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The COVID-19 pandemic led to sustained surveillance efforts, which made unprecedented volumes and types of data available. In Belgium, these data were used to conduct a targeted and regular assessment of the epidemiological situation. In addition, management tools were developed, incorporating key indicators and thresholds, to define risk levels and offer guidance to policy makers. Categorizing risk into various levels provided a stable framework to monitor the COVID-19 epidemiological situation and allowed for clear communication to authorities. Although translating risk levels into specific public health measures has remained challenging, this experience was foundational for future evaluation of the situation for respiratory infections in general, which, in Belgium, is now based on a management tool combining different data sources.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Belgium/epidemiology , SARS-CoV-2 , Health Policy , Public Health , Pandemics , Risk Assessment/methodsABSTRACT
BACKGROUND: While there is increasing evidence for negative physical health consequences of high volumes of sedentary time and prolonged sedentary time in adolescents, the association with cognition is less clear. This study investigated the association of volumes of habitual sedentary time and prolonged sedentary time with executive functions and short-term memory in adolescents. METHODS: This study has a cross-sectional observational study design. Volumes of sedentary time and prolonged sedentary time (accumulated sedentary time spent in bouts of ≥ 30 min) were measured using the Axivity AX3 accelerometer. Six cognitive functions (spatial and verbal short-term memory; and working memory, visuospatial working memory, response inhibition and planning as executive functions) were measured using six validated cognitive assessments. Data were analysed using generalised linear models. RESULTS: Data of 119 adolescents were analysed (49% boys, 13.4 ± 0.6 year). No evidence for an association of volumes of sedentary time and prolonged sedentary time with spatial and verbal short-term memory, working memory, and visuospatial working memory was found. Volumes of sedentary time and prolonged sedentary time were significantly related to planning. One hour more sedentary time or prolonged sedentary time per day was associated with respectively on average 17.7% (95% C.I.: 3.5-29.7%) and 12.1% (95% C.I.: 3.9-19.6%) lower scores on the planning task. CONCLUSIONS: No evidence was found for an association of volumes of habitual sedentary time and prolonged sedentary time with short-term memory and executive functions, except for planning. Furthermore, the context of sedentary activities could be an important confounder in the association of sedentary time and prolonged sedentary time with cognition among adolescents. Future research should therefore collect data on the context of sedentary activities. TRIAL REGISTRATION: This study was registered at ClinicalTrials.gov in January 2020 (NCT04327414; released on March 11, 2020).
Subject(s)
Executive Function , Memory, Short-Term , Male , Humans , Adolescent , Female , Executive Function/physiology , Sedentary Behavior , Cross-Sectional Studies , Cognition/physiologyABSTRACT
BACKGROUND: Despite the availability of physical activity (PA) interventions, many older adults are still not active enough. This might be partially explained by the often-limited effects of PA interventions. In general, health behavior change interventions often do not focus on contextual and time-varying determinants, which may limit their effectiveness. However, before the dynamic tailoring of interventions can be developed, one should know which time-dependent determinants are associated with PA and how strong these associations are. OBJECTIVE: The aim of this study was to examine within-person associations between multiple determinants of the capability, opportunity, motivation, and behavior framework assessed using Ecological Momentary Assessment (EMA) and accelerometer-assessed light PA, moderate to vigorous PA, and total PA performed at 15, 30, 60, and 120 minutes after the EMA trigger. METHODS: Observational data were collected from 64 healthy older adults (36/64, 56% men; mean age 72.1, SD 5.6 y). Participants were asked to answer a time-based EMA questionnaire 6 times per day that assessed emotions (ie, relaxation, satisfaction, irritation, and feeling down), the physical complaint fatigue, intention, intention, and self-efficacy. An Axivity AX3 was wrist worn to capture the participants' PA. Multilevel regression analyses in R were performed to examine these within-person associations. RESULTS: Irritation, feeling down, intention, and self-efficacy were positively associated with subsequent light PA or moderate to vigorous PA at 15, 30, 60, or 120 minutes after the trigger, whereas relaxation, satisfaction, and fatigue were negatively associated. CONCLUSIONS: Multiple associations were observed in this study. This knowledge in combination with the time dependency of the determinants is valuable information for future interventions so that suggestions to be active can be provided when the older adult is most receptive.
