The Lucky Years: How to Thrive in the Brave New World of Health (15 page)

Such technology enables everyone to develop a built-in buddy system. If your friend knows via the app that you are feeling blue, he or she can act to help. At the same time, you will have a record of feeling down over time. Right now we have data only about what you remember when you are in your doctor’s office. By having lots of data over time, patterns and associations can be spotted that would have otherwise gone unnoticed.

While it may seem creepy to think someone can tell your mood
based on smartphone data, such technologies can help people when they are in a vulnerable place and may be prone to depression, such as after a job loss or after giving birth. These technologies take some of the subjectivity out of mental assessments, giving us real measurements. They could also help detect mental illness in loved ones. In the past decade, we’ve suffered a blight of unfortunate gun violence at the hands of individuals who were mentally unstable. How many lives could have been saved had there been interventions before those fateful days? And I’m not just referring to the mass killings that get widespread media attention—the well-known images we can recall from coverage at Newtown, Aurora, Charleston, and Virginia Tech. Since 2006, more than two hundred mass killings have occurred in the United States; they happen on average every two weeks.
⁶

Mass killings, defined as four or more victims, take place far more often than the government reports, and the circumstances of those killings are far more predictable than you may think. The majority of cases are the result of breakups, estrangements, and family arguments, and often they involve a failed safety net. So picture a cell phone app that can provide that safety net. Once you’ve flagged the people you’ve designated in your safety net, the phone can send a message to them that something is wrong when it detects such a scenario, thereby providing a chance to prevent a tragedy. Exactly how these devices could be best used is still being studied, but they offer hope that maybe we can avert some of these horrible events in the future.

The power of Big Data cannot be overstated. In 2013, a French study of nearly half a million people found that those who postpone retirement have less risk of developing Alzheimer’s disease and other forms of dementia.
⁷
In fact, for each additional year of work, the risk of developing dementia is reduced by 3.2 percent. Not only does employment keep people physically active, but those people also stay more socially connected and mentally challenged. They are more likely to be confronted
with new things to learn, which demands more focus and attention in the brain—all good things in terms of preventing mental decline.

That same year, another study found that living near an airport increases the risk for cardiovascular disease.
⁸
Among the 3.6 million people residing near Heathrow Airport in London, where this study was conducted by analyzing data, people who lived in the noisiest areas had a heightened risk for stroke, coronary heart disease, cardiovascular disease, hospital admissions, and death. What’s more, in a similar analysis of data collected on more than 6 million people on Medicare who lived in zip codes around eighty-nine North American airports, researchers discovered that people who lived near airports ranked among the top 10 percent in terms of noise exposure also had a significant increase in the risk of hospital admission for cardiovascular disease, even after adjusting for age, sex, race, zip code–level socioeconomic status and demographics, zip code–level air pollution, and roadway density.
⁹
In fact, they calculated that for older people living near airports, 2.3 percent of hospitalizations for cardiovascular disease could be attributed to aircraft noise!

These studies required researchers to dig into reams of data that were probably not very well organized. Now, what if our hypothetical database of the future, which is filled with people’s information regarding their basic habits and physiology, could ferret out all of these risk factors and important details seamlessly and effortlessly? Imagine the kinds of health management tools we could build around knowing, for example, the potential risks associated with your zip code or choice to retire at age seventy.

For another example, let’s say you’re an avid runner in your forties who has had a nagging pain in your hip. After a few referrals to doctors to help diagnose the problem, it’s determined that you’ve got arthritis blooming in your hip (the same kind that your mother of seventy-five years young has, too; she’s already had one replaced). You’re told to stop running forever and take up a new, nonimpact sport. This isn’t acceptable, and you vow to find an alternative solution. That solution could very well reside in this database, where scores of people like you in age, athletic proclivities, and diagnosis have insights about how to address
your problem and totally overcome it without saying good-bye to the sport. Wouldn’t that be life changing? Wouldn’t the ability to mine other people’s data be worth the risk of entering your own information into that database? I think so.

The point is your health information is part of the solution. You’re not giving
up
anything—you’re
giving
. And you’re going to get back. You want to benefit from every patient who has gone before you. You might even find out that for your particular condition and your personal data, you don’t need to be formally treated at all. Some diseases don’t need traditional medicine, and some should probably be treated differently than they are today. Many cancers, such as low-grade prostate cancer, differentiated thyroid cancers, and some breast tumors, don’t need treatment because their natural history is such that they won’t harm you. But in our one-size-fits-all world, they are treated like any cancer, with aggressive treatments that entail side effects. We’re all told to get a colonoscopy at age fifty, yet most people don’t need it. If you have no polyps removed during the invasive procedure, then you didn’t need it to begin with.

Similarly, asking a healthy man to undergo a prostate biopsy at age fifty just for screening purposes makes no sense. We need technology to determine who needs the tests. Over the next few years, I bet we’ll move from recommending that everyone have a colonoscopy at age fifty to administering a blood test that can tell you whether you have a colon polyp. And if you do, then you can go on to have the procedure. The same is true with drugs like statins and aspirins; these medications have their place in medicine, but we are overusing them because we don’t have a better way to determine exactly who should be taking them and when they should start. It’s the medical equivalent of equipping everyone with an umbrella every day to cover the handful of people who truly need the umbrella when it rains in their city. Given our current lack of data, we just don’t know which diseases need different treatments. Big Data will help us figure out that and so much more.

