COVID-19 Cumulative Incidence Research Articles

SARS-CoV-2 detection in aerosol from community indoor environments

Since its emergence, the COVID-19 pandemic has profoundly and extensively affected global health and society. Numerous studies have focused on detecting SARS-CoV-2 in air samples collected in healthcare indoor spaces, but few have analysed its presence in air samples from other public community spaces. In addition, limited studies have surveyed indoor spaces where it was not known if individuals with COVID-19 were present or had been present at the time of sampling. This study aimed to determine the SARS-CoV-2 genetic load in aerosol samples collected in public community indoor environments where prior knowledge of the presence of infected individuals with COVID-19 cases is not available at the time of sampling.Air samples (N = 497) were collected from healthcare settings, elderly care homes, and educational settings in the Valencian Community, Spain. RNA was extracted and the N1, N2, and E gene fragments of SARS-CoV-2 were quantified using RT-qPCR. SARS-CoV-2 RNA was detected in 8.9 % of air samples. The highest positivity rates were observed in hospitals (16.2 %), elderly care homes (15.3 %), and primary care centres (12.7 %). Concentration of the N1 gene in positive samples ranged 4.3–504 gc/m3 (n = 10), 6.2–77 gc/m3 (n = 8) and 5.1–14 gc/m3 (n = 7), respectively. The genes N2 and E were less frequently detected and generally reported lower concentrations.The frequency of detection of SARS-CoV-2 in aerosols increased at the same time that the population COVID-19 cumulative incidence increased.

Impact of SARS-CoV-2 infection on patients with systemic lupus erythematosus in England prior to vaccination: a retrospective observational cohort study

ObjectivesDetermine the prevaccination healthcare impact of COVID-19 in patients with systemic lupus erythematosus (SLE) in England.DesignRetrospective cohort study of adult patients with SLE from 1 May to 31 October 2020.SettingClinical...

Population-based estimates of COVID-19 period prevalence and cumulative monthly incidence in New York City: A comparison of estimates from three surveys, July–August 2020

Background Diagnosis-based surveillance of COVID-19 underestimates COVID-19 burden. Questions about COVID-19-consistent symptoms were added to three population-based surveys to obtain representative estimates of COVID-19 period prevalence and monthly cumulative incidence. Objective To evaluate if estimates of COVID-19 period prevalence and cumulative monthly incidence differed when collected from surveys with different sampling frames and modes of administration. Methods Data were collected from adult New York City (NYC) residents via the Community Health Survey (CHS) (sampling frame: random digit dial with dual landline and cellphone frame; mode: phone) and the Citywide Mobility Survey (CMS) (sampling frame: probabilistically selected panel; mode: online) in July 2020 and via CHS and Healthy NYC (sampling frame: probabilistically selected panel; mode: online and phone) in August 2020. Persons with COVID-19-like illness (CLI) were identified based on reported symptoms in the past 30 days. To obtain COVID-19 estimates, CLI estimates were adjusted by the proportion of laboratory-confirmed SARS-CoV-2 infections among citywide emergency department CLI visits in which patients received SARS-CoV-2 testing. We used t-tests to compare estimated CLI period prevalence in July 2020 between CHS and CMS and CLI period prevalence and cumulative monthly incidence in August 2020 between CHS and Healthy NYC. Results CLI period prevalence was similar between CHS and CMS during July (12.2% vs. 9.9%, respectively, p=0.511); COVID-19 period prevalence was 1.7% and 1.3%, respectively. In contrast, CLI period prevalence was higher per Healthy NYC during August 2020 than CHS (18.1% vs. 11.3%, p=0.014); COVID-19 period prevalence was 0.7% and 0.4%, respectively. CLI cumulative monthly incidence in August was similar (5.7% and 4.0%, respectively; p=0.246) in both surveys. Conclusions Because estimates of CLI were not consistently different by sampling frame or mode of administration, additional research to understand the cause of differences between CHS and Healthy NYC can support use of symptom-based surveillance to monitor COVID-19 trends.

