SARS-CoV-2 cumulative incidence, United States, August-December 2020

Use of dried blood spots in the detection of coronavirus disease 2019 (COVID-19): A systematic review

INTRODUCTION

COVID-19 disease continues to be a global health concern. The current protocol for detecting SARS-CoV-2 requires healthcare professionals to draw blood from patients. Recent studies showed that dried blood spot (DBS) is a valuable sampling procedure that can collect a low blood volume without the need for the presence of medical practitioners. This study synthesized the available literature on using DBS as a blood collection tool to diagnose COVID-19 disease.

MATERIALS AND METHODS

A comprehensive search utilizing OVID, CINAHL, and Scopus databases was done from inception to March 2023. Five reviewers collected, extracted and organized the study data.

RESULTS

This systematic review included 57 articles. DBS was commonly prepared by finger pricking. Most studies showed more favorable results and longer sample stability (more than 1080 days) with lower storage temperature conditions for the DBS. DBS samples were mostly used for serological assays for COVID-19 disease detection. ELISA was the most used detection method (43.66 %). Diagnostic performance of laboratory tests for COVID-19 using DBS sample showed high sensitivity of up to 100 % for immunoassay tests and 100 % specificity in agglutination, PCR, and DELFIA assays.

CONCLUSION

DBS sampling coupled with serological testing can be an alternative method for collecting blood and detecting COVID-19 disease. These tests using DBS samples showed excellent diagnostic performance across various geographic locations and demographics.

Cost-effectiveness of mask mandates on subways to prevent SARS-CoV-2 transmission in the United States

BACKGROUND

Community-based mask wearing has been shown to reduce the transmission of SARS-CoV-2. However, few studies have conducted an economic evaluation of mask mandates, specifically in public transportation settings. This study evaluated the cost-effectiveness of implementing mask mandates for subway passengers in the United States by evaluating its potential to reduce COVID-19 transmission during subway travel.

MATERIALS AND METHODS

We assessed the health impacts and costs of subway mask mandates compared to mask recommendations based on the number of infections that would occur during subway travel in the U.S. Using a combined box and Wells-Riley infection model, we estimated monthly infections, hospitalizations, and deaths averted under a mask mandate scenario as compared to a mask recommendation scenario. The analysis included costs of implementing mask mandates and COVID-19 treatment from a limited societal perspective. The cost-effectiveness (net cost per averted death) of mandates was estimated for three different periods based on dominant SARS-CoV-2 variants: Alpha, Beta, and Gamma (November 2020 to February 2021); Delta (July to October 2021); and early Omicron (January to March 2022).

RESULTS

Compared with mask recommendations only, mask mandates were cost-effective across all periods, with costs per averted death less than a threshold of $11.4 million (ranging from cost-saving to $3 million per averted death). Additionally, mask mandates were more cost-effective during the early Omicron period than the other two periods and were cost saving in January 2022. Our findings showed that mandates remained cost-effective when accounting for uncertainties in input parameters (e.g., even if mandates only resulted in small increases in mask usage by subway ridership).

CONCLUSIONS

The findings highlight the economic value of mask mandates on subways, particularly during high virus transmissibility periods, during the COVID-19 pandemic. This study may inform stakeholders on mask mandate decisions during future outbreaks of novel viral respiratory diseases.

Correcting for Antibody Waning in Cumulative Incidence Estimation From Sequential Serosurveys

Serosurveys are a widely used tool to estimate the cumulative incidence-the fraction of a population that has been infected by a given pathogen. These surveys rely on serological assays that measure the level of pathogen-specific antibodies. Because antibody levels are waning, the fraction of previously infected individuals that have seroreverted increases with time past infection. To avoid underestimating the true cumulative incidence, it is therefore essential to correct for waning antibody levels. We present an empirically supported approach for seroreversion correction in cumulative incidence estimation when sequential serosurveys are conducted in the context of a newly emerging infectious disease. The correction is based on the observed dynamics of antibody titers in seropositive cases and validated using several in silico test scenarios. Furthermore, through this approach we revise a previous cumulative incidence estimate relying on the assumption of an exponentially declining probability of seroreversion over time, of severe acute respiratory syndrome coronavirus 2, of 76% in Manaus, Brazil, by October 2020 to 47.6% (95% confidence region: 43.5-53.5). This estimate has implications, for example, for the proximity to herd immunity in Manaus in late 2020.

Prevalence of SARS-CoV-2 Infection among Children and Adults in 15 US Communities, 2021

During January-August 2021, the Community Prevalence of SARS-CoV-2 Study used time/location sampling to recruit a cross-sectional, population-based cohort to estimate SARS-CoV-2 seroprevalence and nasal swab sample PCR positivity across 15 US communities. Survey-weighted estimates of SARS-CoV-2 infection and vaccine willingness among participants at each site were compared within demographic groups by using linear regression models with inverse variance weighting. Among 22,284 persons >2 months of age and older, median prevalence of infection (prior, active, or both) was 12.9% across sites and similar across age groups. Within each site, average prevalence of infection was 3 percentage points higher for Black than White persons and average vaccine willingness was 10 percentage points lower for Black than White persons and 7 percentage points lower for Black persons than for persons in other racial groups. The higher prevalence of SARS-CoV-2 infection among groups with lower vaccine willingness highlights the disparate effect of COVID-19 and its complications.

