Science Policy After the COVID-19 Crisis

foreign I'm Tony Mills I'm a senior fellow here at the American Enterprise Institute um I want to welcome you all thank you for coming um particularly those of you who braved the Friday before the holiday weekend to be with us in person but also thanks to those who are tuning in uh virtually um what I thought I would do is before getting into our panel we have a terrific lineup today two excellent panels I'm really excited about the discussions which will cover a range of important and substantive issues relating to the covid-19 crisis but I wanted to start by just saying a few words about why we're having this event here now by way of background um so I think it would be uncontroversial to say that the covid-19 crisis was in fact a crisis however we think about it it's something that we will all be groveling with for uh some time to come um certainly among the most uh important events uh of recent times um uh of course it was a Public Health crisis arguably the worst in a hundred years which resulted in an unspeakable amount of death and illness um but it also has resulted in a set of policy and political uh decisions which were unprecedented the effects of which we're still grappling with and trying to understand and the effects of covet I think could be measured not only in terms of the massive amount of Carnage but also the secondary effects of of the of the crisis I think in terms of learning loss mental health issues um other sorts of medical problems that were exacerbated by by the crisis but also the effect that the crisis had on our social Fabric and our political life if you spend time reading the history of epidemics which is a demoralizing exercise I don't necessarily recommend you'll find that this is a very frequent pattern that one of the the worst effects of epidemics and pandemics uh is to cause a social conflict political conflict and an erosion of social trust and I think we see that today and we'll be grappling with that for a long time to come so all of that would be reason enough I think to have serious reflection on what happened during the coveted crisis what we did well what we did badly what lessons we could learn but there's another arguably more important reason which is that covet is surely not the last Public Health crisis we will face it is very very likely not the last pandemic we will face uh in in the years to come and so while recognizing with I think humility that there are certain deep patterns that can recur when crises like this face us we're not helpless and we do have uh tools that we can use and draw on to think about how to Grapple with crises like this one thing I think we could all agree on regardless of our politics and what we think about covid is that we did not handle covet very well that the response particularly in the United States is not an Exemplar of good governance or good policy and the question that we have to think about today is how we can do better next time what kinds of lessons we can learn uh for policy uh for thinking about how we use evidence for policy making which is the subject of our first panel but also how we think about uh institutional reform and building resilience uh into our federal and other institutions which is what we'll talk more about in the second panel crisis receives the window in which to learn those lessons risks uh closing and it's for this reason that I think we need to have this conversation now one might have expected a National Blue Ribbon commission to look at the covet crisis and to think about what we did well and badly so maybe something akin to what happened after the 9 11 attacks in 2000 2001. um demoralizingly we've seen surprisingly little action of that kind here in Washington um relatively little compared to the scale of the crisis and its importance for thinking about uh the future as it happens a group of experts did get together in 2021 with the purpose of trying to create a commission like the 911 Commission and had inadequate success among policy makers and putting something like that together um fortunately for us they decided to press onward and uh formed the covid crisis Group which was an independent uh group of experts that uh tried to examine what we did well and badly during the crisis it put together a set of recommendations for how we might move forward uh and this uh these recommendations came together in a report a book called lessons from the coveted war that was released just this past spring and so part of what we wanted to do today is highlight the work of that report and engage with its findings and recommendations and so we're fortunate to have a member of the covid crisis group on each of our panels today so we hope this will be the beginning of a set of conversations the uh only the the start of of more to come and we're honored here at AEI to convene this and to contribute to that in a small way so uh without further Ado I'd like to introduce our first panel so we can hear less from me and more from the experts um so joining us on our first panel today we have Mark lipsich um who is a professor of epidemiology at the Harvard Chan School of Public Health and director of the center for communicable disease Dynamics during the pandemic he was named the founding co-director of the new center for forecasting and outbreak analytics within the Center for Disease Control and prevention where he now serves as a senior advisor uh we also have joining us Emily ricotta who's an independent research scholar in the division of intramural research at the National Institute of allergy and infectious diseases one of the divisions within the National Institutes of Health where Emily leads the epidemiology and data management unit last but not least we have Jonathan Fuller who has the dubious distinction of being a fellow philosopher like me he's currently serving as a visiting scholar in the department of bioethics at the National Institutes of Health he's also a professor in the department of history and philosophy of science at the University of Pittsburgh as well as a co-founder and Deputy editor of the excellent Journal which I recommend philosophy of Medicine so with that I'd like to pass it over to mark thanks Tony and thanks for the invitation to be here I'm glad that this panel is happening I think it's a really opportune time it gets less opportune by the day so the sooner we we discuss these things and reach some understanding of how we can move forward together the better I should say that although I do hold a part-time secondment to the CDC I'm speaking in my personal and academic capacity not as a representative of the CDC um I was one of the people that was on the um on the coveted crisis Group which produced this book that many of you may have seen um and lessons from the coveted War um and and Tony asked me to say a couple words about the process of that so um as Tony mentioned the the original idea was to be the sort of groundwork layers for a full-scale investigative commission that might be set up by the government or or in some other way and that began in 2021 through the efforts of um for Philip zelaco who led the 911 Commission as the as the executive director and who worked with four foundations um the Schmidt Futures Foundation the Rockefeller Foundation the school foundation and stand together to try to get some diversity of of funding and diversity of uh and then diversity of viewpoints on the on the planning what was then the covid commission Planning Group and he began assembling that group uh through a sort of snowball process of interviewing people who had been involved in the pandemic in various ways asking some of us to after the interviews to to come and join the the group that was doing these interviews and so we sort of assembled in that way um and as Tony said the goal had been to to put together uh documents and uh oral history and and um some fresh memories for the potential future commission and uh as we all saw that was not adopted in the various pieces of legislation that could have adopted it um and so the the book was written um uh really by Philip uh which is good because he's actually a great writer and some of us are not all as good writers as most of us are not as good writers as he um and I won't go into the details of it I do recommend it I think uh one of the things that's good about it is that it's extremely readable because of his ability to write well uh even about kind of bureaucratic and institutional history which is we've all read books about that those topics that are not well written and it's a lot easier to write about personalities and individual stories but the goal really was to write about the systemic issues and what uh what was decided How It was decided and why it had in many cases to be decided in certain ways that led to less good outcomes than we would have liked um uh but it's it's I think that's part of the value of the book is that it's um it does have individual characters it does it does have stories um but it but it really focuses on the structures and on the um on the unfortunate fact that had we had the best leadership possible at that time in all parts of government we still would have had a really uphill uh battle there were some clear failures of leadership and those are are discussed in the book but um but the system really was not designed it talks about the sort of Grover Cleveland era design of our Public Health System um uh and the the analogy between our the way that we deliver public health and the state militias under the Articles of Confederation so that gives you a taste of of the kind of um level at which it's written and nonetheless it's uh easily readable so um I think I'll leave it there we'll come back to some of the to the topics that are in the book uh during the course of the discussion um I think I I'll Now sort of shift to a few perspectives of my own that I think are consistent with but probably not all in the book uh um uh but I think yeah deep seriously consistent with uh with what the book tries to lay out um so the topic of our panel is the role of Science in pandemic decision making um and the the perspectives that I want to bring are to start by thinking about the kind of time evolution of of that question so um the pandemic at all of its phases was an exercise in decision making under uncertainty and um and the uncertainty in many ways was greatest at the beginning and and narrowed as it went on and as we learn more but it persisted and uh and I think it's helpful to think about how you should act in relation to the science when the science is minimal when it's uh somewhat more established and when it's more mature which we could think of as kind of the beginning the middle in the in the later phases of the pandemic so in the beginning there's a need to kind of be precautionary and use uh use shreds of evidence even shreds of uh indirect evidence to make policies that will delay the bad outcome um and I think there was a process of Education that happened at the beginning of the pandemic where people started to understand what it means to have an exponentially growing threat which can be small today and large tomorrow and where small is easier to control than large and where delay is the name of the game in the sense that as we found in this pandemic if you got coveted in February uh sorry in April of 2020 you faced overloaded hospitals very few medical countermeasures very little evidence about what countermeasures might work and so forth if you got it a year later people knew how to treat the disease there was not yet an antiviral that was specific and good but there was there were steroids there were other procedures in the hospital you were much better off and if it was if it wasn't New York at the height of its first wave you were also less likely to be in an overcrowded Hospital so delay is is valuable flattening the curve as as hackneyed as the term became really does mean that fewer people get infected over the over the course of it as well as those who do get infected get infected later and so really you're you're trying your best as a society to delay the uh the spread we didn't do that as well as we could have and that is that was uh one of the the biggest issues I think it's important to mention the the issue of sort of the complementarity of following science in in that um in that environment um I was one of the few people that among my friends who was in favor initially of Border restrictions um uh it was not a popular view among many public health experts um but but in retrospect I made an assumption that was wrong which was that if we could delay entry by a month of the or or delay the spread of the virus by the month we would have a we would have time to prepare but in fact that delay is only useful if you have time to prepare and because we weren't doing much preparation in February and March that delay was not as valuable as it could have been so I think I was right on the on the principle but wrong in in the event as you get to the and and and the precautionary approach means that you do a lot of interventions that should be marked very big with a very big red asterisk as temporary and based on our current level of ignorance um uh and school closures which I think we'll get to later um is is a good example of that it was absolutely the right thing to close schools initially because we didn't know what the role of children would be in spreading it and um and it made a big uh it made a lot of sense given our understanding of how many respiratory viruses spread but it should have had an asterisk saying this is based on what we know and we're we're going to revisit these decisions and I think that's a general a general issue that I'll mention in a second in the as the pandemic wears on evidence begins to accumulate but it often is not decisive and so the the need to integrate evidence from different sources from social science from um economics from biology from randomized Trials of particular interventions becomes more necessary and as we try to figure out how to live with what turned out to be a very long-term threat the the goal really has to be to make sense of different kinds of evidence none of which is completely conclusive by itself and to treat it as as a multiple input decision making problem and then I think Emily will probably talk about this some in much more detail but I think in the later stages what struck me was the importance of observational evidence as opposed to randomized trial evidence most of what we know about the vaccines right now and the clinical decisions that all of us and our Healthcare Providers make are based on observational studies because we didn't do trials with this variant with this number of vaccines with this immune history they're just a lot of things that we can't do trials on and we have to understand the value of observational evidence and take steps to make it better um uh I want to finish by saying a couple of things I think we can learn from other countries responses and how they integrate science the US has among the most informal processes uh in major