Epidemiologic and Genomic Analysis of SARS-CoV-2 Delta Variant Superspreading Event in Nightclub, the Netherlands, June 2021.

We report a severe acute respiratory syndrome coronavirus 2 superspreading event in the Netherlands after distancing rules were lifted in nightclubs, despite requiring a negative test or vaccination. This occurrence illustrates the potential for rapid dissemination of variants in largely unvaccinated populations under such conditions. We detected subsequent community transmission of this strain.

Hospitalisation rates differed by city district and ethnicity during the first wave of COVID-19 in Amsterdam, The Netherlands.

BACKGROUND: It is important to gain insight into the burden of COVID-19 at city district level to develop targeted prevention strategies. We examined COVID-19 related hospitalisations by city district and migration background in the municipality of Amsterdam, the Netherlands. METHODS: We used surveillance data on all PCR-confirmed SARS-CoV-2 hospitalisations in Amsterdam until 31 May 2020, matched to municipal registration data on migration background. We calculated directly standardised (age, sex) rates (DSR) of hospitalisations, as a proxy of COVID-19 burden, per 100,000 population by city district and migration background. We calculated standardised rate differences (RD) and rate ratios (RR) to compare hospitalisations between city districts of varying socio-economic and health status and between migration backgrounds. We evaluated the effects of city district and migration background on hospitalisation after adjusting for age and sex using Poisson regression. RESULTS: Between 29 February and 31 May 2020, 2326 cases (median age 57 years [IQR = 37-74]) were notified in Amsterdam, of which 596 (25.6%) hospitalisations and 287 (12.3%) deaths. 526/596 (88.2%) hospitalisations could be matched to the registration database. DSR were higher in individuals living in peripheral (South-East/New-West/North) city districts with lower economic and health status, compared to central districts (Centre/West/South/East) (RD = 36.87,95%CI = 25.79-47.96;RR = 1.82,95%CI = 1.65-1.99), and among individuals with a non-Western migration background compared to ethnic-Dutch individuals (RD = 57.05,95%CI = 43.34-70.75; RR = 2.36,95%CI = 2.17-2.54). City district and migration background were independently associated with hospitalisation. CONCLUSION: City districts with lower economic and health status and those with a non-Western migration background had the highest burden of COVID-19 during the first wave of COVID-19 in Amsterdam.

Occupation- and age-associated risk of SARS-CoV-2 test positivity, the Netherlands, June to October 2020.

High coronavirus incidence has prompted the Netherlands to implement a second lockdown. To elucidate the epidemic's development preceding this second wave, we analysed weekly test positivity in public test locations by population subgroup between 1 June and 17 October 2020. Hospitality and public transport workers, driving instructors, hairdressers and aestheticians had higher test positivity compared with a reference group of individuals without a close-contact occupation. Workers in childcare, education and healthcare showed lower test positivity.

COVID-19 burden differed by city districts and ethnicities during the pre-vaccination era in Amsterdam, the Netherlands.

