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Reconciling estimates of global spread and infection fatality rates of COVID-19: An overview of systematic evaluations
Abstract and Figures
Background
Estimates of community spread and infection fatality rate (IFR) of COVID‐19 have varied across studies. Efforts to synthesize the evidence reach seemingly discrepant conclusions.
Methods
Systematic evaluations of seroprevalence studies that had no restrictions based on country and which estimated either total number of people infected and/or aggregate IFRs were identified. Information was extracted and compared on eligibility criteria, searches, amount of evidence included, corrections/adjustments of seroprevalence and death counts, quantitative syntheses and handling of heterogeneity, main estimates, and global representativeness.
Results
Six systematic evaluations were eligible. Each combined data from 10‐338 studies (9‐50 countries), because of different eligibility criteria. Two evaluations had some overt flaws in data, violations of stated eligibility criteria, and biased eligibility criteria (e.g. excluding studies with few deaths) that consistently inflated IFR estimates. Perusal of quantitative synthesis methods also exhibited several challenges and biases. Global representativeness was low with 78‐100% of the evidence coming from Europe or the Americas; the two most problematic evaluations considered only 1 study from other continents. Allowing for these caveats, 4 evaluations largely agreed in their main final estimates for global spread of the pandemic and the other two evaluations would also agree after correcting overt flaws and biases.
Conclusions
All systematic evaluations of seroprevalence data converge that SARS‐CoV‐2 infection is widely spread globally. Acknowledging residual uncertainties, the available evidence suggests average global IFR of ~0.15% and ~1.5‐2.0 billion infections by February 2021 with substantial differences in IFR and in infection spread across continents, countries, and locations.
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... All infected individuals, both symptomatic and asymptomatic, are accounted for in the IFR calculation, and data therein is based on serology. It is important for anyone analyzing or comparing death statistics to use the IFR and not the Case Fatality Rate (CFR) -the ratio between confirmed deaths and confirmed cases [12,13] -because the CFR is based on potentially unreliable death and confirmed case accounts. 5 There is also a lag in time between when people are infected and when they die, and, most importantly, it does not capture the population with innate immunity. ...
... It is more compelling to use the IFR as a metric for comparison for this and future studies. [12] Descriptive statistics on the incidence rates of relevant AEs were calculated as a percentage of the number of unique VAERS IDs and the fully vaccinated population in the United States. 6 Also calculated are the death rates by SARS-CoV-2 for each respective VAERS update date as reported by the Our World in Data collection. ...
... The infection fatality rate (IFR), which is the number of individuals who died from COVID-19 among all infected individuals (both symptomatic and asymptomatic) is estimated to be 0.15% or 1500 individuals per million. [12,13] Thus, if compared to the death rate reported in the VAERS database in the context of the COVID-19 injections, which is 0.0034% or 34 individuals per million, the chance of dying from SARS-CoV-2 is greater than from the injections, based on data collected from the past four months. It is vital to remember here that the actual number of adverse events ongoing are likely being under-reported, and there are likely to be thousands more backlogged due to underrecording. ...
Following the global roll-out and administration of the Pfizer/BioNTech (BNT1 62b2) and Moderna (mRNA-1 273) COVID-1 9 vaccines 1 on December 1 7, 2020 in the United States, and of the Janssen COVID-1 9 Vaccine PF (produced by Johnson & Johnson) on April 1 st, 2021 , tens of thousands of individuals have reported adverse events (AEs) using the Vaccine Adverse Events Reports System (VAERS). This work summarizes this data to date and serves as information for the public and a reminder of the relevance of any adverse events, including deaths, that occur as a direct result of biologicals as prophylactic treatments. This is especially relevant in the context of technologically novel treatments in the experimental phase of development. Analysis suggests that the vaccines are likely the cause of reported deaths, spontaneous abortions, anaphylactic reactions and cardiovascular, neurological and immunological AEs. The precautionary principle promotes transparency and the adoption of preventative measures to address potential risks to the public in the arena of vaccination programs, and it is vital that individuals are informed of these potential risks before agreeing to participate in any medically involved treatment program. VAERS reporting and recording is essential to the proper functioning of this system. It cannot be overemphasized that the public should know how to use this system such that they actually do use it, and that once reports are made, responsible individuals enter each report into the database accordingly.