industrialized countries for integrating scientific input into government decision making on public health and putting aside the FDA where it's quite formalized but for for decisions of the sort of many of the kinds that we had to make in the pandemic there was no centralized group of experts who who filtered evidence from science into policy making in contrast to say the United Kingdom where there was uh Sage which got many criticisms and and is not perfect but it is a formal structure to filter evidence and and provide decision makers with um with a sense of what the scientific Community believes including the the dissent within that scientific community um another lesson and I think that happened informally particularly in many states there were com councils of advisors assembled but there was no structure and those I mean I was on the one in Massachusetts for the governor on several in the Massachusetts for the governor and because it was uh informally assembled that meant it was a several hour a week time commitment not a several not a many hour week time commitment and nobody had the time um to to really we couldn't respond to formal requests for evidence for example um another thing the UK did very well and I'm focused on the UK because it's the one I know the best and because it was one of the best in terms of evidence was to put in place structures to gather evidence they put in place two large surveillances studies that made them understand what was going on at a regional level at all times in a very comparable way across jurisdictions um uh that we never had um and and then there's the preparedness aspect of putting in place data structures and data data authorities for for the federal agencies to collect data from the states and and structures for reporting from States and from Health Care to be more effective and there's a whole discussion to be had about that but I'll just leave it as a teaser there what hi thank you so much for having me here today um I am an infectious disease epidemiologist at the National Institute of allergy and infectious diseases however I am also here in my personal capacity today and give my opinions anything that I say are my own and don't represent that of nyad or the US government so I'm really here today to talk about data so a lot of what Mark said you know having to get that evidence having a lot of misunderstanding and uncertainty as we moved throughout the pandemic how we answer those questions how we become more certain is by gathering data doing research having surveillance systems all of those things and unfortunately data is difficult to collect and manage even at the best of times but it's certainly made substantially more difficult during an epidemic or pandemic situation and so we really you know today I really want to talk about some of the things that went wrong some of the things we could have done better um during the pandemic in terms of data collection and surveillance and research I do want to point out that you know there is a difference in the reasons why we collect data how we collect data and where we collect it from based on whether we're trying to understand uh where a disease is spreading in a community so disease surveillance you know we want to know how many cases of an infection are in certain locations this differs from whether we're trying to answer questions of how well does a vaccine work or how well does an intervention work and so when we talk about these Data Systems we need to separate surveillance and research and other data needs because it's not all just a one-size-fits-all data collection not all data is going to answer every question um so that's really what I want to talk about today my expertise is in the design and conduction of clinical studies not observational studies in particular as well as how and why we collect data how we use it how we manage it so that's that's what I am hoping to bring to this discussion I've had the opportunity to work at a State Health Department I've worked in Academia I've worked with some cdc-funded programs for surveillance so I have I've had a unique opportunity to really understand data collection and use from a wide variety of of different areas so a couple of problems that I want to highlight that we ran into during the covet pandemic although are not surprises to anybody who has done research or surveillance um it's difficult to collect and manage large amounts of data particularly sensitive data so we have to remember that in a pandemic you're collecting information on people's Health on who is sick on demographic information about them we learn a lot about people through the illnesses that they have and that information gets reported and people can have various feelings about that you know some of us don't mind having that information shared other people have reasons why they might not want that data to be shared and so we need to ensure while we're collecting all of this data we're doing it in a sensitive manner we're doing in a way that can protect people's privacy people's information and that's difficult to do especially when you're trying to connect data systems across different platforms um we need to collect data in a real time fashion so having data from three weeks ago doesn't really help us if we're trying to respond to a pandemic so we need to have data on how many people are getting tested are those tests positive or negative are people able to get treatments are they taking those treatments all of these things we need them in real time to understand how the situation is evolving and to understand how we can actually respond to it and how well those responses are working otherwise why are we investing in any of these interventions or things like that so it's not only is it a public health thing but there's also that economic consideration too of making sure that we're using this data to help inform how and where we're doing our Public Health response um in addition to it being collected in real time we need some sort of standardization so just having a bunch of data is better than having no data but it takes a lot of time to manage data um it's you know we in the data science world we joke we spend about 70 percent of our time cleaning data that other people hand us and only about 30 of our time actually doing research with it um and that goes for surveillance as well so if you get data um you know in the US we get surveillance data from state and local Health departments we can get data from healthcare companies from Center for Medicaid services all over the place and none of these Data Systems are standardized or meant to work with one another because that's not how our system works in the US which is problematic when it comes time for a pandemic and we need that data to be interoperable to be able to be joined together so that we can say okay we may not have one federal system of data but we have lots of different data that covers and answers what we need so let's bring it together so we need to be able to collect that data in real time and we need to be able to to have it standardized and interoperable and then we need to have people who can actually analyze it and utilize it and we need to be able to get that data from the people analyzing it up to our decision makers who can then make those decisions based on the data and then provide a response and unfortunately in all of these different settings the United States has fallen short in a pandemic situation Mark mentioned you know we don't really have those oversight organizations who can take data and and the things that we find from it and make it actionable so that's one of the things the report talks a lot about I hope that we get to touch on that more today is you know who maybe is responsible for taking the the data and the insights and actually making them actionable um foreign so there are as I mentioned there's you know we have surveillance we've got research and so we have different places that we can get data from um a lot of people just think oh data appears oh I wish that were the case it doesn't um not in the United States in any way and so we can get data from you know surveillance systems we have existing surveillance systems in the U.S the CDC has the emerging infections program where there are pilot states around the country who report data on certain diseases to the CDC and the CDC collects information we have each state has a list of reportable illnesses that have somebody has it they get to the doctor they've got salmonella that's got to get reported and so we have these systems in place there is data capture there is electronic transfer to the CDC and so one of the things that I think we could talk about is the potential to strengthen those systems to really pay more attention to them invest in them and expand them as one way of helping fix the problem of this lack of data that we have although there are many others we also collect data by doing studies observational studies so these are studies where you're not randomizing people to a particular intervention so randomized controlled trials are our gold standard but we can't always do them like Mark said or they're expensive they're you have to set them up with mindfulness and standardization and protocols and that takes time it can take money you can be in a place where there there's just no way to get enough people enrolled because there aren't enough cases or cases already blew through and now everyone's immune so there are a number of reasons why we can't necessarily use a randomized control trial and we have to turn to something else such as observational studies the problem with those again standardization so we don't have at this moment in time there's not been much work done on standardizing observational studies on making you know pre-thinking about protocols and case report forms things that we could absolutely spend some time on it and prepare for and then have them ready when a pandemic situation emerges and then you know take those into the field and collect that data which can then be used to help inform in place of maybe lacking surveillance systems maybe in place of you know where you'd want to do a control trial but you can't or as we did in the pandemic you know we had our vaccine studies but that left out large groups of the population we weren't able to include pregnant women in early randomized control Trials of vaccines we didn't talk about people with immune compromises and so what do we do we have to rely on observational studies post-market evaluations to assess efficacy of interventions in those populations and so standardizing that making sure that people have um you know the money and the people to actually do that work is very important and it's something that we should be discussing in terms of its own preparedness initiative finally we have real world data as the FDA calls it this is things like Electric chronic health records and we use this data all the time for research it could also be an opportunity for surveillance in the United States though we don't have a centralized Health Care system I don't know if all of you knew that but you know and so our health care relies on working with companies working with different systems at every single Hospital in the country and those systems are not easily accessible they're not necessarily required to send in any data to the CDC in general or in the event of an emergency they're certainly not interoperable I've had the opportunity and Misfortune of having to work with a couple of them and they can be very difficult and time consuming to work with and that really slows us down and that really delays our ability to respond to an emergency and so you know another area of opportunity I think is to really figure out well how could we work with these corporations with these Health Care Systems who collect a lot of our real world data and how can they help us respond to a situation get the data that we need to answer questions do research do surveillance so there's a lot of opportunity I think to work with the data to improve the data and having better data will enable us to prepare and respond to outbreak situations in a much more efficient and and you know concise manner so that's that's what I'm here to talk about today and I'm looking forward to the discussion about that thanks to my co-panelists thanks to Tony American Enterprise Institute the third and similar disclosure is that I'm here talking on behalf of myself representing my own views and not those in my institutions um so we heard a lot about data and I want to turn again to thinking a bit about evidence a related concept roughly when we use data to support certain conclusions or certain decisions and I want to talk also about evidence-based policy which is a term that's often thrown around in government sometimes in public health and can mean different things to different people on some readings evidence-based policy just might mean using some kind of data or rationale to support decisions but that expression evidence base actually comes from medicine in the 1990s the first evidence-based discipline evidence-based medicine which brought with it a a number of different principles and heuristics for thinking about evidence some of which are quite useful in a pandemic especially when we're trying to evaluate Trials of Novel Therapeutics but some of which might be awkward or inappropriate so the ideas of evidence-based medicine can be boiled down to a few um Orthodoxy ebm or evidence-based medicine says that the highest quality evidence comes from randomized controlled Trials of the kind that Emily mentioned in which we're randomizing units usually individuals but sometimes communities or groups to different treatment arms in order to compare the relative effectiveness of different interventions or policies and also we might want to synthesize evidence in a certain way by doing a systematic review of the literature and where possible pooling the results of multiple similar trials together in what's called a meta-analysis to come up with a singular estimate of the quote-unquote effect of an intervention that's often done with drug trials in which we have a similar intervention that might in some cases have a similar effect on different kinds of people and other cases it might not and in which the effectiveness might not be that dependent on let's say social context another idea that often travels with evidence-based medicine can be summarized by the phrase too much medicine which is a movement popular in the United Kingdom and also here in the United States and elsewhere and this is the idea that medicine especially in recent decades has been prone to over treatment over diagnosis too much medicine and so Sciences like clinical epidemiology can serve as an important check on unbridled medical enthusiasm by making sure that we're only using those interventions that are supported by the best evidence that shows that they're effective and safe and that's more of a political kind of Bend to the evidence-based medicine movement that might temper a kind of um you know interventionism or