Science4Parliament podcast – Episode 12 – Dr. Uzma Alam – The Science, Data and Politics Link

Welcome to the Science4Parliament podcast,  the first podcast that aims to foster the relationship between science and decision makers and show how research and innovation are vital to the equitable and sustainable functioning of our societies and economies.
   
 It is presented by Denis Naughten, a directly elected member of parliament in Ireland for nearly three decades. Denis has served as an Irish cabinet minister, and on the Council of European Union Ministers and  is currently chairperson of the Inter-Parliamentary Union Working Group on Science and Technology, based in Geneva, which aims to inspire global parliamentary action through legislative work, in the field of science and technology.
   
The podcast aims to highlight the work of innovative people in the world of science  and to get their perspective of what needs to be done to bring that world and the world of policy, closer together.
   
 In this episode, Denis talks to Dr Uzma Alam,  Program Lead  for Science Policy Engagement with the Science For Africa Foundation in Kenya, whom he met at the  INGSA conference in Kigali, Rwanda. Dr Alam talks  about  the essence of evidence-based decision making, data. Where it comes from and what it can do.

Dr Alam's  recent article on the  potential of Artificial Intelligence (AI) to aid Africa’s pandemic preparedness efforts is available here  : https://www.talkafrica.co.ke/opinion-leveraging-ai-for-africas-pandemic-preparedness-on-africa-day/

More information on Dr. Uzma Alam
Websites:     https://www.researchgate.net/profile/Uzma-Alam
                           https://scienceforafrica.foundation/
X:                      https://x.com/SciforAfrica                     
LinkedIn:      https://www.linkedin.com/in/uzma-alam/
                           https://www.linkedin.com/company/scienceforafricafoundation/ 

To contact  Denis Naughten:
 Email:            Denis.Naughten@oireachtas.ie
 Website:     https://denisnaughten.ie/
X:                      @DenisNaughten
LinkedIn:     linkedin.com/in/denis-naughten-td-77231112

Transcript

Science4Parliament Podcast – Dr Uzma Alam – Episode 12 –The science, data and politics link

 SPEAKERS

Dr. Uzma Alam, Denis Naughten

Denis  00:00

Welcome to the science for Parliament podcast, the first podcast that aims to foster the relationship between science and decision makers. I'm your host, Denis Naughten, and as you know, this podcast aims to highlight the work of innovative people who are trying to bring the world of science and policy closer together. Today I'm talking with Dr Uzma Alam from Kenya, and before we get into asking any questions, can I ask you to pick two numbers, something to add to the conversation, two lighthearted questions. So Uzma, will you pick two numbers for me?  

Uzma  00:34

Sure, thanks for having me on the podcast. So let's go with seven and eight.

Denis  00:38

Seven and eight. That's great. So Uzma, the reason that I wanted to talk to you, you are here in Kigali and you were expressing frustration in terms of academic research and the lack of input from the Global South. So maybe, will you just elaborate on what you're talking about and maybe how you are working to try and address that?

Uzma  01:00

Sure. Thanks. So just in terms of background, so to frame where I'm coming from and where I've developed this understanding. So, by training, I am a researcher. So, I have a PhD in infectious diseases, where I worked on vaccine models, and then went into, you know, pandemic work. And then from there, I moved on into the innovation field. I started a biotech company, using AI, for bacterial detection, and, you know, bringing it to the Global South. And then from there, I moved on into the grant making world. And now I'm finally at that cross section where I'm able to bring all these different experiences together to see how to start influencing policy, because that's the real value of science, right. Science is meant to change lives and economies of our people and our societies. And this is, you know, one of the I see it as one of the low hanging fruits, the nexusis between science and policy, right?

Denis  01:58

There is no point having the evidence unless you can then apply it, and policy and politics is the way to actually apply it in real life, exactly,

Uzma  02:06

Right? And then the question, at the heart of the question that you've asked, right? We need to understand, what is the connection between science, evidence or what is evidence, and how does that link to politics? Three very abstract topics. So scientific thinking, or science, at the core of it, is a way of testing hypothesis in a non-biassed way. So be able to systematically answer a question of interest in a non- biassed way. That is the science piece, right?  What is the output of that, the output of that is data, right? And there's a difference between data and evidence that will come to speak to right? So the output of science is, is data, and then how does this data fit into politics? Because you brought up that word, right? It's critical. So for any politician, or any person in a leader's position, needs to make choices with limited resources, with specific questions in mind. 

