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.

Differences in SARS-CoV-2 infections during the first and second wave of SARS-CoV-2 between six ethnic groups in Amsterdam, the Netherlands: A population-based longitudinal serological study.

BACKGROUND: Surveillance data in high-income countries have reported more frequent SARS-CoV-2 diagnoses in ethnic minority groups. We examined the cumulative incidence of SARS-CoV-2 and its determinants in six ethnic groups in Amsterdam, the Netherlands. METHODS: We analysed participants enrolled in the population-based HELIUS cohort, who were tested for SARS-CoV-2-specific antibodies and answered COVID-19-related questions between June 24-October 9, 2020 (after the first wave) and November 23, 2020-March 31, 2021 (during the second wave). We modelled SARS-CoV-2 incidence from January 1, 2020-March 31, 2021 using Markov models adjusted for age and sex. We compared incidence between ethnic groups over time and identified determinants of incident infection within ethnic groups. FINDINGS: 2,497 participants were tested after the first wave; 2,083 (83·4%) were tested during the second wave. Median age at first visit was 54 years (interquartile range=44-61); 56·6% were female. Compared to Dutch-origin participants (15·9%), cumulative SARS-CoV-2 incidence was higher in participants of South-Asian Surinamese (25·0%; adjusted hazard ratio [aHR]=1·66; 95%CI=1·16-2·40), African Surinamese (28·9%, aHR=1·97; 95%CI=1·37-2·83), Turkish (37·0%; aHR=2·67; 95%CI=1·89-3·78), Moroccan (41·9%; aHR=3·13; 95%CI=2·22-4·42), and Ghanaian (64·6%; aHR=6·00; 95%CI=4·33-8·30) origin. Compared to those of Dutch origin, differences in incidence became wider during the second versus first wave for all ethnic minority groups (all p-values for interaction<0·05), except Ghanaians. Having household members with suspected SARS-CoV-2 infection, larger household size, and low health literacy were common determinants of SARS-CoV-2 incidence across groups. INTERPRETATION: SARS-CoV-2 incidence was higher in the largest ethnic minority groups of Amsterdam, particularly during the second wave. Prevention measures, including vaccination, should be encouraged in these groups. FUNDING: ZonMw, Public Health Service of Amsterdam, Dutch Heart Foundation, European Union, European Fund for the Integration of non-EU immigrants.

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.

Transient Changes in Preexposure Prophylaxis Use and Daily Sexual Behavior After the Implementation of COVID-19 Restrictions Among Men Who Have Sex With Men.

BACKGROUND: We assessed how the Dutch restrictions imposed on March 15, 2020, affected sexual behavior, preexposure prophylaxis (PrEP), and condom use among PrEP users in Amsterdam. METHODS: We used data on (1) PrEP use, (2) anal sex acts, and (3) condom use, per partner type [steady partners (SPs), known casual partners (KCPs), and unknown casual partners (UCPs)], collected daily through a mobile application used between December 1, 2019, and June 30, 2020. We compared the period before versus after March 15, 2020, regarding average proportion of days per week at which each end point was reported and average proportion of anal sex acts covered by PrEP and/or condoms. RESULTS: We included data from 136 men who have sex with men. After March 15, 2020, the proportion of days with anal sex increased with SPs [odds ratio (OR) = 1.26; 95% confidence interval (CI) = 1.10 to 1.44) and decreased with KCPs (OR = 0.73; 95% CI = 0.64 to 0.82) and UCPs (OR = 0.54; 95% CI = 0.48 to 0.61). Shifts in partner types were most profound immediately after March 15, 2020, whereas returning to prerestriction levels mid-May 2020. The proportion of days with PrEP use decreased from 74% before to 58% after March 15, 2020 (P < 0.001). After March 15, 2020, PrEP use during sex decreased with UCPs (ß = -0.36; 95% CI = -0.72 to 0.00) but not with SPs and KCPs. Condom use during sex decreased with KCPs (ß = -0.36; 95% CI = -0.67 to 0.04) and UCPs (ß = -0.24; 95% CI = -0.46 to 0.03) but not with SPs. CONCLUSIONS: MSM decreased sex with casual partners and increased sex with SP, but changes were transient. Decreases in sex acts with casual partners paralleled decreases in PrEP use. However, condom use during sex with casual partners decreased, indicating the importance of continued sexual health services, including sexually transmitted infections screening and PrEP care, during COVID-19 restrictions.