Skip to main content

Our take —

In this observational study of 520 US counties from July through September 2021, those counties with school mask requirements for all students had about half the rates of pediatric COVID-19 cases relative to counties without any mask requirements for students. This association remained after adjustment for several characteristics of the counties that may have otherwise biased results; however, the authors were not able to adjust for other transmission control measures (e.g., staff vaccination rates, physical spacing of students, testing and quarantine measures) that may have influenced COVID-19 case rates. Still, the results provide further suggestive evidence that mask use may be an effective means of controlling SARS-CoV-2 transmission in schools.

Study design

Ecological

Study population and setting

This was an ecological study of 520 US counties between July 1 and September 14, 2021, comparing pediatric COVID-19 case rates between 1) counties with school masking requirements for all students and 2) counties without any masking requirements for students. Counties with heterogeneous school masking policies were excluded from the analysis. Only counties with at least 21 days of case data after the start of the school year were included. Pediatric (<18 years) case rates per 100,000 population were aggregated by week and numbered relative to the start of the school year in each county (i.e., weeks were numbered -3 to 2). Crude comparisons were made via t-test; additionally, the authors used multivariable linear regression adjusting for age, race, ethnicity, pediatric COVID-19 vaccination rate, community transmission rates, population density, social vulnerability score, proportion uninsured, proportion living in poverty, and community COVID-19 vulnerability score.

Summary of Main Findings

Of the 3,142 counties initially sampled, 520 were retained for analysis after the inclusion criteria were applied. The average change in weekly pediatric COVID-19 incidence from one week before to one week after school opening was 18.5 per 100,000 higher in counties without school mask requirements (34.9 per 100,000) than in counties with school mask requirements (16.3 per 100,000). After adjustment for possible confounding variables, school mask requirements were associated with a 1.31 per 100,000 lower daily COVID-19 pediatric case rate (95% CI: 1.11 to 1.51).

Study Strengths

The authors were able to adjust for a wide array of ecological-level variables that may have confounded results.

Limitations

The analysis did not adjust for other county-level differences in SARS-CoV-2 transmission mitigation measures in schools (e.g., ventilation, physical spacing of students, teacher and staff vaccination rates, testing and quarantine measures). These measures may have correlated with mask requirements at the county level; if these measures were partially responsible for lower COVID-19 case rates, the results here may overestimate the beneficial effects of mask requirements. Additionally, the inclusion criteria allowed for classification of a county if there was information on mask requirements in any single school within the district. This may have led to considerable misclassification if schools for which there was no mask requirement information had conflicting mask policies. Only 520 counties were included in the analysis, and these counties may not be representative of the wider U.S. population. No data were available on COVID-19 case rates by student age; there may have been variation in the associations between mask use and COVID-19 incidence by age groups. Finally, this was an ecologic study that did not examine individual-level mask use or COVID-19 outcomes.

Value added

This study adds more evidence derived from ecological, observational data to suggest that masks are effective in reducing SARS-CoV-2 transmission among children in schools.

Our take —

This study described COVID-19-associated hospitalizations in children and adolescents (0-17 years) in the US from March 1, 2020 to August 14, 2021. This study used data from COVID-NET, a hospitalization surveillance network that collects data on laboratory-confirmed COVID-19-associated hospitalizations among children and adults at over 250 acute-care hospitals in 14 states, representing about 10% of the U.S. population. Hospitalization rates peaked in January 2021 (1.5 per 100,000 young children and adolescents) before falling through June 2021 to a low of 0.3. With the emergence of the Delta variant in the United States, child and adolescent COVID-19-associated hospitalizations began to rise again, almost reaching their previous peak at the end of the study period, August 14, 2021 (1.4). The proportion of hospitalized children and adolescents who died during hospitalization (0.7% and 1.8%) or required ICU care (26% and 26%) remained stable before and after the emergence of the Delta variant. Despite limited data, unvaccinated adolescents were 10 times more likely to have a COVID-19-associated hospitalization compared to vaccinated adolescents. This study shows that although pediatric and adolescent hospitalizations started to increase after the emergence of the Delta variant, vaccination (at least in adolescents) dramatically reduced the likelihood of hospitalization among those eligible.

Study design

Ecological

Study population and setting

This surveillance study used data from COVID-NET, a COVID-19-associated hospitalization surveillance network, to describe COVID-19-associated hospitalizations among US children and adolescents (0-17 years) to compare disease severity from before and after the Delta variant became the predominant strain in the US (March 1, 2020 to August 14, 2021). The COVID-NET catchment area consists of 99 counties across 14 states. Cases were defined as residents who were diagnosed with laboratory-confirmed SARS-CoV-2 in the 14 days before, or during, hospitalization. Study staff collected measures of disease severity (length of stay, highest level of breathing support, blood pressure support, and in-hospital death) and vaccination status of all hospitalized children. Age-specific (0-4 years, 5-11 years, and 12-17 years) cumulative and weekly COVID-19-associated hospitalizations per 100,000 children were calculated by dividing COVID-19-associated hospitalizations by 2019 age-specific population estimates for the included counties from the National Center for Health Statistics’ population estimates. The study used COVID-NET vaccination data on hospitalized adolescents (12-17 years) and state immunization databases to calculate cases per 100,000 person-weeks for vaccinated and unvaccinated adolescents. They compared age-specific hospitalizations and hospitalizations among fully vaccinated and unvaccinated adolescents from June 20, 2021 to July 31, 2021. Disease severity measures from March 1, 2020 to June 19, 2021 were compared with those from  June 20, 2021 to July 31, 2021.