Denis  02:07

Yeah, on a daily basis, we have to do it.

Uzma  03:09

 Exactly. So how can such people be rest assured that they're making the right decision? One way of doing it is using data, and that's what we call evidence-based decision making. So evidence is about bringing diverse data sets together to provide you know, the politician or the policy maker, or whoever it is, the information they need to make in right choice or informed choice, a choice that they can stand by, or the best choice in those given circumstances, realising that data keeps changing, right?

Denis  03:41

Sometimes a decision that they can actually defend in the public or in Parliament, 

Uzma  03:45

Very important coming from that perspective, right? So, making the right choices by their taxpayers and making the right, you know, the effective and efficient choices. So that's the link between science data and policy . So having understood that, you ask the question, you know, what's the frustration around the current set of data, or the current set of issues that we have with existing data within the policy space? Simple answer is, representation, yeah. And then you can ask me, What does representation mean? Or, you know, is it just a philosophical argument? But my argument is like, no, it's got real implication. So like I said, it's information to make choices, right? And if you have gaps in this information, then obviously you won't be able to defend the choice. So you have to be very cognizant of what these gaps are. And currently in existing data set, the way our science systems are set up globally drives or has tended to drive gaps in this data. 

 And what are they? Right? So one, a lot of data is predominated from the West, well, the northwest of the planet, the Northwest, right, and the lots of things driving that. And that's a different concept, conversation, we can have the conversation if that's right or wrong, right? But in policy making, like I said, it's an issue because you're not getting the complete information you need, right? So that's, that's one piece. But the second piece is, you know, in the West or the Northwest, when this data exists, it's not representative of the populations of interest or the populations in question. So for example, there are gaps between information from national to sub national level. There are gaps in data that is just lumped together and says, you know, 50% of the population said X, Y and Z, but out of that 50% of the population, what was the gender breakdown, right? And then again, like I said, you know, people can argue this is a philosophical concern. It's, you know, very left wing, or whatever it is, but it's got issues around how you implement so just let me give you a case scenario. Let's say you're looking at flooding. Let's say a parliamentarian wants to make a decision on flooding in country X. Then they go and they look up at the data, and all the data is blocked together, and it will say, you know, the best way scientifically to prevent this flooding is to grow. I don't know, mangroves, wherever, in this country X is right, but is that context real? Would that work? Because that country might have very different regional climates where mangroves might not grow, right? So that's the need for sub national data, right? 

Denis  06:26

So in terms of the currents may not suit mangroves, may not actually resolve the problem

Uzma  06:32

Exactly, right? So that is the importance of understanding your data at that fine level, having this national level representation. That's one piece, right? Let's go to the gender piece. And a very common area where we see fall out because of this lack, or gap in data is in the healthcare sector, especially for women. Let's say we're looking at treating using drug X, okay, covid. Let's go back to covid, right? There was this whole push for using antiretrovirals in communities at an early, early stage rate. But we know that women react very differently to certain medications, including antiretrovirals, compared to men, right? And this is hormonally driven. This is genetically driven. There are lots of factors, right? And so if your population of the country you're looking at is predominantly, you know, 50% women, or 60% women, and yet you're making a decision based on the 10% is that decision going to work? Is it going to be the right decision? And we started this postcard by saying, science needs to be used by politicians. It's a tool for politicians to help them make the best choices, the right choices, in that moment in time. 

Denis  07:48

Now, before we go any further and go into one of our questions, and the first one is, what would you consider to be the most life changing piece of technology? Nice, easy question for you. 

