For instance, one of the best examples of new technology in the field of emergency response is the open source Ushahidi platform. The concept is simple: individuals submit reports of status and need, frequently via SMS, to an office that parses them and places them on a map. The Haiti Crisis Map is a fantastic example. Note, when you visit the site, that the data can be filtered by categories, listed on the right.

Haiti Crisis Map default view

There are a lot of potential uses for this type of technology. The first few that come to mind are that organizations can use the display to plan their deployment, post-disaster, and logistics departments can use the information to develop preliminary plans for the distribution of food, supplies, etc.

The human perception problem relates to the visual display of information. A quick glance at the default map makes it pretty clear where most of the need is. Groups planning a response effort may want to head for the area of the large red “941″ dot. Or do they?

Port au Prince suffered an incredible amount of damage during the earthquake and there is no doubting that there were (and still are) a lot of people there, suffering. Many acute responders spent most of their time locating and rescuing people trapped in collapsed buildings. That is a good thing and to the extent that the Haiti Crisis Map helped lead them to the best places, then it served its purpose and was a huge success.

Disasters are full of stories, however. I pointed out at the Wolfram Data Summit that zooming out and scrolling the map to show more of the bay reveals a different story.

La Gonâve is to the left

La Gonâve is an industrial island with little local food production. Residents rely almost entirely on food supply from Port au Prince. After the earthquake, when the supply lines were cut, food prices skyrocketed on La Gonâve. There was minimal earthquake damage there, but many residents were beginning to starve shortly after the quake because they could no longer afford to purchase food.

Ushahidi captured this story. However, even after zooming out and panning to reveal the island, it looks like a relatively unharmed area in the context of the quake. The human eye is drawn to the large red “2564,” the area from which there clearly there are the most reports. Red, too, is a danger color, so we inherently view the large red dot as being the biggest problem area.

Some of the reports from La Gonâve read:

We have nothing to eat at Lagonave. Please help us. We don’t want to die.

Please, can we still be counted as victims of the drama that occured? I am tired of sending messages to tell you that in La Gonave, we can no longer survive because famin (empty stomach) is overwhelming… URGENT TO RESPOND

The type of aid that Gonâve most needed — food and water — could have been delivered quickly and efficiently without hindering other response efforts. In truth, it was, but perhaps not as early as it could have been; most focus was on the damage and suffering in Port au Prince.

Maps now are a ubiquitous part of the “connected” lifestyle. We use them all over the Internet, when looking for apartments and restaurants to trying to determine bus routes and school districts. When we use them, however, we frequently look for clusters of activity.

In this map, if you live in Arlington and want to find a restaurant, you probably would head into the District instead of to Reston. I would, too, based on the cluster of arrows appearing there.

What we have to remember is that outlying points, in a crisis map, don’t represent fewer opportunities; they represent human suffering. That suffering can take many forms. Just like with La Gonâve, the needs of the people in the less densely represented area on the map may be entirely different from those in the clustered areas. As humans, are we supposed to make judgements about whether suffering from starvation or suffering from homelessness is worse? If we’re going to display these on a map with red dots, how do we convey that the suffering on La Gonâve represents a worthy target for aid?

These are problems of human perception that are neither new or unrecognized. The difference is that technologies like Ushahidi and the Haiti Crisis Map accelerate the pace with which we are able to aggregate information and therefore add more emphasis to the need to develop reasonable methodologies for designing solutions to weaknesses in human perception.

Dynamic mapping technologies are both new and exciting. Humanitarian crises are stressful and demanding. The nexus is an emerging field that promises to improve the efficiency of aid programs and reduce human suffering. It might be time to engage psychologists to help us smooth the rough edges so that these tools can graduate from the periphery of humanitarian aid and become central cores around which we design response efforts.

It is our instinct, and I believe a correct one, to focus on locating and assisting injured survivors in the immediate aftermath of an emergency. The 2010 Haitian earthquake is no exception. There are numerous heart-wrenching stories of children and adults being pulled from the rubble many days after most had given up hope on finding survivors. Once rescued, the survivors require acute medical attention. Longer-term issues can be safely pushed off on logisticians in during the first few days of response as physicians do their work with the injured.

The disease surveillance module in the EMR we built in Haiti

Most medical teams, however, only remain on the ground for a limited amount of time — until the acutely injured have received triage care. When those teams depart, their patients either transfer to another field hospital or are discharged with instructions to seek medical care in the future. They rarely are left with a record of the medical procedures performed on them during the acute care phase.

More people study disaster response these days than every did in the past. Those individuals usually hit the ground running and are able to keep their cool in the face of chaos. There are many people with wonderful intentions but limited training who also respond to disasters. There’s an odd thing that happens with these individuals: while their professional skills probably would be useful during response operations, many people feel like they need to be directly tangibly helping victims in order to be “helping.” IT experts would rather dig holes and move rubble than work on IT systems and administrators would rather cook food and put up tents than help organize papers. Their intentions are good and their actions are helpful, but their expertise in their own professional arena is what truly is needed, especially during a crisis. Hole-digging and food-cooking are tasks that locals can successfully complete — even with no training — with minimal supervision. It is neigh impossible to find a trained systems engineer or administrative assistant wandering the streets among vulnerable local populations following an emergency, especially in resource-poor setting.

The consequence is that minimal efforts are made to organize information during the first few weeks of a disaster response. Log books start and stop, volunteers rotate in and out, and methods of recording information change on a regular basis. People sometimes track information on their own laptops, but this usually is done in triplicate, each copy full of errors, created without the knowledge of the others who are doing the same thing. I certainly don’t blame non-data types for poor data management, especially during the acute response phase of disaster recovery. They are doing the best that they can in a very difficult environment.

