- Last 7 days
twitter.com twitter.comMarco Rubio on Twitter: "79,584 people between 15-24 tested positive in #Florida: -0.95% of them were hospitalized -0.026% of them died Risk from being away from school & sports far greater than risk from #COVID19 Answer is to lower their risk of infection & protect their loved ones at high risk https://t.co/L05nmrYg1J" / Twitter1
Take away: The claim here is ultimately a value judgement, but the data used to support the claim is subject to examination. Overall the source for the presented data was not identifiable and the overall argument requires greater context to evaluate.
The claim: Students are harmed more by not being in school/sports than by COVID-19.
The evidence: The claim is supported by a single data table. Unfortunately, the source for the table presented was not found. It is well documented that the infection fatality rate in age groups 15-24 is lower than for older age groups, however the actual infection fatality rate for this age group is still not entirely clear. As one example, the CDC website has two data sets to estimate COVID-19 deaths. One is provisional COVID-19 deaths with 242 deaths reported in the age group 15-24 in the entire United States. The second dataset estimates COVID-19 deaths based on the increase over the number of expected deaths based on historical data. The second dataset does not present data for 15-24 age group, only "under 25 years." The lower range of the second dataset is ~10,000 deaths in people under 25 years in Florida above the expected deaths for the year. These deaths may be attributed to COVID-19.
Regardless, no data is presented which measures how students are harmed by not being in school. Additional data needs to be presented for the claim to be validated including measurable metrics by which students are harmed. These are not presented in the Twitter post.
Sources: https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm#dashboard https://data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Sex-Age-and-S/9bhg-hcku/data https://experience.arcgis.com/experience/96dd742462124fa0b38ddedb9b25e429 https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/older-adults.html#:~:text=As%20you%20get%20older%2C%20your,than%20people%20in%20their%2050s.
Your Coronavirus Test Is Positive. Maybe It Shouldn’t Be.
Take Away: Diagnostic tests are most useful when they are both sensitive and rapid. The sensitivity of SARS-CoV-2 PCR tests is not the issue, but rather the time it takes to get a result. Additionally, the "90%" statistic is likely misleading due to the data source and not generalisable to all testing results.
The Claim: The usual PCR diagnostic tests may be too sensitive and too slow, with up to 90% of positive cases due to trace amounts of virus.
The Evidence: Polymerase Chain Reaction (PCR)-based tests, which are currently in the most widespread use for detection of SARS-CoV-2 RNA, involves a molecular process that amplifies target DNA sequences in repeated temperature-dependent cycles. The amount of target DNA is measured after each cycle and the number of the cycle when the target can be reliably detected is often referred to as the cycle threshold (Ct). The Ct value is proportional to the amount of starting DNA in the sample and can be used to estimate the viral load of a patient. In some ways this is like a teacher making photocopies of a chapter from a textbook until they have enough for all their students.
However, Ct values are relative measurements and need to be directly compared to controls for every sample - a Ct value taken alone can be meaningless. For instance, consider an infected patient who is tested twice: the first time they are gently swabbed and the sample is relatively dilute, the second time they are vigorously swabbed and the sample is relatively concentrated. The resulting Ct values could be drastically different. Therefore, Ct values need to be considered carefully in the proper context for making medical or policy decisions. The FDA also recommends that a PCR result alone should not be used to determine infection status.
Positive results are indicative of the presence of SARS-CoV-2 RNA; clinical correlation with patient history and other diagnostic information is necessary to determine patient infection status. (1)
Current PCR test results are generally given as a binary positive/negative based on a cutoff value for Ct. The cutoff needs to be determined based on the performance of each individually developed SARS-CoV-2 test, of which there are currently over 160 that have been granted emergency use authorization by the FDA (2). Based on unpublished data from the CDC, setting a stringent Ct cutoff of 30 could return negative results in patients who are both infected and potentially infectious (3 Fig 5). Furthermore, a 30 cycle cutoff would return invalid results for samples which are too diluted. Based on the same CDC data, up to 30% of potentially infectious patients would get invalid results and need to be re-swabbed, thereby extending the time between getting infected and getting a positive result.
The period of time when RNA from SARS-CoV-2 can be detected (and a positive PCR test result returned) may extend up to 12 weeks after recovery, with Ct values trending higher over time (3,4). According to The New York Times article, they looked at Ct values from people who tested positive in Massachusetts in July and found 85-90% of results had Ct values greater than 30. The epidemiology of COVID-19 is highly time and region dependent. Massachusetts had a peak in COVID-19 hospitalizations on April 21 (5), which is 9-12 weeks prior to the testing data analyzed by The NY Times. Therefore, the detection of a large proportion of people with lingering viral RNA is not surprising. These results are likely not universal and can not be applied to other regions, especially where community spread is still significant.
