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Our take —

In this study of 169 K-5th grade schools in the U.S. state of Georgia during late 2020, schools that required masks for teachers and staff had 37% lower incidence of COVID-19 relative to those that did not. Similarly, schools that had some form of improved ventilation (e.g., opened doors or windows) had 39% lower incidence of COVID-19 relative to schools without improved ventilation. However, only a small proportion of schools in Georgia responded to the survey, and the analyses were not adjusted for many possible factors that could have confounded the results, including the presence or absence of other interventions (for example, schools that required masks may have been more likely to improve ventilation). For these reasons, the associations observed here can only be regarded as exploratory.

Study design

Ecological

Study population and setting

This study estimated associations between school-level COVID-19 prevention strategies (including improved ventilation and mask requirements) and COVID-19 incidence. The study included 169 schools (kindergarten – 5th grade) in Georgia, USA that responded to a survey on COVID-19 prevention strategies and reported COVID-19 incidence from November 16 to December 11, 2020. The Georgia Department of Education emailed a survey to all 1,321 public K-5 schools and 140 private schools to measure prevention strategies including mask requirements for staff and students, ventilation improvements (defined as dilution methods, e.g., opening windows and using fans; or filtration methods, e.g., HEPA filters), flexible medical leave for staff, spacing desks six feet or more apart, and placing barriers between all desks. COVID-19 cases were reported by schools to the Georgia Department of Public Health. The authors used negative binomial regression models to estimate risk ratios for each intervention individually, adjusted for county-level COVID-19 incidence.

Summary of Main Findings

Schools had a median of 532 students with a median class size of 19 students; the median proportion of in-person students at each school was 85%. The majority of schools required masks for staff (65%) and students (52%), offered flexible medical leave for staff (82%), and improved ventilation systems (52%). Fewer schools placed barriers between desks (22%) or spaced desks six feet or more apart (19%). COVID-19 incidence among staff and students during the study period was 3.08 per 500 enrolled students, which was lower than the 5.28 per 500 population observed in counties with participating schools during the same period. Mask requirements for staff members were associated with lower COVID-19 incidence (risk ratio (RR): 0.63, 95% CI: 0.47 to 0.85); student mask requirements had an RR of 0.79 but were not statistically significant (95% CI: 0.50 to 1.08). Improved ventilation was also associated with lower incidence (0.61, 0.43 to 0.87). Those with dilution improvements only had lower COVID-19 incidence than those without any improvements (0.65, 0.43 to 0.98), while those with filtration only did not have statistically significant lower incidence (0.69, 0.40 to 1.21) compared to those without improvements.

Study Strengths

The survey elicited some specifics on the type of ventilation improvements enacted by schools.

Limitations

The analyses considered each intervention one at a time, only adjusting for county-level COVID-19 incidence. In addition to the standard concerns over unmeasured confounding from such an analysis (e.g., by socioeconomic status, class size, etc.), interventions were likely correlated with one another. For example, if schools with staff mask requirements were also likely to have improved ventilation, then one cannot conclude from these results that either intervention has an independent effect on COVID-19 incidence. Additionally, COVID-19 incidence was derived from school self-report, and there may have been systematic under-ascertainment that varied along with interventions. Finally, the survey response rate was low (12%), and participating schools may not be representative of all Georgia K-5 schools. In particular, those schools with higher COVID-19 incidence rates or ineffective interventions may have been less likely to participate.

Value added

This is one of the first studies to estimate effects of improved ventilation on COVID-19 outcomes, particularly in educational settings.

Our take —

This study is the largest to date investigating urban-rural disparities in COVID-19 vaccination rates in the US. Through April 2021, urban counties had 6.8% greater first dose vaccine coverage compared to rural counties. This difference persisted when disaggregated by age group and by sex of vaccine recipients. Vaccinated individuals in suburban counties (14%) and in the most rural counties (15%) were more likely to be vaccinated in non-adjacent counties (e.g., further distances) compared to individuals in the most urban counties (10%). The amount of missing data was unclear and may have biased these results, particularly if rural counties reported fewer than the true number of vaccinations given compared to urban counties which would exaggerate estimated disparities.

