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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Depression (16.0%, 95%CI 16.0\u201316.0%) and hypertension (15.3%, 95%CI 15.2\u201315.3%) were the most prevalent conditions among 12.4\u00a0million 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-023-02296-z","type":"journal-article","created":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T09:02:19Z","timestamp":1697446939000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12\u00a0million english primary care records"],"prefix":"10.1186","volume":"23","author":[{"given":"Jennifer","family":"Cooper","sequence":"first","affiliation":[]},{"given":"Krishnarajah","family":"Nirantharakumar","sequence":"additional","affiliation":[]},{"given":"Francesca","family":"Crowe","sequence":"additional","affiliation":[]},{"given":"Amaya","family":"Azcoaga-Lorenzo","sequence":"additional","affiliation":[]},{"given":"Colin","family":"McCowan","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Jackson","sequence":"additional","affiliation":[]},{"given":"Aditya","family":"Acharya","sequence":"additional","affiliation":[]},{"given":"Krishna","family":"Gokhale","sequence":"additional","affiliation":[]},{"given":"Niluka","family":"Gunathilaka","sequence":"additional","affiliation":[]},{"given":"Tom","family":"Marshall","sequence":"additional","affiliation":[]},{"given":"Shamil","family":"Haroon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,16]]},"reference":[{"key":"2296_CR1","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/S2215-0366(15)00505-2","volume":"3","author":"D Vigo","year":"2016","unstructured":"Vigo D, Thornicroft G, Atun R. 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There is overall ethical approval from the NHS Health Research Authority for collection of anonymised data in CPRD Aurum. GP practices consent to share patient data with CPRD for research purposes and individual patients can opt-out of such data-sharing. The informed consent statement was waived by the NHS Health Research Authority (East Midlands - Derby Research Ethics Committee Ethics Committee reference 21\/EM\/0265) due to the fact that individual patients cannot be identified from the database. Further details about CPRD\u2019s process to safeguard patient data can be found at\n                      \n                      All methods were carried out in accordance with relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"For EHR data: Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"220"}}