Community-Level Factors Associated with COVID-19 Cases and Testing Equity in King County, Washington

Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

Access to and utilization of COVID-19 antigen rapid diagnostic tests (Ag-RDTs) among people living with HIV (PLWH): A mixed methods study from Cambodia

Several COVID-19 antigen rapid diagnostic tests have been approved in Cambodia, but no evidence exists about the access to and utilization of these tests. This limits public health interventions to increase testing, especially among vulnerable populations such as people living with HIV (PLWH). We conducted a mixed method study among PLWH in Phnom Penh, Cambodia, between July and August 2022 to understand their current Ag-RDT access and utilization levels, as well as key barriers and drivers. We undertook a cross-sectional survey and focus group discussions among 280 and 10 PLWH, respectively, from five HIV treatment centres using a probability-proportional-to-size and simple random sampling approach. Access was defined as having received a COVID-19 Ag-RDT within the six months and utilization as having administered a COVID-19 Ag-RDT, either to oneself or to others, within the 12 months prior to the study. We calculated means, standard deviations and proportions for continuous and categorical variables, using a linear regression model with random effects to account for clustering. Additionally, we fitted a logistic model with random effects to assess factors associated with Ag-RDT access. For the qualitative data, we used thematic analyses to identify barriers/enablers of Ag-RDT access and utilization. About 35% (n = 101) of PLWH reported having had access to an Ag-RDT test in the past six months. About 11% (n = 32) of the study participants administered the Ag-RDT to themselves, 4% (n = 10) to others and 9% (n = 24) have done both, in the past 12 months. Age and education appeared to be associated with Ag-RDT access in the logistic models. Price and advice from pharmacists were commonly reported to be the main selection criteria for the brand of Ag-RDT chosen. Ag-RDTs are an important diagnostic tool for COVID-19 among PLWH in Cambodia, but familiarity of use and price could hinder better uptake, access and utilization.

Neighborhood Factors Associated with COVID-19 Cases in California

BACKGROUND

There is a need to assess neighborhood-level factors driving COVID-19 disparities across racial and ethnic groups.

OBJECTIVE

To use census tract-level data to investigate neighborhood-level factors contributing to racial and ethnic group-specific COVID-19 case rates in California.

DESIGN

Quasi-Poisson generalized linear models were used to identify neighborhood-level factors associated with COVID-19 cases. In separate sequential models for Hispanic, Black, and Asian, we characterized the associations between neighborhood factors on neighborhood COVID-19 cases. Subanalyses were conducted on neighborhoods with majority Hispanic, Black, and Asian residents to identify factors that might be unique to these neighborhoods. Geographically weighted regression using a quasi-Poisson model was conducted to identify regional differences.

MAIN MEASURES

All COVID-19 cases and tests reported through January 31, 2021, to the California Department of Public Health. Neighborhood-level data from census tracts were obtained from American Community Survey 5-year estimates (2015-2019), United States Census (2010), and United States Department of Housing and Urban Development.

KEY RESULTS

The neighborhood factors associated with COVID-19 case rate were racial and ethnic composition, age, limited English proficiency (LEP), income, household size, and population density. LEP had the largest influence on the positive association between proportion of Hispanic residents and COVID-19 cases (- 2.1% change). This was also true for proportion of Asian residents (- 1.8% change), but not for the proportion of Black residents (- 0.1% change). The influence of LEP was strongest in areas of the Bay Area, Los Angeles, and San Diego.

CONCLUSION

Neighborhood-level contextual drivers of COVID-19 burden differ across racial and ethnic groups.

Early Pandemic Access to COVID-19 Testing in the Somali Community in King County, Washington, USA: a Mixed-Methods Evaluation

BACKGROUND

Racial and ethnic disparities in COVID-19 infection and outcomes have been documented, but few studies have examined disparities in access to testing.