A Design and Analytical Strategy for Monitoring Disease Positivity and Biomarker Levels in Accessible Closed Populations

In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g., in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g., means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone.

Seroincidence of SARS-CoV-2 infection prior to and during the rollout of vaccines in a community-based prospective cohort of U.S. adults

This study used repeat serologic testing to estimate infection rates and risk factors in two overlapping cohorts of SARS-CoV-2 N protein seronegative U.S. adults. One mostly unvaccinated sub-cohort was tracked from April 2020 to March 2021 (pre-vaccine/wild-type era, n = 3421), and the other, mostly vaccinated cohort, from March 2021 to June 2022 (vaccine/variant era, n = 2735). Vaccine uptake was 0.53% and 91.3% in the pre-vaccine and vaccine/variant cohorts, respectively. Corresponding seroconversion rates were 9.6 and 25.7 per 100 person-years. In both cohorts, sociodemographic and epidemiologic risk factors for infection were similar, though new risk factors emerged in the vaccine/variant era, such as having a child in the household. Despite higher incidence rates in the vaccine/variant cohort, vaccine boosters, masking, and social distancing were associated with substantially reduced infection risk, even through major variant surges.

Seroincidence of SARS-CoV-2 infection prior to and during the rollout of vaccines in a community-based prospective cohort of U.S. adults

Background

Infectious disease surveillance systems, which largely rely on diagnosed cases, underestimate the true incidence of SARS-CoV-2 infection, due to under-ascertainment and underreporting. We used repeat serologic testing to measure N-protein seroconversion in a well-characterized cohort of U.S. adults with no serologic evidence of SARS-CoV-2 infection to estimate the incidence of SARS-CoV-2 infection and characterize risk factors, with comparisons before and after the start of the SARS-CoV-2 vaccine and variant eras.

Methods

We assessed the incidence rate of infection and risk factors in two sub-groups (cohorts) that were SARS-CoV-2 N-protein seronegative at the start of each follow-up period: 1) the pre-vaccine/wild-type era cohort (n=3,421), followed from April to November 2020; and 2) the vaccine/variant era cohort (n=2,735), followed from November 2020 to June 2022. Both cohorts underwent repeat serologic testing with an assay for antibodies to the SARS-CoV-2 N protein (Bio-Rad Platelia SARS-CoV-2 total Ab). We estimated crude incidence and sociodemographic/epidemiologic risk factors in both cohorts. We used multivariate Poisson models to compare the risk of SARS-CoV-2 infection in the pre-vaccine/wild-type era cohort (referent group) to that in the vaccine/variant era cohort, within strata of vaccination status and epidemiologic risk factors (essential worker status, child in the household, case in the household, social distancing).

Findings

In the pre-vaccine/wild-type era cohort, only 18 of the 3,421 participants (0.53%) had ≥1 vaccine dose by the end of follow-up, compared with 2,497/2,735 (91.3%) in the vaccine/variant era cohort. We observed 323 and 815 seroconversions in the pre-vaccine/wild-type era and the vaccine/variant era and cohorts, respectively, with corresponding incidence rates of 9.6 (95% CI: 8.3-11.5) and 25.7 (95% CI: 24.2-27.3) per 100 person-years. Associations of sociodemographic and epidemiologic risk factors with SARS-CoV-2 incidence were largely similar in the pre-vaccine/wild-type and vaccine/variant era cohorts. However, some new epidemiologic risk factors emerged in the vaccine/variant era cohort, including having a child in the household, and never wearing a mask while using public transit. Adjusted incidence rate ratios (aIRR), with the entire pre-vaccine/wild-type era cohort as the referent group, showed markedly higher incidence in the vaccine/variant era cohort, but with more vaccine doses associated with lower incidence: aIRRun/undervaccinated=5.3 (95% CI: 4.2-6.7); aIRRprimary series only=5.1 (95% CI: 4.2-7.3); aIRRboosted once=2.5 (95% CI: 2.1-3.0), and aIRRboosted twice=1.65 (95% CI: 1.3-2.1). These associations were essentially unchanged in risk factor-stratified models.

Interpretation

In SARS-CoV-2 N protein seronegative individuals, large increases in incidence and newly emerging epidemiologic risk factors in the vaccine/variant era likely resulted from multiple co-occurring factors, including policy changes, behavior changes, surges in transmission, and changes in SARS-CoV-2 variant properties. While SARS-CoV-2 incidence increased markedly in most groups in the vaccine/variant era, being up to date on vaccines and the use of non-pharmaceutical interventions (NPIs), such as masking and social distancing, remained reliable strategies to mitigate the risk of SARS-CoV-2 infection, even through major surges due to immune evasive variants. Repeat serologic testing in cohort studies is a useful and complementary strategy to characterize SARS-CoV-2 incidence and risk factors.

