Evidence map›Paper›PMID 38942555›Full record

ArticleThe Lancet. Public health2024

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

Andrea L Schaffer, Colm D Andrews, Andrew D Brown, Richard Croker, William J Hulme, Linda Nab, Jane Quinlan, Victoria Speed, Christopher Wood, Milan Wiedemann and 9 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet. Public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Andrea L SchafferBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK. Electronic address: andrea.schaffer@phc.ox.ac.uk.
Colm D AndrewsBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Andrew D BrownBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Richard CrokerBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
William J HulmeBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Linda NabBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Jane QuinlanOxford University Hospitals Trust, Oxford, UK.
Victoria SpeedBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; King's Thrombosis Centre, King's College Hospital, London, UK.
Christopher WoodBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Milan WiedemannBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Jon MasseyBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Peter InglesbyBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Seb C J BaconBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Amir MehrkarBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Chris BatesTPP, TPP House, Leeds, UK.
Ben GoldacreBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Alex J WalkerBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Brian MacKennaBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
OpenSAFELY Collaborative

Funding

Department of Health COV-LT2-0073Department of Health P10607Medical Research Council MC_PC_20030Medical Research Council MC_PC_20058Wellcome Trust 222097
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic disrupted health-care delivery, including difficulty accessing in-person care, which could have increased the need for strong pharmacological pain relief. Due to the risks associated with overprescribing of opioids, especially to vulnerable populations, we aimed to quantify changes to measures during the COVID-19 pandemic, overall, and by key subgroups.

methodsFor this interrupted time-series analysis study conducted in England, with National Health Service England approval, we used routine clinical data from more than 20 million general practice adult patients in OpenSAFELY-TPP, which is a a secure software platform for analysis of electronic health records. We included all adults registered with a primary care practice using TPP-SystmOne software. Using interrupted time-series analysis, we quantified prevalent and new opioid prescribing before the COVID-19 pandemic (January, 2018-February, 2020), during the lockdown (March, 2020-March, 2021), and recovery periods (April, 2021-June, 2022), overall and stratified by demographics (age, sex, deprivation, ethnicity, and geographical region) and in people in care homes identified via an address-matching algorithm.

findingsThere was little change in prevalent prescribing during the pandemic, except for a temporary increase in March, 2020. We observed a 9·8% (95% CI -14·5 to -6·5) reduction in new opioid prescribing from March, 2020, with a levelling of the downward trend, and rebounding slightly after April, 2021 (4·1%, 95% CI -0·9 to 9·4). Opioid prescribing rates varied by demographics, but we found a reduction in new prescribing for all subgroups except people aged 80 years or older. Among care home residents, in April, 2020, parenteral opioid prescribing increased by 186·3% (153·1 to 223·9).

interpretationOpioid prescribing increased temporarily among older people and care home residents, likely reflecting use to treat end-of-life COVID-19 symptoms. Despite vulnerable populations being more affected by health-care disruptions, disparities in opioid prescribing by most demographic subgroups did not widen during the pandemic. Further research is needed to understand what is driving the changes in new opioid prescribing and its relation to changes to health-care provision during the pandemic.

fundingThe Wellcome Trust, Medical Research Council, The National Institute for Health and Care Research, UK Research and Innovation, and Health Data Research UK.

Indexed as

Analgesics, OpioidCOVID-19Interrupted Time Series AnalysisPractice Patterns, Physicians'AdolescentAdultAgedAged, 80 and overCohort StudiesDrug PrescriptionsEnglandFemaleHumansMaleMiddle AgedPandemicsAnalgesics, Opioid

Identifiers

PMID38942555
PMCPMC7616651

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.