- Oct 2020
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For every one confirmed case, Redfield said, the CDC estimates that 10 more people have been infected.
Take away: While this estimate may be the most accurate at the time there are several reasons (addressed below) why any estimate provided at this time may be imprecise. As more data is accrued, including information on the immunological dynamics of the SARS-CoV2 antibodies, we should expect to see a more accurate estimate.
The claim: For every one confirmed case the CDC estimates that another 10 more people have been infected.
The evidence: This estimate was revealed in a press briefing with CDC Director Robert Redfield on June, 25, 2020. It is important to emphasize that this is an estimate extrapolated from the collective data of numerous seroprevalence surveys (antibody tests) performed in different locations across the U.S. While it is most definitely true that the reported case numbers are lower than the actual, given the prevalence of asymptomatic individuals that do not visit medical centers to be tested, the actual figure may be lower or higher than the estimate presented here due to a variety of factors including:
1) Areas surveyed: Indeed, it is known that the number of cases vary disproportionately across different areas of the U.S. According to the CDC, three types of seroprevalence surveys are commonly performed: large-scale geographic, community-level, and special populations. It is important to note that each survey may or may not be completely representative of the specific area yet alone the U.S. as a whole.
2)Type of antibody testing: The FDA reported on the performance of numerous EUA authorized serology tests. The conclusion is that each test has varying levels of accuracy and confidence intervals. As the estimate provided by Redfield was most likely obtained from data derived from the specific test used at each individual surveillance site, the figure may be further skewed by the accuracy of each test.
3)Origin of blood samples: The type of individuals from which the blood samples tested originated may have a significant effect on the Redfield’s estimate. For example, if certain surveillance sites are exclusively testing samples from sick patients, the estimate may be an overestimate as a population presenting COVID symptoms is more likely to test positive than a healthy-looking population. Therefore, a detailed characterization of the individuals from which the blood was obtained would be needed in order to uphold accuracy.
4)Time of tests: As the advent of antibodies can occur a week or longer post-infection, individuals who have recently been infected may not have detectable levels of antibodies and may come up as false negatives. It is also possible for an individual to simply not produce enough antibodies to be detectable by a given serology test. Furthermore, a recent paper published in medrxiv suggests that certain antibodies have reduced titers within 50 days of symptom onset.
Take away: While this estimate may be the most accurate at the time there are several reasons (addressed below) why any estimate provided at this time may be imprecise. As more data is accrued, including information on the immunological dynamics of the SARS-CoV2 antibodies, we should expect to see a more accurate estimate.
The claim: For every one confirmed case the CDC estimates that another 10 more people have been infected.
The evidence: This estimate was revealed in a press briefing with CDC Director Robert Redfield on June, 25, 2020. It is important to emphasize that this is an estimate extrapolated from the collective data of numerous seroprevalence surveys (antibody tests) performed in different locations across the U.S. While it is most definitely true that the reported case numbers are lower than the actual, given the prevalence of asymptomatic individuals that do not visit medical centers to be tested, the actual figure may be lower or higher than the estimate presented here due to a variety of factors including:
1) Areas surveyed: Indeed, it is known that the number of cases vary disproportionately across different areas of the U.S. According to the CDC, three types of seroprevalence surveys are commonly performed: large-scale geographic, community-level, and special populations (1). It is important to note that each survey may or may not be completely representative of the specific area yet alone the U.S. as a whole.
2)Type of antibody testing: The FDA reported on the performance of numerous EUA authorized serology tests (2). The conclusion is that each test has varying levels of accuracy and confidence intervals. As the estimate provided by Redfield was most likely obtained from data derived from the specific test used at each individual surveillance site, the figure may be further skewed by the accuracy of each test.
3)Origin of blood samples: The type of individuals from which the blood samples tested originated may have a significant effect on the Redfield’s estimate. For example, if certain surveillance sites are exclusively testing samples from sick patients, the estimate may be an overestimate as a population presenting COVID symptoms is more likely to test positive than a healthy-looking population. Therefore, a detailed characterization of the individuals from which the blood was obtained would be needed in order to uphold accuracy.
4)Time of tests: As the advent of antibodies can occur a week or longer post-infection, individuals who have recently been infected may not have detectable levels of antibodies and may come up as false negatives. It is also possible for an individual to simply not produce enough antibodies to be detectable by a given serology test. Furthermore, a recent paper published in medrxiv suggests that certain antibodies have reduced titers within 50 days of symptom onset (3).
