Impact of the COVID-19 pandemic on antidepressant prescribing with a focus on people with learning disability and autism: an interrupted time series analysis in England using OpenSAFELY-TPP
Background COVID-19 restrictions led to increased reports of depressive symptoms in the general population and impacted health and social care services. We explored whether these changes affected antidepressant prescribing trends in the general population and those with learning disability or autism...
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BMJ Publishing Group
2025-04-01
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| Series: | BMJ Mental Health |
| Online Access: | https://mentalhealth.bmj.com/content/28/1/e301378.full |
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| author | Ben Goldacre David Evans Orla Macdonald Richard Croker Rebecca Smith Christopher Wood Andrea Schaffer Andrew Brown Brian MacKenna William Hulme Chris Bates Amir Mehrkar Peter Inglesby John Parry Amelia Green Helen Curtis Alex Walker Rose Higgins Sebastian Bacon Simon Davy Tom Ward Louis Fisher Iain Dillingham Milan Wiedemann Lucy Bridges Ben Butler-Cole Colm Andrews Christine Cunningham Victoria Speed Liam C Hart Thomas O’Dwyer Steve Maude |
| author_facet | Ben Goldacre David Evans Orla Macdonald Richard Croker Rebecca Smith Christopher Wood Andrea Schaffer Andrew Brown Brian MacKenna William Hulme Chris Bates Amir Mehrkar Peter Inglesby John Parry Amelia Green Helen Curtis Alex Walker Rose Higgins Sebastian Bacon Simon Davy Tom Ward Louis Fisher Iain Dillingham Milan Wiedemann Lucy Bridges Ben Butler-Cole Colm Andrews Christine Cunningham Victoria Speed Liam C Hart Thomas O’Dwyer Steve Maude |
| author_sort | Ben Goldacre |
| collection | DOAJ |
| description | Background COVID-19 restrictions led to increased reports of depressive symptoms in the general population and impacted health and social care services. We explored whether these changes affected antidepressant prescribing trends in the general population and those with learning disability or autism.Methods With the approval of NHS England, we used >24 million patients’ primary care data from the OpenSAFELY-TPP platform. We used interrupted time series analysis to quantify trends in those prescribed and newly prescribed an antidepressant across key demographic and clinical subgroups, comparing pre-COVID-19 (January 2018–February 2020), COVID-19 restrictions (March 2020–February 2021) and recovery (March 2021–December 2022) periods.Results Prior to COVID-19 restrictions, antidepressant prescribing was increasing in the general population and in those with learning disability or autism. We did not find evidence that the pandemic was associated with a change in antidepressant prescribing trend in the general population (relative risk (RR) 1.00 (95% CI 0.97 to 1.02)), in those with autism (RR 0.99 (95% CI 0.97 to 1.01)) or in those with learning disability (RR 0.98 (95% CI 0.96 to 1.00)).New prescribing post restrictions was 13% and 12% below expected had COVID-19 not happened in both the general population and those with autism (RR 0.87 (95% CI 0.83 to 0.93), RR 0.88 (95% CI 0.83 to 0.92)), but not learning disability (RR 0.96 (95% CI 0.87 to 1.05)).Conclusions and implications In this England study, we did not see an impact of COVID-19 on overall antidepressant prescribing, although unique trends were noted, such as trends in new antidepressant prescriptions which increased in care homes over the pandemic and decreased in the general population and those with autism since recovery. |
| format | Article |
| id | doaj-art-3f05e59bacb34d0cb6411c919c04077d |
| institution | Kabale University |
| issn | 2755-9734 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Mental Health |
| spelling | doaj-art-3f05e59bacb34d0cb6411c919c04077d2025-08-20T03:52:39ZengBMJ Publishing GroupBMJ Mental Health2755-97342025-04-0128110.1136/bmjment-2024-301378Impact of the COVID-19 pandemic on antidepressant prescribing with a focus on people with learning disability and autism: an interrupted time series analysis in England using OpenSAFELY-TPPBen Goldacre0David Evans1Orla Macdonald2Richard Croker3Rebecca Smith4Christopher Wood5Andrea Schaffer6Andrew Brown7Brian MacKenna8William Hulme9Chris Bates10Amir Mehrkar11Peter Inglesby12John Parry13Amelia Green14Helen Curtis15Alex Walker16Rose Higgins17Sebastian Bacon18Simon Davy19Tom Ward20Louis Fisher21Iain Dillingham22Milan Wiedemann23Lucy Bridges24Ben Butler-Cole25Colm Andrews26Christine Cunningham27Victoria Speed28Liam C Hart29Thomas O’Dwyer30Steve Maude3111 Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKSchool of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, UKOxford Health NHS Foundation