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1.
Topics in Antiviral Medicine ; 30(1 SUPPL):301, 2022.
Article in English | EMBASE | ID: covidwho-1880697

ABSTRACT

Background: While the diversity in SARS-CoV-2 transmission across geographies and risk groups is well recognized, there has been limited investigation into spatial heterogeneity at a local scale, that is variability across a single city. Identifying patterns and factors associated with spatial variability requires population representative samples which are challenging to obtain but critical for mitigation strategies including vaccine distribution. Methods: From Jan to May 2021, we sampled 4,828 participants from 2,723 unique households across 100 spatial locations in Chennai, India using a probability proportional to population density sampling approach. All participants provided a blood sample and underwent a household and individual survey. 4,712 samples were tested for antibodies to the Spike protein (anti-Spike IgG) by the Abbott ARCHITECT. SARS-CoV-2 prevalence by spatial location was plotted using splines estimated by generalized additive models. Associations between seroprevalence and spatial attributes (zone, population density), study characteristics (date of sampling), household and individual-level covariates were estimated using Bayesian mixed effects logistic regression accounting for clustering within households and locations. Results: The median age was 38 and 49% self-identified as female. Overall, anti-S IgG prevalence was 61.9% (95% confidence interval [CI]: 60.5-63.3%) but ranged from 41.5% to 73.1% across the 12 zones. Splines indicated statistically significant variation in seroprevalence across the city (Panel A). Mixed effects regression including location and household effects indicated 31% of variance was attributable to location. In adjusted analysis, seroprevalence was significantly associated with population density (OR=1.46 per 100 people/100 sq meter [95%CI: 1.08-1.97];Panel B), age (OR=1.004 [95%CI: 1.0002-1.005]), having an air conditioner (OR=0.65 [95%CI: 0.43-0.98]) and sample timing but not with household crowding (OR=0.97 per person/room [95%CI: 0.75-1.26];Panel C). Significant spatial variation across locations remained after adjustment for these variables, accounting for 28% of variance. Conclusion: We observed substantial spatial heterogeneity of SARS-CoV-2 burden in this high prevalence setting not fully explained by individual, household or population factors. Such local variability in prevalence has implications not only for transmission but for scale-up of vaccines which remain in limited supply in low-and middle-income countries.

2.
Topics in Antiviral Medicine ; 30(1 SUPPL):333, 2022.
Article in English | EMBASE | ID: covidwho-1880443

ABSTRACT

Background: With global vaccine scale-up, the utility of the more stable anti-S IgG assay in seroprevalence studies is limited. P population prevalence estimates of anti-N IgG SARS-CoV-2 using alternate targets (eg, anti-N IgG) will be critical for monitoring cumulative SARS-CoV-2 incidence., We demonstrate the utility of a Bayesian approach that accounts for heterogeneities in SARS-CoV-2 seroresponse (eg, must consider mild infections and/or antibody waning) to ensure anti-N IgG prevalence is not underestimated and correlates not misinterpreted. Methods: We sampled 4,828 participants from 2,723 households across 100 unique geospatial locations in Chennai, India, from Jan-May, 2021 when <1% of the general population was vaccinated. All samples were tested for SARS-CoV-2 IgG antibodies to S and N using the Abbott ARCHITECT. We calculated prevalence using manufacturer cut-offs and applied a Bayesian mixture model. In the mixture model, individuals were assigned a probability of being seropositive or seronegative based on their normalized index value, accounting for differential immune response by age and antibody waning. Regression analyses to identify correlates of infection defined seropositivity by manufacturer cut-offs and the mixture model. Results: The raw SARS-CoV-2 seroprevalence using IgG to S (cutoff=50) and N (cutoff=1.4) were 61.9% (95% confidence interval [CI]: 60.5-63.3%) and 13.7% (CI: 12.8-14.7%), respectively with a correlation of 0.33. With the mixture model, anti-N IgG prevalence was 65.4% (95% credible interval [CrI]: 61.8-68.9). Correlates of anti-N IgG positivity differed qualitatively by the two approaches (Table). Using the manufacturer cut-off, income loss during the pandemic, household crowding and lack of air conditioning were associated with significantly lower anti-N prevalence. By contrast, in the mixture model, many measures of lower socioeconomic status were associated with higher prevalence, associations that were comparable when anti-S was the outcome. The age pattern differed between approaches: the mixture model identified that individuals aged >50 had the lowest seroprevalence, but the highest immune response to infection. Conclusion: With global vaccine scale-up, population prevalence estimates of anti-N IgG will be critical for monitoring cumulative SARS-CoV-2 incidence. We demonstrate the utility of a Bayesian approach that accounts for heterogeneities in SARS-CoV-2 seroresponse to improve accuracy of anti-N IgG prevalence estimates and associated correlates.