Stratified Simple Random Sampling Versus Volunteer Community-Wide Sampling for Estimates of COVID-19 Prevalence

Objectives. To evaluate community-wide prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using stratified simple random sampling. Methods. We obtained data for the prevalence of SARS-CoV-2 in Jefferson County, Kentucky, from adult random (n = 7296) and volunteer (n = 7919) sampling over 8 waves from June 2020 through August 2021. We compared results with administratively reported rates of COVID-19. Results. Randomized and volunteer samples produced equivalent prevalence estimates (P < .001), which exceeded the administratively reported rates of prevalence. Differences between them decreased as time passed, likely because of seroprevalence temporal detection limitations. Conclusions. Structured targeted sampling for seropositivity against SARS-CoV-2, randomized or voluntary, provided better estimates of prevalence than administrative estimates based on incident disease. A low response rate to stratified simple random sampling may produce quantified disease prevalence estimates similar to a volunteer sample. Public Health Implications. Randomized targeted and invited sampling approaches provided better estimates of disease prevalence than administratively reported data. Cost and time permitting, targeted sampling is a superior modality for estimating community-wide prevalence of infectious disease, especially among Black individuals and those living in disadvantaged neighborhoods. (Am J Public Health. 2023;113(7):768-777. https://doi.org/10.2105/AJPH.2023.307303).

Dynamics of SARS-CoV-2 seroassay sensitivity: a systematic review and modelling study

BackgroundSerological surveys have been the gold standard to estimate numbers of SARS-CoV-2 infections, the dynamics of the epidemic, and disease severity. Serological assays have decaying sensitivity with time that can bias their results, but there is a lack of guidelines to account for this phenomenon for SARS-CoV-2.AimOur goal was to assess the sensitivity decay of seroassays for detecting SARS-CoV-2 infections, the dependence of this decay on assay characteristics, and to provide a simple method to correct for this phenomenon.MethodsWe performed a systematic review and meta-analysis of SARS-CoV-2 serology studies. We included studies testing previously diagnosed, unvaccinated individuals, and excluded studies of cohorts highly unrepresentative of the general population (e.g. hospitalised patients).ResultsOf the 488 screened studies, 76 studies reporting on 50 different seroassays were included in the analysis. Sensitivity decay depended strongly on the antigen and the analytic technique used by the assay, with average sensitivities ranging between 26% and 98% at 6 months after infection, depending on assay characteristics. We found that a third of the included assays departed considerably from manufacturer specifications after 6 months.ConclusionsSeroassay sensitivity decay depends on assay characteristics, and for some types of assays, it can make manufacturer specifications highly unreliable. We provide a tool to correct for this phenomenon and to assess the risk of decay for a given assay. Our analysis can guide the design and interpretation of serosurveys for SARS-CoV-2 and other pathogens and quantify systematic biases in the existing serology literature.

Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S

Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection.

Ethnic inequalities in COVID-19 infection, hospitalisation, intensive care admission, and death: a global systematic review and meta-analysis of over 200 million study participants

Background

COVID-19 has exacerbated existing ethnic inequalities in health. Little is known about whether inequalities in severe disease and deaths, observed globally among minoritised ethnic groups, relates to greater infection risk, poorer prognosis, or both. We analysed global data on COVID-19 clinical outcomes examining inequalities between people from minoritised ethnic groups compared to the ethnic majority group.

Methods

Databases (MEDLINE, EMBASE, EMCARE, CINAHL, Cochrane Library) were searched from 1st December 2019 to 3rd October 2022, for studies reporting original clinical data for COVID-19 outcomes disaggregated by ethnicity: infection, hospitalisation, intensive care unit (ICU) admission, and mortality. We assessed inequalities in incidence and prognosis using random-effects meta-analyses, with Grading of Recommendations Assessment, Development, and Evaluation (GRADE) use to assess certainty of findings. Meta-regressions explored the impact of region and time-frame (vaccine roll-out) on heterogeneity. PROSPERO: CRD42021284981.

Findings

77 studies comprising over 200,000,000 participants were included. Compared with White majority populations, we observed an increased risk of testing positive for infection for people from Black (adjusted Risk Ratio [aRR]:1.78, 95% CI:1.59-1.99, I2 = 99.1), South Asian (aRR:3.00, 95% CI:1.59-5.66, I2 = 99.1), Mixed (aRR:1.64, 95% CI:1.02-1.67, I2 = 93.2) and Other ethnic groups (aRR:1.36, 95% CI:1.01-1.82, I2 = 85.6). Black, Hispanic, and South Asian people were more likely to be seropositive. Among population-based studies, Black and Hispanic ethnic groups and Indigenous peoples had an increased risk of hospitalisation; Black, Hispanic, South Asian, East Asian and Mixed ethnic groups and Indigenous peoples had an increased risk of ICU admission. Mortality risk was increased for Hispanic, Mixed, and Indigenous groups. Smaller differences were seen for prognosis following infection. Following hospitalisation, South Asian, East Asian, Black and Mixed ethnic groups had an increased risk of ICU admission, and mortality risk was greater in Mixed ethnic groups. Certainty of evidence ranged from very low to moderate.

Interpretation

Our study suggests that systematic ethnic inequalities in COVID-19 health outcomes exist, with large differences in exposure risk and some differences in prognosis following hospitalisation. Response and recovery interventions must focus on tackling drivers of ethnic inequalities which increase exposure risk and vulnerabilities to severe disease, including structural racism and racial discrimination.

