHA2DS2-VASc score of 2. DESIGN AND SETTING: On behalf of NHS England, a population-based cohort study was conducted. METHOD: The study used primary care data and pseudonymously-linked SARS-CoV-2 antigen testing data, hospital admissions, and death records from England. Cox regression was used to estimate hazard ratios (HRs) for COVID-19 outcomes comparing people with current OAC use versus non-use, accounting for age, sex, comorbidities, other medications, deprivation, and general practice. RESULTS: Of 71 103 people with atrial fibrillation and a CHA2DS2-VASc score of 2, there were 52 832 current OAC users and 18 271 non-users. No difference in risk of being tested for SARS-CoV-2 was associated with current use (adjusted HR [aHR] 0.99, 95% confidence interval [CI] = 0.95 to 1.04) versus non-use. A lower risk of testing positive for SARS-CoV-2 (aHR 0.77, 95% CI = 0.63 to 0.95) and a marginally lower risk of COVID-19-related death (aHR, 0.74, 95% CI = 0.53 to 1.04) were associated with current use versus non-use. CONCLUSION: Among those at low baseline stroke risk, people receiving OACs had a lower risk of testing positive for SARS-CoV-2 and severe COVID-19 outcomes than non-users; this might be explained by a causal effect of OACs in preventing severe COVID-19 outcomes or unmeasured confounding, including more cautious behaviours leading to reduced infection risk.

Published
2022

39. Additional file 1 of 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

Abstract

Additional file 1. This additional piece of analysis uses patients with chronic kidney disease to show how rates can be adjusted to demonstrate the public health burden and to help inform decisions around rollout of vaccine/booster programme for patients at high risk of adverse outcomes, Table S1. Number of fully vaccinated (2 doses + 2 weeks) patients with chronic kidney disease by stage, in OpenSAFELY-TPP, and associated crude and adjusted rates of positive SARS-CoV-2 swab test, COVID-19 related hospital admissions, COVID-19 related critical care admissions and COVID-19 related death, broken down by CKD stage.

Published
2022
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40. A comprehensive high cost drugs dataset from the NHS in England - An OpenSAFELY-TPP Short Data Report

Author

Rowan, Anna, primary, Bates, Chris, additional, Hulme, William, additional, Evans, David, additional, Davy, Simon, additional, A Kennedy, Nicholas, additional, Galloway, James, additional, E Mansfield, Kathryn, additional, Bechman, Katie, additional, Matthewman, Julian, additional, Yates, Mark, additional, Brown, Jeremy, additional, Schultze, Anna, additional, Norton, Sam, additional, J. Walker, Alex, additional, E. Morton, Caroline, additional, Bhaskaran, Krishnan, additional, T. Rentsch, Christopher, additional, Williamson, Elizabeth, additional, Croker, Richard, additional, Bacon, Seb, additional, Hickman, George, additional, Ward, Tom, additional, Green, Amelia, additional, Fisher, Louis, additional, J Curtis, Helen, additional, Tazare, John, additional, M. Eggo, Rosalind, additional, Inglesby, Peter, additional, Cockburn, Jonathan, additional, I. McDonald, Helen, additional, Mathur, Rohini, additional, YS Wong, Angel, additional, Forbes, Harriet, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, J Douglas, Ian, additional, Smeeth, Liam, additional, A Tomlinson, Laurie, additional, W Lees, Charlie, additional, Evans, Stephen, additional, Smith, Catherine, additional, M. Langan, Sinéad, additional, Mehkar, Amir, additional, MacKenna, Brian, additional, and Goldacre, Ben, additional

Published
2021
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41. OpenSAFELY: impact of national guidance on switching anticoagulant therapy during COVID-19 pandemic

Author

OpenSAFELY Collaborative, Curtis, Helen J, MacKenna, Brian, Walker, Alex J, Croker, Richard, Mehrkar, Amir, Morton, Caroline, Bacon, Seb, Hickman, George, Inglesby, Peter, Bates, Chris, Evans, David, Ward, Tom, Cockburn, Jonathan, Davy, Simon, Bhaskaran, Krishnan, Schultze, Anna, Rentsch, Christopher T, Williamson, Elizabeth, Hulme, William, Tomlinson, Laurie, Mathur, Rohini, Drysdale, Henry, Eggo, Rosalind M, Wong, Angel Yun, Forbes, Harriet, Parry, John, Hester, Frank, Harper, Sam, Douglas, Ian, Smeeth, Liam, Goldacre, Ben, and The OpenSAFELY Collaborative

