1 00:00:00,180 --> 00:00:07,110 Recording even as you do it. Okay. 2 00:00:07,120 --> 00:00:12,760 Could you just start by saying your name and what unit of the university you work in at the moment? 3 00:00:13,780 --> 00:00:15,300 Sure. My name is Sarah Hallett. 4 00:00:15,310 --> 00:00:24,370 I'm an associate professor in Health Informatics and a senior research fellow in Biomedical data Science in the Department of Orthopaedics, 5 00:00:24,370 --> 00:00:30,520 Rheumatology, Musculoskeletal sciences. And within that, the centre of statistics and medicine. 6 00:00:31,770 --> 00:00:38,249 And there's also a unit, I think correct me if I'm wrong, that's got the words planetary health in it. 7 00:00:38,250 --> 00:00:40,940 Is that correct? That is my research group that I lead. 8 00:00:40,950 --> 00:00:46,349 So I'm sort of principal investigator, investigator for the Planetary Health Informatics Group. 9 00:00:46,350 --> 00:00:49,460 So that's my own sort of research body. Mm hmm. 10 00:00:49,560 --> 00:00:53,070 So I'd like to talk a little bit more about that later. But just first of all, very briefly, 11 00:00:53,250 --> 00:00:59,970 can you just give me an account of how you got interested in your subject and what the main milestones in your career have been so far? 12 00:01:01,250 --> 00:01:09,690 Um, I trained in engineering, so I'm an engineer by qualification, and I studied engineering science in Pakistan. 13 00:01:10,110 --> 00:01:16,889 And later on through a good scholarship, which is limited to Oxford, 14 00:01:16,890 --> 00:01:21,420 I had the opportunity to undertake my doctoral studies at the engineering department at Oxford, 15 00:01:21,840 --> 00:01:33,300 specialising in biomedical engineering, because that was an is the area of engineering that most interested me and continues to do so. 16 00:01:34,020 --> 00:01:37,739 And then that's the basis of my training. 17 00:01:37,740 --> 00:01:42,540 And my research has since evolved into the field of health informatics. 18 00:01:45,950 --> 00:01:54,120 If you. Okay. And. And yeah. So I'd like to get back to the title of your research group, Planetary Health, which intrigues me. 19 00:01:54,750 --> 00:02:01,830 I mean, I understand that you work in the general area of health informatics data and digital health. 20 00:02:02,070 --> 00:02:05,100 No, digital health isn't quite the way yet in informatics. 21 00:02:05,430 --> 00:02:12,330 And but planetary health seems to take it wider than simply looking at the health of human beings. 22 00:02:12,780 --> 00:02:16,070 Absolutely. And that's the entire idea. It's. 23 00:02:18,280 --> 00:02:22,210 It's still very much human health. 24 00:02:22,240 --> 00:02:35,560 But taking into account not just our, you know, the health of our bodies, but the impact of our environment and more broadly, the climate. 25 00:02:36,800 --> 00:02:40,240 The impact of the changes in all of that on human health. 26 00:02:41,020 --> 00:02:54,490 And that's what. Planetary health, which is, I have to say, a still nascent, still nascent field within within the big field of health informatics. 27 00:02:56,890 --> 00:02:58,960 So I should do it within the field of health care, 28 00:02:59,020 --> 00:03:07,050 know how to promote planetary health within the world of global health or public health is a relatively recent concept. 29 00:03:07,060 --> 00:03:10,120 It has been expedited through, you know, 30 00:03:10,240 --> 00:03:18,460 an accelerated awareness of all the climatic changes around us and the recognition of the impacts of a warming world on women. 31 00:03:19,660 --> 00:03:22,990 So that's what planetary health is about. Why? 32 00:03:23,330 --> 00:03:29,200 Why we so. Within planet within. 33 00:03:30,980 --> 00:03:42,629 Within health care. It was quite clear to see that if we are to take the field of informatics forward, 34 00:03:42,630 --> 00:03:53,310 which is how can we make use of tools and techniques like statistics and artificial intelligence to explore big data from health care, 35 00:03:53,430 --> 00:03:59,190 whether that is at a population level or a patient level to solve global problems. 36 00:03:59,550 --> 00:04:04,820 Then in order to do that, a lot has been done by explore, 37 00:04:05,370 --> 00:04:15,000 leveraging or harnessing just data that is limited to kind of the biology or the genome of the human body. 38 00:04:15,690 --> 00:04:24,230 Less so. Maybe the idea of taking into account environmental climatic links has been explored. 