Welcome to the Policy Panel on Data Gaps. I'm Kunal Sainth, the Director of UNUIDA. Very early in the time when the pandemic, policymakers had a very difficult choice to make. Should we protect lives or should we protect livelihoods? Predicting lives meant bringing on, bringing a lockdown and trying to spread the virus. Predicting livelihoods meant trying to avoid lockdown as much as far as possible and trying to protect and make sure that the poor people, especially an involved sector could go and out and work.  In a way, what was surprising is that the data behind this choice and the evidence based behind this choice was very limited really, on lives because we didn't really have vital registration systems to report deaths properly. There was significant undercounting of COVID-19 motilities either direct because of the virus or indirectly. On livelihoods, what we really saw last year and this year is household surveys, labor force surveys, consumption surveys of any kind, pretty much stopped or were postponed. In that sense, the evidence based making this very difficult policy choice that developing country governments had to face was very much, much more limited and this is the reason why we're having this policy panel today. Because we want to discuss this exactly this kind of issues. What are the data gaps that are there around counting lines lost? What are the data gaps that are there and understanding the impact of the pandemic on livelihoods? Having at the same time, one should also remember in this very dark clouds around data that we've seen in the last two years, there are some silver linings as well. What are they? We've seen fairly innovative methods being used fairly quickly, I would say, on trying to collect data. For example, phone surveys. We've seen that they may not be representative as normal household surveys or labor force surveys, but they have come along and they come along very quickly. We've seen many papers in this conference and in the conference that wider co-hosted the jobs in the conference last week, the papers were essentially presenting findings based on phone surveys. That's an innovation that probably wouldn't have been thought about two years back before the pandemic hit. Now, I should also mention that I see this panel as a mark of respect for the late Beno Undulu because Beno was a former chair at the wider board, was very concerned about this issue. We kept on talking about this until it was untimely passing away early this year. I see this panel in memory of Beno because this is something that many of us who have worked on their big countries were very concerned about. Also, this is something that wider has always been historically interested in and invested in on data. This policy panel brings together our interest in data and this really big challenge we're facing right now on data gaps around lives lost and livelihoods lost. I want to introduce this panel. We have a really remarkable set of panelists here, leading experts drawn from national disaster agencies, international agents in academia. We'll discuss the challenges on data collection in the pandemic and then the implications for the future, including possibly some innovative methods that we've learned in this last couple of years. Let me introduce the panelists in order they will present and I'll ask them to speak. First, we're really pleased I have to say that we have on the leading demographers working on COVID-19 mortality in the Global South. Tom Moultrie is a professor of demography and director in the Center for Actual Research at the University of Cape Town. Among Tom's long-standing research interests are the political sociology of official statistics and improving refining the suite of indirect methods for demographic estimation. Tom, thanks so much for being this panel. Then we have a change in the panelists from the Kenyan National Bureau of Statistics. We have Paul Kenboy-Samue and Paul, welcome. Let me introduce Paul. Paul has over two acres of experience in the Kenyan National Bureau of Statistics. He's the head of research at the Bureau and Paul has worked in survey designs, data collection, processing analysis with strong bias towards poverty and inequality. Really pleased to have Paul in this panel. Moving on to my colleague, Patricia Justino. Patricia is a development economist who works at the interface between developing economics and political science. She's a leading expert on political violence and development. And the co-founder and co-director, the house is in conflict network. She's currently a senior research fellow in UNEWIDER and professional fellow at the Institute of Development Studies, UK, on leave. When we have Lauren Harrison, Lauren, welcome. Lauren is a policy analyst and team lead in the data systems team in the publishing statistics for development in the 21st century, Paris 21 in short. We all know Paris 21. So that's easier to remember on the abbreviation. Finally, but not least, Samuel Anim. Samuel is government statistician at the government statistical Ghanas Institute of Service. Apologies for that. He's also professor of economics whose work, the Department of Economics is Cape Coast Ghana. I should also mention that Samuel is PhD in the University of Manchester where I have an affiliation with. So we have five excellent panelists. And what we're going to do is I'm going to ask each panelist one question first. I would then ask them to respond to that question for five minutes. And I hope we can stick to time because we do want some time for some time for Q&A. And so I'll go by order of presentation, ask each panelist a question and then we'll have approximately 30 minutes for Q&A. And I would really encourage the audience to send in your questions to us in the panel so we can have a really interactive Q&A session to make this panel very lively. So we're looking forward to that. First question, that's to talk. So start my question to you and this is essential as I mentioned your own work on COVID-19 mortality which has been absolutely innovative and part-breaking. There's that and you know we know a lot about COVID-19 mortality in the developed countries. By now, we have very good work than done in the UK, in Europe, the US and so on. But as we know very little we have very little knowledge about COVID-19 mortality in developing countries. So the question I have for you is what do we know about the true mortality due to COVID-19 in developing countries? And link to that, is there a profile of COVID-19 mortality is different in developing countries than in developed countries? You can respond to that and around five minutes please. Thanks. Thank you very much Canal. Thanks very much to the invite. Greetings to all the attendees joining us around the world. And thank you for a great question. You just said that we should try and keep our answers brief. I could certainly talk for an hour, the full hour of the session to try and attempt to comprehensive answer. But I'm not sure you or my fellow panelists or indeed the audience would appreciate that. So I'm going to try to do my best to answer as succinctly as possible. The answer to your first part of your question, what do we know about the true mortality due to COVID in developing countries is straightforward. Surprisingly little, bordering on almost nothing. The more interesting issue for me, I think relates to why this might be the case. And we need to really step through the reasons why we know so little about COVID mortality in developing countries. Firstly, the testing for COVID is wholly inadequate in almost every developing country. This is in part a measure of the functioning of the healthcare system. Does the system have the resources to fund tests, run tests through a lab, collate the data? We know that there's a very strong correlation between, say, the UNHDI and the log number of tests per million population. And so without testing on the appropriate testing routines, one barely gets to the first base. South Africa, for example, my home country has the third highest number of tests per million in continental Africa. But globally, that third position in Africa ranks 123 in terms of the number of tests done per person around the world. Secondly, the identification and recording of COVID deaths requires a highly functional civil registration and vital statistics system. This requires that deaths are notified not only timeously, but also completely, or at least mostly completely. We know with data from the UN Staff's division that the quality of the civil registration of vital statistics systems in most developing countries is exceedingly poor. And that in most of the developing world, in particular in sub-Saharan Africa, there really is very limited functionality to that CRVS system. In the context of the sustainable development goals and this overarching mantra of no one left behind, this really has to be an area of an intensive and extensive work. And I know that Paris 21 and other organizations are taking this forward. Thirdly, the CRVS, that civil registration vital statistics system, has to be combined with capacity within the health system to identify COVID deaths. In other words, we need a cause of death identification system. And again, without the CRVS, there's no going forward, which explains why Swaziland, the country with the fourth most reported deaths per million in continental Africa, comes in at 68th in the global rankings in terms of identified COVID deaths per million. South Africa, my country, has been fortunate in having a reasonably comprehensive testing strategy and a functioning vital registration system, one of the few in continental Sub-Saharan Africa. And that has almost uniquely allowed us to track the evolution of excess mortality. That's mortality above that, which we had expected pre-COVID in almost real time. With colleagues at the South African Medical Research Council, we're able to release data on a Wednesday evening based on deaths which were notified through our civil registration system through to the previous Saturday. So it's a four-day delay between when the deaths, the last point when deaths are recorded until when we release an estimate of those excess deaths. And since the beginning of the epidemic, we've recorded about a quarter of a million excess natural deaths. So that's in the country where there are about 450,000 natural deaths every year. So an excess of a quarter of a million in 14 months is significant. But the number of officially COVID reported deaths is 83,000. And while not all of the excess deaths are COVID, because some will be collateral deaths arising from repeated overburdening of our healthcare system during the surges and the waves, we think that the vast majority of those excess deaths are indeed COVID or COVID related. In fact, we think it's about 85% to 95% of that quarter of a million excess deaths are attributed to COVID. But we don't actually know that and we will never know that until we get the detailed information from the death certificates and the cause of death, which might take two or three more years before we are able to do so. So at the moment, we are basically grappling in the dark. We have excess deaths, which suggests the actual COVID deaths are being underreported by about two and a half to three times. But we don't actually know what those real numbers are, except we see the evolution of excess deaths tracking what we know with the surges and waves of COVID infections. Your second question, just briefly, says, is the profile of COVID deaths different from that elsewhere? Ties with the myth which has been circulating perennially based on misinformed interpretation of data from COVID aggregators, that somehow Africa is immune to the disease, that it's unaffected. This is simply not true. Yes, the population of Africa is younger, but newer variants, for example, Delta, which have affected the young as much, if not more so than the elderly, certainly are having an impact. We see that in recent data