A scrub nurse outside of a mobile OR in Fond Parisien, Haiti

The problem that emerges several months down the line has multiple facets. First and foremost, there are vulnerable children, frequently orphaned, who pass through the medical safety net and find nowhere to land. It is the responsibility of all involved with disaster response to record the presence of these individuals, track their movements, guarantee their safety, and remain with them until they are safely discharged. There is no feeling worse, I can promise you, than receiving a phone call three weeks after discharging an “orphaned” child, only to learn that somebody claiming to be their mother is asking for them at the gate to your field hospital. Well, there is a worse feeling: remembering the child but discovering that you have no record of their visit to your camp, the medical procedures that were performed on them, or their discharge status. Did you just lose a child?

The second major facet to this problem has to do with funding. Funds usually are scraped together in the short term to keep acute operations running. When the media attention shifts, however, and home institutions begin to ask for funding accountability, it is a nightmare to retroactively pull information together. Warehouses fill with all manner of donated goods, some are disbursed, some are stolen, some sit in boxes undiscovered while people yards away desperately need them. Volunteers arrive with large duffel bags full of medicines which are dumped into piles on the ground; some are controlled substances and need to be locked away. It is hard to find a trained pharmacist to assist during emergency response. But when the donors and representatives from funding mechanisms come knocking, they want to know exactly where everything is that was donated to your facility, the condition it currently is in, how you plan to maintain it and where it is when its missing. Writing the 17th draft of an US OFDA funding proposal while trying to manage volunteers and figure out where the patients are, what’s wrong with them, and how you’re going to place orphaned children is a lot for anybody to handle.

These are all reasons why emergency response teams need to consider data management prior to their deployment to the field. When possible, teams should bring dedicated data entry and data management people with them who have the sole responsibility of keeping information organized. That person should curate the single repository of information for the camp and aggregate incoming data from all relevant sources. The data management guru needs to be made aware, prior to deployment, that their job is to keep information organized — not to dig latrines and cook rice.

Information management during a disaster is a day one requirement and its importance is overlooked by many (if not most) teams that deploy during emergency response. When a team waits 3 weeks to bring a data management person to the field, that probably means there will be at least a month’s worth of data lost. From the perspective of a trauma orthopedic surgeon it might not matter. From the perspective of that surgeon’s patients, I can promise you that it does. In Haiti we had dozens of patients who arrived at our field hospital weeks and months after having a limb amputated elsewhere — the stump was infected and they needed care, but they had no medical history. One child arrived on-site 3.5 months after an external fixator was put on her leg following an automobile accident 2 months before the earthquake!

Eirini showing an example patient medical chart

We did our best, in Haiti, to put together a patient tracking system as soon as we could. By the time the hospital closed, we had a database with the medical records of nearly 1000 patients, the pharmacy disbursement records for nearly 7000 prescriptions and the entire stock of the warehouse stored within it. We were able to discharge 200-300 patients with a full print out of their medical history at the camp and a CD with their digital x-ray images on it; for many, it was the first medical record they’d ever had and it was a source of pride.

These frequently are shown broken down by race and ethnic group, too. For example, look at the following two pyramids that show only whites and only blacks in Washington, DC.

There’s a pretty remarkable difference between the two that are broken down by race. You can see at a quick glance that there are few young or old whites (US Census designation) in the District, but there is a far more even distribution of blacks. Other population pyramids overlay the same data for different dates to give an indication of how the population changed over time:

Swiss Population Pyramid

There’s another interesting story here, too, if you stop to consider both the Swiss graphic and the first Washington DC graphic. Look at the population distribution in 1900 in Switzerland. While it shows that the Swiss people lived significantly shorter lives, on average, it also shows that there are far more young people than old people. This is somewhat typical of historical population pyramids, merely revealing that people died at a younger age.

The consequence of having a baby boom — more babies than adults — is that the pool of people that are economically productive will grow in size as the babies enter their adult years. The strain on adults for providing for a cohort of babies larger than themselves dissipates when the economically productive adults start having fewer babies again. The 2008 numbers for Switzerland show a large cohort of tax-paying, economically beneficial 40-50 year olds. They’re saving a lot of money because they have far fewer children in relation to their size as a group. Their tax base supports ongoing social programs in Switzerland. The top pyramid for DC shows a similar story. The positive economic period that follows a baby boom is called The Demographic Dividend and is described in a Rand monograph by David Bloom, David Canning and Jaypee Sevilla (public health professors of several Perihelion members).

The next chapter in the story is an interesting one, though. What happens when the economically productive adults retire and begin to rely on the youth to support their social security and tax-based public services? This potentially puts a huge burden on the country. Many of the discussions about the collapse of Social Security in the United States revolved around exactly this issue: as with Switzerland, there was a post-WWII baby boom. Those baby boomers soon will retire and start to collect Social Security. Those of us who remain in the work force — a far fewer number than will be collecting social security — will struggle to support the tax-based social systems.

Public health programs that reduce morality can lead to baby booms and demographic transition. When fewer people on average die in a population, the population grows. A decline in the birth rate usually follows a decline in the death rate by a number of years. This wikipedia demographic transition chart explains the change. The space between the the death rate and the birth rate curves represents the baby boom.

The Demographic Transition

The point at which the birth rate declines to meet the death rate is what sparks the Demographic Dividend. This can be artificially initiated. For example, China’s economy began to boom when China instituted its (draconian) one-child policy. It set the state for a decline in the size of China’s population and also for economic growth. The economic growth lags behind the decline in birth rate by the number of years that it takes the final large cohort of babies to enter their economically productive years. Ireland’s recent economic boom and decline is an oft-cited example of the demographic transition, the Demographic Dividend, and the consequences of an aging baby boom population.

And to think — one quick glance at a population pyramid can reveal all of that information!