METHODS

We conducted a mixed methods study of access to COVID-19 testing in the Somali immigrant community in King County, Washington, USA, early during the COVID-19 pandemic. In September 2020-February 2021, we conducted quantitative surveys in a convenience sample (n = 528) of individuals who had accessed PCR testing, recruited at King County testing sites near Somali population centers and through social media outreach in the Somali community. We compared self-identified Somali and non-Somali responses using Chi-square and Wilcoxon rank sum tests. We also conducted three Somali-language focus groups (n = 26) by video conference to explore Somali experiences with COVID-19 testing, and in-depth interviews with King County-based policymakers and healthcare workers (n = 13) recruited through the research team's professional network to represent key demographics and roles. Data were analyzed using qualitative rapid analysis to explore the county's COVID-19 testing landscape.

RESULTS

Among 420 survey respondents who had received COVID-19 testing in the prior 90 days, 29% of 140 Somali vs. 11% of 280 non-Somali respondents tested because of symptoms (p = 0.001), with a trend for longer time from symptom onset to testing (a measure of testing access) among Somali respondents (median 3.0 vs. 2.0 days, p = 0.06). Focus groups revealed barriers to testing, including distrust, misinformation, stigma, language, lack of awareness, and transportation. Stakeholders responding from all sectors highlighted the importance of community partnership to improve access.

CONCLUSION

Somali communities experience barriers to COVID-19 testing, as evidenced by the longer time from symptom onset to testing and corroborated by our qualitative findings. These barriers, both structural and community-derived, may be overcome through partnerships between government and community to support community-led, multilingual service delivery and racial representation among medical staff.

Practice- and System-Based Interventions to Reduce COVID-19 Transmission in Primary Care Settings: A Qualitative Study

Using qualitative interviews with 68 family physicians (FPs) in Canada, we describe practice- and system-based approaches that were used to mitigate COVID-19 exposure in primary care settings across Canada to ensure the continuation of primary care delivery. Participants described how they applied infection prevention and control procedures (risk assessment, hand hygiene, control of environment, administrative control, personal protective equipment) and relied on centralized services that directed patients with COVID-19 to settings outside of primary care, such as testing centres. The multi-layered approach mitigated the risk of COVID-19 exposure while also conserving resources, preserving capacity and supporting supply chains.

Comparison of socio-economic determinants of COVID-19 testing and positivity in Canada: A multi-provincial analysis

BACKGROUND

The effects of the COVID-19 pandemic have been more pronounced for socially disadvantaged populations. We sought to determine how access to SARS-CoV-2 testing and the likelihood of testing positive for COVID-19 were associated with demographic factors, socioeconomic status (SES) and social determinants of health (SDH) in three Canadian provinces.

METHODS

An observational population-based cross-sectional study was conducted for the provinces of Ontario, Manitoba and New Brunswick between March 1, 2020 and April 27, 2021, using provincial health administrative data. After excluding residents of long-term care homes, those without current provincial health insurance and those who were tested for COVID-19 out of province, records from provincial healthcare administrative databases were reviewed for 16,900,661 healthcare users. Data was modelled separately for each province in accordance to a prespecified protocol and follow-up consultations among provincial statisticians and collaborators. We employed univariate and multivariate regression models to examine determinants of testing and test results.

RESULTS

After adjustment for other variables, female sex and urban residency were positively associated with testing, while female sex was negatively associated with test positivity. In New Brunswick and Ontario, individuals living in higher income areas were more likely to be tested, whereas in Manitoba higher income was negatively associated with both testing and positivity. High ethnocultural composition was associated with lower testing rates. Both high ethnocultural composition and high situational vulnerability increased the odds of testing positive for SARS-CoV-2.

DISCUSSION

We observed that multiple demographic, income and SDH factors were associated with SARS-CoV-2 testing and test positivity. Barriers to healthcare access identified in this study specifically relate to COVID-19 testing but may reflect broader inequities for certain at-risk groups.