from Kenya, from Namibia. And we have a population in sub-Saharan Africa with extensive comorbidities, particularly diabetes, hypertension, and obesity, which are all co-factors for COVID mortality. The reason why many think that there's no COVID in much of Africa is simply because we do not know where to find the deaths and how to record them. Where countries have been able to do so. We have been able to track those deaths. We've seen it with Peru, which has done a fascinating study amongst developing countries of reconstituting their official estimates relative to what they have now estimated from the death certificate server code. And that is why COVID now tops the list. Things are possible. The work can be done. The resources can be generated. However, the fundamental problem is across most of the developing world really ties not this need to get in a functioning civil registration vital system. Thanks, Kanal. Thank you, Tom. I'm gonna come back to some of the points you raised later on the discussion. That was very, very informative. Thank you. I would move to Paul now. And Paul, the question I had for you is, and specifically in your role in the Kenya National Bureau statistics, what have been the major challenges that you've faced in KNBS and more generally for other satisfy agencies in Africa last year and this year. And what have you tried to do to address this crucial data gaps that have seemed to emerge during the pandemic? So two part question. First, what are the challenges that you faced? And second, what have you tried to do about it if it was possible? Thank you. Thank you very much for the question and thank you for inviting us to participate in this panel. My answer is the first question is the issue of challenges that the Bureau of Statistics has faced. From the emergency of COVID-19 in 2020, it hit Kenya on 12th of March, 2020 and since then there was an immediate measure including the cessation of movement to certain parts of the country, working from home and introduction of cavies. So the first challenge that we faced was the disruption of regular data collection, analysis and report writing in the Bureau. The COVID-19 introduced the lockdowns and therefore we couldn't continue with the data collection. The second challenge that we faced is the timeliness of data dissemination was also disrupted since the report writing workshops had been rescheduled or temporarily suspended. Father, we have the unprecedented demand of data. For example, when the ministry for social protection was looking for the information about the poor households so that they can assist in the social protection targeting. So this information were lacking. We had also budget cuts. As you know, we have to get a lot of money to do a data collection. So this disease introduced the issue of prioritization of the budget and therefore the budget cuts by 50% affected us a lot. We had also the challenge of methodology of data collection from households. That is the data collection had to be switched from computer assisted personal interviews copy to computer assisted telephone interviews copy. There's also, we had also lack of a sampling frame to be used for computer assisted telephone interviews methodology during the switch. We had a problem that we didn't have the phone numbers for those people who we were to conduct. Now, the second part of the question is how do we address this data gap during this pandemic? The bureau has been striving to overcome the shortcomings and the vulnerabilities in data production systems that were brought to light by the pandemic. And these mitigations that were introduced were one we had to switch from collecting data from the field using the copy to cutty and the bureau for the first time utilize the phone interviews. The second one was construction of sample frame for cutting which means the bureau now had to work with other government agencies to get the phone numbers. For the individuals that were to be called or to be interviewed. This means that based on our census 2019 the bureau went for the IDs of these people. So based on the IDs we were able now to combine the IDs with the phone numbers and therefore we will get these people through the phone. Based on the experience of the bureau as incorporated lessons learned from the pandemic to include variables of phone numbers when they are preparing the sample frame. Also this introduced the issue of virtual meetings. So we've been having virtual meetings in everything that we do and therefore this one has disrupted the quality of statistics. Also working in ships has also brought in the issue of quality statistics to come down. Rationalization and prioritization of activities programs due to this the budget cuts the rationalization program was introduced where the key products were prioritized while to overcome the issue of the shortcomings in the budget. Partnership and collaborations was done and the bureau had to reschedule or to work with partnership in order to produce some of the statistical products. The other thing is that the bureau since the social protection required a lot of data to target the poor. The bureau now is working with the social protection using the small area estimation to identify the poor people where they are and therefore keep the database for the national whenever such emergency comes in. We are also working with the citizen generated data to make them official or reporting for SDGs and this we've been working closely with Paris 21 to give us the knowledge on how we can work with this. Then we've been also having capacity building with the big data and other alternatives of data sources. We've been collaborating with ONS of UK to come up with ways of analyzing the big data that is what I can say for now. Thank you. Thank you, Paul. That was very informative and I think in the Q&A That's what I can say for now. Thank you very much. Paul, thanks so much. I should just mention Paul has the video off because of interconnect connectivity problems, but Paul very informative and again, we'll come back to some of the issues you raised about phone surveys and small area estimates and so on. Hello, I've stopped. Thank you very much. Yeah, we can hear you. I've completed the two questions. Thank you very much. We can hear you. I will come back to some of the questions you raised in the Q&A. Now let's move on to Patricia Justino. Patricia being a leading expert on conflict-affected states, particularly where we know with conflict, violent conflict, data collision is always been a challenge, right? I mean, it's always been an issue and you've obviously are very aware of this in your own research. How do you think the pandemic has made things worse in this context? And what does it mean for effective policymaking in this country is for the future? Patricia, all the two. Thank you very much, Kunal, for the invitation to be in this panel. And thank you for all the participants for joining us from many parts of the world. I know many are with us today. So yes, so I've been connecting data in conflict-affected countries for the last 15 years. And it's a challenging, it's probably one of the most challenging environments in the world to do so. And I guess the short answer is the pandemic has made it much more difficult. And we actually had a session on conflict yesterday and we just had a fire chat, fire side chat discussion with Dominic Roon, who's doing a lot of work in his area too. And this comes over and over, all the challenges we face. It's complex, it's dangerous. In some areas, conflict has increased, which makes it even more complicated post-pandemic. Data is available, when available, it's protected by many gatekeepers, including governments. We see a movement towards, in some cases, towards more sort of authoritarianism. And therefore, things are even more difficult to access. We often talking about data that is very sensitive. Even when we access the data, we heavily scrutinize about everything. We have to say, all these challenges are there. They're becoming more difficult. And of course, this has a huge policy implication, because in a lot of these settings, national policies are being done based on very limited evidence. And also for international interventions, as donors focus more and more in conflict-affected countries, as we all know these statistics, the number of the poor is concentrated more and more often in conflict-affected and fragile countries. So therefore, the interest is greater, and so is the need to understand better these issues. But actually, I mean, we know these problems are there, and they're gonna become more difficult, but actually I want to pass on a more positive message here. And the thing is, we have made a great deal of progress over the last decade. And now, actually, we do have a wealth of data from conflict-affected countries that can be made better use of. And it's interesting, I mean, we've seen a wealth of data innovation, which has led to, but also be generated by important theoretical advances in conflict analysis. This is one field where data and theory have gone hand in hand in very interesting ways. And we now have available things like, we can map in much detail conflict events in almost across the entire world, thanks to the efforts of data sets like Akhlet and colleagues at Uppsala University. Of course, there are shortcomings, but it's been done. There's a wealth of information about conflict factors, surveys with ex-combatants and so forth. The consequences of conflict, we know a great deal about the consequences of conflict thanks to efforts to develop household surveys in conflict-affected areas. Some of these are not easy to access, some of these are, again, kept by gatekeepers. So there's a lot of work to be done in terms of compiling, making data accessible, being more transparent about what's out there, but we're making progress. And certainly in terms of quantitative subnational data, I think there's been quite a lot of work. And one interesting thing that I've observed over the last year and a half is that actually the pandemic seems to have forcing everyone to do more originally thinking about how to collect data in the difficult situations. We can't fly, we can't go to the field, we can't access the populations, we can't access our samples. My colleagues in this panel have just gone through all this, discussing all these difficult issues, but actually this is something that conflict researchers face on a daily basis without the pandemic. So we've been thinking very carefully about how to do these things. So I actually think that there might be an opportunity here to innovate and just give you some examples of things that are happening or have been happening in amongst those collecting data in conflict-affected countries that may offer opportunities for others. Is we were discussing and piloting already phone surveys and also methods of collecting surveys using SMS messaging beforehand. The World Bank had, for instance, a large phone survey ongoing in Nigeria in areas controlled by the Boko Horam. And I suspect this may have informed the recent efforts with phone surveys quite well. The use of satellite information has been quite widespread over the last five years. We first started by trying to understand conflict dynamics by looking at things like crop patterns, burn areas and so forth. And then move more recently to using satellite data to trace population movements, especially refugee movements which are very, very difficult to track. And obviously now especially with a fallout from the Afghanistan. From Afghanistan, I mean, this is going to become quite an important issue. Big data is being slowly coming in and being more and more used using social media-based data, information generated by mobile phone GPS locations. I just come across a really interesting project where mobile phone GPS location is being used to understand those that joined the events in the US of January the 6th and tracing exactly who were those present there