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Background: To support the COVID-19 pandemic response, many countries, including Belgium, implemented baseline genomic surveillance (BGS) programs aiming to early detect and characterize new SARS-CoV-2 variants. In parallel, Belgium maintained a sentinel network of six hospitals that samples patients with severe acute respiratory infections (SARI) and integrated SARS-CoV-2 detection within a broader range of respiratory pathogens. We evaluate the ability of the SARI surveillance to monitor general trends and early signals of viral genetic evolution of SARS-CoV-2 and compare it with the BGS as a reference model. Methods: Nine-hundred twenty-five SARS-CoV-2 positive samples from patients fulfilling the Belgian SARI definition between January 2020 and December 2022 were sequenced using the ARTIC Network amplicon tiling approach on a MinION platform. Weekly variant of concern (VOC) proportions and types were compared to those that were circulating between 2021 and 2022, using 96,251 sequences of the BGS. Results: SARI surveillance allowed timely detection of the Omicron (BA.1, BA.2, BA.4, and BA.5) and Delta (B.1.617.2) VOCs, with no to 2 weeks delay according to the start of their epidemic growth in the Belgian population. First detection of VOCs B.1.351 and P.1 took longer, but these remained minor in Belgium. Omicron BA.3 was never detected in SARI surveillance. Timeliness could not be evaluated for B.1.1.7, being already major at the start of the study period. Conclusions: Genomic surveillance of SARS-CoV-2 using SARI sentinel surveillance has proven to accurately reflect VOCs detected in the population and provides a cost-effective solution for long-term genomic monitoring of circulating respiratory viruses.
Subject(s)
COVID-19 , Pneumonia , Humans , SARS-CoV-2/genetics , Pandemics , Sentinel Surveillance , COVID-19/diagnosis , COVID-19/epidemiology , Genomics , HospitalsABSTRACT
BACKGROUND: Non-pharmaceutical interventions (NPIs) were adopted in Belgium in order to decrease social interactions between people and as such decrease viral transmission of SARS-CoV-2. With the aim to better evaluate the impact of NPIs on the evolution of the pandemic, an estimation of social contact patterns during the pandemic is needed when social contact patterns are not available yet in real time. METHODS: In this paper we use a model-based approach allowing for time varying effects to evaluate whether mobility and pre-pandemic social contact patterns can be used to predict the social contact patterns observed during the COVID-19 pandemic between November 11, 2020 and July 4, 2022. RESULTS: We found that location-specific pre-pandemic social contact patterns are good indicators for estimating social contact patterns during the pandemic. However, the relationship between both changes with time. Considering a proxy for mobility, namely the change in the number of visitors to transit stations, in interaction with pre-pandemic contacts does not explain the time-varying nature of this relationship well. CONCLUSION: In a situation where data from social contact surveys conducted during the pandemic are not yet available, the use of a linear combination of pre-pandemic social contact patterns could prove valuable. However, translating the NPIs at a given time into appropriate coefficients remains the main challenge of such an approach. In this respect, the assumption that the time variation of the coefficients can somehow be related to aggregated mobility data seems unacceptable during our study period for estimating the number of contacts at a given time.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , COVID-19/prevention & control , Pandemics/prevention & control , SARS-CoV-2 , Surveys and Questionnaires , Belgium/epidemiologyABSTRACT