Summary of Main Findings

From March 1, 2020 to August 14, 2021, there were 49.7 cumulative COVID-19-associated hospitalizations per 100,000 children and adolescents, aged 0-17 years. The burden was similar among younger children (0-4 years, 69.2 hospitalizations) and adolescents (12-17 years, 63.7 hospitalizations), but lower among children aged 5-11 years (24.0). Weekly hospitalizations were lowest from June 12 to July 3, 2021 (0.3 per 100,000 in younger children and adolescents) after their peak of 1.5 in January 2021. With the predominance of the Delta variant in July and August 2021, weekly incidence increased to 4.7 times the lowest rate from June 2020 (0.3) in the week ending August 14, 2021 (1.4). Across all time periods, COVID-19-associated hospitalizations were more common in children aged 0-4 years and adolescents aged 12-17 years than among children aged 5-11 years. From June 20 to July 31, 2021, the hospitalization rate among unvaccinated adolescents (0.8 per 100,000; 95% CI 0.6, 0.9) was significantly higher than among fully vaccinated adolescents (0.1, 95% CI 0, 0.1) (RR=10.1; 95% CI 3.7, 27.9). Finally, although rates increased with the spread of the Delta variant, disease severity was not significantly different before or after June 19, 2021 – approximately 26% were admitted to the intensive care unit (ICU) during both time periods.

Study Strengths

This study included a large number of child and adolescent COVID-19-associated hospitalizations across the United States over a period of time before and after the emergence of the Delta variant in the US.

Limitations

Due to the short amount of time adolescents have been eligible for the COVID-19 vaccine and the relatively low relative COVID-19-associated hospitalizations (compared to adults), the estimates of the relative risk of hospitalizations in vaccinated versus unvaccinated adolescents is fairly imprecise and did not account for potential confounding variables (such as medical comorbidities). Furthermore, it is possible that cases classified as COVID-19-associated hospitalizations may have included cases where an individual was hospitalized and tested positive for SARS-CoV-2, but was not hospitalized for COVID-19. This would bias the rate estimates upwards. It  is also unclear how the child and adolescent populations within the selected counties changed since 2019, which may bias the rate estimates upwards or downwards. Finally, this study cannot comment on the proportion of children and adolescents infected with SARS-CoV-2 who were sick enough to warrant hospitalization for COVID-19.

Value added

This study presents new data that COVID-19-associated hospitalizations increased with the emergence of the Delta variant and that 0-4 year olds and 12-17 year olds have been more likely to have COVID-19-associated hospitalizations than 5-11 year olds in the United States.

Our take —

This ecological study conducted at the U.S. national level found a negative correlation between apparent COVID-19 convalescent plasma (CCP) utilization and the ratio of COVID-19 deaths to hospitalizations (R = -0.52; p = 0.002). However, the study design is highly susceptible to bias from several sources, including unmeasured confounding and measurement error. Although the authors imply that the results advocate for resuming widespread use of CCP in hospitalized patients, many RCTs and systematic reviews have failed to show a mortality benefit for CCP across various populations. Against this background of existing evidence, an observational study such as this does not add much useful information to help guide clinical practice.

Study design

Ecological

Study population and setting

This was an ecological study at the U.S. national level correlating the ratio of COVID-19 deaths to hospitalizations (case fatality rate) with the ratio of the number of COVID-19 convalescent plasma (CCP) units shipped to the number of hospitalizations (utilization rate) from August 3, 2020 to February 22, 2021. The case fatality rate was calculated from publicly available national-level data on COVID-19 admissions and deaths. The number of units of CCP shipped to US hospitals was recorded by Blood Centers of America; its use as a proxy for CCP usage was checked by correlating CCP units dispensed to those administered through the FDA’s Expanded Access Program. Mortality data was shifted by a varying number of weeks to account for a lag between hospitalization and death. The association between CCP utilization and the COVID-19 case fatality rate was assessed with Pearson’s correlation coefficient and linear regression. Confounders (e.g., mean age of hospitalized patients and total number of hospitalized patients) were considered but not included in analyses due to lack of associations with exposure or outcome. The authors then used results from linear regression to estimate deaths under counterfactual scenarios of CCP usage.

Summary of Main Findings

During the study period, there was a strong negative correlation between weekly CCP utilization and the case fatality rate (R = -0.52; p = 0.002). COVID-19 mortality was 22.3% in the lowest quintile of CCP utilization (12-24%) and decreased to 18% in the highest quintile of CCP use (41-52%). Associations between: a) the case fatality rate and the percent of admissions in those over 65 years old; and b) the case fatality rate and the number of hospitalizations were not statistically significant; these variables were not included in analyses. If the regression coefficient for CCP utilization were applied to a counterfactual scenario of CCP utilization (i.e., peak utilization from August-October 2020), the model implies that an estimated 29,018 fewer deaths would have resulted.

Study Strengths

The authors conducted some limited sensitivity analyses (e.g., changing the lag between hospitalization and mortality, truncating the study period).

Limitations

This was an ecological study, and the analysis thus did not link individual mortality with CCP administration. The study was conducted at the national level without considering county, state, or regional-level differences over time in hospitalization, mortality, and CCP utilization. Changes over time in the patient population affected by COVID-19, and in the care received by these patients, were not adequately accounted for by this analysis. Indeed, no potential confounding variables were included in the analysis and the results are based on a crude linear association. Important potential confounding variables (e.g., the underlying mortality risk profile of the patient population, hospital case burden, other treatments administered, the changing prevalence of SARS-CoV-2 variants, type/location of care facility) were unsatisfactorily considered and/or omitted.

Value added

This study does not add useful evidence to the question of whether COVID-19 convalescent plasma is effective in reducing mortality among hospitalized patients.

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.