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Dennis OConnor

In the coronavirus pandemic, we're making decisions without reliable data - 4 views

www.statnews.com/...ecisions-without-reliable-data

covid testing research pandemic data

shared by Dennis OConnor on 19 Mar 20 - No Cached
  • A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without reliable data
  • This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19.
  • As most health systems have limited testing capacity, selection bias may even worsen in the near future.
  • ...8 more annotations...
  • The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.
  • Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%).
  • Although successful surveillance systems have long existed for influenza, the disease is confirmed by a laboratory in a tiny minority of cases.
  • Some worry that the 68 deaths from Covid-19 in the U.S. as of March 1610 will increase exponentially to 680, 6,800, 68,000, 680,000 … along with similar catastrophic patterns around the globe. Is that a realistic scenario, or bad science fiction? How can we tell at what point such a curve might stop?
  • In the absence of data, prepare-for-the-worst reasoning leads to extreme measures of social distancing and lockdowns.
  • This has been the perspective behind the different stance of the United Kingdom keeping schools open12, at least until as I write this. In the absence of data on the real course of the epidemic, we don’t know whether this perspective was brilliant or catastrophic.
  • One of the bottom lines is that we don’t know how long social distancing measures and lockdowns can be maintained without major consequences to the economy, society, and mental health.
  • At a minimum, we need unbiased prevalence and incidence data for the evolving infectious load to guide decision-making.
  • Dennis OConnor on 19 Mar 20
     
    Dr. Michael Kurisu D.O. "My take is this article is written by a very credible source. John P.A. Ioannidis is from Stanford and great resource. Makes argument that we are basing a LOT of our decisions on faulty or NO data ! Its fascinating to me that there has been less than 10,000 deaths globally and we have had SO MUCH DISRUPTION in the economy. I definitely feel we should be tracking the amount of deaths that are going to occur from people that will be pushed into poverty as well as the number of people being denied access to medical care right now. Yes… with COVID19, it CAN get much worse…. But maybe not… we don't know yet. This article actually increased my morale and put me on track to help GET MORE DATA. Then we can make informed decisions. And then TRACK ALL THE DATA moving forward.
  • Dennis OConnor on 19 Mar 20
     
    DeAunne Denmark, MD, PhD, "Excellent piece spelling out the pervasive and critical issues due to abysmal lack/tardiness in US testing, especially of large populations where initial outbreaks occurred, for those both visibly sick and not. And most importantly, healthcare workers. We cannot even begin to estimate CFR, much less develop reliable projection models, without valid data on everybody who is carrying. "The most valuable piece of information for answering those questions would be to know the current prevalence of the infection in a random sample of a population and to repeat this exercise at regular time intervals to estimate the incidence of new infections."
Dennis OConnor

Which Covid-19 Data Can You Trust? - 0 views

hbr.org/...ch-covid-19-data-can-you-trust

data visualization data covid Trustworthy

shared by Dennis OConnor on 10 May 20 - No Cached
  • incomplete or incorrect data can also muddy the waters, obscuring important nuances within communities, ignoring important factors such as socioeconomic realities, and creating false senses of panic or safety, not to mention other harms such as needlessly exposing private information.
  • Right now, bad data could produce serious missteps with consequences for millions.
  • Whether you’re a CEO, a consultant, a policymaker, or just someone who is trying to make sense of what’s going on, it’s essential to be able to sort the good data from the misleading — or even misguided.
  • ...24 more annotations...
  • These non-transparent, un-validated interventions — which are now being rolled out (or rolled back) in countries such as China, India, Israel and Vietnam — are in direct contravention to the open cross-border collaboration that scientists have adopted to address the Covid-19 pandemic.
  • Data products that are too broad, too specific, or lack context.
  • Public health practitioners and data privacy experts rely on proportionality
  • only use the data that you absolutely need for the intended purpose and no more.
  • Even data at an appropriate spatial resolution must be interpreted with caution — context is key.
  • Simply presenting them, or interpreting them without a proper contextual understanding, could inadvertently lead to imposing or relaxing restrictions on lives and livelihoods, based on incomplete information.
  • The technologies behind the data are unvetted or have limited utility.
  • Both producers and consumers of outputs from these apps must understand where these can fall short.
  • In the absence of a tightly coupled testing and treatment plan, however, these apps risk either providing false reassurance to communities where infectious but asymptomatic individuals can continue to spread disease, or requiring an unreasonably large number of people to quarantine.
  • Some contact-tracing apps follow black-box algorithms, which preclude the global community of scientists from refining them or adopting them elsewhere.
  • common red flags
  • Models are produced and presented without appropriate expertise.
  • Epidemiological models that can help predict the burden and pattern of spread of Covid-19 rely on a number of parameters that are, as yet, wildly uncertain.
  • n the absence of reliable virological testing data, we cannot fit models accurately, or know confidently what the future of this epidemic will look like
  • and yet numbers are being presented to governments and the public with the appearance of certainty
  • Read Carefully and Trust Cautiously
  • Transparency: Look for how the data, technology, or recommendations are presented.
  • Thoughtfulness: Look for signs of hubris.
  • Example: Telenor
  • Expertise: Look for the professionals. Examine the credentials of those providing and processing the data.
  • Open Platforms: Look for the collaborators.
  • technology companies like Camber Systems, Cubeiq and Facebook have allowed scientists to examine their data,
  • The Covid-19 Mobility Data Network, of which we are part, comprises a voluntary collaboration of epidemiologists from around the world analyzes aggregated data from technology companies to provide daily insights to city and state officials from California to Dhaka, Bangladesh
  • This pandemic has been studied more intensely in a shorter amount of time than any other human event.
  • Dennis OConnor on 10 May 20
     
    "This pandemic has been studied more intensely in a shorter amount of time than any other human event. Our globalized world has rapidly generated and shared a vast amount of information about it. It is inevitable that there will be bad as well as good data in that mix. These massive, decentralized, and crowd-sourced data can reliably be converted to life-saving knowledge if tempered by expertise, transparency, rigor, and collaboration. When making your own decisions, read closely, trust carefully, and when in doubt, look to the experts."
Dennis OConnor

Data strategy for achieving a patient-centric future - Partner Content - 0 views

www.mmm-online.com/...eving-a-patient-centric-future

data strategy

shared by Dennis OConnor on 20 Sep 22 - No Cached
  • Dennis OConnor on 20 Sep 22
     
    "Life science companies seeking further advances toward a truly patient-centric future should consider working with an external partner that has extensive experience and a reliable, transparent and proven information portfolio. Leveraging core data linked and integrated with data generated by patients, and providing access to novel, on-demand data sources through a network of curated data partners provides enriched data that goes beyond the patient experience with a particular brand. By understanding the full details of the patient journey, optimal engagement of patients and HCPs can be enabled, thereby delivering the right treatment to the right patient, supporting adoption and adherence and achieving the ultimate goal of patient-centricity."
Dennis OConnor

Anthony S. Fauci, M.D., NIAID Director | NIH: National Institute of Allergy and Infecti... - 0 views

www.niaid.nih.gov/director

covid research reliable-sources

shared by Dennis OConnor on 20 Mar 20 - No Cached
  • Dr. Fauci was appointed Director of NIAID in 1984.
  • Dr. Fauci has advised six Presidents on HIV/AIDS and many other domestic and global health issues.
  • Dennis OConnor on 20 Mar 20
     
    DeAunne Denmark, MD, PhD - Recommends Dr. Anthony Fauci as a highly credible source of information.
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