Evidence map›Paper›PMID 38902726›Full record

ArticleBMC medicine2024

Healthcare utilisation in people with long COVID: an OpenSAFELY cohort study.

Liang-Yu Lin, Alasdair D Henderson, Oliver Carlile, Iain Dillingham, Ben F C Butler-Cole, Michael Marks, Andrew Briggs, Mark Jit, Laurie A Tomlinson, Chris Bates and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
–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

30 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

18 authors.

Liang-Yu LinFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. liang-yu.lin@lshtm.ac.uk.
Alasdair D HendersonFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Oliver CarlileFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Iain DillinghamDepartment of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, NuffieldOxford, OX2 6GG, UK.
Ben F C Butler-ColeDepartment of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, NuffieldOxford, OX2 6GG, UK.
Michael MarksFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Andrew BriggsFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Mark JitFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Laurie A TomlinsonFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Chris BatesTPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK.
John ParryTPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK.
Sebastian C J BaconDepartment of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, NuffieldOxford, OX2 6GG, UK.
Ben GoldacreDepartment of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, NuffieldOxford, OX2 6GG, UK.
Amir MehrkarDepartment of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, NuffieldOxford, OX2 6GG, UK.
Brian MacKennaDepartment of Primary Care Health Sciences, Bennett Institute for Applied Data Science, University of Oxford, NuffieldOxford, OX2 6GG, UK.
OpenSAFELY Collaborative
Rosalind M Eggo *Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Emily Herrett *Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.

Funding

Medical Research Council MC_PC_20058Medical Research Council MR/V015737/1Medical Research Council MR/W016729/1
6 · The paper itself

Abstract

backgroundLong COVID potentially increases healthcare utilisation and costs. However, its impact on the NHS remains to be determined.

methodsThis study aims to assess the healthcare utilisation of individuals with long COVID. With the approval of NHS England, we conducted a matched cohort study using primary and secondary care data via OpenSAFELY, a platform for analysing anonymous electronic health records. The long COVID exposure group, defined by diagnostic codes, was matched with five comparators without long COVID between Nov 2020 and Jan 2023. We compared their total healthcare utilisation from GP consultations, prescriptions, hospital admissions, A&E visits, and outpatient appointments. Healthcare utilisation and costs were evaluated using a two-part model adjusting for covariates. Using a difference-in-difference model, we also compared healthcare utilisation after long COVID with pre-pandemic records.

resultsWe identified 52,988 individuals with a long COVID diagnosis, matched to 264,867 comparators without a diagnosis. In the 12 months post-diagnosis, there was strong evidence that those with long COVID were more likely to use healthcare resources (OR: 8.29, 95% CI: 7.74-8.87), and have 49% more healthcare utilisation (RR: 1.49, 95% CI: 1.48-1.51). Our model estimated that the long COVID group had 30 healthcare visits per year (predicted mean: 29.23, 95% CI: 28.58-29.92), compared to 16 in the comparator group (predicted mean visits: 16.04, 95% CI: 15.73-16.36). Individuals with long COVID were more likely to have non-zero healthcare expenditures (OR = 7.66, 95% CI = 7.20-8.15), with costs being 44% higher than the comparator group (cost ratio = 1.44, 95% CI: 1.39-1.50). The long COVID group costs approximately £2500 per person per year (predicted mean cost: £2562.50, 95% CI: £2335.60-£2819.22), and the comparator group costs £1500 (predicted mean cost: £1527.43, 95% CI: £1404.33-1664.45). Historically, individuals with long COVID utilised healthcare resources more frequently, but their average healthcare utilisation increased more after being diagnosed with long COVID, compared to the comparator group.

conclusionsLong COVID increases healthcare utilisation and costs. Public health policies should allocate more resources towards preventing, treating, and supporting individuals with long COVID.

Indexed as

COVID-19Patient Acceptance of Health CareAdultAgedAged, 80 and overCohort StudiesEnglandFemaleHealth Care CostsHumansMaleMiddle AgedPost-Acute COVID-19 SyndromeSARS-CoV-2State MedicineYoung AdultElectronic health recordsFacilities and services utilizationHealth care costsLong COVID

Identifiers

PMID38902726
PMCPMC11188519

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.