Temperature and COVID-19 Incidence: An Ecologic Study

Background: According to the hypothesis, COVID-19 is less prevalent in regions with warm climates. Contradictory results have led us to investigate the correlation between temperature and the cumulative COVID-19 incidence rate. Methods: We obtained COVID-19 data from CRONALAB, COVID-DASHBOARD, and MCMC databases of Fars Province, Iran, linked the data and finalized daily COVID-19 cases. The daily data on the temperature was gotten from meteorological stations’ reports from March 21, 2020, to March 21, 2021, for each county of Fars Province, Southern Iran. The daily weighted cumulative incidence rate of COVID-19 cases was calculated for all counties, separately. Initially, for uniform data visualization, the average air temperature data were transformed into ranked percentiles. Then, to visually assess the study hypothesis, the distribution of COVID-19 cumulative incidence was visualized on percentiles of temperature. Given the non-linear distribution of the data, we performed exploratory analyses using the generalized additive models and locally weighted (polynomial) regressions to choose the best response function. Then, the generalized linear models were used to parametrically build the model. Results: The generalized additive models showed a small decreasing, near horizontal, linear pattern for COVID-19 incidence rate as the function of temperature (pseudo R2: 0.001, deviance explained: 0.13%, coefficient: -0.02). The GLMs showed head-to-head results (deviance explained: 0.13%, coefficient: -0.02], supported by similar Akaike information criteria (AICs) (34945). However, according to the locally weighted regressions model’s curve, lower COVID-19 incidence rates were recorded on days when the temperatures ranged from 60 to 80 percentiles, equal to 20°C to 25°C in a cold climate and 25°C to 35°C in a warm climate. This is while the rates increased at lower and upper temperatures. Conclusion: Daily COVID-19 incidence rate cannot be explained as a function of daily temperature in Southern parts of Iran. Higher rates of disease transmission out of the range of 20°C to 25°C for cold temperatures and 25°C to 35°C for warm climates might be linked to people’s indoor gatherings, coupled with insufficient ventilation.

COVID-19 Burden in Adult Correctional or Detention Facilities and the Surrounding Communities, January 1, 2020-July 20, 2021.

We estimated the COVID-19 burden in adult correctional or detention facilities and associated counties by state, facility jurisdiction, and county urbanicity. COVID-19 cumulative incidence (cases per 1,000 persons) for each U.S. correctional or detention facility and people ages 18 years and older in the associated county was estimated between January 1, 2020 and July 20, 2021. Across 46 U.S. states, 1,083 correctional or detention facilities in 718 counties were included. The median COVID-19 incidence rate was higher in facilities than in associated counties for 42 of 46 states and for all facility jurisdictions and county urbanicity categories. COVID-19 burden was higher in most facilities than in associated counties. Implementing COVID-19 mitigation measures in correctional settings is needed to prevent SARS-CoV-2 transmission in facilities and associated counties.

The effect of BCG vaccination on infection and antibody levels against SARS-CoV-2—The results of ProBCG: a multicenter randomized clinical trial in Brazil

Evatuate if Bacillus Calmette-Guérin (BCG) vaccine could be used as a tool against SARS-CoV-2 based on the concept of trained immunity. A multicenter, double-blinded, randomized clinical trial recruited health care workers (HCWs) in Brazil. The incidence rates of COVID-19, clinical manifestations, absenteeism, and adverse events among HCWs receiving BCG vaccine (Moreau or Moscow strains) or placebo were compared. BCG vaccine-mediated immune response before and after implementing specific vaccines for COVID-19 (CoronaVac or COVISHIELD) was analyzed. Cox proportional hazard and linear mixed effect modeling were used. A total of 264 volunteers were included for analysis (BCG=134 and placebo=130). The placebo group presented a COVID-19 cumulative incidence of 0.75% vs 0.52% of BCG. The Moreau strain also presented a higher incidence rate (1.60%×0.22%). BCG did not show a protective hazard ratio against COVID-19. In addition, the log (immunoglobulin G) level against SARS-CoV-2 presented a higher increase in the BCG group, whether or not participants had COVID-19, but also without statistical significance. Our results suggest that BCG has a tendency of protection against SARS-CoV-2 and higher immunoglobulin G levels than placebo. The clinical trial was registered at https://clinicaltrials.gov/ (NCT04659941).

Community Mitigation Strategies, Mobility, and COVID-19 Incidence Across Three Waves in the United States in 2020.

