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The Importance of Politics: Who Has Power over the Data and the Decisions?

Here, Eme Owoaje outlines the role of politics in collecting, interpreting, and utilizing data. (Step 3.19)
EME OWOAJE: In this section, we’ll be looking at the importance of politics. Who has power over the data and the decisions? So in this slide, we look at the requirements for data for decision making. There are social requirements, there are financial and technical requirements. I’ll just highlight some of the factors under the various sections that we’ve mentioned. So in social, we look at stakeholder engagement in data collection, analysis, and dissemination. It is extremely important to engage with the stakeholders. Because they must know why you’re collecting the data. They must know the importance of the data. And this helps to ensure buy-in. Then it creates interest in evidence-based decision making. And then, also, the training of individuals engaged in data collection.
That is extremely important. Because without training, your data collection people will just go out and collect whatever data they think is OK. Then the financial aspect, to encourage local funding in data collection analysis and dissemination, this is extremely important as well. Because it fosters the notion that, yes, this data is ours, and we have funded it. And we all know that when people pay for things, they are more likely to be interested in what they pay for rather than things that are given to them free.
Then there’s also make a case for investing in data collection to donors engaged in the health systems of the country, then integrating the various data sources to create a sustainable data network, which is extremely important. Because all levels of stakeholders should be involved in that network so that they will have that data to work with. Then there’s a technical aspect. And this involves employing the use of technological innovations to ensure quality data collection, as we have with the AVADAR. So under the technical section, firstly, we’ll be looking at employing the use of technological innovations to ensure quality data collection as with the AVADAR, which represents [INAUDIBLE] visual AFP detection and reporting, remembering also that AFP is Acute Flaccid Paralysis.
And then we will also look at training and retraining on innovations used for data collection and analysis, checking of data quality, cross-validating, mechanisms for quality assurance. Then the technical aspect also takes care of training personnel at all levels on supervision vision of data collection activities. Political, and cultural, and ideological factors affecting data for decision-making are also very important. Some of these include– culturally, people in some regions may not be forthcoming regarding the information on the number of children they have. Then on the side of the health sector, managers and implementers may inflate data figures to obtain financial rewards for providing certain data. And other aspects could be that there are many vertical programs which result in generation of parallel data.
There is also the factor of political sensitivity and resistance to accepting monitoring data and feedback on quality gaps. This occurs quite often. Data generated from different donor organizations aren’t usually shared. That’s another problem that is encountered in a number of countries. And, finally, policymakers are not trained to understand scientific data. There’s a lot of political sensitivity or resistance to accepting monitoring data or feedback on quality gaps. One has to be a bit liberal with numbers and budgets in planning such programs so that the genuine implementers are not constrained by artificially imposed ceilings.
We have prepared an activity for you to map these concepts to implementation, research, IR competencies so that you can think about how political, cultural, and ideological factors affect data for decision-making. Please see the course site for more details.
So in this slide we’ll be looking at data collection and feedback mechanism. The data is usually collected at the health facility level or the community level and then it’s transmitted to the local governments and the partners at that level, then to the state government and the partners at that level, and then to the federal government and partners at that level. On the right hand side of this slide, we are looking at the various offices and organizations that are involved in the data collection. So at the local government level, we have the disease surveillance and notification officer. We also have the Monitoring and Evaluation– that’s the M&E officer and other ad hoc staff.
At the state government level, you have the state’s Disease Surveillance and Notification Officer, the DSNO, and then the state epidemiologist, you have the state M&E officer, and you have the state primary health care board officials. Then you also have WHO, UNICEF, and other NGOs that are involved in the polio eradication initiative. At the federal government level, you have the federal ministry of health, you have the national primary health care development agency, as well as the National Centers for Disease Control and Prevention, and you have international NGOs involved in the polio eradication initiative. So this is one model of how the data collection process is supposed to work. It is a political process.
In this model, however, it seems that the feedback of the health facilities and the data collection points comes from the federal level. There’s additional value of feedback for the immediate [INAUDIBLE] next supervisory level to cut short time to improve data quality and take corrective actions. This improves ownership at the successive levels. So we’ll be looking at data and power. We have a quote for a global-level policymaker. “At the country level, whoever has the data can interpret the problem and make the decision about how to tackle it, which that means they actually have really a lot of power over how money is spent and how money is requested. So the data is like the gold of this program.”
In some times and places, this wasn’t a problem. ‘ In other times and places, there were controversies about data and data access between governments and other UN agencies and even between different levels of the same UN agency. Ideally, data should be open to all and interpreted and used for decision-making at state and local levels rather than retaining all power and decisions at the national, regional, and headquarter levels. Unless they are under pressure from higher levels simply to show data that looks good rather than data that is good– meaning it also shows problems– implementers at successive levels should own their own data and the credibility attached with it. Ideally, too, data at all levels should be shared.