Identifying potential factors associated with PCR testing for COVID-19 among Australian young people: cross-sectional findings from a longitudinal study

BACKGROUND

Testing has played a crucial role in reducing the spread of COVID-19. Though COVID-19 symptoms tend to be less severe in adolescents and young adults, their highly social lifestyles can lead to increased transmission of the virus. In this study, we aimed to provide population-based estimates of polymerase chain reaction testing (PCR) for the COVID-19 pandemic and identify factors associated with PCR testing in Australian youth using the latest survey data from the Longitudinal Study of Australian Children (LSAC).

METHODS

We used the latest wave (9C1) of the LSAC, collected from 16 to 21-year-old Australians via an online survey between October and December 2020. In total, 2291 youths responded to the questions about COVID-19 testing including factors related to the coronavirus restriction period (CRP) in Australia. Both bivariate and multivariate logistic regression analyses were performed to identify variables (sociodemographic factors and factors related to CRP) associated with COVID-19 testing.

RESULTS

During the study period, 26% (n = 587) of Australian youth aged between 16 and 21 years were tested for COVID-19. The strongest predictor of COVID-19 testing was living in major cities (aOR 1.82, 95% CI:1.34-2.45; p < 0.01). Increased age (aOR 1.97, 1.00-3.89; p < 0.05) and having a pre-existing medical condition (aOR 1.27, 1.02-1.59; p < 0.05) were also significantly associated with a higher likelihood of COVID-19 testing.

CONCLUSION

Age, remoteness and having a pre-existing medical illness were associated with PCR COVID-19 testing among Australian youth aged between 16 and 21 years in the first year of the COVID-19 pandemic. More research is warranted to identify factors associated with other COVID-19 testing methods and address the specific barriers that may limit COVID-19 testing in this age group.

Local-Scale phylodynamics reveal differential community impact of SARS-CoV-2 in metropolitan US county

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

Inequities in spatial accessibility to COVID-19 testing in 30 large US cities

Testing for SARS-CoV-2 infection has been a key strategy to mitigate and control the COVID-19 pandemic. Wide spatial and racial/ethnic disparities in COVID-19 outcomes have emerged in US cities. Previous research has highlighted the role of unequal access to testing as a potential driver of these disparities. We described inequities in spatial accessibility to COVID-19 testing locations in 30 large US cities. We used location data from Castlight Health Inc corresponding to October 2021. We created an accessibility metric at the level of the census block group (CBG) based on the number of sites per population in a 15-minute walkshed around the centroid of each CBG. We also calculated spatial accessibility using only testing sites without restrictions, i.e., no requirement for an appointment or a physician order prior to testing. We measured the association between the social vulnerability index (SVI) and spatial accessibility using a multilevel negative binomial model with random city intercepts and random SVI slopes. Among the 27,195 CBG analyzed, 53% had at least one testing site within a 15-minute walkshed, and 36% had at least one site without restrictions. On average, a 1-decile increase in the SVI was associated with a 3% (95% Confidence Interval: 2% - 4%) lower accessibility. Spatial inequities were similar across various components of the SVI and for sites with no restrictions. Despite this general pattern, several cities had inverted inequity, i.e., better accessibility in more vulnerable areas, which indicates that some cities may be on the right track when it comes to promoting equity in COVID-19 testing. Testing is a key component of the strategy to mitigate transmission of SARS-CoV-2 and efforts should be made to improve accessibility to testing, particularly as new and more contagious variants become dominant.

BNT162b2 mRNA Vaccine Effectiveness Given Confirmed Exposure: Analysis of Household Members of Coronavirus Disease 2019 Patients

BACKGROUND

Although BNT162b2 vaccine-efficacy analyses have been published, the effectiveness of the vaccine in preventing coronavirus disease 2019 given confirmed exposure has not been previously demonstrated, even though it has policy implications, such as the need for self-quarantine when exposure has occurred.

