Evidence map›Paper›PMID 35074819›Full record

Observational studyBMJ open2022

Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register.

Rebecca Bendayan, Zeljko Kraljevic, Shaweena Shaari, Jayati Das-Munshi, Leona Leipold, Jaya Chaturvedi, Luwaiza Mirza, Sarah Aldelemi, Thomas Searle, Natalia Chance and 7 more

Open access · goldAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
2.8field-weighted citation impact, top 9% of its field
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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Article
  9. Review
  10. A network approach to lifestyle behaviors and health outcomes in people with mental illness: the MULTI+ study III.European psychiatry : the journal of the Association of European Psychiatrists · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Making meaning of multimorbidity and severe mental illness: A viewpoint.The Australian and New Zealand journal of psychiatry · 2024
    Article
  16. 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

17 authors at 2 institutions in 1 country.

Rebecca BendayanDepartment of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK rebecca.bendayan@kcl.ac.uk.ORCID 0000-0003-1461-556X
Zeljko KraljevicDepartment of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK.
Shaweena ShaariNIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK.
Jayati Das-MunshiDepartment of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Leona LeipoldNIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK.ORCID 0000-0002-7712-4293
Jaya ChaturvediDepartment of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK.
Luwaiza MirzaNIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK.
Sarah AldelemiNIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK.
Thomas SearleDepartment of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK.
Natalia ChanceNIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK.
Aurelie MascioDepartment of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK.
Naoko SkiadaDepartment of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK.
Tao WangDepartment of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK.
Angus RobertsDepartment of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK.
Robert StewartNIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK.
Daniel BeanDepartment of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK.
Richard DobsonDepartment of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK.
King's College London · GBUniversity of London · GB

Funding

Medical Research Council MC_PC_17214Medical Research Council MR/S00310X/1Medical Research Council MR/T045302/1Medical Research Council MR/V049879/1Wellcome Trust
6 · The paper itself

Abstract

objectivesThe first aim of this study was to design and develop a valid and replicable strategy to extract physical health conditions from clinical notes which are common in mental health services. Then, we examined the prevalence of these conditions in individuals with severe mental illness (SMI) and compared their individual and combined prevalence in individuals with bipolar (BD) and schizophrenia spectrum disorders (SSD).

designObservational study.

settingSecondary mental healthcare services from South London

participantsOur maximal sample comprised 17 500 individuals aged 15 years or older who had received a primary or secondary SMI diagnosis (International Classification of Diseases, 10th edition, F20-31) between 2007 and 2018. MEASURES: We designed and implemented a data extraction strategy for 21 common physical comorbidities using a natural language processing pipeline, MedCAT. Associations were investigated with sex, age at SMI diagnosis, ethnicity and social deprivation for the whole cohort and the BD and SSD subgroups. Linear regression models were used to examine associations with disability measured by the Health of Nations Outcome Scale.

resultsPhysical health data were extracted, achieving precision rates (F1) above 0.90 for all conditions. The 10 most prevalent conditions were diabetes, hypertension, asthma, arthritis, epilepsy, cerebrovascular accident, eczema, migraine, ischaemic heart disease and chronic obstructive pulmonary disease. The most prevalent combination in this population included diabetes, hypertension and asthma, regardless of their SMI diagnoses.

conclusionsOur data extraction strategy was found to be adequate to extract physical health data from clinical notes, which is essential for future multimorbidity research using text records. We found that around 40% of our cohort had multimorbidity from which 20% had complex multimorbidity (two or more physical conditions besides SMI). Sex, age, ethnicity and social deprivation were found to be key to understand their heterogeneity and their differential contribution to disability levels in this population. These outputs have direct implications for researchers and clinicians.

Indexed as

Biomedical ResearchBipolar DisorderMental DisordersSchizophreniaAdolescentHumansLondonMultimorbidityState Medicineepidemiologyhealth informaticsmental healthpsychiatrypublic health

Identifiers

PMID35074819
PMCPMC8788233
OpenAlexW4207032307

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.