Skip main navigation

New offer! Get 30% off your first 2 months of Unlimited Monthly. Start your subscription for just £35.99 £24.99. New subscribers only T&Cs apply

Find out more

Roundtable discussion: Incentivizing Quality Data for Decision-Making

Watch as Patrick K Kayembe tells Olakunle Alonge about his experience in incentivizing the collection of quality data (Step 3.21).
OLAKUNLE ALONGE: Hello. Welcome. I’m Olakunle Alonge. And with me today is Professor Patrick Kayembe from the DRC. And we’re going to be talking about data for decision making, specifically around some of the issues with poor quality data, the reasons for these issues, and some of the solutions. Patrick, thanks a lot for joining me in this conversation. I really want you, based on your experience in the DRC and in many other countries working in surveillance and in collecting data for epidemiology, for implementation, what are some of the reasons why you think data is not being used as effectively as it could in public health programming.
PATRICK KAYEMBE: Thank you for this interesting question.
Data are not being used for many reasons. So one of the reasons is that people in charge– so we’re supposed to be using this data. We don’t have that skills. They don’t think that this data is important for to make to make decisions, to see it, to monitor what’s going on, let’s say for health programs are being implemented to see where to [INAUDIBLE] and where to bring changes and correction. They don’t have that. It’s a program for [INAUDIBLE]. They don’t have that. And when the data is available, sometimes the data is very poor quality. So there’s nothing you can pull out from that, from that data. So they are incomplete, timely. So it’s [INAUDIBLE].
And then that way you see that in Africa, most of the time, people do conduct surveys to generate some new data. Because the existing data, the data being collected through the National Information System is of very poor quantity.
OLAKUNLE ALONGE: I mean, this is really insightful. So it’s not a matter of people really know the details. There’s an agreement, a consensus, the data is helpful. Well we don’t use data because of– one of the reasons is the poor quality of the data. The data is not collected in a timely fashion. Some of the people are collecting data, they don’t also see the usefulness of it. Thanks a lot for those points. Now in order to improve the quality of data, what are some of the strategies that have been tried. And I really want you to comment especially on this idea of providing monetary incentives to improve the quality of data. And what are some of the pitfalls?
Or what are some of the advantages and disadvantages of this?
PATRICK KAYEMBE: Yes. There are many strategies that have been tried in, let’s say, in the Democratic Republic of Congo. So one of them train people. So you train people so they know exactly how to collect the data, what kind of data they need to collect, and supervising them. But the thing is, once you train somebody and somebody has are skills so that this one will leave, so would be hired by other organization out there. So really, there’ll be a [INAUDIBLE],, you’re looking at him. [INAUDIBLE] And that person will be gone. And then we end up appointing another one, not trained, not having the necessary skills to collect good quality data. So that’s one of the problems.
Another problem is that giving people incentives so that they can commit to the work, the problem with giving incentives is good. It gives people some money. But the problem is how do you sustain that. So it’s a problem of sustainability. So how long are you going to be giving them money to be doing that. That’s the problem. So I think that the best strategy would be to make people understand why. That is very important. So why do they collect them and why they should even collect them and use them locally before sending them to the higher level. Because what is happening is that people collect the data don’t understand exactly why they have to collect them.
They just collect to send. They don’t use them for their own planning, locally planning and [INAUDIBLE] monitoring locally. So they’re just used to send this information to the higher level and don’t understand what they’re doing. And we have experience of people. So we appointed– they feel that they are being punished. So why do they ask me to be filling this form? So because of they don’t like me so they do it to punish me. And then the people don’t do it. And then they make up data. They make up data just because this is additional work they’re asking them to do, and then end up with a very [INAUDIBLE].
So the [INAUDIBLE] at training people, supervising them, and giving some incentives. But it’s mostly making them understand why this is useful, why they should keep doing it correctly.
OLAKUNLE ALONGE: Well, I mean this is very insightful, these points that you’ve highlighted that, indeed, the census should not be a stand alone approach. What is really most important is for people to see the utility of the data that they are collecting and therefore, to have motivation to collect the data. And on top of that, to then put incentives. I think that’s a very good point. I would just like to add from the experience, what are some of the challenges?
