Trial Report The effectiveness of COVID-19 vaccines to prevent long COVID symptoms: staggered cohort study of data from the UK, Spain, and Estonia, 2024, Català +

The effectiveness of COVID-19 vaccines to prevent long COVID symptoms: staggered cohort study of data from the UK, Spain, and Estonia
Martí Català; Núria Mercadé-Besora; Raivo Kolde; Nhung T H Trinh; Elena Roel; Edward Burn; Trishna Rathod-Mistry; Kristin Kostka; Wai Yi Man; Antonella Delmestri; Hedvig M E Nordeng; Anneli Uusküla; Talita Duarte-Salles; Daniel Prieto-Alhambra; Annika M Jödicke

Background
Although vaccines have proved effective to prevent severe COVID-19, their effect on preventing long-term symptoms is not yet fully understood. We aimed to evaluate the overall effect of vaccination to prevent long COVID symptoms and assess comparative effectiveness of the most used vaccines (ChAdOx1 and BNT162b2).

Methods
We conducted a staggered cohort study using primary care records from the UK (Clinical Practice Research Datalink [CPRD] GOLD and AURUM), Catalonia, Spain (Information System for Research in Primary Care [SIDIAP]), and national health insurance claims from Estonia (CORIVA database). All adults who were registered for at least 180 days as of Jan 4, 2021 (the UK), Feb 20, 2021 (Spain), and Jan 28, 2021 (Estonia) comprised the source population. Vaccination status was used as a time-varying exposure, staggered by vaccine rollout period. Vaccinated people were further classified by vaccine brand according to their first dose received. The primary outcome definition of long COVID was defined as having at least one of 25 WHO-listed symptoms between 90 and 365 days after the date of a PCR-positive test or clinical diagnosis of COVID-19, with no history of that symptom 180 days before SARS-Cov-2 infection. Propensity score overlap weighting was applied separately for each cohort to minimise confounding. Sub-distribution hazard ratios (sHRs) were calculated to estimate vaccine effectiveness against long COVID, and empirically calibrated using negative control outcomes. Random effects meta-analyses across staggered cohorts were conducted to pool overall effect estimates.

Findings
A total of 1 618 395 (CPRD GOLD), 5 729 800 (CPRD AURUM), 2 744 821 (SIDIAP), and 77 603 (CORIVA) vaccinated people and 1 640 371 (CPRD GOLD), 5 860 564 (CPRD AURUM), 2 588 518 (SIDIAP), and 302 267 (CORIVA) unvaccinated people were included. Compared with unvaccinated people, overall HRs for long COVID symptoms in people vaccinated with a first dose of any COVID-19 vaccine were 0·54 (95% CI 0·44–0·67) in CPRD GOLD, 0·48 (0·34–0·68) in CPRD AURUM, 0·71 (0·55–0·91) in SIDIAP, and 0·59 (0·40–0·87) in CORIVA. A slightly stronger preventative effect was seen for the first dose of BNT162b2 than for ChAdOx1 (sHR 0·85 [0·60–1·20] in CPRD GOLD and 0·84 [0·74–0·94] in CPRD AURUM).

Interpretation
Vaccination against COVID-19 consistently reduced the risk of long COVID symptoms, which highlights the importance of vaccination to prevent persistent COVID-19 symptoms, particularly in adults.

Funding
National Institute for Health and Care Research.


Link | PDF (The Lancet Respiratory Medicine)

 

What about non-mild long-term health outcomes that don't directly correlate with the severity of an acute infection, since the rest has been known for years?

 

Even if true, too bad the medical profession has been selling the idea that "it's just a cold now" for years and vaccination rates barely cross 15%. Every country went with a "vaccine-only" strategy, then got bored with it quickly in a way that will depress all vaccination rates for years. Good job, geniuses.

I couldn't really find anything about it, but it still doesn't seem to consider "time since vaccination", which more often than not is equivalent to # of vaccinations, however that is fading quickly and is largely irrelevant as long as variants keep cropping up, which is being maximized.

If the protection is short-lived, and vaccination rates remain close or fall to single digit %, none of this matters. A common maxim of warfare is that no matter how well you plan, the enemy also makes plans. Well, this is an enemy that can't even make plans, and it's kicking our asses because we can't bothered to make plans that account for reality. May as well make a cavalry charge across a chasm and stick to the plan. We are truly no smarter than our ancestors who hooted around fires.

 

Can someone please figure out if this estimate includes vaccine effectiveness? E.g., are they comparing the LC rate among people infected, or the LC rate among all people?

 

This data includes vaccine effectiveness (LC rate among all people). Their data suggests that the vaccines had a tremendous impact at preventing a Covid-19 infection altogether (roughly 3.5 times better odds at not getting infected), at a larger rate than what I have seen in more rigorous studies (albeit with smaller sample sizes).

I have calculated all the cohorts together which yielded:

Vaccinated: 10 161 929
infected : 179 859
i.e. 1.77%

Unvaccinated: 10 363 535
infected : 639 238
i.e. 6.17%

There's also very large differences amongst the various cohorts on the percentage of infected people. In some unvaccinated cohorts more than 17% get infected, in some less than 0.3% get infected (the same of course applies to the vaccinated cohorts). The differences are really massive. I can't say whether there's methodological problems (for example identifying an infection) or if these are due to natural differences in the different cohorts (age, gender, comborbidties, cohorts being situation in places where infection waves are hitting taking place etc.).

The differences in the LC rates are similar but not quite as big, showing that there are definite problems in collecting the data like this (from 25% to 1.7%).

I don't know if they have also got some data somewhere on the rate of LC amongst those infected. I assume they do, maybe they also discuss this somewhere.