 So hi everyone. Can you hear me there? Mic test. Good. Yeah, so we are going to give some of our colleagues a few minutes so that they can join. So maybe we start at one or five. Okay. Mic check. Will Fred, can you hear me? Mic check. Good afternoon. Hello Grant. Say something. Can you hear me? Yeah. Mic test again. Okay. I think we can start. Hi everyone. Welcome to this data use session. Discussing a little bit or sharing experience and practice from the field. On the use of data. In routine information. So this session we are going to be a couple of presenters. I and Elaine will be chairing this particular session, but we have filled experience from Togo coffee will be kind of presenting. We also have a story from Tanzania. Clement Kinga will be presenting. And also Haywood will be presenting his story from Chinzali, Zambia, and of course, Eric will be also presenting his story from the use of data use packages. One of the key thing about this particular session is that last year. In the DHSU annual conference, the theme of the DHSU annual conference was data use. And it was more or less focusing on how to turn DHS to information interaction. Now this particular year we actually kind of taking a stock and see what has happened within the year. And if we have learning experiences from pushing DHS to into actionable decisions. Maybe a mic to you to speak a little bit before. Well, for now you've said it said it very eloquently there. And as you said last year we looked at the kind of absence of data use stories that have been documented. So this year we're really interested in in hearing from the experience over the year around data use. So I won't say much. So we can hand it over to the first presenter. Hello, can you hear me? Okay. Let me quickly tell you some two stories from the experience we have in Togo regarding data use. The first one is about district and the second one about region monitoring. This is a photo that we took some some month ago. Some of you may have recognized the guy back in the, the photo. Can anyone guess? Karin Prosper. Yeah, who said that? Yeah, it was Adam actually. I went into the field and to observe a district monitoring monthly monitoring meeting and I'm not on the field on the photo because I was the one taking the photos. So it was very interesting. They were all together of a 20 help facility managers all around the district management officers looking at data. Data was examined being examined in two ways. The first one is data quality. How qualitative was the data. So they will run this WHO based data review into the DHS to and they will identify missing data inconsistent data and they will discuss with the community managers to understand the challenges and address them. And secondly, they are looking at performance. How performance the district is and which ones of the help facilities are dragging the district back or which one are pushing the district forward. They will use this kind of data to of table to it is actually in the DHS to what you see is the Alma card. I don't know how many of you are familiar with the Alma card Alma is the African leaders alliance against malaria. So they, they, they, they put malaria in a high level engagement from university level and primary level level. So they set a series of indicators they have to be monitoring every quarter and they have to report on it to be accountable on it. So we took that Alma card and we actually customize this into the DHS to building it directly into DHS to as the facilities are reporting the, their, their performance. It is being built directly into the DHS to so that the district can look at its performance, the region and the whole country. So this card was, was very interesting because they were discussing it. And one thing that we appreciated there was the data availability data was available to inform this card because it was something very critical at the highest level. And one of those health facility managers tell actually told us a story. He said three years back he was appointed as a facility manager. It was in 2017. He went there and there was no data. He had to reconstitute all the data from the latest or the registries that he can found there. But three years after in 2020, he got appointed to another health facility. Then because we rolled out DHS to in 2018, he was able to find the data all the way down. And that helped him a lot in doing what in doing two things. First of all, doing the review of the performance of the facility for that year. And second one, built the facilities micro plan for the next year. So data was available. The second thing that we noticed was the denominator challenge. We noticed that some facilities were above 100% where such facilities were constantly below 50%. And the explanation was that some populations are very moving. And some of those facilities were actually at the border between Togo and Ghana. So people will be coming from Ghana to attend the facility. And that can sometimes skyrocket the performance. But we discussing with them, we have identified that it is very easy to have the denominators at the high level, at the country, district level, but it's more delicate to have it at the facility level. That's why denominator distribution has become a subject of concern for us. And we are doing research on it. The third thing is that almost no analysis were done at the facility level. And we are just populating the data and DHS to and then waiting by the end of the year to discover how performant they have been. But at the district level, the analysis capacities were limited. Most of the time it is the HMIS team that was in charge for analysis of the data. The program offices were not so familiar with it. So what we decided is to provide them with a simple guide for data analysis at the district level and then at the facility level. That was the experience from the district level. That is the second story from the region level. What you're seeing in red is one of the six health districts in Togo. It is called the plateau region. The plateau region, as you can see on the map, is the largest. And it also includes the largest number of facilities of district 12 facilities actually. So when a friend of mine, medical doctor, colleague of mine was appointed last year as health region manager, he wanted to have an overview of the performance of his region. And he discovered that the region was actually a bad performer in health