Evidence map›Paper›PMID 41394534›Full record

ArticleBMJ medicine2025

Opioid prescribing to people on orthopaedic waiting lists during the covid-19 pandemic in England: retrospective cohort study using linked electronic health record data in OpenSAFELY-TPP.

Rose Higgins, Rebecca M Smith, Iain Dillingham, Jane Quinlan, Victoria Speed, Helen J Curtis, Christopher Wood, Milan Wiedemann, Meghna Jani, Sebastian C J Bacon and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Rose HigginsNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-5295-4370
Rebecca M SmithNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0003-3964-6733
Iain DillinghamNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Jane QuinlanOxford University Hospitals NHS Foundation Trust, Oxford, UK.ORCID https://orcid.org/0000-0002-0212-4459
Victoria SpeedNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Helen J CurtisNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0003-3429-9576
Christopher WoodNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0001-5184-0606
Milan WiedemannNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Meghna JaniCentre for Musculoskeletal Research, Centre for Epidemiology Versus Arthritis, The University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-1487-277X
Sebastian C J BaconNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-6354-3454
Amir MehrkarNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-2098-1278
Ben GoldacreNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-5127-4728
OpenSAFELY CollaborativeNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Brian MacKennaNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-3786-9063
Andrea L SchafferNuffield Department of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-3701-4997

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To quantify the changes in opioid prescribing over time to a population with high rates of opioid use to understand the impact of longer elective wait times during the covid-19 pandemic. Design: With the approval of NHS England, a retrospective cohort study using linked electronic health record data in OpenSAFELY-TPP. Setting: Primary and secondary care electronic health records of people registered at general practices in England that use TPP SystmOne software, covering about 43% of the total registered population in England, linked to data from the Waiting List Minimum Dataset (WLMDS) within the OpenSAFELY-TPP platform, which is part of the NHS England OpenSAFELY covid-19 service. Participants: 63 850 eligible patients on the waiting list for elective trauma procedures or orthopaedic procedures whose wait ended in admission between May 2021 and April 2022. Main outcome measures: Opioid prescribing to eligible patients before referral to the waiting list, while waiting for treatment, and after discharge from treatment. Opioids were classified based on their strength (weak, moderate, or strong opioids) and duration of action (immediate release Results: Of 63 850 people on elective trauma or orthopaedic waiting lists whose wait ended during the study period (median age 61 years, 54.6% female), 20.5% waited for more than 52 weeks to be admitted. In the three months before their waiting list referral date, 9890 (15.5%) participants had three or more opioid prescriptions, and 3790 (5.9%) were prescribed a strong opioid. Weekly opioid prescribing rates per 100 people on the waiting list were stable over time, with prescription rates peaking immediately after treatment and plateauing about three months after treatment. Comparing the three month period before the waiting list referral date to the period four to six months after the waiting list end date, changes in the proportion of people with three or more prescriptions for an opioid during that period were -1.6% (95% confidence interval -2.2% to -1.0%) for people on the waiting list for 18 weeks or less, -1.1% (-1.7% to -0.5%) for people waiting for 19-52 weeks, and -0.5% (-1.4% to 0.4%) for people waiting for more than 52 weeks. Conclusions: In this study, one in five people who received treatment for an elective orthopaedic procedure between May 2021 and April 2022 waited for more than one year. Nearly one in seven people were prescribed opioids long term before their referral date to the waiting list, and only small reductions in long term opioid prescribing were observed after a patient's procedure, regardless of length of time spent on the waiting list.

Indexed as

COVID-19EpidemiologyPain management

Identifiers

PMID41394534
PMCPMC12699563

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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.