METHODS

In a retrospective cohort study, we used data collected between 20 December 2020 and 17 March 2021 from the second largest healthcare provider in Israel to analyze the probability of an additional household infection occurring within 10 days after an index infection. In model 1, vaccine effectiveness was described for Fully Vaccinated individuals (7 or more days from second dose) vs either Unvaccinated individuals or those Recently Vaccinated Once (0-7 days from the first dose, presumably still unprotected). Secondary analyses included correction for differing testing rates. In model 2, we conducted a separate analysis of households comprised of only adults with the same vaccination status.

RESULTS

A total of 173 569 households were included, of which 6351 had an index infection (mean [standard deviation] age, 58.9 [13.5] years); 50% were women. Adjusted vaccine effectiveness of Fully Vaccinated compared with Unvaccinated participants was 80.3% (95% confidence interval [CI], 73.5-85.4) and 82.0% (95% CI, 75.6-86.8) compared with those Recently Vaccinated Once.

CONCLUSIONS

The BNT162b2 vaccine is effective in high-risk real-life exposure scenarios, but the protection afforded in these settings is lower than that previously described. Individuals with a confirmed significant exposure to severe acute respiratory syndrome are still at risk of being infected even if fully vaccinated.

Factors Associated with Delayed Diagnosis among Patients with COVID-19 in Okinawa, Japan

The delayed presentation and diagnosis of COVID-19 can contribute to spread of the disease to others but can also cause severe conditions. This study examined factors associated with delayed diagnosis among patients with COVID-19 in Okinawa, Japan. We used the data from 7125 reported cases of people living in Okinawa prefecture with symptom onset between September 2020 and March 2021. The outcome variable was the number of days from symptom onset to diagnosis. The predictor variables included age, sex, occupation, residential area, presumed infection route, and the day of the week. Cox regression analysis was used to compare the outcome between categories for each predictor variable. The median number of days from onset to diagnosis was 3 days, with an interquartile range of 1 to 5 days. Significantly more time from onset to diagnosis was observed in patients in their 60s vs. those in their 20s (hazard ratio: 0.88; 95% confidence interval: 0.81-0.96); hospitality workers were compared to office workers (0.90; 0.83-0.97), patients with unknown infection routes to those with known infection routes (0.77; 0.70-0.84), and those with symptom onset on Sundays/national holidays to those with symptom onset on weekdays (0.90; 0.85-0.96).

Feasibility and lessons learned on remote trial implementation from TestBoston, a fully remote, longitudinal, large-scale COVID-19 surveillance study

Longitudinal clinical studies traditionally require in-person study visits which are well documented to pose barriers to participation and contribute challenges to enrolling representative samples. Remote trial models may reduce barriers to research engagement, improve retention, and reach a more representative cohort. As remote trials become more common following the COVID-19 pandemic, a critical evaluation of this approach is imperative to optimize this paradigm shift in research. The TestBoston study was launched to understand prevalence and risk factors for COVID-19 infection in the greater Boston area through a fully remote home-testing model. Participants (adults, within 45 miles of Boston, MA) were recruited remotely from patient registries at Brigham and Women’s Hospital and the general public. Participants were provided with monthly and “on-demand” at-home SARS-CoV-2 RT-PCR and antibody testing using nasal swab and dried blood spot self-collection kits and electronic surveys to assess symptoms and risk factors for COVID-19 via an online dashboard. Between October 2020 and January 2021, we enrolled 10,289 participants reflective of Massachusetts census data. Mean age was 47 years (range 18–93), 5855 (56.9%) were assigned female sex at birth, 7181(69.8%) reported being White non-Hispanic, 952 (9.3%) Hispanic/Latinx, 925 (9.0%) Black, 889 (8.6%) Asian, and 342 (3.3%) other and/or more than one race. Lower initial enrollment among Black and Hispanic/Latinx individuals required an adaptive approach to recruitment, leveraging connections to the medical system, coupled with community partnerships to ensure a representative cohort. Longitudinal retention was higher among participants who were White non-Hispanic, older, working remotely, and with lower socioeconomic vulnerability. Implementation highlighted key differences in remote trial models as participants independently navigate study milestones, requiring a dedicated participant support team and robust technology platforms, to reduce barriers to enrollment, promote retention, and ensure scientific rigor and data quality. Remote clinical trial models offer tremendous potential to engage representative cohorts, scale biomedical research, and promote accessibility by reducing barriers common in traditional trial design. Barriers and burdens within remote trials may be experienced disproportionately across demographic groups. To maximize engagement and retention, researchers should prioritize intensive participant support, investment in technologic infrastructure and an adaptive approach to maximize engagement and retention.