Study design

Ecological

Study population and setting

This study sought to characterize disparities in COVID-19 vaccination rates among US states between urban and rural populations. The study used data reported to the US CDC from December 10, 2020 to April 10, 2021 by health departments, pharmacies, and federal entities in the immunization information systems, the Vaccine Administration Management System, or directly submitted by these entities. County-level data was analyzed for all adults (18 years or older) living in 49 states and DC who received at least one vaccine dose. Hawaii and eight counties in California were excluded due to data-sharing restrictions of county-level information. Individuals receiving their first dose of vaccine were classified as four urban and two rural categories according to their county of residence. Urban counties included large central metropolitan, suburbs (large fringe metro), medium metro, and small metro, while rural was comprised of rural counties (micropolitan), and most rural counties (noncore). Four jurisdictions did not report for rural counties and therefore, 45 jurisdictions were included. Vaccine recipients were stratified by age group (18 to 64, and 65 years+), sex, jurisdiction, and by urban/rural residence.

Summary of Main Findings

Overall, COVID-19 vaccination rates were higher in urban areas (46%) compared to rural (39%). This disparity persisted across all age groups and by sex. In 36 jurisdictions (72% of 45 states and DC), coverage was higher in urban counties, in 5 jurisdictions (10%), it was the same in both rural and urban counties, and in 5 jurisdictions (10%) it was significantly higher among rural counties compared to urban. Nearly all (98%) people were vaccinated in their state of residence, and 67.1% were vaccinated in their county of residence. More individuals in large fringe metropolitan counties (suburban, 14%) and noncore counties (most rural, 15%) reported traveling to nonadjacent counties compared with those in the most urban counties (10%).

Study Strengths

The study used county-level data from nearly every state in the US and DC, making it one of the most complete accounts of vaccination in the US. Given anecdotal reports of individuals traveling long distances to receive vaccines, they also disaggregated their findings based on county of residence vs. where individuals were actually vaccinated. Additionally, they disaggregated by major sociodemographic factors (sex and age) in order to identify any differences in disparities there also.

Limitations

The study used first dose as the outcome of interest, therefore, these findings do not reflect full vaccination rates, which may have even greater disparities. The study was unable to examine race/ethnicity disparities that may be correlated to driving some of these disparities, given this data was missing for 40% of their dataset. The absolute number of individuals were not often reported in their analysis, rather the authors reported the proportion of those vaccinated by their county of residence, adjacent county, and nonadjacent county. This makes it difficult to understand missingness in their data and whether potential selection bias occurred in their findings. Finally, it is unclear what is driving these disparities, and no specific causal factor can be identified from the data analyzed.

Value added

Vaccination access among urban vs. rural areas is a major public health issue, and this is the largest study to date from the US directly comparing vaccine coverage in these areas.

Our take —

Between August and December 2020, nearly 64,000 COVID-19 cases were detected among children ages 5-17 years in Florida, though 60% of these cases were not deemed to be school-related. During the study period, a median of 70% of registered students across districts attended full-time in-person school, and <1% of registered students were identified as having had school-related COVID-19. Factors associated with higher COVID-19 student case rates in a school district included earlier in-person reopenings, absence of mask mandates, and higher county-level background COVID-19 incidence. However, the validity of study findings are limited by possible misclassification of COVID-19 cases attributed to transmission within or outside of schools and the lack of adjustment for other SARS-CoV-2 control measures implemented within schools.

Study design

Ecological

Study population and setting

The investigators estimated a student COVID-19 case rate for the state of Florida by dividing the number of confirmed COVID-19 cases in children aged 5-17 years by the total number of registered students in a school district. After estimating a COVID-19 student case rate between August and December 2020 for each school district, the investigators tested for associations between factors (e.g., date of reopenings, presence or absence of mask mandates, background COVID-19 incidence) and student COVID-19 case rates at the school district level.

Summary of Main Findings

From August 10 to December 21, 2020, 63,654 COVID-19 cases were reported among children ages 5-17 in Florida, 39.4% (n = 34,959) of which were classified as school-related cases (defined as a case who had been on school campus during the 14 days preceding symptom onset or testing, regardless of where the infection was acquired). Nearly 700 SARS-CoV-2 clusters were detected in approximately 10% of Florida’s 6,800 public schools; 110 of these outbreaks were attributed to extracurricular activities (i.e., sporting events, non-school-sanctioned gatherings, mass transit to school). Among 2,809,553 registered students in Florida, a median of 70% of students across Florida’s 67 school districts attended school in-person during the study period, yielding a median COVID-19 case rate of 1,280 cases per 100,000 registered students (school district range: 394–3,200). COVID-19 student case rates were significantly higher in school districts with earlier in-person re-openings (before August 16), with an absence of mask mandates in the school district re-opening plans, with smaller county population sizes, and with higher background county-level COVID-19 incidence.

Study Strengths

Contact tracing investigations were able to determine activities associated with a subset (81%) of outbreaks.