39 00:04:24,530 --> 00:04:34,009 Why we are now able to start looking into this is because we now have as we know, there's been a data explosion. 40 00:04:34,010 --> 00:04:44,270 We live in the age of big data and data driven analytics made possible because, you know, we are seeing. 41 00:04:46,360 --> 00:04:49,750 Terabytes of data come through daily care and so on. 42 00:04:50,110 --> 00:04:56,020 But also because climate data has become basically freely available, 43 00:04:56,110 --> 00:05:03,250 you can basically leverage satellite imagery data and try and glean some friends from it. 44 00:05:03,430 --> 00:05:10,749 So my group is what our niche is, is trying to kind of do something which is not without its challenges, 45 00:05:10,750 --> 00:05:17,409 which is and we mesh environment and climate information together with what 46 00:05:17,410 --> 00:05:24,730 we have available in the form of data on health and derive insights from it. 47 00:05:25,720 --> 00:05:34,930 By using data describing tools like artificial intelligence and so on to understanding both just generally the health gaps, 48 00:05:35,230 --> 00:05:39,810 but also the impact of climate change is an interesting guess. 49 00:05:41,010 --> 00:05:44,479 Have you got an example you can give? Not counting? 50 00:05:44,480 --> 00:05:48,910 CO But we haven't quite got to that yet. But from from your from your previous work, 51 00:05:48,910 --> 00:05:54,730 have you got an example where you can show how incorporating this climate data has has led to new insights? 52 00:05:55,420 --> 00:06:10,450 So yeah, a couple of studies both from because you know, by by virtue of the way it works, the research is not limited to any kind of geography. 53 00:06:10,450 --> 00:06:20,829 So I can give you an example of a study that we've we are doing on on data from children who move from place to place. 54 00:06:20,830 --> 00:06:28,150 So we have know, for example, that we've been looking at over a million children moving from place to place in Europe, trying to see. 55 00:06:29,070 --> 00:06:35,660 By. By following their movement and their health profiles. 56 00:06:36,860 --> 00:06:46,280 Can we learn retrospectively the impact of, let's say, more green environments versus more brown environments or more urban rural environments? 57 00:06:46,760 --> 00:06:50,810 On on health impacts of children as they grew. 58 00:06:51,530 --> 00:07:04,040 And obviously you can do this as a clinical trial because you can sort of you can't randomly allocate children to good and bad environments, 59 00:07:04,370 --> 00:07:10,699 but with access to routinely collected health information such as those from GP's and with 60 00:07:10,700 --> 00:07:16,639 access to environmental and chronic climatic level indicators such as from satellite imagery, 61 00:07:16,640 --> 00:07:22,730 we are now being able to kind of retrospectively study the movement of children. 62 00:07:23,370 --> 00:07:27,949 Now that's one example from Europe in South Asia, because my background is in Pakistan, 63 00:07:27,950 --> 00:07:33,980 So I have some collaborations there and what we're one of the one of the applications is to look at. 64 00:07:38,200 --> 00:07:41,370 A big problem is pollution. 65 00:07:41,380 --> 00:07:49,440 So South Asia is home to at least two cities like Karachi and Bombay, which are the top three polluted cities. 66 00:07:49,450 --> 00:08:05,710 Air pollution rise in the world is a big problem. And so we're looking at brick kilns and they produce a lot of air pollution, but also heat. 67 00:08:06,130 --> 00:08:16,090 So the air pollution is affecting people's respiratory health in real time and the heat. 68 00:08:17,420 --> 00:08:20,960 Is obviously contributing to global warming as well. 69 00:08:22,250 --> 00:08:25,969 A lot of these industries are not regularised. 70 00:08:25,970 --> 00:08:32,230 They are not officially. They're not in the official sort of radar. 71 00:08:32,410 --> 00:08:40,560 So can we use artificial intelligence to remotely identify such you know, 72 00:08:40,800 --> 00:08:48,270 it is such industries geographically where they are geographically located over vast expanses of rural areas. 73 00:08:48,280 --> 00:08:52,810 And so we've shown how you can basically start to do that with artificial intelligence. 74 00:08:53,950 --> 00:09:05,770 And then that can serve as as an input for policymakers and lawmakers wants to then do monitoring or surveillance or policing of that activity. 