Uzma 08:00

Oh, wow. So I'm not going to say AI as much as it's the buzzword, and, you know, to disappoint you, but I truly believe it was the internet. Yeah, it, it's really, it's, it's been a tool that's opened up the world. So there is, especially for, you know, people myself coming from, from, you know, developing world where we didn't have access to textbooks. I remember, you know, going to school and, and I know this is against IP and stuff, but I remember if my mom had to, you know, actually go photocopy pages of certain textbooks, and just knowing that this information is at your fingertips now, and it's there and available, yeah, and not only that, even for my parents, like, you know, with me travelling a lot and, you know, having lived internationally now and worked, you know, across 3, 4, different continents. You know, I've relied heavily on the internet to not only keep this contact with them, but also manage, as as a woman, manage, you know, this, this life, life, work, balance. I've been able to pay the bills online. I've been able to, you know, chat with the doctor online. I've been able to order the medicines to eat online. I've been able to see, you know, when my dad got a traffic ticket that he has forgotten to pay, whose passport needs to be, you know, renewed. So, yes, for me, the internet has been life changing.

Denis  09:16

So coming back to the conversation that we've been having, what advice would you give to members of parliament when they're looking at these issues? So, we've two sets of very different issues. One is the data set itself and maybe the bias that's within that data set. The other is the geographic location of that data set. You know, you cannot take evidence that's generated in the northwest part of the globe, and apply that and shoehorn it into a policy decision, let's say, in Sub Saharan Africa. So, what advice would you give to members of parliament to test the evidence that's being presented to them in. First instance, and maybe then, how could they try and address that, but test it? First of all?

Uzma  10:05

Wow, that's, that's a great question. So, this takes us back to the original conversation, right? What is data and evidence? And you know, when I spoke about data, I just gave one dimension of it. It's what scientifically generated. But data also comes beyond that, right? Data comes from lived, what we call lived experiences. So how people would react to something of the knowledge people have, stuff that's not necessarily captured. So one way, you know, advice to politicians would be to use this evidence that they have as a start, to engage with their membership and their constitution, to really start having these conversations and saying, look, this is what our understanding is. What difference is there? And what you what do you need to add to this? And I can give you a perfect example of this. During Covid 19, WHO Africa had put out its covid 19 research and development, mappery, and it had 17 different priorities of what the areas of interest should be across the world for Covid, right? And one of them was strengthening or use of intensive care units for Covid 19 patients. And this was at the start. There was a big thing to get Covid 19 very likely going to end up into intensive care. But when we actually contextualised what that means in our field, right, within Africa, it was immediately apparent, even talking to the population, that this wasn't of concern to us. We don't have intensive care units, so it wasn't a priority, right? 

Denis  11:28

Wasn't a priority that worked there in the first place. 

Uzma  11:30

So what we were able to do through engaging with, again, with policy makers, civil society, the private sector, and our researchers, we were able to really understand what should be the priorities for Africa, right? And we were able to identify seven different new priorities. Then actually, then got adopted into the global roadmap. And, you know, from Africa, they started informing the research and development in Latin America, right? And I think there's, and I know when you said, you know, you can't play and plug, but there are some mechanisms to do that, right? So that's one piece. Politicians should use that as a tool for engaging and learning from it. 

Denis  12:12

So in other words, that it would be the starting point to start that engagement, the conversation. 

Uzma  12:17

And not only that, it creates buy in, right? So, it ensures that you are by the time you release the policy you want, or whatever you've got your constituency lined up behind you. So, it's a really powerful tool, and that's a way to fill that gap. That's one piece and then another piece. And this is something that's emerging with this the science policy Nexus is, you know, how can politicians work and policy makers and parliamentarians work with researchers right from the beginning, and it's amazing if you do start having that conversation. So, I support, I've done a lot of work with African Union. I support the Government of Malawi on the Science Technology Innovation Development Programme. I've worked with, you know, governments in Egypt and Kenya. And so, when you actually start having these engagements and bringing you the two there's more in common that's already happening, but it's just very framed differently. So, it's building that relationship. That's one piece, and then the third piece, I think, you know, that's a newer thought. And again, we've used this, and I'll give you a concrete example of this is they are, and I'm not asking, you know, our members of parliament to go do this. I know how busy they are, but they are tools that are available that can somehow fill these gaps, right? So, for example, in clinical trials, is this place for synthetic data, data that exists, but also in evidence, and especially in disease outbreak, there is a way of extrapolating from pre-existing data sets, if you understand the context and building and I'll give you an example of this. So, when Covid, like I said, you know, I've worked across the science value chain, and I've still got my foot in in both worlds, the science world and the policy world. 