interventionist strain okay so what do we need for evidence-based policy in public health um I I suggest to you that we need to think a little bit differently like Mark mentioned we need in public health science and decision making to rely on a diversity of evidence to answer the diverse kinds of questions that we we want to we want to have answers to we want to understand the mode of transmission of a pathogen infection fatality rates the role of schools and transmission the role of Therapeutics the role of vaccines and different policies in order to mitigate transmission and so on and different kinds of evidence play different roles we wouldn't use a natural history study in which we're not intervening in order to understand the effectiveness of an intervention obviously so that means that we need a kind of approach to evidence-based Public Health that recognizes the value of diverse evidence including evidence Beyond randomized trials we also need diverse modes of thinking and reasoning as well for partly for a reason that Mark mentioned and that's that in an evolving Public Health Emergency there are just different phases in which we have relative amounts of uncertainty and different amounts of data and evidence available we might reason in a more precautionary fashion early on and just require having modeling results or evidence or data that makes plausible certain assumptions and outcomes or plausibly supports the use of certain policies that we might then collect more data on and re-evaluate as we go along and so relying on just the highest quality evidence at the early stage might on the contrary actually lead us to be too cautious and not act in order to revert a catastrophe so because we need diverse evidence we also need diverse experts and Public Health Sciences and interdisciplinary interdisciplinary field we need diverse experts to avoid blind spots that particular scientific disciplines might suffer from as well as the kind of reason together to come up with an understanding of complex health problems and a pandemic is is a prime example of a complex health problem but this of course means that different scientific communities that are used to handling different kinds of evidence experimental evidence observational evidence could come up could could end up butting heads or clashing or having differing understandings of What kinds of studies we need and what kinds of studies are appropriate I think partly to deal with this challenge of enter the need for interdisciplinarity but the fact that different disciplines are different ways of thinking through evidence and decision making I actually want a second Mark's call for a scientific Advisory Group for emergencies in the United States a sage group similar to what's what they have in the United Kingdom and also what advises the World Health Organization as a kind of standing group that's there an inter-pandemic periods that involves all the relevant kinds of scientific expertise from epidemiology and other Public Health Sciences as well as from social sciences including economics that can help to answer important scientific questions in between Public Health emergencies and to think through what kinds of evidence or processes we might need when an emergency hits that's independent of government both for the uh for the perception of having freedom from political interference but also actually for uh for the purpose of being relatively independent of political influence and this is a group of people that shouldn't be ad hoc but should be there as I mentioned in between crises the second thing I think we need is we need government institutions to lead the way on preparing for the kinds of research studies we might want having dedicated funding streams for these uh coming up with protocols in order to standardize research across multiple areas when it comes time to collect the relevant data identify important priorities for research and gaps in research and then be able to kick in and partner with private and public groups based on existing relationships in order to get the studies done quickly when we need them this these tasks shouldn't just be left to Academia even though Academia did a great job during the pandemic at answering many of the questions that we needed to have answered okay um second thing I want to say is that was that was under the heading of kind of evidence-based policy fit for Public Health but we also need to talk not just about evidence when we're talking about evidence-based policy but we also need to talk about values and and reasoning so we hear phrase we hear phrases like evidence-based policy follow the science which are kind of been All To Us by now oftentimes they're invoked just to kind of put a stamp or justification on a policy and sometimes one might be doing no more than saying look there is some data there is some evidence that might be relevant to this particular problem but data as I suggest at the beginning only becomes evidence when we actually use it to support a conclusion in the context of an argument we're making and the argument we're making often depends on what kind of evidence we need and how strong that evidence needs to be so I suggested that for kind of a precautionary reasoning or planning for reasonable worst case scenarios we might just need modeling results or we might just need evidence to make certain outcomes bad outcomes plausible and that might be the best we're able to do in early conditions of uncertainty on the other hand if we're trying to justify mandating certain interventions we might want to know specifically how those interventions might prevent harm to third parties how they might stop the spread of infection for instance one person to another and when we're doing when we're doing harm benefit analysis and really trying to weigh the harms and benefits of an intervention there we need lots of evidence about the diverse effects that our interventions have that might not be available early on okay so this clearly requires not just evidence but also it requires thinking about values as well so what is it what is it we're trying to achieve here are we trying to prevent harm to vulnerable groups that might have been made vulnerable because of a legacy of injustices that put them at heightened risk for uh during a pandemic um it also means that because values and decision making are necessary in evidence-based policy that when groups disagree over the relevant values in decision making it can sometimes look like they're disagreeing over the evidence that's also relevant to making that decision so it's sometimes can seem easier to say look I actually disagree with the values or priorities that you're relying on but I'm going to criticize the strength of evidence for your Acclaim the the poor study that you're using to support it because that makes my argument against your positions seem more objective and politically neutral and so we have to we have to make sure that we're not letting disagreements over values and decision making masquerade as disagreements over evidence and policy as they are want to do in a the heightened political environment of a pandemic so if there's a more concrete proposal here it might be that we need to focus on communicating the values and the kinds of reasoning we're relying on not just invoke data or evidence in order to when we're justifying Public Health decisions and we need to submit this to to public scrutiny as well and not just you know make data or evidence open for viewing the last point I want to make is that um you know might seem trite but in public health we need Public Health approaches to problems and not just medical approaches as important in meta as medicine is during a Public Health crisis like a pandemic that means that we need to think about epidemics um according to what they really are they're problems of a population they have a life of their own we need to adopt certain tools to understand them at the population level in different ways for thinking about how to intervene and track the spread of an epidemic so to give you a couple examples of this we might deploy interventions for different purposes we might employ face masks in order to protect individuals from from becoming infected and harmed and that might lead us to look for certain kinds of evidence that tells us whether or not they're good for a certain pathogen at doing this but we might also want to look at the ability of interventions to stop spread to others to pull in another example testing for infection can have various purposes it can have the purpose of surveillance it can have the purpose of diagnosing individual patients with having disease it can also have the purpose of screening individuals who might be contagious to others and so in order to think about how to rationally deploy things like tests and treatments we need to think about what kind of outcomes we're trying to achieve and those must include Public Health outcomes that are directed at the community and not exclusively outcomes that are directed at sick individual patients um so this is maybe one kind of philosophical way of supporting the um the common refrain that we need to support public health and not just clinical medicine and that includes at the levels of Regulation policy guidance and and scientific advising as well um so I think with that I'll leave it there and turn it back over to Tony nope thank you all so there's a lot of a lot of topics that I want to come back to I thought maybe to begin before getting into the some of the Weeds on uh standards of evidence uh that John was alluding to which I'd like to probe Mark and Emily on as well I thought one thing we could start with is thinking about what we did wrong initially so this was a theme at Mark and Emily a lot of what you were saying so something that I think everyone agrees with is that as the pandemic unfolded a lot of the interventions we had you know were controversial not always effective in a lot of our polarization resulted from uh debates about masks and different contexts and ultimately vaccines and so forth um in the one of the ironies of course is that we've been better up front we would have actually been able to be more precise in our interventions and had fewer you know Hammer type uh interventions but the fact was that we didn't necessarily had a have a very clear picture on what was going on so I guess the first question I want to ask both Mark and Emily is why was that what what was it uh that we lacked that didn't give us the kind of picture that we would have needed in order to have early more bespoke kinds of policy interventions to avoid the hammer type interventions we wound up with and it became so controversial so data you know we didn't have the data we also assumed that you know the first case was really got into the U.S um in in March of 2020 when in fact it turns out we probably had people who'd come in infected earlier than that and once they're you know once you turn on that tap the water is running you can't put it back up in the tap and so not knowing that people had come and infection infected where they were who they might have been infection infecting um that all led to you know just compounding cases spreading where we had no idea and so if you don't know how do you intervene how do you contact tracing works a lot better if you have a one person and their 20 to 30 contacts than if you have 20 people and their 2000 contacts you know everything increases exponentially as you go so the earlier you have data the earlier you know what the situation is the earlier you can respond and they don't have to be such huge disruptive interventions and so I think that the lack of data but also the lack of wanting to really believe that this was going to be a problem in America for us I think definitely contributed to our early stumbling you know this is really one of the first big times that a pandemic impacted Americans directly sure we had some cases of Ebola that we had to worry about yes we've had flu but yeah everyone gets the flu right you know um so this was really one where I think people more people wanted to just not have that data not have that evidence and say well we it's we don't have anything to say that it's here and allowed that to kind of that ignorance that deliberate uh not knowing um to to let things get out of hand and had we had data had we had surveillance systems had we started collecting things early we would have known better how and where to respond yeah I agree with all of that and I think it was partly willful decision to to not respond in the first two months in the sort of January February I was one person but not the only person in February to say this is going to be a global pandemic and it's going to infect a majority of the people in the world and and it's going to be a big thing and I wasn't the only one because it was clear to infectious disease epidemiologists that if there are cases in dozens of countries as there were by then that it's the weakest link problem that uh an epidemic spreading anywhere becomes a risk of an epidemic spreading to new places and even if China as it did can control it at the source once it's out in the rest of the world uh it's it's really the weakest link and and the chance that it gets back in the bottle is is nil um and we really didn't spend the first two months gearing up and preparing much as as much as we could have um and that includes preparing together better data um I think there was some specific institutional and kind of weedy things about the inability to use for example a study set up for flu in Seattle to to detect covid which eventually was broken through but that that lost time we could have we could have had more flexible abilities to to see what was going on had we been planning had we planned our human subjects approvals uh in advance to to cover a little bit more uh of emergency needs um but I think I basically agree with Emily that it was a lack of data but also a lack of a lack of putting positioning the ability to respond uh to what was clear to many people was a threat even if we didn't have the data to prove it was yet a threat in the U.S and one of the other things is that you know and this is talked a lot in the talked about a lot in the report is that I feel like a lot of agencies didn't know whose responsibility it was to be responding to these things and collecting data everyone assumes the CDC is well it's an infectious disease it's a CDC but that's actually not the case um you know the CDC can make recommendations but it's up to the states to do a lot of the response if it's an imported case the Department of Homeland Security actually has jurisdiction over a certain imported infectious diseases and so I think there was just a lot of confusion too about who should even be doing the responding and who should have been collecting this data and what's the purview of different agencies and that's something that I don't know if it ever really got cleared up you know the development of the the White House kovid Commission didn't really seem to help things there was a lot of movement around in there