Abstract

BACKGROUND: Early in the COVID-19 pandemic, the National Health Service (NHS) recommended that appropriate patients anticoagulated with warfarin should be switched to direct-acting oral anticoagulants (DOACs), requiring less frequent blood testing. Subsequently, a national safety alert was issued regarding patients being inappropriately coprescribed two anticoagulants following a medication change and associated monitoring. OBJECTIVE: To describe which people were switched from warfarin to DOACs; identify potentially unsafe coprescribing of anticoagulants; and assess whether abnormal clotting results have become more frequent during the pandemic. METHODS: With the approval of NHS England, we conducted a cohort study using routine clinical data from 24 million NHS patients in England. RESULTS: 20 000 of 164 000 warfarin patients (12.2%) switched to DOACs between March and May 2020, most commonly to edoxaban and apixaban. Factors associated with switching included: older age, recent renal function test, higher number of recent INR tests recorded, atrial fibrillation diagnosis and care home residency. There was a sharp rise in coprescribing of warfarin and DOACs from typically 50-100 per month to 246 in April 2020, 0.06% of all people receiving a DOAC or warfarin. International normalised ratio (INR) testing fell by 14% to 506.8 patients tested per 1000 warfarin patients each month. We observed a very small increase in elevated INRs (n=470) during April compared with January (n=420). CONCLUSIONS: Increased switching of anticoagulants from warfarin to DOACs was observed at the outset of the COVID-19 pandemic in England following national guidance. There was a small but substantial number of people coprescribed warfarin and DOACs during this period. Despite a national safety alert on the issue, a widespread rise in elevated INR test results was not found. Primary care has responded rapidly to changes in patient care during the COVID-19 pandemic.

Published
2021

42. Association between warfarin and COVID-19-related outcomes compared with direct oral anticoagulants: population-based cohort study

Author

OpenSAFELY Collaborative, Wong, Angel YS, Tomlinson, Laurie A, Brown, Jeremy P, Elson, William, Walker, Alex J, Schultze, Anna, Morton, Caroline E, Evans, David, Inglesby, Peter, MacKenna, Brian, Bhaskaran, Krishnan, Rentsch, Christopher T, Powell, Emma, Williamson, Elizabeth, Croker, Richard, Bacon, Seb, Hulme, William, Bates, Chris, Curtis, Helen J, Mehrkar, Amir, Cockburn, Jonathan, McDonald, Helen I, Mathur, Rohini, Wing, Kevin, Forbes, Harriet, Eggo, Rosalind M, Evans, Stephen JW, Smeeth, Liam, Goldacre, Ben, and Douglas, Ian J

Abstract

BACKGROUND: Thromboembolism has been reported as a consequence of severe COVID-19. Although warfarin is a commonly used anticoagulant, it acts by antagonising vitamin K, which is low in patients with severe COVID-19. To date, the clinical evidence on the impact of regular use of warfarin on COVID-19-related thromboembolism is lacking. METHODS: On behalf of NHS England, we conducted a population-based cohort study investigating the association between warfarin and COVID-19 outcomes compared with direct oral anticoagulants (DOACs). We used the OpenSAFELY platform to analyse primary care data and pseudonymously linked SARS-CoV-2 antigen testing data, hospital admissions and death records from England. We used Cox regression to estimate hazard ratios (HRs) for COVID-19-related outcomes comparing warfarin with DOACs in people with non-valvular atrial fibrillation. We also conducted negative control outcome analyses (being tested for SARS-CoV-2 and non-COVID-19 death) to assess the potential impact of confounding. RESULTS: A total of 92,339 warfarin users and 280,407 DOAC users were included. We observed a lower risk of all outcomes associated with warfarin versus DOACs [testing positive for SARS-CoV-2, HR 0.73 (95% CI 0.68-0.79); COVID-19-related hospital admission, HR 0.75 (95% CI 0.68-0.83); COVID-19-related deaths, HR 0.74 (95% CI 0.66-0.83)]. A lower risk of negative control outcomes associated with warfarin versus DOACs was also observed [being tested for SARS-CoV-2, HR 0.80 (95% CI 0.79-0.81); non-COVID-19 deaths, HR 0.79 (95% CI 0.76-0.83)]. CONCLUSIONS: Overall, this study shows no evidence of harmful effects of warfarin on severe COVID-19 disease.