COVID-19 Health Disparities and Adverse Social Determinants of Health

Racial and ethnic disparities in healthcare and health outcomes are longstanding. The real-time emergence of COVID-19 disparities has heightened the public and scientific discourse about structural inequities contributing to the greater risk of morbidity and mortality among racial and ethnic minority populations and other underserved groups. A key aspect of assuring health equity is addressing social determinants that lead to adverse health outcomes among minoritized groups. This article presents an exploratory social determinants of health (SDOH) conceptual framework for understanding racial and ethnic COVID-19 disparities, including factors related to health and healthcare, socioeconomics, and environmental determinants. The model also illustrates the backdrop of structural racism and discrimination, which directly affect health and COVID-19 exposure risk, and thus transmission, infection, and death. We also describe a special SDOH collection in the PhenX Toolkit (consensus measures for Phenotypes and eXposures), which includes established measures to promote standardization of assessment and the use of common data elements in research contexts. The use of common constructs, measures, and data elements are important for data integration, understanding the causes of health disparities, and evaluating interventions to reduce them. Substandard SDOH are among the primary drivers of health disparities-and scientific approaches to address these key concerns require identification and leveled alignment with the root causes. The overarching goal of this discussion is to broaden the consideration of mechanisms by which populations with health disparities face additional SARS-CoV-2 exposure risks, and to encourage research to develop interventions to reduce SDOH-associated disparities in COVID-19 and other conditions and behaviors.

Extracting Patient-level Social Determinants of Health into the OMOP Common Data Model

Deficiencies in data sharing capabilities limit Social Determinants of Health (SDoH) analysis as part of COVID-19 research. The National COVID Cohort Collaborative (N3C) is an example of an Electronic Health Record (EHR) database of patients tested for COVID-19 that could benefit from a SDoH elements framework that captures various screening instruments in EHR data warehouse systems. This paper uses the University of Washington Enterprise Data Warehouse (a data contributor to N3C) to demonstrate how SDoH can be represented and managed to be made available within an OMOP common data model. We found that these data varied by type of social determinants data and where it was collected, in the time period that it was collected, and in how it was represented.

COVID-19 Home Monitoring: An Institutional Approach to Bridging Care During a Pandemic

Introduction: Coronavirus disease 2019 (COVID-19) has widened patient care gaps and created gaps in medical student clinical training. The care gaps are often most obvious in primary care medicine clinics (PCMCs) where residents and medical students care for a vulnerable population. Materials and Methods: We designed an outpatient telehealth program to support and monitor PCMC patients who had been diagnosed or were suspected to have COVID-19 and were confined to their homes due to public health mandated isolation. To support the program, we recruited medical student volunteers. We recruited patients from our institution's primary care clinic who were recently diagnosed with COVID-19 and were currently not hospitalized. Feasibility of the home monitoring program (HMP) was assessed and mortality data for all patients were collected. Results: Over 800 monitoring phone calls were placed during the 8-month study period to 296 patients, with an average of 2.79 calls per patient. A total of 30 medical students participated. A total of four patients died during the study period. Conclusions: Our institution was able to rapidly design and implement a COVID-19 HMP integrated with our primary care clinic to ensure continued access to care during a pandemic.

Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types

Background

Despite increased testing efforts and the deployment of vaccines, COVID-19 cases and death toll continue to rise at record rates. Health systems routinely collect clinical and non-clinical information in electronic health records (EHR), yet little is known about how the minimal or intermediate spectra of EHR data can be leveraged to characterize patient SARS-CoV-2 pretest probability in support of interventional strategies.

