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dc.contributor.authorCooper, Jennifer
dc.contributor.authorNirantharakumar, Krishnarajah
dc.contributor.authorCrowe, Francesca
dc.contributor.authorAzcoaga-Lorenzo, Amaya
dc.contributor.authorMcCowan, Colin
dc.contributor.authorJackson, Thomas
dc.contributor.authorAcharya, Aditya
dc.contributor.authorGokhale, Krishna
dc.contributor.authorGunathilaka, Niluka
dc.contributor.authorMarshall, Tom
dc.contributor.authorHaroon, Shamil
dc.date.accessioned2023-11-15T10:30:03Z
dc.date.available2023-11-15T10:30:03Z
dc.date.issued2023-10-16
dc.identifier293881978
dc.identifierc99b0958-9b34-4396-bad4-703298160690
dc.identifier37845709
dc.identifier85174280779
dc.identifier.citationCooper , J , Nirantharakumar , K , Crowe , F , Azcoaga-Lorenzo , A , McCowan , C , Jackson , T , Acharya , A , Gokhale , K , Gunathilaka , N , Marshall , T & Haroon , S 2023 , ' Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records ' , BMC Medical Informatics and Decision Making , vol. 23 , 220 . https://doi.org/10.1186/s12911-023-02296-zen
dc.identifier.issn1472-6947
dc.identifier.otherJisc: 1432394
dc.identifier.otherORCID: /0000-0002-9466-833X/work/145516127
dc.identifier.otherORCID: /0000-0003-3307-878X/work/145517206
dc.identifier.otherJisc: 1432394
dc.identifier.otherPubMedCentral: PMC10580600
dc.identifier.otherpii: 10.1186/s12911-023-02296-z
dc.identifier.urihttps://hdl.handle.net/10023/28701
dc.descriptionThis study was undertaken as part of a National Institute for Health Research (NIHR) Intelligence for Multiple Long-Term Conditions (AIM) funded project. OPTIMising therapies, disease trajectories, and AI assisted clinical management for patients Living with complex multimorbidity (OPTIMAL study) Award ID: NIHR202632 https://fundingawards.nihr.ac.uk/award/NIHR202632 .en
dc.description.abstractBackground Primary 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. Methods This 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. Results Depression (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. Conclusions The 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.
dc.format.extent16
dc.format.extent2538668
dc.language.isoeng
dc.relation.ispartofBMC Medical Informatics and Decision Makingen
dc.subjectPrevalenceen
dc.subjectMental healthen
dc.subjectElectronic health recordsen
dc.subjectRenalen
dc.subjectCardiovascularen
dc.subjectMetabolicen
dc.subjectZA4050 Electronic information resourcesen
dc.subjectRA0421 Public health. Hygiene. Preventive Medicineen
dc.subject3rd-DASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subject.lccZA4050en
dc.subject.lccRA0421en
dc.titlePrevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care recordsen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.contributor.institutionUniversity of St Andrews. Population and Behavioural Science Divisionen
dc.identifier.doi10.1186/s12911-023-02296-z
dc.description.statusPeer revieweden


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