 So when Covid 19 broke out, you know, obviously it hadn't broken out in Africa. There was this whole talk about, you know, Africa is going to be really hard hit, and they're going to be, you know, XYZ. But if you actually looked at what was published at that time, there was nothing related to Africa, right? So, a group of colleagues and myself, all diaspora, and it's interesting, we actually took time of the advantage of the lockdown and the different time differences, and we actually worked around the clock for five days. So, we literally passed on because we had a colleague in in the US, had a colleague in Australia. You know, at that time, I just come to Kenya, so we were like a relay, exactly, but the relay, right? We started looking at this data and seeing what was the issue. And there were lots of data sets that we didn't have. That we didn't have access to, or just didn't exist for Africa, but what we were able to do with mathematical modelling and mathematical techniques, use the pre-existing data sets and actually fill in the gaps, right and then test how strong is that evidence, because you need to test and believe it. Or not. You know, what our first analysis showed was that Africa is not going to hit as hard, yeah, and so the fact that what got published or not is another entirely different conversation that we should have around open science. But that was the first model that really showed, and I think that goes back to that power of decision making, because those models were being used by politicians for decision making. And what our model showed is that, you know, we didn't need lockdowns. We needed lockdowns in the air spaces, but we didn't need lockdowns in other spaces. It really showed the power of of these gaps that we are talking about. And the other piece of what I spoke about earlier, you know, understanding your population and contextualising it.

Denis 15:40

I'm going to come back to that in a second, but I'm going to come back to your questions, the conversational questions. So, the question is, what is your favourite restaurant meal? 

Uzma 15:40

Wow, so I'm a foodie. And I'm lucky that I travel a lot, so I really like experiencing local cuisine. So I don't have one particular favourite, but I do enjoy food,

Denis  16:04

So lots of different foods and dishes, so well, that's great. I'm going to pick up on two points that you've just made. First of all, in relation to the data and the data gaps. So, isn't part of the ability to model and fill in those data gaps is based on having existing data, and doesn't that give a justification for investment in research and the collating of data in the first place? So that's the first question. It I'll come to the other one then. 

Uzma 16:33

Oh, absolutely right. You cannot expect to have data if you've not invested in that research, right? And you cannot invest in the moment when the outbreak, for example, is happening. It's a long-term investment, and it is something that you know triples in the value. If we just, if we just look at the investments made in programmes of public health, even if the disease outbreaks, if you do the comparison according to investments, is what's come out, there's much you invest fewer dollars for better outcomes, in the sense that, you know, people just see the negative when it happens, when there's an outbreak, but you know, the flip side of it is how many lives have been presented or how many outbreaks have been prevented by the science and research to start with, and we never quantify that. So, there's a very strong case for science. Science drives society. It's the basis of changing science, so it needs to be a long-term investment. So yes, there is need, totally need for investment. But this idea of, and I'm not trying to say that we don't need to invest in fill in these gaps, definitely. And I've just given you two examples, dimensions right at the national level and gender, but there's other levels that are missing, right? Language, and I've given you the example of what, where data can come from, yeah. So, so they're more dimensions, right? And I'm not trying to say that we don't need to invest to fill these, but what I'm saying, you know, in the current situations that we have, there are tools that, instead of just saying, okay, I'm making a decision because the data doesn't exist. You know, the better option is like, okay, understanding the limitations in the data you have, and saying, you know, based on this living data, you know, you can use x tool to test this evidence. So, it's an alternative way of working. But it's not saying that, no, we're not going to invest, or you don't need to invest, because these tools exist. No, it's, it's, it's a workaround occur on what is going on. 