programs. So he wanted to have a clear overview of the programs and of the districts. So he asked his region managers to provide him with data. That was the nice excel sheet he was provided with. And the funny thing is that it was pulled, the data was pulled out of DHS2 and then pushed into excel to show him actually the situation. He was so dissapointed that he sent that to us saying that you guys have been working on DHS2 over years, but that's what I'm getting. So what can we do? So we convened with him a meeting where we provide him with what we can actually be able to design a DHS2 as a dashboard. He was so amazed that he said, I want to have this now. So actually at the moment we're speaking, they are in a workshop to actually configure those dashboards for him so that he can be able to be seeing the performance not only of his programs but also for the district because we told him that he can be seeing the health district. I was saying that they have 12 health districts with a color code that enables him to see which one is in green and which one is in red. So what are we planning to do moving forward? The first thing is that we have six regions in the country. The plan is that if once the experience is successful in one region, we can scale it up by the other regions. The second thing is that we have started discussing with the minister himself so that he can have these dashboards directly in his office so that he can be able to be discussing with the regions. The improvement we plan regarding the system itself is about SMS alert. Someone was talking about it in the previous sessions. Actually he has to go to the DHS to see what is going on. But if we can configure SMS that can alert him once a district is below or above a threshold, then he can go now to the DHS more frequently. And we have targeted, we have triggered a positive divine research, which is about what? When we will be looking at those districts and those regions, we will be identifying those that are well-performing and learning from their performance, we can replicate them to the other districts without putting not so much money. So those are the stories from Togo and I thank you for your attention. Thanks, Kofi, for this wonderful experience. Like the way you kind of ended up with talking about getting information out of the DHS to customize message for these decision-makers, managers, so that they can make some action out of that. Our presenter is Clement Kehinga from Hispansania. He will join us online. Is he there? Clement, can you hear us? Technology. Clement Kehinga, if you can unmute yourself? Yes. Can you hear me? Yes. Okay, thank you. You can take it over. I will be changing the slides for you here. Hello, my name is Kehinga. I'm presenting the development and the use of national alumnus H and H, square card in Tasmania. Next slide. And this photo is launched the day when we launched the square card. It was launched next slide. Can you see the slides? Yes, I can see. It is the historic background of the square card is that between 2009 and 2015 we have the national roadmap of the strategic plan to improve reproductive and maternal newborn and child health and adolescent health, which called for periodic tracking of performance of interventions. But at that time we did not, we only have DHS-2 data, which one helps to analyze and develop Excel templates. And the knowledge of using that technology was not very universal. So the ministry in the section needed some sort of tool for tracking and for accountability. And that's why we allowed the decision to develop the reproductive maternal newborn and child health square card based on the arm malaria square card. Next slide. So I will give you a short background of our square card. Toward the end of the implementation of our national strategy for maternal health, child health and adolescent health development, we developed a short-term plan which we called the shape and plan for the years 2014 to 2015. And we decided that it was the time also to include the problem of the arm malaria square card. So we started to work on that. And the good part of it, the new health sector strategy also called for the square card to be included in our plan to be used nationally, regionally, and also the local governments and the councils. So it was a high policy decision that gave us the big push to develop that square card. Next slide. So in 2014, that's with the support from the U.S. and the other partners. We developed the first square card and we used the normal color quality of green for target achieved on track, year of progress but not efforts and ready not on track and the grade for that are not available. And it was launched by the president of Tanzania, he's excellent. So this square card was launched together with the shape and the one plan so that during the first two years, that document, the square card should go hand in hand. And it includes indicators for tracking key maternal and the child health indicators to identify botanical drugs and it would have action for national and regional and the local government decisions. So in collaboration with the U.S. and the other partners, the MOH has been continuing strengthening this square card as a management accountability tool. And now from 2015, it's seven years of operations next. Let me now share with you the implementation levels and updates. Our square card is implemented at the national, regional and district level. I heard from my previous presenter that it goes to the facilities. For us, the facilities, because of the problem of the nominator, like what has been said about Togo, we said that our calculations will start at the district, regional and national for his facilities, the justice, correct the aggregated data. The square card does address priority indicators with variable data that was agreed by state stakeholders. The first square card was for January to March 2014. And the information comes from the most of the information comes from HIMS, the HSO, but there are three indicators come from other systems. The one indicate on national insurance and the other indicate on vaccination. They come from other systems, but most of the data comes from THS or HIMS. And all the accounts, we have currently we have 184 of them. They are responsible for data collection, quality auditing and electronic capture of the data. Next. We will show you the key success factors. One is the use of the square card as a