ArticlePloS one2017
Analysis of diagnoses extracted from electronic health records in a large mental health case register.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- An analysis of the global, regional, and national epidemiology and trends of Alzheimer's disease and other dementias linked to smoking from 1990 to 2021 and projections to 2050.Tobacco induced diseases · 2025Article
- Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach.Journal of biomedical informatics · 2022Article
- The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning.JMIR medical informatics · 2020Article
- Stratifying risk for dementia onset using large-scale electronic health record data: A retrospective cohort study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2020Article
- Identifying Veterans Using Electronic Health Records in the United Kingdom: A Feasibility Study.Healthcare (Basel, Switzerland) · 2019Article
- Automatic mining of symptom severity from psychiatric evaluation notes.International journal of methods in psychiatric research · 2018Article
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Authors and funding
5 authors.
Funding
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Abstract
The UK government has recently recognised the need to improve mental health services in the country. Electronic health records provide a rich source of patient data which could help policymakers to better understand needs of the service users. The main objective of this study is to unveil statistics of diagnoses recorded in the Case Register of the South London and Maudsley NHS Foundation Trust, one of the largest mental health providers in the UK and Europe serving a source population of over 1.2 million people residing in south London. Based on over 500,000 diagnoses recorded in ICD10 codes for a cohort of approximately 200,000 mental health patients, we established frequency rate of each diagnosis (the ratio of the number of patients for whom a diagnosis has ever been recorded to the number of patients in the entire population who have made contact with mental disorders). We also investigated differences in diagnoses prevalence between subgroups of patients stratified by gender and ethnicity. The most common diagnoses in the considered population were (recurrent) depression (ICD10 codes F32-33; 16.4% of patients), reaction to severe stress and adjustment disorders (F43; 7.1%), mental/behavioural disorders due to use of alcohol (F10; 6.9%), and schizophrenia (F20; 5.6%). We also found many diagnoses which were more likely to be recorded in patients of a certain gender or ethnicity. For example, mood (affective) disorders (F31-F39); neurotic, stress-related and somatoform disorders (F40-F48, except F42); and eating disorders (F50) were more likely to be found in records of female patients, while males were more likely to be diagnosed with mental/behavioural disorders due to psychoactive substance use (F10-F19). Furthermore, mental/behavioural disorders due to use of alcohol and opioids were more likely to be recorded in patients of white ethnicity, and disorders due to use of cannabinoids in those of black ethnicity.
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