Prevalence, risk factors and characterisation of individuals with long COVID […] in over 1.5 million COVID cases in England, 2024, Wang+

Prevalence, risk factors and characterisation of individuals with long COVID using Electronic Health Records in over 1.5 million COVID cases in England
Han-I Wang; Tim Doran; Michael G Crooks; Kamlesh Khunti; Melissa Heightman; Arturo Gonzalez-Izquierdo; Muhammad Qummer Ul Arfeen; Antony Loveless; Amitava Banerjee; Christina Van Der Feltz-Cornelis

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.


Link | PDF (Journal of Infection) [Open Access]

 

Didn't note any mention of ME/CFS.

 

It's not, but whatever.

This is a pretty strong deviation from other studies in many aspects, but it looks mainly because of what they choose to look for.

4.5 years and the research is still so damn weak. You can find spicier homeopathic water than this.

 

There is this overarching problem here where clinicians mainly focus on symptoms that can be treated, which leaves behind symptoms that can't, which makes them under-recorded, which makes it impossible to develop treatments, which makes them less focused on, which makes them less recorded, and so on and on and on...

And then there's what clinical coders choose to focus on. And then there's what researchers choose to look for. It's the classic telephone game, where the final output has almost nothing to do with the initial input.

Sometimes it really feels like a miracle that this profession has ever managed to achieve anything at all. Oh, right, technology, aka knowledge that does work. Without this, barely anything seems to work.

 

Heightman and Van Der Feltz-Cornelis are familiar names from FND papers.

 

FND features though doesn't look like a significant risk factor —

However Somatic Symptom Disorder —

I would expect pre-existing SSD might be misdiagnosed ME/CFS in a number of cases, and quite possibly FND also.

 

The list of risk factors is mostly the ingredients in a recipe for "Who will get gaslighted today?"