Published
2021

43. Trends and clinical characteristics of COVID-19 vaccine recipients: a federated analysis of 57.9 million patients’ primary care records in situ using OpenSAFELY

Author

Curtis, Helen J, Inglesby, Peter, Morton, Caroline E, MacKenna, Brian, Green, Amelia, Hulme, William, Walker, Alex J, Morley, Jessica, Mehrkar, Amir, Bacon, Seb, Hickman, George, Bates, Chris, Croker, Richard, Evans, David, Ward, Tom, Cockburn, Jonathan, Davy, Simon, Bhaskaran, Krishnan, Schultze, Anna, Rentsch, Christopher T, Williamson, Elizabeth J, Rowan, Anna, Fisher, Louis, McDonald, Helen I, Tomlinson, Laurie, Mathur, Rohini, Drysdale, Henry, Eggo, Rosalind M, Wing, Kevin, Wong, Angel Ys, Forbes, Harriet, Parry, John, Hester, Frank, Harper, Sam, O'Hanlon, Shaun, Eavis, Alex, Jarvis, Richard, Avramov, Dima, Griffiths, Paul, Fowles, Aaron, Parkes, Nasreen, Douglas, Ian J, Evans, Stephen Jw, Smeeth, Liam, Goldacre, Ben, and (The OpenSAFELY Collaborative)

Subjects
parasitic diseases
Abstract

BACKGROUND: On 8 December 2020 NHS England administered the first COVID-19 vaccination. AIM: To describe trends and variation in vaccine coverage in different clinical and demographic groups in the first 100 days of the vaccine rollout. DESIGN AND SETTING: With the approval of NHS England, a cohort study was conducted of 57.9 million patient records in general practice in England, in situ and within the infrastructure of the electronic health record software vendors EMIS and TPP using OpenSAFELY. METHOD: Vaccine coverage across various subgroups of Joint Committee on Vaccination and Immunisation (JCVI) priority cohorts is described. RESULTS: A total of 20 852 692 patients (36.0%) received a vaccine between 8 December 2020 and 17 March 2021. Of patients aged ≥80 years not in a care home (JCVI group 2) 94.7% received a vaccine, but with substantial variation by ethnicity (White 96.2%, Black 68.3%) and deprivation (least deprived 96.6%, most deprived 90.7%). Patients with pre-existing medical conditions were more likely to be vaccinated with two exceptions: severe mental illness (89.5%) and learning disability (91.4%). There were 275 205 vaccine recipients who were identified as care home residents (JCVI group 1; 91.2% coverage). By 17 March, 1 257 914 (6.0%) recipients had a second dose. CONCLUSION: The NHS rapidly delivered mass vaccination. In this study a data-monitoring framework was deployed using publicly auditable methods and a secure in situ processing model, using linked but pseudonymised patient-level NHS data for 57.9 million patients. Targeted activity may be needed to address lower vaccination coverage observed among certain key groups.

Published
2021

44. Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY

Author

Walker, Alex J, MacKenna, Brian, Inglesby, Peter, Tomlinson, Laurie, Rentsch, Christopher T, Curtis, Helen J, Morton, Caroline E, Morley, Jessica, Mehrkar, Amir, Bacon, Seb, Hickman, George, Bates, Chris, Croker, Richard, Evans, David, Ward, Tom, Cockburn, Jonathan, Davy, Simon, Bhaskaran, Krishnan, Schultze, Anna, Williamson, Elizabeth J, Hulme, William J, McDonald, Helen I, Mathur, Rohini, Eggo, Rosalind M, Wing, Kevin, Wong, Angel Ys, Forbes, Harriet, Tazare, John, Parry, John, Hester, Frank, Harper, Sam, O'Hanlon, Shaun, Eavis, Alex, Jarvis, Richard, Avramov, Dima, Griffiths, Paul, Fowles, Aaron, Parkes, Nasreen, Douglas, Ian J, Evans, Stephen Jw, and (The OpenSAFELY Collaborative)

