COVID-19 testing in schools: Perspectives of school administrators, teachers, parents, and students in Southern California

BackgroundSchool-based COVID-19 testing is a potential strategy to facilitate the safe reopening of schools that have been closed due to the pandemic. This qualitative study assessed attitudes toward this strategy among four groups of stakeholders: school administrators, teachers, parents, and high school students. MethodsFocus groups and interviews were conducted in Los Angeles from December 2020 to January 2021 when schools were closed due to the high level of COVID transmission in the community. ResultsFindings indicated similarities and differences in attitudes toward in-school COVID-19 testing. All groups agreed that frequent in-school COVID-19 testing could increase the actual safety and perceived safety of the school environment. School administrators and teachers expressed pessimism about the financial cost and logistics of implementing a testing program. Parents supported frequent testing but expressed concerns about physical discomfort and stigma for students who test positive. Teachers and parents noted that testing would prevent parents from sending sick children to school. Students were in favor of testing because it would allow them to return to in-person school after a difficult year of online learning. ConclusionIn-school COVID-19 testing could be a useful component of school reopening plans and will be accepted by stakeholders if logistical and financial barriers can be surmounted and stigma from positive results can be minimized.

SARS-CoV-2 Seroprevalence Among Firefighters in Los Angeles, California

ObjectiveWe estimate the seroprevalence of SARS-CoV-2 antibodies among firefighters in the Los Angeles, California fire department in October 2020 and compare demographic and contextual factors for seropositivity. MethodsWe conducted a serologic survey of firefighters in Los Angeles, California, USA, in October 2020. Individuals were classified as seropositive for SARS-CoV-2 if they tested positive for immunoglobulin G, immunoglobulin M, or both. We compared demographic and contextual factors for seropositivity. ResultsOf 713 participants, 8.9% tested positive for SARS-CoV-2 antibodies. Seropositivity was not associated with gender, age, or race/ethnicity. Furthermore, firefighters who worked in zip codes with lower income or higher share of minority population did not have higher rates of SARS-CoV-2 infection. Seropositivity was highest among firefighters who reported working in the vicinity of Los Angeles International Airport, which had a known outbreak in July 2020. ConclusionsSeroprevalence among firefighters was no higher than seroprevalence in the general population, suggesting that workplace safety protocols, such as access to PPE and testing, can mitigate increased risk of infection at work. Workplace safety protocols for firefighters also eliminated differences in disease burden by geography and race/ethnicity observed in the general population.

Manufacturer Signal-to-Cutoff Threshold Underestimates Cumulative Incidence of SARS-CoV-2 Infection: Evidence from the Los Angeles Firefighters Study

While SARS-CoV-2 serologic testing is used to measure cumulative incidence of COVID-19, appropriate signal-to-cut off (S/Co) thresholds remain unclear. We demonstrate S/Co thresholds based on known negative samples significantly increases seropositivity and more accurately estimates cumulative incidence of disease compared to manufacturer-based thresholds.

Estimation of COVID-19 risk-stratified epidemiological parameters and policy implications for Los Angeles County through an integrated risk and stochastic epidemiological model

