This theory does not only dishonor individual creates unnecessary suffering. How is it possible to set whether they are winning the fight against covid. We just held an event last week. If no mortality data was being recorded in the Public Domain according to who mentality fatality report. Will be talking about one strategy. From the measurement record in realtime. When they cannot generate him causes of death. An where were death in the community are left out. We will look att examples. From much better debt. Dot registration. Has still struggled to prove produce accurate and timely data. Will be focusing on the big picture. What can we do to develop modern systems. Wh what other Innovative Strategies are being tested and how can we move this issue up the agenda as it deserves. We are really lucky to be joined today by the experts in the field. They advised that brazilian government on this issue. The assistant director general. A delivery for impact at the World Health Organization. Ct they have just watched their own initiative. And Aaron Nichols who the registrations of the u. S. Centers for disease control. Lets get started. Thank you so much to all of you. If you will just give me one moment to share my screen input as into a slide mode here. Presentation mode. Presentation mode. Good morning and good afternoon even good evening. And thank you for joining us here. This is an important topicth and it really goes to the heart of the adage of know if note your response. It is a phraseope that was coind over a decade ago. How do we know that covid pandemic. We believe everybody should know that. The shape that data given driven response. It doesnt represent death and concerned cases due to covid. We need to1 in terms of understanding. Particularly the members of death. In the middle and countries. This is in ati focus on one measure excess mortality that we feel captures the full scale of the toll of the pandemic. Now it should be no news. Since the early days there has been shortages and concerns over testing. N over the scope and the scale. Not to mention some high income can. Testing has been argued its actually a next dental service. As it cannot. A cabinet. I will have argued recently and absent of the vaccine. Widespread testing is crucial to halting transmission and into something which we can all concerned confirmed. It is highly variable actually often inadequate and have been inadequate to the relationship and the need. Given the scarcity that it has tended to be focused on symptomatic cases. To identify cases in clinical settings. The question here is that what are the implications for that scale for that detection of the total burden of the pandemic. And as we have seen from this guardian. Com. This undercount cases the Community Meaning that the who want that it could be the tip of the iceberg in terms of the actual number of cases sedetected. The key picture that im trying to paint for you today is what a murky picture we have because we do not have the count and the data enhanced. In many places. That dashboard tallied over 960,000 in front case. Again, we need to unpack a little bit the numbers that are coming from countries that lack. In the Civil Registration programs. And the cause of death gave us systems that were rooted in the Health Sector that are not yet capable of delivering and providing timely data on the time of death. In those kind of context those are two issues that arise in particular. Pertains to the deck numbers themselves. Were just trying to get them how to get them. That is going to depend on the adaptation and adoption of important guidance issued by the World Health Organization in terms of how to correctly certified in code a death. This guidance which i have looked at can get a little complicated when it concerns that morbidity for example. Where there is presence. The upshot is. The all of guided the estimates. Hi the suspected mortality. The second issue howeverrt that i want to call attention to deals with what is left out of the frame completely with an exclusive focus on covid death in mortality. Namely, there are death that occur in locations far away from hospitals. Alluded to that reality in her opening remarks. We leave out those that arise fromve overextended Health Systems. In deaths that occur because people avoid where delay seeking hospital care for fear of infection where they fear they might be infected. It also excludes emergency room death for those that are brought in deaths hospitals that are not counted. All of these forces are acting in many placesalal where the majority of death as a man that pointed out even before the pandemic were occurring. This really contributes to a murky picture. We only have hypothesis at the moment about the indication that we have that there may be comparatively you are or unexpectedly located at the moment o of covid in the deaths occurring during the pandemic i should say across much of the continent. S is this really a factor of limited Testing Programs so that we dont have a window into the pandemic. Make the virus have come early already. We might have missed the arrival of the virus. Rr it may just had a later arrival. For the competing causes of death. And they have a mitigating effect of the age structure. In terms of person out mortality by cause. We think it is a simple and fairly comprehensive way of capturing in a timely manner the full human cost cost of the pandemic. I want to explain this mortality interest to parks. The first part of measuring it. Is to focus on the enumeration of all deaths that are occurring now regardless of cause by age, and location. To get that on the weekly basis. Tracking the current level of my mortality. The second piece. Of measuring expert mortality is to establish a baseline of expected death or historically observed death for the same week. With todays mortality. This mortality can be attributed. Also to the causes of death that are a results of that