BackgroundThe Belgian COVID-19 vaccination campaign aimed to reduce disease spread and severity.AimWe estimated SARS-CoV-2 variant-specific vaccine effectiveness against symptomatic infection (VEi) and hospitalisation (VEh), given time since vaccination and prior infection.MethodsNationwide healthcare records from July 2021 to May 2022 on testing and vaccination were combined with a clinical hospital survey. We used a test-negative design and proportional hazard regression to estimate VEi and VEh, controlling for prior infection, time since vaccination, age, sex, residence and calendar week of sampling.ResultsWe included 1,932,546 symptomatic individuals, of whom 734,115 tested positive. VEi against Delta waned from an initial estimate of 80% (95%â¯confidence interval (CI):â¯80-81) to 55% (95%â¯CI:â¯54-55) 100-150 days after the primary vaccination course. Booster vaccination increased initial VEi to 85% (95%â¯CI:â¯84-85). Against Omicron, an initial VEi of 33% (95%â¯CI:â¯30-36) waned to 17% (95%â¯CI:â¯15-18), while booster vaccination increased VEi to 50% (95%â¯CI:â¯49-50), which waned to 20% (95%â¯CI:â¯19-21) 100-150 days after vaccination. Initial VEh for booster vaccination decreased from 96% (95%â¯CI:â¯95-96) against Delta to 87% (95%â¯CI:â¯86-89) against Omicron. VEh against Omicron waned to 73% (95%â¯CI:â¯71-75) 100-150 days after booster vaccination. While recent prior infections conferred higher protection, infections occurring before 2021 remained associated with significant risk reduction against symptomatic infection. Vaccination and prior infection outperformed vaccination or prior infection only.ConclusionWe report waning and a significant decrease in VEi and VEh from Delta to Omicron-dominant periods. Booster vaccination and prior infection attenuated these effects.
Subject(s)
COVID-19 Vaccines , COVID-19 , Humans , SARS-CoV-2 , Belgium/epidemiology , COVID-19/epidemiology , COVID-19/prevention & control , Vaccine Efficacy , HospitalizationABSTRACT
OBJECTIVES: Vaccine effectiveness against transmission (VET) of SARS-CoV-2-infection can be estimated from secondary attack rates observed during contact tracing. We estimated VET, the vaccine-effect on infectiousness of the index case and susceptibility of the high-risk exposure contact (HREC). METHODS: We fitted RT-PCR-test results from HREC to immunity status (vaccine schedule, prior infection, time since last immunity-conferring event), age, sex, calendar week of sampling, household, background positivity rate and dominant VOC using a multilevel Bayesian regression-model. We included Belgian data collected between January 2021 and January 2022. RESULTS: For primary BNT162b2-vaccination we estimated initial VET at 96% (95%CI 95-97) against Alpha, 87% (95%CI 84-88) against Delta and 31% (95%CI 25-37) against Omicron. Initial VET of booster-vaccination (mRNA primary and booster-vaccination) was 87% (95%CI 86-89) against Delta and 68% (95%CI 65-70) against Omicron. The VET-estimate against Delta and Omicron decreased to 71% (95%CI 64-78) and 55% (95%CI 46-62) respectively, 150-200 days after booster-vaccination. Hybrid immunity, defined as vaccination and documented prior infection, was associated with durable and higher or comparable (by number of antigen exposures) protection against transmission. CONCLUSIONS: While we observed VOC-specific immune-escape, especially by Omicron, and waning over time since immunization, vaccination remained associated with a reduced risk of SARS-CoV-2-transmission.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , COVID-19/prevention & control , SARS-CoV-2 , BNT162 Vaccine , Bayes Theorem , Belgium/epidemiology , Contact Tracing , Vaccine Efficacy , Immunization, SecondaryABSTRACT
BACKGROUND: To design efficient mitigation measures against COVID-19, understanding the transmission dynamics between different age groups was crucial. The role of children in the pandemic has been intensely debated and involves both scientific and ethical questions. To design efficient age-targeted non-pharmaceutical interventions (NPI), a good view of the incidence of the different age groups was needed. However, using Belgian testing data to infer real incidence (RI) from observed incidence (OI) or positivity ratio (PR) was not trivial. METHODS: Based on Belgian testing data collected during the Delta wave of Autumn 2021, we compared the use of different estimators of RI and analyzed their effect on comparisons between age groups. RESULTS: We found that the RI estimator's choice strongly influences the comparison between age groups. CONCLUSION: The widespread implementation of testing campaigns using representative population samples could help to avoid pitfalls related to the current testing strategy in Belgium and worldwide. This approach would also allow a better comparison of the data from different countries while reducing biases arising from the specificities of each surveillance system.
