The Culprit Behind Disease: It's More Tricky Than You Think

In my last post, I touched on a few of the dramatic differences - inequalities -  in the health status of the U.S. population. I promised that I'd begin shedding some light on the causes of these health inequalities. And I will.  But in order to do so properly, I'll need to step back for a moment and talk about a crucial epidemiologic principle: causality. 


A natural human tendency is to side with personal anecdote over data-driven automaticity.  The power of emotion comes through in a news clip depicting a mother with an autistic child, weeping as she describes taking him to routine immunizations like a responsible parent.

 Data-driven automaticity, on the other hand, is text in a journal article, sometimes translated on airwaves through the remarkably monotone voice of a bespectacled gray-haired gentleman in an imposing white coat, who uses phrases like "The hypothesis of association appears poorly justified, with reasonable certainty, given our existing body of evidence" to say things like "No."

In the eyes of the general population, it's no contest.*

(* The hyperlink opens to an article which covers a recent grand preventive medicine snafu in translating an "evidence-grounded" message for the general public:  the mammogram screening controversy. Communication problems partially furthered the general public outcry that followed new mammogram screening guidelines calling for raising the age when women should start getting screened.)

The power of emotion and anecdote is so great as to render former Playboy models reater authorities on neurobiology than the hyper-educated, bespectacled physician. Despite a lack of scientific connection between vaccines and autism, emotion fuels a sizeable and growing group of fervently anti-vaccine parents. (Whose impact, by the way, is already evident in places like Marin County, dealing with a sudden surge in kids coming down with preventable whooping cough and diphtheria.)


The science of epidemiology, unfortunately, may partially be responsible for the case of the doctor who seems to have a knee-jerk tendency to qualify and add caveats to every recommendation.  A truth of life is that the more you delve deeper into learning something, the less you realize is absolutely certain. (Besides death, I suppose, and taxes.) Epidemiology takes this to the hilt: take a few classes in the subject and you feel as though a joyriding teen just vandalized your brain. Suddenly, everything you thought you knew, and everything you thought you knew about knowing how to know, is suspect.  (See what I mean? I've just been studying epidemiology, and that's a classic post-epidemiology-studying statement.) Doctors' seemingly cautious, throat-clearing flourishes simply reflect belief in the limits of scientific analysis. (Or, for the cynics among us, fear of getting sued.)

This article takes it to the hilt, in true epidemiology style: can we really believe most of what we find in evidence based medicine?

As irritatingly exhausting as it is, epidemiology is also incredible. It combines everything you learned in grade school about the "scientific method" - forming and testing a hypothesis, performing experiments, analyzing results - with the thinking calisthenics of philosophy and logic, the liberal arts perspective of history and sociology, and the formula-based precision of calculus, accounting and statistics.  You can see why it's exhausting; almost as exhausting as writing that sentence.

At the core of epidemiology is piecing apart the puzzle of what causes disease and what prevents disease.

It's a puzzle that most of us have pondered at one time or another. You hear of the tragic situation of an avid cell-phone user who is diagnosed with brain cancer, for example, and wonder whether cell phones cause cancer. Or, you get a call from Aunt Mildred, who just got the flu shot and now feels horrible, like she's getting the flu. She's convinced the flu shot "caused" her to get even sicker.

A patient in San Francisco may buy a bottle of kombucha after a friend raves about its energy and health benefits (not to mention an enticing label boasting of potential benefits for everything from digestive health to warding off cancer). A Berkeley patient, on the other hand, might avoid peanuts on Tuesday, fast on kale juice on Wednesday and otherwise "is gluten/soy/dairy-free because everything else causes my system to be messed up."

We make associations all the time. But isolating whether X caused Y  is a trickier proposition. It respects the fact that sometimes, things just happen to coexist by random, non-causal chance. "Causality", though, requires more than one isolated person (or even a few people) happening to have a certain outcome when they have a specific "exposure" - whether a flu shot or cell phone.

 And even when you can show two things are associated  - ie, more cell phone users tend to have brain cancer - does not mean that the link is causal, i.e. that cell phone use causes brain cancer.

Our classic go-to example is matches and lung cancer. If you did a study where you tracked rates of matches and rates of lung cancer in a population, you could quickly see an association between numbers of matches and cancer. It could look quite convincing, with rising numbers of matches linked to higher cancer rates. You could probably repeat the study in many different settings and come up with the same result.

But matches aren't the cause of lung cancer. The culprit is what the matches are used for - i.e., smoking cigarettes, which we commonly accept promotes lung cancer. (Bear with me here and pretend that cigarette lighters haven't been invented yet, so people use matches to light up. You could do the same thing with "lighter use" if you wanted.)