3.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333553

ABSTRACT

BACKGROUND: The absence of systematic surveillance for SARS-CoV-2 has curtailed accurate appraisal of transmission intensity. Our objective was to perform case detection of an entire rural community to quantify SARS-CoV-2 transmission using PCR and antibody testing. METHODS: We conducted a cross-sectional survey of the prevalence and cumulative incidence of SARS-CoV-2 infection in the rural town of Bolinas, California (population 1,620), four weeks following shelter-in-place orders. Residents and county essential workers were tested between April 20th-24th, 2020. Prevalence by PCR and seroprevalence combining data from two forms of antibody testing were performed in parallel (Abbott ARCHITECT IgG to nucleocapsid protein and in-house IgG ELISA to the receptor binding domain). RESULTS: Of 1,891 participants, 1,312 were confirmed Bolinas residents (>80% community ascertainment). Zero participants were PCR positive. Assuming 80% sensitivity, it would have been unlikely to observe these results (p<0.05) if there were >3 active infections in the community. Based on antibody results, estimated prevalence of prior infection was 0.16% (95% CrI: 0.02%, 0.46%). Seroprevalence estimates using only one of the two tests would have been higher, with greater uncertainty. The positive predictive value (PPV) of a positive result on both tests was 99.11% (95% CrI: 95.75%, 99.94%), compared to PPV 44.19%-63.32% (95% CrI range 3.25%-98.64%) if only one test was utilized. CONCLUSIONS: Four weeks following shelter-in-place, active and prior SARS-CoV-2 infection in a rural Northern California community was extremely rare. In this low prevalence setting, use of two antibody tests increased the PPV and precision of seroprevalence estimates.

5.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-296875

ABSTRACT

We report very low SARS-CoV-2 seroprevalence in two San Francisco Bay Area populations. Seropositivity was 0.26% in 387 hospitalized patients admitted for non-respiratory indications and 0.1% in 1,000 blood donors. We additionally describe the longitudinal dynamics of immunoglobulin-G, immunoglobulin-M, and in vitro neutralizing antibody titers in COVID-19 patients. Neutralizing antibodies rise in tandem with immunoglobulin levels following symptom onset, exhibiting median time to seroconversion within one day of each other, and there is >93% positive percent agreement between detection of immunoglobulin-G and neutralizing titers.

