key: cord-0957930-y96pxmag
authors: Neuberger, F.; Grgic, M.; Diefenbacher, S.; Spensberger, F.; Lehfeld, A.-S.; Buchholz, U.; Haas, W.; Kalicki, B.; Kuger, S.
title: COVID-19 infections in day care centres in Germany: Social and organizational determinants of infections in children and staff in the second and third wave of the pandemic
date: 2021-06-09
journal: nan
DOI: 10.1101/2021.06.07.21257958
sha: 19ee382ae8b4e98aa85bffe622ee755d6cfd8576
doc_id: 957930
cord_uid: y96pxmag

Background: During the SARS-CoV-2 pandemic, German early childhood education and care (ECEC) centres organized childrens attendance variably (i.e., reduced opening hours, emergency support for few children only or full close- down). Further, protection and hygiene measures like fixed children/staff groups, ventilation and surface disinfection were introduced among ECEC centres. To inform or modify public health measures in ECEC, we investigate the occurrence of SARS-CoV-2 infections among children and staff of ECEC centres in light of social determinants (socioeconomic status of the children) and recommended structural and hygiene measures. We focus on the question if the relevant factors differ between the 2nd (when no variant of concern (VOC) circulated) and the 3rd wave (when VOC B.1.1.7 predominated). Methods: Based on panel data from a weekly online survey of ECEC centre managers (start August 2020, ongoing) including approx. 8500 centres, we esti- mate the number of SARS-CoV-2 infections in children and staff using random- effect-within-between (REWB) panel models for count data in both waves. Results: In centres with a high proportion of children with low socioeconomic status (SES), the risk of infections in staff and children is more than doubled in both waves. Fixed child/staff cohorts seem more important in the 3rd wave. Contribution: ECEC centres with a large proportion of children from a low SES background and lack of using fixed child/staff cohorts experience higher COVID-19 rates. Centres should be supported in maintaining recommended measures over the long run. Preventive measures such as vaccination of staff should be prioritised in centres with large proportions of low SES children.

In calendar week (CW) 16 (middle of April) 2021 Germany was in its 3rd pandemic COVID-19 wave. While the 1st wave in spring 2020 was relatively brief followed by a phase of low incidence during summer 2020, the 2nd wave started approximately in CW 40/2020 and lasted until the first weeks in 2021. The 3rd wave followed on foot and was characterized by a parallel rise of the proportion of specimens diagnosed as the VOC B.1.1.7 ("British variant"). To curb high incidences, the German government ordered national lockdowns. One related objective was to reduce the number of children in early childhood education and care (ECEC) centres to reduce the number of contacts among and between parents, staff and children [1] [2] [3] [4] [5] .

During the 1st wave, a "strict" lockdown was introduced during which only children of parents providing essential services (i.e., jobs necessary for peoples daily lives such as physicians or food vendors) and children in need of child welfare services (e.g., cases of maltreatment) could attend ECEC centres [5] . During the 2nd wave, a "strict" lockdown was installed from CW 51/2020 until CW 04/2021. Throughout this "strict" lockdown, children of all families could attend ECEC in principle. However, most federal states appealed to parents to keep their children at home if possible [1] [2] [3] [4] .

During the 3rd pandemic wave (starting in CW 05/2021, ongoing until at least CW 22/2021), implementation of measures was largely dependent on the incidence of individual counties. Hence, ECEC attendance regulations differed widely across regional meso and micro levels as did, in consequence, the numbers of attending children.

Before circulation of VOC B.1.1.7 (i.e., prior to the 3rd wave), children 1-11 years old were under-represented among COVID-19 cases compared to their proportion in the general population and they were particularly under-represented among cases experiencing severe outcomes, such as hospitalization, requiring respiratory support or death [6] . Two systematic literature reviews conducted in 2020, i.e. before circulation of VOC B.1.1.7, concluded that they were less susceptible than adults [7, 8] . Data on infectiousness have shown equivocal results. In household studies children were rarely identified as primary cases [6] and gave rise to a lower (secondary) attack rate ((S)AR) of 7.9% (95% confidence interval (CI), 1.7%-16.8%) compared to that of adults (15%, 95% CI, 6.2%-27%) [9] . In German ECEC centres, children with COVID-19 infections have led to an average SAR of 1.7% and small outbreak sizes (average size: 3-4 cases) [10] .