Funding

ESRC:ES/W000849/1.

Differential COVID-19 infection rates in children, adults, and elderly: Systematic review and meta-analysis of 38 pre-vaccination national seroprevalence studies

Background

Debate exists about whether extra protection of elderly and other vulnerable individuals is feasible in COVID-19. We aimed to assess the relative infection rates in the elderly vs the non-elderly and, secondarily, in children vs adults.

Methods

We performed a systematic review and meta-analysis of seroprevalence studies conducted in the pre-vaccination era. We identified representative national studies without high risk of bias through SeroTracker and PubMed searches (last updated May 17, 2022). We noted seroprevalence estimates for children, non-elderly adults, and elderly adults, using cut-offs of 20 and 60 years (or as close to these ages, if they were unavailable) and compared them between different age groups.

Results

We included 38 national seroprevalence studies from 36 different countries comprising 826 963 participants. Twenty-six of these studies also included pediatric populations and twenty-five were from high-income countries. The median ratio of seroprevalence in elderly vs non-elderly adults (or non-elderly in general, if pediatric and adult population data were not offered separately) was 0.90-0.95 in different analyses, with large variability across studies. In five studies (all in high-income countries), we observed significant protection of the elderly with a ratio of <0.40, with a median of 0.83 in high-income countries and 1.02 elsewhere. The median ratio of seroprevalence in children vs adults was 0.89 and only one study showed a significant ratio of <0.40. The main limitation of our study is the inaccuracies and biases in seroprevalence studies.

Conclusions

Precision shielding of elderly community-dwelling populations before the availability of vaccines was indicated in some high-income countries, but most countries failed to achieve any substantial focused protection.

Registration

Open Science Framework (available at: https://osf.io/xvupr).

Age-stratified infection fatality rate of COVID-19 in the non-elderly population

The largest burden of COVID-19 is carried by the elderly, and persons living in nursing homes are particularly vulnerable. However, 94% of the global population is younger than 70 years and 86% is younger than 60 years. The objective of this study was to accurately estimate the infection fatality rate (IFR) of COVID-19 among non-elderly people in the absence of vaccination or prior infection. In systematic searches in SeroTracker and PubMed (protocol: https://osf.io/xvupr), we identified 40 eligible national seroprevalence studies covering 38 countries with pre-vaccination seroprevalence data. For 29 countries (24 high-income, 5 others), publicly available age-stratified COVID-19 death data and age-stratified seroprevalence information were available and were included in the primary analysis. The IFRs had a median of 0.034% (interquartile range (IQR) 0.013-0.056%) for the 0-59 years old population, and 0.095% (IQR 0.036-0.119%) for the 0-69 years old. The median IFR was 0.0003% at 0-19 years, 0.002% at 20-29 years, 0.011% at 30-39 years, 0.035% at 40-49 years, 0.123% at 50-59 years, and 0.506% at 60-69 years. IFR increases approximately 4 times every 10 years. Including data from another 9 countries with imputed age distribution of COVID-19 deaths yielded median IFR of 0.025-0.032% for 0-59 years and 0.063-0.082% for 0-69 years. Meta-regression analyses also suggested global IFR of 0.03% and 0.07%, respectively in these age groups. The current analysis suggests a much lower pre-vaccination IFR in non-elderly populations than previously suggested. Large differences did exist between countries and may reflect differences in comorbidities and other factors. These estimates provide a baseline from which to fathom further IFR declines with the widespread use of vaccination, prior infections, and evolution of new variants.

Quantifying the relationship between sub-population wastewater samples and community-wide SARS-CoV-2 seroprevalence

Robust epidemiological models relating wastewater to community disease prevalence are lacking. Assessments of SARS-CoV-2 infection rates have relied primarily on convenience sampling, which does not provide reliable estimates of community disease prevalence due to inherent biases. This study conducted serial stratified randomized samplings to estimate the prevalence of SARS-CoV-2 antibodies in 3717 participants, and obtained weekly samples of community wastewater for SARS-CoV-2 concentrations in Jefferson County, KY (USA) from August 2020 to February 2021. Using an expanded Susceptible-Infected-Recovered model, the longitudinal estimates of the disease prevalence were obtained and compared with the wastewater concentrations using regression analysis. The model analysis revealed significant temporal differences in epidemic peaks. The results showed that in some areas, the average incidence rate, based on serological sampling, was 50 % higher than the health department rate, which was based on convenience sampling. The model-estimated average prevalence rates correlated well with the wastewater (correlation = 0.63, CI (0.31,0.83)). In the regression analysis, a one copy per ml-unit increase in weekly average wastewater concentration of SARS-CoV-2 corresponded to an average increase of 1-1.3 cases of SARS-CoV-2 infection per 100,000 residents. The analysis indicates that wastewater may provide robust estimates of community spread of infection, in line with the modeled prevalence estimates obtained from stratified randomized sampling, and is therefore superior to publicly available health data.

How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trials

OBJECTIVE

Reports of efficacy, effectiveness and harms of COVID-19 vaccines have not used key indicators from evidence-based medicine (EBM) that can inform policies about vaccine distribution. This study aims to clarify EBM indicators that consider baseline risks when assessing vaccines' benefits versus harms: absolute risk reduction (ARR) and number needed to be vaccinated (NNV), versus absolute risk of the intervention (ARI) and number needed to harm (NNH).