Abstract

BACKGROUND: Long COVID describes new or persistent symptoms at least 4 weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were recently created. AIM: To describe the use of long-COVID codes, and variation of use by general practice, demographic variables, and over time. DESIGN AND SETTING: Population-based cohort study in English primary care. METHOD: Working on behalf of NHS England, OpenSAFELY data were used encompassing 96% of the English population between 1 February 2020 and 25 May 2021. The proportion of people with a recorded code for long COVID was measured overall and by demographic factors, electronic health record software system (EMIS or TPP), and week. RESULTS: Long COVID was recorded for 23 273 people. Coding was unevenly distributed among practices, with 26.7% of practices having never used the codes. Regional variation ranged between 20.3 per 100 000 people for East of England (95% confidence interval [CI] = 19.3 to 21.4) and 55.6 per 100 000 people in London (95% CI = 54.1 to 57.1). Coding was higher among females (52.1, 95% CI = 51.3 to 52.9) than males (28.1, 95% CI = 27.5 to 28.7), and higher among practices using EMIS (53.7, 95% CI = 52.9 to 54.4) than those using TPP (20.9, 95% CI = 20.3 to 21.4). CONCLUSION: Current recording of long COVID in primary care is very low, and variable between practices. This may reflect patients not presenting; clinicians and patients holding different diagnostic thresholds; or challenges with the design and communication of diagnostic codes. Increased awareness of diagnostic codes is recommended to facilitate research and planning of services, and also surveys with qualitative work to better evaluate clinicians' understanding of the diagnosis.

Published
2021

45. Comparative effectiveness of ChAdOx1 versus BNT162b2 COVID-19 vaccines in Health and Social Care workers in England: a cohort study using OpenSAFELY

Author

Hulme, William J, primary, Williamson, Elizabeth J, additional, Green, Amelia, additional, Bhaskaran, Krishnan, additional, McDonald, Helen I, additional, Rentsch, Christopher T, additional, Schultze, Anna, additional, Tazare, John, additional, Curtis, Helen J, additional, Walker, Alex J, additional, Tomlinson, Laurie, additional, Palmer, Tom, additional, Horne, Elsie, additional, MacKenna, Brian, additional, Morton, Caroline E, additional, Mehrkar, Amir, additional, Fisher, Louis, additional, Bacon, Seb, additional, Evans, Dave, additional, Inglesby, Peter, additional, Hickman, George, additional, Davy, Simon, additional, Ward, Tom, additional, Croker, Richard, additional, Eggo, Rosalind M, additional, Wong, Angel YS, additional, Mathur, Rohini, additional, Wing, Kevin, additional, Forbes, Harriet, additional, Grint, Daniel, additional, Douglas, Ian J, additional, Evans, Stephen JW, additional, Smeeth, Liam, additional, Bates, Chris, additional, Cockburn, Jonathan, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, Sterne, Jonathan AC, additional, Hernán, Miguel, additional, and Goldacre, Ben, additional

Published
2021
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46. Risk of severe COVID-19 outcomes associated with immune-mediated inflammatory diseases and immune modifying therapies: a nationwide cohort study in the OpenSAFELY platform

Author

MacKenna, Brian, primary, Kennedy, Nicholas A., additional, Mehkar, Amir, additional, Rowan, Anna, additional, Galloway, James, additional, Mansfield, Kathryn E., additional, Bechman, Katie, additional, Matthewman, Julian, additional, Yates, Mark, additional, Brown, Jeremy, additional, Schultze, Anna, additional, Norton, Sam, additional, Walker, Alex J., additional, Morton, Caroline E, additional, Harrison, David, additional, Bhaskaran, Krishnan, additional, Rentsch, Christopher T., additional, Williamson, Elizabeth, additional, Croker, Richard, additional, Bacon, Seb, additional, Hickman, George, additional, Ward, Tom, additional, Davy, Simon, additional, Green, Amelia, additional, Fisher, Louis, additional, Hulme, William, additional, Bates, Chris, additional, Curtis, Helen J., additional, Tazare, John, additional, Eggo, Rosalind M., additional, Evans, David, additional, Inglesby, Peter, additional, Cockburn, Jonathan, additional, McDonald, Helen I., additional, Tomlinson, Laurie A., additional, Mathur, Rohini, additional, Wong, Angel YS, additional, Forbes, Harriet, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, Douglas, Ian J., additional, Smeeth, Liam, additional, Lees, Charlie W, additional, Evans, Stephen JW, additional, Goldacre, Ben, additional, Smith, Catherine, additional, and Langan, Sinéad M., additional