Limitations

The procedure for determining whether COVID-19 cases were school-related depended on accuracy and completeness of case information, requiring detailed contact investigations in most instances; cases were, therefore, likely differentially misclassified based on factors like school district, testing availability, and completeness of contact investigations. Additionally, outside of mask mandates, the investigators did not explore or adjust for the impact of other control measures (e.g., classroom cohorting, quarantining of potentially exposed students and staff) on observed SARS-CoV-2 case rates in the study period. Because case rate estimates were aggregated for the entire study period (August to December 2020), the investigators could not examine how time-varying ecological factors (i.e., background COVID-19 incidence) impacted SARS-CoV-2 epidemic trajectories in school-aged children across school districts. Two of Florida’s largest school districts (Miami-Dade and Broward Counties) were excluded from analyses because of delayed school reopenings, which limits possible inference. Lastly, analysis at the school district level may have masked important differences in SARS-CoV-2 transmission and mitigation measures within individual schools.

Value added

This is among the first studies to examine factors associated with SARS-CoV-2 burdens among school-aged children in the United States, during an initial phase of school reopenings when background COVID-19 incidence was high.

Our take —

This study reports the results of a national rapid antigen testing campaign in Slovakia, which was associated with a greater than 50% reduction in estimated SARS-CoV-2 prevalence over a one week period. Results should be interpreted cautiously, as mass testing occurred in the context of many other SARS-CoV-2 control measures.

Study design

Ecological, Modeling/Simulation

Study population and setting

Between October 23 and November 8, 2020, The Slovak Ministry of Health implemented SARS-CoV-2 testing nationally using rapid viral antigen tests in three phases: (1) a pilot phase (October 23-25) in four high-incidence counties; (2) a mass testing campaign implemented nationally in 79 counties (October 31-November 1; round 1); and (3) follow-up testing one week later in 45 counties with the highest SARS-CoV-2 prevalence (November 7-8; round 2). Residents were instructed to present to central testing hubs in their jurisdictions, staffed with over 60,000 trained employees nationally, during each testing campaign phase. Testing was performed using the SD-Biosensor Standard Q rapid antigen test on nasopharyngeal swabs. A national lockdown was imposed in Slovakia at the time of mass testing campaigns, which included business and school closures (for students ages 10 years and above): residents were instructed to stay at home and leave only for essential purposes (i.e., travel to/from work, accompanying children to school, seeking medical care). After estimating changes in SARS-CoV-2 prevalence (defined as the proportion of SARS-CoV-2 tests performed during a campaign phase with positive results) between mass testing campaigns, the authors used an epidemic microsimulation model to evaluate whether observed changes in SARS-CoV-2 prevalence could be attributed to scale-up of rapid antigen testing.

Summary of Main Findings

Nearly 5.3 million rapid antigen tests were conducted across the three testing phases, detecting 50,466 positive cases overall. Population coverage of antigen tests during the pilot, round 1, and round 2 phases was 65%, 66%, and 62%, respectively (84-87% of the census age-eligible population). Test positivity in the pilot phase was 3.91%, 1.01% during round 1, and 0.62% in round 2. Specificity of the rapid test was estimated to be 99.85%. In the 45 counties included in rounds 1 and 2, the estimated SARS-CoV-2 prevalence decreased by 58% (95% CI: 56–63%) between campaigns, with substantial heterogeneity observed between counties. In the four counties included in the pilot phase, infection prevalence decreased by 82% (95%CI: 81-83%) between the pilot and round 2. In microsimulation models, the authors assumed varying levels of effectiveness of other SARS-CoV-2 control measures which were implemented at the same time as testing (e.g., lockdowns, school closures): the only scenario that sufficiently reproduced observed reductions in SARS-CoV-2 prevalence between testing campaigns was a scenario in which confirmed COVID-19 cases isolated from household contacts, suggesting that declines in SARS-CoV-2 prevalence were unlikely to have occurred in the absence of the mass testing campaign.

Study Strengths

This study analyzes an impressive amount of SARS-CoV-2 rapid antigen testing data from three testing campaigns, including one conducted at a national scale in Slovakia.

Limitations

Participation in viral antigen testing was voluntary and required travel to testing sites; the estimated SARS-CoV-2 prevalence, therefore, may not accurately reflect true SARS-CoV-2 transmission dynamics in the population. Despite using an epidemic simulation model to estimate the potential impact of mass testing on SARS-CoV-2 transmission, declines in SARS-CoV-2 prevalence may not be attributable to scale-up of viral antigen tests. Mass testing campaigns were also conducted in the context of a national lockdown, where residents were instructed to only leave their homes for essential purposes. No empirical data were presented on isolation or quarantine of infected cases and their contacts, respectively, which is essential to concluding whether observed reductions in cases were attributable to mass testing or other interventions. While the test used was a rapid antigen test, it was conducted by tens of thousands of trained professionals using nasopharyngeal swabs; thus, results may not be generalizable to most other settings.