Environmental, contact or fomite transmission are all terms that are used interchangeably, and their association with the COVID-19 pandemic is controversial. Ferretti estimates that 10% of transmissions may be due to "environmental" factors, i.e. contact transmission [11] . Meyerowitz concludes that there is currently no conclusive evidence for fomite or direct contact transmission of SARS-CoV-2 in humans [12] .

Transmission of SARS-CoV-2 occurs mainly through the respiratory route. Within the respiratory routel, both short and long range transmissions are believed to play a role [12] [13] [14] . Exhaled aerosols can float in the air for hours [15] , and half life of viable virus in small particles is estimated as approximately one hour [16] . Once certain boundary data, such as room size, duration of exposure, number of persons exposed and type and degree of ventilation are known, it has been possible to predict the attack rate of outbreaks [17] . Conversely, at least among households, ventilation was shown to be protective for secondary infections [18] . These findings have led to the recom-mendations to keep a minimum distance of 1.5 m to other persons, wear masks and ventilate rooms where several persons are present at the same time.

In principle, these recommended behaviours provide protection in the context of ECEC centres as well. However, as most transmission studies have been conducted among adults, the evidence base for preventive recommendations among children is largely unexplored. For children at preschool age, it cannot be expected to keep a distance of 1.5 m to peers or staff, nor to wear masks. Recommended measures for ECEC centres have thus focused on organisational changes as well as infection control / hygiene recommendations for staff and parents.

Before the pandemic, the following (pedagogical) group concepts typically existed in German ECEC settings: (i) fixed groups, (ii) open groups (children can freely choose and switch between groups), and (iii) mixed concept, e.g. a fixed group in the morning and free roaming in the afternoon. These concepts leave all options open how staff is assigned to groups. An important organisational change in ECEC centres was the recommendation or order (depending on incidence rate) to switch not only to fixed groups, but to also keep educators of a given group constant (fixed staff assignment to a particular group). In addition, infection control and hygiene recommendations included regular ventilation of rooms and regular disinfection of surfaces [19] . 4 tem started to collect weekly data in CW 36/2020. It has been set up to monitor the re-opening and closing of ECEC centres, the rate of children attending settings and the staff in attendance during the pandemic. Furthermore, information on COVID-19 infections in ECEC centres and the implementation of infection control and hygiene measures has been collected. Hence, it offers a unique database to identify determinants that may influence the frequency of COVID-19 infections among children and staff in ECEC centres.

To inform public health policy, we analysed data from the ECEC centre registry. We put a focus on the questions (1) to understand relevant determinants, risk and protective factors for COVID-19 occurrence in ECEC centres, (2) whether factors associated with infections differ between children and staff, and (3) as the 3rd wave is largely driven by the mutated VOC B.1.1.7 [20] with possibly different epidemiological properties if the relevant factors differ between the 2nd and 3rd wave. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted June 9, 2021. ; https://doi.org/10.1101/2021.06.07.21257958 doi: medRxiv preprint children from households with low socioeconomic status (SES) and the group concept prior to the pandemic. The weekly questionnaire collects information about the current week and contains time-varying variables such as the number and age of children currently attending the ECEC centre, the number of staff working at the ECEC centre in general as well as in the current week, the currently applied group concept, if staff was assigned firmly to groups (fixed staff assignment (only asked if ECEC previously used a fixed or mixed group concept)), application of certain hygiene measures, as well as the number of children, staff and parents who are tested positive for COVID-19.

We use the number of reported infections per week as a dependent variable. To measure infections in staff and children, ECEC centre managers were asked if they had any new laboratory confirmed cases of COVID-19 in children or staff. Infections were reported for staff members and for children separately. For data protection reasons, detailed information on infections in staff was only asked in ECEC centres with at least 7 staff members (which applies to 97% of our sample). The serial interval of COVID-19 (i.e., the average interval from the onset of illness in an infectious / case to the onset of illness of a case infected by that case) is estimated to have a duration of 5 days [21, 22] .

