Evidence map›Paper›PMID 37845709›Full record

ArticleBMC medical informatics and decision making2023

Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records.

Jennifer Cooper, Krishnarajah Nirantharakumar, Francesca Crowe, Amaya Azcoaga-Lorenzo, Colin McCowan, Thomas Jackson, Aditya Acharya, Krishna Gokhale, Niluka Gunathilaka, Tom Marshall and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.1field-weighted citation impact, top 11% 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

10 citing papers in PubMed, 10 citations in OpenAlex.

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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

11 authors at 2 institutions in 1 country.

Jennifer CooperInstitute of Applied Health Research, Health Data Science and Public Health, University of Birmingham, Birmingham, UK.
Krishnarajah NirantharakumarInstitute of Applied Health Research, Health Data Science and Public Health, University of Birmingham, Birmingham, UK. k.nirantharan@bham.ac.uk.
Francesca CroweInstitute of Applied Health Research, Health Data Science and Public Health, University of Birmingham, Birmingham, UK.
Amaya Azcoaga-LorenzoSchool of Medicine, University of St Andrews, Fife, UK.
Colin McCowanSchool of Medicine, University of St Andrews, Fife, UK.
Thomas JacksonClinician Scientist in Geriatric Medicine, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
Aditya AcharyaInstitute of Applied Health Research, Health Data Science and Public Health, University of Birmingham, Birmingham, UK.
Krishna GokhaleInstitute of Applied Health Research, Health Data Science and Public Health, University of Birmingham, Birmingham, UK.
Niluka GunathilakaInstitute of Applied Health Research, Health Data Science and Public Health, University of Birmingham, Birmingham, UK.
Tom MarshallInstitute of Applied Health Research, Health Data Science and Public Health, University of Birmingham, Birmingham, UK.
Shamil HaroonInstitute of Applied Health Research, Health Data Science and Public Health, University of Birmingham, Birmingham, UK.
University of Birmingham · GBUniversity of St Andrews · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrimary care electronic health records (EHR) are widely used to study long-term conditions in epidemiological and health services research. Therefore, it is important to understand how well the recorded prevalence of these conditions in EHRs, compares to other reliable sources overall, and varies by socio-demographic characteristics. We aimed to describe the prevalence and socio-demographic variation of cardiovascular, renal, and metabolic (CRM) and mental health (MH) conditions in a large, nationally representative, English primary care database and compare with prevalence estimates from other population-based studies.

methodsThis was a cross-sectional study using the Clinical Practice Research Datalink (CPRD) Aurum primary care database. We calculated prevalence of 18 conditions and used logistic regression to assess how this varied by age, sex, ethnicity, and socio-economic status. We searched the literature for population prevalence estimates from other sources for comparison with the prevalences in CPRD Aurum.

resultsDepression (16.0%, 95%CI 16.0-16.0%) and hypertension (15.3%, 95%CI 15.2-15.3%) were the most prevalent conditions among 12.4 million patients. Prevalence of most conditions increased with socio-economic deprivation and age. CRM conditions, schizophrenia and substance misuse were higher in men, whilst anxiety, depression, bipolar and eating disorders were more common in women. Cardiovascular risk factors (hypertension and diabetes) were more prevalent in black and Asian patients compared with white, but the trends in prevalence of cardiovascular diseases by ethnicity were more variable. The recorded prevalences of mental health conditions were typically twice as high in white patients compared with other ethnic groups. However, PTSD and schizophrenia were more prevalent in black patients. The prevalence of most conditions was similar or higher in the primary care database than diagnosed disease prevalence reported in national health surveys. However, screening studies typically reported higher prevalence estimates than primary care data, especially for PTSD, bipolar disorder and eating disorders.

conclusionsThe prevalence of many clinically diagnosed conditions in primary care records closely matched that of other sources. However, we found important variations by sex and ethnicity, which may reflect true variation in prevalence or systematic differences in clinical presentation and practice. Primary care data may underrepresent the prevalence of undiagnosed conditions, particularly in mental health.

Indexed as

HypertensionMental HealthCross-Sectional StudiesFemaleHumansMalePrevalencePrimary Health CareCardiovascularElectronic health recordsMental healthMetabolicPrevalenceRenal

Identifiers

PMID37845709
PMCPMC10580600
OpenAlexW4387667970

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.