Blind spots: how a lack of reliable data undermines development

(c)Markel Rodondo, Conakry, Guinea

Despite its critics, vaccines are the most cost-effective and efficient forms of preventing disease. Yet millions of kids die from vaccine-preventable diseases – like pneumonia and measles – every year.

Despite routine vaccination and vaccination campaigns, many developing countries have repeated outbreaks of disease.  In 2013, Médecins Sans Frontières vaccinated 2.5 million people in response to measles outbreaks; half of those were in the Democratic Republic of Congo. (MSF, 2014)

So where do governments and aid agencies go wrong in not getting the vaccines kids need?

A lack of data – in many forms – is crucial. And this is where governments, in a particular, have a responsibility to improve data collection. Many country governments have blind spots when it comes to how many of their nation’s children are properly vaccinated, or even where their people live.

As Kazungu and Adetifa observe:

Good quality vaccination data is required to understand inequities in access to vaccines. Most vaccination coverage estimates in LMICs are from administrative reports that tend to overestimate coverage, due to errors in the number of vaccine doses administered and/or invalid assumptions about the size of the target population of children. (Jacob Kazungu, 2017)

Much of this lies at the foot of governments. Morten Jerven argues – with good reason – that the interest, or more often the lack thereof, in gathering data on their populations’ health, wealth and wisdom is often down to whether its in the best interests of the politicians. As he states:

Data collection is often done in accordance with a governance imperative: statistics are collected and compiled in order to facilitate particular policy agendas. Conversely, a lack of data may signal and facilitate inaction. (Jerven, 2013)

Overestimation of vaccination coverage is unfortunately a common theme throughout many states in Africa. In 2009, Burkina Faso experienced a measles outbreak with over 50,000 cases – to the surprise of many, it turns out. “[the country’s] vaccine coverage estimates preceding this outbreak suggested the country was near elimination, not at risk for a large outbreak.” (Nita Bharti, 2016)

It’s not only a lack of data on vaccination coverage rates that have an impact on disease outbreaks. The ability of a country to be able to provide vaccines for their country’s children in the first place has an impact. The price a country pays for a vaccine is a large part of this. Pharmaceutical companies are notoriously opaque about pricing – and the pneumonia vaccine is a great example of this.

What each country pays for this vaccine is largely a secret. Countries of like-sized economic status – take the BRICS, for example – don’t know what each other is paying for the pneumonia vaccine.  This lack of data “impacts… international or national policy evaluation, and these evaluations in turn have direct implications for issues of governance.” (Jerven, 2013) In this case, whether or not a government can afford to buy a life-saving vaccine that could protect kids against a disease that kills a million children each year.

Médecins Sans Frontières launched a campaign – called A Fair Shot – in part to highlight this lack of data around the price of the pneumonia vaccine. Here’s a short, funny video that explains the part of the campaign where prices are hidden:

The campaign was designed to ask two pharmaceutical companies – Pfizer and GlaxoSmithKline – to reduce the price of the pneumonia vaccine to $5 per child in developing countries. In the early part of the campaign, it highlighted that countries don’t know what other countries are paying for the vaccine, therefore they had to negotiate a price with the companies ‘blind’.

To highlight the issue, and solve the problem, MSF, working with the Guardian newspaper, decided to crowdsource the prices of the pneumonia vaccine in different countries. They asked readers and supporters to visit their local pharmacy or hospital and ask for the price of the vaccine, and then report it to the Guardian or to MSF.

MSF and the Guardian used social media posts to boost this ask:

The request to ask the public to send data on vaccine prices worked. MSF and the Guardian were able to source prices for the pneumonia vaccine from countries and sources that had hitherto been unknown or unavailable.

While these blind spots – or lack of data – in vaccination exist, there are way around them. Governments can do more, “but data availability is a good indicator of political commitment.” (Jerven, 2013) Governments will source the data they require, and respond to the needs accordingly – but only if the political will to do so is there.

 

Works Cited

Jacob Kazungu, I. A. (2017, February 15). Crude childhood vaccination coverage in West Africa: Trends and predictors of completeness. Wellcome Open Research.

Jerven, M. (2013). Poor numbers: how we are misled by African development statistics and what to do about it. Ithaca: Cornell University Press.

MSF. (2014). International Activity Report 2013. Médecins Sans Frontières .

Nita Bharti, A. D. (2016, October). Measuring populations to improve vaccination coverage . Scientific Reports.

 

Humanitarian Data in a Development Context

Humanitarian Data in a Development Context
Image Source: Google

Big data is an opportunity for the entire global community to better understand what is happening around us in real time, all over the world. If in 2017 there are more than 7 billion mobile phones in the world, around 6 billion of them are used by people from developing countries. This leads to the production of large amounts of data as these people go about their daily lives.