It's quickly apparent how carefully teasing apart the cause of an outcome is crucial to effective policy. Even though matches were associated with cancer in our study, calling for a policy that bans matches would hardly target the root cause of cancer. And it would negatively affect people who use matches for other things, like moms lighting candles on birthday cakes, or overachieving eighth-graders reproducing that authentic yellowed hue of 18th-century parchment paper for a history project on the Declaration of Indepdence.

Epidemiology lays out how you can take data that tracks exposures (ie, matches) and outcomes (ie, cancer) for a large number of people and analyze it to see if 1) there actually is a "real", non-chance kind of link and 2) if  that link could be causal. It's the holy grail of the science: trying to figure out if a curious pattern we perceive or wonder about is actually real on a population scale. In other words - does Aunt Millie's experience with that flu shot translate to a "real" side effect seen by the population? Is there evidence that the flu shot can "cause" the flu in people?  (For the record, the answer is no.  Get  your flu shot!)

An inherent conflict between science and our mind: human beings naturally "want" the world to follow intuitive observations. And sometimes, intuition is in fact the way the world works. But sometimes, science doesn't follow our logic.  After all, the earth is round and rotates around the sun - even though the sun seems to circle above us, rising and setting on a fixed, flat horizon.

That our observations may not necessarily explain the full picture holds with the realm of causality as well. Take the following example. The circle is what epidemiologists call a "causal pie": it represents the component causes of a disease. Each thing in the circle has to happen for a person to get the disease. If just one piece is "prevented", the person won't get the disease.

But there's a catch: for most diseases, no-one knows what all the pieces of the pie actually are. Our best guess at the pie's structure come from our basic knowledge, scientific experiments and observations - a collection of inputs based on theories and the patterns we see.

For example, if the disease is being overweight, a very simple model might say that A represents a gene for overweight, B is overeating, and C is not exercising.

Now, let's say that in Population 1, everyone has a gene for overweight (A) and everyone overeats (B). About 10% of people don't exercise (C) - and are therefore overweight, since they have A, B, and C and complete the pie. Thus, it's observed that the 10% of the population who are overweight ahappen to be non-exercisers. It's the only thing that seems to differentiate the overweight group from the rest.

 So what would an observer logically conclude about this population? Being overweight a behavioral thing - it's caused mainly by not exercising. Exercise seems like the "magic switch" that "prevents" and "cures" overweight.

Now take another population. In this population, everyone is a non-exerciser (they have C) and everyone overeats (B). 10% of the population has a gene for being overweight - and thus, this 10% ends up becoming overweight, with pieces A,B, and C completed.

 Then, scientists come up with a miracle drug that blocks the gene and give it to overweight people, and voila!  The drug cures weight gain in 10% of the population.

In this population, the magic switch seems to be a gene - the only thing that seems to differentiate the overweight group from the rest. What do people in this population conclude? Being overweight is genetic - it's caused by a gene, not behavior.

In reality, preventing any one of the pie pieces could have prevented overweight effectively. In fact, if a smart scientist somehow knew that overeating - something that no-one thought -  caused overweight, and somehow was able to prevent people from overeating  - then he could prevent everyone in both populations from being overweight. This intervention would have a larger scale impact in both populations.

Obviously, real life is much more complex. But the point of all this holds: When thinking about what causes a disease, the pie pieces that are the most common causes in a population are often the least recognized.   On the other hand, the pieces that are present in a relatively small percent of people are quickly identified as the "magic switch", and are often the focus of prevention efforts.

Sometimes, these observably different pieces indeed represent the best - and most "do-able" - approach to prevent disease, given our incomplete knowledge of the pie's full structure. But sometimes, the prevention strategy with largest potential impact rests on undiscovered, commonly present pie pieces - those that affect most of the population. Finding these pieces requires thinking outside the box.

 For example, some scientists are starting to wonder whether being overweight is linked to a commonly present virus. (To see how this could happen, replace the "overeating" pie piece B with "virus".) Mind-boggling, no?

There you have it - a quick intro to causality. Next post- as promised - applying causality to a practical situation: health disparities and health outcomes.

Differences in Health: Thinking about the Puzzle

Show me someone who eagerly goes through 23+ years of school, and still eagerly seeks more - and I'll show you someone whose hobby is thinking.

I've always been the type of person who likes to think about things. Not just academic things; food, movies, books, art are all fair game. Sampling a carrot-ginger bisque at a farmers' market, my mind will try to pick apart the ingredients; coming across a snazzy web page, I'll ponder its construction;  reading a thought-provoking essay in the New Yorker, I'll wonder how many caffeine-fueled hours of coffee-shop-loitering it took to produce that article. (For the record, people who tell you they love to write are usually lying: writing itself is like slow torture, but the finished product is usually well worth it).