... infected groups of patients, with the lowest risk among those who are both vaccinated and previously infected. [1][2][3][4][5] Before Omicron, when vaccine coverage was lower, severe outcome risks estimated by others were higher than we report here; [44][45][46][47][48][49][50][51][52][53] IFRs by single-year of age were estimated to reach 1% from age 60 years, 3% from age 70 years, 8% from age 80 years and 20% from age 90 years. 48 Post-Omicron, fewer seroprevalence-based estimates are available, but among Danish blood donors aged 17-72 years, more than 95% of whom were vaccinated twice, the IFR from January to March 2022 was 0.02% among the oldest adults (61-72 yr). ...
Background:
Population-based cross-sectional serosurveys within the Lower Mainland, British Columbia, Canada, showed about 10%, 40% and 60% of residents were infected with SARS-CoV-2 by the sixth (September 2021), seventh (March 2022) and eighth (July 2022) serosurveys. We conducted the ninth (December 2022) and tenth (July 2023) serosurveys and sought to assess risk of severe outcomes from a first-ever SARS-CoV-2 infection during intersurvey periods.
Methods:
Using increments in cumulative infection-induced seroprevalence, population census, discharge abstract and vital statistics data sets, we estimated infection hospitalization and fatality ratios (IHRs and IFRs) by age and sex for the sixth to seventh (Delta/Omicron-BA.1), seventh to eighth (Omicron-BA.2/BA.5) and eighth to ninth (Omicron-BA.5/BQ.1) intersurvey periods. As derived, IHR and IFR estimates represent the risk of severe outcome from a first-ever SARS-CoV-2 infection acquired during the specified intersurvey period.
Results:
The cumulative infection-induced seroprevalence was 74% by December 2022 and 79% by July 2023, exceeding 80% among adults younger than 50 years but remaining less than 60% among those aged 80 years and older. Period-specific IHR and IFR estimates were consistently less than 0.3% and 0.1% overall. By age group, IHR and IFR estimates were less than 1.0% and up to 0.1%, respectively, except among adults aged 70-79 years during the sixth to seventh intersurvey period (IHR 3.3% and IFR 1.0%) and among those aged 80 years and older during all periods (IHR 4.7%, 2.2% and 3.5%; IFR 3.3%, 0.6% and 1.3% during the sixth to seventh, seventh to eighth and eighth to ninth periods, respectively). The risk of severe outcome followed a J-shaped age pattern. During the eighth to ninth period, we estimated about 1 hospital admission for COVID-19 per 300 newly infected children younger than 5 years versus about 1 per 30 newly infected adults aged 80 years and older, with no deaths from COVID-19 among children but about 1 death per 80 newly infected adults aged 80 years and older during that period.
Interpretation:
By July 2023, we estimated about 80% of residents in the Lower Mainland, BC, had been infected with SARS-CoV-2 overall, with low risk of hospital admission or death; about 40% of the oldest adults, however, remained uninfected and at highest risk of a severe outcome. First infections among older adults may still contribute substantial burden from COVID-19, reinforcing the need to continue to prioritize this age group for vaccination and to consider them in health care system planning.
... The COVID-19 pandemic declared on March 11th, 2020, had a devastating impact on people's physical and mental health across the globe. Sars-COVID 2 generated an infectious disease that took the lives of millions and infected around 1.5 to 2 billion people globally (Ioannidis, 2021). Governments introduced major lockdowns and tight restrictions to curb the spread and protect people from infecting one another. ...