the sorts of factors i outlined a moment ago. Major outlets. They are using the mortality. Such as these. Thats all well and good. But work to bring us back to our main concern here. What about places on the globe that are notha any of the support of the bloomberg philanthropies. They are levered existing sources of data. To create excess. In part they are reliant on the technical package that we had produced in partnershipe with the World Health Organization. Regional partners in africa and asia. This technical package is existing countries along a spectrum of system readiness if you well. I will show the graph from brazil. The point i like to make here. You will hear much more about brazil shortly. Is that rapid mortality surveillance was actually a kind of innovative use of existence in public data. They have to make fairly minor innovations. What we had found. In these settings systems do lack high coverage. They make the incident that more. In much africa and south asia. They have a need to measure the mortality both from the community because theres such a big community. And Health Systems. In order to form a complete picture. In such circumstances they have shown the communitybased surveillance piece of the Technical Work that needs to de accomplished is a bit more challenging than getting that silly base help. The vital strategies have been working out with a few countries. And routine Health Information system and based reporting that to a weekly basis. This is mostly to people that are familiar with Health Information systems. The communitybased surveillance. They are actively detected and reported on. Iv we are supporting the government of colombia and that government of england dash to undertake this work. In colombia the intention is to reach remote and harder to access parts of the population in the country. And in bangladesh we are leveraging an expanding model of active vital event notification to be able to identify that on a more rapid basis. Under the Civil Registration system there. As we are beginning to support countries to produce the data the question arise. In addition to advocacy and serving as the corrective for misinformation access mortality data can be viewed in conjunction used in conjunction with other core indicators to assess geographic disparities for example. Or perhaps even to chart the lagging because mortality is a leading indicator the Impact Public Health and social measures that may have an impact on both the number of cases in the number of deaths that are served. If there is available cause of death data. It may be possible to understand the excess mortality more in terms of the specific causes of death in private. Certainly that majority will be there. The quotient that is left over in the additional excess due to other causes may be due to some importante system breakdowns and Health System. And knowing the specific causes can help to pinpoint action. He this is before the survey for addressing the situation. And lastly, this is more significant everything. We may have the opportunity to discuss this further. We can shore up the registration during the pandemic that is rapid mortality. In fact, we are working with at least one country intending to undertake rapid mortality surveillance coming up i just want twopoint out that we know that knowledge is key to the response. A focus on solely covid cases and that insufficient to is insufficient to understand the true magnitude of the pandemic. In measuring excess mortality is one very familiar and at leastrt relatively straightforward thing to do to fill the faith of knowing the epidemic in terms of crucial statistics. I would argue that it also adds to the urgency of pre these are i believe, the longterm solutions for ssm that can indeed meet the needs of future Public Health emergency. And the director of civil reservation service. Sums it up nicely at that. These are the words i will leave you with. She said we had realized the need for a resilience. In the emergency such as covid. To meet the needs of the population but at vulnerable and marginalized populations in the country. Ze with that i would like to conclude thank you. And hand back. I would like to turn over to it over to vital strategies doing work in brazil looking at the excess mortality settlement. We hope that you will tell if the findings from brazil. And how you use seen it used in policy would be great. Amanda. Morning. Good afternoon. Good evening to everyone. This is helping me with the presentation. This is the. With the help involvement. The data force. You combine to data source. We have that from the Minnesota Health team. We have to history. They go to the cause of events. We are looking at the data. Because of the places where we see the resistance. We compare it today. We have the gap. And an age group. And we apply the collection. You can see on the right it is for one seat. And you can see the gap between the blue line and the red light. The blue light at scr. It is an example of how we did this we did for 2019 and also we head in 2020. The assignments. Where there is a reference. And always to make small areas beautiful. There is a mixed method. The next flight please with the excess mortality. We have left of those. Two age groups it is a clear path and so much is. My stage were region. Next please be an example. We have to go there. Have exactly race. Definition also using the data. We did every distribution of data. We work with those. And the translate to brown. In the mix of population. So here it is. In brazil. The consortium. Of the Health Department is that the mortality rate. This that model. They have just started collecting data we are using the data that we had corrected. But for the obvious reasons of the country. Some parts. You can see is from of march. The end of may and beginning of june. Starts to decline. It is moving into country territory. Even the end of the time. And going so effective. You are