(4) Li N, Wang X, Lv T. Prolonged SARS-CoV-2 RNA Shedding: Not a Rare Phenomenon. J Med Virol 2020 Apr 29. doi: 10.1002/jmv.25952.
- Sep 2020
If we are now going to hold our nation hostage because of this obsession over PCR (polymerase chain reaction) swab tests, we should at the very least make certain they’re accurate. What happens when we have expedited and chaotic test results driving an epidemic curve rather than actual symptoms? You get what happened to Ohio Governor Mike DeWine last Thursday. He tested positive for the virus after experiencing absolutely no symptoms. But because he is such a VIP, he got a second, more accurate test that showed he was in fact negative for SARS-CoV-2. The same thing happened to Detroit Lions quarterback Matthew Stafford, who tested negative after receiving a false positive and was therefore allowed out of coronavirus prison.
Take away: Current polymerase chain reaction (PCR) testing technology is very sensitive and specific. Even for rapidly developed new tests for the novel coronavirus, SARS-CoV-2, available clinical data indicates they are highly accurate.
The claim: SARS-CoV-2 testing is unreliable and plagued by false positive results.
The evidence: Any diagnostic test has some degree of error that is typically very low for FDA approved products. For SARS-CoV-2 tests, which detect the presence of the virus that causes COVID-19, although rare, it is possible to get a positive result when you may not have been exposed or infected by the virus. In other words, a false positive. So how frequently do false positives occur?
There is no universal false positive rate for SARS-CoV-2 test results because there are dozens of different tests that have been developed and deployed, each with their own error rate. As of August 26, 2020, there are 146 commercial diagnostic tests that have received emergency use authorization from the FDA. Data from clinical performance testing submitted to the FDA indicates that PCR tests are highly accurate. For example, the specific PCR test mentioned by the author, Quest Diagnostics SARS-CoV-2 rRT-PCR, obtained 100% correct results in clinical evaluation studies (n = 60), and 100% true negative results in a random population of samples from before the pandemic (n = 72).
Additional considerations: In addition to PCR technology-based tests, which detect the viral RNA genome and require lab processing, there are antigen tests, which use antibodies to detect viral proteins and can be rapidly performed in point-of-care settings. Antigen tests are much easier to perform than PCR tests, but they can be less sensitive. For example, the LumiraDx SARS-CoV-2 Ag Test, when compared to PCR, has an overall agreement of 96.9%.
The author provides two anecdotes of high-profile personnel who obtained false positive test results. For the Ohio Governor, his initial positive was from an antigen test, not a PCR test. The NFL quarterback is part of a unique population that is presumed to be largely SARS-CoV-2 negative but is being tested frequently and repeatedly. This scenario increases the probability that a positive test result may be false. However, the NFL in early August said it has conducted over 75,000 tests, so unless there are many additional cases of false positives, this suggests that their testing methodology is over 99.99% accurate.
It is so mild that half of infected people are asymptomatic, shown in early data from the Diamond Princess ship, and then in Iceland and Italy.
The takeaway: Reported numbers of asymptomatic individuals are discordant but generally are less than 20% of reported cases.
The claim: Half of people infected with COVID-19 are asymptomatic.
The evidence: 17.9% of the Diamond Princess ship were asymptomatic (1). Only 48 out of 473 total cases were from asymptomatic individuals in Iceland (2). The initial analysis of China's asymptomatic cases was 1% (3). A research article summarizing data from China and Italy lists China's asymptomatic cases as 80.9% and Italy's asymptomatic cases as 8.5% (4). It appears that mild symptoms and asymptomatic cases were combined in reference 4 for China's data as mild symptom numbers were N/A.
Therefore, there is no consensus on the number of asymptomatic individuals. Additional clarity is needed in the data before conclusions can be made based on the number of asymptomatic individuals.
- Aug 2020
COVID-19 first appeared in a group of Chinese miners in 2012
Take away: The COVID-19 virus (SARS-CoV2) did not exist in 2012, however a related virus was isolated from bats in 2013.
The claim: The same virus that is causing the COVID-19 pandemic existed in miners in 2012.
The evidence:RaTG13, a virus that was isolated from bats by the Wuhan Institute of Virology in 2013 is the closest known relative to SARS-CoV2, the virus that causes COVID-19 (Ge 2016, Zhou 2020). This bat virus is not the same virus as SARS-CoV2, but is closely related (96% identical DNA). The virus was isolated from bats, not humans. However, it was isolated from a cave near where workers the previous year became sick and some died, and may be linked to the illnesses. The SARS-CoV2 virus shows a number of key adaptations that likely makes it much more infectious in humans than the related bat virus (Wrobel, 2020).