75 00:09:06,400 --> 00:09:11,170 And you can you can link that to the health data from from that region as well, Can you? 76 00:09:11,650 --> 00:09:15,580 Yes, exactly. Yes. 77 00:09:15,920 --> 00:09:19,570 So So more on the kind of maybe. 78 00:09:20,860 --> 00:09:27,009 Yes, we can. And then just maybe sort of substantiated with this slightly with another study. 79 00:09:27,010 --> 00:09:37,659 But a related kind of example is that we've started to look at a lot has been done, as I was saying, on health, 80 00:09:37,660 --> 00:09:45,030 in health indicators know there are studies like the global burden of disease and so on which have been tracking for the last 20, 81 00:09:45,040 --> 00:09:53,080 30 years how it is the burden incidence of various diseases around the world at a global level. 82 00:09:54,130 --> 00:10:00,430 And what we're doing is to now map that to environmental indicators. 83 00:10:00,700 --> 00:10:08,890 And this is something that can be done at a global level. We are doing it for South Asian Middle East to try and see, as the health indicators have, 84 00:10:09,460 --> 00:10:12,490 as the burden of disease has gone up over the last 30 years. 85 00:10:13,840 --> 00:10:23,499 Is there a relationship or is it a correlation between that and the kind of the increase in some of the climatic indicators like global land, 86 00:10:23,500 --> 00:10:27,940 surface temperatures, alcohol, dioxide and so on? So that's another study. 87 00:10:27,940 --> 00:10:34,360 And we're beginning to see some interesting correlation, you know, in different parts of South Asia for that. 88 00:10:35,020 --> 00:10:42,430 And that's a that's a really good introduction to the that the scope of your your research and where you get your data and so on. 89 00:10:42,580 --> 00:10:45,760 So I think that's now arrived at the COVID pandemic. 90 00:10:46,300 --> 00:10:52,390 Can can you remember where you were or how you first came to hear that there was an outbreak of respiratory disease. 91 00:10:52,480 --> 00:10:56,139 Yeah, I mean, yeah, yeah, yeah. Very clear like it was yesterday. 92 00:10:56,140 --> 00:11:04,750 It was in fact in this very, very space that I am with today's call only because it's Friday afternoon and I had to go in for something to my home. 93 00:11:04,750 --> 00:11:14,590 I was right here. My mother had just landed in from Pakistan for her annual summer break, I think. 94 00:11:14,950 --> 00:11:25,450 And obviously Boris Johnson was announcing on the TV behind us on the 20th or something of March 2020 that we're all going to have to stay at home. 95 00:11:25,600 --> 00:11:27,430 And the world started to sort of close in. 96 00:11:28,610 --> 00:11:35,710 But that's just, you know, you know, you have these big sporadic memories and that's my first memory of lockdown, lockdown in the UK. 97 00:11:39,830 --> 00:11:50,960 I had, incidentally, just I was just coming back to work after my second maternity leave during that same time as well. 98 00:11:51,350 --> 00:12:03,950 So what that meant was that I, I came back to work after a nine month maternity leave in, in the middle of a lockdown situation, i.e., working. 99 00:12:04,190 --> 00:12:11,870 Having to work from home. And because of the lockdown restrictions and nursery closures, etc. 100 00:12:12,260 --> 00:12:18,860 I had a nine year old primary school child and a nine month old. 101 00:12:21,280 --> 00:12:30,520 A baby at home as well. And so that. That is my second kind of recollection of what lockdown and COVID meant, 102 00:12:30,910 --> 00:12:38,889 but what it meant in terms of work and how it impacted research and how the how, 103 00:12:38,890 --> 00:12:44,530 like so many other researchers around Oxford built around the academic community, 104 00:12:44,530 --> 00:12:53,560 around the world, things had to be pivoted, felt around COVID, not just because it had to be done, 105 00:12:53,890 --> 00:13:02,080 but because we wanted to contribute what we could from our area of work to. 106 00:13:03,830 --> 00:13:07,950 Driving rapid. Evidence around. 107 00:13:08,970 --> 00:13:10,650 So where, for example, 108 00:13:11,040 --> 00:13:21,480 folks in the Jenner Institute were doing all of the important stuff like the vaccine and all what we were able to do because we work with large scale, 109 00:13:22,050 --> 00:13:28,830 routinely collected health data, health information such as from GP's in hospitals and pharmacies and registries and all of that. 110 00:13:30,120 --> 00:13:33,810 We tried and used or gleaned all of that information. 