METHODS

We used a multimethod approach, including a scoping review of the literature; calculation of risk reductions and harms from data concerning five major vaccines; analysis of risk reductions in population subgroups with varying baseline risks; and comparisons with prior vaccines.

FINDINGS

The scoping review showed few reports regarding ARR, NNV, ARI and NNH; comparisons of benefits versus harms using these EBM methods; or analyses of varying baseline risks. Calculated ARRs for symptomatic infection and hospitalisation were approximately 1% and 0.1%, respectively, as compared with relative risk reduction of 50%-95% and 58%-100%. NNV to prevent one symptomatic infection and one hospitalisation was in the range of 80-500 and 500-4000. Based on available data, ARI and NNH as measures of harm were difficult to calculate, and the balance between benefits and harms using EBM measures remained uncertain. The effectiveness of COVID-19 vaccines as measured by ARR and NNV was substantially higher in population subgroups with high versus low baseline risks.

CONCLUSIONS

Priorities for vaccine distribution should target subpopulations with higher baseline risks. Similar analyses using ARR/NNV and ARI/NNH would strengthen evaluations of vaccines' benefits versus harms. An EBM perspective on vaccine distribution that emphasises baseline risks becomes especially important as the world's population continues to face major barriers to vaccine access-sometimes termed 'vaccine apartheid'.

Population-based correlates of COVID-19 infection: An analysis from the DFW COVID-19 prevalence study

BACKGROUND

COVID-19 has resulted in over 1 million deaths in the U.S. as of June 2022, with continued surges after vaccine availability. Information on related attitudes and behaviors are needed to inform public health strategies. We aimed to estimate the prevalence of COVID-19, risk factors of infection, and related attitudes and behaviors in a racially, ethnically, and socioeconomically diverse urban population.

METHODS

The DFW COVID-19 Prevalence Study Protocol 1 was conducted from July 2020 to March 2021 on a randomly selected sample of adults aged 18-89 years, living in Dallas or Tarrant Counties, Texas. Participants were asked to complete a 15-minute questionnaire and COVID-19 PCR and antibody testing. COVID-19 prevalence estimates were calculated with survey-weighted data.

RESULTS

Of 2969 adults who completed the questionnaire (7.4% weighted response), 1772 (53.9% weighted) completed COVID-19 testing. Overall, 11.5% of adults had evidence of COVID-19 infection, with a higher prevalence among Hispanic and non-Hispanic Black persons, essential workers, those in low-income neighborhoods, and those with lower education attainment compared to their counterparts. We observed differences in attitudes and behaviors by race and ethnicity, with non-Hispanic White persons being less likely to believe in the importance of mask wearing, and racial and ethnic minorities more likely to attend social gatherings.

CONCLUSION

Over 10% of an urban population was infected with COVID-19 early during the pandemic. Differences in attitudes and behaviors likely contribute to sociodemographic disparities in COVID-19 prevalence.

Stress during pregnancy: An ecological momentary assessment of stressors among Black and White women with implications for maternal health

BACKGROUND

Stress can lead to adverse physiological and psychological outcomes. Therefore, understanding stress during pregnancy provides insight into racial disparities in maternal health, particularly Black maternal health.

OBJECTIVES

This study aimed to describe (1) daily exposure to self-reported stress levels during pregnancy, and (2) sources of stress among participants that identified as Black or White using data collected via ecological momentary assessment.

METHODS

We leveraged survey data from the Postpartum Mothers Mobile Study, a prospective longitudinal study using ecological momentary assessment data collection methods to describe patterns of stress during pregnancy. This article is descriptive and documents patterns of self-reported stress levels and sources of stress. Frequencies and percentages of stress responses were computed to describe these patterns.

RESULTS

The sample (n = 296) was 27% Black (n = 78) and 63% White (n = 184). Results were based on at least one measurement of that stress level during pregnancy. A similar number of Black and White participants reported no stress during pregnancy. White (85%-95%) and Black (60%-70%) participants reported low to moderate levels of stress. Black participants (38%) and White participants (35%) reported experiencing high stress. Black and White participants reported similar sources of stress: stress from a partner, too many things to do, a baby or other children, and financial concerns. White participants reported work as a top stressor, and Black participants reported financial issues as a top source of stress.

CONCLUSION

This study provides insight into daily exposure to stress that has implications for maternal health. We described patterns of self-reported stress and sources of stress among Black and White participants. The daily exposures to stress reported by this sample exist within a context of root causes of structural inequities in education, health care, income, wealth, and housing that must be addressed to achieve maternal health equity.