After laboratory diagnosis, 1-3 days may pass until the result of a test is available at the county health department [23] . Hence, we included the variable "number of infections" with a lead of one week in our models and estimated the rate of infection in CW X+1 with data from CW X. We do not analyse infections in parents, as the link to the ECEC centre is not necessarily given here.

As possible predictors for the number of reported infections, we use variables which are either time-constant or time-variant.

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Type of provider: The type of provider (public, private for-profit, ecclesiastical or other non-profit) of the ECEC centres is included, since it might be associated with the (systematic) implementation of particular hygiene measures.

Socioeconomic status: COVID-19 infections are known to follow a social gradient [24, 25] . To control for social composition, ECEC managers were asked to estimate the proportion of children with low socioeconomic status (SES) on a 4 point Likert scale (i.e., below 10% children with low SES background, 11% to 30%, 31% to 60%, orabove 60%, respectively).

Group concept prior to the pandemic: To grasp differences in the set-up of the institutions which might make it difficult to implement certain measures such as e.g. group separation, we include the type of pedagogical group concept before the pandemic in our model.

We include these variables as time-constant dummy variables in the models.

Currently applied group concept: Managers indicated which group concept was currently in use (i.e., fixed, open, partly open see above). We analysed the currently applied group concept as this may facilitate close contact to more or less children or staff in the ECEC centres. We distinguished between the three categories "open group concept", i.e. attendants could mix freely, "partly open" and "fixed", i.e. strict assignment of children to only one group.

Infection control and hygiene measures: Managers specified which other measures they took: (1) Regular ventilation of the rooms, (2) regular surface disinfection (e.g., furniture surfaces, door handles or toys), and (3) a fixed staff assignment to groups (only asked for fixed or mixed group concepts).

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The copyright holder for this preprint this version posted June 9, 2021. ; https://doi.org/10.1101/2021.06.07.21257958 doi: medRxiv preprint Control variables 7-day incidence on county level: Laboratory confirmed COVID-19 cases are notified to the local health authority (LHA) in accordance with the German Protection against Infection Act (IfSG) 1 . The LHA transmits reported cases via the respective federal state health authority to the RKI. The 7-day incidence includes the number of newly reported cases within seven days per 100,000 population.

Number of Children: Managers indicated per week how many children aged 0-2, 3-5, and 6 years and older attended the ECEC centres. These numbers may change due to (perhaps only regionally observed) holidays as well as regional outbreaks and measures taken by the federal state or county.

We use the latter two variables, 7-day incidence on county level and number of children attending, to control for possible exposure to the virus.

Our data provide various information about ECEC centres, namely on time-constant variables related to the centre from the baseline questionnaire, on average differences in the time-varying variables between ECEC centres and on changes within a centre as well. Since the Poisson distribution is known to approximate incidence counts from a wide variety of underlying processes [26], we use a random-effect panel poisson model for count data with demeaned data to approximate incidence counts (see formula 1). That allows us to estimate the effects of time-constant variables, between-unit differences and within-unit changes on the incidence count at the same time [27, 28] .

1 https://www.gesetze-im-internet.de/ifsg/, visited 13.4.2021

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The copyright holder for this preprint this version posted June 9, 2021. and υ 1t are unit-and time-fixed effects. Exponential coefficients could be interpreted as incidence rate ratios, hence how much the expected count changes multiplicatively when x increases by one.

As the occurrence of COVID-19 infections varies by time and region, we include a county's 7-day incidence as (log) exposure A [29] with a regression coefficient constrained to 1, allowing the model to represent rates instead of counts. This is equivalent to standardizing the dependent variable with the offset variable (see formula 2, equivalent with formula 3).

By doing so, we include the assumption that an ECEC centre in a county with twice as many infections also reports twice as many cases in the ECEC centre. Since the likelihood of an occurrence of a COVID-19 infection does not only depend on regional conditions, but is also strongly dependent on the number of persons in the respective facilities, we add within-and between-effects for the number of children of all age groups (0-2, 3-5, 7 plus) in our model. As we tend to refrain from interpreting these variables directly, they are included in the model as mere controls and are only shown in the appendix tables. 1 Results Overall, the rate of infections in staff is higher compared to that of children, but the ratio clearly increases in the 3rd wave.

