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Abstract
Background The COVID-19 pandemic affected how care was delivered to vulnerable patients, such as those with dementia or learning disability.
Objective To explore whether this affected antipsychotic prescribing in at-risk populations.
Methods With the approval of NHS England, we completed a retrospective cohort study, using the OpenSAFELY platform to explore primary care data of 59 million patients. We identified patients in five at-risk groups: autism, dementia, learning disability, serious mental illness and care home residents. We calculated the monthly prevalence of antipsychotic prescribing in these groups, as well as the incidence of new prescriptions in each month.
Findings The average monthly rate of antipsychotic prescribing increased in dementia from 82.75 patients prescribed an antipsychotic per 1000 patients (95% CI 82.30 to 83.19) in January–March 2019 to 90.1 (95% CI 89.68 to 90.60) in October–December 2021 and from 154.61 (95% CI 153.79 to 155.43) to 166.95 (95% CI 166.23 to 167.67) in care homes. There were notable spikes in the rate of new prescriptions issued to patients with dementia and in care homes. In learning disability and autism groups, the rate of prescribing per 1000 decreased from 122.97 (95% CI 122.29 to 123.66) to 119.29 (95% CI 118.68 to 119.91) and from 54.91 (95% CI 54.52 to 55.29) to 51.04 (95% CI 50.74 to 51.35), respectively.
Conclusion and implications We observed a spike in antipsychotic prescribing in the dementia and care home groups, which correlated with lockdowns and was likely due to prescribing of antipsychotics for palliative care. We observed gradual increases in antipsychotic use in dementia and care home patients and decreases in their use in patients with learning disability or autism.
- COVID-19
- delirium & cognitive disorders
- adult psychiatry
- impulse control disorders
Data availability statement
Data are available in a public, open access repository. Access to the underlying identifiable and potentially re-identifiable pseudonymised electronic health record data is tightly governed by various legislative and regulatory frameworks and is restricted by best practice. The data in OpenSAFELY are drawn from general practice data across England where TPP and EMIS are the data processors. TPP developers (CB, JC, JP, FH and SH) and EMIS developers (SO'H, AE, RJ, DA, IW and NP) initiate an automated process to create pseudonymised records in the core OpenSAFELY database, which are copies of key structured data tables in the identifiable records. These are linked onto key external data resources that have also been pseudonymised via SHA-512 one-way hashing of NHS numbers using a shared salt. DataLab developers and principal investigators (AG, AW, RC, HC, BB-C, CA, CM, DE, PI, ID, JM, LF, SB, WH and SD) hold contracts with NHS England and have access to the OpenSAFELY pseudonymised data tables as needed to develop the OpenSAFELY tools. These tools in turn enable researchers with OpenSAFELY Data Access Agreements to write and execute code for data management and data analysis without direct access to the underlying raw pseudonymised patient data, and to review the outputs of this code. All codes for the full data management pipeline—from raw data to completed results for this analysis—and for the OpenSAFELY platform as a whole are available for review at https://github.com/OpenSAFELY. The data management and analysis code for this paper was led by AG.
This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
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Data availability statement
Data are available in a public, open access repository. Access to the underlying identifiable and potentially re-identifiable pseudonymised electronic health record data is tightly governed by various legislative and regulatory frameworks and is restricted by best practice. The data in OpenSAFELY are drawn from general practice data across England where TPP and EMIS are the data processors. TPP developers (CB, JC, JP, FH and SH) and EMIS developers (SO'H, AE, RJ, DA, IW and NP) initiate an automated process to create pseudonymised records in the core OpenSAFELY database, which are copies of key structured data tables in the identifiable records. These are linked onto key external data resources that have also been pseudonymised via SHA-512 one-way hashing of NHS numbers using a shared salt. DataLab developers and principal investigators (AG, AW, RC, HC, BB-C, CA, CM, DE, PI, ID, JM, LF, SB, WH and SD) hold contracts with NHS England and have access to the OpenSAFELY pseudonymised data tables as needed to develop the OpenSAFELY tools. These tools in turn enable researchers with OpenSAFELY Data Access Agreements to write and execute code for data management and data analysis without direct access to the underlying raw pseudonymised patient data, and to review the outputs of this code. All codes for the full data management pipeline—from raw data to completed results for this analysis—and for the OpenSAFELY platform as a whole are available for review at https://github.com/OpenSAFELY. The data management and analysis code for this paper was led by AG.
Supplementary materials
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Footnotes
OM and AG contributed equally.
Contributors OM and AG are joint first authors. OM, BM, AG and HC conceptualised the study. OM, BM, AG, AW, DE, CA, HC, PI, ID, JM, LF, SB, SD, TW, WH, BB-C and CM curated the data. AG did the formal analysis. BG acquired funding for the study and provided the study resources. AG, AW and DE contributed to the investigation. OM, BM, AG, HC and AW contributed to the methodology. OM, BM, AB, PI and RC contributed to the development of the codelists. BM led the project administration and supervised the project. BG, AW, DE, CA, PI, ID, JM, AG, LF, SB, SD, TW, WH and BB-C contributed to study software. AW and HC contributed to data validation. OM, BM and AG, contributed to data visualisation. OM and AG wrote the original draft. OM, BM, AG, HC, AW and AM edited subsequent drafts. All authors reviewed the final manuscript. BM and AM were responsible for information governance. All authors had final responsibility for the decision to submit for publication. BG is guarantor of the OpenSAFELY project.
Funding This research used data assets made available as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058). In addition, the OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073) and Health Data Research UK (HDRUK2021.000, 2021.0157). BG has also received funding from: the Bennett Foundation, the Wellcome Trust, NIHR Oxford Biomedical Research Centre, NIHR Applied Research Collaboration Oxford and Thames Valley, the Mohn-Westlake Foundation; all Bennett Institute staff are supported by BG’s grants on this work. BM is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data. ID holds grants from NIHR and GSK. OM is employed by Oxford Health as Lead Learning Disabilities pharmacist and was funded by Buckinghamshire, Oxfordshire and Berkshire West Integrated Care System and by the NIHR Oxford Health Biomedical Research Centre (grant NIHR203316) for this project.
Disclaimer The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, UK Health Security Agency or the Department of Health and Social Care. Funders had no role in the study design, collection, analysis and interpretation of data; in the writing of the report and in the decision to submit the article for publication.
Competing interests No, there are no competing interests.
Patient and public involvement statement This analysis relies on the use of large volumes of patient data. Ensuring patient, professional and public trust is therefore of critical importance. Maintaining trust requires being transparent about the way OpenSAFELY works, and ensuring patient voices are represented in the design of research, analysis of the findings, and considering the implications. For transparency purposes we have developed a public website (https://opensafely.org/) which provides a detailed description of the platform in language suitable for a lay audience; we have participated in two citizen juries exploring public trust in OpenSAFELY; we are currently co-developing an explainer video; we have ‘expert by experience’ patient representation on our OpenSAFELY Oversight Board; we have partnered with Understanding Patient Data to produce lay explainers on the importance of large datasets for research; we have presented at a number of online public engagement events to key communities; and more. To ensure the patient voice is represented, we are working closely with appropriate medical research charities.
Provenance and peer review Not commissioned; externally peer reviewed.
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