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BACKGROUND: Fathers are important in establishing healthy behaviors in their children, but are rarely engaged in lifestyle programs. Focusing on physical activity (PA) of both fathers and their children by engaging them together in PA (i.e. "co-PA") is therefore a promising novel strategy for interventions. The study aim was to investigate the effect of the 'Run Daddy Run' on co-PA and PA of fathers and their children, and secondary outcomes such as weight status and sedentary behaviour (SB). METHODS: This study is a non-randomized controlled trial (nRCT), including 98 fathers and one of their 6 to 8 years old children (intervention = 35, control = 63). The intervention was implemented over a 14-week period, and consisted of six (inter)active father-child sessions and an online component. Due to COVID-19, only 2/6 sessions could be implemented as planned, the remaining sessions were delivered online. In November 2019-January 2020 pre-test measurements took place, and post-test measurements in June 2020. Additional follow-up test was conducted in November 2020. PA (i.e. LPA, MPA, VPA and volume) of fathers and children were objectively measured using accelerometry, co-PA and the secondary outcomes were questioned using an online questionnaire. RESULTS: Significant intervention effects were found for co-PA (+ 24 min./day in the intervention compared to the control group, p = 0.002), and MPA of the father (+ 17 min./day, p = 0.035). For children, a significant increase in LPA (+ 35 min./day, p < 0.001) was found. However, an inverse intervention effect was found for their MPA and VPA (-15 min./day, p = 0.005 and - 4 min./day, p = 0.002, respectively). Also decreases in fathers' and children's SB were found (-39 min./day, p = 0.022 and - 40 min./day, p = 0.003, respectively), but no changes in weight status, the father-child relationship, and the PA-family health climate (all p > 0.05). CONCLUSION: The Run Daddy Run intervention was able to improve co-PA, MPA of fathers and LPA of children, and decreasing their SB. Inverse intervention effects were however found for MPA and VPA of children. These results are unique given their magnitude and clinical relevance. Targeting fathers together with their children might be a novel and potential intervention strategy to improve overall physical activity levels, however, further efforts should however be made to target children's MPA and VPA. Last, replicating these findings in a randomized controlled trial (RCT) is recommended for future research. TRIAL REGISTRATION NUMBER: This study is registered as a clinical trial (clinicaltrials.gov, ID number: NCT04590755, date: 19/10/2020).
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COVID-19 , Humans , Child , Male , COVID-19/prevention & control , Exercise , Life Style , Health Behavior , Accelerometry , FathersABSTRACT
Interest is growing in neighborhood effects on health beyond individual's home locations. However, few studies accounted for selective daily mobility bias. Selective mobility of 470 older adults (aged 67-94) living in urban and suburban areas of Luxembourg, was measured through detour percentage between their observed GPS-based paths and their shortest paths. Multilevel negative binomial regression tested associations between detour percentage, trips characteristics and environmental exposures. Detour percentage was higher for walking trips (28%) than car trips (16%). Low-speed areas and connectivity differences between observed and shortest paths vary by transport mode, indicating a potential selective daily mobility bias. The positive effects of amenities, street connectivity, low-speed areas and greenness on walking detour reinforce the existing evidence on older adults' active transportation. Urban planning interventions favoring active transportation will also promote walking trips with longer detours, helping older adults to increase their physical activity levels and ultimately promote healthy aging.