Summarizing the impact of community-based mitigation strategies and mobility on COVID-19 infections throughout the pandemic is critical for informing responses and future infectious disease outbreaks. Here, we employed time-series analyses to empirically investigate the relationships between mitigation strategies and mobility on COVID-19 incident cases across US states during the first three waves of infections. We linked data on daily COVID-19 incidence by US state from March to December 2020 with the stringency index, a well-known index capturing the strictness of mitigation strategies, and the trip ratio, which measures the ratio of the number of trips taken per day compared with the same day in 2019. We utilized multilevel models to determine the relative impacts of policy stringency and the trip ratio on COVID-19 cumulative incidence and the effective reproduction number. We stratified analyses by three waves of infections. Every five-point increase in the stringency index was associated with 2.89% (95% confidence interval = 1.52, 4.26%) and 5.01% (3.02, 6.95%) reductions in COVID-19 incidence for the first and third waves, respectively. Reducing the number of trips taken by 50% compared with the same time in 2019 was associated with a 16.2% (-0.07, 35.2%) decline in COVID-19 incidence at the state level during the second wave and 19.3% (2.30, 39.0%) during the third wave. Mitigation strategies and reductions in mobility are associated with marked health gains through the reduction of COVID-19 infections, but we estimate variable impacts depending on policy stringency and levels of adherence.

SARS-CoV-2 infection in Africa: a systematic review and meta-analysis of standardised seroprevalence studies, from January 2020 to December 2021

IntroductionEstimating COVID-19 cumulative incidence in Africa remains problematic due to challenges in contact tracing, routine surveillance systems and laboratory testing capacities and strategies. We undertook a meta-analysis of population-based seroprevalence...

COVID-19 vaccination, incidence, and mortality rates among indigenous populations compared to the general population in Brazil: Describing trends over time

BackgroundChronic social and health inequities faced by indigenous peoples in Brazil foretell the detrimental impact of COVID-19.MethodsWe use de-identified, publicly available data from the Ministry of Health from March/2020 - December/2021 to describe vaccination coverage, cumulative incidence, and cumulative mortality rates due to COVID-19 among indigenous peoples. We also compare vaccination coverage among indigenous peoples with that reported for older adults, who were simultaneously included as a priority group in the vaccination strategy. Finally, we compared COVID-19 incidence and mortality rates in the indigenous population with that reported for the general Brazilian population.FindingsWe found important heterogeneities in vaccination coverage across the 34 indigenous districts, and a lower overall coverage among indigenous peoples compared to older adults. We observed higher COVID-19 cumulative incidence rates among indigenous populations compared to the general Brazilian population. Although mortality rates were seemingly lower, data should be interpreted with caution due to a younger age structure and more frequent underreporting of cases and deaths among indigenous populations. After the beginning of COVID-19 vaccination program, we observed a decrease in both incidence and mortality rates among indigenous peoples in all Brazilian regions.InterpretationThe COVID-19 pandemic has had a heavy toll on vulnerable populations. Although social and geographic isolation challenges the implementation of any vaccination program for indigenous populations, prior experience suggests that the COVID-19 vaccination strategy lacked effectiveness. The absence of a coordinated strategy to reinforce the importance of the vaccine and other prevention methods, to guarantee the access to trustworthy information, and to respond with the necessary resources in extreme situations, resulted in lower COVID-19 vaccination coverage, higher incidence rates, and preventable deaths due to COVID-19 among indigenous peoples in Brazil.FundingThis work was not supported by specific funding.

COVID-19, green space exposure, and mask mandates

IntroductionMask-wearing and social distancing are critical prevention measures that have been implemented to stem the spread of COVID-19. The degree to which these measures are adhered to in the US, however, may be influenced by access to outdoor resources such as green space, as well as mask mandates that may vary by state. PurposeTo examine the association between the presence or absence of statewide mask mandates and green space exposure with COVID-19 cumulative incidence in the US. MethodsIn October 2020, COVID-19 case data for each US county was downloaded from USA Facts, in addition to statewide mask mandates from a database maintained by the American Association of Retired Persons. The Normalized Difference Vegetation Index from the US Geological Survey (USGS), was used as a measure of greenspace, while the 2016 National Land Cover Database was used to assess tree canopy exposure as an alternative measure of greenspace. We performed generalized linear regression to evaluate associations with COVID-19 incidence, adjusting for potential confounders such as other environmental factors (i.e., air pollution and climate) and socio-economic factors derived from the CDC social vulnerability index. In addition, we also performed spatial regression analyses to account for spatial autocorrelation across counties. ResultsCounties with mandatory mask-wearing policies had a lower cumulative incidence of COVID-19 (B = −0.299, SE = 0.038). Among environmental factors, precipitation (B = 0.005, SE = 0.001) and PM 2.5 (B = 0.072, SE = 0.012) were associated with a higher incidence of COVID-19, while tree canopy (B = −0.501, SE = 0.129) was associated with a lower risk of COVID-19. COVID-19 incidence was higher in counties with socially vulnerable populations regarding socioeconomic status, minority status, and housing and transportation. ConclusionMandatory mask regulation, exposure to green space, and reduced exposure to air pollution may reduce COVID-19 incidence in the US. Additional public health policies should consider ways to mitigate environmental conditions that may contribute to the risk of COVID-19, especially for vulnerable populations.

Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses

BackgroundThere is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts.MethodsThis study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000.ResultsNinety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 90 days) have a better fit.ConclusionOur findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.

Health and healthcare variables associated with Italy's excess mortality during the first wave of the COVID-19 pandemic: An ecological study

BackgroundHealthcare factors have strongly influenced the propagation of COVID-19. This study aims to examine whether excess mortality during the first phase of the COVID-19 outbreak in Italy was associated with health, healthcare, demographic, and socioeconomic, provincial-level indicators. MethodsThis ecological study concerns the raw number of deaths reported from February 1 to April 30, 2020 and the mean number of deaths occurred during the same months from 2015 to 2019, per province. Information on socioeconomic factors and healthcare settings was extracted from updated databases on the Italian National Institute of Statistics (ISTAT) website. A multivariate model and four multilevel models were constructed to test the association between excess mortality and the analysed indicators across 107 Italian provinces. ResultsThe hospitalization rate in long-term care wards and the cardiovascular disease mortality rate correlate positively with excess mortality (p <0.05), while higher densities of licensed physicians and of general practitioners are associated with lower excess mortality (p <0.05). After controlling for the COVID-19 cumulative incidence in each province, only the density of licensed physicians remains negatively associated with excess mortality (p <0.01). ConclusionSome health and healthcare variables (in particular, the density of physicians) are strongly associated with excess mortality during the first wave of the COVID-19 pandemic in Italy and should be targeted to increase the resilience of health systems.

GIS-based spatio-temporal analysis and modeling of COVID-19 incidence rates in Europe

The COVID-19 epidemic has emerged as one of the most severe public health crises worldwide, especially in Europe. Until early July 2021, reported infected cases exceeded 180 million, with almost 4 million associated deaths worldwide, almost a third of which are in continental Europe. We analyzed the spatio-temporal distribution of the disease incidence and mortality rates considering specific periods in this continent. Further, we applied Global Moran's I to examine the spatio-temporal distribution patterns of COVID-19 incidence rates and Getis-Ord Gi* hotspot analysis to represent high-risk areas of the disease. Additionally, we compiled a set of 40 demographic, socioeconomic, environmental, transportation, health, and behavioral indicators as potential explanatory variables to investigate the spatial variations of COVID-19 cumulative incidence rates (CIRs). Ordinary Least Squares (OLS), Spatial Lag model (SLM), Spatial Error Model (SLM), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) regression models were implemented to examine the spatial dependence and non-stationary relationships. Based on our findings, the spatio-temporal distribution pattern of COVID-19 CIRs was highly clustered and the most high-risk clusters of the disease were situated in central and western Europe. Moreover, poverty and the elderly population were selected as the most influential variables due to their significant relationship with COVID-19 CIRs. Considering the non-stationary relationship between variables, MGWR could describe almost 69% of COVID-19 CIRs variations in Europe. Since this spatio-temporal research is conducted on a continental scale, spatial information obtained from the models could provide general insights to authorities for further targeted policies.

Populations in Low-Magnesium Areas Were Associated with Higher Risk of Infection in COVID-19's Early Transmission: A Nationwide Retrospective Cohort Study in the United States.