6.
Sleep ; 44(SUPPL 2):A262-A263, 2021.
Article in English | EMBASE | ID: covidwho-1402635

ABSTRACT

Introduction: COVID-19 is an infectious respiratory illness that was declared a pandemic in March 2020. During the course of COVID-19, studies have demonstrated worsening sleep quality and anxiety. No studies have examined age-related and sex-specific associations between COVID-19 anxiety and sleep in aging populations. We examined associations between COVID-19 anxiety and sleep, and evaluated age and sex as moderators, in middle-aged/older adults. Methods: Two hundred and seventy-seven middle-aged/older adults aged 50+ (Mage=64.68, SD=7.83;44% women) living in the United States who were cognitively healthy (no cognitive impairment/dementia/ neurological disorders) completed an online Qualtrics survey in July/August 2020 measuring sleep (Pittsburgh Sleep Quality Index;PSQI) and COVID-19 anxiety (Coronavirus Anxiety Scale;CAS). Multiple regressions examined whether CAS was independently associated with or interacted with age or sex in its associations with PSQI total score/subscores (sleep quality, sleep duration, sleep efficiency, daytime dysfunction), controlling for age, education, number of medical conditions, sleep/pain medication use, and COVID-19 status. Results: CAS interacted with age (B=-.008, SE=.003 p=.02, R-squared=.02), not sex (p=.31), in its association with sleep duration. Higher CAS was associated with shorter sleep duration in oldestolder adults (∼73 years old;B=.12, SE=.05, p=.01) and younger-older adults (∼65 years old;B=.07, SE=.03, p=.02), not middle-aged adults (∼57 years old, p=.47). CAS interacted with age (B=.01, SE=.004, p=.02), not sex (p=.56), in its association with sleep efficiency. Higher CAS was associated with worse sleep efficiency in oldest-older adults (B=.14, SE=.05, p=.009) and younger-older adults (B=.08, SE=.04, p=.03), not middle-aged adults (p=.60). Higher CAS was associated with greater daytime dysfunction (B=.26, SE=.07, p<.001) and higher PSQI total score (B=.82, SE=.33, p=.01), and did not interact with age or sex (ps>.05). Conclusion: Increased COVID-19 anxiety is associated with several aspects of worse sleep (shorter sleep duration, sleep efficiency) in older adults but not middle-aged adults. Generally, in middle-aged/older adults, higher COVID-19 anxiety is associated with worse daytime dysfunction and overall sleep quality. Sex does not moderate these associations. Increased COVID-19 morbidity and mortality in aging populations may translate to increased anxiety and subsequent sleep disruptions. Interventions aimed at mitigating negative pandemicrelated psychological and sleep outcomes may be particularly relevant for older adults.

7.
Transfusion ; 60(SUPPL 5):287A-288A, 2020.
Article in English | EMBASE | ID: covidwho-1044919

ABSTRACT

Background/Case Studies: One of the essential tasks of operating a transfusion medicine service is the management of blood inventory. In response to the uncertainty in blood use and blood supply brought about by the COVID-19 pandemic, a Blood Inventory Management Dashboard (BIMD) for the University Hospitals Healthcare System (UHHS) was developed and implemented. Study Design/Methods: A multidisciplinary working group consisting of blood bank medical directors, laboratory managers, and information technology specialists were involved in the design of the BIMD. Hospital IT resources were prioritized to develop the dashboard. The dashboard informatics design pulls data from our blood bank information system (HCLL version 2015, SP1, WellSky® (pka Mediware®) (Overland Park, KS)). Reports are then generated using Microsoft SQL ServerData Tools 2015 (Redmond, WA). Data elements include: available blood components, total number of uncrossmatched red blood cells (RBC) divided based on ABO group and Rh type, patients who are high-volume users, and number of blood components issued and used. Inventory was trended over 7 and 30 days and graphically displayed. As a refinement, the dashboard was modified to display color coded inventory per level status (adequate, guarded, and critical). The dashboard display is configured in a tabular and graphical format via Microsoft Excel 2016 (Redmond, WA). Results/Findings: Beginning on March 22, 2020, the dashboard has been automatically distributed prior to 8 AM daily via email to the health system Incident Command Center, Blood Bank medical directors, and other organizational leadership. It immediately became a crucial tool for planning and served as a mechanism to identify the need to move blood inventory throughout the system over the course of the pandemic and permitting identification of high volume users for interventions of ordering additional inventory based on anticipated needs. The number of blood component units are now tracked across all the hospitals within our system. The breakout by ABO and Rh type is a novel and valuable feature of the dashboard and presents valuable summary data that are not available in the laboratory information system. Conclusions: The development of an automated dashboard enables monitoring blood inventory levels across a multiple-hospital health system. The dashboard provides data broken down by ABO and Rh type, data not readily retrievable in the laboratory information system. The dashboard provides tracking of blood product utilization, and the ability to respond dynamically to blood needs throughout the system over the course of the pandemic. The BIMD has proven to be a key tool to inform decision making by organizational and blood bank leadership in the COVID-19 pandemic and in developing future contingency plans in managing blood bank inventory during unforeseen emergencies.

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