Ge XY, Wang N, Zhang W, Hu B, Li B, Zhang YZ, Zhou JH, Luo CM, Yang XL, Wu LJ, Wang B. Coexistence of multiple coronaviruses in several bat colonies in an abandoned mineshaft. Virologica Sinica. 2016 Feb 1;31(1):31-40.
Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, Si HR, Zhu Y, Li B, Huang CL, Chen HD. A pneumonia outbreak associated with a new coronavirus of probable bat origin. nature. 2020 Mar;579(7798):270-3.
Wrobel AG, Benton DJ, Xu P, Roustan C, Martin SR, Rosenthal PB, Skehel JJ, Gamblin SJ. SARS-CoV-2 and bat RaTG13 spike glycoprotein structures inform on virus evolution and furin-cleavage effects. Nature Structural & Molecular Biology. 2020 Aug;27(8):763-7.
twitter.com twitter.comAlex Berenson on Twitter: "1/ TL:DR - @who published a massive review/meta-analysis of interventions for flu epidemics in 2019, found "moderate" evidence AGAINST using masks. (They actually missed the 2015 Vietnam study, yet another brick in the wall.) 2019. So recent, yet so long ago. What's changed?" / Twitter1
@who published a massive review/meta-analysis of interventions for flu epidemics in 2019, found "moderate" evidence AGAINST using masks.
Take away: In their 2019 report the WHO actually recommended for, not against, the use of masks in severe influenza epidemics or pandemics, contrasting the statement made in this tweet. Further, recent evidence overwhelmingly supports the benefit of masks for preventing the spread of SARS-CoV2, the virus that causes COVID-19.
The claim: Overall the claim here appears to be that masks are ineffective against the spread of SARS-CoV2, the virus that causes the clinical syndrome known as COVID-19. The evidence used in support of this claim is that “the WHO found ‘moderate’ evidence AGAINST using masks” in their 2019 report on the use of non-pharmaceutical interventions for mitigating influenza pandemics.
The evidence: This overall claim is poorly supported by data and the evidence used to support this claim is incorrectly characterized by the claimant. Narrowly, the claim that the WHO recommended against mask use is patently false. In their report, the WHO reviewed 10 separate studies and did conclude that there was scant evidence that masks significantly decreased spread of the flu. However, they found no evidence that masks increased spread, and based on mechanistic plausibility (i.e. masks are barriers that prevent droplets from passing between people) and the low risk/high reward, they made a conditional recommendation for mask use in severe influenza epidemics or pandemics.
While influenza does not behave exactly like the SARS-CoV2 virus, the similarities in mode of transmission make it reasonably likely that masks would also have protective effects against the spread of this virus is well. The best evidence is hard data, and that too increasingly points to the benefit of masks for slowing down or preventing the transmission of SARS-CoV2. A recent summary of that data is available here.
- Jul 2020
When virus levels in the population are very low, the chances of a test accurately detecting Covid-19 could be even less than 50 per cent
Take away: Real-world evidence from countries like New Zealand, that already have very low disease incidence, suggests that the concerns for false positives raised in this article are overhyped.
The claim: "When virus levels in the population are very low, the chances of a test accurately detecting Covid-19 could be even less than 50 per cent..."
The evidence: The author explains theoretical scenarios where, when rates of true COVID infections are low, the rate of true positives (test positive and have COVID) may be equal to or less than false positives (test positive but do not have COVID). The background here is that no test is perfect and every screening test used in medicine has some percentage of false negatives and false positives. Several anecdotes are cited in support, however real world data from countries that already have very low disease incidence, suggests that the concerns or false positives raised in this article are unfounded. New Zealand, for example, has tested an average of 2127 people per day from July 1-22, with an average of 1.2 positive cases identified per day—an average % positive of only 0.07%. In order for the authors assumptions to hold, all of the positive tests reported there would have to be false positives—highly unlikely as New Zealand still has symptomatic patients. Therefore, real-world evidence from standard PCR based COVID testing in low incidence populations suggests that the concern for high rates of false positives raised in this article is overhyped.
Are there parts that we should have closed? – It’s not obvious from what we know so far. I don’t think it is. There are no activities that we can point to as extremely vulnerable. There aren’t.
This is an oversimplification that dismisses research findings on particularly vulnerable activities. For instance, superspreading events have been linked to certain activities, as detailed here. For instance, a study of 110 case-patients from 11 clusters in Japan, linked all clusters to closed environments, including fitness centers, shared eating environments, and hospitals. The authors found an 18.7x risk of transmission in closed environments than open environments. Therefore, it is inaccurate to say "there are no activities that we can point to as extremely vulnerable."