and it's been used to also in the ongoing legal actions. There's a lot of talk about early warning systems. This has always been there. It's a big topic. You want to understand where the next conflict is going to happen. So there's a lot happening in terms of machine learning models forecasting conflict and it's becoming more sophisticated as the underlying data also improves. Again, also using big data and so forth. There's challenges that remain on things like understanding the long-term effects of conflict and with the pandemic happening, this is going to come up. And challenge is happening because all these innovations have been happening in the last 8, 9 years or so forth. And so we don't really have good ways of going back in time and understanding what was there. Can we kind of take lessons from previous conflicts? We had a paper yesterday looking at the effect of the Ebola pandemic on violent conflicts alongside another paper looking at the effects of COVID-19 on conflict and finding very similar results. So we want to learn from past experiences. There's a big weakness, but there are large improvements now in digitalizing archival data and also accessing intelligent military data as it gets declassified. So lots happening. So I am hoping that actually what the pandemic will offer is an opportunity to do things better and accessing. These are some of the poorest, most vulnerable population in the world also living in areas which sometimes are almost impossible to access. So I am quite hopeful that we can all think more creatively about how to access these populations and understand better these really difficult contexts. So that's me. Thank you, Kanal. Patricia, thanks so much. Actually, you made a very important and really interesting point that as we right now have a situation where many countries, especially the global spot, not only the global south are facing re-challenging data. This is a challenge that you've always faced and others who work with fragile states face for a long time. And so you'd already started thinking about innovations which perhaps others can learn from. That's a really interesting point. And I think we can come back to this later on in the Q&A. Thank you, Patricia. Let me move on to Lauren, Harrison. Lauren, you've already heard now the role of Paris 21 in the remarks that Paul made. So it's a nice way to introduce your work and Paris 21's work. So what do you see is the role of initially international issues like that as Paris 21 improving statistical systems in low-income countries, not just now for them for now, but for the future so that we can have more resilience to future crisis in terms of data or over to you. Thanks so much, Kunal. And to my fellow panelists, it's indeed such a pleasure and really an honor to be able to join you today and talk about one of our favorite things at Paris 21, how to solve the issue of data gaps. So coming to your question, I wanna talk about, when I think about resilience I'm thinking about it in a couple of ways. One is more on the operations piece and that's asking how do we enable and empower countries to become more nimble and responsive in moments of crisis? And the second is more what we might think of as the substantive output piece of how do we enable and empower countries to address key data gaps that ensure no one is left behind in crisis moments. And when we're talking about international initiatives I think that like Paris 21, there's really three ways where I think we play a role. And that's by adapting, assisting and amplifying. So first on adaptation, my colleagues have already mentioned a few points and we're probably all really tired of thinking about how to adapt, we've all had to adapt. But very early on in the pandemic, Paris 21 started thinking about COVID-19 as what we might think of as a dual shock for statistical systems in low and middle income countries because we saw a rapid increase in demand for data and information. And at the same time, a precipitous decline in operational space to collect data and produce information. There are many success stories, several from the colleagues that have already spoken on how NSOs and other agencies have been innovative and adapted under these conditions. A couple of examples from our shop. One is the one that Paul brought up earlier on how we've been working with KNBS to integrate quality criteria for non-traditional data sources like citizen-generated data in their new national quality assurance framework, which is forthcoming, which has been exciting. Another example is in the Philippines where we worked with the Philippine Statistical Research and Training Institute and the Philippine Statistics Authority to train local government units to use really rich granular information when they were capturing in their community-based monitoring systems to close some local data gaps. Both of these examples illustrate not only how international initiatives can continue to play a role in sustaining support during the pandemic, but also on adopting new data sources to close data gaps, which we know is probably part of the solution. It's often that we need practical tools and capacity development to make this really useful and practical for NSOs and other stakeholders in low and middle-income countries. So international initiatives like Paris 21, of course, we've also had to work on adaptation. Both the initiatives I just mentioned have been delivered more or less remotely with very little face-to-face interaction. But since the side of the pandemic, we've also launched two free e-learning courses on communicating gender statistics in partnership with UN Women. We've seen over 1,000 course participants and already have examples of impact from more inclusive land use planning and Trinidad and Tobago to data-driven advocacy on violence against women during the pandemic in Jamaica. And I think that the e-learning courses we found, these have been a really important example of adaptation for a couple of reasons. One is kind of the obvious one, that this is about democratizing and scaling access to training and tools for a wider group of people. But also it speaks substantively to a broader challenge with data gaps that we're observing, which is that it's really not just a production problem, it's also a communication problem. And this speaks a little bit to the bottlenecks that Patricia was talking about. How do you get the data that is there into the hands of the people who can use it and to make more informed decisions? So that's adaptation. Thinking about assistance, it's about supporting countries to meet evolving needs and challenges. I've already mentioned we've adapted modalities for engagement, but international initiatives like Paris 21 play an important role in tailoring guidance and tools to address the challenges that countries face in a changing data landscape, whether that landscape is shifting because of a crisis like COVID-19, or just the evolution of the modern data ecosystem. In the aftermath of the crisis, we released guidelines on how to develop a business continuity plan, for example. So this was meant to complement more medium term planning for like a national strategy for the development of statistics. Statistical planning, I think, is a really important way in which we can think ahead and help countries become more resilient. But we're also looking at how statistical planning can interact more structurally with issues around data gaps and inclusion. For the past three years, we developed and piloted a framework with UN Women to assess gender statistics and mainstream gender and statistical planning. And we've already seen how this process has actually strengthened the responsiveness of NSOs to one example of a key gender data gap, the gender data gap. In Senegal, for example, the NSO leveraged insights from the Gender Statistics Assessment to establish a dedicated pillar related to gender in their new NSDS. In the Maldives, the National Bureau of Statistics is working with the Ministry for Gender, Family and Social Services to establish a new interagency group to coordinate gender statistics. So these examples demonstrate how targeted assistance can help address some of the critical data gaps that we've seen during COVID-19 and ultimately set the stage to close those gender data gaps in the future and build resilience for more inclusive statistical systems and crisis. Third and finally and very briefly on amplifying. And I think that this is for international initiatives like Paris 21, we take really seriously our role in ensuring the needs and experiences of countries are amplified and reflected and communicated back to the wider international community. This is especially critical to address the resource shortfall we often see. Our partner report on support for statistics has reported for a number of years running that financing for core statistical systems like CRVS and others is really stagnant. And yet we also know that the need for data and information is only growing. For this reason, as the secretary for the Bern network, Paris 21 is developing a new clearinghouse on financing development data, which will be an innovative online platform to better match donor supply of financing with information on country needs for financing so that we have a better sense and countries have better representation and a tool that partners can easily use to understand the landscape of those needs better both in moments of crisis and in moments of relative calm. We look forward to launching a prototype for this new tool during the World Data Forum next month. And so those are my kind of three points on why I think international initiatives really can play a role in improving resilience. That's adopting, assisting and amplifying. Thanks so much for your time. Over to you Paul. Hello, Arun. Thanks so much. That's really very useful. And I'm going to come back later on the discussion around some of the issues around citizen led data collection, for example, that was very interesting. Paul mentioned that when you did too, because you might want to think about other ways of collecting data in the standard way that economists collect data. So qualitative data, for example, could be very important for the future. So I'll come back to that maybe in the Q&A. And now we are very pleased to have Samuel Anim. Samuel, there were some technology problems I know at the beginning, but I'm glad you could join us. And great to see you again. And I've already introduced you at the beginning. So let me get to the question directly then. The question I have for you, something that you've already touched on a little bit in the discussion, is the pandemic is going to be smarter and more efficient ways of collecting data? I mean, so we already see examples that Paul mentioned, Lawrence mentioned, Patricia mentioned too, that we had to find ways around this problem that we had. We couldn't really do the kind of data collection we could do before. And so we've seen some really interesting ways of collecting data in the last two years. So first of all, how do you think this is particularly in the case of Ghana and where you will sit as the government statistician, the Ghana Statistical Service? How do you see this in your own work? And also, what can we learn from this? What is what we observe for the future? And also I realize that you are both the producer of data in a sense as the government statistician, but you're also the user of data as a professor of economics in the U.C. Cape Coast. So again, I'm quite curious to see how you see both sides of this question of data quality. Thanks, Sam, go ahead. And thank you very much, Kunal. Indeed, I did have challenges, but thankfully just after your introduction, I got in to listen to all the other panelists. And thank you for all the listeners on this session. Indeed, the issues that have been mentioned by my colleagues do highlight some of the innovations that took place at the height of the pandemic. But what I want to