discussing about the registration. The next flight please. As an example in a different phase. And the last one we call that a pandemic. In the Central Region has bolivia. And you see that it starts to peek there. In the peak of excess mortality is the end of july. It is still going on the state. In the right. We just head release date. Then it starts. In the region. Next, please. So here is the map. The last numbers in the right percentage, so numbers but we have, the most popular states in the country with higher numbers. And in the right percentages so we see 74 of the increase in the amazon states pixel amazon states was early beginning of march why it is so. In the most remote areas we have the same status as in the south. Starts ins the north and in the southeast. And also youhe can see the south the last states, boarding with uruguay andnd argentina, still small increasing death mortality. Still ongoing in this area. From the north, south east to the current countryside and now its moving to the south. Southo brazil. Shows very well. Next, please. Who is dying . We can see much more, men dying compared to women, compared to women 18 . But some states we have 67 increase in the north region. Its a huge difference among men and women and also in the age group in the right, under 60 years old and in the orange it 60 or more years old. So the impact in the group that we expect too much mortality was in the average 28 , but in some areas we are higher and also more balances with the elderly and people under 60. So also we have this the entire population. We will see in the next slide, please, so who is dying . Black people, black and brown. Its ongoing work. Vulnerable populations and this kind of analysis would help more, both populations. Black and brown people had High Percentage of mortality then white. Also the average ongoing work, so the first state here is sao paulo. Sao paulo is most richest state in the country with 60 of population white, but you see the excess mortality in the black and brown was 32 compared to 11 among the white population. And in the other states you can see similar. More black and brown people dying than whites dying with excess mortality. When we moved to the right, 3059 years old we can see that in sao paulo, the state, we had 42 excess mortality among black and Brown Age Group compared to 24 , the whites population also in this age group. The other states have more balanced distribution also high in this age group of course, 3059, but were discussing with states, how can we use the excess mortality to give better picture of impacts of the pandemic. Next,po please. So the excess mortality dashboard that deals with states, it has been used for decisionmakers, citizen society, researchers and others, discussing how to use this. National newspapers showing, have been following the debates that we cant excess mortality and has been using this information as well. Nextew please. So also the dashboard is a source of data. Also agencies fake yields have been using the excess mortality data. For instance, 100,000 deaths by covert, there was massive numbers saying 100,000 deaths in fact, wasnt caused by covert. What was the cause . People just die anyway, so this kind of agency, they started to use the excess mortality channel show no, look at the excess mortality. People are dying not just because they are dying of the disease that they could die, but they are dying, there is an excess mortality, that is, it is exactly hyper sometimes its the same number of causes of death. So excess mortality around 80 now. But we are still working on this estimate because of [inaudible] media has been asking for more analysis. Please, the next one. Here it is, the dashboard with conass that i showed for some of you. Anthony dashboard now were using Vital Statistics and comparing Vital Statistics and civil resistance by states using estimated excess mortality as well. This is a new one. We Just Launched with the ministry, and here its not so updated. Because there is a rate higher compared to hear but theres a lower completeness so thats why we need this data. We realize we needed to correct for the delayed registration. Because when the pandemic moves through [inaudible] its bigger compared to overnight. Now its importantnt to correct for this, and were discussing about it. By now it should be very discussed the correction. Thats what i have to show you. I would like to thank you for this opportunity to show the excess mortality rate. Thank you so much, dr. Marinho potentially interesting and particularly completely obvious the implications of this data for help system response come for geographical focus, for reaching foldable groups, implications are very clear so that consummate for the presentation. Lets now turn to erin nichols. You obviously been working with us on the global perspective bue you also said India Centers for disease control. Whats your view on sort of the state of the system at this stage and what else do we need to do better on . Good day, everyone. My name is erin nichols. I lead a small team focus on global registration of Vital Statistics his improvements situated at cdc. Since 2015 we have partnered with vital strategies and w. H. O. To the bloomberg data for Health Initiative to support one of the growing number of initiatives that in the last ten to 15 years have focus on approving Civil Registration of Vital Statistics, or crvs in low and middle income countries. Laid out the forces behind the challenges that were trying to tackle through that initiative. So now parallel to this ongoing moment for crvs improvement theres this critical demand on the mortality data. Surveillance collects throughout cdc a look at what data is available across a multitude of existing surveillance platforms and thinking about how these platforms can be leveraged to compile mortality information. A a unique open is to System Integration and convergence as Data Collection ef