Published
2021
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47. Linked electronic health records for research on a nationwide cohort of more than 54 million people in England:data resource

Author

Wood, Angela, Denholm, Rachel, Hollings, Sam, Cooper, Jennifer, Ip, Samantha, Walker, Venexia, Denaxas, Spiros, Akbari, Ashley, Banerjee, Amitava, Whiteley, William, Lai, Alvina, Sterne, Jonathan, Sudlow, Cathie, CVD-COVID-UK Consortium, Douiri, Abdel, Akinoso-Imran, Abdul Qadr, Jonas, Adrian, Shah, Ajay, Handy, Alex, Davies, Alun, Kurdi, Amanj, Hansell, Anna, Docherty, Annemarie, Pherwani, Arun, Dashtban, Ashkan, Bray, Ben, Cairns, Ben, Goldacre, Ben, Humberstone, Ben, Mateen, Bilal, Doble, Brett, Roberts, Brian, Morris, Carole, Dale, Caroline, Rogers, Caroline, Wolfe, Charles, Tomlinson, Christopher, Lawson, Claire, Du Toit, Clea, Berry, Colin, Smith, Craig, O’Connell, Dan, Harris, Daniel, Brind, David, Cromwell, David, Hughes, David, Martos, David Moreno, Ringham, Debbie, Lawler, Deborah, Lowe, Deborah, Nikiphorou, Elena, Withnell, Eloise, Di Angelantonio, Emanuele, Morris, Eva, Birney, Ewan, Falck, Fabian, Torabi, Fatemeh, Greaves, Felix, Falter, Florian, Zaccardi, Francesco, Kee, Frank, Davies, Gareth, Nicholson, George, Curry, Gwenetta, Zhang, Haoting, Hemingway, Harry, Wilde, Harry, Abbasizanjani, Hoda, Wu, Honghan, Tang, Howard, Wang, Huan, Mordi, Ify, MacArthur, Jackie, Lyons, Jane, Beveridge, Jennifer, Barrett, Jessica, Wu, Jianhua, Thygesen, Johan, Danesh, John, Dennis, John, Boyle, Jon, Halcox, Julian, Khunti, Kamlesh, Cheema, Kate, Brown, Katherine, Li, Ken, Kavanagh, Kim, North, Laura, Pasea, Laura, Ellins, Libby, Pierotti, Livia, Wright, Lucy, Martin, Lydia, Morrice, Lynn, Mamas, Mamas, Bennie, Marion, Barber, Mark, Macleod, Mary Joan, Caputo, Massimo, Buch, Maya, Mizani, Mehrdad, Katsoulis, Michalis, Gravenor, Mike, Inouye, Mike, Skrypak, Mirek, Gerstung, Moritz, Pirmohamed, Munir, Glickman, Myer, Herz, Naomi, Davies, Neil, Hall, Nick, Samani, Nilesh, Seminog, Olena, Lorgelly, Paula, Machado, Pedro, Li, Qiuju, Goldacre, Raph, Carragher, Raymond, Sofat, Reecha, Takhar, Rohan, Lyons, Ronan, Priedon, Rouven, Griffiths, Rowena, Payne, Rupert, Kolamunnage-Dona, Ruwanthi, Salim, Safa, Padmanabhan, Sandosh, Onida, Sarah, Kent, Seamus, Bacon, Seb, Manohar, Sinduja, Babu-Narayan, Sonya, Keene, Spencer, Varma, Susheel, Lawrence, Thomas, Wang, Tianxiao, Wilkinson, Tim, Norris, Tom, Palmer, Tom, Nafilyan, Vahé, Wood, Angela [0000-0002-7937-304X], and Apollo - University of Cambridge Repository