Value added

This is among the first studies to assess the impact of mass rapid viral antigen testing on SARS-CoV-2 transmission dynamics.

Our take —

The study, available as a preprint and thus not yet peer-reviewed, sought to describe the COVID-19-related mortality disparity among Native Americans in the US. The study found a standardized mortality ratio of 2.77 compared to white populations, and this was even higher in some states, with South Dakota having a mortality ratio of 9.7 as compared to the white population. They found that the standardized mortality ratio was highly correlated with cthe perent of Native Americans living on reservations. The study had many limitations due to its ecological study design, including use of data collected as far back as 2014, and potential underreporting of Native American race/ethnicity. Regardless, results show a high level of disparity in Native American mortality from COVID-19 compared to other racial/ethnic populations in the US.

Study design

Cross-Sectional, Ecological

Study population and setting

The study sought to describe risk factors for COVID-19 infection and related mortality among Native American/American Indian communities in the US. COVID-19-related death counts from the National Center for Health Statistics from January 1, 2020, through January 16, 2021 were used. Midyear population estimates of 2019 were drawn from the US Census Bureau data from 10 states: Arizona, California, Oklahoma, New Mexico, Washington, New York, South Dakota, Minnesota, Utah, and Mississippi. The American Community Survey (ACS) and the Behavioral Risk Factor Surveillance System (BRFSS) were used to estimate potential risk factors that may impact transmission and mortality. For the ACS, the analysis used 2014 – 2018 data to estimate the type of health insurance, income-poverty ratio, and household living arrangements. They also extracted data on frontline worker status using data from 2018. They used the BRFSS from 2011 to 2018 to estimate smoking status and health conditions including asthma, chronic obstructive pulmonary disease (COPD), kidney disease, cancer, heart disease, diabetes, and obesity. More recent ACS or BRFSS versions were not yet available. Finally, they utilized data from MultiState, which generates a rating of open-ness during the pandemic based on state policies and capacity/industry restrictions. They categorized race as non-Latino Native American (including American Indian and Alaskan Native), non-Latino white, non-Latino Black, and Latino, and using the My Tribal Area tool, integrated 2014 – 2018 ACS estimates of Native Americans living on- vs. off-reservation. They generated standardized mortality ratios compared to the 3 other racial categories overall and by state. They disaggregated this based on reservation living status, occupation, and chronic health conditions and behavioral risk factors, generating correlation estimates for each.

Summary of Main Findings

In this study, 2,789 COVID-19-related deaths were estimated from January 1, 2020 to January 16, 2021 among Native Americans. They estimated a crude death rate of 1.63 times that among the US white population, and a standardized mortality ratio of 2.77. This was greater than the standardized mortality ratio within the Black population (1.64) and the Latino population (1.81). Stratifying by state, they found geographic differences as well, with South Dakota having the highest standardized mortality ratio at 9.7 compared to the state’s White population, while California had the lowest at 1.6 times the mortality to the state’s White population. The standardized mortality ratios for the 10 states were correlated with increasing percentages of Native Americans living on reservations (correlation = 0.8). In their sociodemographic and behavioral correlations, they found the income-poverty ratio was highly negatively correlated with the standardized mortality ratio (-0.86).

Study Strengths

The study made use of a wide range of data to describe the health disparities impacting Native Americans, an often underreported population. They disaggregated by meaningful variables indicative of structural risks of disease, such as living on a reservation which may impact access to health services, and living in multigenerational or crowded households, and having insurance. They also examined individual-level factors, such as clinical risks through COPD and diabetes.

Limitations

The study’s primary limitation was that they used many different data sources which may have different reporting guidelines and criteria. Therefore, these results paint an overall picture of Native American health and health disparities, but do not generate individual-level estimates of risk factors and are limited to standardization by age and place alone.. They also limited their analysis to individuals reporting Native American as their only race, which likely underreports the true number of Native American people in the US. This standardization does not reflect differences in the underlying clinical health between white and Native American populations likely due to differences in access to health services and clinical care, and may be biased. They also used data from prior years going as far back as 2014 which may not reflect more recent trends in disease and social factors.

Value added

This is a large study of Native American people in the US, reflecting the health disparities they face compared to white and other racial/ethnic groups.

