The road from Addis Ababa to Adama is a study in the contrasts of contemporary Africa. Leaving Addis toward the southeast, we see the gleaming concrete and steel shell of the nearly completed tram line that promises to relieve some of the congestion on the city’s clogged roads. We pass block after block of empty housing developments, which, in theory, will fill with the new transit lines.

But the roads themselves remain pockmarked with potholes and bathed in dust and debris. Poorly fed horses stand in the middle of the street; gaunt and unconcerned, routing traffic around them. Older women bend down, their faces straining, carrying hefty loads of discarded plastic bottles wrapped in gauze tarps, presumably for sale or recycling. Alongside the road, drivers of makeshift horse-carts ferry food and other goods to points unknown, reminiscent of scenes out of any number of decades past.

And then, as if turning the page from one era to the next, we arrive at the on-ramp to the recently completed Addis-Adama Expressway. Built mostly with Chinese labor and funded with low-cost Chinese capital, the expressway appears almost like a mirage of an onrushing high-speed Africa. Endless fields of green tef grain speed past. Wind farms spin on hilltops, powering waves of rural electrification. Our pace is now more than double what it was minutes ago as we accelerate towards Adama and the regional public health laboratory for the Oromia region. Traffic, however, remains strikingly below the levels we have just left behind, possibly because few can afford the new thoroughfare’s toll.

In some ways, like the expressway that leads toward it, the Adama regional laboratory reads like a vibrant sign of an emerging but incomplete African future. It was built in 2013 with funding from the US Centers for Disease Control and USAID and operates at reasonable capacity. Brand new lab equipment hums with life in almost every room. Behind an alarmingly marked glass door sits an industrial negative-pressure storage chamber for samples of highly contagious MDR tuberculosis. The Adama laboratory seems like a model of clean, efficient diagnostic technology. And yet, there is still much work to be done.

In the regional lab director’s furniture-packed second-floor office, we call up samples of GIS maps, web applications and Tableau dashboards to review. The director is impressed and enthusiastic.  Although Adama regional laboratory is stocked with some of the best in medical diagnostic equipment, its lab information system lacks an analytics front-end. So their diagnostic output tends to be conceptualized in terms of individual patients or samples rather than in terms of populations or overall laboratory processes. GIS and Tableau might change that.

Returning to Addis after a long day of project evaluation, a thought occurs to me. If the laboratories can be networked into the type of integrated and visually rich information environment we’ve been envisioning, then the future of Ethiopia’s public health system might be one where the transfer of essential medical data is no longer contingent on the horses standing in our path.

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It’s a wet and chilly Monday morning outside the Addis Ababa Regional Laboratory (AARL). The thick metal gates open wide and a motorcycle with knobby tires and a tall yellow container attached to its rear roars up to the front reception area. In large block letters the container reads, “Ethiopia Mail.” Inside the yellow container is a battered blue cooler with peeling biohazard stickers affixed to both sides. Specimens in Ethiopia move through an agreement with the postal service.

The driver dismounts, removes the blue cooler, and carries it over to the receptionist’s desk. She extracts a pair of translucent plastic packs, which in turn contain a set of thin, stoppered vials full of dark red blood. The vials each have barcode stickers, which are read by a hand scanner. The receptionist also enters several columns of information manually into a spreadsheet. This includes information on the specimen’s origin and its departure and arrival time, which is connected to the laboratory information system.

We’re witnessing the intake process for potentially HIV positive and high-viral-load blood specimens.

Notebooks and pens in hand, we pepper the staff with questions: Is there a regular schedule for the referral of testing samples from healthcare facilities to the regional lab? Yes. Can we have a copy for mapping? Of course, but it’s all on paper. How often does any particular facility send samples to the lab? Once per week. Do you know how long it takes to return the results? It should be about one week, but it could be longer.  Does part of the intake process always involve manual data entry? Yes, unfortunately, but some diagnostic machines such as CD4 and hematology are linked directly to the LIS. Is there anyone checking on this data in terms of structure and quality? The lab has a data quality team of 5 persons. Are all of your machines functioning right now? There’s one that’s been out for a little while, but it should be on the repair list.

Later, when we’re back at EPHI, I check my freshly built map. It tracks the national inventory of CD4 counters, and, sure enough, there’s a point for the AARL indicating that a machine is in need of repair.  All of this process graphing points to positive signs that our spatial data integrations could work.