do is to give us some background to the pandemic so that we can have whatever ingenuity that we're talking about during the pandemic in terms of the data that we collected. We all should appreciate the fact that on the heels of the pandemic, we were achieving some good results from the data revolution that started in 2014. And what I mean by good results was that we had quite a number of national statistical agencies in the global south putting their data ecosystems in place, i.e., making sure that their laws are working and also going into good collaborations and partnership that would essentially help them explore the four tenets of the global data revolution. And also as a background, we should also know that at the height of the pandemic, quite a number of countries were preparing for the 2020 round of population and housing sensors. Indeed, in the case of Kenya, they had just finished and released their preliminary results. And in the case of Ghana, we were at the height of the preparation ahead of the sensors taken. So keeping these two things in mind that ahead of the pandemic, these things were happening. It helps us to understand whatever ingenuity that took place during the pandemic. If you carefully look at the data revolution which has four tenets, three of the tenets, i.e., the data landscape, the innovation and the SDGs, four directly in line with the discussion that we are having in terms of data gaps from the production side. So I would touch on the accessibility side, which is the fourth pivot of the global data revolution. So whatever ingenuity that we're thinking about would make, should make reference to these three pillars of the data revolution, i.e., the SDGs that got all of us to continue to produce data in large quantities, the innovations that all of us now on this panel discussion, we're talking about citizens generated data, big data, administrative data. Indeed, these were the things that we were looking at when we had to deal with the over 231 indicators for the SDGs. And as I said earlier on the issue of the data landscape, got us to think about the environment, specifically the legal environment. Also it is important for us to know that the pandemic to a very large extent engendered desperation. And what that means is that quite a number of countries got into interventions without taking into consideration what the data is telling us and moving forward what we should look at from the data perspective. So clearly once we're thinking about the ingenuity, that led to quite a number of NSOs coming up with different sources of data in response to it. We should also keep in mind that we made some mistakes along the line because the interventions that we put in place preceded whatever data considerations that we should have during the pandemic. And the last thing that I want to talk about as background is the sensors had to, the sensors taking how to battle with the risk of increasing the infection rate, that is a COVID-19 infection rate, and also the sensors had to battle with. And for that matter, all other in-person data collection had to battle with the panic in terms of whether is not going to increase the infection rate. So keeping all this in mind, my perspective is that if you want to have a conversation around the ingenuity of data collection processes during the pandemic, there are quite a number of things that we need to take into consideration. And I'll quickly run through these things. The first is it is really giving us the opportunity to revisit the rudiment of quality statistics. And when I say quality statistics, all of us would make reference to the 10 fundamental principles of quality statistics. And we begin to ask ourselves whether these new sources of data collection really are reliable, really are representative, really address things that are academics over the period we've taken for granted. And Kunal, if you permit me, I would want to stretch the argument on data collection away from the field exercise itself when we are interacting with the respondents and begin to think about what the pandemic taught us from the perspective of those who are going to collect the data, how they are identified, how they are selected throughout the process. And the modes of training earlier on, one of the panelists talked about virtual engagement and also as academics, whether and also as policymakers, whether the few papers around recall ability to capture responses as they are in consistencies between responses, given that you have one respondent and different enumerators go there and how these feed in our discourses whenever we are doing some analysis. So the discussion around ingenuity should move away from the source of data, primarily saying that it is traditional sources of data, it is non-traditional sources of data, and step back to begin to think about who is collecting the data, especially when we are dealing with telephone interview, for instance, when we are dealing with online interviews, for instance, how do these eventually play out with the quality of the data that we're going to collect? And that is what I'm saying we need to reflect on the issue of the rudiments of quality statistics. The other thing that I think should be central to the conversation has to do with the partnerships and as I indicated earlier on, quite a number of things were happening ahead of the pandemic. So in the case of NANA, where we relied extensively on core detail records to look at mobility and the infection rate, this was an activity that we were already undertaking, but at the outset of COVID-19, we've now found the real need to do it in the context of the pandemic. So partnerships, collaborations are one of the things that I talked about as results that we're achieving on the heels of the data revolution that started in 2014. And premise on what I talked about earlier on in terms of the panic and the relationship between the pandemic and infection, the pandemic and response rates is the whole conversation around publicity, education and advocacy, once you are thinking about new ways of data collection because if we continue to work in this atmosphere, what we're going to see is how to deal with non-response. I don't know the experience of my colleague from Kenya, but when we were using the telephone interview away from the in-person interview for tracking the pandemic on businesses, one of the things that we had to grapple with is high non-response rate relative to what we would have been able to achieve if we were doing in-person training. So the whole conversation around publicity, education and advocacy has now become more relevant in the space of data collection within the pandemic. So a few things that I think we should reflect on as lessons moving forward is harnessing the options that are available to us for non-responses in the bid to ensure that we have quality data. And more importantly, we need to work aggressively towards identifying and addressing the biases associated with these new sources of data. This brings to the fore my third issue that hybridization of different approaches of data collection. And yet when I say hybridization, I'm not looking at only traditional and non-traditional data sources. Within traditional data sources, how do we think about hybridization and within non-traditional? How do you think about hybridization and also across traditional and non-traditional data sources? And this is the way we need to go and we need to consciously work towards reaching the point where, as I always say, data is now a byproduct, especially when countries in the global South are mimicking what is happening on the other side of the globe where digitalization is intensive. And if you go to the Scandinavian countries, specifically Denmark that we are collaborating with, but we are moving towards a situation where we tend to have sensors, pure base and not demonstrating data. This thinking should inform how we are working towards it over the inter-sensor period and you do that. Lastly, I want to emphasize the need for national statistical agencies to anticipate shock. Now we were all caught by surprise with COVID-19 and I'm asking myself, what about another shock that is going to come in a different perspective? I.e. with quote and unquote, with this rush towards digitalization and the use of non-traditional data sources if technology fails us, how are we going to prepare ourselves as NSOs to continue to provide data in areas that are needed for that reason? And thank you very much. I'll pause here and I'm hoping to further elaborate on some of the issues that I've talked about. Thank you. Thanks, Sam. That was very, very interesting and extremely useful to think about for us. I want to just, and I'm going to raise a couple of issues here and I don't want to respond to it from you all right now, because I want to also get to the questions. One thing I think we should do as we get back to a point where we might be able to do in-person interviews, in-person surveys, I want to see some pilots done of seeing the reliability of phone surveys versus in-person surveys. Because I'm not convinced yet the phone surveys are all that reliable. So I want to see pilots where you do the same survey for the same person over the phone and then in-person, try to see the difference there. I also would like to see much better thinking through retrospective questions. And perhaps again, trying to do a pilot on retrospective questions versus questions that are asked in the baseline. Because in this particular case of the pandemic, we had a lot of surveys, which are asking retrospective questions. We don't really know how accurate those questions, the data was from that. So I would like to see much more of thinking by the international community on those sorts of issues before we get to the next situation of another possibility of where we can't get in-person. I would also like to see, speaking as an economist, I would also like to see much more thoughts on qualitative methods, much more thoughts of citizen-led methods, much more thoughts of participatory methods, because those are easier to do in the situation in the pandemic. We have not, as economists especially, invested enough thought on those methods. I remember the World Bank had, it wasn't a poor project where they tried to bring in participatory methods. It went, it just went on, it was sidelined after some time. You didn't really pick it up, especially among economists. I think it's important for us to rethink these issues and try to be prepared in a much better way on these methods, because we know exactly the problem with large-scale surveys that are in-person surveys. And even though we thought of very innovative ways to handle that, they may not be foolproof. So I think those are things I think we should all think about for the way forward. Now, I have several questions in the Q&A already, and they are actually mostly to Paul and to Lauren. So maybe start with those questions, and then I'll have some questions for all of you. So Paul, I have a few questions for you, which you might have already seen in the chat, but let me read them out for the benefit of the audience. So the question are as follows. First question is from Hyde-Syn DeVries, who asked the question that, so did COVID accelerate the use and shift towards big data at KNBS, such as financial transactions and mobile phone geolocation data? So was there a pandemic kind of a shock towards big data collection in KNBS? Let me ask the second question to you Paul at the same time. So the question is then, and this is a very specific question, can one access the survey data that you've collected from the KNBS platform? Is that publicly available or not? So that's probably quite a straightforward answer that yes or no. And if I don't, if you don't mind, I have a third question for you. The third question is, what would you advise students and other researchers into switching from Cappy to Catty, which you mentioned in your own remarks, what are