Lunchtime pandemic reading.
Standard disclaimer: this is a roundup of informative pieces I've read that interest me on the severity of the crisis and how to manage it. I am not a qualified medical expert in ANY sense; at best I am reasonably well-read laity. ALWAYS prioritize advice from qualified healthcare experts over some person on Facebook.
This is also available as an email newsletter at https://lunchtimepandemic.substack.com if you prefer the update in your inbox.
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A troubling sign: a day before a Florida DPH manager was removed from her job, she was instructed to delete COVID-19 data. "One day before a top Florida Department of Health data manager was taken off her role maintaining the state’s COVID-19 dashboard, officials had directed her to remove data from public view that showed Floridians reported symptoms of the disease before cases were officially announced, according to internal emails obtained by the Tampa Bay Times. According to the emails, department staff gave the order shortly after reporters requested the same data from the agency on May 5. The data manager, Rebekah Jones, complied with the order, but not before she told her supervisors it was the “wrong call.” Jones also told CBS12 in Tallahassee on Monday that she refused to “manually change data to drum up support for the plan to reopen” the state."
Source: https://www.tampabay.com/news/health/2020/05/19/florida-health-department-officials-told-manager-to-delete-coronavirus-data-before-reassigning-her-emails-show/
Disease has no political allegiance. SARS-CoV-2 doesn't care about opinion polls, public standing, or our feelings. Suppressing or lying about data only serves to make the virus and its impact that much stronger. Continue to hold your elected officials accountable to data and facts.
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NEJM has more on the data gap. "That the United States is failing such a simple test of its capacity to protect public health is shocking. Collecting and reporting public health data are not rocket science. Other countries, notably Canada and Belgium, are already reporting nationwide data on testing at the individual level, including individual demographic data (using ranges for each person to protect privacy) and other key attributes for each test. The United States was once a leader in collecting systematic federal data on population health. Now our national disease-tracking effort seems stuck with well-meaning but scattershot efforts by tech companies using cellular phone signals, social media surveys, online searches, and smart thermometers as we try to guess where Covid-19 outbreaks may be lurking. Small one-off studies using convenience samples have popped up to try to fill the vacuum with basics such as percentages of cases that are asymptomatic and of symptomatic people who seek care. Because of sampling bias, these studies are producing wildly different and nearly uninterpretable results. Estimates are so wide ranging that modelers have little choice but to default back to imprecise assumptions."
Source: https://www.nejm.org/doi/full/10.1056/NEJMp2014836
Part of the problem is also willful ignorance. Many people of many different political orientations are willfully ignoring data, from not wanting to collect more ("more testing makes our numbers look bad") to not wanting to enforce the conclusions it offers ("we've got to open up").
It is shocking that in an age when we have the most sophisticated data analysis tools ever available to humanity that we're using data so poorly to solve this pandemic. History will be - and should be - unkind to those who ignore what data and evidence are shouting from the rooftops.
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Unusual skin conditions continue to be a hallmark of COVID-19. From the Lancet: "Clinical manifestations of coronavirus disease 2019 (COVID-19) are rare or absent in children and adolescents; hence, early clinical detection is fundamental to prevent further spreading. We report three young patients presenting with chilblain-like lesions who were diagnosed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Two of them were asymptomatic and potentially contagious. Skin lesions, such as erythematous rashes, urticaria, and chicken pox-like vesicles, were reported in 18 (20·4%) of 88 patients with COVID-19 in a previous study.3 These symptoms developed at the onset of SARS-CoV-2 infection or during hospital stay and did not correlate with disease severity."
Source: https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30402-3/fulltext
Keep these unusual skin lesions in mind, especially for children. They MIGHT be an indicator of COVID-19, requiring further testing to verify.