Using Big Data Safely and Responsibly as a Public Good

Some of the UN Global Pulse initiatives that rely on user generated data online include the following example projects. These demonstrate how big data and mapping techniques are important for both humanitarian action and development:

  • Estimating Socioeconomic Indicators From Mobile Phone Data in Vanuatu. This ongoing project takes into consideration results from recent studies that show that data from mobile phones (Call Details Records and airtime credit purchases) can help in the process of understanding socioeconomic factors where official statistics are absent. The research project uses data from a local telecom operator in Vantau in order to compare if the officially provided statistical data in terms of education and household issues is accurate enough.
  • Exploring the Potential of Mobile Money Transactions to Inform Policy. The project analyses data provided by one of Uganda’s mobile operators to understand if the usage of mobile banking services depends on social networks, time and location. The result of this still ongoing project would help local authorities better understand the decision making process behind these services.
  • Informing governance with social media mining. This project analyzes the first live TV Presidential debates in Uganda in 2016, and it’s direct impact public opinions expressed on Facebook and social media in general. The analysis included 50,000 Facebook posts published publicly during the first two presidential debates on TV. The results of the project confirmed the positive impact of TV debates on democracy in Uganda.

Using Big Data for Mapping Our Future

Haiti in 2010 is considered as the initial moment in digital humanitarianism. And the most used platform for the biggest part of the digital response was Ushahidi. It was created in Kenya to help with tracking the violence after the elections. People used Ushahidi earlier in 2008 so that anyone could send in reports of violence via a web-form or SMS. Then they added the results to a Google map of Kenya (Read, Taithe, Mac Ginty, 2016, p. 9).

By learning from the past and by finding ways to protect the privacy of online users, organizations such as UN Global Pulse already have projects that use the electronically generated data from subscribers around the world. Of course, this data is useful for various purposes. But in most of the cases the gathered data is for creating maps. For example, all over the UN system there are maps. Maps of human rights violations, maps of poverty, maps of crop yields, etc.

In most of the cases, these maps are somehow static and don’t provide 100% reliable data in real time. As Patrick Meier argues, “the radical shift from static, “dead” maps to live, dynamic maps, requires that we reconceptualize the way we think about maps and use them”(Meier 2012, p. 89).

Dodge and Perkins (2009) suggest that “essential to new mapping techniques are imaging technologies, in particular satellite data”. And this results in “radically reshaping the ways different groups comprehend space and place” (Dodge and Perkins 2009, p.497). But they both remind us that “although access to much of this imagery is free, this disguises the powerful interests of corporations such as Google and Microsoft, who produce and own the images and control what we see and thus how we see the world through them” (Dodge and Perkins 2009, p.497).

The Duality of Big Data

In fact, a telecom company is able to track where its users move in real time. And by using this data, it’s possible to create maps of the movements of the population for example. This data can contain information about people going after a disaster, or people going to schools, clinics, etc. Current technology provides methods to create precise maps of people’s behavior in certain situations.

In other words it all depends on how we use and interpret big data. Big data seems to rely on human interpretation. Crawford et al (2013) note that we need to “more broadly consider the human impact – both short and long term – of how data is being gathered and used” (Crawford et al 2013, p. 4.). And “the technologies required to interrogate big data may mean that its use is restricted to a privileged few” (Read, Taithe, Mac Ginty, 2016, p. 11.). Boyd and Crawford argue that big data is ‘a cultural, technological and scholarly phenomenon’ combining technology (advanced computation power and algorithmic accuracy), analysis (identifying patterns to make claims) but also mythology; the belief that it offers new and higher knowledge ‘with the aura of truth, objectivity, and accuracy’ (Read, Taithe, Mac Ginty, 2016, p. 10.)

References

Boyd and Crawford, “Critical Questions,” 663., Last Checked: 1/10/2017, Retrieved from: https://people.cs.kuleuven.be/~bettina.berendt/teaching/ViennaDH15/boyd_crawford_2012.pdf

Crawford, K., Faleiros, G., Luers, A., Meier, P., Perlich, C., and Thorp, J. (2013) Big Data, Communities and Ethical Resilience: A Framework for Action. White Paper for PopTech and RockfellerFoundation. Last Checked 01/10/2017, Retrieved from: https://www.rockefellerfoundation.org/report/big-data-communities-and-ethical-resilience-a-framework-for-action/

Dodge M, Perkins C., The ‘view from nowhere’? Spatial politics and cultural significance of high-resolution satellite imagery. Geoforum. 2009 Jul;40(4):497-501.

Meier, P. 2012: Crisis Mapping in Action: How Open Source Software and Global Volunteer Networks Are Changing the World, One Map at a Time, Journal of Map & Geography Libraries

Read, R., Taithe, B., Mac Ginty, R. 2016: Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly, forthcoming. Last Checked: 1/10/2017, Retrieved from: http://dx.doi.org/10.1080/01436597.2015.1136208