Evidence map›Paper›PMID 40295114›Full record

ArticleBMJ mental health2025

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.

Christine Cunningham, Orla Macdonald, Andrea Schaffer, Andrew Brown, Milan Wiedemann, Rose Higgins, Chris Bates, John Parry, Louis Fisher, Helen Curtis and 22 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

32 authors.

Christine CunninghamNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Orla MacdonaldOxford Health NHS Foundation Trust, Oxford, UK orla.macdonald@oxfordhealth.nhs.uk.ORCID 0000-0003-2774-0001
Andrea SchafferNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID 0000-0002-3701-4997
Andrew BrownNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID 0000-0002-3688-8393
Milan WiedemannUniversity of Oxford, Oxford, UK.
Rose HigginsNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID 0000-0002-5295-4370
Chris BatesTPP, Leeds, UK.
John ParryThe Phoenix Partnership, Leeds, UK.
Louis FisherUniversity of Oxford, Oxford, UK.
Helen CurtisNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Amir MehrkarNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Liam C HartNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
William HulmeUniversity of Oxford, Oxford, UK.
Victoria SpeedNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Tom WardUniversity of Oxford, Oxford, UK.
Richard CrokerNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID 0000-0002-8114-9186
Christopher WoodNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID 0000-0001-5184-0606
Alex WalkerNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Colm AndrewsNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Ben Butler-ColeNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
David EvansNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Peter InglesbyNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Iain DillinghamNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Simon DavyNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Lucy BridgesNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID 0000-0002-4407-9596
Thomas O'DwyerNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID 0000-0002-4373-1597
Steve MaudeNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Rebecca SmithNuffield Department of Primary Care Health Sciences, The Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Amelia GreenNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Ben GoldacreNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Brian MacKennaNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Sebastian BaconNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.

Funding

National Institute for Health for Care Research NIHR NIHR135559, COV-LT2-0073UK Department of Health and Social CareWellcome TrustWellcome Trust 222097/Z/20/Z
6 · The paper itself

Abstract

backgroundCOVID-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.

methodsWith 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.

resultsPrior 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.

Indexed as

Antidepressive AgentsAutistic DisorderCOVID-19DepressionDrug PrescriptionsLearning DisabilitiesPractice Patterns, Physicians'AdolescentAdultAgedChildEnglandFemaleHumansInterrupted Time Series AnalysisMaleAntidepressive AgentsCOVID-19DepressionPSYCHIATRY

Identifiers

PMID40295114
PMCPMC12039045

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.