Despite the omnipresence of rhythm in music, movement, circadian cycles, and learning processes, this research topic is the first volume to offer interdisciplinary approaches the topic. Empirical research on timing and precision from the microbiological level of synapses to the macro level of elite solo and ensemble performances is presented. The volume provides results gained by the use of microscopes, motion capture systems, medical equipment such as imaging scans, as well as artistic and educational experience.
The goal of this research topic is to present current, scientific studies that examine the ways in which rhythm effects human biology, behavior, perception, and art. Reciprocally, science can learn from studies conducted with artists about how they experience, express and synchronize rhythms.
... By 2021, the global mortality rate had reached 0.15% with 1.5-2.0 billion infections. 1 The severity of this viral infection ranges from mild common cold symptoms to life-threatening severe acute respiratory failure. Studies have indicated that COVID-19 tends to progress more rapidly and have a more severe course, especially in the elderly (>65 years), individuals with chronic diseases such as diabetes and cardiovascular diseases, and those using immunosuppressive drugs. 2 Inflammatory or autoimmune disorders have been associated with an increased risk of infection and a more severe disease course, attributed to the disease itself, comorbidities, and immunosuppressive treatments, particularly corticosteroids. ...
Coronaviruses are a group of enveloped viruses with nonsegmented, single-stranded, and positive-sense RNA genomes. Coronaviruses belong to the “Coronaviridae family”, which causes various diseases, from the common cold to SARS and MERS. In March 2020 the World Health Organization declared the SARS-Cov-2 virus a global pandemic. We performed a review to describe existing literature about Corona Virus Disease 2019 (COVID-19) history, Symptoms, Epidemiology, Clinical features, Clinical manifestations, Diagnosis, Treatment, Prevention.
International human rights standards and bioethical norms with regard to informed consent for all medical interventions logically apply to COVID-19 vaccines. This invasive medical procedure carries both known and unknown risks. Over the past two years, COVID-19 vaccine mandates significantly infringed on the individual’s right to medical self-determination, violating Article 7 of the International Covenant on Civil and Political Rights, and Article 5 of the Oviedo Convention. The COVID-19 era practices to ostracize, spurn, pressure, mandate, pay, fraudulently induce, and shame people into getting vaccinated against their will violated key Bioethical standards and long-established International Human Rights Law norms jus cogens and obligations erga omnes. Voluntariness, like so many other notions, is not an over-simplified yes-or-no concept but a matter of degree and understanding all the relevant facts in casu. Coercion itself encompasses a broad range of permutations, from applying physical force at one end to applying subtle emotional pressure on the other. Any kind of pressure put on an individual impedes and therefore nullifies the voluntariness of his or her decision, irrespective of the degree. When Government and Corporate COVID-19 biomedical medical paternalists pressured citizens to take the COVID-19 vaccines through threats of punishment if they did not and promises of reward if they did, it failed voluntariness on all counts.
Background
Mechanisms of pulmonary thrombosis (PT) in COVID-19 are unknown. Thromboembolism and local pulmonary inflammation have been suggested as the main factors. However, robust evidence is still lacking because this was mainly based on retrospective studies, in which patients were included when PT was suspected. On the other hand, the number of thrombi within lung opacification, and the association with percentage of pulmonary involvement (TLI) related to COVID-19 were not evaluated. The main objective was to determine the number and percentage of thrombi surrounded by lung opacification (TSO) in each patient, as well as their relationship with TLI.
Methods
Consecutive patients with COVID-19 pneumonia performed computed tomography pulmonary angiography. We determined TLI and TSO in patients with PT. TLI was automatically calculated by artificial intelligence analysis. TSO was defined when there was lung opacification ≤ 10 mm from each pulmonary vessel with a thrombus. Analyses at patient level (TLI and percentage of TSO) and at thrombi level (TLI and TSO) were performed.