Subjects
Male, COVID-19/diagnosis, 030204 cardiovascular system & hematology, Cohort Studies, 0302 clinical medicine, COVID-19 Testing, Epidemiology, Electronic Health Records, Primary Health Care/statistics & numerical data, 030212 general & internal medicine, Child, Stroke, Cardiovascular Diseases/diagnosis, education.field_of_study, General Medicine, Middle Aged, Hospitalization, England, Cardiovascular Diseases, Child, Preschool, Cohort, Female, Medical emergency, Medical Record Linkage, Cohort study, Adult, medicine.medical_specialty, COVID-19 Vaccines, Adolescent, Population, MEDLINE, 03 medical and health sciences, Young Adult, Intensive care, medicine, Humans, England/epidemiology, education, Aged, Hospitalization/statistics & numerical data, Primary Health Care, business.industry, SARS-CoV-2, Public health, Research, Infant, Newborn, COVID-19, Infant, medicine.disease, United Kingdom, business
Abstract

Objective To describe a novel England-wide electronic health record (EHR) resource enabling whole population research on covid-19 and cardiovascular disease while ensuring data security and privacy and maintaining public trust. Design Data resource comprising linked person level records from national healthcare settings for the English population, accessible within NHS Digital’s new trusted research environment. Setting EHRs from primary care, hospital episodes, death registry, covid-19 laboratory test results, and community dispensing data, with further enrichment planned from specialist intensive care, cardiovascular, and covid-19 vaccination data. Participants 54.4 million people alive on 1 January 2020 and registered with an NHS general practitioner in England. Main outcome measures Confirmed and suspected covid-19 diagnoses, exemplar cardiovascular conditions (incident stroke or transient ischaemic attack and incident myocardial infarction) and all cause mortality between 1 January and 31 October 2020. Results The linked cohort includes more than 96% of the English population. By combining person level data across national healthcare settings, data on age, sex, and ethnicity are complete for around 95% of the population. Among 53.3 million people with no previous diagnosis of stroke or transient ischaemic attack, 98 721 had a first ever incident stroke or transient ischaemic attack between 1 January and 31 October 2020, of which 30% were recorded only in primary care and 4% only in death registry records. Among 53.2 million people with no previous diagnosis of myocardial infarction, 62 966 had an incident myocardial infarction during follow-up, of which 8% were recorded only in primary care and 12% only in death registry records. A total of 959 470 people had a confirmed or suspected covid-19 diagnosis (714 162 in primary care data, 126 349 in hospital admission records, 776 503 in covid-19 laboratory test data, and 50 504 in death registry records). Although 58% of these were recorded in both primary care and covid-19 laboratory test data, 15% and 18%, respectively, were recorded in only one. Conclusions This population-wide resource shows the importance of linking person level data across health settings to maximise completeness of key characteristics and to ascertain cardiovascular events and covid-19 diagnoses. Although this resource was initially established to support research on covid-19 and cardiovascular disease to benefit clinical care and public health and to inform healthcare policy, it can broaden further to enable a wide range of research.

Published
2021

48. Recording of “COVID-19 vaccine declined” among vaccination priority groups: a cohort study on 57.9 million NHS patients’ primary care records in situ using OpenSAFELY

Author

Curtis, Helen J, primary, Inglesby, Peter, additional, MacKenna, Brian, additional, Croker, Richard, additional, Hulme, William, additional, Rentsch, Christopher T, additional, Bhaskaran, Krishnan, additional, Walker, Alex J, additional, Morton, Caroline E, additional, Evans, David, additional, Mehrkar, Amir, additional, Bacon, Seb, additional, Bates, Chris, additional, Hickman, George, additional, Ward, Tom, additional, Morley, Jessica, additional, Cockburn, Jonathan, additional, Davy, Simon, additional, Schultze, Anna, additional, Williamson, Elizabeth, additional, McDonald, Helen I, additional, Tomlinson, Laurie, additional, Mathur, Rohini, additional, Eggo, Rosalind M, additional, Wing, Kevin, additional, Wong, Angel YS, additional, Forbes, Harriet, additional, Tazare, John, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, O’Hanlon, Shaun, additional, Eavis, Alex, additional, Jarvis, Richard, additional, Avramov, Dima, additional, Griffiths, Paul, additional, Fowles, Aaron, additional, Parkes, Nasreen, additional, Evans, Stephen JW, additional, Douglas, Ian J, additional, Smeeth, Liam, additional, and Goldacre, Ben, additional

Published
2021
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49. Mortality among Care Home Residents in England during the first and second waves of the COVID-19 pandemic: an analysis of 4.3 million adults over the age of 65