Our take —

This ecological study on the impact of SARS-CoV-2 mitigation measures in the US found that county-level case and death daily growth rates declined following the implementation of mask mandates and increased following restaurant reopenings. However, due to several key limitations in the study design and statistical analyses, results from this study should be interpreted very cautiously.

Study design

Ecological

Study population and setting

The study used county-level data on SARS-CoV-2 cases and deaths as well as the implementation dates of mask mandates and on-premise dining reopenings to evaluate the impact of masks mandates and restaurant reopenings on subsequent SARS-CoV-2 spread and mortality in the US. Dates of policy implementation were extracted from state and county websites, while case and death data were extracted from state and county public health department websites. Primary outcomes included daily growth rates for cases and deaths, which were estimated as the change in log cases or deaths from the previous day.

Associations between mask mandates and restaurant reopenings with the primary study outcomes were estimated using weighted least squares regression, and were assessed 20 days within implementation as well as 21-40 days, 41-60 days, etc. through 100 days following implementation. Associations between mitigation measures and outcomes in the pre-implementations periods were also examined. Models included adjustment for bar closures, stay-at-home orders, gathering size limitations, daily testing rates, county fixed effects, and day.

Summary of Main Findings

This study found that mask mandates, which were implemented in 75% of US counties, were associated with lower daily case and death growth rates (0.5 and 0.7 percentage point daily decrease within 1 to 20 days after implementation, respectively; p<0.001) following their passage. While counties allowing for reopening of on-premises dining (98% of all counties) did not experience an initial rise in cases within the first 40 days, they did experience a significant increase in both the daily growth rates of cases by 41 days and deaths by 61 days after reopening (p<0.001 for both).

Study Strengths

Data used for this study were gathered directly from primary sources (i.e., local government websites).

Limitations

There were several major limitations to this study. First, there were numerous SARS-CoV-2 mitigation policies implemented over the same calendar timeframes as mask mandates and restaurant restrictions/reopenings, and it is unclear why the impacts of only two of a large number of county-level mitigation measures were assessed in this study. Relatedly, while the authors controlled for several mitigation measures that may have confounded associations between mask mandates and restaurant restrictions with daily changes in cases and deaths, it is unclear whether all mitigation measures implemented within counties were actually included in the analysis and the extent to which those mitigation measures included temporally overlapped with one another: substantial overlap (i.e., statistical collinearity) would likely impact meaningful estimation of exposure effects. Third, cases and death rates change over time due to infectious disease dynamics, which were not accounted for in the analysis. Fourth, and perhaps most critically, authors controlled for daily testing rates in their adjusted models. Testing rates are arguably a proxy for case rates, one of the two primary study outcomes, thus potentially rendering effect estimates from the case rate models uninterpretable. Lastly, the primary outcomes were based on daily reported changes in cases and deaths, which are likely subject to extreme variations due to reporting/surveillance biases.

Value added

This study contributes to a large body of ecological studies examining the impacts of SARS-CoV-2 mitigation measures on virus associated cases and deaths.

Our take —

In a study of US health departments from June to July 2020, higher COVID-19 caseloads per health department staff member correlated with poorer timeliness of COVID-19 case investigations and lower yield in the number of close contacts elicited from each COVID-19 case investigation. Given the inclusion of data from only 56 health departments and narrow data collection period, results may not reflect contact tracing performance outcomes of health departments not included in this study or at different stages of the SARS-CoV-2 pandemic.

Study design

Ecological

Study population and setting

Between June 25 and July 31, 2020, investigators calculated COVID-19 contact tracing performance for 56 CDC-funded health departments in the United States. These indicators included average caseload per investigator (ratio of confirmed or probable COVID-19 cases to number of case investigators), average contact tracing load per investigator (ratio of elicited close contacts from case investigations to number of case investigators), case investigation timeliness (proportion of confirmed or probably COVID-19 cases contacted for a case interview within 24 hours of reporting to the health department), contact tracing timeliness (proportion of close contacts elicited from case investigations notified of exposure within 24 hours), and contact tracing yield (ratio of close contacts elicited from a case investigation to number of index COVID-19 cases interviewed). Contact tracing performance metrics were compared using the Spearman correlation coefficient (r).