Results
We diagnosed PT in 70 out of 184 patients. Three (2–8) thrombi/patient were detected. The median percentage of TSO was 100% per patient (75–100%), and TLI was 19.9% (4.6–35.2) in all patients. Sixty-five patients (92.9%) were above the random scenario (in which the percentage of TSO should correspond to the percentage of lung involvement in each patient), and had more percentage of TSO than TLI in each patient. Most thrombi (n = 299, 75.1%) were TSO. When evaluating by TLI (< 10%, 10–20%, 20–30%, and > 30%), percentage of TSO was higher in most groups. Thrombi were mainly in subsegmental/segmental arteries, and percentage of TSO was higher in all locations.
Conclusion
Thrombi in COVID-19 pneumonia complicated with PT were found within lung opacities in a higher percentage than lung involvement, regardless of the proportion of pulmonary infiltrates and clot location, supporting the hypothesis that COVID-19 could promote local pro-thrombotic phenomena rather than “classic thrombo-embolism”. These data expand understanding of PT in COVID-19 and support a partial justification for why thromboprophylaxis does not prevent PT. Further studies should focus on new strategies to reduce the thrombotic risk.
An unprecedented global social and economic impact as well as a significant number of fatalities have been brought on by the coronavirus disease 2019 (COVID-19), produced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Acute SARS-CoV-2 infection can, in certain situations, cause immunological abnormalities, leading to an anomalous innate and adaptive immune response. While most patients only experience mild symptoms and recover without the need for mechanical ventilation, a substantial percentage of those who are affected develop severe respiratory illness, which can be fatal. The absence of effective therapies when disease progresses to a very severe condition coupled with the incomplete understanding of COVID-19’s pathogenesis triggers the need to develop innovative therapeutic approaches for patients at high risk of mortality. As a result, we investigate the potential contribution of promising combinatorial cell therapy to prevent death in critical patients.
Today’s world is mostly affected by the COVID-19 pandemic; due to this, so many employees are unemployed. This virus initially started in China and later spread to all countries. The vaccine or treatment is not available as of now. Illness like fever, cold, pneumonia, etc., is symptoms. The self-employees, business persons, and labor workers typically face health and employment issues. The pandemic origins lockdown so that many people worldwide and developing countries face unemployment problems. The economy balancing is very tough to handle country GDP (gross domestic price). In this situation, machine learning-based COVID-19 impact estimation application design must cross the limitations above. Therefore, the Random Forest Optimization (RFO) machine learning model is implemented for this work. The following model classifies the affecting parameters to unemployment risk. This performance measures expecting application robustness, and metrics like accuracy 98.45%, recall 97.23%, sensitivity 95.34%, F measure 93.78%, and sensitivity 99.32% had been attained. These measures outperform methodology and compete with future applications related to data sensitivity estimation. The RFO-based COVID-19-based unemployment is an estimation model that gives social and ethical elements strength. This application is most useful for the present economy and proposes challenges to machine learning models.KeywordsUnemploymentCOVID-19RFO ClassifierMachine learning model
Background
Many studies report the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies. We aimed to synthesize seroprevalence data to better estimate the level and distribution of SARS-CoV-2 infection, identify high-risk groups, and inform public health decision making.Methods
In this systematic review and meta-analysis, we searched publication databases, preprint servers, and grey literature sources for seroepidemiological study reports, from January 1, 2020 to December 31, 2020. We included studies that reported a sample size, study date, location, and seroprevalence estimate. We corrected estimates for imperfect test accuracy with Bayesian measurement error models, conducted meta-analysis to identify demographic differences in the prevalence of SARS-CoV-2 antibodies, and meta-regression to identify study-level factors associated with seroprevalence. We compared region-specific seroprevalence data to confirmed cumulative incidence. PROSPERO: CRD42020183634.ResultsWe identified 968 seroprevalence studies including 9.3 million participants in 74 countries. There were 472 studies (49%) at low or moderate risk of bias. Seroprevalence was low in the general population (median 4.5%, IQR 2.4-8.4%); however, it varied widely in specific populations from low (0.6% perinatal) to high (59% persons in assisted living and long-term care facilities). Median seroprevalence also varied by Global Burden of Disease region, from 0.6% in Southeast Asia, East Asia and Oceania to 19.5% in Sub-Saharan Africa (p
Background:
Serology tests can identify previous infections and facilitate estimation of the number of total infections. However, immunoglobulins targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported to wane below the detectable level of serologic assays (which is not necessarily equivalent to the duration of protective immunity). We estimate the cumulative incidence of SARS-CoV-2 infection from serology studies, accounting for expected levels of antibody acquisition (seroconversion) and waning (seroreversion), and apply this framework using data from New York City and Connecticut.