111 00:13:34,740 --> 00:13:41,910 We tried and kind of apply analytical tools that we usually do the same methodology that we work with, 112 00:13:41,910 --> 00:13:50,460 which is in statistics and informatics to do routinely collected data, which is also something that we we work with. 113 00:13:50,700 --> 00:13:52,799 But to answer COVID related questions, 114 00:13:52,800 --> 00:14:00,480 So what is the clinical risk profile of what increases your risk of contracting COVID or being infected with COVID? 115 00:14:00,870 --> 00:14:08,270 And of course, two, three, two and a half years down the lane, our answers to that question are very different than much. 116 00:14:08,310 --> 00:14:13,840 We have a lot greater understanding of it. More than biological and epidemiology levels. 117 00:14:14,600 --> 00:14:21,800 But at that time, you know, we were trying to kind of basically dig out from data, available data what we could about, you know, 118 00:14:22,460 --> 00:14:29,960 for example, just having a history of respiratory illnesses, obesity, cardiovascular disease put you at a high risk of having COVID. 119 00:14:30,710 --> 00:14:36,960 Once you've had COVID, what are you most at high risk of in terms of outcomes? 120 00:14:36,960 --> 00:14:42,050 So who's, you know, at high risk of mortality, hospitalisations, etc., etc. 121 00:14:42,290 --> 00:14:48,680 And in some of this work, even informed policies like the government's sort of shielding policy. 122 00:14:51,200 --> 00:15:05,180 The the e-mails policy on to go ahead with the the the mutual trials on sorry not the EMEA is guidance on European Medicines Agency. 123 00:15:05,240 --> 00:15:14,270 Yes the European Medicines Agency guidance on hydroxychloroquine which was one of the trials on drugs under trial in W.H.O., 124 00:15:14,270 --> 00:15:17,329 ended up stopping the hydroxychloroquine trial. Based on that, 125 00:15:17,330 --> 00:15:25,190 even though the US President had been saying something completely different to our work once epidemiology questions 126 00:15:25,490 --> 00:15:34,070 using dramatic tools and techniques and big health data is what we were doing during during the pandemic. 127 00:15:34,490 --> 00:15:41,540 You know, can you can you tell me something about the because obviously you were using data from lots and lots and lots of different countries, 128 00:15:41,750 --> 00:15:49,750 which presumably was collected using different criteria. And and there wasn't standardisation from one country to another. 129 00:15:50,270 --> 00:15:54,020 But I understand that you participated in various international collaborations and 130 00:15:54,020 --> 00:16:00,349 consortia which helped to solve that problem so that the data was comparable to Italy. 131 00:16:00,350 --> 00:16:05,210 I mean, this is a world that was led predominantly by Professor Peter Alzheimer, 132 00:16:05,600 --> 00:16:13,969 Professor of Pharmacology, device Epidemiology department, as I am, and yes, in a nutshell, 133 00:16:13,970 --> 00:16:17,959 to consortia like the observational Data Science initiated, 134 00:16:17,960 --> 00:16:27,470 which the global community working in dramatics applied to the all to be ready to go and of course, like everybody else. 135 00:16:31,540 --> 00:16:40,570 This consortia had been working for a long time on an underlying problem, which is which pre-dates COVID, which is how do you make heterogeneous, 136 00:16:41,020 --> 00:16:48,430 patchy health data from different settings and context compatible, interoperable, 137 00:16:48,970 --> 00:16:55,270 harmonised and be able to talk to each other so that they can be data and then be used or compared? 138 00:16:56,800 --> 00:16:59,620 Any any kind of a apples to apples manner. 139 00:17:01,180 --> 00:17:11,889 And we were lucky enough to be able to leverage the sort of the methodology that had been developed around this question and apply it to COVID, 140 00:17:11,890 --> 00:17:17,440 because from COVID, we were able to kind of leverage data from all sort of contributing data partners. 141 00:17:18,010 --> 00:17:21,400 This model operates on a kind of contributory basis. 142 00:17:21,640 --> 00:17:27,370 So it comes within it falls within what you might call Federated Network Analytics. 