Coronavirus disease (COVID-19)

As COVID-19 vaccine effectiveness against SARS-CoV-2 infection was lower for cases of the Omicron vs the Delta variant, understanding the effect of vaccination in reducing risk of hospitalisation and severe disease among COVID-19 cases is crucial.

To evaluate risk reduction of hospitalisation and severe disease in vaccinated COVID-19 cases during the Omicron BA.1-predominant period in Navarre, Spain.

A case-to-case comparison included COVID-19 epidemiological surveillance data in adults ≥ 18 years from 3 January–20 March 2022. COVID-19 vaccination status was compared between hospitalised and non-hospitalised cases, and between severe (intensive care unit admission or death) and non-severe cases using logistic regression models.

Among 58,952 COVID-19 cases, 565 (1.0%) were hospitalised and 156 (0.3%) were severe. The risk of hospitalisation was reduced within the first 6 months after full COVID-19 vaccination (complete primary series) (adjusted odds ratio (aOR): 0.06; 95% CI: 0.04–0.09) and after 6 months (aOR: 0.16; 95% CI: 0.12–0.21; pcomparison < 0.001), as well as after a booster dose (aOR: 0.06: 95% CI: 0.04–0.07). Similarly, the risk of severe disease was reduced (aOR: 0.13, 0.18, and 0.06, respectively). Compared with cases fully vaccinated 6 months or more before a positive test, those who had received a booster dose had lower risk of hospitalisation (aOR: 0.38; 95% CI: 0.28–0.52) and severe disease (aOR: 0.38; 95% CI: 0.21–0.68).

Full COVID-19 vaccination greatly reduced the risk of hospitalisation and severe outcomes in COVID-19 cases with the Omicron variant, and a booster dose improved this effect in people aged over 65 years.

It sparked considerable attention from international media when Denmark lifted restrictions against COVID-19 in February 2022 amidst widespread transmission of the new SARS-CoV-2 Omicron variant and a steep rise in reported COVID-19 mortality based on the 30-day COVID-19 death count.

Our aim was to investigate how coincidental infections affected COVID-19 mortality estimates following the introduction of the Omicron variant in late 2021.

We compared the 30-day COVID-19 death count with the observed mortality using three alternative mortality estimation methods; (i) a mathematical model to correct the 30-day COVID-19 death count for coincidental deaths, (ii) the Causes of Death Registry (CDR) and (iii) all-cause excess mortality.

There was a substantial peak in the 30-day COVID-19 death count following the emergence of the Omicron variant in late 2021. However, there was also a substantial change in the proportion of coincidental deaths, increasing from 10–20% to around 40% of the recorded COVID-19 deaths. The high number of 30-day COVID-19 deaths was not reflected in the number of COVID-19 deaths in the CDR and the all-cause excess mortality surveillance.

Our analysis showed a distinct change in the mortality pattern following the introduction of Omicron in late 2021 with a markedly higher proportion of people estimated to have died with, rather than of, COVID-19 compared with mortality patterns observed earlier in the COVID-19 pandemic. Our findings highlight the importance of incorporating alternative mortality surveillance methods to more correctly estimate the burden of COVID-19 as the pandemic continues to evolve.

During the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.

To develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.

Data were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms’ performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified.

We estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits.

Implementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.

The SARS-CoV-2 variant of concern Omicron was first detected in Italy in November 2021.

To comprehensively describe Omicron spread in Italy in the 2 subsequent months and its impact on the overall SARS-CoV-2 circulation at population level.

We analyse data from four genomic surveys conducted across the country between December 2021 and January 2022. Combining genomic sequencing results with epidemiological records collated by the National Integrated Surveillance System, the Omicron reproductive number and exponential growth rate are estimated, as well as SARS-CoV-2 transmissibility.

Omicron became dominant in Italy less than 1 month after its first detection, representing on 3 January 76.9–80.2% of notified SARS-CoV-2 infections, with a doubling time of 2.7–3.3 days. As of 17 January 2022, Delta variant represented < 6% of cases. During the Omicron expansion in December 2021, the estimated mean net reproduction numbers respectively rose from 1.15 to a maximum of 1.83 for symptomatic cases and from 1.14 to 1.36 for hospitalised cases, while remaining relatively stable, between 0.93 and 1.21, for cases needing intensive care. Despite a reduction in relative proportion, Delta infections increased in absolute terms throughout December contributing to an increase in hospitalisations. A significant reproduction numbers’ decline was found after mid-January, with average estimates dropping below 1 between 10 and 16 January 2022.

Estimates suggest a marked growth advantage of Omicron compared with Delta variant, but lower disease severity at population level possibly due to residual immunity against severe outcomes acquired from vaccination and prior infection.

During the coronavirus disease (COVID-19) pandemic, key persons who were formally or informally active in community organisations and networks, such as sports clubs or cultural, educational, day care and healthcare facilities, occupied a key position between governments and citizens. However, their experiences, the dilemmas they faced and the solutions they generated when implementing COVID-19 measures in their respective settings are understudied.