Methods:
We estimated time from seroconversion to seroreversion and infection fatality ratio (IFR) using mortality data from March to October 2020 and population-level cross-sectional seroprevalence data from April to August 2020 in New York City and Connecticut. We then estimated the daily seroprevalence and cumulative incidence of SARS-CoV-2 infection.
Results:
The estimated average time from seroconversion to seroreversion was 3-4 months. The estimated IFR was 1.1% (95% credible interval: 1.0-1.2%) in New York City and 1.4% (1.1-1.7%) in Connecticut. The estimated daily seroprevalence declined after a peak in the spring. The estimated cumulative incidence reached 26.8% (24.2-29.7%) at the end of September in New York City and 8.8% (7.1-11.3%) in Connecticut, higher than maximum seroprevalence measures (22.1% and 6.1%), respectively.
Conclusions:
The cumulative incidence of SARS-CoV-2 infection is underestimated using cross-sectional serology data without adjustment for waning antibodies. Our approach can help quantify the magnitude of underestimation and adjust estimates for waning antibodies.
Objectives
Coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 has been affecting many people on earth and our society. Japan is known to have relatively smaller number of its infections as well as deaths among developed nations. However, accurate prevalence of COVID-19 in Japan remains unknown. Therefore, we conducted a cross-sectional study to estimate seroprevalence of SARS-CoV-2 infection.
Methods
We conducted a cross-sectional serologic testing for SARS-CoV-2 antibody using 1000 samples from patients at outpatient settings who visited the clinic from March 31 to April 7, 2020, stratified by the decade of age and sex.
Results
There were 33 positive IgG among 1000 serum samples (3.3%, 95%CI: 2.3–4.6%). By applying this figure to the census of Kobe City (population: 1,518,870), it is estimated that the number of people with positive IgG be 50,123 (95%CI: 34,934–69,868). Age and sex adjusted prevalence of positivity was calculated 2.7% (95%CI: 1.8–3.9%), and the estimated number of people with positive IgG was 40,999 (95%CI: 27,333–59,221). These numbers were 396 to 858-fold more than confirmed cases with PCR testing in Kobe City.
Conclusions
Our cross-sectional serological study suggests that the number of people with seropositive for SARS-CoV-2 infection in Kobe, Japan is far more than the confirmed cases by PCR testing.
Substantial COVID-19 research investment has been allocated to randomized clinical trials (RCTs) on hydroxychloroquine/chloroquine, which currently face recruitment challenges or early discontinuation. We aim to estimate the effects of hydroxychloroquine and chloroquine on survival in COVID-19 from all currently available RCT evidence, published and unpublished. We present a rapid meta-analysis of ongoing, completed, or discontinued RCTs on hydroxychloroquine or chloroquine treatment for any COVID-19 patients (protocol: https:// osf.io/QESV4/). We systematically identified unpublished RCTs (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, Cochrane COVID-registry up to June 11, 2020), and published RCTs (PubMed, medRxiv and bioRxiv up to October 16, 2020). All-cause mortality has been extracted (publications/preprints) or requested from investigators and combined in random-effects meta-analyses, calculating odds ratios (ORs) with 95% confidence intervals (CIs), separately for hydroxychloroquine and chloroquine. Prespecified subgroup analyses include patient setting, diagnostic confirmation, control type, and publication status. Sixty-three trials were potentially eligible. We included 14 unpublished trials (1308 patients) and 14 publications/preprints (9011 patients). Results for hydroxychloroquine are dominated by RECOVERY and WHO SOLIDARITY, two highly pragmatic trials, which employed relatively high doses and included 4716 and 1853 patients, respectively (67% of the total sample size). The combined OR on all-cause mortality for hydroxychloroquine is 1.11 (95% CI: 1.02, 1.20; I² = 0%; 26 trials; 10,012 patients) and for chloroquine 1.77 (95%CI: 0.15, 21.13, I² = 0%; 4 trials; 307 patients). We identified no subgroup effects. We found that treatment with hydroxychloroquine is associated with increased mortality in COVID-19 patients, and there is no benefit of chloroquine. Findings have unclear generalizability to outpatients, children, pregnant women, and people with comorbidities.