Trust, Oxford, UKBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK1Exeter IBD Pharmacogenetics Research Group, Exeter, UK2 University of Oxford, Oxford, UK1 Centre for Big Data Research in Health, UNSW Sydney, Sydney, New South Wales, AustraliaMedical University of South Carolina, Charleston, South Carolina, USA4 NHS England, Redditch, UKUniversity of Oxford, Oxford, UKTPP, Leeds, UK11 Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKTPP, Leeds, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK2 University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK11 Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK2 Nuffield Department of Primary Care, Oxford University, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK2 King`s Thrombosis Centre, Department of Haematological Medicine, King`s College Hospital NHS Foundation Trust, London, UKBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UKBackground COVID-19 restrictions led to increased reports of depressive symptoms in the general population and impacted health and social care services. We explored whether these changes affected antidepressant prescribing trends in the general population and those with learning disability or autism.Methods With the approval of NHS England, we used >24 million patients’ primary care data from the OpenSAFELY-TPP platform. We used interrupted time series analysis to quantify trends in those prescribed and newly prescribed an antidepressant across key demographic and clinical subgroups, comparing pre-COVID-19 (January 2018–February 2020), COVID-19 restrictions (March 2020–February 2021) and recovery (March 2021–December 2022) periods.Results Prior to COVID-19 restrictions, antidepressant prescribing was increasing in the general population and in those with learning disability or autism. We did not find evidence that the pandemic was associated with a change in antidepressant prescribing trend in the general population (relative risk (RR) 1.00 (95% CI 0.97 to 1.02)), in those with autism (RR 0.99 (95% CI 0.97 to 1.01)) or in those with learning disability (RR 0.98 (95% CI 0.96 to 1.00)).New prescribing post restrictions was 13% and 12% below expected had COVID-19 not happened in both the general population and those with autism (RR 0.87 (95% CI 0.83 to 0.93), RR 0.88 (95% CI 0.83 to 0.92)), but not learning disability (RR 0.96 (95% CI 0.87 to 1.05)).Conclusions and implications In this England study, we did not see an impact of COVID-19 on overall antidepressant prescribing, although unique trends were noted, such as trends in new antidepressant prescriptions which increased in care homes over the pandemic and decreased in the general population and those with autism since recovery.https://mentalhealth.bmj.com/content/28/1/e301378.full |
| spellingShingle | Ben Goldacre David Evans Orla Macdonald Richard Croker Rebecca Smith Christopher Wood Andrea Schaffer Andrew Brown Brian MacKenna William Hulme Chris Bates Amir Mehrkar Peter Inglesby John Parry Amelia Green Helen Curtis Alex Walker Rose Higgins Sebastian Bacon Simon Davy Tom Ward Louis Fisher Iain Dillingham Milan Wiedemann Lucy Bridges Ben Butler-Cole Colm Andrews Christine Cunningham Victoria Speed Liam C Hart Thomas O’Dwyer Steve Maude Impact of the COVID-19 pandemic on antidepressant prescribing with a focus on people with learning disability and autism: an interrupted time series analysis in England using OpenSAFELY-TPP BMJ Mental Health |
| title | Impact of the COVID-19 pandemic on antidepressant prescribing with a focus on people with learning disability and autism: an interrupted time series analysis in England using OpenSAFELY-TPP |
| title_full | Impact of the COVID-19 pandemic on antidepressant prescribing with a focus on people with learning disability and autism: an interrupted time series analysis in England using OpenSAFELY-TPP |
| title_fullStr | Impact of the COVID-19 pandemic on antidepressant prescribing with a focus on people with learning disability and autism: an interrupted time series analysis in England using OpenSAFELY-TPP |
| title_full_unstemmed | Impact of the COVID-19 pandemic on antidepressant prescribing with a focus on people with learning disability and autism: an interrupted time series analysis in England using OpenSAFELY-TPP |
| title_short | Impact of the COVID-19 pandemic on antidepressant prescribing with a focus on people with learning disability and autism: an interrupted time series analysis in England using OpenSAFELY-TPP |
| title_sort | impact of the covid 19 pandemic on antidepressant prescribing with a focus on people with learning disability and autism an interrupted time series analysis in england using opensafely tpp |
| url | https://mentalhealth.bmj.com/content/28/1/e301378.full |
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