Abstract
Background COVID-19 lockdowns led to increased reports of depressive symptoms in the general population and impacted the health and social care services of people with learning disability and autism. We explored whether the COVID-19 pandemic had an impact on antidepressant prescribing trends within these and the general population.
Methods With the approval of NHS England, we used >24 million patients’ primary care data from the OpenSAFELY-TPP platform. We identified patients with learning disability or autism and used an 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 lockdown (March 2020-February 2021) and the recovery period (March 2021-December 2022).
Results Prior to COVID-19 lockdown, antidepressant prescribing was increasing at 0.3% (95% CI 0.2% to 0.3%) patients per month, in the general population and in those with learning disability, and 0.3% (95% CI 0.2% to 0.4%) in those with autism. We did not find evidence that the pandemic was associated with a change in trend of antidepressant prescribing in the general population (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 lockdown was 13% and 12% below expected if COVID-19 had 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 Pre-COVID-19, antidepressant prescribing was increasing at 0.3% per month. While we did not see an impact of COVID-19 on overall prescribing in the general population, prescriptions to those aged 0-19, 20-29, and new prescriptions were lower than pre-COVID-19 trends would have predicted, but tricyclics and new prescriptions in care homes were higher than expected.
What is already known on this topic
⇒ The prescribing of antidepressants in the UK has been increasing for more than a decade.
⇒ Studies globally have found differing impacts of COVID-19 on mental health outcomes in the general population, by age, sex, socio-economic status, and care home status.
What this study adds
⇒ This study describes the impact of COVID-19 on antidepressant prescribing in England with additional follow-up through December 2022, with a focus on people with a learning disability or autism.
How this study might affect research, practice, or policy
⇒ This study demonstrates how the pandemic did not lead to an increase in antidepressant prescriptions in the general population, but more is needed to ensure that antidepressants are used appropriately within vulnerable populations.
⇒ Improvements are needed in the documentation of diagnosis when prescribing medicines.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
The OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z) and MRC (MR/V015737/1, MC_PC_20059, MR/W016729/1). In addition, development of OpenSAFELY has been funded by the Longitudinal Health and Wellbeing strand of the National Core Studies programme (MC_PC_20030: MC_PC_20059), the NIHR funded CONVALESCENCE programme (COV-LT-0009), NIHR (NIHR135559, COV-LT2-0073), and the Data and Connectivity National Core Study funded by UK Research and Innovation (MC_PC_20058) and Health Data Research UK (HDRUK2021.000). 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 BGs grants on this work. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
London - City & East Research Ethics Committee gave ethical approval for this work. REC reference 20/LO/0651.
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Data Availability
The dataset analysed within OpenSAFELY is based on > 24 million people currently registered with GP surgeries using TPP SystmOne software. All data were linked, stored and analysed securely using the OpenSAFELY platform, https://www.opensafely.org/, as part of the NHS England OpenSAFELY COVID-19 service. Data include pseudonymised data such as coded diagnoses, medications and physiological parameters. No free text data are included. All code is shared openly for review and re-use under MIT open licence. Detailed pseudonymised patient data is potentially re-identifiable and therefore not shared. Data management and analysis was performed using Python 3. All code for data management and analysis, as well as codelists, is shared openly for inspection and re-use.
https://github.com/opensafely/antidepressant-prescribing-lda