The overarching objective of this study was to provide the descriptive epidemiology of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in Qatar by addressing specific research questions through a series of national epidemiologic studies. Sources of data were the centralized and standardized national databases for SARS-CoV-2 infection. By July 10, 2020, 397,577 individuals had been tested for SARS-CoV-2 using polymerase-chain-reaction (PCR), of whom 110,986 were positive, a positivity cumulative rate of 27.9% (95% CI 27.8–28.1%). As of July 5, case severity rate, based on World Health Organization (WHO) severity classification, was 3.4% and case fatality rate was 1.4 per 1,000 persons. Age was by far the strongest predictor of severe, critical, or fatal infection. PCR positivity of nasopharyngeal/oropharyngeal swabs in a national community survey (May 6–7) including 1,307 participants was 14.9% (95% CI 11.5–19.0%); 58.5% of those testing positive were asymptomatic. Across 448 ad-hoc testing campaigns in workplaces and residential areas including 26,715 individuals, pooled mean PCR positivity was 15.6% (95% CI 13.7–17.7%). SARS-CoV-2 antibody prevalence was 24.0% (95% CI 23.3–24.6%) in 32,970 residual clinical blood specimens. Antibody prevalence was only 47.3% (95% CI 46.2–48.5%) in those who had at least one PCR positive result, but 91.3% (95% CI 89.5–92.9%) among those who were PCR positive > 3 weeks before serology testing. Qatar has experienced a large SARS-CoV-2 epidemic that is rapidly declining, apparently due to growing immunity levels in the population.
Background
Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.
Methods
On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.
Results
The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1–2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2–99.7%) and sensitivity was 82.8% (95% CI 76.0–88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7–1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3–4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000–35 000) using unweighted prevalence] people were infected in Santa Clara County by late March—many more than the ∼1200 confirmed cases at the time.
Conclusion
The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.
The ability to preferentially protect high-risk groups in COVID-19 is hotly debated. Here, the aim is to present simple metrics of such precision shielding of people at high risk of death after infection by SARS-CoV-2; demonstrate how they can estimated; and examine whether precision shielding was successfully achieved in the first COVID-19 wave. The shielding ratio, S, is defined as the ratio of prevalence of infection among people in a high-risk group versus among people in a low-risk group. The contrasted risk groups examined here are according to age (≥70 vs <70 years), and institutionalised (nursing home) setting. For age-related precision shielding, data were used from large seroprevalence studies with separate prevalence data for elderly versus non-elderly and with at least 1000 assessed people≥70 years old. For setting-related precision shielding, data were analysed from 10 countries where information was available on numbers of nursing home residents, proportion of nursing home residents among COVID-19 deaths and overall population infection fatality rate (IFR). Across 17 seroprevalence studies, the shielding ratio S for elderly versus non-elderly varied between 0.4 (substantial shielding) and 1.6 (substantial inverse protection, that is, low-risk people being protected more than high-risk people). Five studies in the USA all yielded S=0.4–0.8, consistent with some shielding being achieved, while two studies in China yielded S=1.5–1.6, consistent with inverse protection. Assuming 25% IFR among nursing home residents, S values for nursing home residents ranged from 0.07 to 3.1. The best shielding was seen in South Korea (S=0.07) and modest shielding was achieved in Israel, Slovenia, Germany and Denmark. No shielding was achieved in Hungary and Sweden. In Belgium (S=1.9), the UK (S=2.2) and Spain (S=3.1), nursing home residents were far more frequently infected than the rest of the population. In conclusion, the experience from the first wave of COVID-19 suggests that different locations and settings varied markedly in the extent to which they protected high-risk groups. Both effective precision shielding and detrimental inverse protection can happen in real-life circumstances. COVID-19 interventions should seek to achieve maximal precision shielding.