Summary of Main Findings

During the reporting period of approximately one month, health departments reported a median caseload per investigator of 31 (range: 1–96 cases) and a median of 29 elicited close contacts requiring follow-up per investigator (range: 0.5–200). Among cases prioritized for interview, a median of 1.15 close contacts were elicited per case, and 42/53 (79%) health departments elicited fewer than two close contacts per case. A median of 57% of COVID-19 cases were interviewed within 24 hours of initial case reporting to the health department, and a median of 55% of close contacts were notified of their exposure within 24 hours of initial interview with the index case. Twelve (25%) health departments reported reaching fewer than 32% of elicited close contacts within a 24-hour period. Investigator caseload and case investigation timeliness (correlation coefficient: –0.68) and contact tracing yield (correlation coefficient: –0.60), respectively, were inversely correlated. Correlations were similar across health departments with different staffing models (i.e., allocating different staff members to case investigations and contact tracing, using the same staff members for case investigations and contact tracing, or a mix).

Study Strengths

The study used health system capacity metrics as indicators of contact tracing performance. Indicator correlations were also compared across health departments that allocated staff to case investigations and contact tracing efforts in different ways, which supports the consistency of reported correlations across different contact tracing models.

Limitations

The study’s unit of analysis was the health department, rather than the individual case investigator or contact tracer; this prevented investigators from attributing contact tracing performance outcomes to unmeasured differences in staff member characteristics (e.g., individual caseload, training, years of experience) beyond the contextual factors (i.e., average caseload per investigator) reported in this study. Furthermore, the reported correlations in this study may be entirely spurious; these unadjusted estimates do not control or account for other factors, like SARS-CoV-2 incidence and laboratory capacity, that could confound the relationship between health department characteristics and contact tracing performance indicators. Because data from only 56 health departments were included in this study, the results may not be generalizable to health departments that did not participate. Similarly, because data were collected in a narrow timeframe (June–July, 2020), these results may not reflect contact tracing performance at different stages in the SARS-CoV-2 pandemic with varying health department staffing capacity and contact tracing demands.

Value added

This is among the first studies to correlate health workforce capacity and health systems indicators to performance outcomes of COVID-19 contact tracing efforts.

Our take —

In a large survey of over 300,000 US residents aged 13 and older between June 3 and July 27, 2020, 85% of respondents said they were “very likely” to wear a mask while grocery shopping, while only 40% were “very likely” to wear a mask while visiting family and friends. In an analysis conducted at the state level, the authors estimated that an increase of 10% in self-reported mask wearing by this measure was associated with a more than threefold increase in the odds of epidemic control, as defined by an estimated SARS-CoV-2 reproduction number below 1. Although these results support the consensus that mask use is an effective component of transmission control, they should be interpreted cautiously for multiple reasons including survey representativeness, crude measures of exposure and outcome, and likely unmeasured confounding.

Study design

Cross-sectional; Ecological

Study population and setting

This study related US state-level self-reported mask use to transmission control, as measured by an estimate of the reproduction number (Rt) of SARS-CoV-2. Mask use was assessed with a survey delivered by the SurveyMonkey.com online platform and answered by 378,207 individuals 13 years or older between June 3 and July 27, 2020. Results were analyzed as unweighted data and by weighting for age, race, sex, education, geography, and political affiliation to reflect the composition of the U.S. population. Respondents were asked how likely they were to wear a mask “while grocery shopping” or “while visiting with family or friends in their homes,” on a four-point scale ranging from “very likely to “not at all.” A binary classification of mask-wearing was created, defined as responding “very likely” to both questions. Individual mask use data were then aggregated at the state-level each week: this was the primary exposure measurement of mask community use used in subsequent models. Logistic regression models were fit to aggregated state-level weekly estimates of Rt that were dichotomized as either below or above 1 (epidemic slowing vs. epidemic growing). Models were adjusted for several possible confounding variables, including physical distancing (defined by state-level weekly time spent at home relative to a baseline period, measured with aggregated mobility data from Google), state population density, proportion of non-white respondents, and a linear time trend.

Summary of Main Findings

A high proportion (84.7%) of respondents reported that they would be very likely to wear masks at the grocery store, while only 40.2% reported they would be very likely to wear masks while visiting family and friends; 39.8% answered “very likely” to both questions. Self-reported mask use increased linearly with age and was higher among women, nonwhite people, and people with lower income. Mask use varied considerably by geographic region, and was highest on both coasts, along the southern border, and in urban areas. In multivariable logistic regression, self-reported mask use was associated with transmission control (defined as Rt <1): a 10% increase in mask use had an estimated odds ratio for epidemic control of 3.53 (95% CI: 2.03 to 6.43). Results were broadly similar, though attenuated in some instances, under other assumptions including alternative Rt estimates, an alternative definition of mask wearing, dichotomizing Rt at different thresholds, and using self-reported community contacts rather than mobility data. A separate analysis found no association between state-level mask mandates and changes in self-reported mask use.