management through ATO levels. It means from more district regions in the central level and to some extent the facility level. Number two, the decentralization of the square card and the use at the lower level from the national to include the regions and the law government. Third, the integration into management processes and the use at the critical level, high level. And fourth, wide dissemination and public sharing and another success factor is the evaluation and documentation of the practice. Let us start with the first. Next. So on the use of the management through ATO levels. It is used by the government in both national, regional, district level and law government. We have already updated the square card every quarter time remaner. Usually 30 days after data has been entered into the system. That is one we can export the data into the square card. Number two, all the indicators that are extracted from the HIMS so are fully populated. There are two components. The first component is the safety data which comes from the HIMS and the second component is the denominator which comes from the Bureau of statistics the population data. Indicators are reviewed based on the latest strategic plans like now we have new strategic plans also the square cards were designed in 2015 but now we have two different new strategic plans but the square card is still responding and the wide range of stakeholders at both levels are using the square card and therefore logging the authorities to use the square card. As a function of the square card they are also using the action tracker. Our square card is the action tracker that can be documented and used for decision making next. The second one is the centralization of the square card and the user to region on the ROGA levels. The square card is being used at the region on the ROGA levels by the government local partners. In the initial slide, in the previous slide it is the user to the high level. Now this is the user to the local level. When a NGO is working in the region or the district they use the specific square card for that district. Secondly, they use the regions user square card to support the prioritized resources, to program planning and also to discuss the nature accountability of the accountability within the ROGA. The most part from all levels, they use the square card to more need to progress and support implementations and the regions ROGA have improved the safety delivery based on the square card. In fact, they are using it for, if they start from the square card they are being used for for for budgeting, they are being used for expansion of services and also to collect if there are any shortfalls next. Then the third one is the integration into existing management processes and they use it in political level. You see in the technical working groups, also there are a number of routine meetings and other things. So now the square card, using those meetings, the square card is reviewed nationally, regionally in the ROGA in the existing management meetings including technical working groups. The actions are captured in meetings and in the action tracker and the implementation is also documented at your levels. And the square card, they also use the first provision of performance management. In fact, the indicators that are in the square card somewhere from the from the integrated management tools. The square card is also used for planning, budgeting. Our budgeting cycle learns from July to June. So they use this data to form the square card. They use it for budgeting. Next slide. Kihinga, you have one minute remaining. Yes. Okay, thank you. Let us skip this one. Let us go to the conclusion. The next. Yes, this is why they do it succeed. The involvement of the president himself of the president who have made the square card one of the agenda during their visit to the region district and number two is the high profile of the ranch of the square card. All the high officials were there. They started with the high awareness in the country about the need to improve the health of the mothers and children next. The other factor is the simple color code. The next factor that for our success is the discussion of the square card at your levels district and the regions. And also the regular HMI is just two data at your levels. And also the country technical skills from the MOH and the other partners next. So based on the success of our square card, there have been a number of other implementers also coming with the square card because the success of the square card has been influenced and they also want to emulate on their success. For example, we have the square card now, we have the nutrition square card, we have the basket funding square card with the environmental health square card, but the RMSH was the first. So these are all products of data based on that success. Next slide please. I thank you for your attention and that's the story of our square card in Tanzania. Thank you very much. Thank you for that interesting presentation linking up the square card with the political awareness on the country. Now it's up to Artur with Shinsali story. While Artur is setting up there, there's a question coming in on the chat. Can you hear me? There's a question on the chat from Yvonne that Clement or Joseph if you could respond within the chat if you don't get time to it at the end. Thank you. Where are we? We're going to switch countries and go to Shinsali in Zambia. If you can see which side is it? Shinsali is a really remote district in northern Zambia 12 hours away from the capital and it's really in the middle of nowhere. It's been implementing DHS since 2006 DHS 2 since 2014 and like every other rural district in Zambia it enters data at district level. There was a Gavi project that focused on data entry at facility level that started exactly the time that COVID came in. I was in there drawing up the implementation plan when COVID struck and fortunately Gavi said carry on, let's do it anyway. I came back a year later after the project had been implemented and I used I spent a month there doing participatory research talking to all the people involved about 50 people in the district went out and visited the six high turnover districts and using a data collection tool that was standardized. In reality what happened was the first six months of the project was spent converting ordinary face-to-face learning materials into distance learning and we