Significance
Infection with the novel coronovirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a worldwide pandemic of COVID-19 disease. Efforts to design local, regional, and national responses to the virus are constrained by a lack of information on the extent of the epidemic as well as inaccuracies in newly developed diagnostic tests. In this study we analyze data from testing randomly selected Indiana state residents for infection or previous exposure to SARS-CoV-2 and derive estimates of the statewide COVID-19 prevalence in an attempt to address potential biases arising from nonresponse and diagnostic testing errors.
To make informative public policy decisions in battling the ongoing COVID-19 pandemic, it is important to know the disease prevalence in a population. There are two intertwined difficulties in estimating this prevalence based on testing results from a group of subjects. First, the test is prone to measurement error with unknown sensitivity and specificity. Second, the prevalence tends to be low at the initial stage of the pandemic and we may not be able to determine if a positive test result is a false positive due to the imperfect test specificity. The statistical inference based on a large sample approximation or conventional bootstrap may not be valid in such cases. In this paper, we have proposed a set of confidence intervals, whose validity doesn't depend on the sample size in the unweighted setting. For the weighted setting, the proposed inference is equivalent to hybrid bootstrap methods, whose performance is also more robust than those based on asymptotic approximations. The methods are used to reanalyze data from a study investigating the antibody prevalence in Santa Clara County, California in addition to several other seroprevalence studies. Simulation studies have been conducted to examine the finite-sample performance of the proposed method.
OBJECTIVE
To examine whether the age distribution of COVID-19 deaths and the share of deaths in nursing homes changed in the second versus the first pandemic wave.
ELIGIBLE DATA
We considered all countries that had at least 4000 COVID-19 deaths occurring as of January 14, 2020, at least 200 COVID-19 deaths occurring in each of the two epidemic wave periods; and which had sufficiently detailed information available on the age distribution of these deaths. We also considered countries with data available on COVID-19 deaths of nursing home residents for the two waves.
MAIN OUTCOME MEASURES
Change in the second wave versus the first wave in the proportion of COVID-19 deaths occurring in people <50 years (“young deaths”) among all COVID-19 deaths and among COVID-19 deaths in people <70 years old; and change in the proportion of COVID-19 deaths in nursing home residents among all COVID-19 deaths.
RESULTS
Data on age distribution were available for 14 eligible countries. Individuals <50 years old had small absolute difference in their share of the total COVID-19 deaths in the two waves across 13 high-income countries (absolute differences 0.0-0.4%). Their proportion was higher in Ukraine, but it decreased markedly in the second wave. The odds of young deaths was lower in the second versus the first wave (summary prevalence ratio 0.81, 95% CI 0.71-0.92) with large between-country heterogeneity. The odds of young deaths among deaths <70 years did not differ significantly across the two waves (summary prevalence ratio 0.96, 95% CI 0.86-1.06). Eligible data on nursing home COVID-19 deaths were available for 11 countries. The share of COVID-19 deaths that were accounted by nursing home residents decreased in the second wave significantly and substantially in 8 countries (prevalence ratio estimates: 0.36 to 0.78), remained the same in Denmark and Norway and markedly increased in Australia.
CONCLUSIONS
In the examined countries, age distribution of COVID-19 deaths has been fairly similar in the second versus the first wave, but the contribution of COVID-19 deaths in nursing home residents to total fatalities has decreased in most countries in the second wave.