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ObjectiveWe studied the association between the coronavirus disease 2019 (COVID-19) pandemic, including the restrictive measures, and metabolic risk factors for cardiovascular disease (CVD) in women and men. Next, we analysed whether changes in these metabolic risk factors were mediated by psychological and behavioural mechanisms. DesignIn this natural experiment, we assessed changes from baseline in metabolic CVD risk factors in the exposed group (whose follow-up measurements were taken during the pandemic), and compared these to the changes in the control group (whose follow-up measurements were taken before the pandemic). ParticipantsThis study used data from 6962 participants from six different ethnic groups (Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan) of the HELIUS study, based in Amsterdam, the Netherlands. We included women and men without prior CVD, who participated in both the baseline (2011-2015) and follow-up measurements (2019-2021). Outcome measuresChanges between baseline and follow-up measurements in six metabolic CVD risk factors were calculated for systolic and diastolic blood pressure (SBP, DBP), total cholesterol (TC), fasting plasma glucose (FPG), haemoglobin A1c (HbA1c), and estimated glomerular filtration rate (eGFR). ResultsThe exposed group experienced somewhat less favourable changes over time in SBP, DBP and FPG (the latter only in women) than the control group, while temporal changes in HbA1c and eGFR were more favourable among the control group. For instance, SBP was 1.119 mmHg [0.046, 2.193] higher in exposed than non-exposed women, and 1.380 [0.288, 2.471] in men. Changes in SBP and DBP were partially mediated by changes in behavioural factors, most notably BMI and alcohol consumption. ConclusionsThe COVID-19 pandemic, including the restrictive lockdown measures, is associated with a deterioration of several CVD risk factors in women and men. These findings may aid in decision making concerning the management of and the recovery following the pandemic. Article SummaryO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIThe COVID19 pandemic lockdown measures led to a pause in the data collection for the prospective, population-based HELIUS study, which shaped a natural experiment. C_LIO_LINatural experiments, as quasi-experimental designs, are generally considered stronger than cross-sectional studies. C_LIO_LIThrough inverse-probability weighting, this study aimed to account for baseline differences between the control and exposed group. C_LIO_LIWe could not adjust for differences in follow-up time that occurred as a result of the restrictive measures, which may have affected estimates of variables that change with age. C_LIO_LIThe effects of certain mediators may be underestimated, as the data available for defining these variables were largely based on self-reports. C_LI
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BackgroundSurveillance 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. MethodsWe analyzed 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 modeled 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. Findings2,497 participants were tested after the first wave; 2,083 (83{middle dot}4%) were tested during the second wave. Median age at first visit was 54 years (interquartile range=44-61); 56{middle dot}6% were female. Compared to Dutch-origin participants (15{middle dot}9%), cumulative SARS-CoV-2 incidence was higher in participants of South-Asian Surinamese (25{middle dot}0%; adjusted hazard ratio [aHR]=1{middle dot}66;95%CI=1{middle dot}16-2{middle dot}40), African Surinamese (28{middle dot}9%;aHR=1{middle dot}97;95%CI=1{middle dot}37-2{middle dot}83), Turkish (37{middle dot}0%;aHR=2{middle dot}67;95%CI=1{middle dot}89-3{middle dot}78), Moroccan (41{middle dot}9%;aHR=3{middle dot}13;95%CI=2{middle dot}22-4{middle dot}42), and Ghanaian (64{middle dot}6%;aHR=6{middle dot}00;95%CI=4{middle dot}33-8{middle dot}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 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. InterpretationSARS-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. FundingZonMw, Public Health Service of Amsterdam, Dutch Heart Foundation, European Union, European Fund for the Integration of non-EU immigrants.
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BackgroundEthnic minorities have higher rates of SARS-CoV-2 diagnoses, but little is known about ethnic differences in past exposure. We aimed to determine whether prevalence and determinants of SARS-CoV-2 exposure varied between six ethnic groups in Amsterdam, the Netherlands. MethodsParticipants aged 25-79 years enrolled in a population-based prospective cohort were randomly selected within ethnic groups and invited to test for SARS-CoV-2-specific antibodies and answer COVID-19 related questions. We estimated prevalence and determinants of SARS-CoV-2 exposure within ethnic groups using survey-weighted logistic regression adjusting for age, sex and calendar time. ResultsBetween June 24-October 9, 2020, we included 2497 participants. Adjusted SARS-CoV-2 seroprevalence was comparable between ethnic-Dutch (25/498; 5.5%, 95%CI=3.2-7.9), South-Asian Surinamese (22/451; 4.8%, 95%CI=2.1-7.5), African Surinamese (22/400; 8.2%, 95%CI=3.0-13.4), Turkish (30/408; 7.8%, 95%CI=4.3-11.2) and Moroccan (32/391; 7.0%, 95%CI=4.0-9.9) participants, but higher among Ghanaians (95/327; 26.5%, 95%CI=18.7-34.4). 57.1% of SARS-CoV-2-positive participants did not suspect or were unsure of being infected, which was lowest in African Surinamese (18.2%) and highest in Ghanaians (90.5%). Determinants of SARS-CoV-2 exposure varied across ethnic groups, while the most common determinant was having a household member suspected of infection. In Ghanaians, seropositivity was associated with older age, larger household sizes, living with small children, leaving home to work and attending religious services. ConclusionsNo remarkable differences in SARS-CoV-2 seroprevalence were observed between the largest ethnic groups in Amsterdam after the first wave of infections. The higher infection seroprevalence observed among Ghanaians, which passed mostly unnoticed, warrants wider prevention efforts and opportunities for non-symptom-based testing.