Study Strengths

This study employed self-reported mask-wearing behavior, rather than mask policies, as the exposure of interest. The sample size was large and was weighted to match the distribution of some US demographic variables. The authors performed a range of sensitivity analyses.

Limitations

Self-reported mask-wearing behavior is subject to bias; for example, respondents may have provided answers in line with perceived social desirability, and this may have occurred differently across geographic regions. The survey was administered via a web platform, and thus respondents are more likely to have internet access than the broader U.S. population, and may have been non-representative in other ways. Moreover, those who responded to the survey may have systematically differed from those who did not respond, which is particularly concerning given that the response rate was only 11%. The measure of dichotomous mask-wearing was crude and may have ignored meaningful gradations in behavior. The outcome measure was similarly crude, and more problematic, since the time-varying reproduction number at the state level is determined by a heterogeneous array of factors, many of which relate to geographically specific transmission dynamics. It is unlikely that the potential confounding variables included in the model (including a linear time trend) adequately accounted for determinants of Rt that may also be related to self-reported mask use. Lastly, self-reported mask use may have been affected by characteristics of local epidemics that were not entirely accounted for by adjusting for prior peak Rt (e.g., test positivity rate, local hospital capacity, local social distancing policies).

Value added

Many prior ecological studies of mask-wearing effectiveness relied on mask mandates and policies; this is one of the few to measure self-reported mask use at a large scale.

Our take —

This study used sewage samples from Alameda and Marin Counties in California, USA, to demonstrate that population-level wastewater sampling can be used to identify SARS-CoV-2 variants circulating in the local population. The authors presented a methodology that can be used to generate genomic data from wastewater samples and showed that the majority of variants detected were also available in publicly available sequences from clinical patient samples. These findings suggest that wastewater sampling may be an effective tool for detecting circulating SARS-CoV-2 variants even in the absence of, or prior to, sequencing of patient-derived samples.

Study design

Ecological

Study population and setting

This study included data from 27 samples from wastewater treatment facilities from Alameda and Marin Counties in Northern California, USA. These samples, all 24-hour 1-liter composite samples of raw sewage (meaning sewage samples were collected every 15 minutes over the course of 24 hours and then combined), were collected between May 19 and July 15, 2020. The goal of the study was not only to detect SARS-CoV-2 in wastewater, but to assemble whole SARS-CoV-2 virus genomes and to compare observed variants to those found in publicly available sequences from clinical patient samples in California.

Summary of Main Findings

The authors found multiple SARS-CoV-2 genotypes in sewage known to be present in local communities. Assembled whole genomes from wastewater samples matched published genomes from clinical samples. In addition, the majority of individual variants (even those at low frequencies) detected within wastewater samples were also observed in patient-derived genomes from California, and that variants found in two or more samples were 2.3 times more likely to be observed in California or US patient-derived genomes than variants observed only once. This suggests that the variation observed in wastewater samples is representative of the SARS-CoV-2 variation circulating in the sampled population — individuals in Alameda and Marin counties, California. The authors also found evidence of new variants not yet found in patient-derived samples from California and suggested that these variants may be detected in clinical patient samples in the near future. On a technical side, the authors addressed the difficulties inherent to isolating virus in wastewater samples, and reported that both the RNA extraction method and the initial concentration of SARS-CoV-2 in the sample have a significant effect on genome recovery and coverage.

Study Strengths

This study demonstrated that wastewater samples can be used not only to detect the presence of SARS-CoV-2 in the community, but also to detect distinct genotypes circulating in the local population. The authors presented a comparison of RNA extraction and sequencing methods that may help to inform future wastewater sequencing studies, and their findings suggest that wastewater sampling may be an effective tool for surveying SARS-CoV-2 variants at a population scale.

Limitations

The authors provided some evidence that common variants occur at similar frequencies in wastewater samples but, in the absence of phylogenetic context or additional technical replicates, it is difficult to evaluate if variant frequencies in wastewater samples can be used to track the spread and abundance of particular viral lineages.

Value added

This study demonstrated that sequencing of wastewater samples can detect SARS-CoV-2 and identify multiple genotypes known to be circulating in local communities.

Our take —

A national study in Israel found that SARS-CoV-2 test positivity and incidence increased across pediatric and adult age groups following school reopenings in May 2020; increases in SARS-CoV-2 hospitalizations and deaths, however, were not observed until later in the summer, coinciding with the lifting of other restrictions on large social gatherings. This study had major limitations, including the use of aggregate national data and short time intervals in analysis, restricted to just three weeks following school reopenings.