and so there was just a lot of confusion about responsibility early on too which again slowed us down in order to get the relevant data that the book talks about a lot as we need to have the ability to rapidly scale up and distribute tests and the kind of tests we need to collect the information we want so that we're able to to as the book kind of suggests get an eye on where the enemy is how quickly it's spreading and then eventually to allow us to more safely return to work school and other environments which it took Americans a long time to do um partly perhaps because we didn't have a kind of national testing strategy of the kind that certain groups like The Safra Center were kind of working on developing and then eventually collaborating with others such as the covet collaborative in order to think uh carefully about but like Emily suggested there was no kind of national federal strategy for thinking about how what we what we would need to do and how we would need to to do it in order to you know surveil covet effectively and then also return people safely to their various environments as that requires for instance just having the production capacity in place the kind of Partnerships in place in order to manufacture the tests once we once we have the sequence of the virus and are able to and are able to produce them yeah and another thing that the book mentions uh is the sort of lack of an industrial policy so the end of a pre-existing non-suspicious or at least not overly suspicious relationship between government and and Private Industry so it gives the example of South Korea which because of their example with Myrrh is a previous coronavirus had established relationships with companies that could build tests and had set up the the infrastructure not the not the physical production capacity to my knowledge although maybe some of that but the agreements and the and the relationships and the knowledge of who could do it so that uh when this virus came South Korea was in the position to scale up the manufacture of tests that doesn't give you a policy that's a separate problem but um but at least the supply of tests I mean it's easy to point to the early failures of CDC in the development of the test but an alternative or a complementary strategy is to have the capacity to build large amounts of them so I want to come back to the some of these institutional questions but before doing that I want to get into some of uh issues of evidence and data that that you raise so John I know something you alluded to in your in your opening remarks this is also something that you've you've written about is that one of the difficulties of grappling with complex problems where you have multiple kinds of expertise that's required is that you can have conflict among domains of experts and clashing standards of what constitutes good evidence or what kinds of methods we can use and one of the sort of cultural clashes that you've you've written about is that between the domains in medicine and public health that rely on principally randomized control trials and evidence that's generated by that and the domains that rely on modeling especially if you're thinking about population level evidence I wonder if you could just say a little bit more about those two approaches and particularly how that shaped a lot of the debate about covet because I think part of what happened in a lot of these policy discussions is that we had public controversy but if you sort of lift the hood you would find that the sort of a complex situation within the expert Community as well and these two things were not always clearly identified and it seemed to muddy the waters in some cases yeah I mean I think one thing I want to start off by saying at the outset is that there's absolutely an important role for clinical trials and especially randomized clinical trials in a pandemic we need them to understand the effectiveness of Novel interventions like vaccines we need them in order to figure out which of the many possible repurposed drugs we might consider are actually effective and safe and there are great examples from the UK with the recovery trial with the remap cap Critical Care platform trial in the United States where they delivered good high quality and fairly timely results to help inform the care of patients so I'm not somebody who thinks that we simply don't need clinical trials in medicine but there can be more tension when we're thinking about the relative roles of different kinds of evidence in examples like face masks and maybe I'll stick with that example because it's one in which right from the beginning of the pandemic to just a few months ago there have been disagreements among different sectors of you might say of the scientific Community who are used to dealing with different kinds of interventions or problems so very early on in the pandemic there was the question of whether or not we should be using face masks in order to protect ourselves or others and um there were a few individuals in the evidence-based medicine Community who pointed out that from previous reviews of the evidence including randomized and non-randomized studies face masks for other respiratory pathogens studies of the effectiveness of those face masks were often of low of low or uncertain quality and and didn't give us a clear direction of whether or not face mask might be useful in this pandemic so arguing from a kind of lack of evidence some cautioned against deploying face masks they might be analogous to a drug that has some kind of theoretical plausibility But ultimately turns out not to be effective others however who are prop who are possibly a little bit more acclimatized to the reality of Public Health decision making suggested that um you know there's another kind of approach we can take to the question of a face mask and that's that we can ask ourselves um what are the what are the kind of worst case outcomes we're trying to prevent what are the plausible harms of this intervention face mask wearing and um given that there is some uncertainty about um about whether or not face mask might be useful here um you know is there a different way we can reason not asking ourselves how good is the evidence and is it strong is it high quality here but rather on balance of plausible benefits and harms is it a prudent thing to do to recommend wearing face masks specifically for the uh for in the community among the asymptomatic that's a different question about whether to wear face mask when you have symptoms of covet in there the guidance was a little bit more clear that it was a it was a good thing to do fast forward three years later when we have collected some more uh quite a bit more data on the ability of face masks to slow Community transmission some of this is observational in data for instance looking at different regions that um Implement to different policies mask mandates versus not as well as a as a two or three um you know randomized trials comparing different individuals or communities that used face mask to slow Community transmission um and so despite the fact that we have we have some more study we have some more studies in the context of the pandemic and actually a wealth of different kinds of evidence um from before the pandemic about the ability of face masks in the laboratory setting to stop transmission of respiratory particles we have a wealth more evidence during the pandemic about the role of pre-symptomatic and asymptomatic spread of SARS Cove II in a community transmission we have a wealth more evidence from observational epidemiology demonstrating that Airborne transmission with through small respiratory particles is a an important and probably the dominant mode of spread of stars code 2 there Still Remains some disagreement among certain kinds of scientists so a Cochrane systematic review which is the kind of gold standard for evaluating evidence in evidence-based medicine a few months ago did an update of the literature on face masks focusing only on randomized controlled trials in which face masks were used during the pandemic but actually principally in studies dating back before the pandemic context of influenza and other viruses and trying to revisit the question of whether or not face masks are effective for instance for a mitigating Community transmission and concluded that it was that they probably had little or no effect based on evidence of variable quality but often according to the the review of lower uncertain quality in which adherence to face Mass was highly variable in which the settings in which the role of face Master Study was also highly variable ranging from providers in healthcare settings to um community members living in villages and so essentially use this kind of Orthodox evidence-based medicine approach of aggregating together the studies in order to come up with a single pooled estimate for the ability of face masks compared to no face masks to slow Community transmission or decrease it and then some studies as well comparing different kinds of face coverings respirators to surgical cloth face masks so that review included concluded based on kind of an orthodox evidence-based medicine approach that face masks are probably not affected but if you look to many members of the Public Health Community they're unconvinced um why because they're looking at a broader range and diversity of evidence and they're not asking the question what is the effect size of face masks against respiratory pathogens they're instead asking how can we understand the results of multiple different kinds of studies in context and understand the roles of so so the role in mechanism by which this intervention might play might have a function or might have a role in mitigating person-to-person transmission so it's not just it's it's it's not just that they're looking at a broader range of evidence but also that they're looking at this evidence in a different way a way that I would think of as kind of trying to explain the results of different kinds of studies holistically rather than pulling together the results in order to come up with a more precise statistical estimate of a single effect size which in this case just doesn't exist because the range of studies that are that are being looked at are so heterogeneous in terms of the policies that are being studied the the context the settings the outcomes and so on so there really is no single singular effect size of mask wearing against respiratory pathogens there to be found in the first place so this is this is an example of a kind of what you might call scientific culture Clash where a certain group of people who are used to evaluating certain kinds of interventions in medicine using favored methodologies or trying to apply those in a different context in which to my mind those methodologies have important limitations it seems like a a good example of where needing a space in which to have these kinds of debates and informing policy would have been very helpful because it strikes me listening to you that the description you gave about the state of the science and disagreement and the reasoning that goes into the policy recommendations is stark contrast with what we tended to get in sort of media and the public sphere and and I think arguably contributed to a lot of polarization and trust issues um I want to make sure that we have time for questions from the audience so I've a lot more I'd like to ask but if there are questions please feel free start with Bob Seattle and I'm I'm your judgment about whether now given the technical capacity that we have and the sequencing capacity that existed in that area and the folks that were doing the testing knew that there was Community transmission going on did that information filter up and would we make that same mistake again or do you think that the current policies would allow that information to start informing policy earlier on you want to take that one I'll start but I'm I'm certainly don't know the detailed timeline of of where information got from that I think part of the the part that the point I was trying to make really was that for a long time it was against it was inconsistent with their human subjects approvals that they had to do so they their study had been reviewed as the Seattle flu study they were not allowed to test it for other things test the samples for other things so there was a delay in being able to do that um uh uh due to the limitations of of how the consent forms were written um I think once the case was reported it was I think it made national news as I recall and it wasn't a problem of getting the information out then I think there was I mean Public Health agencies always have the problem that they they like to stick to data they don't like to speculate and I I think actually it's public health agencies need to do some controlled speculation um with again asterisk saying I'm speculating now this is not data this is informed expert opinion if there's a case in Seattle which we found in that way and there are cases in multiple other countries then it is it's stupid to think there are not cases in lots of other places in the United States the fact that we haven't seen them is sort of needs to be noted but is not but but the story should not be we have a case in Seattle the story should be we have a case in Seattle and uh one roll of infectious disease models and of of epidemiological reasoning is to say if there's a case in Seattle we have reason to believe for all these other reasons that there are cases elsewhere and that we need to be preparing for that and certain Public Health departments that were in conversation with a good epidemiologist or had them themselves we're thinking that way very early on but not not all and I think one of the things you're probably trying to get at is if an academic institution develops a great test why can't we use that information rather than waiting for a CDC or FDA authorized test and you know there's there's something to be said on both sides of that discussion so should we maybe um as a country be a little more flexible in allowing evidence to come from different sources such as Academia yeah I think so um however is there a reason why every random Joe on the street can't you know Elon Musk can't get into the testing industry please um and just come up with any tests there's a lot of there's a lot of garbage that can get out into the system and we can run the risk of you know if somebody has a false positive and we've got our policy that says well you have to stay home for 14 days and now this person potentially loses 14 days worth of income when it was a garbage test and shouldn't have you know that's when we get into trouble and so we need to balance between innovating and allowing non the non-standard the non-historically standardized groups who are the ones who always made those tests and always did we have to balance incorporating them and allowing them to to come to the table with also making sure that we're still following rigorous clinical standards rigorous Health standards because there are implications for using this for having this data