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Environment Design , Walking , Humans , Aged , Transportation , Residence CharacteristicsABSTRACT
BACKGROUND: There has been an increased focus on active transport, but the measurement of active transport is still difficult and error-prone. Sensor data have been used to predict active transport. While heart rate data have very rarely been considered before, this study used random forests (RF) to predict transport modes using Global Positioning System (GPS), accelerometer, and heart rate data and paid attention to methodological issues related to the prediction strategy and post-processing. METHODS: The RECORD MultiSensor study collected GPS, accelerometer, and heart rate data over seven days from 126 participants living in the Ile-de-France region. RF models were built to predict transport modes for every minute (ground truth information on modes is from a GPS-based mobility survey), splitting observations between a Training dataset and a Test dataset at the participant level instead at the minute level. Moreover, several window sizes were tested for the post-processing moving average of the predicted transport mode. RESULTS: The minute-level prediction rate of being on trips vs. at a visited location was 90%. Final prediction rates of transport modes ranged from 65% for public transport to 95% for biking. Using minute-level observations from the same participants in the Training and Test sets (as RF spontaneously does) upwardly biases prediction rates. The inclusion of heart rate data improved prediction rates only for biking. A 3 to 5-min bandwidth moving average was optimum for a posteriori homogenization. CONCLUSION: Heart rate only very slightly contributed to better predictions for specific transport modes. Moreover, our study shows that Training and Test sets must be carefully defined in RF models and that post-processing with carefully chosen moving average windows can improve predictions.
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Geographic Information Systems , Walking , Humans , Heart Rate , Machine Learning , Surveys and Questionnaires , AccelerometryABSTRACT
BACKGROUND: A healthy lifestyle decreases the risk of developing type 2 diabetes mellitus. The current cross-sectional study aimed to describe self-reported lifestyle behaviours and compare them to current health guidelines in European Feel4Diabetes-families at risk for developing type 2 diabetes across six countries (Belgium, Finland, Spain, Greece, Hungary and Bulgaria). METHODS: Parents and their children were recruited through primary schools located in low socio-economic status areas. Parents filled out the FINDRISC-questionnaire (eight items questioning age, Body Mass Index, waist circumference, PA, daily consumption of fruit, berries or vegetables, history of antihypertensive drug treatment, history of high blood glucose and family history of diabetes), which was used for the risk assessment of the family. Sociodemographic factors and several lifestyle behaviours (physical activity, sedentary behaviour, water consumption, fruit and vegetable consumption, soft drink consumption, sweets consumption, snack consumption, breakfast consumption) of both adults and children were assessed by parental questionnaires. Multilevel regression analyses were conducted to investigate families' lifestyle behaviours, to compare these levels to health guidelines and to assess potential differences between the countries. Analyses were controlled for age, sex and socio-economic status. RESULTS: Most Feel4Diabetes-families at risk (parents and their children) did not comply with the guidelines regarding healthy behaviours, set by the WHO, European or national authorities. Less than half of parents and children complied with the physical activity guidelines, less than 15% of them complied with the fruit and vegetable guideline, and only 40% of the children met the recommendations of five glasses of water per day. Clear differences in lifestyle behaviours in Feel4Diabetes-families at risk exist between the countries. CONCLUSIONS: Countries are highly recommended to invest in policy initiatives to counter unhealthy lifestyle behaviours in families at risk for type 2 diabetes development, taking into account country-specific needs. For future research it is of great importance to focus on families at risk in order to counter the development of type 2 diabetes and reduce health inequity.
