Prevalence and risk factors for long COVID among adults in Scotland using electronic health records, 2024, Jeffrey et al.

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study
Karen Jeffrey; Lana Woolford; Rishma Maini; Siddharth Basetti; Ashleigh Batchelor; David Weatherill; Chris White; Vicky Hammersley; Tristan Millington; Calum Macdonald; Jennifer K. Quint; Robin Kerr; Steven Kerr; Syed Ahmar Shah; Igor Rudan; Adeniyi Francis Fagbamigbe; Colin R. Simpson; Srinivasa Vittal Katikireddi; Chris Robertson; Lewis Ritchie; Aziz Sheikh; Luke Daines

BACKGROUND
Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development.

METHODS
In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98–99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status.

FINDINGS
Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38–67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4–26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p < 0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive.

INTERPRETATION
The prevalence of long COVID presenting in general practice was estimated to be 0.02–1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach.

FUNDING
Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.


Link | PDF (Lancet: eClinicalMedicine) [Open Access]

 

Might as well look for marriage troubles from divorce records in countries (or times) where divorce is illegal, or at best highly frown upon and limited geographically or to select, say rich, people. Extremely unreliable.

Scientists need quality primary data to work with. Here the primary data is impossible to build to a quality level. So the whole thing is stuck in loops where decisions influence records which then influence decisions. I have never seen such a level of systemic dysfunction. Well, apart from, you know, us, going on for decades. Just stunning ineptitude and failure across the board.