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.

The post Labs for Life: Ethiopia (Part Four) appeared first on Direct Relief.

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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