To conclude, while this estimate may be the most accurate at the time given the available data, many factors can affect the figure and, in some instances, more information is needed as it is unclear exactly how this number was obtained from the information provided in the press briefing.
Sources: 1) https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/about-serology-surveillance.html
3) https://www.medrxiv.org/content/10.1101/2020.07.09.20148429v1
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www.americanthinker.com www.americanthinker.com
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The CDC summarized it succinctly, “For 6% of the deaths, COVID-19 was the only cause mentioned.
The takeaway: >50% of the adult US population has at least one chronic condition. Therefore exclusion of deaths from people with comorbidity will not predict how COVID-19 affects >50% of the adult US population.
The claim: Only 6% of deaths were caused by COVID-19 alone.
The evidence: The CDC website does state that "For 6% of the deaths, COVID-19 was the only cause mentioned." (1) The same web page also states "Data during the period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more."
Additionally, in the USA 6 out of 10 adults have one chronic disease and 4 out of 10 adults have two or more chronic conditions (2). Based on this data, COVID-19 deaths in people with chronic conditions should not be excluded because >50% of the adult population has at least one chronic condition.
Sources:
2 https://www.cdc.gov/chronicdisease/resources/infographic/chronic-diseases.htm
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www.stltoday.com www.stltoday.com
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The positivity rate — the percentage of tests with positive results — is 6.5%, well below the 10% recorded recently, he said.
This is a particularly important metric for any testing program, because it gives a sense of whether enough testing is being done to accurately capture the true positive rate. For instance, the WHO recommends that to ensure adequate testing, the % positive rate should be at or below 5% for at least 14 consecutive days: https://coronavirus.jhu.edu/testing/testing-positivity
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- Sep 2020
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www.scientificamerican.com www.scientificamerican.com
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COVID-19 Can Wreck Your Heart, Even if You Haven’t Had Any Symptoms
Take Away: SARS-CoV-2 infection has been clearly linked to heart muscle injury in those with severe COVID-19 illness. However, at present, there is insufficient data to determine the impact of mild or asymptomatic COVID-19 on the hearts of previously healthy individuals.
The Claim: COVID-19 can wreck your heart, even if you haven’t had any symptoms.
The Evidence: Several articles, including this August 31st piece (1), have raised the alarm about dangerous effects of mild or even asymptomatic cases of COVID-19 on the heart of infected individuals.
In support of this argument, there have been numerous reports, some of which are cited in the article above, documenting severe heart inflammation (myocarditis) and injury (e.g. cardiomyopathy and/or heart failure) in patients with COVID-19. However, most of these documented cases were in individuals with severe cases of COVID-19. At present, the evidence for clinically significant heart injury (requiring treatment or special precautions) from mild or asymptomatic COVID-19, is much less clear, especially in those with no prior evidence of heart disease.
One recent study reported that 78% of patients from an unselected cohort (including patients with asymptomatic, mild, and severe cases) had evidence of myocarditis (via MRI or blood testing) following COVID-19 infection (2). This study clearly demonstrated the link between COVID-19 and myocarditis by examining tissue from biopsies of the heart (the gold standard definitive diagnosis of myocarditis) of patients with the most severe cases. The study went on to show that, on average, patients who were treated for COVID-19 at the hospital (presumably more severe cases) and patients who were treated at home (presumably asymptomatic to moderate cases) both had blood test levels or MRI findings suggesting elevated myocarditis compared to non-COVID-19 infected patients with similar health profiles.
A key limitation here is “average”. The study was not designed or powered to look for the rate of myocarditis in only previously healthy patients with mild or asymptomatic COVID-19. This study included asymptomatic patients in the analysis, but without knowing their prior health or comparing their findings to other healthy non-COVID patients, it is not possible to infer the risk of myocarditis to this population. To their credit, the authors of the study discuss this limitation in their conclusions.
Despite this, the study was widely covered as evidence that ”COVID-19 can wreck your heart, even if you haven’t had any symptoms.“ In order to answer that question, we need research looking selectively healthy patients with mild or asymptomatic COVID-19 as outlined above.
Until that research is conducted, we might look at COVID within the same context as a number of other well studied viruses, many of which generally cause mild illness, that have also been shown to lead to heart injury and inflammation (3).
Disclaimer: This content is not intended as a substitute for professional medical advice. Always seek the advice of a qualified health provider with any questions regarding a medical condition.
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www.dailymail.co.uk www.dailymail.co.uk
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Take away: People are infectious for only part of the time they test positive. The tests for COVID-19 were granted emergency status by the FDA so some debate concerning the most ideal number of cycles is to be expected. It is worth noting that the FDA has the disclaimer "Negative results do not preclude 2019-nCoV infection and should not be used as the sole basis for treatment or other patient management decisions. Negative results must be combined with clinical observations, patient history, and epidemiological information (2)."