143 00:17:27,630 --> 00:17:29,440 The kind of the spirit behind that is that. 144 00:17:30,570 --> 00:17:40,380 Data owners anywhere in the world who are interested in contributing their data to generate evidence and do research. 145 00:17:42,450 --> 00:17:50,670 They can basically map their data to be a Sandy data model, which is one model of data sharing, 146 00:17:51,210 --> 00:17:56,100 and that enables them to then participate in network study, to collaborate a lot of questions around it. 147 00:17:56,100 --> 00:18:07,140 And that was the kind of classical or the perfect opportunity for many collaborators around the world with either. 148 00:18:08,190 --> 00:18:15,240 We were either data hoarders or data orders or had access to relevant data to be able to kind of contribute to that. 149 00:18:18,680 --> 00:18:24,920 And produce results that were globally important but also relevant for their specific geographies and regions. 150 00:18:26,110 --> 00:18:31,269 And did you have a particular focus yourself within that context on on what kind 151 00:18:31,270 --> 00:18:35,220 of data or what from what geographical regions you were you were looking for? 152 00:18:35,260 --> 00:18:46,360 If you if you're talking about kind of the work we did. In in the kind of observational data space where this is a pandemic. 153 00:18:47,650 --> 00:18:54,070 And certainly we quite quickly came to the realisation of was that. 154 00:18:56,290 --> 00:19:01,510 A lot of these data that we were that we was possibly in leverage. 155 00:19:02,920 --> 00:19:07,090 In the form of an adverse study or an exclusion. 156 00:19:07,510 --> 00:19:14,770 Gentleman's reports were coming from, let's say, the global north, if you will. 157 00:19:16,090 --> 00:19:23,890 I'm not a big fan of kind of. Is the appropriate terminology. 158 00:19:24,280 --> 00:19:33,750 But a lot of the a lot of the data is coming from this intense period where it's even going to be more accurate from higher income settings. 159 00:19:33,760 --> 00:19:38,500 In higher income settings we get from Northwest or from from places like. 160 00:19:40,210 --> 00:19:52,970 China and South Korea. So to question one of the efforts we did do was to try and leverage by inviting 161 00:19:52,970 --> 00:20:00,020 data partners from some kind of resource limited settings around the world 162 00:20:01,010 --> 00:20:14,210 from where we knew from previous collaboration there was just as good quality data to participate in this kind of international activity around COVID. 163 00:20:14,630 --> 00:20:20,990 And in a sense, as many people would say, that, you know, there were some silver linings to COVID, 164 00:20:21,410 --> 00:20:25,340 and one of that was accelerated research, lots of things that couldn't have happened. 165 00:20:25,340 --> 00:20:30,110 I mean, the vaccine is what's a better example than the vaccine being developed in three months or. 166 00:20:34,160 --> 00:20:41,480 But that is another example with previously there would have been lots of issues and lots of issues and lots of bureaucracy and loopholes. 167 00:20:41,750 --> 00:20:53,900 Look, sorry to get through. It was possible and there was interest in motivation from our collaborators in regions like Brazil and Pakistan. 168 00:20:55,090 --> 00:21:11,229 Specifically to join this goal during the journey of harmonising data to international standards and be able to kind of generate evidence, 169 00:21:11,230 --> 00:21:17,560 do research from that. And so that's been an interesting and maybe interesting success story. 170 00:21:19,750 --> 00:21:23,950 In the sense also that beyond COVID, that sort of collaboration and. 171 00:21:27,220 --> 00:21:39,520 Culture of evidence based research for evidence generation and evidence based decision making has the support of continued. 172 00:21:40,150 --> 00:21:45,620 And I hope that that's something that can great. That's very good news. 173 00:21:45,810 --> 00:21:51,950 And whether other COVID related projects that you particularly led on. 174 00:21:57,410 --> 00:22:11,880 And we think now. So there is a number of studies who went around the simulation space and there was some studies within the university. 175 00:22:14,440 --> 00:22:22,840 And in other words, more and more internationally, we were looking at all sorts of things like risk factors, 176 00:22:23,170 --> 00:22:30,100 treatments, vaccines around the ideas, questions of efficacy and safety. 