the cost and quality implications of this different data collection formats? Actually, that's quite an interesting question that cuts across to other NSOs too. So Paul, maybe you could start and then I will also have a question for Lauren and then questions for the other panelists. Thank you. Thank you very much for these questions. Actually, on the issue of the big data, we are switching on slowly to big data because we need also to build capacity on how to deal with the big data, but we are switching it slowly into that. So on that question, I don't have so much because we are building capacity through ONS. Now on the issue of availability of the information in the website, Pothole, we've been migrating because of the service provider. And when we migrate, we lost, we didn't migrate what we call NADA. The Canada is where we host most of our results from the surveys. So it is underway, so it will be there in due course and most of the reports will be available there. On the issue of the older reports, we have a working, you can go back to the website and look at the older reports on CPI or other, they're all in that web. The next question was about what advice we should give to students or researchers to switch from copy to copy in light of the COVID-19. Now, actually to say the truth is that when using copy, you are directly asking questions to individuals and you can see the answers and the body language. You can understand whether somebody is cheating or saying the truth. When you're using a copy, you'll never know whether the person is saying the truth or not. So in terms of copy, you can guess the answers straight and when somebody is lying, you can repeat the question. But for copy, you'll never know. So that one lowers the quality of data. But when it comes to the issue of cost, the cost in copy is very low because what you need is to have people and you are training them through the virtual and then the cost for transportation to the location where the households are is cut down. So in this case, the cost will go down but the quality will also go down based on that. I think those are the questions that were said. Thank you so much, Paul. Very, really good responses and very useful and really good to see so much of interest from the audience or the work that KNBS is doing. Now, Lauren, there's a question for you too which is also pretty specific actually. The question is that interactive data portal that you mentioned, will it be an open source platform? Open source platforms are extremely critical for resources in developing countries. Yes, thanks so much for the question. So the Clearinghouse platform is basically designed to pipe in multiple data sources on financing for statistical systems and budgeting for statistical systems. So the modeling part is still underway. The data sourcing is still underway. What we're releasing next month is a prototype of the platform. What we're also working on is a governance arrangement to make sure that we can use this data responsibly and at the same time produce the most value obviously for users. So that's a, you probably are picking up the long-winded answer to the question without actually answering the question. We are still in the process of defining the parameters for the platform, but open source certainly is a priority for us. What that will look like in practice for this platform I think is still to be determined. And I am not the person working on that specific aspect but it is certainly something that's a priority for Paris 21 and we're very aware that more open source is better and that is certainly a priority as we're working on the design. So I would say, stay tuned, join us at the World Data Forum in October and there'll be more information on the functionalities and how to use the platform going forward. We really, really appreciate the interest. Thank you, Laura. Do you want telling us when the dates of the World Data Forum is so that perhaps people can tune in? Ooh, that's a great question. I don't know the bottom of my head. I think it's the first week of October. I think I wanna say the sixth, seventh, eighth somewhere around there. All right, excellent. And that's supposed to be in Paris 21 website on the website you would have just be the dates at some point. Yes, I think we'll have some information on our events coming up but also there is a really nice platform already for the event. It's a hybrid event. So you can join in-person or online registration is open and if you're interested in data gaps there will be much, much content there to continue to explore these interesting issues. Excellent, thank you. There are still a couple of questions but I'm going to just ask a question and whoever from the Tom, Patricia and Sam can just jump in because we have literally like three minutes left for this panel. So the one question I think is a really interesting question is about mobility data. Google mobility data is now, actually we've seen several papers being used have used this data. So do you think that's the kind of data that we should be trying to collect more and how useful is that? So that any of you can answer that. And let me just put in the second question so that one of you can answer that about ethics of data collection especially when you can't do the standard constant forms and so on in-person. So is there a difference between collecting data through the over a phone versus in-person surveys in those ethics and so on? That's a very important question as we start moving more towards phone service and so on. Who wants to answer the first question on mobility? Tom and then maybe I think Patricia might also want to answer the next one. Yeah, just very briefly, I mean, time is running out. The question of the utility mobility data I think is crucial. I think it is certainly one of the ways which the world is going. I think certainly in the developing world in sub-Saharan Africa, you have to ask who has a smartphone connected to Google or Apple? And that's obviously a major concern. But beyond that I think we need to also almost press pause now. The COVID epidemic provided a spur for the most unbelievable unleashing of data collection exercises, particularly in the global south. We found mobile phone data being used to try and track the epidemic. And so we need to think about the ethics and morality and data privacy issues of unleashing all that data before we understand fully what the modalities of it are. So I'd like to emphasize the ethics of it. I'd like to also say we should avoid throwing out the baby with the bathwater. We need civil registration, vital statistics systems. If we, without those, we are never gonna get to the kind of data richness that we have at present in the developed world. So I'll leave it there and let Patricia pile in. Thank you. Thanks Tom. But to share the ethics question, do you have anything, any thoughts? Yeah.  I mean, just to reiterate what I said, this mobility data was being used before to do things like tracking pilot events, protests and so forth in a variety of ways. And I see a lot of value now. Obviously, who should also mention spending patterns and all the rest of it that's been used by various, in various ways. I see a lot of value in this data versus two spot patterns of vulnerability. You see people moving in and out of different places. You can also, it's also being used with the patterns of displacement, for instance. You can see, suddenly a whole village it moves out somewhere. You see there is a problem coming up in that area. So it's being used in a variety of ways. And, but like Tom rightly says, I mean, the ethics issues are huge. Like to give you an example, when we first start thinking about these issues in terms of using phones in very remote conflict affected areas. One thing that you find very quickly is giving out mobile phones is not a great idea because that person becomes a target for armed groups. So there's all these really subtle issues that need to be taken into consideration. And we know also that some of these data is being used by different governments to track activists, journalists, et cetera. So as researchers, we have a lot of responsibility to act that. And we don't have time in this panel to discuss more, I can talk about for a long time. But one of the issues is what Kanaal mentioned before. The use and the qualitative understanding of some of these contexts is going to become really crucial. I suspect in the times to come. Thank you, Patricia. I have one, we're going to go slightly over time, but this is the last session for this day. So we could probably do that. I'm going to ask Samuel the one last question that came from the audience. That's a really important question in my view. And it's about opportunity that we are, opportunity of data collection with a new method of data collection. Do we see a difference in optimality in terms of the new methods versus the older methods, which were obviously much more large-scale because there is samples and so on? Or do you think we can still maintain the optimality that we need as researchers in the data collection that we are doing with new methods? Sam, over to you. Thank you very much, Kunal. Just a second, let me touch on the previous question on whether the mobility data can give us some economic indicators. And my answer is absolutely yes. At least from three perspectives, especially at a time that countries are beginning to pick GPS coordinates on all their structures in the rounds of the 2020 population housing sensors, there's huge potential to track where people are going to in terms of essential services and also where people are going to in terms of the district capitals and so on. And these are economic indicators at the geography level, which would help us understand a lot of the cluster-level effects that when we are doing analysis, we are not able to do that. And at least it's going to give us some small-area estimates on expenditure and especially for countries in the global south that do not have economic statistics at the lower geography level. Certainly this cost of mobile phone expenditure could be one of the indicators for economic and statistics. To your question on objectivity, Kunal, as I said, we are moving to a point where data is becoming a by-product. If you think about big data, it is a by-product. If you think about administrative data, it is a by-product. So certainly that objectivity comparison will be far-fetched in terms of the traditional and the non-traditional data sources. I think what we should begin to do is think about the adjustment factors, if any, so that once, as you rightly said, if data is collected using in-person.