It shouldn’t just flow from the lower levels to the higher levels, but aggregated data should be open for use by people at lower levels. But the point here is that data sharing and use is always political. And so these things need to be plugged into health programs from the outset with the understanding that there will be political and power reasons to keep datas secret. We have an example in this slide that addresses controversies over data at the Nigerian Emergency Operations Center, EOC. As mentioned in the previous lecture, the EOC in Nigeria was an important site of data for decision-making, but sharing data was, according to some interviewees, a challenge for some.
Agencies and individuals, who had controlled certain data for years, we’re now asked to share. According to one interviewee, when the EOC was started– for some people to have it, that is the data, be completely open to all the partners and be second-guessed in terms of their interpretation of that data was so threatening to them. So in this slide, we continue with the controversies over data at the Nigerian EOC. We have a quote from another global-level policymaker. “At one point in Nigeria, we had to go up to the Minister and say, ‘this is your government data, but nobody has access to it, why?’
And the response he gave was, ‘You,’ and by ‘you,’ he meant all the polio partners and external people. ‘You have been doing this in our country for decades and yet I don’t have any data managers in my ministry who can actually do the kinds of analyses you do, and you are supposed to be building our capacity, what’s wrong?’ And it was just such a telling moment because he was basically say, ‘I would like to own the data, but I can’t and by nature of your presence, you’re kind of tying our hands and we have to listen to what you’re saying about what this data says.’
And that led to a data-sharing guideline for the EOC, and it basically outlines which server this data would sit on, who had access to it, all of this stuff, and it led to a much more equitable way of sherry the data and analyzing it.” In this slide, we’ll be having an exercise looking at national level data sharing. So I’d like you to look at the health program that you’re familiar with, perhaps polio eradication in your country, and outline the following. The different actors. The data needs. Access to needs. Control of data. Outline how roles and responsibilities could be defined to promote collaboration rather than competition.
We have another example in this slide and we’ll be looking at global data sharing agreements. Global data sharing for polio has seen many challenges, and data sharing agreements have been critical. As we’ve mentioned, data leadership is about power. So we have another quote from an interviewee. “I think one of the solutions are lessons here is in any kind of global effort like this is put effort into those data sharing agreements at the beginning rather than running into tension and letting it slow you down later and just being open about this. It needing to be an explicit thing that’s built into the work. I also think, and within that the idea that the government always needs to own the data.
The server that the data sits on needs to be government owned and government managed. The people who are managing and analyzing the data should ideally be governments and without a kind of explicit agreement on that, again, from the beginning. It’s been very hard to retrofit that and kind of pass the data back to the government.” In this slide, we’ll just briefly look at data management. Data management includes all aspects of data planning, handling, analysis, documentation, and storage. It takes place in all stages of the data lifecycle. So on the right hand side of the slide, you can see the various stages of data management.
So starting from creating data to processing data, analyzing the data, preserving it, giving access to data, and then reusing it. So data management is technical work because it can also be a political process. Ownership issues should be well-defined and I believe we talked about that in the earlier slides. So what’s the takeaway from this module that we’ve gone through? Build open data into project culture from the beginning and the specifics. In big programs implemented through big partnerships or in partnerships with governments, all data should be available in the public domain and open for discussion.
The other thing is that one person in the polio program explained the importance of data sharing by saying, “Better interpretation comes with collective wisdom and experience.” So implementation science in today’s course themes has evolved. The importance of incentives– how good is the data? The promise and limits of technology– how and when can technology improve data? We’ve shown some examples of that. Then the portals of politics– who has the power over data and the decisions? We’ve discussed that as well. So we’d like to link back to implementation science and we’d like you to think of the following for each topic.
And then we’d like you to summarize the implementation problems for data, for decision-making, provide examples of the root causes of the problem, and then also implementation strategies deployed to address the problems based on the contexts.

Eme Owoaje, MBBS, MPhil, FWACP
College of Medicine, University of Ibadan, Nigeria

At the end of this lecture, you are asked to reflect on the same questions that were posed at the beginning of this section on Challenges and Strategies in Data Use:

  1. The importance of incentives: How good is the data?

  2. The promise and limits of technology: How and when can technology improve data?

  3. The importance of politics: Who has power over the data and the decisions?

Review your response to these questions in Introduction to Challenges and Strategies in Data Use. Now that you have completed the lecture, what have you discovered? Did any of your responses align with those you learned in the lecture? Was there any answers to these questions that surprised you to learn about?

Please share your thoughts in the discussion.

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Collecting and Using Data for Disease Control and Global Health Decision-Making

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