for being identified as a case for you know putting money into that region because we think there's transmission and so I think probably there will be more conversation now going forward about how we can incorporate uh different you know academic institutions or maybe private companies who come in they have you know new genome technology but I I do think that we need to make sure that there are still standards and regulation there because it could go badly there's a lot of damage that they could do that maybe people wouldn't think of initially because oh hey it's just more data it's more information so um so thinking about new technologies coming online and and garbage and garbage out I'd like to talk briefly ask you briefly about the waste water approaches which were essentially new technology that was deployed as the pandemic was unfolding and yielded some very interesting uh uh predictive forecasting information and uh the much more recent use of large language models in the context of SARS cov2 genomes as opposed to language um to predict the future evolution of the spike protein and in both cases these are new approaches that um are are out have been historically outside the public health and medical Arenas but um as a result of the pandemic uh were deployed and are being deployed and uh they certainly don't fit the mode of random randomized controlled trials um yet they do represent where technology and I would say uh bleeding edge curiosity based science can be deployed during a public health emergency to yield positive outcomes for the for the public I'm wondering if any of you can respond to that yeah absolutely um so Wastewater surveillance has actually been done for polio um we we've looked to we've used it as a mechanism for public health surveillance in the past and so I think as long as it is done in uh you know in ethical fashion and it is done in such a way that it is able to answer the questions that you have why not if you know if we've got money if we've got people to do it um and why not have that data I I think maybe people disagree with me but I think it's a great way it also removes the need for humans in that situation a little bit you take them out because you can observe their Wastewater you don't have to then have people going to the doctor you can go into areas where maybe there's not as much Access to Health Care maybe they can't get testing but you can get their waste water so really it could be a good way to innovate getting information about people that are historically excluded from our surveillance systems because of other issues that we can definitely talk about in terms of AI and the language learning I actually think that this is a really great area for the utilization of that um you know I'm an epidemiologist I do I do machine learning I'm usually very much a proponent of taking that carefully making sure that we really think through but when it comes to predictive modeling of things like Spike mutations or Target sites for new vaccines or things like that we can really utilize AI we have a lot of data good quality data good quality sequences uh you know structural models of the virus and it's different proteins that we can put in so you've got good quality data that you can put into an AI system and then model things that aren't going to necessarily immediately impact a patient and their well-being and so we can say okay so maybe here's some different target options of Spike mutations and now we can design studies to test those and do that through a more traditional you know observational studies and then randomized controls if we need to and so I definitely think that those are two instances of these newer Technologies or repurposing of older technologies that we can use in a pandemic situation and in an environment where it's not going to where it could contribute an ad to our body of knowledge as opposed to adding more conflicting knowledge or adding more you know low quality evidence so I wouldn't say oh yes let's go out and apply AI to everything it'll save the world just as with mathematical models you have to be mindful of how you're using them how you're building them same thing applies but I think there's definitely room for these newer Technologies given that we have people thinking through the risk benefit trade-off given that we make sure that we're using them in an ethical fashion given that we have good quality data going in I think we have time for one more question so right in the front here Lou Gagliano uh when we think about the fact that our Health Care system is both the public and a private system we don't have Universal system the opportunity to to make it better next time how much of it is structural how much of it is policy and I know they're related but how and when you look at the two sectors the private and the public side the structural versus the policy side how does that play out in your mind in terms of making it better next time I'll start I I think the the federal structure of our public health system is the biggest constraint because it's probably not going to go away meaning just dis decentralized and at the state or local level um I think that policies can can improve our decision making by in allow by empowering those more decentralized units of government to use data and and information better and that's one thing that we're very focused on at our CDC Center for forecasting outbreak analytics is working with state and local authorities to Pilot and and then spread the best analytic approaches really as a hedge against Federal inability to National inability to respond it's good to have capacity at multiple levels there's a Workforce issue there as well um and then I think at the same time we need to redundantly in a way find better Roots as as all the panelists have said to get health care data Healthcare System data from the private Health Care System integrated into public health that doesn't happen as much as it should it does happen to some degree but there's an opportunity to make it timely to I in my personal view to to find better ways to collaborate rather than rather than a sort of data buying a block of data and transferring it which means it's going to be old before you can use it and and that you don't necessarily know all the details of what the data mean but to build collaborative arrangements with health Health Care Systems and public health to do analyzes that's one thing we're trying to work through also in the CDC Center so um I think it the the structure is in a way a constraint and the the policy can can work within that constraint but I I think we have a lot we can improve through those kinds of approaches so unfortunately we are out of time and I want to make sure that we have a chance to thank our panelists and prepare for our next panel which would also be terrific so please stick around and join me in thanking foreign we're going to move on to our second panel I'm very excited uh very excited for this actually one is I have my good friend Dr Janet Woodcock who's the principal Deputy FDA commissioner has been at the FDA for a number of years and has has helped reorganize and restructure offices and centers and is probably one of the best Executives I know in the public and private sector and you know she has always been willing to do the hard work that others are unwilling to do and make the hard decisions and then have a continuing our Brain Trust we have Dr Mark McClellan uh who ran two federal agencies including serving as FDA commissioner and so I'm a Eternal pragmatist so that this is fun for me because we are going to spend some time talking about how to fix agencies and organizations right during the pandemic the biomedical Innovation complex worked pretty well right we got vaccines we had Therapeutics it took some time but it happened in record time and you know all of us are probably sitting here today probably because those things actually happened and we eventually got them out into the community the public health infrastructure unfortunately operated largely in a silo fragmented across localities States and the FEDS and then as we think about agencies right we had three primary agencies we had the CDC the FDA and the NIH and they all function very differently during the pandemic and so I My Hope was to spend some time talking about you know how the fda's successes and it was not perfect but it did a pretty darn good job and what we can learn from that thinking about how to fix the CDC and the NIH going forwards and make a more robust Public Health infrastructure uh Dr Woodcock a few thoughts from you to start us off [Music] okay that we don't have a systemic approach to Medical product development testing and response it's very very fragmented and all those cracks showed like with the pandemic right and um CDC struggles I think a great deal because of the distributed nature of the public health system in other countries that you know had a more nationalized public health system and Health Care system too we're able to respond more like systematically because they they were unified and there was some kind of central approach unified approach so I think um that we we suffer from this um you know which we celebrate our Federated nature but we also are uh we're victims of it in some extent because yeah for the from FDA standpoint say take the Public Health Data take the data about vaccination and its Effectiveness we had to re we relied on um Scandinavia and Israel for those days and everybody's beating up on us why are you quoting uh those data but they had all the information and um I know the biologic Center who had set up a surveillance program say for Adverse Events for off the health records but the vaccine who was vaccinated was not in the medical records because it was done through a different system and the states with their own privacy laws and their own approaches we couldn't get those data so we couldn't link Adverse Events or outcomes like what did you get coveted or not we couldn't link that to whether you were vaccinated or not so we were unable to draw conclusions about vaccine efficacy and safety from The Real World data in the United States the CDC had a relatively and when you say small it's just the fact that you need many thousands of people so they had an academic linked you know linked program that they were using for that and but that had such small data we had some really bad problems with uh false signals which is what you get with small small data that incomplete data and so forth for vaccine Adverse Events vaccines are really really safe or they should be right and so you need hundreds of thousands of exposures to look for Adverse Events or you should I mean if you only need 100 people then you don't have a viable vaccine right so and I think that was repeated the same for um Mark tell me if I'm talking too much okay so the same for say the clinical um development programs I was the therapeutic lead at operational warp speed okay and so we we funded you know getting um we funded a lot of stuff and that was fairly successful but there was not a clinical trials Network in the United States we could utilize the Indus the industry does in the United States most of the development clinical development work and frankly the pandemic planning the um U.S had done presumed there would be influenza a pandemic flew and there would be treatments already under and Diagnostics already and everything and so the clinical the development clinical development and evaluation was something is completely neglected in a sense of thinking about how large of an effort that would be and at the end of the day it was the Industrial efforts the clinical trials and so forth in the United States that actually gave the leading data that we were able to they did the vaccine trials they were supported by warp speed obviously but industry did the therapeutic trial supported to a great extent by warp speed of getting but not totally that got these um treatments available in vaccines available whereas the UK was able to put together the recover trial through their National Health System with large pragmatic trial and showed a very cheap agent corticosteroids were useful in the late stage of disease which is the ards type of problem and you know we've been Mark you probably remember when you were training we've been arguing in medicine for 40 years about this whether or not steroids should be used in ards and at least for this type for coven related respiratory failure the steroids proved to be life-saving and are now you know a Cornerstone of the therapy so but they were only able to do that the cheap widely available could be used in any country you know many different formulations of it they were able to show that because they had a network a clinical they were able to wrap assemble clinical Network and I will say one more thing before I stop and I'm sorry but I talked to Martin Landry was one of the pis over in the UK in this and with his permission I for coin Landry's law and Landry's law is that the number of patients enrolled at any site is inversely proportional to the number of professors and I would say that you know the NIH networks are all in academic Health Centers enough said all right whereas the industry preferred sites are usually not because they are focused on patient enrollment and so forth so and they actually were the ones that delivered the industry site so I'll stop there pulling on that thread mark a question and something that we all have thought about you know what makes an agency successful and agencies our organizations just like businesses one of the things we talked about is performance metrics and accountability and a clear budget how do you think the FDA is distinct from the CDC and the NIH in that regards picking up on Janet's Point has a very clear nationally focused Mission we don't have state and local you know drug review boards we've got one National system I am concerned about some of the recent you know court cases that have kind of challenged some of that but we have one National system that is able to put a lot of expertise into answering questions about safety and effectiveness of drugs you just heard Janet Singh as we move into having better far from perfect but better post-market electronic data available learning a lot more from Real World evidence too and overall for FDA that's tied to clear metrics you know so a lot of the budget for the drug and biologic Center is related to being able to get to clear guidance to Industry and clear response and all of that came in very handy in the um in the covid response I do want to like back up a little bit in talking about metrics to where I think they really are needed as you know um Mark lipsitz and I were both involved in the covid Planning Group effort which was intended to support what could potentially be a bipartisan commission to look at what worked what didn't you know people really disagree on on some of these issues now but that's why you need kind of a deep thoughtful bipartisan look we you know did it after 9 11 we've done after other major national crises and Phil zelikal who