Hello, I'd like to thank the KCA and NYU for hosting this event today And what I'd like to do is ask the panelists took the speakers to come back up to the front We're gonna start some questions So again, we have William Hwang who was the surgeon who spoke Robert Altar one of the medical oncologist Janice Dutcher and Jeff Shea And I'll start off with a few questions One question to the surgeon is what do you do and you mentioned the surveillance for patients with some of these small tumors Is your approach similar in a patient for example, who's 20 years old with a small three centimeter under three centimeter tumor Versus someone who may be in their 70s or 80s So that's an excellent question. I think as a surgeon when we see patients who come to us with a small tumor We have to really take a look at two things and this is the discussion I have with them Not just a tumor, but also the patient themselves. So, you know, it's unrealistic to think that a patient with who's only in their 20s or 30s Would want to follow their tumor for the next 20 30 years waiting for it to grow And so often someone like that will tell them well, you really should have this treated But this is not an urgent issue And we can address this when it's appropriate for you, but that's frequently for most people almost immediately I think for older patients who find out that they have this in the setting of a lot of other medical conditions They often take more importance than this tiny little kidney tumor and we'll probably You know follow them every six months or so or follow them with imaging that they're getting image for for other reasons and only if there's A demonstrable change in their tumor will we then say you should probably consider having these treatments So it's on an individual basis that that we follow tumors and by no means What was I suggesting that in someone who's completely healthy and someone who has a normal life expectancy that we would necessarily watch But I think a good example is I had a lady who was pregnant And so during her pregnancy she had an ultrasound and found a kidney tumor and we certainly waited till after she delivered before treating her Okay, thank you and some of the questions on high-dose interleukin so Janice you talked a little bit about the the treatment and patients have asked often You know how many cycles can I get can I get treated you know come in five days ten five? Do I come back again? How often can you repeat these treatments? Well, I Tend either so you can repeat the cycles depending on the toxicity that people had previously What I always tell people is your body remembers because it isn't immune-activating drugs So your reaction to it the second time is going to be greater than the first time so the first week The second week you get usually fewer doses and then the clinical trials say after three months you repeat it But I tend to watch How things go before I will give another cycle and see because there are people that I've only been able to give One two-week cycle to and then they had other issues I want a woman developed a rotator cuff injury, which is pro-inflammatory And I couldn't give her more I'll to and over the course of a year Oliver London I just went away without any more treatment because the body remembers and she still had this immune reaction going other people Will give more frequently and try to get like four cycles in and over the course of the year It really depends on each individual person and I think Bob would agree because he gives a lot of I'll too as well That it's different for different people. I don't First of all, we learn from Jan We try to buy by the god-mines So I like to give you the five those of their five days of therapy obviously And again, the great thing about the toxicities that they are in particular in person So I give them not the nine day break. I've given the 16 day breaks. I have come in two weeks later Then I wait for four weeks scan them and see how they do I'd like to write on each individual course of therapy if they do well Then we like to sort of promote a stable disease or better Discussing patient potential benefit of doing it again I find the maximum of the courses I've given is four courses Which is a lot of toxicity Again as one is towering therapy last we stopped giving So I believe with Jan a lot of times that therapy is just stopped and the patient's benefit still evolves and beneficial and we can get a lot of duration of time between Coming off IO2 until you need to initiate the next therapy. So again once it's great years is Fantastic Do you see that I'd also like to say I have actually given boosters booster courses like people have gotten a course of IO2 Things got better and they were stable and go on and on and on and then maybe something starts to grow again And I've given it again and they've knocked it down again So I think you know a lot of us as he said we don't just do what the book says We kind of vary it with the patient and kind of experiment a little bit along the way With success The two kind of gases they are talking about the IO2 and the ferro which one Which kind of gas you like Which side of them? Yeah, you know the bookini is in IO2 and the ferro right so you want to do combination those are all gases right one is The old product oh stepping on the gas. Yeah. Yeah, that's from you. You from you The gas the gas, okay um Well, you get higher response rate with IO2 There were there are old data that bond presented with high very high doses of interferon where there are complete responders I just that the problem with interferon is you have to keep giving it You know, it's three times a week forever or for however long you can tolerate it Whereas is IO2 it's short and sweet. Yeah, you're in the hospital. You have toxicity, but when it's over you're done You know you walk away from it So I I'm I've always been and I'm probably always will be proponent of IO2 because I just think it's it's easy The person goes back to a normal life You know interfere on this like having the flu forever people lose 30 pounds.  Um, you know, I've seen a couple of patients that have come in and they were started on a full dose They had some toxicity and now they're off that drug onto the next drug What do you recommend for patients? So, um, that's for the oncologist on the panel. I keep the patient who has a disease So you have to know who your patient is first. Um I you know We're seeing this disease happening in younger patients. So you believe that they're More able to tolerate side effects that I probably give them the standard doses as recommended based upon the data that's been presented And I think the data that's been presented is how we should all uh apply our therapies The one variant would be that with that will be with suthent or suthent Which as a standard dose given the typical 28 days and 14 days off have been significantly toxic patients young and old And I've adjusted the therapies to rather do the 28 days around 14 days off the 14 days in around seven days off And there's data to allow us to believe that that is as powerful Actually better tolerated as well as as effective. So That's the only change I've done. I really try to treat patients with the recommended doses Accepted patients function performance as or at least of them as a 75 dose and escalate up Um, I believe that it's easy to treat a patient and accept the dose and accept toxicity Um, when the focus is the cancer, but I like to have my patient believe the focus should be them And then we can take care of the cancer at the same time So for them to tolerate dose and if we feel something tolerated very well, we can try to escalate it up Without the impact that they believe that the most max dose is going to be the one dose that's going to be effective So I'd rather tell I tell my patients you are walking the marathon So to sort of take a day by day to eventually get to a finish line Is much better than to do all your energy up front and bad side effects of the therapy Then you have to sort of scale back So I have no problem starting at a moderate dose and escalate to a full dose based upon the tolerability But on patients who have a good functional performance that is a good treatment full dose I just like to say that after Going through the clinical trials with these drugs at full doses When I treat people off study, I actually tend to start low and build up Because I just have found so many people will bolt from the toxicity if they have a really bad hand flicks and room are really bad hypertension They don't want to be on the drug anymore and we saw that a lot in the adjuvant trial because It you know had a different motivational level but There's data now from canada and from europe of a two week on One week off schedule for student at nibb and I think is far better tolerated and that you can give up full dose But if I'm going to do the four week on two Two weeks off I tend to start One week at one dose and build up the next week and then build up because I just think the goal is to keep people on the drug Like you said a marathon and I think however you can do it to keep people's You know on treatment and doing well The best way the better way to do it I mean there are some data suggesting 50s better than 37.5, but You know most people can't stay on 50. I I think Very few people can stay on 50 for a long period of time So I think the goal is to keep people on the drug keeps them stable keeps the disease under control And that has to be done in a way that they can continue to live their lives Just I have to tell you one anecdote which was a fellow that was taking seraphimib for a while And he had terrible diarrhea with it And he was a golfer And he basically said if I can't golf I don't live So he basically you know and people take oral drugs. They do what they want to do.  I tend to start either Depends on the patient so depends on the patient depends on depends on their Other conditions that they may have blood pressure or so forth I tend to start at an intermediate dose and work my way up Rarely do I start full dose And work my way down and then it also depends on also Different ethnicities. So, uh, you know What I've anecdotally in my in my practice, uh, I've seen that patients of asian descent don't tolerate These tyrosine kinase inhibitors so well. So I started a lower dose and worked my way up Um, so you it's art of medicine. Yeah, so Only half a day a week. So I don't see that But uh, but I do I you know, I do love my patients. So I pay attention So the the idea is this I think it depends. So for from an elderly patient In the for isomac 70s 80s You don't want to get a full dose because you're You are going to knock down their performance there. They'll do the miserable So you can start with to me like for those patients. I really start with very low You know, like was over there was our 200 to see where they tolerate 400 maybe not come to 600 really go up to that You know, so so it really depends on who the patient you are seeing if it's a very very young patient Like 40 some years old I want to give you the most benefit. So I keep in like full dose like 50 and to see whether you can tolerate And to see whether it is really working. If it not if it's working as great And maybe if he's having trouble tolerating, I can start to reduce it So that you can do either way But I think it's very important to see what the patient wants and what the patient really expects Um, and again, if you're dealing with clear cell kidney cancer, that's what we most deal with That is Super important Even the patient with succumbate or whatever they progress To lift them up or to dope them They slow down their disease So we can see that they progress based on radiographically, of course, but they feel better But the formula is really taken off. Boom. It's gone. So that that's you have to understand the disease That's what the mechanism is important. It's not because they take out other things It's just because they on top of they learn something else. So that's your treatment always in your mind So James in your half day clinic, you know, they're not become a little bit very long Yeah, they're they're long half days. I like to talk to too long So I don't know I think I'm like But no, it's fine. It's fine. That's not painful because the day that I enjoy it So I know that we are still, uh, you know, we we still treat patients Same size fits all, you know, we're giving everyone the same agents What can patients do to learn more about the genomics or metabolomics of their disease? Yeah, so so I would say that don't don't try to learn it Because even even think about if you ask your doctor, no, none of them know If you can ask them, do they know about it? You can spend $10,000 to sequence it Do they know it? Do they know how to deal with it? No, they don't So basically it's a waste at this moment that if you know, so to me, it's really People need to really try to learn more.  