Subject(s)
Diabetes Mellitus, Type 2 , Adult , Child , Cross-Sectional Studies , Diabetes Mellitus, Type 2/epidemiology , Diabetes Mellitus, Type 2/etiology , Diabetes Mellitus, Type 2/prevention & control , Europe/epidemiology , Humans , Life Style , Parents , Sedentary Behavior , Self ReportABSTRACT
INTRODUCTION: The pathogenesis of COVID-19 depends on the interplay between host characteristics, viral characteristics and contextual factors. Here, we compare COVID-19 disease severity between hospitalized patients in Belgium infected with the SARS-CoV-2 variant B.1.1.7 and those infected with previously circulating strains. METHODS: The study is conducted within a causal framework to study the severity of SARS-CoV-2 variants by merging surveillance registries in Belgium. Infection with SARS-CoV-2 B.1.1.7 ('exposed') was compared to infection with previously circulating strains ('unexposed') in terms of the manifestation of severe COVID-19, intensive care unit (ICU) admission, or in-hospital mortality. The exposed and unexposed group were matched based on the hospital and the mean ICU occupancy rate during the patient's hospital stay. Other variables identified as confounders in a Directed Acyclic Graph (DAG) were adjusted for using regression analysis. Sensitivity analyses were performed to assess the influence of selection bias, vaccination rollout, and unmeasured confounding. RESULTS: We observed no difference between the exposed and unexposed group in severe COVID-19 disease or in-hospital mortality (RR = 1.15, 95% CI [0.93-1.38] and RR = 0.92, 95% CI [0.62-1.23], respectively). The estimated standardized risk to be admitted in ICU was significantly higher (RR = 1.36, 95% CI [1.03-1.68]) when infected with the B.1.1.7 variant. An age-stratified analysis showed that among the younger age group (≤65 years), the SARS-CoV-2 variant B.1.1.7 was significantly associated with both severe COVID-19 progression and ICU admission. CONCLUSION: This matched observational cohort study did not find an overall increased risk of severe COVID-19 or death associated with B.1.1.7 infection among patients already hospitalized. There was a significant increased risk to be transferred to ICU when infected with the B.1.1.7 variant, especially among the younger age group. However, potential selection biases advocate for more systematic sequencing of samples from hospitalized COVID-19 patients.
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COVID-19 , SARS-CoV-2 , Aged , Belgium/epidemiology , COVID-19/epidemiology , Hospitalization , HumansABSTRACT
This retrospective multi-center matched cohort study assessed the risk for severe COVID-19 (combination of severity indicators), intensive care unit (ICU) admission, and in-hospital mortality in hospitalized patients when infected with the Omicron variant compared to when infected with the Delta variant. The study is based on a causal framework using individually-linked data from national COVID-19 registries. The study population consisted of 954 COVID-19 patients (of which, 445 were infected with Omicron) above 18 years old admitted to a Belgian hospital during the autumn and winter season 2021-2022, and with available viral genomic data. Patients were matched based on the hospital, whereas other possible confounders (demographics, comorbidities, vaccination status, socio-economic status, and ICU occupancy) were adjusted for by using a multivariable logistic regression analysis. The estimated standardized risk for severe COVID-19 and ICU admission in hospitalized patients was significantly lower (RR = 0.63; 95% CI (0.30; 0.97) and RR = 0.56; 95% CI (0.14; 0.99), respectively) when infected with the Omicron variant, whereas in-hospital mortality was not significantly different according to the SARS-CoV-2 variant (RR = 0.78, 95% CI (0.28-1.29)). This study demonstrates the added value of integrated genomic and clinical surveillance to recognize the multifactorial nature of COVID-19 pathogenesis.
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COVID-19 , SARS-CoV-2 , Adolescent , Belgium/epidemiology , COVID-19/epidemiology , Cohort Studies , Humans , Retrospective Studies , SARS-CoV-2/genetics , SeasonsABSTRACT
BACKGROUND: In Belgium, current research on socio-economic inequalities in the coronavirus disease 2019 (COVID-19) crisis has mainly focused on excess mortality and data from the first epidemiological wave. The current study adds onto this by examining the association between COVID-19 incidence and area deprivation during the first five wave and interwave periods, thus adding a temporal gradient to the analyses. METHODS: We use all confirmed COVID-19 cases between March 2020 and June 2021 in Belgium, aggregated at the municipality-level. These data were collected by the national laboratory-based COVID-19 surveillance system. A level of area deprivation was assigned to each Belgian municipality using data of three socio-economic variables: the share of unemployed persons in the active population, the share of households without a car and the share of low-educated persons. The spatio-temporal association between COVID-19 incidence and area deprivation was assessed by performing multivariate negative-binomial regression analyses and computing population attributable fractions. RESULTS: A significant association between COVID-19 incidence and area deprivation was found over the entire study period, with the incidence in the most deprived areas predicted to be 24% higher than in the least deprived areas. This effect was dependent on the period during the COVID-19 crisis. The largest socio-economic inequalities in COVID-19 infections could be observed during wave 2 and wave 3, with a clear disadvantage for deprived areas. CONCLUSION: Our results provide new insights into spatio-temporal patterns of socio-economic inequalities in COVID-19 incidence in Belgium. They reveal the existence of inequalities and a shift of these patterns over time.