Municipal and neighbourhood level wastewater surveillance and subtyping of an influenza virus outbreak

Recurrent influenza epidemics and pandemic potential are significant risks to global health. Public health authorities use clinical surveillance to locate and monitor influenza and influenza-like cases and outbreaks to mitigate hospitalizations and deaths. Currently, global integration of clinical surveillance is the only reliable method for reporting influenza types and subtypes to warn of emergent pandemic strains. The utility of wastewater surveillance (WWS) during the COVID-19 pandemic as a less resource intensive replacement or complement for clinical surveillance has been predicated on analyzing viral fragments in wastewater. We show here that influenza virus targets are stable in wastewater and partitions favorably to the solids fraction. By quantifying, typing, and subtyping the virus in municipal wastewater and primary sludge during a community outbreak, we forecasted a citywide flu outbreak with a 17-day lead time and provided population-level viral subtyping in near real-time to show the feasibility of influenza virus WWS at the municipal and neighbourhood levels in near real time using minimal resources and infrastructure.

Municipal and neighbourhood level wastewater surveillance and subtyping of an influenza virus outbreak

Recurrent influenza epidemics and pandemic potential are significant risks to global health. Public health authorities use clinical surveillance to locate and monitor influenza and influenza-like cases and outbreaks to mitigate hospitalizations and deaths. Currently, global integration of clinical surveillance is the only reliable method for reporting influenza types and subtypes to warn of emergent pandemic strains. The utility of wastewater surveillance (WWS) during the COVID-19 pandemic as a less resource intensive replacement or complement for clinical surveillance has been predicated on analyzing viral fragments in wastewater. We show here that influenza virus targets are stable in wastewater and partitions favorably to the solids fraction. By quantifying, typing, and subtyping the virus in municipal wastewater and primary sludge during a community outbreak, we forecasted a citywide flu outbreak with a 17-day lead time and provided population-level viral subtyping in near real-time to show the feasibility of influenza virus WWS at the municipal and neighbourhood levels in near real time using minimal resources and infrastructure.

Severe Acute Respiratory Syndrome Coronavirus 2 Incidence and Risk Factors in a National, Community-Based Prospective Cohort of US Adults

BACKGROUND

Prospective cohort studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence complement case-based surveillance and cross-sectional seroprevalence surveys.

METHODS

We estimated the incidence of SARS-CoV-2 infection in a national cohort of 6738 US adults, enrolled in March-August 2020. Using Poisson models, we examined the association of social distancing and a composite epidemiologic risk score with seroconversion. The risk score was created using least absolute shrinkage selection operator (LASSO) regression to identify factors predictive of seroconversion. The selected factors were household crowding, confirmed case in household, indoor dining, gathering with groups of ≥10, and no masking in gyms or salons.

RESULTS

Among 4510 individuals with ≥1 serologic test, 323 (7.3% [95% confidence interval (CI), 6.5%-8.1%]) seroconverted by January 2021. Among 3422 participants seronegative in May-September 2020 and retested from November 2020 to January 2021, 161 seroconverted over 1646 person-years of follow-up (9.8 per 100 person-years [95% CI, 8.3-11.5]). The seroincidence rate was lower among women compared with men (incidence rate ratio [IRR], 0.69 [95% CI, .50-.94]) and higher among Hispanic (2.09 [1.41-3.05]) than white non-Hispanic participants. In adjusted models, participants who reported social distancing with people they did not know (IRR for always vs never social distancing, 0.42 [95% CI, .20-1.0]) and with people they knew (IRR for always vs never, 0.64 [.39-1.06]; IRR for sometimes vs never, 0.60 [.38-.96]) had lower seroconversion risk. Seroconversion risk increased with epidemiologic risk score (IRR for medium vs low score, 1.68 [95% CI, 1.03-2.81]; IRR for high vs low score, 3.49 [2.26-5.58]). Only 29% of those who seroconverted reported isolating, and only 19% were asked about contacts.

CONCLUSIONS

Modifiable risk factors and poor reach of public health strategies drove SARS-CoV-2 transmission across the United States.

CalScope: Monitoring SARS-CoV-2 Seroprevalence from Vaccination and Prior Infection in Adults and Children in California May 2021– July 2021

Background

Understanding the distribution of SARS-CoV-2 antibodies from vaccination and/or prior infection is critical to the public health response to the pandemic. CalScope is a population-based serosurvey in 7 counties in California.

Methods

We invited 200,000 randomly sampled households to enroll up to 1 adult and 1 child between April 20, 2021 and June 16, 2021. We tested all specimen for antibodies against SARS-CoV-2 nucleocapsid and spike proteins, and each participant completed an online survey. We classified participants into categories: seronegative, antibodies from infection only, antibodies from infection and vaccination, and antibodies from vaccination only.

Results

11,161 households enrolled (5.6%), with 7,483 adults and 1,375 children completing antibody testing. As of June 2021, 33% (95%CI [28%, 37%]) of adults and 57% (95%CI[48%, 66%]) of children were seronegative; 18% (95%CI[14%, 22%]) of adults and 26% (95%CI[19%, 32%]) of children had antibodies from infection alone; 9% (95%CI[6%,11%]) of adults and 5% (95%CI[1%, 8%]) of children had antibodies from infection and vaccination; and 41% (95%CI[37%, 45%]) of adults and 13% (95%CI [7%, 18%]) of children had antibodies from vaccination alone.

Conclusions

As of June 2021, a third of adults and most children in California were seronegative. Serostatus varied regionally and by demographic group.