was involved in leading that helped bring this whole effort together we didn't get that commission there was a effort by Senator Burr Senator Murray and the last Congress to get that over the line with some of the other end of year legislation at the so-called prevent act this one didn't quite come together so it's very important to have meetings and discussions like this to think about like what worked and what didn't and and try to get past some of the kind of high-level talking points that people have and one of the things we found in this effort was you know the reality was is a little bit different than what different people are summarizing one of the things that's most critical here is we don't have and still don't have a unified National strategy for how you bring different components of the federal government together so they can do each of the things that they need to do as part of an effort for National Response in a crisis like this and support the state and local responses we are a federal government we are a federal country every part of the country has somewhat different governance institutions capabilities and that can be a good thing so we're so diverse but but that means you need Federal support to make it easier for these the things that can and should be done at the local level to be done effectively and FDA working with industry was able to do this especially for warp spray warp speed kind of signature success in the pandemic of getting vaccines tested at large scale scale mass produced and available the other components were more problematic and you mentioned there's roles for CDC there's also big roles for CMS CMS proved once again that it's a critical public health agency with all these flexibilities and implemented quickly to deliver Care at home and so forth and CDC tried as well and we get back to some of the failures in in all of these areas but while we had the best treatments and vaccines we had by 2020 by late 2020 the largest availability of good diagnostic tests too including ones that people can use at home we did have some real problems in translating that into impact and part of that I know the CDC has been blamed for and we can talk more about that too part of it I think goes beyond that because any infectious disease threat going forward requires a different kind of response than we had in the 20th century so it's no longer good enough to go door to door and find like a locally spreading infection and try to understand it you know grow it in the lab or whatever from now on these these infections can potentially spread globally super quickly but we have the technology to manage that any new infectious disease threat should be something we should be able to sequence genomically in a matter of days if that we were able to do that with covid we should be able to produce large-scale so-called PCR tests this is basic technology and make those available not only in Public Health Labs but in healthcare organizations that do most of the testing around the country we've seen this happen with you know impacts response now two we should have treatments off the shelf because we know what kind of virus or infectious agencies is that we can apply we can try to apply in the kind of testing framework that jant was talking about quickly we have synthetic biology that enables us to make monoclonal antibodies and and other Technologies treatments in a matter of weeks to months so that they could work and manufacture them at scale and vaccines too but we also need along with that a capacity to engage the public so they understand what what's going on what we do and don't know at each step of the ways we're starting to detect the infection hopefully understanding it and taking good steps quickly to contain spread and respond and that requires not just CDC but also asper our assistant secretary for preparedness and response and it requires the Health Care system to act differently to to we had heroic Health Care responses during the pandemic as New York and other parts of the country were hit first and then hit repeatedly by successive waves where we have struggled a bit more within to Janet's Point engaging people about whether they want to get vaccinated or not for identifying people who are at high risk making sure they had tests available and had access to treatments that prevention community-based side of Health Care was much more uneven again there were some really bright spots organizations that got out there and were already doing like virtual visits and knew who all our high-risk patients were and pre-positioned tests and had discussions with them or uses community health workers to help engage them and there are a lot of parts of the country where we just didn't have that instruction that infrastructure in place we had you know could bring in temporary um you know vaccine centers at football stadiums but that's not really an infrastructure that's geared to the fact that for any infectious disease threat that comes along we ought to be able to identify it quickly identify where and how it's spreading contain it through these other steps but that requires not just you know new accountability to CDC but I think some new accountability in in healthcare as well if you think about where we're moving in our health care System it's more about you know can we identify health risk before they progress the Technologies are there test and treat applies to virtually every health problem today and it's not only medical responses that we need but that is an important part of it so there's a lot to learn here that we haven't really put together yet that aren't I don't think is a partisan set of issues but I really appreciate Us coming out of their talk about so I've talked about some of problems and some of the opportunities and hoping the rest of our time we can can move forward from that I was going to say in some sense actually it's not necessarily even a surprise that the CDC struggled because we didn't actually necessarily set it up for success right because we've tasked the agency with addressing Public Health everything yeah yeah and then we are surprised when uh there's a once in a century pandemic and they focused on a variety of other components of Public Health and that Readiness and response function has atrophied so the city all that that has been relatively flat over some decades took a hit with the budgetary challenges with the Great Recession of 2008 and hadn't really recovered by the time covid came around just what emphasize is that unlike FDA which is a national structure for getting safe and effective treatments to people and using them protect and promote Health CDC is as Jana said very much a federal agency so most of its limited budget goes as kind of pass-throughs with some CDC oversight to State and local public health offices there are over 3 300 state and local public health offices across the country and with limited grant funding and that funding through the way Congress appropriates for CDC split into a bunch of different silos some of which are about emergency response some of which are about you know other good public health goals your point you know smoking cessation maternal Health filling in gaps in our health care System around infectious diseases HIV patients that get fired by our health care providers it's understandable that it's hard for them to put all that together I think there is a path forward and and you know the new CDC director incoming CDC directors talk about this more Partnerships with with Health Care maybe more Partnerships with FDA certainly more Partnerships at the state and local level with you know in North Carolina some of our our effective responses were getting out into rural communities who's there and trusted well like the AG extension service you know that could be a good point of contact for for farmers and Frontline Health Care Providers but they need support to do this so something that you know where CDC could help can't do it alone but also where health care and and social service providers could be involved too do you think that perhaps a more focused mission for the CDC with Staffing and culture built around that could help it's a very broad Mission if you look at um you know the the CDC has on its website it's kind of broadly supporting the Public Health Community this idea of uh what's called Public Health 3.0 which is recognizing that public health is is not certainly not just about you know hygiene and and making sure the water is clean and the the foods are safe and so forth important collaborations with FDA there too I think um but also about you know all these opportunities with technology with medical technology all the opportunities with understanding how behavioral choices and and constraints that people face influence their public their uh their health outcomes but that is so broad I mean think about budgets you know we spend about thirteen thousand dollars per person on Health Care in the United States we spend about you know thirty five hundred four thousand on Social Services you know all those things affect Health it's been about 300 per capita or 350 Maybe between CDC funding and other federal and state and local public health funding you see how this has got to be a partnership in order to work better absolutely although I would say for the CDC the question is is what is the return on the investment for the population in investment and say chronic disease and is CDC the right lever to do that and should another agency be taking on some of those functions maybe at a more local or state level rather than a federal level and that would allow the CDC to sort of Blossom in its pandemic response and infectious disease response and perhaps have a different Workforce yeah I think we may be starting to take steps in that direction I think it um is another area where I think would be really productive to have some discussions about you know how can you do that better so take the healthy people 2030 goals you know yeah so these are all goals out there kind of below them like well you know who's exactly supposed to be doing what to get there and uh every year we make new goals or every 10 years and the population keeps getting worse on all the measures that's right so we're doing we're not doing something right okay so I think this is despite by the way us having better knowledge of what you know behavioral non-medical steps can improve public health and having better drugs early Diagnostics than ever before right that also could contribute to all of these CDC does not have the budget to be held accountable to get to those goals by themselves right we're starting to see in many um Healthcare organizations this is a CMS priority um I've heard about you know shifting from fee for service payment to paying for accountability for for for Better Health outcomes so more payments to Health Care Providers are moving towards safer diabetes you know did you screen your population of patients effectively did you get too effective hemoglobin A1c level you know indicating good diabetes diabetic control through a whole combination of mechanisms you know new treat new diabetes treatment food is medicine prescriptions help with engaging people and using you know personal health apps to get healthier so that's something that's CMS can help with and I agree with your point about um you know State and and local levels you know we're seeing a lot of really Innovative work happening you know across blue states red States you know Indiana North Carolina Massachusetts wherever on trying to integrate social services and Community Resources around people and um and and have some tracking and accountability at the local level for sort of the equivalent of these healthy people 20 30 goals but this is way bigger than something that CDC can work on a lot I absolutely agree and one of the questions to ask is is you know should a function reside at the federal level or should it might be better at the state level level or even at the local level and it's who is most effective to undertake the job and coordinate it along those lines I had a question actually For You Janet I was thinking about sort of clinical trials we talked about how the biomedical Innovation complex worked quite well Marie and we're all thankful for that the NIH is a Powerhouse for basic science what do you think are there or do you think that there are lessons for what we should focus some of the nih's translational science efforts on well you know I have long been a proponent um if the NIH is going to sponsor clinical trials that they really ought to think about the infrastructure uh funding and infrastructure rather than funding what I call the SCT so I won't mention that here but the small not very good trials right that they have traditionally funded as part of like r01 grants and so forth you know what we lack in the United States because we have left the clinical development to Industry and that's fine they do it efficiently they do it effectively and so forth and so on but then when a crisis arises like this or for public health issues the industry isn't necessarily going to pursue right because they have a different objective or set of objectives we really lack an infrastructure to do that as Mark said now we have the opportunity for example we could harness real World evidence we could do large-scale experiments we could really see because some of the things you raised Mark they they cry for cluster randomization because look we've been having a healthy people 2000 for what or whatever a thousand for 30 years and we haven't made progress against so we need evidence driven or data driven interventions uh even social interventions rather than than best intention interventions that we keep doing even though they're not working okay so I do think but I don't think the NIH is set up for that and I I don't know the answer for it but what I would say is there's a big gap in the public sector of research and that is the huge gap on the clinical evaluation is a giant Gap and I wanted to you wanted to ask about Diagnostics so the real issue there the covet Diagnostics I think really took off that the Congress gave a billion dollars I think to NIH for the for the uh rad X Program and they put in a program they developed standards okay and reference standards and you know serum okay and so forth so you know manufacturing standard test bets exactly algorithm and they helped with it right and so manufacturers are sending us covid tests and they tested them in 30 people okay and they were supposed to be used by millions right and they didn't work and this was universities too who did this right but if we had offered a standard evaluation protocol with that that a third party could do we could run those through very quickly and go and a lot of those on the market because the data would have been generated certification of conformity in a known test bed is something that is used in the Telecom industry all the time that's what I mean this is isn't rocket science has been around for like 20 years but you know so they were all out there little diagnostic companies