Author

Schultze, Anna, primary, Nightingale, Emily, additional, Evans, David, additional, Hulme, William, additional, Rosello, Alicia, additional, Bates, Chris, additional, Cockburn, Jonathan, additional, MacKenna, Brian, additional, Curtis, Helen J, additional, Morton, Caroline E, additional, Croker, Richard, additional, Bacon, Seb, additional, McDonald, Helen I, additional, Rentsch, Christopher T, additional, Bhaskaran, Krishnan, additional, Mathur, Rohini, additional, Tomlinson, Laurie A, additional, Williamson, Elizabeth J, additional, Forbes, Harriet, additional, Tazare, John, additional, Grint, Daniel, additional, Walker, Alex J, additional, Inglesby, Peter, additional, DeVito, Nicholas J, additional, Mehrkar, Amir, additional, Hickman, George, additional, Davy, Simon, additional, Ward, Tom, additional, Fisher, Louis, additional, Green, Amelia CA, additional, Wing, Kevin, additional, Wong, Angel YS, additional, McManus, Robert, additional, Parry, John, additional, Hester, Frank, additional, Harper, Sam, additional, Evans, Stephen JW, additional, Douglas, Ian J, additional, Smeeth, Liam, additional, Eggo, Rosalind M, additional, Goldacre, Ben, additional, and Leon, David A, additional

Published
2021
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50. Rates of serious clinical outcomes in survivors of hospitalisation with COVID-19: a descriptive cohort study within the OpenSAFELY platform

Author

Tazare, John, Walker, Alex J, Tomlinson, Laurie, Hickman, George, Rentsch, Christopher T, Williamson, Elizabeth J, Bhaskaran, Krishnan, Evans, David, Wing, Kevin, Mathur, Rohini, Wong, Angel YS, Schultze, Anna, Bacon, Seb, Bates, Chris, Morton, Caroline E, Curtis, Helen J, Nightingale, Emily, McDonald, Helen I, Mehrkar, Amir, Inglesby, Peter, Davy, Simon, MacKenna, Brian, Cockburn, Jonathan, Hulme, William J, Warren-Gash, Charlotte, Bhate, Ketaki, Nitsch, Dorothea, Powell, Emma, Mulick, Amy, Forbes, Harriet, Minassian, Caroline, Croker, Richard, Parry, John, Hester, Frank, Harper, Sam, Eggo, Rosalind M, Evans, Stephen JW, Smeeth, Liam, Douglas, Ian J, and Goldacre, Ben

Abstract

BackgroundPatients with COVID-19 are thought to be at higher risk of cardiometabolic and pulmonary complications, but quantification of that risk is limited. We aimed to describe the overall burden of these complications in survivors of severe COVID-19.MethodsWorking on behalf of NHS England, we used linked primary care records, death certificate and hospital data from the OpenSAFELY platform. We constructed three cohorts: patients discharged following hospitalisation with COVID-19, patients discharged following hospitalisation with pneumonia in 2019, and a frequency-matched cohort from the general population in 2019. We studied eight cardiometabolic and pulmonary outcomes. Absolute rates were measured in each cohort and Cox regression models were fitted to estimate age/sex adjusted hazard ratios comparing outcome rates between discharged COVID-19 patients and the two comparator cohorts.ResultsAmongst the population of 31,716 patients discharged following hospitalisation with COVID-19, rates for majority of outcomes peaked in the first month post-discharge, then declined over the following four months. Patients in the COVID-19 population had markedly increased risk of all outcomes compared to matched controls from the 2019 general population, especially for pulmonary embolism (HR 12.86; 95% CI: 11.23 - 14.74). Outcome rates were more similar when comparing patients discharged with COVID-19 to those discharged with pneumonia in 2019, although COVID-19 patients had increased risk of type 2 diabetes (HR 1.23; 95% CI: 1.05 - 1.44).InterpretationCardiometabolic and pulmonary adverse outcomes are markedly raised following hospitalisation for COVID-19 compared to the general population. However, the excess risks were more comparable to those seen following hospitalisation with pneumonia. Identifying patients at particularly high risk of outcomes would inform targeted preventive measures.FundingWellcome, Royal Society, National Institute for Health Research, National Institute for Health Research Oxford Biomedical Research Centre, UK Medical Research Council, UK Research and Innovation, Health and Safety Executive.

Published
2021

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