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In 2009, Steve Jobs received a liver transplant—not in northern California where he lived, but across the country in Memphis, Tennessee. Given the general complications of both travel and a transplant, Jobs’ decision may seem like an odd choice. But it was a strategic move that almost certainly got him a liver much more quickly than if Jobs had just waited for a liver to become available in California. Eight years later, the Apple founder’s procedure continues to highlight the state of transplants in the US: when it comes to organs, we have a big math problem.

Today, there’s a much greater need than there are organs to go around. It’s a problem currently being tackled in part by mathematicians and developers, who are crafting clever algorithms that aim to make organ allocation as fair as possible. But it’s complicated math that’s done against a backdrop of sticky ethical issues, and the debates surrounding it are heated and contentious.

The problem(s)

Before we can understand how researchers are using math to take on the bigger issues plaguing organ allocation, we have to understand what those issues are and where current strategies—mathematical or otherwise—have failed.

Organs are allocated differently depending on the organ. For hearts and lungs, the time between organ procurement and transplantation has to be kept very short. So, a recipient is first searched for within a limited radius from the donor hospital. If no suitable person is found, the radius is extended out.

For organs like kidneys and livers, the timeframe is longer, and recipients are searched for within the donor hospital’s service area, of which there are 58 in the US (they’re organized into 11 larger regions). Allocation is managed by the United Network for Organ Sharing (UNOS), a private, non-profit organization contracted by the federal government to run the Organ Procurement and Transplantation Network (OPTN).

When, for example, a liver is available for transplant, the hospital where it’s located will contact the nearest organ procurement organization (OPO). That OPO will evaluate the quality of the organ and work with the OPTN’s system to find a match for the liver (see the sidebar for details on the matching process). The recipient search uses an algorithm to find the sickest patient match on the transplant list within the donor hospital’s UNOS region, and the algorithm confirms that the organ can reach its recipient within the appropriate timeframe. Sickness for liver transplant patients is measured using the Model for End-Stage Liver Disease (MELD) score, which uses measures of the patient’s health status to determine how likely they are to die if they don’t get a transplant within 90 days. The higher the score, the sicker the patient.

Making a match Both the organ donor and transplant candidate have key information entered into the OPTN computer network. When an organ becomes available, transplant candidates are first eliminated by the OPTN’s matching software based on incompatible blood type, body size, condition severity, and waiting time. Different organs have other specific factors that may further eliminate candidates. For hearts and lungs, body size is particularly important because the organs have to fit inside the recipient’s ribcage. For livers, patients within the donor service area of the available organ are first offered the organ based on the severity of their MELD score (or PELD score for children) with those in the worst condition getting offered the organ first. Rejection risks are higher for kidneys and intestines, so immune matches are an additional requirement for transplantation. And since those waiting for intestines often experience a shrinkage of their abdominal cavity, donors have to be smaller than the recipients. Once all that’s accounted for, kidneys and intestines are distributed within the donor service area of the hospital based on severity of condition, like livers. Both the organ donor and transplant candidate have key information entered into the OPTN computer network. When an organ becomes available, transplant candidates are first eliminated by the OPTN’s matching software based on incompatible blood type, body size, condition severity, and waiting time. Different organs have other specific factors that may further eliminate candidates. For hearts and lungs, body size is particularly important because the organs have to fit inside the recipient’s ribcage. For livers, patients within the donor service area of the available organ are first offered the organ based on the severity of their MELD score (or PELD score for children) with those in the worst condition getting offered the organ first. Rejection risks are higher for kidneys and intestines, so immune matches are an additional requirement for transplantation. And since those waiting for intestines often experience a shrinkage of their abdominal cavity, donors have to be smaller than the recipients. Once all that’s accounted for, kidneys and intestines are distributed within the donor service area of the hospital based on severity of condition, like livers.

In 2000, a document known as the Final Rule laid out a regulatory framework for the OPTN. For some of the guidelines laid out there, we’re doing pretty well. The Final Rule says policies should be based on sound medical judgement and should be reviewed periodically and revised when seen fit, for example. But with one particular goal, we’re failing—Final Rule says where you live shouldn’t have an effect on the likelihood of receiving an organ. Steve Jobs’ example is one entry in a lot of data that tells us otherwise.

There are major geographic disparities in organ allocation in this country. “There are areas that are organ rich and don’t have much demand, and some areas that are organ poor and have huge demand,” says Dorry Segev, the associate vice chair for research and a professor of surgery at Johns Hopkins. And these drastic differences in supply and demand have major implications for patients.

As Segev tells Ars, in some areas of the country, a person with a MELD score of 38, which is very high, would have an 85 percent chance of getting a liver in 90 days and a 15 percent chance of dying. But in other areas, someone with the same score would only have a 15 percent chance of getting a liver and an 85 percent chance of dying. “It turns out that because of the way the lines are drawn, in some areas of the country you have to achieve a much higher MELD score to get a transplant than you do in other parts of the country,” says Ryutaro Hirose, chair of the UNOS Liver and Intestinal Organ Transplantation Committee.

That’s what ultimately led to Jobs’ Tennessee-based transplant. Where Jobs lived in California, median waiting times for a liver transplant were very high, but in Tennessee, the supply and demand gap was much smaller. So, Jobs listed himself in Tennessee and, when a liver match became available, he hopped on a jet to get it. It likely dropped his wait time down from years to months.

Listing yourself for an organ in different regions or in multiple regions at once is completely legal. There are some extra costs involved beyond the ones required to travel to meet your organ at a moment’s notice, but what Jobs did is something that plenty of people do all the time.

“This builds a system by which those who are rich and powerful can get organs from anywhere in the country,” says Segev. “Those who don’t have the resources to travel, don’t have access to a private jet, don’t have the resources to even get evaluated by other transplant centers, and are stuck with the care around where they live, they get disenfranchised by the system.”

While it may be allowed, the fact Jobs needed to look for options throughout the country is a sign of how bad the geographic disparity problem has become. And if there’s a wealth gap attached to a US system, the problems soon expand to other social identities and create advantaged and disadvantaged groups despite the goal being equality.

“The biggest problem with geographic disparities is that they lead to disparities in both race/ethnic distribution of organs and socioeconomic status,” says Sommer Gentry, professor of mathematics at the US Naval Academy.

These disparities create a lot of organ waste. Areas with low demand compared to supply have the luxury of being choosy about which organs to use. “The areas that have the lowest need for organs actually squander the less ideal but still very viable organs in their area,” says Segev. In fact, if a transplant hospital refuses enough marginal organs, their OPO will eventually stop offering them, and the organ may never even be extracted. So, while someone who really needs the organ might live within transport distance, they’ll lose out because they aren’t within the designated region. “So, in general we are using fewer organs in the US because there are some advantaged areas and disadvantaged areas,” says Segev.