they might have good PCR scientists and this and that but you know they didn't have the clinical and people couldn't get a hold of the variants um you know um and so forth but if we pulled our efforts and put pulled reference standards in place you could run a lot of different diagnostic tests through there very rapidly give the evidence to the FDA it would be standardized so we wouldn't have to question it we wouldn't have to inspect it and that's what happened and when that happened and all these tests became available because they could be easily clinically evaluated and these include direct to Consumer tasks and tests you can use at home with uh with rapid response time it might not be quite as sensitive but still can be a really really valuable part of detecting and containing an outbreak and one thing we you and I are big proponents both of Mark is real world evidence and moving trials out of of what Jana would say are the small not very good trials into a broader you know larger Community setting right like HCA should have a massive clinical trials apparatus like all these large Health Systems in the community should have that and that's right yeah it should be part of clinical care and so what do you think that the nih's translational efforts could Target to help promote that well I think NIH can help I think FDA can actually do some further steps to help here too and it certainly is a high priority for the current commissioner as well as as Janet to try to make progress the the hardest thing about doing trials in a real world setting is that you actually have to deliver real world care and the solution of wealth let's just expand our funding for traditional NIH trials by bringing in you know protocol reviewers and and uh local oversight of trial conditions and data collectors and all of that just doesn't lend itself to the delivery of care and routine practice remember Healthcare organizations are pretty stretched these These Days financially so I have to do something simple the good news is that we've got a lot better electronic data than we used to a lot of that data if it matters for payment is pretty darn accurate we've got Health Care Systems that are no longer you know working at the one-off clinician level so we're not talking about like you know a traditional small uh crummy trial that would have come from like hey uh you know one academic at this one institution periamp with a few of his or her friends say we can produce a study where you really would worry about is that particular clinician really implementing objective standards and and write the right kind of consent process and are they collecting data accurately we're talking about systems that have systematic implementation of electronic record systems of clinical protocols supported by emrs whole teams of people who are working together so holding those individual clinicians accountable for well how exactly is the trial going in the same way that we did in the old style you know investigator-led research is just not right and and getting to ways of kind of certifying Health Systems is having good patient protections consent oversight good objective data collection that they're already using for their own care and their own payments in many cases are based on that data that's the future but I don't think Janet that either the NIH funding for you know sort of these platform kinds of practical randomized clinical trials or frankly a lot of the FDA you know oversight regulations have yet you know matched up with the potential there well this morning before I came down here I was talking to the people in the UK where there was a group there on the revision of the good clinical practice guidelines which are ich guidelines and they've the pragmatic trial list which is really what we're talking about here have stage a little bit of a revolution against that and which is appropriate I mean trials should be fit for purpose okay and um there aren't quite regulatory trials and we should decide what kind of decisions you're going to make out of that information and you should design the trial to yield that data and information in a reliable way so I think we will be revising including revising like how who's responsible and how can you do distributed and we've sent out some guidance and information on that because yeah we have to recognize that the world is changing the way things are done is changing that would have uh you know but the question is what Brian's question I think is what would have enabled the U.S to put together more rapidly okay uh clinical trials say out of warp speed I had a tremendous amount of problems getting that done at enough scale right and um it's because we didn't they were small uh accident networks um but you know we didn't have the kind of scope say that uh because um the UK the recovery trial managed to incorporate even investigational agents eventually and test them or Reaper they did a lot of repurposed agents but they did that could be a good use for them H is translational research efforts setting up those trials or even helping come up with real world evidence measures like how do you clean EHR data for example yeah but then you'd need a unit or group it and I choose focus on clinical evaluation and methodology and if you look around the country oh well okay we have some okay but I'm talking about Institute an Institute right where are the Departments of clinical evaluation around the country at medical schools they aren't any this is a sideline for most people it isn't supported by robust Grant so I think even for NIH it's traditional role could be if they sort of open their eyes about this at least to support not just the basic science but the clinical evaluation and the science of clinical evaluation that might be nih's true role not supporting the actual evaluation itself that might be a bridge too far I mean they do some of that I mean I think that's a really good point because that could rapidly speed up development decrease cost and also increase access all in one specific policy change when it might improve the quality overall in the United States of Investigations you know I've published on the fact that most of the trials that were set up in the United States by the academic centers were were inadequate they weren't adequately powered they didn't enroll enough they couldn't answer the questions we had thousands and thousands tens of thousands of patients enrolled in trials that were not able to answer the questions they were asking on your face so you know have raising the level of expertise around clinical evaluation not having it simply be I mean at warp speed I had to bring in clinops people from industry and they were the they were the people who knew actually how to do this and get this done at scale so you know we just don't have that set of expertise in the government except maybe some you know Points of Light here and there but you really need a kind of machine okay to really get this done at scale this would be a significant change at NIH to do something like that at scale um I think the other pieces are lining up you know I work with a group called um uh um advancing clinical trials at the point of Care Coalition which includes a number of these Health Systems which to Janna's Point um just don't feel like the the pre-market clinical trials are answering all the questions that they'd like to answer you know what are the long-term effects of these treatments what do I do about you know drug interactions is this the right dosage you know if I have a side effect what's the best way to manage it and those are impossible questions to answer in a standard you know pre-market NIH type clinical trial they are exactly the kinds of questions that you could answer at a much if you had much larger scale platforms and we're focusing on the key data elements to collect right we already at that point have a good understanding of of major side effects mechanism of action uh things like that and these organizations that act at point of care would say like you know these are the kinds of questions that that we want answers whether it's around cardiovascular disease drugs or the new diabetes drugs that may be very helpful for pre-diabetes may be helpful for obesity but may you know have some unknown as of yet consequences for you know with long-term use at higher doses new treatments for neurodegenerative diseases comparative effectiveness I am not quite seeing you know the the NIH you know big change to get there one other possibility might be the new funding for arpa H so this is a program to that's intended support Advanced you know transformative projects and you know maybe a jump start here around a few platforms could would lead to the desired goal of the our page program you know faster better evidence and transformation I'm on the pcori board and I've been trying to push them toward you know they just there was just a study published this sounds like why hadn't we done this electric convulsive therapy versus ketamine for severe depression all right and you know so that's a little edgy that's a little bit more you know that but those are the questions Health Care Systems really want and insurers need to answer should what should we pay for or if people they have this chronic disease and they've been on this regimen and you need to they're not responding or whatever you need another what should you go to and right now it's all like well you know my mentor taught me this or you know this is what it is in your SCT small crummy observational studies yeah and it is an art instead of it's it's not evidence driven yeah and that's much of health care right now and yet it's amenable to um to actually hypothesis driven you know evidence generation if we'd only organize it a little bit and that means like Mark said that those who are would be participating in this Health Care System insurers have to see the value of this information to them yeah yeah I think we'll open it up to questions a microphone is coming to you there you go thank you Luigi Agliano as a data person we have more data flying around in this country that we are not using well and uh I guess I'm thinking of LeapFrog the leap for our group who about 20 years ago revolutionized getting Hospital data okay measuring a good hospital and not on an ABCD scorecard pretty simple to understand yes there's data behind it they're Gathering to determine what is a B C or D what did that lead to it led to in part value-based Contracting because guess what insurance companies don't want to send who they're going to have to pay for nor do the employers to a d Hospital so why we can't take that lesson and morph it and I'll give you one absurd example of an insurance company who's decided to pay for getting people to walk companion dogs for people at end of life because that companion dog delivers more value to that person who's remote and alone than any other thing so we have an example of what is going on on the private side and that we are not morphing to the public side and it's about time that we grow up as a country and learn from what's going on from the blue standards of care that are going on in North Carolina Utah Massachusetts Etc thank you I think it's a great point and it just highlights how important it is to shift out of the traditional way we've paid in health care for stuff that's done in hospitals and institutions and recognize that all these factors influence Health to make that work though you need to like leapfrog really emphasize you need to measure what matters and and create a way of aligning payments with that and a lot of this is happening in the private sector as you said but some of those examples are in Medicare Advantage Programs this is publicly funded privately delivered with some accountability and there are some things we could do to improve that program but it very much aligns with the the stuff we've been talking about here today I I would say the challenge is in measurement right now because we don't have those networks because we don't have the ability to measure stuff with scale in the real world and we don't and we spend billions of dollars on translational research publicly funded but don't have these sort of questions in mind we do I mean we do know the measures it's like this is like a measure of patient experience caregiver experience or standard ways to do that we've got digital tools that can help they're not as widely accepted and they're not as widely used but that's an issue where policy can make a difference the regulatory steps like Janet was describing around hey if these measures you know digitally collected remotely could be used for clinical trials including practical clinical trials and if CMS would put maybe more emphasis on you know paying for what people really care about that could help Advance these approaches too yeah what the the knife edge you walk though is a lot of these I have some friends who are actually involved in some of these programs implementing or being nature is micromanagement so you want to you want to Value the outcome not micromanage the people because then they have to fill in like the providers a lot of forms and I did this and I'd screamed this for this and pretty soon they are spending an hour on all this stuff that people could dream up that would be helpful for the patient so you know I think that the design of the programs needs to be evaluated and I think Medicare Advantage does that to some extent right and there's more we could do again it should be evidence-based is this way of holding people accountable actually change their behavior yeah yeah right and having that infrastructure to answer that question quickly in real time that's right and that's money because we don't have that we don't have that right and we had to end you know we talked you also talked about vaccinations and football fields and I would take that step further and say it's fine to have the football field be the vaccination site but that we should have the plan in place and the Machinery ready to go to set that up quickly and so a lot of I think lessons that we're pulling from this is that we need to be pragmatic and that we need to be flexible and ready to go and that a lot of our infrastructure is not that way and that infrastructure that's sitting there unused and has billions of dollars spent on it every year is not necessarily what we need we need the ability to rapidly scale and deploy and probably some creativity because I think in the correct you need creativity in advance so I think you mentioned AG extension whatever Services okay so when we had uh we got the mono Colonial antibodies at work speed we've got them developed we paid for them we had them in hand and we couldn't deliver them okay I wasn't in charge of that for quite a while until um stage of resolution about it okay we couldn't deliver them and if we thought in advance okay who do people turn to it turned out the ambulance services the EMTs they were fabulous at this okay they knew their communities but we had to send it to them okay um the I think the AG extension services for rural areas we really