We aimed to understand how key persons in different community organisations and networks experienced and responded to the COVID-19 measures in the Netherlands.

Between October 2020 and December 2021, the Corona Behavioural Unit at the Dutch national public health institute, conducted qualitative research based on narratives derived from 65 in-depth interviews with 95 key persons from 32 organisations and networks in eight different sectors.

Firstly, key persons enhanced adherence and supported the resilience and well-being of people involved in their settings. Secondly, adherence was negatively affected where COVID-19 measures conflicted with important organisational goals and values. Thirdly, small changes and ambiguities in COVID-19 policy had substantial consequences, depending on the context. Fourthly, problem-solving was achieved through trial-and-error, peer support, co-creation and transparent communication. Lastly, the COVID-19 pandemic and measures highlighted inequalities in access to resources.

Pandemic preparedness requires organisational and community preparedness and a multidisciplinary public health approach. Structural engagement of governments with key persons in community organisations and networks is key to enhance public trust and adherence to pandemic measures and contributes to health equity and the well-being of the people involved.

Compliance with infection prevention and control (IPC) measures is critical to preventing COVID-19 transmission in healthcare settings.

To identify and explain factors influencing compliance with COVID-19-specific IPC measures among healthcare workers (HCWs) in long-term care facilities (LTCF) in Finland.

The study included a web-based survey and qualitative study based on the Theoretical Domains Framework (TDF). The link to the anonymous survey was distributed via email to LTCFs through regional IPC experts in December 2020. Outcome was modelled using ordinary logistic regression and penalised ridge logistic regression using regrouped explanatory variables and an original, more correlated set of explanatory variables, respectively. In-depth interviews were conducted among survey participants who volunteered during January–March 2021. Data were analysed thematically using qualitative data analysis software (NVIVO12).

A total of 422 HCWs from 17/20 regions responded to the survey. Three TDF domains were identified that negatively influenced IPC compliance: environmental context and resources, reinforcement and beliefs about capabilities. Twenty HCWs participated in interviews, which resulted in identification of several themes: changes in professional duties and lack of staff planning for emergencies (domain: environmental context and resources); management culture and physical absence of management (domain: reinforcement), knowledge of applying IPC measures, nature of tasks and infrastructure that supports implementation (domain: beliefs about capabilities), that explained how the domains negatively influenced their IPC behaviour.

This study provides insights into behavioural domains that can be used in developing evidence-based behaviour change interventions to support HCW compliance with pandemic-specific IPC measures in LTCFs.

After an outbreak of the SARS-CoV-2 Beta variant in the district of Schwaz/Austria, vaccination with Comirnaty vaccine (BNT162b2 mRNA, BioNTech-Pfizer) had been offered to all adult inhabitants (≥ 16 years) in March 2021. This made Schwaz one of the most vaccinated regions in Europe at that time (70% of the adult population took up the offer). In contrast, all other Austrian districts remained with low vaccine coverage.

We studied whether this rapid mass vaccination campaign provided indirect protection to unvaccinated individuals such as children (< 16 years) living in the same district.

To study the effect of the campaign we used two complementary approaches. We compared infection rates among the population of children (< 16 years) in Schwaz with (i) the child population from similar districts (using the synthetic control method), and (ii) with the child population from municipalities along the border of Schwaz not included in the campaign (using an event study approach).

Before the campaign, we observed very similar infection spread across the cohort of children in Schwaz and the control regions. After the campaign, we found a significant reduction of new cases among children of −64.5% (95%-CI: −82.0 to −30.2%) relative to adjacent border municipalities (using the event study model). Employing the synthetic control method, we observed a significant reduction of −42.8% in the same cohort.

Our results constitute novel evidence of an indirect protection effect from a group of vaccinated individuals to an unvaccinated group.

Underlying conditions are risk factors for severe COVID-19 outcomes but evidence is limited about how risks differ with age.

We sought to estimate age-specific associations between underlying conditions and hospitalisation, death and in-hospital death among COVID-19 cases.

We analysed case-based COVID-19 data submitted to The European Surveillance System between 2 June and 13 December 2020 by nine European countries. Eleven underlying conditions among cases with only one condition and the number of underlying conditions among multimorbid cases were used as exposures. Adjusted odds ratios (aOR) were estimated using 39 different age-adjusted and age-interaction multivariable logistic regression models, with marginal means from the latter used to estimate probabilities of severe outcome for each condition–age group combination.

Cancer, cardiac disorder, diabetes, immunodeficiency, kidney, liver and lung disease, neurological disorders and obesity were associated with elevated risk (aOR: 1.5–5.6) of hospitalisation and death, after controlling for age, sex, reporting period and country. As age increased, age-specific aOR were lower and predicted probabilities higher. However, for some conditions, predicted probabilities were at least as high in younger individuals with the condition as in older cases without it. In multimorbid patients, the aOR for severe disease increased with number of conditions for all outcomes and in all age groups.

While supporting age-based vaccine roll-out, our findings could inform a more nuanced, age- and condition-specific approach to vaccine prioritisation. This is relevant as countries consider vaccination of younger people, boosters and dosing intervals in response to vaccine escape variants.