Nationally Representative Social Contact Patterns among U.S. adults, August 2020-April 2021

The response to the COVID-19 pandemic in the U.S prompted abrupt and dramatic changes to social contact patterns. Monitoring changing social behavior is essential to provide reliable input data for mechanistic models of infectious disease, which have been increasingly used to support public health policy to mitigate the impacts of the pandemic. While some studies have reported on changing contact patterns throughout the pandemic., few have reported on differences in contact patterns among key demographic groups and none have reported nationally representative estimates. We conducted a national probability survey of US households and collected information on social contact patterns during two time periods: August-December 2020 (before widespread vaccine availability) and March-April 2021 (during national vaccine rollout). Overall, contact rates in Spring 2021 were similar to those in Fall 2020, with most contacts reported at work. Persons identifying as non-White, non-Black, non-Asian, and non-Hispanic reported high numbers of contacts relative to other racial and ethnic groups. Contact rates were highest in those reporting occupations in retail, hospitality and food service, and transportation. Those testing positive for SARS-CoV-2 antibodies reported a higher number of daily contacts than those who were seronegative. Our findings provide evidence for differences in social behavior among demographic groups, highlighting the profound disparities that have become the hallmark of the COVID-19 pandemic.

Post-lockdown changes of age-specific susceptibility and its correlation with adherence to social distancing measures

Social distancing measures are effective in reducing overall community transmission but much remains unknown about how they have impacted finer-scale dynamics. In particular, much is unknown about how changes of contact patterns and other behaviors including adherence to social distancing, induced by these measures, may have impacted finer-scale transmission dynamics among different age groups. In this paper, we build a stochastic age-specific transmission model to systematically characterize the degree and variation of age-specific transmission dynamics, before and after lifting the lockdown in Georgia, USA. We perform Bayesian (missing-)data-augmentation model inference, leveraging reported age-specific case, seroprevalence and mortality data. We estimate that overall population-level transmissibility was reduced to 41.2% with 95% CI [39%, 43.8%] of the pre-lockdown level in about a week of the announcement of the shelter-in-place order. Although it subsequently increased after the lockdown was lifted, it only bounced back to 62% [58%, 67.2%] of the pre-lockdown level after about a month. We also find that during the lockdown susceptibility to infection increases with age. Specifically, relative to the oldest age group (> 65+), susceptibility for the youngest age group (0–17 years) is 0.13 [0.09, 0.18], and it increases to 0.53 [0.49, 0.59] for 18–44 and 0.75 [0.68, 0.82] for 45–64. More importantly, our results reveal clear changes of age-specific susceptibility (defined as average risk of getting infected during an infectious contact incorporating age-dependent behavioral factors) after the lockdown was lifted, with a trend largely consistent with reported age-specific adherence levels to social distancing and preventive measures. Specifically, the older groups (> 45) (with the highest levels of adherence) appear to have the most significant reductions of susceptibility (e.g., post-lockdown susceptibility reduced to 31.6% [29.3%, 34%] of the estimate before lifting the lockdown for the 6+ group). Finally, we find heterogeneity in case reporting among different age groups, with the lowest rate occurring among the 0–17 group (9.7% [6.4%, 19%]). Our results provide a more fundamental understanding of the impacts of stringent lockdown measures, and finer evidence that other social distancing and preventive measures may be effective in reducing SARS-CoV-2 transmission. These results may be exploited to guide more effective implementations of these measures in many current settings (with low vaccination rate globally and emerging variants) and in future potential outbreaks of novel pathogens.

Prevalence of current and past COVID-19 in Ohio adults

Purpose To estimate the prevalence of current and past COVID-19 in Ohio adults. Methods We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of non-ignorable non-response bias. Results The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%-2.0%), corresponding to ∼85,000 prevalent infections (95% credible interval: 6,300-177,000) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2%-2.7%), corresponding to ∼118,000 Ohio adults (95% credible interval: 22,000-240,000). Estimates did not change meaningfully due to non-response bias. Conclusions Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts.

Accuracy of Case-Based Seroprevalence of SARS-CoV-2 Antibodies in Maricopa County, Arizona

We conducted a community seroprevalence survey in Arizona, from September 12 to October 1, 2020, to determine the presence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We used the seroprevalence estimate to predict SARS-CoV-2 infections in the jurisdiction by applying the adjusted seroprevalence to the county's population. The estimated community seroprevalence of SARS-CoV-2 infections was 4.3 times greater (95% confidence interval = 2.2, 7.5) than the number of reported cases. Field surveys with representative sampling provide data that may help fill in gaps in traditional public health reporting. (Am J Public Health. 2022;112(1):38-42. https://doi.org/10.2105/AJPH.2021.306568).

Public health impact of the Pfizer-BioNTech COVID-19 vaccine (BNT162b2) in the first year of rollout in the United States

BACKGROUND

As the body of evidence on COVID-19 and post-vaccination outcomes continues to expand, this analysis sought to evaluate the public health impact of the Pfizer-BioNTech COVID-19 Vaccine, BNT162b2, during the first year of its rollout in the US.