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In the following, we briefly discuss the development of our time-varying indicators. Factors associated with COVID-19 infections in ECEC centres Table 1 shows results from the REWB models. It contains 4 models for different dependent variables, analysing the number of infections within different time frames (i.e. wave 2 and 3 of the pandemic) in both staff and children. Models 1 and 2 in Table   1 estimate the number of infections in staff, Models 3 and 4 the number of infections in children. Models 1 and 3 cover a 21-week time frame from CW 36/2020 to CW 04/2021 (i.e., 2nd wave, the grey area in Figure 1 ), while Models 2 and 4 cover a 12-week time frame from CW 05/2021 to CW 16/2021 (i.e., 3rd wave, the white area in Figure 1 ). Note that the dependent variable has a 1-week lead, hence the 12-week time frame from CW 05/2021 to 16/2021 is represented by the number of weeks, Num.

groups: week=11 in Models 2 and 4).

We do not find any significant differences for different types of providers, except a negative effect of private providers on infections in children in wave 3 (Model 4). The

proportion of low SES children is found to be a significant predictor for the rate of infections in staff as well as in children with higher proportions of children from a low SES background going along with a higher infection rate. For the two categories with the largest proportions of children with lower SES background, that is 31% to 60% and above 60% (compared to less than 10% low SES children), we find significant positive effects in all four models. For 11% to 30% of low SES children (compared to less than 10% low SES children), effects are significant only in Models 2 and 4 (i.e., 3rd

wave), but not in Models 1 and 3 (i.e., 2nd wave). Further, effect sizes of SES tend to be larger in wave 3 compared to wave 2 for both infections in staff and children, with . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted June 9, 2021. ; https://doi.org/10.1101/2021.06.07.21257958 doi: medRxiv preprint 2.14 3.02 2.42 3.33 * * * p < 0.001; * * p < 0.01; * p < 0.05. Source: Survey data collected by the German Youth Institute. Second wave assumed to last from calendar week (CW) 36/2020-04/2021, and third wave from CW 05/2021-16/2021. REWB poisson model with twoway fixed effects, offset for county incidence (data collected by the Robert Koch-Institute), dependent variable with 1 week lead. Coefficients are displayed as log incidence rate ratio, standard errors in parentheses, controlled for number of children in different age groups (within and between effects), see Appendix Table A2 for full model, own calculations. one exception for staff regarding the effect of the largest proportion of children with low SES background, which is larger in wave 2 than in wave 3. The variable group concept prior to the pandemic did not yield any significant effects.

Between-units effects compare averages across CWs between ECEC centres and are prone to endogeneity, that is, a positive effect (indicating more infections) of implementing a protection measure could stem from the fact that centres in counties with . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted June 9, 2021. ; very low COVID-19 incidences tend to implement protection measures to a lower extent than centres in counties with higher incidences.

We do not find significant between-units effects regarding the currently applied group concept. We find a significant positive between-units effect for regular ventilation for infections in staff in wave 2 (Model 1), indicating that, controlling for the factors in the model, ECEC centres that report to adhere to the recommendation to ventilate rooms frequently more strictly on average (i.e., implement this measure in more weeks) have higher average infections in staff in the 2nd wave. We also find positive between-units effects for fixed group assignment of staff (Models 1, 3, and 4).

As mentioned above, those effects most likely stem from a higher number of weeks with ventilation and with fixed group assignment in centres located in counties with high COVID-19 incidences. We find no between-unit effects of regular surface disinfection. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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Implementing regular disinfection of surfaces from one week to the next is associated with a significant increase of infections among staff in the 3rd wave (Model 2).

Further analysis showed that this effect is likely to stem from ECEC centres which started disinfection when local county incidences were already on the rise in the 3rd wave. Centres that reported to newly implemented surface disinfection in the 3rd wave have a county average COVID-19 incidence of 83.5/100,000 before they start, and an incidence of 115/100,000 after having started with disinfection. Hence, we assume that the start of surface disinfection was a reaction to locally rising incidence rates.