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The objective of this study was to investigate the incidence and risk factors associated with COVID-19 vaccine breakthrough infections. We included all persons ≥18 years that had been fully vaccinated against COVID-19 for ≥14 days, between 1 February 2021 and 5 December 2021, in Belgium. The incidence of breakthrough infections (laboratory confirmed SARS-CoV-2-infections) was determined. Factors associated with breakthrough infections were analyzed using COX proportional hazard models. Among 8,062,600 fully vaccinated adults, we identified 373,070 breakthrough infections with an incidence of 11.2 (95%CI 11.2-11.3)/100 person years. Vaccination with Ad26.COV2.S (HR1.54, 95%CI 1.52-1.56) or ChAdOx1 (HR1.68, 95%CI 1.66-1.69) was associated with a higher risk of a breakthrough infection compared to BNT162b2, while mRNA-1273 was associated with a lower risk (HR0.68, 95%CI 0.67-0.69). A prior COVID-19-infection was protective against a breakthrough infection (HR0.23, 95%CI 0.23-0.24), as was an mRNA booster (HR0.44, 95%CI 0.43-0.45). During a breakthrough infection, those who had a prior COVID-19 infection were less likely to have COVID-19 symptoms of almost all types than naïve persons. We identified risk factors associated with breakthrough infections, such as vaccination with adenoviral-vector vaccines, which could help inform future decisions on booster vaccination strategies. A prior COVID-19 infection lowered the risk of breakthrough infections and of having symptoms, highlighting the protective effect of hybrid immunity.
Subject(s)
COVID-19 Vaccines , COVID-19 , Ad26COVS1 , Adult , BNT162 Vaccine , Belgium/epidemiology , COVID-19/epidemiology , COVID-19/prevention & control , Humans , Incidence , Prospective Studies , Risk Factors , SARS-CoV-2/geneticsABSTRACT
BACKGROUND: During the first half of 2021, we observed high vaccine effectiveness (VE) against SARS-CoV2-infection. The replacement of the alpha-'variant of concern' (VOC) by the delta-VOC and uncertainty about the time course of immunity called for a re-assessment. METHODS: We estimated VE against transmission of infection (VET) from Belgian contact tracing data for high-risk exposure contacts between 26/01/2021 and 14/12/2021 by susceptibility (VEs) and infectiousness of breakthrough cases (VEi) for a complete schedule of Ad26.COV2.S, ChAdOx1, BNT162b2, mRNA-1273 as well as infection-acquired and hybrid immunity. We used a multilevel Bayesian model and adjusted for personal characteristics (age, sex, household), background exposure, calendar week, VOC and time since immunity conferring-event. FINDINGS: VET-estimates were higher for mRNA-vaccines, over 90%, compared to viral vector vaccines: 66% and 80% for Ad26COV2.S and ChAdOx1 respectively (Alpha, 0-50 days after vaccination). Delta was associated with a 40% increase in odds of transmission and a decrease of VEs (72-64%) and especially of VEi (71-46% for BNT162b2). Infection-acquired and hybrid immunity were less affected by Delta. Waning further reduced VET-estimates: from 81% to 63% for BNT162b2 (Delta, 150-200 days after vaccination). We observed lower initial VEi in the age group 65-84 years (32% vs 46% in the age group 45-64 years for BNT162b2) and faster waning. Hybrid immunity waned slower than vaccine-induced immunity. INTERPRETATION: VEi and VEs-estimates, while remaining significant, were reduced by Delta and waned over time. We observed faster waning in the oldest age group. We should seek to improve vaccine-induced protection in older persons and those vaccinated with viral-vector vaccines.
