Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records
Abstract Background 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 ov...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02296-z |
| _version_ | 1797559240379858944 |
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| author | Jennifer Cooper Krishnarajah Nirantharakumar Francesca Crowe Amaya Azcoaga-Lorenzo Colin McCowan Thomas Jackson Aditya Acharya Krishna Gokhale Niluka Gunathilaka Tom Marshall Shamil Haroon |
| author_facet | Jennifer Cooper Krishnarajah Nirantharakumar Francesca Crowe Amaya Azcoaga-Lorenzo Colin McCowan Thomas Jackson Aditya Acharya Krishna Gokhale Niluka Gunathilaka Tom Marshall Shamil Haroon |
| author_sort | Jennifer Cooper |
| collection | DOAJ |
| description | Abstract Background 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. |
| first_indexed | 2024-03-10T17:42:37Z |
| format | Article |
| id | doaj.art-396b8843c9a44f3c9288c6e44c073748 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-10T17:42:37Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-396b8843c9a44f3c9288c6e44c0737482023-11-20T09:38:08ZengBMCBMC Medical Informatics and Decision Making1472-69472023-10-0123111610.1186/s12911-023-02296-zPrevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care recordsJennifer Cooper0Krishnarajah Nirantharakumar1Francesca Crowe2Amaya Azcoaga-Lorenzo3Colin McCowan4Thomas Jackson5Aditya Acharya6Krishna Gokhale7Niluka Gunathilaka8Tom Marshall9Shamil Haroon10Institute of Applied Health Research, Health Data Science and Public Health, University of BirminghamInstitute of Applied Health Research, Health Data Science and Public Health, University of BirminghamInstitute of Applied Health Research, Health Data Science and Public Health, University of BirminghamSchool of Medicine, University of St AndrewsSchool of Medicine, University of St AndrewsClinician Scientist in Geriatric Medicine, Institute of Inflammation and Ageing, University of BirminghamInstitute of Applied Health Research, Health Data Science and Public Health, University of BirminghamInstitute of Applied Health Research, Health Data Science and Public Health, University of BirminghamInstitute of Applied Health Research, Health Data Science and Public Health, University of BirminghamInstitute of Applied Health Research, Health Data Science and Public Health, University of BirminghamInstitute of Applied Health Research, Health Data Science and Public Health, University of BirminghamAbstract Background 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.https://doi.org/10.1186/s12911-023-02296-zElectronic health recordsPrevalenceCardiovascularRenalMetabolicMental health |
| spellingShingle | Jennifer Cooper Krishnarajah Nirantharakumar Francesca Crowe Amaya Azcoaga-Lorenzo Colin McCowan Thomas Jackson Aditya Acharya Krishna Gokhale Niluka Gunathilaka Tom Marshall Shamil Haroon 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 Electronic health records Prevalence Cardiovascular Renal Metabolic Mental health |
| title | Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records |
| title_full | Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records |
| title_fullStr | Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records |
| title_full_unstemmed | Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records |
| title_short | Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records |
| title_sort | prevalence and demographic variation of cardiovascular renal metabolic and mental health conditions in 12 million english primary care records |
| topic | Electronic health records Prevalence Cardiovascular Renal Metabolic Mental health |
| url | https://doi.org/10.1186/s12911-023-02296-z |
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