1. Burden of HAI across three COVID-19 waves
In our study of COVID-19 HAI, we report numbers of HAI that are comparable to other centres3,4. Interestingly, our study spans the first three waves of the COVID-19 pandemic in Belgium; the number and percentages of HAI in our centre were stable across the first two waves, and increased towards the third wave, despite more control measures in place by that time of the pandemic. This may reflect more infectious variants as well as more systematic screening of all hospital admissions. Indeed, the percentage of symptomatic HAI patients with time was lowest during the third wave, probably reflecting vaccination rollout, earlier diagnoses and more exhaustive testing across the hospital.
2. Characteristics of HAI vs CAI
We then went on to describe symptomatic HAI and compared them to CAI. Again, there may have been some selection bias reflected in our results; for example, patients with a HAI are expected to be in poorer health before infection, since they were already hospitalised.
In the univariate analysis, some factors seemed to support this selection of sicker patients: our HAI patients were older, had higher frailty scores, more pronounced smoking habits and more comorbidities. Higher percentage of malignancies, kidney disease and older age were also observed in a British study on HAI versus CAI COVID-1914. With increased age, frailty, comorbidities, and initial reason for hospitalization in mind, it might not come as a surprise that HAI patients had a longer hospital stay from the time of COVID-19 diagnosis. Furthermore, more HAI patients were admitted to the ICU during their stay, compared to CAI. Finally, the mortality was also significantly higher in HAI, compared to CAI patients. Again, these differences are probably due to the selected population, rather than a causal link with HAI per se. Other studies had similar findings which confirm the extent of the problem of HAIs in hospitalized patients7.
Apparent differences in the presence or absence of symptoms between HAI and CAI patients might be due to the timing of COVID-19 diagnoses. Indeed, as HAI might be diagnosed at an earlier stage of the disease, because of timeliness of disease detection and laboratory testing – especially at later waves of COVID-19, certain symptoms might not yet have been present in some HAI cases. Similarly for laboratory data, in particular the significantly lower CRP levels in HAI patients which may be indicative of an earlier stage of COVID-1913. Other confounders for some laboratory findings, for e.g. D-dimers, are the underlying pathologies of hospitalized patients.
In the multivariate analysis frailty (+), thrombocytes (+) and CRP (-) were significantly associated with HAI. These may become important parameters to take into account when trying to decide if a patient has acquired his/her infection in hospital. Of note, BMI was not significantly associated with having a HAI, compared to a CAI, but we must stress we did not assess associations with severe outcomes – so our results cannot draw any conclusion on the link between BMI amongst HAI and severe COVID-19 disease.
3. Cluster analysis
In part B of the results’ section, we report correlations between clinical criteria for the diagnosis of an HAI and genetic sequencing data. When considering bias and limitations, it is true that we were only able to sequence a proportion of the HAI samples.
Despite this, our sequencing analyses allowed us to validate the ECDC definitions for HAI. Sequencing can therefore nicely complement descriptive analyses to describe clusters and seems to be a great tool to better understand COVID-19 transmission within hospitals. Indeed, due to the remaining uncertainties around the incubation period, pre-symptomatic transmission and asymptomatic infections, a definite international consensus on the definition of a HAI is yet to be defined.
We described 12 clusters involving HAI. Some of the clusters we described were large, stressing once again the extreme infectiousness of this infectious agent. In some, different wards and floors were affected by the same cluster (cluster 10): movement of (undetected) infected patients and healthcare workers across wards might have caused transmission. Indeed, looking at figure 2, many clusters have at least a healthcare worker as part of the cluster, thereby suggesting that healthcare workers were involved in nosocomial transmission. A narrative review by Abbas and co-workers highlights the important implications of SARS-CoV-2 transmission to and from healthcare workers25. While protection of healthcare workers is key in prevention of nosocomial COVID-19 outbreaks, preventive measures are predominantly focused on the use of personal protective equipment, such as masks. It seems vital that other preventive measures are also well established, such as physical distancing, appropriate workload, adequate training, population-wide vaccination, etc.25. Our findings support this fully.
With regard to cluster 10, it is not so surprising that the viral strain circulating in this cluster was of the B.1.1.7 lineage. Indeed, we now know that the transmissibility of that SARS-CoV-2 strain is estimated to be 1.56 times higher than the previously reported strains10. It is also very interesting to see that while several SARS-CoV-2 lineages were circulating in HAI patients earlier in the pandemic, 100% of the isolates from HAI samples were of the B.1.1.7 from the beginning of 2021 onwards.