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Additional peer-reviewed data on obesity and COVID-19. "This study reports a significant association between the prevalence of obesity and severe COVID-19, including critical COVID-19, and suggests that obesity might be a risk factor of pejorative evolution of COVID-19, increasing the risk of ICU admission. Preliminary analyses from Lille University Hospital, using the same cohort of patients, have also reported a higher prevalence of invasive mechanical intubation in male patients and those with higher BMI, especially at least 35 kg/m2 in ICU patients with COVID-19. Given the dual pandemics of COVID-19 and obesity in high-income countries, our findings have major implications for the clinical care of patients with obesity and COVID-19, as well as for public health interest."
Source: https://www.thelancet.com/journals/landia/article/PIIS2213-8587(20)30160-1/fulltext
The study makes clear that more study is needed to determine if there's a causal effect with COVID-19 and negative outcomes, but for now, the correlative data is enough to say that if a patient is obese - BMI > 30 kg/m2 - they have higher risks for negative outcomes.
That would, in America, put 40% of the population in the at-risk category.
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An excellent, thorough summary of questions to ask about epidemiological models for COVID-19 any time you're reading a study or model about the disease. "1. What is the purpose and time frame of this model? For example, is it a purely statistical model intended to provide short-term forecasts or a mechanistic model investigating future scenarios? These two types of models have different limitations.
2. What are the basic model assumptions? What is being assumed about immunity and asymptomatic transmission, for example? How are contact parameters included?
3. How is uncertainty being displayed? For statistical models, how are confidence intervals calculated and displayed? Uncertainty should increase as we move into the future. For mechanistic models, what parameters are being varied? Reliable modeling descriptions will usually include a table of parameter ranges — check to see whether those ranges make sense.
4. If the model is fitted to data, which data are used? Models fitted to confirmed Covid-19 cases are unlikely to be reliable. Models fitted to hospitalization or death data may be more reliable, but their reliability will depend on the setting.
5. Is the model general, or does it reflect a particular context? If the latter, is the spatial scale — national, regional, or local — appropriate for the modeling questions being asked and are the assumptions relevant for the setting? Population density will play an important role in determining model appropriateness, for example, and contact-rate parameters are likely to be context-specific."
Source: https://www.nejm.org/doi/full/10.1056/NEJMp2016822
This is an outstanding list of questions to keep in mind whenever reading scientific journalism in general, but especially about COVID-19. Treat every news story with the same level of scrutiny, because without it, we risk accepting incorrect conclusions that will prolong the pandemic and magnify its impact.
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A reminder of the simple daily habits we should all be taking.
1. Wash/sanitize your hands every time you are in or out of your home for any reason. Consider also spraying the bottoms of your shoes with a general disinfectant (alcohol/bleach/peroxide) when you return home.
2. Wear gloves and a mask when out of your home. Consider wearing a face shield.
3. Stay home as much as possible.
4. Get your personal finances in order now. Cut all unnecessary costs.
5. Donate any PPE you can. https://getusppe.org/give/
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Common misinformation debunked!
There is no genomic evidence at all that COVID-19 arrived before 2020 in the United States and therefore no hidden herd immunity:
Source:
There is no evidence SARS-CoV-2 was engineered, nor that it escaped a lab somewhere.
Source: https://www.washingtonpost.com/world/2020/01/29/experts-debunk-fringe-theory-linking-chinas-coronavirus-weapons-research/
Source: https://www.nature.com/articles/s41591-020-0820-9
Source: https://www.nationalgeographic.com/science/2020/05/anthony-fauci-no-scientific-evidence-the-coronavirus-was-made-in-a-chinese-lab-cvd/
There is no evidence a flu shot increases your COVID-19 risk.
Source: https://www.factcheck.org/2020/04/no-evidence-that-flu-shot-increases-risk-of-covid-19/
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A common request I'm asked is who I follow. Here's a public Twitter list of many of the sources I read.
https://twitter.com/i/lists/1260956929205112834
This list is biased by design. It is limited to authors who predominantly post in the English language. It is heavily biased towards individual researchers and away from institutions. It is biased towards those who publish or share research, data, papers, etc. I have made an attempt to follow researchers from different countries, and also to make the list reasonably gender balanced, because multiple, diverse perspectives on research data are essential.