Information bias of vaccine effectiveness estimation due to informed consent for national registration of COVID-19 vaccination: estimation and correction using a data augmentation model (preprint)

Background: Registration in the Dutch national COVID-19 vaccination register requires consent from the vaccinee. This causes misclassification of non-consenting vaccinated persons as being unvaccinated. We quantified and corrected the resulting information bias in the estimation of vaccine effectiveness (VE). Methods: National data were used for the period dominated by the SARS-CoV-2 Delta variant (11 July to 15 November 2021). VE ((1-relative risk)*100%) against COVID-19 hospitalization and ICU admission was estimated for individuals 12-49, 50-69, and [≥]70 years of age using negative binomial regression. Anonymous data on vaccinations administered by the Municipal Health Services were used to determine informed consent percentages and estimate corrected VEs by iterative data augmentation. Absolute bias was calculated as the absolute change in VE; relative bias as uncorrected / corrected relative risk. Results: A total of 8,804 COVID-19 hospitalizations and 1,692 COVID-19 ICU admissions were observed. The bias was largest in the 70+ age group where the non-consent proportion was 7.0% and observed vaccination coverage was 87%: VE of primary vaccination against hospitalization changed from 75.5% (95% CI 73.5-77.4) before to 85.9% (95% CI 84.7-87.1) after correction (absolute bias -10.4 percentage point, relative bias 1.74). VE against ICU admission in this group was 88.7% (95% CI 86.2-90.8) before and 93.7% (95% CI 92.2-94.9) after correction (absolute bias -5.0 percentage point, relative bias 1.79). Conclusions: VE estimates can be substantially biased with modest non-consent percentages for registration of vaccination. Data on covariate specific non-consent percentages should be available to correct this bias.

The estimated disease burden of acute COVID-19 in the Netherlands in 2020, in disability-adjusted life-years (preprint)

Background. The impact of COVID-19 on population health is recognised as being substantial, yet few studies have attempted to quantify to what extent infection causes mild or moderate symptoms only, requires hospital and/or ICU admission, results in prolonged and chronic illness, or leads to premature death. We aimed to quantify the total disease burden of acute COVID-19 in the Netherlands in 2020 using the disability-adjusted life-years (DALY) measure, and to investigate how burden varies between age-groups and occupations.Methods. Using standard methods and diverse data sources (mandatory notifications, population-level seroprevalence, hospital and ICU admissions, registered COVID-19 deaths, and the literature), we estimated years of life lost (YLL), years lived with disability, DALY and DALY per 100,000 population due to COVID-19, excluding post-acute sequelae, stratified by 5-year age-group and occupation category.Results. The total disease burden due to acute COVID-19 was 286,100 (95% CI:281,700–290,500) DALY, and the per-capita burden was 1640 (95% CI:1620–1670) DALY/100,000, of which 99.4% consisted of YLL. The per-capita burden increased steeply with age, starting from 60–64 years, with relatively little burden estimated for persons under 50 years old.Conclusions. SARS-CoV-2 infection and associated premature mortality was responsible for a considerable direct health burden in the Netherlands, despite extensive public health measures. DALY were much higher than for other high-burden infectious diseases, but lower than estimated for coronary heart disease. These findings are valuable for informing public health decision-makers regarding the expected COVID-19 health burden among population subgroups, and the possible gains from targeted preventative interventions.

Testing behaviour and positivity for SARS-CoV-2 infection: Insights from web-based participatory surveillance (preprint)

BackgroundVoluntary testing for SARS-CoV-2 infection is an integral component of an effective response to the COVID-19 pandemic. It is essential to identify populations at a high risk for infection but who are less likely to present for testing. Here, we use internet-based participatory surveillance data from the Netherlands to identify sociodemographic and household factors that are associated with a lower propensity to be tested and, if tested, with a higher risk of a positive test result.MethodsMultivariable analyses using generalised estimating equations for binomial outcomes were conducted to estimate the adjusted odds ratios of testing and of positivity associated with participant and household characteristics.ResultsBased on five months (17 November 2020 to 18 April 2021) of weekly surveys obtained from 12,026 participants, males (adjusted odds ratio for testing (ORt): 0.92; adjusted odds ratio for positivity (ORp): 1.30, age-groups <20 (ORt: 0.89; ORp: 1.27) 50-64 years (ORt: 0.94; ORp: 1.06) and 65+ years (ORt: 0.78; ORp: 1.24), diabetics (ORt: 0.97; ORp: 1.06), and sales/administrative employees (ORt: 0.93; ORp: 1.90) were distinguished as lower propensity/higher positivity factors.ConclusionsThe factors identified using this approach can help identify potential target groups for improving communication and encouraging testing among those with symptoms and thus increase the effectiveness of testing, which is essential for the response to the COVID-19 pandemic and for public health strategies in the longer term.

Estimating the asymptomatic proportion of SARS-CoV-2 infection in the general population: Analysis of a nationwide serosurvey in the Netherlands (preprint)

BackgroundThe proportion of SARS-CoV-2 positive persons who are asymptomatic - and whether this proportion is age-dependent - are still open research questions. Because an unknown proportion of reported symptoms among SARS-CoV-2 positives will be attributable to another infection or affliction, the observed, or crude proportion without symptoms may underestimate the proportion of persons without symptoms that are caused by SARS-CoV-2 infection. MethodsBased on a large population-based serological study comprising test results on seropositivity and self-reported symptom history conducted in April/May 2020 in the Netherlands (n=3147), we estimated the proportion of reported symptoms among those persons infected with SARS-CoV-2 that is attributable to this infection, where the set of relevant symptoms fulfills the ECDC case definition of COVID-19, using inferential methods for the attributable risk (AR). Generalised additive regression modelling was used to estimate the age-dependent relative risk (RR) of reported symptoms, and the AR and asymptomatic proportion (AP) were calculated from the fitted RR. ResultsUsing age-aggregated data, the estimated AP was 70% (95% CI: 65-77%). The estimated AP decreased with age, from 80% (95% CI: 67-100%) for the <20 years age-group, to 55% (95% CI: 48-68%) for the 70+ years age-group. ConclusionWhereas the crude AP represents a lower bound for the proportion of persons infected with SARS-CoV-2 without COVID-19 symptoms, the AP as estimated via an attributable risk approach represents an upper bound. Age-specific AP estimates can inform the implementation of public health actions such as targetted virological testing and therefore enhance containment strategies.