Background: During the first wave of COVID-19 in Amsterdam, the Netherlands, a disproportional number of COVID-19 hospitalizations occurred in individuals with an ethnic minority background and in individuals living in city districts with a lower socioeconomic status (SES). In this study, we assessed whether these disparities continued throughout the second wave, when SARS-CoV-2 testing was available to anyone with symptoms but prior to the availability of COVID-19 vaccination. Methods: Surveillance data on all notified SARS-CoV-2 cases in Amsterdam between 15 June 2020 and 20 January 2021 were matched to municipal registration data to obtain the migration background of cases. Crude and directly age- and sex-standardized rates (DSR) of confirmed cases, hospitalizations, and deaths per 100,000 population were calculated overall, and by city districts, and migration backgrounds. Rate differences (RD) and rate ratios (RR) were calculated to compare DSR between city districts and migration backgrounds. We used multivariable Poisson regression to assess the association of city districts, migration backgrounds, age, and sex with rates of hospitalization. Results: A total of 53,584 SARS-CoV-2 cases (median age 35 years [IQR = 25-74]) were notified, of whom 1,113 (2.1%) were hospitalized and 297 (0.6%) deceased. DSR of notified infections, hospitalization, and deaths per 100,000 population were higher in lower SES peripheral city districts (South-East/North/New-West) than higher SES central districts (Central/West/South/East), with almost a 2-fold higher hospitalization DSR in peripheral compared to central districts (RR = 1.86, 95%CI = 1.74-1.97). Individuals with a non-European migration background also had a higher COVID-19 burden, particularly with respect to hospitalization rates, with a 4.5-fold higher DSR for individuals with a non-European background compared to ethnic-Dutch (RR 4.51, 95%CI = 4.37-4.65). City districts, migration backgrounds, male gender, and older age were independently associated with COVID-19 hospitalization rates. Discussion: Individuals with a non-European background and individuals living in city districts with lower SES continued to independently have the highest COVID-19 burden in the second wave of COVID-19 in Amsterdam, the Netherlands.

Self-reported symptoms as predictors of SARS-CoV-2 infection in the general population living in the Amsterdam region, the Netherlands.

INTRODUCTION: Most COVID-19 symptoms are non-specific and also common in other respiratory infections. We aimed to assess which symptoms are most predictive of a positive test for SARS-CoV-2 in symptomatic people of the general population who were tested. METHODS: We used anonymised data of all SARS-CoV-2 test results from the Public Health Service of Amsterdam from June 1,2020 through August 31, 2021. Symptoms were self-reported at time of requesting a test. Multivariable logistic regression models with generalized estimating equations were used to identify predictors of a positive test. Included symptoms were: cough, fever, loss of smell or taste, muscle ache, runny nose, shortness of breath, and throat ache; adjustments were made for age and gender, and stratification by month. RESULTS: Overall, 12.0% of 773,680 tests in 432,213 unique individuals were positive. All symptoms were significantly associated with a positive test result, the strongest positive associations were: cough (aOR = 1.78, 95%CI = 1.75-1.80), fever (aOR = 2.11, 95%CI = 2.07-2.14), loss of smell or taste (aOR = 2.55, 95%CI = 2.50-2.61), and muscle ache (aOR = 2.38, 95%CI = 2.34-2.43). The adjusted odds ratios for loss of smell or taste slightly declined over time, while that for cough increased. CONCLUSION: Cough, fever, loss of smell or taste, and muscle ache appear to be most strongly associated with a positive SARS-CoV-2 test in symptomatic people of the general population who were tested.

Factors Associated With Using the COVID-19 Mobile Contact-Tracing App Among Individuals Diagnosed With SARS-CoV-2 in Amsterdam, the Netherlands: Observational Study.

