Prevalence, risk factors and characterisation of individuals with long COVID using Electronic Health Records in over 1.5 million COVID cases in England.

OBJECTIVES: This study examines clinically confirmed long-COVID symptoms and diagnosis among individuals with COVID in England, aiming to understand prevalence and associated risk factors using electronic health records. To further understand long COVID, the study also explored differences in risks and symptom profiles in three subgroups: hospitalised, non-hospitalised, and untreated COVID cases. METHODS: A population-based longitudinal cohort study was conducted using data from 1,554,040 individuals with confirmed SARS-CoV-2 infection via Clinical Practice Research Datalink. Descriptive statistics explored the prevalence of long COVID symptoms 12 weeks post-infection, and Cox regression models analysed the associated risk factors. Sensitivity analysis was conducted to test the impact of right-censoring data. RESULTS: During an average 400-day follow-up, 7.4% of individuals with COVID had at least one long-COVID symptom after acute phase, yet only 0.5% had long-COVID diagnostic codes. The most common long-COVID symptoms included cough (17.7%), back pain (15.2%), stomach-ache (11.2%), headache (11.1%), and sore throat (10.0%). The same trend was observed in all three subgroups. Risk factors associated with long-COVID symptoms were female sex, non-white ethnicity, obesity, and pre-existing medical conditions like anxiety, depression, type II diabetes, and somatic symptom disorders. CONCLUSIONS: This study is the first to investigate the prevalence and risk factors of clinically confirmed long-COVID in the general population. The findings could help clinicians identify higher risk individuals for timely intervention and allow decision-makers to more efficiently allocate resources for managing long-COVID.

Translating and evaluating historic phenotyping algorithms using SNOMED CT.

OBJECTIVE: Patient phenotype definitions based on terminologies are required for the computational use of electronic health records. Within UK primary care research databases, such definitions have typically been represented as flat lists of Read terms, but Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) (a widely employed international reference terminology) enables the use of relationships between concepts, which could facilitate the phenotyping process. We implemented SNOMED CT-based phenotyping approaches and investigated their performance in the CPRD Aurum primary care database. MATERIALS AND METHODS: We developed SNOMED CT phenotype definitions for 3 exemplar diseases: diabetes mellitus, asthma, and heart failure, using 3 methods: "primary" (primary concept and its descendants), "extended" (primary concept, descendants, and additional relations), and "value set" (based on text searches of term descriptions). We also derived SNOMED CT codelists in a semiautomated manner for 276 disease phenotypes used in a study of health across the lifecourse. Cohorts selected using each codelist were compared to "gold standard" manually curated Read codelists in a sample of 500 000 patients from CPRD Aurum. RESULTS: SNOMED CT codelists selected a similar set of patients to Read, with F1 scores exceeding 0.93, and age and sex distributions were similar. The "value set" and "extended" codelists had slightly greater recall but lower precision than "primary" codelists. We were able to represent 257 of the 276 phenotypes by a single concept hierarchy, and for 135 phenotypes, the F1 score was greater than 0.9. CONCLUSIONS: SNOMED CT provides an efficient way to define disease phenotypes, resulting in similar patient populations to manually curated codelists.

Healthcare use by people who use illicit opioids (HUPIO): development of a cohort based on electronic primary care records in England.

Background: People who use illicit opioids such as heroin have substantial health needs, but there are few longitudinal studies of general health and healthcare in this population. Most research to date has focused on a narrow set of outcomes, including overdoses and HIV or hepatitis infections. We developed and validated a cohort using UK primary care electronic health records (Clinical Practice Research Datalink GOLD and AURUM databases) to facilitate research into healthcare use by people who use illicit opioid use (HUPIO). Methods: Participants are patients in England with primary care records indicating a history of illicit opioid use. We identified codes including prescriptions of opioid agonist therapies (methadone and buprenorphine) and clinical observations such as 'heroin dependence'. We constructed a cohort of patients with at least one of these codes and aged 18-64 at cohort entry, with follow-up between January 1997 and March 2020. We validated the cohort by comparing patient characteristics and mortality rates to other cohorts of people who use illicit opioids, with different recruitment methods. Results: Up to March 2020, the HUPIO cohort included 138,761 patients with a history of illicit opioid use. Demographic characteristics and all-cause mortality were similar to existing cohorts: 69% were male; the median age at index for patients in CPRD AURUM (the database with more included participants) was 35.3 (interquartile range 29.1-42.6); the average age of new cohort entrants increased over time; 76% had records indicating current tobacco smoking; patients disproportionately lived in deprived neighbourhoods; and all-cause mortality risk was 6.6 (95% CI 6.5-6.7) times the general population of England. Conclusions: Primary care data offer new opportunities to study holistic health outcomes and healthcare of this population. The large sample enables investigation of rare outcomes, whilst the availability of linkage to external datasets allows investigation of hospital use, cancer treatment, and mortality.