Evidence map›Paper›PMID 41565343›Full record

ArticleBMJ (Clinical research ed.)2026

Time trends in newly recorded diagnoses of 19 long term conditions before, during, and after the covid-19 pandemic: population based cohort study in England using OpenSAFELY.

Mark D Russell, Andrea Schaffer, Katie Bechman, Mark Gibson, Jon Massey, Rose Higgins, Brian MacKenna, Peter Inglesby, Seb Bacon, Amir Mehrkar and 14 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ (Clinical research ed.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

24 authors.

Mark D RussellCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK mark.russell@kcl.ac.uk.ORCID https://orcid.org/0000-0001-8171-7772
Andrea SchafferBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Katie BechmanCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK.
Mark GibsonCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK.
Jon MasseyBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Rose HigginsBennett 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.
Peter InglesbyBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Seb 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.
Ben GoldacreBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Edward AlveynCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK.
Victoria AllenCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK.
Zijing YangCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK.
Samir PatelCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK.
Maryam A AdasCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK.
Gurjinder SandhuKing's College Hospital NHS Foundation Trust, London, UK.
Elizabeth PriceGreat Western Hospital NHS Foundation Trust, Swindon, UK.
Rouvick M GamaDepartment of Inflammation Biology, King's College London, London, UK.
Kate BramhamCentre for Nephrology, Urology, and Transplantation, King's College London, London, UK.
Matthew HotopfInstitute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
Sam NortonCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK.
Andrew P CopeCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK.
James B GallowayCentre for Rheumatic Diseases, King's College London, SE5 9RJ, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate temporal changes in rates of newly recorded diagnoses for 19 long term conditions in England in relation to the covid-19 pandemic by disease, age group, sex, socioeconomic status, and ethnicity.

designPopulation based cohort study.

settingPrimary care and hospital admission data, with the approval of NHS England.

participants29 995 025 individuals registered with general practices in England contributing data to the OpenSAFELY-TPP platform.

main outcome measuresTemporal trends in age and sex standardised incident and prevalent diagnosis rates for 19 long term conditions between 1 April 2016 and 30 November 2024. Differences between expected and observed diagnosis rates after the onset of the covid-19 pandemic were compared using seasonal autoregressive integrated moving-average models, based on modelled projections of expected rates from pre-pandemic patterns.

resultsAll 19 conditions showed a sharp decline in newly recorded diagnoses during the first year of the pandemic, followed by variable recovery. As of November 2024, cumulative reductions in diagnoses remained evident for conditions such as depression (734 800 (27.7%) fewer diagnoses than expected; 95% prediction interval (PI) 703 100 to 766 400), asthma (152 900 (16.4%) fewer diagnoses; 95% PI 137 500 to 168 300), chronic obstructive pulmonary disease (COPD) (90 100 (15.8%) fewer diagnoses; 95% PI 81 400 to 98 900), psoriasis (54 700 (17.1%) fewer diagnoses; 95% PI 50 100 to 59 200), and osteoporosis (54 100 (11.5%) fewer diagnoses; 95% PI 47 100 to 61 100). Conversely, diagnoses of chronic kidney disease have increased by 34.8% above expected levels during the pandemic recovery period, corresponding to 359 000 additional diagnoses (95% PI 333 500 to 384 500). Unadjusted subgroup analyses stratified by ethnicity and socioeconomic status indicated that, after an initial decrease, dementia diagnosis rates have risen above pre-pandemic levels for people of white ethnicity and in less deprived socioeconomic areas, but not for those from other ethnicities and more deprived areas.

conclusionsSince the covid-19 pandemic, there have been fewer diagnoses than expected for conditions such as depression, asthma, COPD, and osteoporosis, in contrast with a rapid increase in diagnoses of chronic kidney disease since 2022. Unadjusted analyses stratified by ethnicity and socioeconomic status suggest differential patterns of recovery, particularly for individuals with dementia. This study highlights the potential for near real time monitoring of disease epidemiology using routinely collected health data, informing strategies to enhance case detection and investigate inequities in healthcare.

Indexed as

COVID-19AgedChronic DiseaseCohort StudiesEnglandFemaleHumansPandemicsSARS-CoV-2

Identifiers

PMID41565343
PMCPMC12821157

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.