BACKGROUND: Worldwide, efforts are being made to stop the COVID-19 pandemic caused by SARS-CoV-2. Contact tracing and quarantining are key in limiting SARS-CoV-2 transmission. Mathematical models have shown that the time between infection, isolation of cases, and quarantining of contacts are the most important components that determine whether the pandemic can be controlled. Mobile contact-tracing apps could accelerate the tracing and quarantining of contacts, including anonymous contacts. However, real-world observational data on the uptake and determinants of contact-tracing apps are limited. OBJECTIVE: The aim of this paper is to assess the use of a national Dutch contact-tracing app among notified cases diagnosed with SARS-CoV-2 infection and investigate which characteristics are associated with the use of the app. METHODS: Due to privacy regulations, data from the app could not be used. Instead, we used anonymized SARS-CoV-2 routine contact-tracing data collected between October 28, 2020, and February 26, 2021, in the region of Amsterdam, the Netherlands. Complete case logistic regression analysis was performed to identify which factors (age, gender, country of birth, municipality, number of close contacts, and employment in either health care or education) were associated with using the app. Age and number of close contacts were modelled as B-splines due to their nonlinear relationship. RESULTS: Of 29,766 SARS-CoV-2 positive cases, 4824 (16.2%) reported app use. Median age of cases was 41 (IQR 29-55) years, and 46.7% (n=13,898) were male. In multivariable analysis, males (adjusted odds ratio [AOR] 1.11, 95% CI 1.04-1.18) and residents of municipalities surrounding Amsterdam were more likely to use the app (Aalsmeer AOR 1.34, 95% CI 1.13-1.58; Ouder-Amstel AOR 1.96, 95% CI 1.54-2.50), while people born outside the Netherlands, particularly those born in non-Western countries (AOR 0.33, 95% CI 0.30-0.36), were less likely to use the app. Odds of app use increased with age until the age of 58 years and decreased sharply thereafter (P<.001). Odds of app use increased with number of contacts, peaked at 8 contacts, and then decreased (P<.001). Individuals working in day care, home care, and elderly nursing homes were less likely to use the app. CONCLUSIONS: Contact-tracing app use among people with confirmed SARS-CoV-2 infection was low in the region of Amsterdam. This diminishes the potential impact of the app by hampering the ability to warn contacts. Use was particularly low among older people, people born outside the Netherlands, and people with many contacts. Use of the app was also relatively low compared to those from some other European countries, some of which had additional features beyond contact tracing, making them potentially more appealing. For the Dutch contact-tracing app to have an impact, uptake needs to be higher; therefore, investing more into promotional efforts and additional features could be considered.

The effect of flight-related behaviour on the risk of venous thrombosis after air travel.

In a case-control study including 11,033 participants (The Multiple Environmental and Genetic Assessment of risk factors for venous thrombosis study) on risk factors of venous thrombosis, we studied the effect of flight-related behaviour on the risk of venous thrombosis after air travel. Patients and control subjects received a questionnaire on risk factors for venous thrombosis, including recent travel history and details of their last flight. From this population, 80 patients and 108 control subjects were selected who had recently (<8 weeks) travelled for more than 4 h by aeroplane. Window seating compared to aisle seating increased the risk twofold [odds ratio (OR) 2.2; 95% confidence interval (CI): 1.1-4.4], particularly in those who were obese (OR 6.1; 95% CI: 0.5-76.2). Anxiety (OR 2.5; 95% CI: 0.9-7.0) and sleeping (OR 1.5; 95% CI: 0.7-3.1) may increase the risk slightly. The risk was not affected by alcohol consumption (OR 1.1; 95% CI: 0.5-2.4). Flying business class may lower the risk (OR 0.7; 95% CI: 0.2-1.8). We did not find a protective effect for several measures currently part of standard advice from airlines and clinicians, i.e. drinking non-alcoholic beverages, exercising or wearing stockings. The effect of behavioural factors during flying on the risk of venous thrombosis after air travel is limited. Current advice on prevention of travel-related thrombosis may have to be reconsidered.

Fluid loss does not explain coagulation activation during air travel.

The mechanism of air travel-related venous thrombosis is unclear. Although immobility plays a pivotal role, other factors such as fluid loss may contribute. We investigated whether fluid loss occurred more in individuals with coagulation activation after air travel than in subjects without. As a secondary aim, we investigated whether fluid loss per se occurred during air travel. In this crossover study, 71 healthy volunteers were exposed to eight hours of air travel, eight hours immobilization in a cinema, and a daily-life control situation. Markers of fluid loss (haematocrit, serum osmolality and albumin) and of coagulation activation were measured before and after each exposure. The study included 11 volunteers with and 55 volunteers without coagulation activation during the flight. The change in parameters of fluid loss was not different in volunteers with an activated clotting system from those without (difference between groups in haematocrit: -0.6%, 95% confidence interval [CI]: -1.9 to 0.6). On a group level, mean haematocrit values decreased during all three exposures. However, in some individuals it increased, which occurred in more participants during the flight (34%; 95% CI 22 to 46) than during the daily-life situation (19%; 95% CI 10 to 28). These findings do not support the hypothesis that fluid loss contributes to thrombus formation during air travel.