METHODS

A combined Markov decision tree model compared clinical and economic outcomes of the Pfizer-BioNTech COVID-19 Vaccine (BNT162b2) versus no vaccination in individuals aged ≥12 years. Age-stratified epidemiological, clinical, economic, and humanistic parameters were derived from existing data and published literature. Scenario analysis explored the impact of using lower and upper bounds of parameters on the results. The health benefits were estimated as the number of COVID-19 symptomatic cases, hospitalizations and deaths averted, and Quality Adjusted Life Years (QALYs) saved. The economic benefits were estimated as the amount of healthcare and societal cost savings associated with the vaccine-preventable health outcomes.

RESULTS

It was estimated that, in 2021, the Pfizer-BioNTech COVID-19 Vaccine (BNT162b2) contributed to averting almost 9 million symptomatic cases, close to 700,000 hospitalizations, and over 110,000 deaths, resulting in an estimated $30.4 billion direct healthcare cost savings, $43.7 billion indirect cost savings related to productivity loss, as well as discounted gains of 1.1 million QALYs. Scenario analyses showed that these results were robust; the use of alternative plausible ranges of parameters did not change the interpretation of the findings.

CONCLUSIONS

The Pfizer-BioNTech COVID-19 Vaccine (BNT162b2) contributed to generate substantial public health impact and vaccine-preventable cost savings in the first year of its rollout in the US. The vaccine was estimated to prevent millions of COVID-19 symptomatic cases and thousands of hospitalizations and deaths, and these averted outcomes translated into cost-savings in the billions of US dollars and thousands of QALYs saved. As only direct impacts of vaccination were considered, these estimates may be conservative.

Inferring the COVID-19 infection fatality rate in the community-dwelling population: a simple Bayesian evidence synthesis of seroprevalence study data and imprecise mortality data

Estimating the coronavirus disease-2019 (COVID-19) infection fatality rate (IFR) has proven to be particularly challenging –and rather controversial– due to the fact that both the data on deaths and the data on the number of individuals infected are subject to many different biases. We consider a Bayesian evidence synthesis approach which, while simple enough for researchers to understand and use, accounts for many important sources of uncertainty inherent in both the seroprevalence and mortality data. With the understanding that the results of one's evidence synthesis analysis may be largely driven by which studies are included and which are excluded, we conduct two separate parallel analyses based on two lists of eligible studies obtained from two different research teams. The results from both analyses are rather similar. With the first analysis, we estimate the COVID-19 IFR to be 0.31% [95% credible interval (CrI) of (0.16%, 0.53%)] for a typical community-dwelling population where 9% of the population is aged over 65 years and where the gross-domestic-product at purchasing-power-parity (GDP at PPP) per capita is $17.8k (the approximate worldwide average). With the second analysis, we obtain 0.32% [95% CrI of (0.19%, 0.47%)]. Our results suggest that, as one might expect, lower IFRs are associated with younger populations (and may also be associated with wealthier populations). For a typical community-dwelling population with the age and wealth of the United States we obtain IFR estimates of 0.43% and 0.41%; and with the age and wealth of the European Union, we obtain IFR estimates of 0.67% and 0.51%.

SARS-CoV-2 incidence and risk factors in a national, community-based prospective cohort of U.S. adults

BACKGROUND

Epidemiologic risk factors for incident SARS-CoV-2 infection as determined via prospective cohort studies greatly augment and complement information from case-based surveillance and cross-sectional seroprevalence surveys.

METHODS

We estimated the incidence of SARS-CoV-2 infection and risk factors in a well-characterized, national prospective cohort of 6,738 U.S. adults, enrolled March-August 2020, a subset of whom (n=4,510) underwent repeat serologic testing between May 2020 and January 2021. We examined the crude associations of sociodemographic factors, epidemiologic risk factors, and county-level community transmission with the incidence of seroconversion. In multivariable Poisson models we examined the association of social distancing and a composite score of several epidemiologic risk factors with the rate of seroconversion.

FINDINGS

Among the 4,510 individuals with at least one serologic test, 323 (7.3%, 95% confidence interval [CI] 6.5%-8.1%) seroconverted by January 2021. Among 3,422 participants seronegative in May-September 2020 and tested during November 2020-January 2021, we observed 161 seroconversions over 1,646 person-years of follow-up (incidence rate of 9.8 per 100 person-years [95%CI 8.3-11.5]). In adjusted models, participants who reported always or sometimes social distancing with people they knew (IRRalways vs. never 0.43, 95%CI 0.21-1.0; IRRsometimes vs. never 0.47, 95%CI 0.22-1.2) and people they did not know (IRRalways vs. never 0.64, 95%CI 0.39-1.1; IRRsometimes vs. never 0.60, 95%CI 0.38-0.97) had lower rates of seroconversion. The rate of seroconversion increased across tertiles of the composite score of epidemiologic risk (IRRmedium vs. low 1.5, 95%CI 0.92-2.4; IRRhigh vs. low 3.0, 95%CI 2.0-4.6). Among the 161 observed seroconversions, 28% reported no symptoms of COVID-like illness (i.e., were asymptomatic), and 27% reported a positive SARS-CoV-2 diagnostic test. Ultimately, only 29% reported isolating and 19% were asked about contacts.

INTERPRETATION

Modifiable epidemiologic risk factors and poor reach of public health strategies drove SARS-CoV-2 transmission across the U.S during May 2020-January 2021.

FUNDING

U.S. National Institutes of Allergy and Infectious Diseases (NIAID).