Study design

Ecological

Study population and setting

On May 3, 2020, schools in Israel were partially reopened following nearly two months of closure (since March 14). By May 17, schools throughout Israel were completely reopened through the end of the academic year (June 19 for secondary schools and June 30 for elementary schools). While Israel imposed standard public health mitigation measures within schools upon reopening (i.e., mask use, maintaining physical distance from others), they did not employ cohort-based mitigation measures (e.g., keeping the same students and teachers together in the same classroom) in contrast to other countries. Using age-disaggregated COVID-19 incidence, hospitalization, and mortality estimates, the authors investigated the impact of partial and complete school reopenings on SARS-CoV-2 transmission dynamics, comparing changes in SARS-CoV-2 test positivity, incidence (7-day average of new cases per 100,000 persons), hospitalizations, and deaths in school-aged children/adolescents (ages 19 and under) and the general adult population (ages 20 and above) in the week before and the two to three weeks following school reopenings.

Summary of Main Findings

In the 21-27 days following school reopenings, SARS-CoV-2 test positivity increased significantly across adult age groups but not among children <10 years (Rate Ratio [before vs. after reopening]: 1.46, 95% CI: 0.85–2.51) or 10-19 years (RR 0.93, 95% CI: 0.65–1.34). Accounting for the number of SARS-CoV-2 tests performed, COVID-19 incidence increased significantly in adults and children <10 years (Incidence Rate Ratio [IRR]: 2.2, 95% CI: 1.56–3.11) and 10-19 years (IRR 1.29, 95% CI: 0.94–1.76) in the two and three weeks following school reopenings. By July 31, children 19 years and younger accounted for nearly one-third (29%) of SARS-CoV-2 infections in Israel, but this increase in SARS-CoV-2 test positivity and incidence grew most dramatically in children and adolescents (<20 years) after schools closed permanently for the academic year (June 19 and 30 for secondary and elementary schools, respectively). COVID-19 hospitalizations and deaths remained relatively stable in the 49-day period following school reopenings, increasing significantly in the 21-28 days and 28-34 days, respectively, following easing of restrictions on attending large social gatherings (on June 12) (hospitalizations: IRR 3.95, 95% CI: 3.2–4.8; mortality: IRR 4.0, 95% CI: 1.9–8.3). SARS-CoV-2 test positivity and 7-day average COVID-19 incidence increased significantly across pediatric and adult age groups after June 12, when restrictions on attending large social gatherings were eased.

Study Strengths

This study adjusted for changes in testing rates over time, which may have reduced impacts of surveillance biases introduced by changing test guidelines over calendar time. Israeli surveillance protocols following school reopenings, including testing of asymptomatic household contacts of school-attending children infected with COVID-19, likely resulted in more accurate estimates of SARS-CoV-2 burdens among school-aged children, compared to symptomatic testing only. Lastly, the study’s use of multiple SARS-CoV-2 transmission metrics, from test positivity to mortality, provided a detailed picture of the COVID-19 epidemic in the context of school reopenings.

Limitations

This study had several key limitations. First, trends in COVID-19 test positivity, incidence, hospitalizations, and deaths were only compared between the seven days before school were reopened and the two to three weeks after schools were reopened; this narrow time interval prohibited assessment of potential downstream impacts (i.e., COVID-19 hospitalizations and deaths) that could occur following multiple generations of SARS-CoV-2 transmission. Moreover, it may take several weeks or longer before outbreaks or transmission in schools to occur; if such transmission events occurred, they would not have been captured in this study. Second, because school reopenings occurred within days of other restrictions on social gatherings being lifted, the independent effect of school reopenings on SARS-CoV-2 transmission cannot be disentangled from other policy changes. Third, because children infected with SARS-CoV-2 are more likely than adults to present asymptomatically, estimates of SARS-CoV-2 infections in pediatric age groups may be underestimated. Fourth, given the author’s exclusive use of national data, the estimates generated in their analyses do not account for background subnational differences in COVID-19 incidence, hospitalizations, or deaths. Fifth, the absence of any information on control measures implemented in schools during reopenings, or how these control measures may have differed across schools, makes the results difficult to interpret or to generalize to other settings. Lastly, because schools in Israel were reopened at a time when community SARS-CoV-2 transmission had declined substantially since March 2020, the generalizability of these results may be limited to settings with low COVID-19 burdens at the time of school reopenings.

Value added

This is among the first studies globally to estimate impacts of school reopenings on national age-specific SARS-CoV-2 transmission dynamics.