Some variants of SARS-CoV-2 are associated with increased transmissibility, increased disease severity or decreased vaccine effectiveness (VE). In this population-based cohort study (n = 4,204,859), the Delta variant was identified in 5,430 (0.13%) individuals, of whom 84 were admitted to hospital. VE against laboratory confirmed infection with the Delta variant was 22.4% among partly vaccinated (95% confidence interval (CI): 17.0−27.4) and 64.6% (95% CI: 60.6−68.2) among fully vaccinated individuals, compared with 54.5% (95% CI: 50.4−58.3) and 84.4% (95%CI: 81.8−86.5) against the Alpha variant.

Background COVID-19 vaccines have been crucial in the pandemic response and understanding changes in vaccines effectiveness is essential to guide vaccine policies. Although the Delta variant is no longer dominant, understanding vaccine effectiveness properties will provide essential knowledge to comprehend the development of the pandemic and estimate potential changes over time. Methods In this population-based cohort study, we estimated the vaccine effectiveness of Comirnaty (Pfizer/BioNTech; BNT162b2), Spikevax (Moderna; mRNA-1273), Vaxzevria (AstraZeneca; ChAdOx nCoV-19; AZD1222), or a combination against SARS-CoV-2 infections, hospitalisations, intensive care admissions, and death using Cox proportional hazard models, across different vaccine product regimens and age groups, between 15 July and 31 November 2021 (Delta variant period). Vaccine status is included as a time-varying covariate and all models were adjusted for age, sex, comorbidities, county of residence, country of birth, and living conditions. Data from the entire adult Norwegian population were collated from the National Preparedness Register for COVID-19 (Beredt C19). Results The overall adjusted vaccine effectiveness against infection decreased from 81.3% (confidence interval (CI): 80.7 to 81.9) in the first 2 to 9 weeks after receiving a second dose to 8.6% (CI: 4.0 to 13.1) after more than 33 weeks, compared to 98.6% (CI: 97.5 to 99.2) and 66.6% (CI: 57.9 to 73.6) against hospitalisation respectively. After the third dose (booster), the effectiveness was 75.9% (CI: 73.4 to 78.1) against infection and 95.0% (CI: 92.6 to 96.6) against hospitalisation. Spikevax or a combination of mRNA products provided the highest protection, but the vaccine effectiveness decreased with time since vaccination for all vaccine regimens. Conclusions Even though the vaccine effectiveness against infection waned over time, all vaccine regimens remained effective against hospitalisation after the second vaccine dose. For all vaccine regimens, a booster facilitated recovery of effectiveness. The results from this support the use of heterologous schedules, increasing flexibility in vaccination policy.

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Objective To analyze the association between radiologists’ performance and image position within a batch in screen reading of mammograms in Norway. Method We described true and false positives and true and false negatives by groups of image positions and batch sizes for 2,937,312 screen readings performed from 2012 to 2018. Mixed-effects models were used to obtain adjusted proportions of true and false positive, true and false negative, sensitivity, and specificity for different image positions. We adjusted for time of day and weekday and included the individual variation between the radiologists as random effects. Time spent reading was included in an additional model to explore a possible mediation effect. Result True and false positives were negatively associated with image position within the batch, while the rates of true and false negatives were positively associated. In the adjusted analyses, the rate of true positives was 4.0 per 1000 (95% CI: 3.8–4.2) readings for image position 10 and 3.9 (95% CI: 3.7–4.1) for image position 60. The rate of true negatives was 94.4% (95% CI: 94.0–94.8) for image position 10 and 94.8% (95% CI: 94.4–95.2) for image position 60. Per 1000 readings, the rate of false negative was 0.60 (95% CI: 0.53–0.67) for image position 10 and 0.62 (95% CI: 0.55–0.69) for image position 60. Conclusion There was a decrease in the radiologists’ sensitivity throughout the batch, and although this effect was small, our results may be clinically relevant at a population level or when multiplying the differences with the number of screen readings for the individual radiologists. Key Points • True and false positive reading scores were negatively associated with image position within a batch. • A decreasing trend of positive scores indicated a beneficial effect of a certain number of screen readings within a batch. • False negative scores increased throughout the batch but the association was not statistically significant.

Objectives To determine the agreement between artificial intelligence software (AI) and radiographers in assessing breast positioning criteria for mammograms from standard digital mammography and digital breast tomosynthesis. Methods Assessment of breast positioning was performed by AI and by four radiographers in pairs of two on 156 examinations of women screened in Bergen, April to September 2019, as part of BreastScreen Norway. Ten criteria were used; three for craniocaudal and seven for mediolateral-oblique view. The criteria evaluated the appearance of the nipple, breast rotation, pectoral muscle, inframammary fold and pectoral nipple line. Intraclass correlation and Cohen’s kappa coefficient (κ) were used to investigate the correlation and agreement between the radiographer’s assessments and AI. Results The intraclass correlation for the pectoral nipple line between the radiographers and AI was >0.92. A substantial to almost perfect agreement (κ > 0.69) was observed between the radiographers and AI on the nipple in profile criterion. We observed a slight to moderate agreement for the other criteria (κ = 0.06–0.52) and generally a higher agreement between the two pairs of radiographers (mean κ = 0.70) than between the radiographers and AI (mean κ = 0.41). Conclusions AI has great potential in evaluating breast position criteria in mammography by reducing subjectivity. However, varying agreement between radiographers and AI was observed. Standardized and evidence-based criteria for definitions, understandings and assessment methods are needed to reach optimal image quality in mammography.

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