Introducing fixed group assignment of staff is associated with fewer infections in both staff and children for all models looking at wave 3 (Mod. 2 and 4). The measures effect is a more than halving of the infection rate in staff and children. In the second wave models, we found no effect for infections in staff but a significant positive effect for infections in children. The small positive effect in model 3 might be again read as anticipation, hence ECEC centres start with fixed staff assignments to groups in the face of rising incidence rates, presumably reacting to regional orders for implementation which are linked to high incidence rates.

To challenge our results, we additionally ran a variety of models as robustness checks, i.e., several models with protective measures only (Appendix Tables A6-A9, models that include the offset as variable (Appendix Table A10 ) and models with alternative wave cut-off points (Appendix Tables A11 and A12). All tests by and large confirm the above results in relation to the sign and the effect strength and can be found in appendix tables A6-A12. Considering protective measures, the very low number of within changes, especially in ventilation and disinfection, leads to a strong dependence on few observations only and the effects should therefore only be interpreted with caution. We further tested if our data fits the poisson specification by analyzing the conditional mean and variance of the outcome variables in our models, finding underdispersion in all our models. Underdispersion leads to overestimated standard errors in poisson models [30] . As we prefer to not correct for that bias, e.g. by using a quasipoisson model, (which would lead to smaller confidence bounds), our signifi-. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted June 9, 2021. ;  cance levels could be characterized as conservative. This approach seems appropriate, especially in view of the fact that our data do not represent a true random selection. We tried alternative specifications for underdispersed data, i.e. using a generalized poisson distribution (see Appendix Table A13 ) that by and large confirms our results. A notable exception is the within-effect of fixed staff assignment for infections in children in wave 3 (Mod. 4) which is considerably smaller and insignificant when a generalized poisson distribution is assumed.

Our study investigated factors associated with infections in staff and children in ECEC centres in Germany during the 2nd and 3rd wave. We found no effects of provider type or type of group concept before the pandemic. We found ECEC centres with a larger proportion of children from a low SES background to have the highest risk of infections, and we found this effect to be increasing in wave 3 compared to wave 2. This effect is probably the most robust and one of the main findings of our analyses based on data from the ECEC centre registry. The social gradient of COVID-19 does not stop at the ECEC centret's door, indicating that children, families and staff in corresponding centres need special support, e.g. by prioritising vaccinations.

The change of group concepts towards a more restrictive group separation was one of the essential recommended or ordered measures since the beginning of the pandemic. A large proportion of ECEC centres in our sample followed this recommendation, especially in the 3rd wave (Appendix Figure A4 ). Although we have only little within-variance in wave 3, the significant association of switching from a fixed to a partly open concept with infections in staff (model 2) shows that failure to follow these recommendations, especially in wave 3, puts staff health at risk. Our results suggest that it is beneficial to keep up a fixed group concept in all ECEC centres during the pandemic.

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The copyright holder for this preprint this version posted June 9, 2021. ; https://doi.org/10.1101/2021.06.07.21257958 doi: medRxiv preprint It is well recognized that the main route of SARS-CoV-2 transmission is through the respiratory mode, and that airborne transmission is of major importance. Our findings tend to confirm this for ECEC settings. In our data, regular ventilation had a significant protective effect on infections in one of the four models, specifically on staff in the 2nd wave (model 1). Overall, implementation of regular ventilation as a protective measure in ECEC centres was quite complete with only very little variance.

Hence, insignificance is probably due to very low case numbers resulting in low overall variance. Nevertheless, even if regular ventilation was widely implemented during the whole period under study, we observed a small, but constant decline in implementation. Our findings support the assumed importance of the routine to ventilate regularly, although this should be easier in the spring, summer or autumn months because of higher outside temperatures.

In line with controversial findings regarding the role of contact or fomite transmission, we find a significant positive within result (representing more infections) for regular surface disinfection only in one of our four models. Definitively we have no evidence that surface disinfection prevents COVID-19 cases, but it is unlikely that it creates cases. We believe that the most likely explanation is that ECEC managers anticipate or react to local outbreaks or increasing COVID-19 incidence with implementing hygiene measures, such as disinfection of surfaces. Hence, we do not know to which extent our results are biased due to anticipation.