Methods and findings

We modeled patient pretest probability for SARS-CoV-2 test positivity and determined which features were contributing to the prediction and relative to patients triaged in inpatient, outpatient, and telehealth/drive-up visit-types. Data from the University of Washington (UW) Medicine Health System, which excluded UW Medicine care providers, included patients predominately residing in the Seattle Puget Sound area, were used to develop a gradient-boosting decision tree (GBDT) model. Patients were included if they had at least one visit prior to initial SARS-CoV-2 RT-PCR testing between January 01, 2020 through August 7, 2020. Model performance assessments used area-under-the-receiver-operating-characteristic (AUROC) and area-under-the-precision-recall (AUPR) curves. Feature performance assessments used SHapley Additive exPlanations (SHAP) values. The generalized pretest probability model using all available features achieved high overall discriminative performance (AUROC, 0.82). Performance among inpatients (AUROC, 0.86) was higher than telehealth/drive-up testing (AUROC, 0.81) or outpatient testing (AUROC, 0.76). The two-week test positivity rate in patient ZIP code was the most informative feature towards test positivity across visit-types. Geographic and sociodemographic factors were more important predictors of SARS-CoV-2 positivity than individual clinical characteristics.

Conclusions

Recent geographic and sociodemographic factors, routinely collected in EHR though not routinely considered in clinical care, are the strongest predictors of initial SARS-CoV-2 test result. These findings were consistent across visit types, informing our understanding of individual SARS-CoV-2 risk factors with implications for deployment of testing, outreach, and population-level prevention efforts.

Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina

Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal-geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection (p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models fit better than global models, suggesting nonstationary correlations between a region and its neighbors. Characterized by temporal-geospatial patterns, disparities in COVID-19 infection rate and their PIDRs are different from the mortality and morbidity of COVID-19 patients. Our findings suggest the importance of prioritizing different populations and developing tailored interventions at different times of the pandemic.

Cumulative Impacts and COVID-19: Implications for Low-Income, Minoritized, and Health-Compromised Communities in King County, WA

Few studies have assessed how the intersection of social determinants of health and environmental hazards contributes to racial disparities in COVID-19. The aim of our study was to compare COVID-19 disparities in testing and positivity to cumulative environmental health impacts, and to assess how unique social and environmental determinants of health relate to COVID-19 positivity in Seattle, King County, WA, at the census tract level. Publicly available data (n = 397 census tracts) were obtained from Public Health–Seattle & King County, 2018 ACS 5-year estimates, and the Washington Tracking Network. COVID-19 testing and positive case rates as of July 12, 2020, were mapped and compared to Washington State Environmental Health Disparities (EHD) Map cumulative impact rankings. We calculated odds ratios from a series of univariable and multivariable logistic regression analyses using cumulative impact rankings, and community-level socioeconomic, health, and environmental factors as predictors and having ≥ 10% or < 10% census tract positivity as the binary outcome variable. We found a remarkable overlap between Washington EHD cumulative impact rankings and COVID-19 positivity in King County. Census tracts with ≥ 10 % COVID-19 positivity had significantly lower COVID-19 testing rates and higher proportions of people of color and faced a combination of low socioeconomic status–related outcomes, poor community health outcomes, and significantly higher concentrations of fine particulate matter (PM_2.5). King County communities experiencing high rates of COVID-19 face a disproportionate cumulative burden of environmental and social inequities. Cumulative environmental health impacts should therefore systematically be considered when assessing for risk of exposure to and health complications resulting from COVID-19.