Many studies have confirmed the important roles of nutritional status and micronutrients in the COVID-19 pandemic. Magnesium is a vital essential trace element that is involved in oxidative stress, inflammation, and many other immunological functions and has been shown to be associated with the outcome of COVID-19 infection. Here, we conducted a nationwide retrospective cohort study in the United States involving 1150 counties, 287,326,503 individuals, and 5,401,483 COVID-19 confirmed cases as of 30 September 2020 to reveal the infection risk of the populations distributed in low-magnesium areas in the early transmission of COVID-19. Our results indicate that the average county-level COVID-19 cumulative incidence in low-magnesium areas was significantly higher than in the control areas. Additionally, a significant negative nonlinear association was found between environmental magnesium concentration and the county-level COVID-19 cumulative incidence. Furthermore, the populations distributed in low environmental magnesium areas faced a higher COVID-19 infection risk (RR: 1.066; CI: 1.063–1.068), among which females (RR: 1.07; CI: 1.067–1.073), the 0–17 years subgroup (RR: 1.125; CI: 1.117–1.134), the 65+ years subgroup (RR: 1.093; CI: 1.087–1.098), black people (RR: 1.975; CI: 1.963–1.986), populations outside metro areas, and counties with a smaller population experienced higher risk of infection by COVID-19 than other subgroups. Considering that the magnesium intake of about half the population of the United States is below the daily required dose, our study will contribute to the creation of long-term public health strategies to help protect against COVID-19.

Similar COVID-19 incidence to the general population in people with opioid use disorder receiving integrated outpatient clinical care

IntroductionDuring the COVID-19 pandemic, limited access to health care augmented COVID-19 risk in subjects with opioid use disorder (OUD). The aim of the study was to compare COVID-19 incidence in individuals with OUD receiving continuous clinical care with that of the general population. MethodsA prospective cohort study was carried out from March 2020 to March 2021 comparing COVID-19 cumulative incidence of individuals presenting an OUD receiving integrated clinical care with that of an age-reference general population, in three public outpatient treatment centers for addiction in Barcelona, Spain. ResultsOver the study period, 366 individuals received clinical care. Mean age: 48.2±8.9 years, 280 (76.5%) were men and 283 (77.3%) native Spanish. All subjects were on opioid agonist therapy. Prevalence of communicable diseases were: HIV infection in 109 (29.8%) and hepatitis C in 46 (12.6%). Psychiatric comorbidity was present in 207 (56.6%), and 119 (32.5%) had >1 chronic medical disease. COVID-19 was diagnosed in 10 patientsa cumulative incidence of 2,732 casesx100,000 people/year (C.I.95%: 1,318–4,967). There were no differences compared to the age-general population: 2,856 casesx100,000 people/year (C.I.95%: 2,830–2,880) (p=0.81). In the bivariate analysis, hypertension (5[50.0%] vs. 53[14.9%], p=0.01) and cardiovascular chronic diseases (2 [20.0%] vs. 8 [2.2%], p=0.03) were more prevalent in patients with OUD and COVID-19. ConclusionsIndividuals with OUD who received integrated clinical care had a COVID-19 incidence comparable to the general population. Ensuring comprehensive healthcare is essential to prevent the clinical impact of COVID-19 on individuals with OUD.

The determinants of the changing speed of spread of COVID-19 across Italy.

Abstract The COVID-19 epidemic showed inter-regional differences in Italy. We used an ecological study design and publicly available data to compare the basic reproduction number (R0), the doubling time of the infection (DT) and the COVID-19 cumulative incidence (CI), death rate, case fatality rate (CFR) and time lag to slow down up to a 50-days doubling time in the first and the second 2020 epidemic waves (δDT50) by region. We also explored socio-economic, environmental and lifestyle variables with multiple regression analysis. COVID-19 CI and CFR changed in opposite directions in the second vs. the first wave: the CI increased sixfold with no evidence of a relationship with the testing rate; the CFR decreased in the regions where it was initially higher but increased where it was lower. The R0 did not change; the initially mildly affected regions, but not those where the first wave had most severely hit, showed a greater δDT50 amplitude. Vehicular traffic, average temperature, population density, average income, education and household size showed a correlation with COVID-19 outcomes. The deadly experience in the first epidemic wave and the varying preparedness of the local health systems might have contributed to the inter-regional differences in the second COVID-19 epidemic wave.

