Eurosurveillance - Volume 29, Issue 35, 29 August 2024

COVID-19 remains a major infectious disease with substantial implications for individual and public health including the risk of a post-infection syndrome, long COVID. The continuous changes in dominant variants of SARS-CoV-2 necessitate a careful study of the effect of preventative strategies.

We aimed to estimate the effectiveness of post-vaccination, post-infection and hybrid immunity against severe cases requiring oxygen support caused by infections with SARS-CoV-2 variants BA1/2 and BA4/5+, and against long COVID in the infected population and their changes over time.

We used a Cox regression analysis with time-varying covariates and calendar time and logistic regression applied to national-level data from Czechia from December 2021 until August 2023.

Recently boosted vaccination, post-infection and hybrid immunity provide significant protection against a severe course of COVID-19, while unboosted vaccination more than 10 months ago has a negligible protective effect. The post-vaccination immunity against the BA1/2 or BA4/5+ variants, especially based on the original vaccine types, appears to wane rapidly compared with post-infection and hybrid immunity. Once infected, however, previous immunity plays only a small protective role against long COVID.

Vaccination remains an effective preventative measure against a severe course of COVID-19 but its effectiveness wanes over time thus highlighting the importance of booster doses. Once infected, vaccines may have a small protective effect against the development of long COVID.

Respiratory sentinel surveillance systems leveraging computerised medical records (CMR) use phenotyping algorithms to identify cases of interest, such as acute respiratory infection (ARI). The Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) is the English primary care-based sentinel surveillance network.

This study describes and validates the RSC’s new ARI phenotyping algorithm.

We developed the phenotyping algorithm using a framework aligned with international interoperability standards. We validated our algorithm by comparing ARI events identified during the 2022/23 influenza season in England through use of both old and new algorithms. We compared clinical codes commonly used for recording ARI.

The new algorithm identified an additional 860,039 cases and excluded 52,258, resulting in a net increase of 807,781 cases (33.84%) of ARI compared to the old algorithm, with totals of 3,194,224 cases versus 2,386,443 cases. Of the 860,039 newly identified cases, the majority (63.7%) were due to identification of symptom codes suggestive of an ARI diagnosis not detected by the old algorithm. The 52,258 cases incorrectly identified by the old algorithm were due to inadvertent identification of chronic, recurrent, non-infectious and other non-ARI disease.

We developed a new ARI phenotyping algorithm that more accurately identifies cases of ARI from the CMR. This will benefit public health by providing more accurate surveillance reports to public health authorities. This new algorithm can serve as a blueprint for other CMR-based surveillance systems wishing to develop similar phenotyping algorithms.