worked very closely with South Africa to implement distance learning but it took six months so the actual project itself was six months total of which the first six weeks was teaching people how to enter the data and it was entering data is not very difficult if you can use a WhatsApp message you know how to enter data into DHS too. That was not the big deal what was interesting was that people then started to own their data, they tried to in the attempt to understand their data and they attempted to use the data and I think that quote on the right-hand side says it all we used to just send the data to the district now it's ours and we own it and we try to make it better the other major thing that happened was the district information officer was really insulted when I said you just glorified data entry clock after the facility started to enter the data she suddenly realized that all she had been doing for the last five years was entering data and she was suddenly liberated to actually do some work in data quality control and data use and I think that was one of the most important things about this project was entering data into the facility liberates the district information officer to do other more useful things the other thing that happened was that the district health management team suddenly had a focus for their activities they were suddenly the immunization officer was looking at his EPI data the mother and child health program officer suddenly could see that people were coming to immunization I mean for antinatal care all of them were coming into town and there was no antinatal care being done in the periphery and she started to realize the functioning of the program based on data the other thing that was really surprising was and I had quite good entry into the ministry of health the M&E office I mean M&E director was an old friend of mine and they suddenly realized who there's a whole lot more that we can do and they started looking at data use guidelines they started saying oh that's what we need to do and they put a lot of effort into developing data guidelines we managed to convince them that data use should be introduced in the pre-service facility they had been developing pre-service guidelines but they didn't contain data use and we started talking to them about what we've been doing in Chinsali oh yeah we put that into the curriculum good idea Arthur it was really really fascinating they also started looking at monitoring evaluation frameworks yeah and then the normal ministry of health blah blah about feedback mechanisms and community health information systems that nothing has ever happened but there was discussions about data at every level but there were major problems it wasn't all rosy and we used I don't know if anybody knows the prison framework but we used the prison framework to look at the organizational, technical and human factors and by far the biggest challenge was the organizational side of things this people had the data in front of them but they didn't have any idea what to do with it there's no point having data if you're not told what you're expected to do with it so at each facility was doing completely different things with their data and there was no standardization no ability to compare districts or facilities that's where we should we'll use in future the scorecard that's a really nice concept the other thing is demand for data unless there's an institutional demand for data nobody's going to use it if it's not in your job description if it's not part of your monthly activities then people are not going to use data and that has to come from all levels but especially from the higher level for planning monitoring and supervision the third big organizational problem was that the data all traditionally just went upwards and nothing came back again and people get very discouraged when they don't get feedback and there's actually no system in Zambia to send nobody uses DHS to feedback features to provide feedback to the facility level collectors the population has been mentioned everywhere and we're going to talk about it later this afternoon but that's another major issue technical DHS2 those of you who worked with Zambia DHS2 and I've seen Nora shaking her head it's a mess but it kind of muddles along and is functional needs major revision but people are used to it and they know how to do the real problem is that the parallel systems don't feed into DHS2 so we have no logistics data there's an EPI program we were supposed to implement the EPI app but we didn't only 40 of the 100 data elements no sorry not 40% of the data elements required for the EPI app are actually available so 60% no vaccine availability data no cold chain data no human resource data so the EPI app is I don't know whether anybody is able to use that app but certainly I haven't ever seen it properly used and that was supposed to be core to it human side yeah I mean there's an amazing amount of skill at health facility level to use computers I mean most of the people at facility level actually had their own computer many of them were using their own computers for health facility work but that had never been tapped into and it is still not tapped into and there's a huge potential at facility level for computer skills the people at facility level they also they're not exposed they had never been a single training anywhere in Chinsali people at facility level on information systems they're just not exposed and as a result there's very little discussion data use work practices had actually changed that was what I went to go and look at data use work practices had changed considerably with the entry of data timing timeliness and completeness of reporting and I think to me the biggest this is my lesson not for is that distance learning really works at facility level even a rushed hastily cobbled together unprofessional distance learning which is what we had worked incredibly well to get people motivated and the key to the distance learning working was to train local district health management team members to be the trainers so we had four DHMT members who then became trainers and they then became the biggest advocates of DHS and with the district working towards data use then the entire the district team working towards data use the entire district started working the data review meetings that Coffey was talking about they held them and they were absolute waste of time before but now