Maybe it's sufficient But if they have some other mutation like bad one mutation or steady two mutation, then you have to be very careful about it Okay, so that's the way you just think about it. And if we don't you have Clear cell is very simple. Clear cell is just VHL disease on top of they are something else And that's the part we started to learn what are the g maybe at play and that's what where the convergence is And then if you treated two drugs or three drugs Blocking a different, you know, you know converge they have different conversion point If you find out those conversion point on a very specific disease, maybe you can get rid of it Because if you're doing one you block into one this or easily get to another way to converge a second point So the easiest way is just, you know block two or three you think and do that Um, successfully you may actually get rid of disease before like every chance to develop resistance But it takes time to try to understand how it's actually involved. Every disease is a bit different Even every individual patient is different So so and then nowadays you do sequencing and the sequencing Yeah, it depends where you sequence you sequence the primary is not going to be the same as the sequence the metastasis So if I if you guys anyone want to sequence your tumor and if you have metastatic disease sequence the metastasis Don't waste your time on sequencing the primary because that's Let me not be the cone actually cause your problem. That's just part of the game So anyway, so I don't I don't think this is a very good answer The problem is really it spends so much money and really don't you know And you mean things like foundation medicine these types of Yes, no, no, no the idea is basically they need a very very big number and really really clinical annotated And really try to understand which disease is doing what and then what we call a worker is Novartis is kind of one of the things where we can do that is basically a big clinical trial Um, and then we collect all the DNA We collect everything because the the regular DNA is easier to analyze is DNA does not You know so stable millions years, you know, that's why you can sequence dinosaurs There's no what kind of DNA they had and that you by sequencing that you can try to kind of not correlate clinical response And they can figure out who will do well who will not do well who will respond to who will not And based on that in the future you should be able to collect The patient who have that kind of patient and should be treated one way We're going to see some of that information over the next few years from the adjuvant trial Or from several of the adjuvant trials because I think they've all collected tissue And the the the one that was sponsored by the nci the ecog Akron cooperative group multi-center group They actually allowed clear cell with other histologic Types, so there's going to be a lot of information, but I think you know what what we're saying here It's just not ready for primetime Yeah, you know, it's interesting because what you're saying also goes all the way down to the disease state that I see Which is localized kidney tumors people People often say well, you know what? Why are you operating on me? Shouldn't you biopsy this shouldn't you see what it is before you take it out? And the problem is that unlike some other cancers biopsy isn't really telling us that much yet And we are not good And we don't have enough information from a biopsy at this point to really say to you Oh, well, this is a two centimeter tumor. You can watch it for the rest of your life This is a two centimeter tumor and this is going to rapidly progress and metastasize So until we have that ability, you know, we we we sort of use really Rhythm entry instruments. How big is it? You know, is it anywhere else? Things like that. So It's interesting and I think also with the follow-up imaging for patients who are theoretically free of disease You want to be able to tease out who's going to recur? And who's going to need additional therapy until You know, we're more advanced with uh, figuring that out It's just one size fits all you image everybody and you image everybody regularly and you spend the healthcare dollars on that So I can keep asking a million questions, but I'm going to go ahead and open it to the audience If anyone has any questions Anything that doesn't have to be related to what we've talked about anything with kidney cancer or cancer in general Maybe I should sit there. I will ask questions Well here actually since uh Let me ask a question as a surgeon. I'm not a medical oncologist. Obviously with all these different tkis now What is what do most people use as their first, uh, drug of choice now that You know Is there a favorite amongst you or is it sort of uh, and what do you do to choose? So I would have to say for me, uh I separate the standard therapies Uh and the clinical trials I try to encourage patients to enroll in clinical trials, especially since a lot of the new Immunotherapies are available only via clinical trials. I'll try to encourage patients to Seek out clinical trials other either at my institution or other institutions many of these clinical trials with the checkpoint inhibitors that the panelists spoke about are Comparison studies so they have The checkpoint inhibitor nevolumab for example versus one of the standards su-tent Or the checkpoint inhibitor versus another standard. So you have an opportunity Uh to get a drug that may you know may or may not be better than the actual standard Short of a clinical trial of patients unable to go on a clinical trial depends on you know as as uh, dr. Alter talked about the the prognostic factors of that patient. So Uh in a patient who has poor risk disease, you know unwell person I I tend to choose temsarylimus In a patient with good or intermediate risk again, this is It's one size kind of fits all I'm using one of the tkis of either votrient or or su-tent so, uh depends on the the the patient so I can give you some Depends a lot on the patient and what they do. So I've had uh, for example some Athletes or musicians who are very concerned about the hand foot syndrome the painful calluses that may That occur more often in patients that take su-tentative. So in that case, I would use the votrient instead Conversely in patients for example, who I know have abnormal liver function test abnormalities or Issues with their liver. We tend to see more abnormalities in in those liver studies In patients who take votrient. So I would stray away from that So it varies any any patient with a non-clear cell. I'm talking about clinical trials So that's how that's how I choose So, okay. So, uh, almost two years ago, uh, there was a clinical trial that compared the two common drugs the two active drugs that we use for queer cell kidney cancer that was Votrient opazotinib and su-tent or su-nitinib and fearful of the toxicity of su-tent There was a drug votrient that got after you improved and they went right on to a clinical trial comparing the Challenger that being votrient to the chanting that being su-tent and A lot of centers were involved as a international study And the data pretty much showed that the drugs were similar in how effective they were against the patient But the tolerability of votrient was Superior Based upon that There still is habit for me where a lot of physicians believe that su-tent was the end opioid drug where therapies were Again, we've been using this drug since early 2006 with very good results and based upon the Responses patients have had it's very hard to sort of knock off the chanting coming in with a newer drug We always take the patients quality by functional goals when it comes down to therapy A lot of times votrient would be an excellent option to patients because the tolerability is Against deemed to be superior enough based upon a clinical trial There's also another study was a patient survey where actually patients were exposed to both drugs in different sequences and patients Gave a diary and they submitted their thoughts. What was the better agent again? They were not knowing what they were taking And the patients by 71 to 20 percent margin believe that votrient was a better tolerated drug So taking that into account. I like to have these therapies as you know, there's the patient and there's the disease And patients who have volume of disease that I believe needs shrinkage Faster, I think as soon as soon as a better drug to stop But again, we have learned to adapt the dosing of it to make it much more tolerable Because that was not presented on the actually that was not represented on the clinical trial compared to drugs So I would say we've soon ended up as a great drug to use for patients volumes of disease patients We're trying to adapt more towards their Quality of life and the recognition that we're trying to slow down the disease or stabilize the disease that the judge has said Stay with disease is an accomplishment that we try to achieve as medical oncologists Patients of course want shrinkage and disappearance. So we always again have to explain the goals, but I tend to look at the patient again If the patient has good functional performance status and they have bulky disease I think that a drug that can reduce that is important The patients who have not threatened disease and I believe this is again that walking the marathon approach I think votrient's an excellent option Okay, I would also say that a lot it depends on where you're being treated if you're treating it Being treated in a place that has a lot of kidney cancer And the staff and the doctors the nursing staff and the doctors are very familiar with the drug You know, you're going to get A lot of help taking the drug if you're not in a place where that's the case But your physician is feels comfortable giving this drug It's more important that you get the drug that that physician knows the best Because it's you know, a lot this is outpatient treatment There's a lot of coaching that goes on The nursing staff have to be tuned in and you have to feel comfortable that you can call somebody Whenever you need to because there are things that come up along the way So I think familiarity with the drug and the toxicities and the management is key to giving the drug successfully And to taking the drug successfully On the part of the patient So I I think again that you need to know what you're comfortable doing I will say in that my only quibble with that study that was the comparison was that the quality of life Questionnaires were timed to hit suit and at its worst That's the four weeks before the break So I I'll be a little skeptical about that But but otherwise, um, I I understand the data and I still um I've actually had more success with cement than I have had with water So I think we all have it's the it's the blind men in the elephant We all have a piece of the picture and you know, again, it's our comfort zone in terms of what we ask patients to do Is it better to I think clinical trials If you can access to a big clinical center clinical trial will be the way to go because Now all the clinical trials had to had compared to standard In the past clinical trial is like, you know, we don't have this we don't have that as we don't know where it works It's why we're just sorry everything But now the clinical trial at least in kidney cancer. They are all very very reasonably designed And we really need to and we really understand what's really going on That's why a lot of treatment the face one the phase three trials or From line trial is always the other Talk about you to pair a suit and Which you know some of the trials suit and was pd1 suit and was pd1 plus amastin And he thinks like that and those are the you building the composition of immunotherapy and try to figure out What's really going on if don't have the excess I would still depends on how you How fast your disease progressed you really don't want to wait. I have some patients with oh, I'm slow When you go to a break of the other places and the other thing you have to understand Continue when kids bigger they continue to evolve So you don't want to get it too big because you want to evolve they will acquire some other bad mutation And that's when they take off when they take off you are dealing with different monster Well, there are people where it doesn't take