46 Comments

This is interesting. Thanks for sharing. Yet another advantage of a national health care system (“Medicare for all”) would be better, more consistent, more centralized data. Health care (not just health data) in this country is in shambles. Exhibit A, a million Americans dead and one of the very highest death rates from covid in the world. It’s a global embarrassment that none of our leaders is talking about. A million dead and we’re just going about our business as if there’s not a huge problem here in this wealthy nation.

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I stopped the video in the middle to send Dr. Rivers' statement "but if every jurisdiction is collecting different data or collecting it in different ways, it's really hard to aggregate that into a national picture that really gives us a sense of what's happening across the country," to my husband, who is a data scientist working for a formulation and contract manufacturing pharmaceutical company. Over and over again, he has heard various groups and projects say that they've got good data to work with, only to find, when the data is finally made available to him, that there are gaps and holes and no two sources collected the same data. Lots of lip service from the company about using data science and machine learning going forward, but no real investment in improving the data collection systems, programs, etc. Various groups that would need to participate in improving and using new data collection systems are not motivated to do so because it would be extra work for an outcome that they aren't invested in. My husband would like all data collection to be motivated by a project statement--the generators of the data need to be invested from the start, to understand how the improvements will benefit them in the long run. It is much more easily said than done, when one part of the company (or country) doesn't do the same thing as the other part of the company (or country). For my husband, better data means better modeling, control, etc. The money it saves the company or the ways it improves process outcomes can sometimes be abstract to the operators or researchers who collect the data. For the country, better data means better public health outcomes for the country, which can feel very nebulous and abstract to too many citizens. Better communication about why consistent data is so important is key to improving the situation. In a nutshell, that is what both Dr. Jetelina and Dr. Rivers are doing. Difficult problem. Thank you both for working on it!

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Super fascinating! Full disclosure: I was a railroad data geek. Gathering, collecting and getting data from disparate sources to play nicely together was one of my work things.

Complete, timely and accurate data doesn't exist anywhere, and is likely impossible, but shouldn't be an excuse for not trying to improve. Glad to hear there are people working at it.

I wonder if the most bang for the buck might occur by working from the bottom up. That is, getting the actual data workers together to suggest standards rather than trying to mandate standards from the top down that would get stuck in the bureaucracy.

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May 4, 2022·edited May 4, 2022

Thanks for all the great work you do in pointing out gaps in our nation's pandemic response.

With the upcoming wave of high school and college graduations, given the CDC's new "risk assessment" map based on hospitalizations (2-3 week lagging indicator), I am concerned that there is no easy way for elderly grandparents to assess their own risk. Many of them will be traveling by plane to attend indoor, unmasked graduation ceremonies, particularly in areas of high transmission such as the northeast.

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In a distributed public health surveillance system, such as we have in the US, actors at different points in the system have different strengths and opportunities, and different functions. An epidemiologist working at the local has all kinds of local context for the case reports, and the flows of syndromic surveillance and laboratory data, that are available in real time from their community. But they don't automatically have a view of similar data from neighboring communities or states, or for the US as a whole. An epidemiologist working at the national level may be able to see patterns across data from multiple jurisdictions that would not be noteworthy in the various jurisdictions themselves. So communication between workers at various levels is paramount in making the system work well. Also, the purposes of data collection and analysis are different at the local, state and national levels. Case reports for reportable diseases at the local level are most important for driving followup and intervention around individual cases, and for local cluster and outbreak detection. Discerning larger patterns over time and space is important, but not the only reason for data collection. At the national level, the detection of patterns over time and space, and their investigation, is the primary reason for data collection. With modern information systems, a national worker can have access to raw data from the local level just about as fast as the local worker, but won't have the advantage of the cleaning and editing that happen at the local level.

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CMS is also the governing body over all plans subject to the Affordable Care Act, which includes millions of people with private insurance. The hospital data they require for the PHE is for all patients at the hospital -- not just Medicare and Medicaid patients. https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

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I had an idea about a way to get decent data about case counts: Why not have a system where a random group of individuals in each state, a group that's representative of the state's demographics, are selected to test themselves say once or twice a week. They could be paid in cash or lottery tickets or something. Or, we could make it like jury duty -- no pay, just your turn to do it this month. Have people send in photos of their rapid test results and use time stamps on them to make sure they're genuine.

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When I worked on data analysis for fisheries, we often had to address problems that arose from different groups and agencies having different standards and priorities for the data they collected.

It was not unusual to encounter situations where a group would collect data on “their favorite species” and lump the rest into “other”. Of course, each group would have a different favorite species (depending on what they were studying), which made the “other” category awfully nebulous, and well, … oh well.

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Thank you. I’m envious that you had the chance to meet with Dr. Rivers, and very appreciative for the excellent interview and questions.

I’m a data geek, as are we all… the painful lack of data has made communicating the concerns and risks much too difficult and has allowed disinformation to prosper because we lacked the information we needed to counter it.

And I agree with @Dan Jepson about seeking input from the PH community.

I suspect Caitlin can leverage a bit of the work on crowdsourced data and statistical analys to make sense of home RAgtest results, but her point about reporting bias were spot on.

Thanks!

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Data geek here, but I prefer the term "Quant" as I've been called one. So data collection standardization is to me paramount. The worst of all worlds is to run an info aggregation system reporting to external end users, where the daily, weekly, and quarterly input is coming in subject to periodic and arbitrary changes in protocols from the reporting entities. Here in Oregon, the OHA has made numerous changes in reporting since early 2020. Usually the changes have been improvements as more reporting data elements were added to the picture, but others were a bit baffling to me at least. There are too many semi-independent reporting baronies and fiefdoms, both public and private. Here in Oregon we were flying blind for quite a while since there was woefully little testing and elr data was lagging but now we are up to speed through the very hard work of the folks there. Widely available private testing though has muddied the picture again alas.

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What can I say! long neglect is hard to fix quickly

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Great informative discussion! After 17+ years on the public health disease intervention front lines several critical points/needs were identified/mentioned. Please seek input from local, district, regional, and statewide public health staff on what is needed and known/anticipated barriers as a critical step in developing any strategy prior to implementation. I acknowledge the messiness of this approach or procedure process.

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This is really helpful information. It has been much harder than I would have ever expected to get data-based answers to the basic questions people are interested in-what is my risk? What is my communities risk? Is the vaccine still working?

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Wonderful work! Thank you for being such a good influence on the influencers! How exciting to get this validation. This is yet another sign that for all its imperfections and mistakes, the right person is leading CDC. I recently heard a one hour interview with Dr Walensky and realized how we have all been hearing only soundbites from her for 2 years. It was good to see her sharp intelligence, humility and determination to keep learning and improving shine through. Sharing it here with your readers https://milkeninstitute.org/video/rochelle-walensky-cdc-director

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founding

Dr. Jetelina, could you address this preprint study (see below) that purports to show that the mRNA vaccines did not reduce all-cause mortality but that the adenovirus-vector vaccines did? I also include the perspective and analysis of the preprint study by Martin Kulldorff, an epidemiologist. Thanks.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4072489

https://brownstone.org/articles/have-people-been-given-the-wrong-vaccine/

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Excellent. Thanks again.

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