the health workers we gave the data review meetings some structure they looked forward to this and it wasn't about finding faults with districts it was really focusing on performance because all the data quality stuff had been done before and you focused on performance at the data review meetings rather than problems yeah so again we won't talk about populations we they will talk about that later but there was a field of blooming flowers in terms of data use unfortunately the project stopped money was withdrawn technical support didn't come and the whole thing collapsed the people are still there with their skills they're still going to their data review meetings they're still entering their data 100% completeness and timeliness but the whole motivation the whole drive has kind of gone away the ministry doesn't have the money to follow it up the ministry was really excited by it but they're unable to follow it through and it's stuck a little bit of water what have I done okay you can see it anyway a little bit of water and those flowers would come back to life it's not going to happen overnight it will happen how much time are you good zero okay that's fine that's all I need we need guidelines on data use we need ongoing distance education for example half of the staff has moved so we need to train the new people coming in that process is now stopped the district health management team is all excited to have the power to change things and there's no ability to bring in new processes and new systems and there's very little that we can do about setting using data in planning monitoring and evaluation but the potential is there we've planted the seed it just needs to be watered and the water time is a little bit short so we'll just go ahead and jump to our next presenter Eric can you hear us there joining us hi Wilfred yes I can hear you can you hear me yes take it away thank you can you allow me to share my screen please oh thank you Wilfred can I share my slide I don't know whether this is the one that is shared I think it should be able to share your screen okay just refresh and then yeah I think it should be the same please share oh so we share can you see my screen hello Wilfred yes we can we can see your screen okay thank you very much for taking time to listen in and also share in these data use experiences and practices my name is Eric me I'm Babazi and I will share with you some of the experiences we have seen in Uganda and also in other countries that we have worked with and also supported the concept of data use really would make a lot of sense starting to look at the data sources because wherever we have been in most countries the sources are partly to partly contribute to how we ultimately use the data in country so health data largely is captured at health facility or within the community and depending on the point of contact today we are talking about non health areas and so we may have to thinking about how to work with the sources where this data comes from the tools that we use are largely standardized and aggregate at different levels and also to note that data national level are also accessed through both electronic and manual system so for data use packages wherever we have to support them it's important to take note of some of these levels of access and where the data is ultimately coming from and of course at the national level DHIS2 has played a very key role in providing electronic records for use I thought I would also speak a little bit on other sources of data because again these contribute a lot on how we use the data in country where I come from we have data from administrative levels from inventory supervision management meetings where for example in Uganda they are able to track the number of times national level teams are able to do support supervision at lower levels so it's important to take note of some of these and also the population best health surveys that deal with district health surveys and national health surveys we have data from institutions and academic data as well and then health information research that do clinical trials and longitudinal community studies so in the health domain the sources are really quite broad and pulling all these sources together to make sense is really quite key important. We are having currently a discussion on how to pull data on civil registration and vital statistics to be able to contribute to some of the program data that we are looking at at the national level I'm sorry to interrupt you there Eric there's a lot of background noise so I would just ask people to make sure that they're muted. Thank you Thank you Nora. Also we have data on Sentinel events as well as civil registration and vital statistics so in terms of packages really that we have been dealing with a lot largely it's data on data first of all generated through the DHIS as the main repository and then also data on using generic applications like scorecard bottleneck analysis and also the data quality apps yeah so we we've looked at large delay and by and large the analysis and visualization features from DHIS and these are for all teams that are using DHIS too and of course the additional applications like scorecard bottleneck analysis the data quality applications one that used to be called the WHO data quality application and so these play a very key role in terms of how to use the data that is being generated and so how the applications are presented becomes very very important in some of the screenshots you see for example these are screenshots that were taken from one of the facility offices where they are able to generate these visualizations and then pin them on the dashboard on the notes board but of course this does not really quite often demonstrate the use of data pinning the the charts doesn't really help that so we started pushing to see more and more of how this data can be used beyond looking at a dashboard beyond looking at a notes board beyond looking at a scorecard showing performance trends using some of these tools so in terms of experiences on how this has been used by and large DHIS2 plays a very key and significant role in the provision of data and for our case it's compilation of the annual health sector performance report which is really a key report that is important for the country but also data is comprised in that report is generated from some of these features secondly we have been working with a program that