they could pair up with the emergency AMT services and they could deliver like facts but that's creativity like who do the people actually interact with who do they work with who do they trust who's there okay and then how do we would we arrange to send them stuff so that we reach all our population because the football fields work if you live near a football stadium right or whatever but then you need the Personnel to to give it and so forth so I really think in times of Peace we shouldn't just think of the public health system and all the people in there we got to think of who's capable and I think one of the threads I want to pull on there is you're also talking about a decentralized system coupled with some Central organization yeah but having EMS is not having someone in DC Atlanta you know Boston Chicago telling them what to do right it's a d they get the supplies they need right and maybe there's some low you know Federal coordination but that it is often a local or a state effort and we can't underestimate their importance I agree and that's where we need to think of that in advance because it took us a while and then we had to mobilize all those people we had the dialysis centers they were they are good at IVs and stuff like that and well the Home Health people were able to help with delivering the monoclonals and so forth but you know we had to dream all this up in the middle of a crisis and then reach out to all these people I'll tell you the ambulance people and some of the community organizers in different communities were fabulous at organizing vaccination in delivery of Therapeutics to their people but they weren't the people came to mind instantly we are seeing some good examples of everything he said so that's the good news the the challenge is how do you make that scalable you know how do you make it easier to replicate and sustain financially and most of the funding for that some of it's going to come from CDC and public health but as I talked about before there's just not a whole lot of resources there a lot more around social service programs where many states you know Indiana has done a great job of integrating eligibility programs and participation programs in in their social programs can help a lot with these issues and linking it to healthcare providers who are trying to address social needs for for individuals and on the health care side lots of health care providers around the country are playing key roles in local and state public responses you know the best responses around the country were invariably a combination of state and local government and Business Leaders and especially Healthcare leaders including ambulance providers and and so forth um but we we aren't systematically giving them the data that they need um you know we've got fragmented Public Health Data got Health Care data that could support all of this with further steps like we've been talking about in the in in CMS and our our big um Healthcare payment programs and I hope that's an area for Progress you know there are again some states and localities that are doing it well this is all connected Brian back to your point about you know what are the goals that we're trying to work on together and can we get to some more real accountability you know are we are we truly ready have we done a real stress test for the next Regional State response to a public health emergency with the right kind of federal support are we doing the the right things to address other local public health threats like opioid use disorder maternal health and equities and things like that and I I think going to that actually we often give localities funding tied to very narrow goals without providing small fragmenting sources right and so they end up with like 15 20 different funding streams there are tiny bits of money with broad goals and no flexibility about how to use them and organize them that's not the right way to do it no it's not it's not thoughtful or practical yes thanks I wanted to uh I wanted to give you an opportunity to talk a bit about various uh regulatory approval processes so uh the acronym eua became uh familiar to all of us during the pandemic um and so I wanted to hear a bit about the role of different Pathways to approval especially during a pandemic how they're functioning how they could be doing better and I think it's important to talk about this because people sometimes can get confused about why things are approved why approval is sometimes revoked why it might take a while to get full approval and so on so what's the role of these different Pathways within the FDA and are they doing well could they be doing better yeah well I think the emergency authorization pathway is a very good one and function very well for the country and enabled the FDA to get out things what it basically does it's no longer investigational and you don't have all the informed consent and all these you know it's deemed as good enough in considering that emergency that uh it could be made available authorized for availability without you know being investigational anymore so without IND and all the you know all the different things that go along with that on the other hand it doesn't require the um and that's I do think what confused the population because for example for the vaccines they probably had some of the largest vaccine trials ever done okay and we had a huge amount of data on safety and effectiveness of these so we weren't cutting Corners there but there's all sorts of things required for reliability of manufacturer and everything that we could cut off in an emergency vaccine going to be stable for a year exactly it didn't matter it was flying off the shelves right so we could do all those things and have those flexibilities with the eua but I think the major problem with it is it confused the public who was thinking it was a lesser stand rendered overall and they were getting substandard stuff where we've taken shortcuts and of course this was combined with the social narrative that was out there which was very confusing I think too the medical community much of us at least that you know this was some kind of plot or something before we stood on the public right so I think um we probably need to pay more attention if something like this happen again to how we position this and how what the narrative is around why we're doing this we tried but we aren't nobody's like really good at communicating extremely well these com more complex Concepts during an emergency yeah but the actual mechanism functioned extremely well in the FDA you know put out dozens of eua products and then okay revoked then would be revoked because more data came out that you know or there was a better alternative that became available for example things like that um and that's hard to explain to the public that happens all the time in medicine you know we have a cancer drug and then we get a better one well they aren't really quote revoke but nobody's using the old one anymore because something more effective or safer came along so explaining that Natural Evolution that is accelerated during crisis is difficult for the public another issue we haven't solved yet which is effective communication especially in emergency when right some is known the knowledge level improves and changes uh quickly but lots is unknown and conveying that in a way that that's accurate and practical is another area I think where you know more of these Partnerships could be helpful I mean CDC can be a good source of analytic information especially if they're able to get good data um but if you look at National polls across Republicans Democrats people getting their information from you know different frames they do have some commonalities around trusting their local health care provider clinician pharmacist trusting others in their Community who could be Allied who could partner you know we're talking about like AG extension things like that before but that's a different threat strategy for not only you know describing hey the zua has been approved but for helping people get the right practical information for them from a trusted Source about what what it means for them I don't think we solved that problem yet either yeah we had and I know the CDC had we had a huge partner Network work of all these different uh patients and consumer organizations and you know all kinds of organizations and we had weekly phone calls with them and we gave them materials and we gave them talking points and we answered their questions and everything but we still it was still very difficult to get the sort of reliable valid information out into everybody's hands and have it heard and that might have been for other reasons but yeah those we I think there was a big effort to bring in the local communities and a recognition that those are the faith communities were reached out to that those are the folks that people listen to okay and Trust um so we are a country of countries yeah that's for sure that's right yeah yeah well I I have one question actually I wanted to ask that's been sort of burning it's about diagnostic testing and so originally when we actually had the early stages of the pandemic we didn't have access to tests and there were a variety of reasons why that happened and I guess my question for each of you is what do you think we should do to make sure that that doesn't happen in the future very um plan and the plan is what Mark said we get the PCR we get the virus or whatever it is the agent we get it in we um there is a collective systematic effort to develop reference panels we get serum from infected people we get people other possible interfering Sierra we get all that make a panel and then we invite people industry academic Labs the CDC whoever to contribute to that now you need a gold standard okay but we have that we can do these uh you know you can do the blots and you can do the PCR and everything on this reference panel and you can characterize it and that's I can't even emphasize enough what an important development tool that is and people do not have I mean you have to do testing of robustness and proficiency testing and so forth like that to make sure the test is actually doable in people's hands right but this having these resources available to various parties uh so they could do rapid development and generate the evidence needed for an eua right is it just can't be but we have to have a plan to do that rather than just put the plan together to add to that I think one thing that that I I think there's a lot of agreement there's a lot of issues with disagreement about you know how to respond respond to the next um public health emergency but where I think there is agreement is that the technology is there through through genomics and PCR to make available um very quickly and there will be other tests that build on it for you know more rapid uh um findings and and things that people can do on their own at home but through our existing laboratory Network including not just the Public Health Labs which we kind of relied on at the beginning of of covid and um uh not just through CDC but through all yeah of the the labs that want to help I mean we saw this in covet with you know Trevor bedford's a lab in in uh in Seattle trying to do early diagnosis so we've got the the capacity to do this when there's a new right threat and so like number one we all agree on is can we get those tests out to healthcare partner organizations all across the country to enable us to do a hopefully a quick assessment of just how much this new agent is spreading and if you work with the healthcare organizations all these labs are connected via nationally consistent electronic standards in ways that could feed back that data at least at an aggregate level to local responders each state and and local public health agency the the healthcare organizations they work with as well as the the CDC and and whoever is in charge of of national planning for the Emergency Response at that point like I said the beginning we haven't worked that out yet that seems like some common ground and that that could then you know couple that with Janet's reference testing for any new tests that come along to scale those up and make available the best ones that'd be a really different response than we were able to pull off and right absolutely yeah so Mark's talking about the very beginning as this threat comes through we need the um we need really good Laboratories to be working together other to do that surveillance they have to agree on the standards okay they have to get together we had a lot of people who were fighting with each other right about you know this and that and the other thing and then as that moves forward that out of that effort comes a reference panel really but you have to objectively decide you're going to construct that right and um it's going to be a healthcare Public Health effort together that's right yeah you got to get those samples from health care so you can create that and then the diagnostic companies can really they're the ones who can do this at scale right let's get over it okay and so they can do the you know the easy test the you know plate tests that every or this strip test they could do all this stuff but they need the tools they need the uh they need to get the substrates uh given to them they need a flexible platform upon which the multiple parties can compete to develop tests that meet a standard and then on top of that for rollout yeah they need to be able to have the logistics to get the test out but then also get the data in a standardized form you know so there needs to be standards development around public health and Health Data so that it's more portable and then on top of that you need to sort of like Starbucks can measure the number of cups of coffee sold at probably each of their stores across the world to be able to at the local state or federal level get those data and de-identified and secure in it made that sound a little complicated but we do some things like this now you know we do have a electronic reporting for like today um coveted cases from from hospitals and emergency rooms we did have some states and localities adopting electronic standards for sharing um uh lab report data positive and negative and you know de-identified and and protected we just haven't done it nationally yet and it seems like a you know great combination of what we can support nationally through CDC FDA and out at CMS and informing and supporting a very well done local responses that are faster more informed and therefore more effective well thank you thank you all for joining us we'll end on that note um and hopefully I think that we have a series of ideas and thorough policy principles for a bipartisan way forwards and hopefully the next time we run into a pandemic maybe 30 40 years from now maybe sooner maybe sooner we'll be in a better place right thank you [Applause]

Channel: American Enterprise Institute

Views: 7,764

Keywords: AEI, American Enterprise Institute, politics, news, education

Id: Ky3AryeC13M

Length: 139min 43sec (8383 seconds)

Published: Fri Jun 30 2023