Guinea Worm and the ABCs: What Can Education Learn from Health?

Guinea Worm and the ABCs: What Can Education Learn from Heath?
© Juliette Humble/DFID

Guinea worm disease may soon be eradicated from the earth—only a few cases remain. In some eradication efforts, it has become so difficult to find new cases, that data informants are paid more and more (see amounts here) to report the cases that remain: fewer cases, higher reward for finding one. The disease is being wiped out through scarcity-induced reporting incentives. In the Millenium Development Goals, health’s flagship effort was to reduce under-five mortality by 66 percent or 2/3: a reduction of about 70 points. The target was missed by about 15 points, but 55 points of reduction were achieved. A similar goal in education was to increase completion of primary school. The goal, implicitly, was to increase this by about 25 percent: from about 80 percent to 100 percent. The goal was missed by about half, only about 10 or 15 points were achieved. Measurement is only part of the story. But, because of measurement, the health sector knew to set targets that were ambitious but doable, and knew more or less how. Measurement helped engineer the improvement. Education had less ambition, and achieved less. It also measured less, and at the outset had a less clear idea about how ambitious to be, because it lacked the data. We now have lots of primary school completion, but, says the UNESCO Institute for Statistics, 500 million children in school are barely learning.

What are some examples of data innovations in health that education could emulate?

Education hardly uses household surveys. In the last few decades the health sector has had Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). These helped bolster confidence on how ambitious to be, and in using data to drive goal-achievement. Data-based evidence is not just for policy, but for everyday management. Between MICS and DHS there have been some 600 (to my informal count) country/year applications, in low-income and lower-middle income countries. That’s a lot of surveys and a lot of knowledge! Education has nothing like it. Oddly, some of the more interesting things we know in education about the wealth-education relationship comes from such surveys (work done by Deon Filmer at the World Bank - the surveys have some education questions). In education, we have learning assessment surveys (school-based and household-based). These are powerful, but there are not nearly enough. Systems still mostly do not know how to use them; the way health uses data as a tool to tackle problems.

These issue-specific forms of information help in a particular way: being issue focused, they are helpful in demonstrating that there can be a measure / act / re-measure / report virtuous cycle. This not only helps fight the disease or condition in question and show success, but also helps create demand for more data, including systemic data. A concern over whether these surveys help undermine more system-type efforts can arise. The health sector has responded by developing tools such as DHIS2, a reasonably generic modular system that can be used to improve or even create administrative health information systems. Funders and developers have persisted for decades, have used prototyping and design-based thinking, and have not let technology get ahead of them. Today there is a large user base, and some educators are using it for monitoring education. Education has had similar efforts such as Global Ed*Assist, OpenEMIS, StatEduc, and PREMISE. But they have not received the steadiness of support and persistence of effort that DHIS2 has and have not “jelled” as DHIS2 has.

Another lesson from health is that as things improve, further success requires finer data, as has been noted by colleagues working on the Neglected Tropical Diseases (NTDs) Envision Project. Says my colleague Margaret Baker: “Analysis of … data shows that increasingly, low coverage districts are due to boutique issues that affect a relatively small number of districts but which will impede NTD programs from reaching control and elimination goals. These include: … urban centers; nomadic/migrant populations; border towns; where there is insecurity; reaching both in-school and out-of-school SAC...” We in the education sector still measure for, and tend to plan for, the average. It is improving, but it is the spirit that still pervades.

Innovation proceeds. A recent advance has been the use of informants equipped with cell phones who are responsible for detection of problems, or reporting on certain issues, but tied to particular geographies. This can be used for sentinel-type report-and-respond systems for malaria control (e.g., Coconut), for Neglected Tropical Diseases, or for general purpose reporting when some special need arises (e.g., PMA2020). The Guinea worm “case auction” approach mentioned in the introduction is an example. Disease reduction can be directly traced to the use of these approaches.

In education, the other big challenge is getting the data actually used, and linked to a “systems” view of the education sector, but that’s for a different blog.