In the 3rd wave, some ECEC centres switched to a more flexible staff assignment to groups and changed their group concept between CW 07 and 08 (see Appendix Figure A3b , A4b), presumably due to a shortage of staff. This was associated with an increase of infections in staff in the 3rd wave. Thus, our results confirm that it should be beneficial for ECEC centres to maintain this practice in order to prevent infections, particularly among staff. Nevertheless, if all children attend ECEC it may be extremely difficult for many centres to assign staff firmly to just one group. After all, staff resources may be limited as typically, they are still based on pre-pandemic needs. Because of this, the measure was only recommended but not prescribed by law.

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Overall, it must be mentioned that there is a certain chance that especially the results of surface disinfection and ventilation are driven by very few units within-changes (Appendix Figure A3b) , as is further shown in the robustness section in the appendix (e.g., the within effect of surface disinfection is dependent on the wave definition, see appendix tables A11 and A12). This limitation does not hold true for the effect of fixed staff assignment on infections in staff, which remains significant in wave 3 in all robustness checks. We therefore strongly support the recommendation to keep up fixed staff assignment in all ECEC centres wherever possible.

We acknowledge as a general limitation that a managers decision to implement specific measures in their ECEC centre (and the according report in our questionnaires)

is not always followed and translated into every-day practice by all staff members.

Further, some measures are conceptually similar, e.g. a fixed group concept (with consequent separation of children) and a fixed staff assignment which includes that also staff does not move between groups. As both show significant effects, this suggests that strict contact restrictions are likely to be one of the most effective protective measures, especially in the 3rd wave.

Although it is unknown which infections are due to a VOC or not, the 3rd wave was dominated increasingly by the VOC B. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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19 work in contact with children e.g. due to infection risks for elderly persons during the pandemic. In order to better prepare ECEC centres for such exceptional situations in the future, it is essential to finally eliminate the staff shortage that was already prevalent before the pandemic.

In summary, our panel data suggest that the COVID-19 pandemic affects ECEC centres particularly when they serve children with low SES. Although many ECEC centres are grappling with staffing difficulties it is important to maintain -to the best possible degree -fixed group assignments among children and fixed staff assignments to groups. In addition, generous and frequent ventilation may aid in preventing infections. As vaccinations become increasingly available, particularly staff of ECEC centres with a large proportion of children from a low SES background should be prioritized.

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The copyright holder for this preprint this version posted June 9, 2021. 1-11, 2020. URL https://www.gov.uk/government/publications/ investigation-of-novel-sars-cov-2-variant-variant-of-concern-20201201.

[32] Autorengruppe Bildungsberichterstattung. Bildung in Deutschland 2020. pages 1-361, 2020. URL https://www.bmbf.de/de/bildung-in-deutschland-2020-11888.

html.

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The copyright holder for this preprint this version posted June 9, 2021. ; Figure A4 : Group concepts: (a) Applied paedagogical group concept in day care centres (b) Share of day care centres with changes in paedagocial concept (n/N per week) 

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The copyright holder for this preprint this version posted June 9, 2021. ; 3.48 * * * p < 0.001; * * p < 0.01; * p < 0.05. Source: Survey data collected by the German Youth Institute. Second wave assumed to last from calendar week (CW) 36/2020-06/2021, and third wave from CW 07/2021-16/2021. REWB poisson model with twoway fixed effects, offset for county incidence (data collected by the Robert Koch-Institute), dependent variable with 1 week lead. coefficients are displayed as log incidence rate ratio, standard errors in parentheses, own calculations. 12 * * * p < 0.001; * * p < 0.01; * p < 0.05; † p < 0.1. Source: Survey data collected by the German Youth Institute. Second wave assumed to last from calendar week (CW) 36/2020-04/2021, and third wave from CW 05/2021-16/2021. REWB generalized poisson model with two-way fixed effects, offset for county incidence (data collected by the Robert Koch-Institute), dependent variable with 1 week lead. Coefficients are displayed as log incidence rate ratio, standard errors in parentheses, own calculations.

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