 And I think another point you sort of raised, and I want to address, and I sort of did, when I gave you that example of Covid, right? A lot of the issues that we are facing currently are global. I mean, Covid was a classical one, and I don't know why it took us so long to realise that. Climate's another one. And all these are cross border issues, the migration issue, right? Human migration is another one. And you know, it's not good enough saying that. You know people sitting in parliament in the Northwest want to make decisions on these but yet the data is not coming from where the problem is the biggest. And there is learning, and it's again, right? There's this piece of collaboration. I spoke about collaborating with, with your constituency, but there's this piece of collaborating with, with your neighbour, right? You have shared resources, said, human resources, the shared natural resources. There's this thing of, you know, diplomacy across the lines for science, and, you know, sharing those resources. So there is cross learning. You know, it's there's this notion that science has come from the West, and you know, the West has influenced it, but the reality that is a lot happening in the South that can be learned and taken from from our experiences. And I keep going back to covid because, because there was so much data from it. So I don't know if people are familiar with this, this data, but 70% of the public health innovations that came out for covid or not, from the global north west, yeah, Northwest. They were driven from, from the other ends. And again, it was because our communities need to be very resilient and innovate and are innovative because we have limited resources into human instinct you need to survive so you find those ways around. It so again, you know, I see data as this real opportunity for politicians to really get to where they want, you know, not to only drive what their taxpayers expects, but, you know, it sounds weird, but it's actually a tool to get them on their own journey of where they want to be to actually, you know, influence lives and, you know, make those right decisions and be able to stand by them and build relationships and be able to connect with your neighbouring countries.

Denis  20:27

It's not just anymore about learning from the neighbouring country, but you can learn from countries on the other side of the globe now, and I think it's a cultural thing that we all need to change

Uzma  20:37

Yeah, and borrow. I mean, it's partnerships, right? It's equitable partnerships, and that's the word it should be equitable partnerships.

Denis  20:44

Absolutely it needs to be equitable partnerships, but just on partnerships. And you touched on it previously, and that was this idea of a relationship between the policy makers and decision makers and the scientists in Co-creating data and evidence. If you were in parliament in Kenya tomorrow morning and you were reaching out, how would you go about starting that conversation?

Uzma  21:12

No, my member of parliament is here, so maybe he should be answering this question. You should put him in the hot spot. But here's the thing you know we need so there's a awakening both at both ends. There's an awakening within the scientists and the researchers that that science needs to feed into, into the policy path. There's that awakening. And then I believe, you know, there is this awakening within parliaments to across, across the world. Climate has been a big driver of this, that we need to do something, and it just can't be the old way of making decisions. Yeah, so that there is awakening. So this is a real opportunity and the time to leverage this. And having said that, between both communities and let's not forget the private sector and civil society, they're equally important place in this co creation, because there's no point of view designing a policy that's not going to be used or updated can be the best policy in the world, in your best you know, the best evidence thrown behind it, but it won't succeed in the implementation. So, this is important to bring all your stakeholders, all strands together, yeah. 

 But having said that, having this new awakening, this new era of how science and policy interact. So even that it does interact, and it does influence politics, you know, they are champions. That's what I'd like to call champions within both, both, right? And I would truly believe you're, you know, having brief conversations we've had, you know, you're, you're the icon of one of those in the fact that you're here at a science conference, right? Yeah? Says, says a lot. So, I would, and, you know, going back to this question, you know, tomorrow morning, I would go look for this champion and support them. And believe me, they are. So, I think when, when we started, you know, when we had our first coffee, I remember telling you that, you know, the last one year I'd been working across Africa to really understand what the policy needs and gaps of use of AI in health. And, you know, somehow, we managed engaging with 47 out of the 54 countries. And in each of these countries, we were able to identify a champion then who, you know, was able to snowball this effect. And okay, given AI is a hot topic and is of more interest, but they are, they're there. And I think it's an awakening, and it's and I think again, and I'm not, this is not a bribe or whatever, right? It's platforms like such the, you know, the podcast and stuff, that need to put this information out, and it's a way of thinking and changing the narrative. So I think I'd use my position in parliament for that one day to really start thinking about science, diplomacy, data, gender and champions

Denis  23:43

Uzma, I really enjoyed this conversation here in the open air, and people might have heard the birds tweeting in the background, but it was really great to have this conversation with you today. It was really so enlightening, as well. As always, the podcast is available, on all the usual platforms. And if people have questions, if they email me at denis.naughten@oireachtas.ie and we can get back to you. So thanks very much. And Dr Uzma, thank you too.

Uzma 24:12

Thank you. The pleasure is all mine. And look forward to you know, more engagement.