One of the LIS staff from EPHI has accompanied us to AARL. We brief the lab director about the BD-PEPFAR program and our GIS project for viral load testing and equipment maintenance tracking. Meanwhile, the LIS staff downloads five years of viral load specimen data for us onto a USB stick. We’ll be able to pair this dataset with the one from EPHI.

Our assessment takes about an hour and a half. Anmol has a conference call to make, so the rest of us decide to walk the ½km back to the hotel to work. Before we’re even past the laboratory gates a man walks up beside me and spits on the ground, getting a few flecks of it on my pants. I try to explain that everything’s fine and not to worry while I walk, but he insists on wiping the side of my leg with a cloth.

Abruptly the man turns to leave. I quickly realize that my Android is no longer in my pocket. I stop him just before he darts into the street. He returns my phone with a sheepish shrug. I suppose no harm has been done, but from now on I’ll keep my phone in my zippered pocket.

Back in our hotel conference room, Adam loads the AARL data into Tableau while Jessica and Anmol take over on Excel. We grind through another marathon geocoding and data cleaning session. By late-afternoon Monday, our vision is blurry, and the team is in dire need of food and caffeine.

But there’s good news: the data model derived from our EPHI work last week turns out to work for us once again, with only minor variations. We can see age and gender distributions sprouting, along with the spatial distribution of the specimen network for Addis subdivided by scheduled day, testing frequencies and viral load results. Now that we’ve completed data cleanup and formatting on a second major lab, there’s every reason to believe that most every LIS system in Ethiopia — at least those from the same vendor — ought to allow us to back out a spatial understanding of the specimen referral network.

My long-deferred idea from four years ago is one small step closer to reality.

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Most of the hard work in GIS has nothing to do with making maps. The heart of the matter, from a functional point of view at least, is not the pretty pictures but the quality, sourcing, content and structure of the data which feeds those images. Without quality data, there can be no quality GIS.

This lesson has been driven home with a vengeance over the past couple of days, as we’ve struggled to formulate a prototype that is capable of visualizing Ethiopia’s viral load specimen referral network. From where exactly will the data come? In what shape is it? How much work is required to clean it up? Do we even have sufficient permissions to proceed?

The team huddles on Wednesday afternoon after the initial conceptual GIS presentation and concludes that the only viable short-term strategy is to zero in on EPHI itself. We’re going to base ourselves at the prototype stage on historical data from EPHI’s laboratory information system (LIS). The LIS integrates several testing systems, including biology, chemistry, hematology, CD4 and viral load, as well as TB sequencing. Since it’s the national reference laboratory, EPHI receives at least a few specimens regularly from a landscape of clinical sites scattered throughout the country. From referral locations and key indicators such as testing totals, turnaround time, and test results, their LIS should contain enough information to illustrate at least one slice of Ethiopia’s viral load specimen referral network.

Once we’ve decided on a course of action, Adam and Anmol run over to track down Tigist, EPHI’s IT director. Explaining our plan, they’re able to secure permission from her to utilize the past five years of viral load data for our initial experiment in specimen referral mapping. We’re in business.

By the morning, it’s clear once again though that even modest projects with sufficient clearance face enormous challenges. The LIS was not designed with spatial analysis in mind, so it doesn’t yield easily to our goals. Like many datasets over which one has no authorship or control, there is a wide, muddy field to cross in terms of data cleaning and organization before it can become even moderately readable in GIS.

On Thursday, while one of our team members recovers from illness, the remaining group digs into the data with a local advisor, assigned to train with us, from Heal TB. The stark reality is that there is no standard in place for location descriptions, nor a matching set of IDs to leverage into viable data integration units. The geocoding problem, in particular, or the identification and attachment of coordinate points for mapping, beckons us to climb down deeper and deeper into the muck of tedious detail.

But the team is persistent. After several hours’ worth of careful code-matching, spreadsheet restructuring, field parsing and manual lookups our raw material starts to look like it contains the basis of cartographic form.  We have ourselves a working core dataset.  

Back at the hotel Thursday night, we rejoin our recovering colleague and manage to convince the concierge to open a quiet conference room up on the 12th floor for us to work. Up until the wee hours Friday morning we’re merging datasets, measuring indicators, and hammering out the first draft mapping applications in ArcGIS Online. Around 2 am East Africa time is the very first moment any of us sees this network on-screen. Even partially complete, it’s sort of exhilarating.