46. Racial and Ethnic Disparities in COVID-19 Incidence among Persons with HIV in a Multisite-Cohort

Abstract Background Little is known about how race and ethnicity, imperfect (albeit accessible) proxies for structural racism, impact COVID-19 incidence among people with HIV (PWH). We report the cumulative incidence and incidence rate ratios (IRR) for COVID-19 in a long-term multi-site cohort of PWH across the US Figure 1. Cumulative incidence of COVID-19 in the CNICS cohort Methods We examined COVID-19 cumulative incidence and IRR among PWH in care between 3/1/2020 and 12/31/2020 at seven sites in the CFAR Network of Integrated Clinical Systems (CNICS) cohort. We define COVID-19 incident case as having a laboratory-confirmed (RT-PCR/Ag) SARS-CoV-2 positive result or diagnosis verified by chart review. Reinfections were excluded. Results are presented as monthly and quarterly cumulative incidence and IRR with 95% CI stratified by CD4 count, self-reported race/ethnicity, and site. Follow-up was censored on the earliest of diagnosis of COVID-19 disease, loss to follow up, or 12/31/2020 Results Among 15,780 PWH in care in the CNICS cohort during the study period, 62% were non-white, with a median (IQR) age of 52 (IQR 40-59), 95% were on antiretroviral therapy, 17% had a CD4 count less than 350, and 6% less than 200. Overall, 651 PWH tested positive for COVID-19 for a cumulative incidence of 4.13%. COVID-19 cumulative incidence increased from 0.77% at the end of the first quarter to 4.12% by the end of December 2020. At the peak of the pandemic in December 2020, the cumulative incidence in Black PWH was 1.68 fold higher than in white PWH (p=.033) and 2.35 fold higher in Hispanics than in whites (P&amp;lt; .0001), figure 1. Similarly, the IRR for COVID-19 was 1.71 (95% CI 1.42-2.07) for Black and 2.40 (95% CI 1.91-3.01) for Hispanic PWH relative to white. Although there was variation across sites, reflecting geographic differences in pandemic waves and access to COVID-19 testing, overall individual trends remained the same. COVID-19 cumulative incidence was similar across CD4 cell count strata Conclusion Our results suggest effects of structural racial disparities on COVID-19 incidence in this diverse population of PWH across the US, with higher and disproportionate rates of COVID-19 in Black and Hispanic PWH. Incidence estimates are conservative because testing was not uniform, and no systematic testing was conducted Disclosures Edward R. Cachay, MD, MAS, Gilead Science (Grant/Research Support, Advisor or Review Panel member)Merck Sharp &amp; Dohme Corp (Grant/Research Support) Adrienne Shapiro, MD, PhD, Vir Biotechnology (Scientific Research Study Investigator) Darcy Wooten, MD, MS, Nothing to disclose Rachel A. Bender Ignacio, MD MPH, Abbvie (Individual(s) Involved: Self): Consultant; SeaGen (Individual(s) Involved: Self): Consultant Greer A. Burkholder, MD, MSPH, Eli Lilly (Grant/Research Support) Heidi Crane, MD, MPH, ViiV (Grant/Research Support)

Relationship between out-of-hospital cardiac arrests and COVID-19 during the first and second pandemic wave. The importance of monitoring COVID-19 incidence.

BackgroundThe relationship between COVID-19 and out-of-hospital cardiac arrests (OHCAs) has been shown during different phases of the first pandemic wave, but little is known about how to predict where cardiac arrests will increase in case of a third peak.AimTo seek for a correlation between the OHCAs and COVID-19 daily incidence both during the two pandemic waves at a provincial level.MethodsWe considered all the OHCAs occurred in the provinces of Pavia, Lodi, Cremona, Mantua and Varese, in Lombardy Region (Italy), from 21/02/2020 to 31/12/2020. We divided the study period into period 1, the first 157 days after the outbreak and including the first pandemic wave and period 2, the second 158 days including the second pandemic wave. We calculated the cumulative and daily incidence of OHCA and COVID-19 for the whole territory and for each province for both periods.ResultsA significant correlation between the daily incidence of COVID-19 and the daily incidence of OHCAs was observed both during the first and the second pandemic period in the whole territory (R = 0.4, p<0.001 for period 1 and 2) and only in those provinces with higher COVID-19 cumulative incidence (period 1: Cremona R = 0.3, p = 0.001; Lodi R = 0.4, p<0.001; Pavia R = 0.3; p = 0.01; period 2: Varese R = 0.4, p<0.001).ConclusionsOur results suggest that strictly monitoring the pandemic trend may help in predict which territories will be more likely to experience an OHCAs’ increase. That may also serve as a guide to re-allocate properly health resources in case of further pandemic waves.