Here’s one more case in point: Every year, approximately 300,000 Americans with appendicitis undergo emergency surgery under the assumption that if their appendix is not immediately removed, it will burst—with
potentially fatal consequences. Your appendix is a little tubelike sac that’s attached to the lower part of your large intestine on the right side. You can certainly live without it; it’s likely a vestigial organ from our evolutionary past, though some say it could help reboot the digestive system with good bacteria after a diarrheal illness. But if you have to lose it due to infection, often from bad bacteria, it won’t cause any known health problems.

Surgical treatment for appendicitis began in the 1880s. But is surgery always necessary? Today we know that the length of time that an appendix is inflamed isn’t linked to the risk of it bursting. During the Cold War, when American sailors spent six months or more on nuclear submarines and were prohibited from surfacing, those who developed appendicitis didn’t have the luxury of undergoing surgery. They were instead treated with antibiotics. And this course of therapy worked great overall; there were no deaths or complications reported. But this approach wasn’t widely publicized. In 1961, during the height of the Cold War, Leonid Rogozov, a Russian doctor stationed in Antarctica, became desperate when his appendix became inflamed, so he cut it out himself. It would take another fifty-four years for us to realize that invasive surgery isn’t always necessary. The self-surgery that saved Rogozov’s life became big news in the Soviet press, and the doctor went on to specialize in surgery, dying in 2000 from lung cancer at the age of sixty-six.

In 2015, five small European studies involving about one thousand patients showed that antibiotics can cure some patients with appendicitis; roughly 70 percent of patients who took the antibiotics did not need surgery.
¹⁰
And those who wound up having an appendectomy after trying antibiotics first did not face any more complications than those who underwent surgery immediately. The opportunity to avoid more than two hundred thousand surgeries annually has been available since the dawn of antibiotics more than eighty years ago, but we didn’t have the data to help us realize it. Now we do.

In the spring of 2015, a headline caught my attention that spotlights another angle to this data story. It read, “Like Sleeping Beauty, some research lies dormant for decades . . .”
¹¹
A new study from the Indiana
University Bloomington School of Informatics and Computing’s Center for Complex Networks and Systems Research attempted to answer the question of why some research papers and discoveries fade into obscurity for years, sometimes decades, and then suddenly the research explodes and finally has everyone’s attention.

Surprisingly, the top journals for sleeping beauties are among the most prestigious:
Proceedings of the National Academy of Sciences
(in which this particular study was published),
Nature
, and
Science
. The fields with the highest rate of delayed acknowledgment included physics, chemistry, multidisciplinary science, mathematics, and general and internal medicine. Several papers in these categories experienced hibernation periods of more than seventy years. The “drowsiest sleeping beauty” in the study came from Karl Pearson, an influential statistician at the turn of the twentieth century. His paper, entitled “On Lines and Planes of Closest Fit to Systems of Points in Space,” was published in 1901 in
Philosophical
magazine but did not “awaken” until 2002. Four of the top fifteen sleeping beauties identified by this latest study were published more than one hundred years ago! What the IU researchers determined is that a paper can be ahead of its time—unable to gain the attention of others given the prevailing assumptions and general philosophies of the day.

Findings like this would make anyone wonder what gems exist already in the literature to solve serious challenges today in health care. Although it was previously thought that long-dormant studies are few and far between, this new study shows that that clearly isn’t the case. We must figure out the trigger mechanisms for awakening these sleeping beauties.

Collecting and recording health data goes far back in history. It’s actually as old as writing itself, but it would take centuries for such data to be gathered and documented in a way that would be useful to the public. It would take, in fact, until the Middle Ages for the world to have its first public health data set, thanks in part to the spread of the bubonic plague.

The title page for
Bills of Mortality
from 1664 and 1665.

In 1538, the English passed a law that required death certificates for burials. Apparently, the government was concerned that people were cheating on their taxes by pretending to be dead. Although the printing press had been invented nearly a century earlier, it wasn’t until 1600 that someone thought of gathering all the death certificates and compiling them in order to see generally what was going on. Then the
king would know who had died recently and would have a broad sense of his population’s dynamics, for births were also listed. Eventually, this led to one-page weekly reports that documented who died and from what. These reports were called Bills of Mortality, and one particular set would go down in history as being among the most important texts in human history.

The Bills of Mortality is probably a literary collection you never studied in school.
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It kept track of the rise and fall of the bubonic plague that struck England in 1664 and 1665. By most measures, it was the first record in the world of the spread of disease. Many of the recorded causes of death have odd-sounding names for us in modern times. Those who were charged with recording the deaths were not trained medical professionals, so they often fell short of knowing how to articulate the exact cause of death and would list peculiar or vague causes. Some deaths, for example, were listed as “griping in the guts,” “grief,” “suddenly,” “frighted,” and “stopping of the stomach.” The recordings reveal that infant mortality rates were high. The children who died were usually categorized by their ages rather than listed according to the illnesses that might have killed them. Hence, infants younger than a month old were listed as “chrisomes” and babies who hadn’t finished teething were listed as “teeth.”