Impact of both socioeconomic level and occupation on antibody prevalence to SARS-CoV-2 in an Egyptian cohort: The first episode

We studied the impact of socioeconomic level on the anti-SARS-CoV-2-antibodies prevalence in an Egyptian cohort. The low socioeconomic standard group (LSS) included 51 humans, 30 females (F) and 21 males (M). The high socioeconomic standard group (HSS) included 55 subjects, 24 F and 31 M. Of the 30 LSSF, 6 were immunoglobulin M (IgM), 21 immunoglobulin G (IgG), and 6 double positive. Of the 21 LSSM, 5 were IgM, 12 IgG, and 5 double positive. Of the 24 HSSF, 6 were IgM, 11 IgG, and 5 double positive. Of the 31 HSSM, 6 were IgM, 14 IgG, and 4 double positive. Of the 51 LSS humans, 26 were symptomatic (S) and 25 asymptomatic (AS). Of the 26 S, 20 were IgG and 8 IgM/IgG double positive. Of the 25 AS, 13 were IgG and 3 IgM/IgG double positive. Of the 55 HSS humans, 38 were S and 17 AS. Of the 38S, 24 were IgG and 11 IgM positive of whom, 9 were double positive. Of the 17 AS, one was IgG and one IgM positive. The IgM prevalence was higher among the HSS humans. The IgG prevalence was significantly higher among the LSS humans. In the two different socioeconomic standards, the prevalence of either IgM or IgG was higher among F. An inverse correlation was observed between age and the anti-SARS-CoV-2-antibodies prevalence except for LSSF-IgG and LSSM-IgM. In conclusion, socioeconomic standard, gender, and age impact humoral responses to SARS-CoV-2 with a clear heterogeneity in individualized responses to the infection in terms of symptoms.

Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions

Analyzing the myriad ways in which structural racism systemically generates health inequities requires engaging with the profound challenges of conceptualizing, operationalizing, and analyzing the very data deployed-i. e., racialized categories-to document racialized health inequities. This essay, written in the aftermath of the January 6, 2021 vigilante anti-democratic white supremacist assault on the US Capitol, calls attention to the two-edged sword of data at play, reflecting long histories of support for and opposition to white supremacy and scientific racism. As illustrated by both past and present examples, including COVID-19, at issue are both the non-use (Edge #1) and problematic use (Edge #2) of data on racialized groups. Recognizing that structural problems require structural solutions, in this essay I propose a new two-part institutional mandate regarding the reporting and analysis of publicly-funded work involving racialized groups and health data and documentation as to why the proposed mandates are feasible. Proposal/part 1 is to implement enforceable requirements that all US health data sets and research projects supported by government funds must explicitly explain and justify their conceptualization of racialized groups and the metrics used to categorize them. Proposal/part 2 is that any individual-level health data by membership in racialized groups must also be analyzed in relation to relevant data about racialized societal inequities. A new opportunity arises as US government agencies re-engage with their work, out of the shadow of white grievance politics cast by the Trump Administration, to move forward with this structural proposal to aid the work for health equity.

The effect of in-person primary and secondary school instruction on county-level SARS-CoV-2 spread in Indiana

Objective

To determine the county-level effect of in-person primary and secondary school reopening on daily cases of SARS-CoV-2 in Indiana.

Methods

This is a county-level population-based study using a panel data regression analysis of the proportion of in-person learning to evaluate an association with community-wide daily new SARS-CoV-2 cases. The study period was July 12-October 6, 2020. We included 73 out of 92 (79.3%) Indiana counties in the analysis, accounting for 85.7% of school corporations and 90.6% of student enrollment statewide. The primary exposure was the proportion of students returning to in-person instruction. The primary outcome was the daily new SARS-CoV-2 cases per 100,000 residents at the county level.

Results

There is a statistically significant relationship between the proportion of students attending K-12 schools in-person and the county level daily cases of SARS-CoV-2 28 days later. For all ages, the coefficient of interest (β) is estimated at 3.36 (95% CI: 1.91—4.81; p < 0.001). This coefficient represents the effect of a change the proportion of students attending in-person on new daily cases 28 days later. For example, a 10 percentage point increase in K-12 students attending school in-person is associated with a daily increase in SARS-CoV-2 cases in the county equal to 0.336 cases/100,000 residents of all ages.

Conclusion

In-person primary and secondary school is associated with a statistically significant but proportionally small increase in the spread of SARS-CoV-2 cases.