The claim: Up to 90 percent of people diagnosed with coronavirus may not be carrying enough of it to infect anyone else
The evidence: Per Walsh et al. (1), SARS-CoV-2 virus (COVID-19) is most likely infectious if the number of PCR cycles is <24 and the symptom onset to test is <8 days. RT-PCR detects the RNA, not the infectious virus. Therefore, setting the cycle threshold at 37-40 cycles will most likely result in detecting some samples with virus which is not infectious. As the PCR tests were granted emergency use by the FDA (samples include 2-9), it is not surprising that some debate exists currently about where the cycle threshold should be. Thresholds need to be set and validated for dozens of PCR tests currently in use. If identifying only infectious individuals is the goal, a lower cycle number may be justified. If detection of as many cases as possible to get closer to the most accurate death rate is the goal, setting the cycle threshold at 37-40 makes sense. A lower threshold will result in fewer COVID-19 positive samples being identified. It is worth noting that the emergency use approval granted by the FDA includes the disclaimer that a negative test does not guarantee that a person is not infected with COVID-19. RNA degrades easily. If samples are not kept cold or properly processed, the virus can degrade and result in a false negative result.
Source: 1 https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa638/5842165
2 https://www.fda.gov/media/134922/download
3 https://www.fda.gov/media/138150/download
4 https://www.fda.gov/media/137120/download
5 https://www.fda.gov/media/136231/download
6 https://www.fda.gov/media/136472/download
7 https://www.fda.gov/media/139279/download
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www.spectator.co.uk www.spectator.co.uk
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Detection of viruses using Polymerase Chain Reaction (PCR) is helpful so long as its accuracy can be understood: it offers the capacity to detect RNA in minute quantities, but whether that RNA represents infectious virus is another matter. RT-PCR uses enzymes called reverse transcriptase to change a specific piece of genetic material called RNA into a matching piece of genetic DNA. The test then amplifies this DNA exponentially; millions of copies of DNA can be made from a single viral RNA strand.
Take away: The claim that virus can be detected for a long time but is not infectious needs further clarification. This claim was based on a Lancet article (1). Within the Lancet article, some of the studies cited detected RNA in stool/blood/seminal fluid samples instead of nasal swabs. Other studies cited did not test infectious nature of virus detected by PCR. It is several logic steps to travel from detecting virus in stool/blood/seminal fluid in Lancet article to concluding that PCR of nasal swabs for COVID-19 results in large numbers of false positives.
The claim: RNA from coronavirus is present and can be detected for a long time but may not be infectious.
The evidence: The Spectator article links to the article "SARS-CoV-2 shedding and infectivity" in the Lancet (1). This article cites seven articles to support the statement that RNA persists long after virus is not infectious. Of these articles, only one reports that virus was detected at ~30 days but could not be cultured beyond three weeks (2). This article also states that detection was easier in stool samples than nasal samples after the first five days. Several articles cited by source 1 did not report infectivity of virus detected (3, 4, 5, and 7). Of the two remaining articles, virus was detected in serum/blood (6 and 8). In the serum study, 58% of tested specimens were infectious (6).
Source:
1 https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30868-0/fulltext
2 https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(03)13412-5/fulltext
3 https://pubmed.ncbi.nlm.nih.gov/15030700/
4 https://pubmed.ncbi.nlm.nih.gov/27682053/
5 https://pubmed.ncbi.nlm.nih.gov/29648602/
6 https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(16)30243-1/fulltext
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www.primarydoctor.org www.primarydoctor.org
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Take away: Though many articles are referenced, additional context is needed because the conclusions of the publications do not always agree with the conclusion of the author of this article. Additionally, publications which conflict with the claims in this article are not presented.
The claim: Masks are neither effective nor safe.
The evidence: The data in the articles referenced here is inconclusive regarding whether masks are or are not effective. Though several studies referenced here did not see a statistically significant difference between those who wore masks and controls, several facts need to be considered. The sample size of people who became sick in the individual studies was small (often <10 people). Compliance in the mask group was not enforced. A number of the articles referenced are pre-prints lacking peer-review and validation. Some of the articles compare N95 masks to surgical masks but do not have a control no mask group. Additionally, the claim of the author of this article sometimes differs from the conclusions written by the authors of the publications cited. Publications which contradict the conclusions of the author are not presented (1, 2).
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