AML-355: Incidence and Severity of COVID-19 among Inpatients with Hematologic Malignancies

Context:COVID-19 has become the largest health crisis in recent history, with complex healthcare challenges in frontline and critical care settings or in assisting patients with chronic diseases and cancer. Hematologic malignancies (HM) represent an additional factor for severe COVID-19, and mortality rates of 21%–62% for HM patients have been reported.Objective:To assess the incidence and severity of COVID-19 among HM inpatients.Design:We performed a retrospective analysis of electronic health charts of inpatients at Hospital Municipal São José (HMSJ) in Joinville, Brazil.Setting:This one-year period was selected considering that the first COVID-19 case in Joinville was reported on March 13. Local transmission was initially uncommon.Patients or Other Participants:Diagnosis of HM under the WHO classification and hospital stay for more than 48 hours were required. One hundred seventeen consecutive HM inpatients and 152 consecutive inpatients in the one-year period before COVID-19 in Joinville were included.Intervention:No intervention was performed.Main Outcome Measures:Mortality and incidence of COVID-19, severe disease, and thrombotic events.Results:COVID-19 cumulative incidence for HM inpatients was 25%, with one COVID-19 positive test every 73 patient hospital days. Very likely nosocomial transmission was seen in 19 cases (66%). Chemotherapy was administered within 30 days of symptom onset in 19 patients (66%). Severe neutropenia and severe thrombocytopenia were seen in 10 (34%) and 13 (45%) patients, respectively. For HM inpatients with and without COVID-19, the most common diagnoses were acute leukemia (34% and 23%, respectively; p=0.23), lymphoma (28% and 34%; p=0.65), and multiple myeloma (10% and 18%; p=0.4) Severe COVID-19 was established in 22 patients (76%), with 16 (55%) intensive care unit (ICU) admissions and 19 deaths (66%). TEs were reported in six patients. Thrombocytopenia prevented anticoagulation in 11 patients (50%). In-hospital mortality was 66% for HM inpatients versus 32% for HM inpatients without COVID-19 (p=0.002) and 23% for pre-COVID-19 HM inpatients (p<0.001).Conclusions:Cumulative incidence of COVID-19 in HM inpatients and the higher mortality over this one-year period are very alarming. As vaccination efforts are currently in expansion worldwide, it is uncertain whether hospital care for HM will carry on without inpatient transmission. Collaborative studies reporting on COVID-19 outcomes of HM inpatients are needed to guide decisions in this population.

COVID-19 cumulative incidence, asymptomatic infections, and fatality in Long Island, NY, January-August 2020: A cohort of World Trade Center responders.

BackgroundNew York City and Long Island, NY were early foci of the COVID-19 epidemic in the US. The effects of COVID-19 on different sub-populations, and its key epidemiologic parameters remain unknown or highly uncertain. We investigated the epidemiology of COVID-19 from January to August of 2020 in an established academic monitoring cohort of N = 9,697 middle-aged World Trade Center responders residing in Long Island, NY.MethodsA seroprevalence survey and a series of cross-sectional surveys were nested in a prospective cohort study. Measures included IgG antibody testing, SARS-CoV-2 polymerase chain reaction (PCR) testing, review of electronic medical records, and surveys of symptoms. Correlates of infection were analyzed with multivariable logistic regression.ResultsThe cohort was predominantly men in their mid-fifties; 6,597 cohort members were successfully contacted (68%); 1,042 (11%) individuals participated in the seroprevalence survey; and 369 individuals (5.6% of 6,597 study participants) underwent PCR testing. The estimated standardized cumulative incidence was 21.9% (95%CI: 20.1–23.9%), the asymptomatic proportion was 16.4% (36/219; 95%CI: 11.8–22.0%), the case hospitalization ratio was 9.4% (36/385; 95%CI: 6.6–12.7%), the case fatality ratio was 1.8% (7/385; 95%CI: 0.7–3.7%), and the hospitalization fatality ratio was 8.3% (3/36; 95%CI: 1.8–22.5%). Confirmed SARS-CoV-2 infection was associated with younger age, race/ethnicity, and being currently employed.ConclusionsThe results of the present study suggest a high cumulative incidence of SARS-CoV-2 among WTC responders in the spring and summer of 2020 and contribute to narrowing the plausible range of the proportion of infections that exhibit no symptoms. An increased risk of infection among younger employed individuals is likely to reflect a higher probability of exposure to the virus, and the racial disparities in the infection risk warrant further investigation.