Friday morning, we’re back at EPHI to reconnect with primary stakeholders and review our progress. They’re a little bit stunned.  In just one week, we have pulled together a functioning model web application that enables spatial analysis of a laboratory network, in a way never before seen. Hotspot analytics are enabled in the desktop and the browser. Core indicators have been loaded into a geodatabase. By the light of our Powerpoint slides, feedback comes fast and furious from all corners of the CDC conference room.

As the first week comes to a close, the BD-PEPFAR group has its marching orders: widen the sphere of activity from EPHI to the regions, refine the data model and expand from the initial prototype phase towards viable and shareable GIS tools. 

Starting Monday, the team is heading from EPHI to the regional labs, beginning at the Addis Ababa Regional Lab and moving the following week to the Adama Regional Lab. Ethiopia’s laboratory landscape is taking shape.

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Representatives from the Ministry of Health and the Clinton Health Access Initiative are here, alongside multiple branches of the Ethiopian Public Health Institute (EPHI), the CDC, and my team of invaluable professional volunteers in the BD-PEPFAR Labs for Life program. That team includes Adam Yeung, a health data and market analysis specialist from BD’s France offices, Anmol Chopra, a research and development specialist from BD’s India offices, and my new colleague at Direct Relief, Jessica White, who has just joined us from Stanford University. We’re here to teach basic skills in geographic information systems (GIS) to our local counterparts and to build a set of prototype mapping applications targeted towards strategic improvements in Ethiopia’s national system for diagnosing and treating HIV and tuberculosis.  

Every day, across Ethiopia, blood is being drawn and sputum collected from people suspected of infection with some of the world’s deadliest diseases. Without timely results their conditions will worsen, many of their lives will be threatened, and the risk of transmission to others will increase. Those specimen samples, once collected, are sent to hospitals and regional laboratories, usually in special packaging designed to be carried safely by the Ethiopian postal service. CD4 counts are run, viral loads are tested and TB samples are sequenced, then returned back to health clinics and hospitals where they inform medical judgments about treatment regimens and patient well-being. Sometimes, perhaps far more often than anyone would like, no diagnosis is possible because the specimens arrive at their destination unable to be read. In that case people who may be carrying serious viral infections, do not learn their status and cannot be treated appropriately.

The diagnostic system is literally the lifeblood of the healthcare system, constantly producing the epidemiological equivalent of actionable intelligence. It’s maybe a truism, but without accurate diagnosis there can be no effective treatment. Yet up until very recently there was no systematic overview of Ethiopia’s national laboratory system because there was no map. Questions like, “where are MDR-TB cases emerging most rapidly?” or, “which labs are having the hardest time meeting quality standards,” or  “what is the best way to scale up HIV viral load testing throughout the country?” had to be answered for the most part without detailed spatial information. As a result, analysis was delayed too long, hypotheses went untested, leads went unfollowed, and epidemiology lagged behind the events of the world, sometimes to alarming extents.

The BD-PEPFAR Labs for Life program is a public-private partnership which aims to make a systematic, long-term intervention in the laboratory systems of multiple countries in Africa and Asia through a combination of training, improvements in equipment and supplies, and targeted informatics. GIS is being used in this program to establish baseline conditions for spatial analysis of specimen referral, viral load testing and core lab systems effectiveness.  The maps that we build here, even if only in their prototype form, will hopefully allow EPHI and others in the CDC and the MOH to make more rapid, intelligent and targeted decisions based on detailed, accurate and timely spatial information.  

By the end of today’s initial training exercise the rain has subsided, my voice has recovered and I’ve somehow managed not to slip on the floor and break my neck. We have people lined up three and four deep to install software on their laptops and get logged in to ArcGIS Online to join groups we have set up as virtual collaborative workspaces. There’s a palpable sense of anticipation around the projects we’re planning to do together. Although GIS is brand new to many of the people in the room, we have an excellent mix of experience and enthusiasm.  In the hallway I debrief with Gonfa Ayana, the Director of the Regional Lab Capacity Building program. He’s eager to connect us with data and set up targeted training sessions around specific problem sets.

Our BD-PEPFAR team is in Ethiopia until the 4th of September, during which time I’ll be relaying our progress and conveying the ups and downs of geographic analysis and map development for some of the world’s most challenging problems, as our work unfolds, in near real time. Check back for updates.

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