Or in terms of when you have multiple programs or you are needing different kinds of data, and you have systems being set in place for collecting this data, what are some of the repercussions of that to collecting good quality data in a place like the DRC?
PATRICK KAYEMBE: Yeah. These are very important points. So you see, the problem that we have, what we call the National Information System, that should be collecting information for all programs that are being implemented. But what is happening that all partner coming in, each partner wants to have its own system. So they’re setting up parallel systems and then sending the form to the field so a health professional in the health center might find himself filling 20, 30 forms. And this is becoming a– so it’s something that’s overwhelming. And then he doesn’t fill them correctly because it’s just a lot of work. And people, a partner, are not willing to [INAUDIBLE] system. So everybody who wants to set his own [INAUDIBLE]..
We need– what is important is that everybody to come up with– to be certain that the [INAUDIBLE] system that’s in place so that the system can be generating very, very, very good data so everybody that can use. And the system should be flexible to accommodate new programs. And this is being achieved now. And then, we have this– [INTERPOSING VOICES] –that has been implemented around the country. So but still, we have this problem. Because now every health district is connected. But the problem is that sending the data on time is still a problem. So we hope that this technology may be a solution. But another thing is that internet connectivity is not that good.
And so this is impacting the quality of the data, and [INAUDIBLE] mostly sending this data on time.
OLAKUNLE ALONGE: I mean, this is really very important, the issues that you have raised. So even aside from addressing the issue of incentives, aside from the issue of educating the data collectors on the use or the utility of the data so that they are motivated to collect good quality data and teaching them how to use the data for action, there is also the challenges with infrastructure. and then having the right infrastructure. Even when we trying to [INAUDIBLE] multiple data systems, you still have to have a very strong infrastructure to support that. And so the long term, we are going to be wrapping up now.
I would really just want to say one thing that you would like your guests to take away with them in terms of if you have the opportunity to change the system and to improve the system so that data can be used for decision making. What’s the one thing that should be done?
PATRICK KAYEMBE: That’s a tough one. One thing that is important, so I think that people should understand that we’re not collecting data just for the sake of collecting data. So collecting data because the data is showing exactly what is going on, what we are doing, and where we should go. So it is very important. So there is no program without data.
OLAKUNLE ALONGE: Thank you. I mean, that [INAUDIBLE] I think you’ve said it. That really, at the end of the day, it boils down to people understanding why data is important. And when they do that, they will find innovative ways to ensure the quality of the data. Thanks a lot, Patrick. It’s really been great to discuss with you.
PATRICK KAYEMBE: Thank you for having me.
OLAKUNLE ALONGE: Yeah. Thank you. And so our audience, again, thank you for joining us. We hope that this conversation has been helpful to understand some of the issues with regards to data quality in sub-Saharan Africa and some of the challenges and solutions and some particular ways on how to do it better. So thanks again. This is where we wrap it up. And until we meet another time, bye for now.

Patrick K Kayembe, MD, PhD, MPH
School of Pubilc Health, University of Kinshasa, Democratic Republic of the Congo (DRC)

Olakunle Alonge, MD, MPH, PhD
Bloomberg School of Public Health, Johns Hopkins University, USA

At the end of the discussion, Dr. Kayembe shares one take-away for you, the audience. He says: “I think that people should understand that we’re not collecting data just for the sake of collecting data. So collecting data because the data is showing exactly what is going on, what we are doing, and where we should go. So it is very important. So there is no program without data.”

Reflect on this statement. How will this advice impact your own efforts in your program or context?

This article is from the free online

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

Created by
FutureLearn - Learning For Life

Reach your personal and professional goals

Unlock access to hundreds of expert online courses and degrees from top universities and educators to gain accredited qualifications and professional CV-building certificates.

Join over 18 million learners to launch, switch or build upon your career, all at your own pace, across a wide range of topic areas.

Start Learning now