deals with section reproductive health as well as gender based violence that generates data using scorecards important to note that data generated from DHIS2 using the scorecards has facilitated prioritization of indicators generating targets and also priority data sets for analysis this particular area we worked with a couple of members of the Ministry of Health and to generate something called an indicator handbook and that handbook summarizes key indicators that are used in monitoring the project and the interventions then linkages and integration of key areas these we have gone ahead to link for example HR data generated from the address with the DHIS2 to be able to address issues of denominators I had I think Henry talk about this earlier on we've also the packages have the awareness and SPA planning we have the bottleneck analysis currently in around 35 districts that are using it to do research to do district planning and the data that is really generated from the bottleneck analysis which we worked on recently facilitated planning for this cycle of using the bottleneck analysis we have seen also this in other countries in Tanzania I think also in Rwanda they are trying to use the bottleneck and the scorecard to do the same there is a video on this that we have posted in the chat the participants can watch it in the free time then linkages within the community initiatives we have a story of district that where politicians used data generated in the DHIS2 chats to to create a boa hole to identify places where they had the number of problems and then create boa holes there and further experiences on data used as seen in the DHIS have spread the facilities conducting weekly CMEs what we call Continuous Education in Facilities and these ones that are done based on some of the analysis that is generated from the apps and the systems we have district quarterly and monthly data review meetings in some of those screenshots based on the chats I need to also mention that the data use in a big way is contributing to innovations for example we did generate work with the community to develop the smart display app which was to further show and simplify the way this data is analyzed and visualized in different places currently in the ministry on the ministries to monitor screens we have deployed that in the city hall in the main CBD and so on then also to mention that routine audits have been conducted based on the data that is generated from these chats routine orientations on data reviews I mentioned that and then basic infrastructure support on internet and devices which is really by and large generated based on the reporting rate completeness for example within the HIS too that helps us to understand which facilities are not reporting why are they not reporting and so on so these country experiences have quite been enhanced by the use of data and the HIS too and the application packages that fall within them there are a couple of focus areas which we think are very key for data use in most of the countries that we have worked with and this is investment in analysis and interpretation this is very key there is no way they can use it and we are also understanding with time that just generally having dashboard, spinning them on walls doesn't really help much then additionally we are seeing data engaging and engaging middle-level managers one for them to generate demand so that data is generated based on demand and then two training and capacity building for these middle-level managers and then the other key component is dissemination of data use products earlier I mentioned about the indicator handbook which is a very short summarized manual for indicators here we also talk about bulletins this could be either monthly or quarterly or even weekly bulletins performance so that action is able to be taken we talked about league tables that need to be also generated and distributed then the last part is the tracking of actions and this is a bit new we have not really used this this application this called action tracker that we need to have a bit more increased investment and roll out of applications such as BNA and action trackers so that if you are able to monitor the investment on some of these data use packages and applications then some of the drawbacks we have observed one of course large datasets is one of the key for example for the case of Uganda then operational level not involved in the design work overload for facilities we have clinicians turning into data collection and that becomes a bit of a challenge in complete and inconsistent data then missing key data points I mentioned earlier point on linking with different with different sectors and then parallelism where we have you know different programs running different systems as well as start turnover I think I made a timer and I want to stop here thank you very much we started five also we just have one minute remaining to end our session we have heard a lot of themes here institutional data use from we have heard a lot about this core card to promote data use at the national level sub national level and etc but I've not really heard a lot about this data use at the facility level and I think that's where the data source is coming from maybe I could pick a brain of Nora because somebody mentioned Nora if you can speak something about that anything but for one minute Eric pointed out that one of the big problems is the large data sets and I think that if we can start to tackle those and try and get them reduced I think we may sort of start to have a way forward because if you talk about data use at facility and you've got data sets that look like this where on earth do you start what do you define as important what do you define as what you must concentrate on and what do you define as you must have good data quality on so we seriously need to reduce our data sets and we have historical reasons of why we collect these massive lists of mobility and mortality and age and gender groups and things like that and we need to start tackling those and I'm quite sure that if we reduce that the facility people can look at can understand and move forward thank you our time is up if you have any comments, questions on data use we are on the COP there's a data use channel there you can post there some questions and comments and we can continue discussion there thank you very much for your attendance and let's clap for each other thank you