177 00:22:31,480 --> 00:22:35,410 Effectiveness and on. That's all. 178 00:22:35,420 --> 00:22:41,610 That's. That's what I'm thinking off the top of my head. And Oh, right. 179 00:22:41,630 --> 00:22:49,760 I'm just looking at the time, so I'm just going to switch to your own sort of personal experience of the pandemic. 180 00:22:49,760 --> 00:22:55,310 So you told me your situation you were just back from maternity leave and shop within your four walls at home. 181 00:22:55,940 --> 00:23:01,670 How threatened did you feel by the infection itself, by the possibility of catching the infection yourself? 182 00:23:03,020 --> 00:23:09,730 I think just as everyone else knew, there was so little knowledge at home, especially early on in the pandemic. 183 00:23:12,700 --> 00:23:20,530 More kind of reason for apprehension. When you combine the lesson information we have, the more we tend to panic and so on. 184 00:23:21,100 --> 00:23:27,820 All the panic is what's the word I would use. I think there was a sense of the seriousness and the gravity of the situation. 185 00:23:28,360 --> 00:23:40,480 At the same time, I think I distinctly remember the sense of reassurance as well in knowing that this is we're all in it. 186 00:23:41,060 --> 00:23:46,600 It's not something that is. It's just I mean, I don't have to go through this alone. 187 00:23:48,860 --> 00:23:54,010 We're all in it. You know, it's it's you know, the prime minister has been affected of this country. 188 00:23:54,020 --> 00:24:05,360 People around the world are being affected. It's not something that Kuwait in itself wasn't bringing inequity. 189 00:24:05,360 --> 00:24:18,459 It did, however, I have to say. Highlight existing and underlying inequities in our health care systems and more broadly in our societies. 190 00:24:18,460 --> 00:24:25,900 We saw that through how various countries responded and the border restrictions imposed and so on. 191 00:24:27,610 --> 00:24:31,330 But that's probably a whole other topic for another day. 192 00:24:32,440 --> 00:24:39,370 But on the whole, there was a kind of a mixed mixed sense of obviously some apprehension, 193 00:24:39,970 --> 00:24:44,910 but also some some sort of reassurance in the sense that we weren't going to. 194 00:24:46,180 --> 00:24:51,040 Interesting now that you had previously asked about what is so. 195 00:24:52,310 --> 00:24:57,080 In fact, I forgot to mention one of our ongoing studies is about. 196 00:24:59,390 --> 00:25:12,799 Inequities in health care. And with regards to bias in data and bias in our technology and how that can play 197 00:25:12,800 --> 00:25:17,030 out in a few bias in the health care that people receive and that in our society. 198 00:25:17,450 --> 00:25:25,730 And for that we are in partnership with the data reaching Q2 and looking at for the 199 00:25:26,180 --> 00:25:36,290 first time an in-depth study of health care records of with regards to ethnicity. 200 00:25:38,370 --> 00:25:46,620 From everyone in England and Wales. So that's that's, that's also something really special in how it played out in terms of COVID 19. 201 00:25:47,100 --> 00:25:50,370 And that's also an area that we're looking at as well. 202 00:25:50,910 --> 00:25:55,860 Mm hmm. So I think we've more or less ran out of time. 203 00:25:56,790 --> 00:26:02,489 You mentioned earlier that the the work that you did with low and middle income countries 204 00:26:02,490 --> 00:26:07,980 had had long term benefits in terms of a greater willingness to collaborate and so on. 205 00:26:08,520 --> 00:26:13,919 Are there any other ways in which, as you say, as a sort of silver lining to the pandemic, 206 00:26:13,920 --> 00:26:23,820 your work has been accelerated or changed or made broader as a result of the work that you did during COVID? 207 00:26:28,330 --> 00:26:39,160 I think yes. I think it made us realise as researchers the plasticity and. 208 00:26:40,810 --> 00:26:46,600 The versatility of our work and how we can be adaptable. 209 00:26:47,640 --> 00:26:51,210 To the need of the new global need of the day, if you will. 210 00:26:53,870 --> 00:26:57,870 And. Yeah, I mean, I. That's just kind of a more high level thing. 211 00:26:57,900 --> 00:27:03,480 I don't know if you can think of be more specific recognition or flying effect, if I can think of anything. 212 00:27:06,130 --> 00:27:10,030 Okay. I think that's great. Thank you very much indeed. 213 00:27:10,360 --> 00:27:14,940 It's a pleasure to talk to you. And I will turn to recording.