off. Yes. Yes. Yes, but there's so the small ones This one the small one you monitor that if they only one centimeter, you know I have some patients that I don't treat for two years because their disease just don't do anything But you have to understand 10 years 10 years. Yeah, but you have to understand the biology Well, that you know what I would say is get two points on the curve at least and see how fascinating Especially somebody that is late recurrence where you know that things have been quiet for a long period of time There are other people where every scan it's a little bit bigger It's a little bit bigger. Well, you know, and there's some people who are in the next scan It's a lot bigger. Well, those people are not going to respond to immunotherapy at least not up front You may have to calm it down with the tki shrink it as Bob was saying And make sure that it's under control and then consider giving me immunotherapy The sequencing is still tricky. I mean we still don't understand it yet I mean there were data Early on that if you gave interleukin right after tki you would have some toxicities that were unexpected But part of that was that gave it a little too soon after the tki You need to take a couple of months break and then you can go on with the immunotherapy So What I always tell people you're going to need all of the drugs once you start on drugs You're going to need all of the drugs. So just you know, but we don't really Truthfully know the best sequence and so it's still judgment. It's still what we call clinical judgment Somebody's going slowly and they've had a long hiatus I would go with immunotherapy if somebody's got disease that came back in six months You could try immunotherapy or you could say well, maybe we should just go in with the targeted tki But you know, there's no and and you know worrying about the mutations because once they start to grow They really can grow. That's another that's another subcategory is I guess the big thing is it's not all one disease And each individual person is going to have their own pattern and what we do as a doctor is try to figure out what the pattern is So we can give you the best what what about in that patient? That has that long hiatus 2001 nephrectomy And then now has you know, 2015 Small lung nodules.  Do you go in? Resect? Do we ablate? Do we start treating? normal you know I So there's a couple things that have been proven in retrospective studies to make a difference I think some of the things that you brought out are very important to look at One is what was the duration of time between which The person developed a recurrence and so, you know We've had success in patients who have Six seven years and they develop a solitary recurrence and just removing it And I have a few patients where literally every three or four years I go back in and take a little something else out But that's uh, that's the biology of disease. It's not really us being successful surgeons or clinicians treating them successfully It's really that this is how their disease sort of putters along If you're talking about someone who has lung nets and then a renal lesion as well It you know the more disease burden there is in multiple places the less enthusiastic I am to try to go and pluck everything out or send them to an orthopedic oncologist or send them to a Uh, you know a brain might be different. You'd obviously treat that but you know, it really depends on how long it took to come back And I think what the the volume of diseases And again, also the one thing also is to get multiple points So, you know, you see one thing six six months or six years later And then you scan them again after three months just to watch it and now it's everywhere clearly going in to take that out would be a bad idea So I I sort of take the a little bit more aggressive approaching in terms of surgery because I at this moment I believe if you have limited disease Surgery may be the only bad thing you have at this moment unless we really develop some way to cure the disease If the promotion of the patient is very very well if you're limited the surgeon is very very competent So that's why where you get your surgery done and where do you get it's done? It's very very important lots of some of my patients that I you know Have this recurrence It's just like some other patient we talked about that That had disease like 10 years ago now Lymphono recurrence Plus lung recurrence maybe And the question is whether you want what do you want to do it? So it really depends on the patient What you know, what will how aggressive they want their performance status they have and where they want to get So the patient I've had the two of them or three of them just operated And they're fine. So so it really depends on On the course, you know, and of course you have some patients operate no matter how we operate three months later something Those are patients that that's not going to be a very good surgical tender if they just recurred like after three months Yes Again So the question is is Is it detrimental to take one sequence versus the other or taking a certain sequence Will prevent you from being able to get another drug? So I would imagine something like maybe like if you get a tki and then an mTOR Like synitinib and then avrolimus does this mean that you and you progress on the synitinib Does this mean you can't come back to another drug within the family of synitinib? So I it doesn't limit as far as we know it doesn't limit the your ability to get other or or You so patients can respond and can develop stable disease and one of our panels later today Will talk to you. He's on his seventh agent Some of which have been tki some of which have been mTORs some of which have been experimental drugs And has been on the current drug for many years now Similar drug to the initial drug that he started on but on the other hand if you Disease that's been stable or quiet and then develop metastatic disease Again with that lung hiatus my preference my bias is to give immunotherapy Because you've demonstrated a long period of time and then you have disease that's not growing quickly My concern was starting with the tki and then going to an mTOR is that every time you change It usually means there's been something changing with the disease depending on what your threshold is To change drugs and a person might miss their opportunity to get at least get I'll do at this point in time Because performance status is key to tolerating the drug and to responding to the drug. It means your immune system is still cooking Now that may or may not be the situation with the checkpoint inhibitors We don't know because a lot of those people have had prior treatment We don't know whether the performance status is much of an issue But you know file two it seems to be sort of a Surrogate for being in good immunological shape And just back to your patient, you know one thing that might be a consideration that that situation that you hypothesized I would be nervous about doing more surgery on a kidney that's already had a partial nephrectomy When they have other sites of disease, but they might be a candidate for something like cryo Or rfa to the kidney and systemic treatment for the lung nodules, and I still would prefer And their data actually that's what I want to say there's data in animals and I think now Brendan curtey in Portland is doing a study of Radiation basically serving as an antigen release And then giving interleukin two afterwards as sort of a vaccination and then hydrocyle two In animals it's it has a profound effect. It really is a remarkable effect on tumor shrinkage We don't have any data To the tumor well to the kidney Yes, so That's that's not a rule by by any means and I think it has a lot to do with the comfort of the surgeon Um, you know, there's you may have heard the word vhl passed out multiple times You know these people who have vhl syndrome will develop kidney tumors repeatedly throughout their lives, right and so Uh, many of them end up getting their kidneys operated on three or four or five times in the same kidney. So Uh, a lot of it has to do with where the recurrence is located, for instance, and also with, um With the comfort level of the surgeon, but it's not a rule that once you've operated on it that you won't have it I think the issue Is if it's your only kidney The likelihood that something could go awry during A repeat or redo partial refractomy Is greater and the possibility that you end up losing the entire kidney is higher And in that case that could leave you possibly with no kidneys and dialysis which for some people may be a situation That's worse than having you know, a solitary or a small growing tumor Now we are looking into other alternatives rather than removing it or going in and cutting it out Oblating it freezing it and you know The use of that is very different than what I was describing in my talk about Someone who has two functioning kidneys and a small real mass You know, your your particular situation is different. You know, you had a recurrence You already had an operation on it and in that case ablating it may be very reasonable But that's really up to the comfort level of the surgeon and also I think part of it's where the tumor recurrence is located And also what that, you know, the person's kidney function is is going to come come into play here Yes And I just want to say I'm not going to cut everybody off. We'll take one more question now and we'll break for lunch And then um, there The session after lunch There will be lots of opportunity for questions. Okay. Yes Well, uh, you know, one thing to keep in mind is that we don't screen for kidney cancer Okay, so, uh, you know, and that's a whole other topic, but To screen You really have to fulfill certain criteria and one is not just finding every cancer but also meaning and making a meaningful Difference by screening and also having treatments that are acceptable and doesn't result in more harm than Good, I think, you know, you always hear about that news about breasts and prostate and perhaps the downsides of screening But with kidney cancer the bottom lines we don't screen and I think that Because of how frequently people are imaged nowadays It's it's almost like a pseudo screening. They're they're actually being screened without knowing it We do screen for people who have hereditary diseases that may You know predispose them to kidney cancer, but it's not a disease that we screen So there's really nothing that we are interested in doing right now To try to find kidney cancer if that makes any sense I think having having said that I would encourage Everyone here and your family members to see their doctors at least once a year have complete blood counts Have your analysis looking for blood in the urine. These are simple tests that can be done That can detect these uh malignancies as well You know in 1980 they did a study for patients We developed lung cancer They thought they would want to be ideal to screen both smokers And get them x-rays and they calculated that the amount of x-rays Uh, it would take to find one lung cancer is 170,000 x-rays And they said it was probably not financially worth it Well, just recently they approved on patients who have smoking to do cat scans and screened lung cancer I've just discussed that the oncology means this past year so 30 years later they find that screening the right patient population may be beneficial There are so many different risk factors for kidney cancer and so vast But there was also Numerous factors and no risk factors who could get kidney cancer for no reason whatsoever So who is the right person to screen and I give Dr. Molina You know the simple urine test may detect a small microscopic Trace of blood in the urine that you would never see for years upon years But the doctor may find it so that will probably be the best screening And a routine Dr. Cenk about these ones here Thank you


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