SummaryO_ST_ABSBackgroundC_ST_ABSHealth disparities have emerged with the COVID-19 epidemic because the risk of exposure to infection and the prevalence of risk factors for severe outcomes given infection vary within and between populations. However, estimated epidemic quantities such as rates of severe illness and death, the case fatality rate (CFR), and infection fatality rate (IFR), are often expressed in terms of aggregated population-level estimates due to the lack of epidemiological data at the refined subpopulation level. For public health policy makers to better address the pandemic, stratified estimates are necessary to investigate the potential outcomes of policy scenarios targeting specific subpopulations. MethodsWe develop a framework for using available data on the prevalence of COVID-19 risk factors (age, comorbidities, BMI, smoking status) in subpopulations, and epidemic dynamics at the population level and stratified by age, to estimate subpopulation-stratified probabilities of severe illness and the CFR (as deaths over observed infections) and IFR (as deaths over estimated total infections) across risk profiles representing all combinations of risk factors including age, comorbidities, obesity class, and smoking status. A dynamic epidemic model is integrated with a relative risk model to produce time-varying subpopulation-stratified estimates. The integrated model is used to analyze dynamic outcomes and parameters by population and subpopulation, and to simulate alternate policy scenarios that protect specific at-risk subpopulations or modify the population-wide transmission rate. The model is calibrated to data from the Los Angeles County population during the period March 1 - October 15 2020. FindingsWe estimate a rate of 0.23 (95% CI: 0.13,0.33) of infections observed before April 15, which increased over the epidemic course to 0.41 (0.11,0.69). Overall population-average IFR(t) estimates for LAC peaked at 0.77% (0.38%,1.15%) on May 15 and decreased to 0.55% (0.24%,0.90%) by October 15. The population-average IFR(t) stratified by age group varied extensively across subprofiles representing each combination of the additional risk factors considered (comorbidities, BMI, smoking). We found median IFRs ranging from 0.009%-0.04% in the youngest age group (0-19), from 0.1%-1.8% for those aged 20-44, 0.36%-4.3% for those aged 45-64, and 1.02%-5.42% for those aged 65+. In the group aged 65+ for which the rate of unobserved infections is likely much lower, we find median CFRs in the range 4.4%-23.45%. The initial societal lockdown period avoided overwhelming healthcare capacity and greatly reduced the observed death count. In comparative scenario analysis, alternative policies in which the population-wide transmission rate is reduced to a moderate and sustainable level of non-pharmaceutical interventions (NPIs) would not have been sufficient to avoid overwhelming healthcare capacity, and additionally would have exceeded the observed death count. Combining the moderate NPI policy with stringent protection of the at-risk subpopulation of individuals 65+ would have resulted in a death count similar to observed levels, but hospital counts would have approached capacity limits. InterpretationThe risk of severe illness and death of COVID-19 varies tremendously across subpopulations and over time, suggesting that it is inappropriate to summarize epidemiological parameters for the entire population and epidemic time period. This includes variation not only across age groups, but also within age categories combined with other risk factors analyzed in this study (comorbidities, obesity status, smoking). In the policy analysis accounting for differences in IFR across risk groups in comparing the control of infections and protection of higher risk groups, we find that the strict initial lockdown period in LAC was effective because it both reduced overall transmission and protected individuals at greater risk, resulting in preventing both healthcare overload and deaths. While similar numbers of deaths as observed in LAC could have been achieved with a more moderate NPI policy combined with greater protection of individuals 65+, this would have come at the expense of overwhelming the healthcare system. In anticipation of a continued rise in cases in LAC this winter, policy makers need to consider the trade offs of various policy options on the numbers of the overall population that may become infected, severely ill, and that die when considering policies targeted at subpopulations at greatest risk of transmitting infection and at greatest risk for developing severe outcomes.

COVID-19 Antibody Seroprevalence in Santa Clara County, California

BackgroundAddressing COVID-19 is a pressing health and social concern. To date, many epidemic projections and policies addressing COVID-19 have been designed without seroprevalence data to inform epidemic parameters. We measured the seroprevalence of antibodies to SARS-CoV-2 in a community sample drawn from Santa Clara County. MethodsOn April 3-4, 2020, we tested county residents for antibodies to SARS-CoV-2 using a lateral flow immunoassay. Participants were recruited using Facebook ads targeting a sample of individuals living within the county by demographic and geographic characteristics. We estimate weights to adjust our sample to match the zip code, sex, and race/ethnicity distribution within the county. We report both the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We also adjust for test performance characteristics by combining data from 16 independent samples obtained from manufacturers data, regulatory submissions, and independent evaluations: 13 samples for specificity (3,324 specimens) and 3 samples for sensitivity (157 specimens). ResultsThe raw prevalence of antibodies to SARS-CoV-2 in our sample was 1.5% (exact binomial 95CI 1.1-2.0%). Test performance specificity in our data was 99.5% (95CI 99.2-99.7%) and sensitivity was 82.8% (95CI 76.0-88.4%). The unweighted prevalence adjusted for test performance characteristics was 1.2% (95CI 0.7-1.8%). After weighting for population demographics of Santa Clara County, the prevalence was 2.8% (95CI 1.3-4.7%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 54,000 (95CI 25,000 to 91,000 using weighted prevalence; 23,000 with 95CI 14,000-35,000 using unweighted prevalence) people were infected in Santa Clara County by early April, many more than the approximately 1,000 confirmed cases at the time of the survey. ConclusionsThe estimated population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection may be much more widespread than indicated by the number of confirmed cases. More studies are needed to improve precision of prevalence estimates. Locally-derived population prevalence estimates should be used to calibrate epidemic and mortality projections.