Subject(s)
COVID-19 , Vaccines , Ad26COVS1 , Aged , Aged, 80 and over , BNT162 Vaccine , Bayes Theorem , Belgium/epidemiology , COVID-19/prevention & control , Contact Tracing , Humans , Middle Aged , RNA, Viral , SARS-CoV-2 , Vaccination , Vaccine EfficacyABSTRACT
BACKGROUND: SARS-CoV-2 strains evolve continuously and accumulate mutations in their genomes over the course of the pandemic. The severity of a SARS-CoV-2 infection could partly depend on these viral genetic characteristics. Here, we present a general conceptual framework that allows to study the effect of SARS-CoV-2 variants on COVID-19 disease severity among hospitalized patients. METHODS: A causal model is defined and visualized using a Directed Acyclic Graph (DAG), in which assumptions on the relationship between (confounding) variables are made explicit. Various DAGs are presented to explore specific study design options and the risk for selection bias. Next, the data infrastructure specific to the COVID-19 surveillance in Belgium is described, along with its strengths and weaknesses for the study of clinical impact of variants. DISCUSSION: A well-established framework that provides a complete view on COVID-19 disease severity among hospitalized patients by combining information from different sources on host factors, viral factors, and healthcare-related factors, will enable to assess the clinical impact of emerging SARS-CoV-2 variants and answer questions that will be raised in the future. The framework shows the complexity related to causal research, the corresponding data requirements, and it underlines important limitations, such as unmeasured confounders or selection bias, inherent to repurposing existing routine COVID-19 data registries. TRIAL REGISTRATION: Each individual research project within the current conceptual framework will be prospectively registered in Open Science Framework (OSF identifier: https://doi.org/10.17605/OSF.IO/UEF29 ). OSF project created on 18 May 2021.
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BACKGROUND: The aim of this study was to investigate bidirectional associations between (prolonged) sitting time and sleep duration in 12- to 14-year-old adolescents using a between-subjects and within-subjects analyses approach. METHODS: Observational data were used from 108 adolescents (53% girls; mean age 12.9 (SD 0.7) years) from six schools in Flanders, Belgium. The Axivity AX3 triaxial accelerometer, worn on the thigh, was used to assess daily total sitting time and daily time spent in sedentary bouts of ≥30 min (as a proxy for prolonged sitting time). The Fitbit Charge 3 was used to assess nightly sleep duration. Both monitors were worn on schooldays only (ranging from 4 to 5 days). Linear mixed models were conducted to analyse the associations, resulting in four models. In each model, the independent variable (sleep duration, sitting time or prolonged sitting time) was included as within- as well as between-subjects factor. RESULTS: Within-subjects analyses showed that when the adolescents sat more and when the adolescents spent more time sitting in bouts of ≥30 min than they usually did on a given day, they slept less during the following night (p = 0.01 and p = 0.05 (borderline significant), respectively). These associations were not significant in the other direction. Between-subjects analyses showed that adolescents who slept more on average, spent less time sitting (p = 0.006) and less time sitting in bouts of ≥30 min (p = 0.004) compared with adolescents who slept less on average. Conversely, adolescents who spent more time sitting on average and adolescents who spent more time sitting in bouts of ≥30 min on average, slept less (p = 0.02 and p = 0.003, respectively). CONCLUSIONS: Based on the between-subjects analyses, interventions focusing on reducing or regularly breaking up sitting time could improve adolescents' sleep duration on a population level, and vice versa. However, the within-subjects association was only found in one direction and suggests that to sleep sufficiently during the night, adolescents might limit and regularly break up their sitting time the preceding day. TRIAL REGISTRATION: Data have been used from our trial registered at ClinicalTrials.gov ( NCT04327414 ; registered on March 11, 2020).