I've heard that thinking too much is the root of all misery, the flip side of the "ignorance is bliss" argument. (Or, perhaps, that is something I thought up while in a state of thought-induced angst.) Indeed, thinking has gotten me into trouble a few times (see: accidentally walking into glass doors, wandering into oncoming traffic at red light, struggling for eons over exactly which falafel joint to visit for lunch.)  If the mind were a muscle, mine often feels like a marathon runner's toes: weathered, achy and bruised, under-appreciated and overused.

Maybe thinking isn't for the faint of heart. And yet, as the eternal optimist that I am, I see it as a the first step to positive change, to solving problems. It's hard to get to the stage of doing unless you know 1) what you want to do and 2) why it makes sense to do it. And thus, enter the stage of thinking.


 In health, history supports this notion. A deadly cholera epidemic in London in the 1800s was unraveled by a physician who thought about the underlying pattern of cases - instead of simply treating every case as it appeared - and localized the source to a faulty water pump. A life-saving antibiotic, penicillin, was accidentally discovered by a scientist who thought about the strange mold that had grown on his bacteria cultures -  instead of passively discarding it a spoiled experiment - and found that the mold actually killed bacteria.

Thinking is the championed cause of public health and preventive medicine. In a test tube, a bacteria triggers infection; a mutation of a cell incites cancer; an inherited faulty gene produces a characteristic disease. But in society, illness doesn't follow pure test-tube predictability. Public health physicians tease apart the puzzle of disease, outlining patterns of individual illness with tools of epidemiology.  Recalling the words of esteemed epidemiologist Dr. Roy Acheson:: "Why did this person get this disease at this time?"

After seeing patients in the hospital, I'd spend many hours thinking about this question - about patients' differential experience of illness.  I was inspired by the pockets of intelligence, quality and compassion present in the US medical system - the caring colleagues who put needs of patients before their own, mentors who patiently taught residents, episodes of high-quality care successfully healing patients with devastating disease.  And yet, I was troubled by the gaps in health: patients who couldn't afford drugs or lacked insurance; who died, suffered through emergency surgery or were committed to expensive, lifelong drug therapy for  preventable and easily fixable conditions.

I thought: why were some patients able to quit smoking, but not others? Why did some patients seem to end up on five different blood pressure medications, while others needed none? Why did certain patients return time and again to the emergency room with the same, chronic symptoms, while others were permanently cured?

 Why, fundamentally, did some people seem to have better chances of better health?

Thinking led me to others thinking about the same questions, and I began finding clues: the role of environment and the community on health. Social influences - peer community, education, the "built environment" affects health by molding behavior, self-esteem, self-efficacy, and prioritization of health. 

Take this 2006 study in the Public Library of Science, where researchers' analysis of health data revealed a United States of "Eight Americas" - eight broad geographic regions with strikingly different levels of health. They reported the following findings: 

• The ten million Americans living in the healthiest region - "America 1" - enjoyed one of highest average life expectancies in the world, even higher than long-lived residents of Japan. 
• Meanwhile, the residents in the "lower America" regions had average life expectancies "more typical of middle income or low-income countries". The lifespan difference separating groups at both extremes stretched to nearly thirty-five years.  
• American Indian men in South Dakota died at age 58, on average, nearly 20 years earlier than white men living in the rural north. 
• Most differences in the death rates were from differences in rates of violence, injury and chronic disease - in other words, conditions which could be treated or prevented. 
• The gaps in health outcomes between groups - and the relative order of the groups - had not changed significantly since 1987. 
• Insurance coverage ("access to care") and use of health care services (ie number of visits to a clinic or emergency room) did not fully explain the differences in health outcomes. That is, the difference in death rates separating groups was much greater than the difference in rates of health insurance coverage.    

This national pattern of wide variation in health translates to the county and state level. "Shortened Lives", a groundbreaking 4-part series in the Contra Costa Times, reported on how decades of variation in life expectancy -  and marked differences in rates of homicide, asthma, heart disease and cancer - separated Bay Area counties that were just digits apart in zip code:

•  Life expectancies ranged from 87.1 years in the Walnut Creek suburb to 71 years in crime-heavy Sobrante Park outside of Oakland. 
• Residents of "hardscrabble" East Bay neighborhoods had rates of heart disease and cancer almost tripling those in wealthier residents.  
• Hospitalization rates for children with asthma soared in the lowest-income neighborhoods outside of Oakland, with nearly a fourth of children returning to the hospital within less than a year of being discharged. 
• The variation was not a discrete "rich-are-healthy", "poor-are-unhealthy" pattern - it instead showed a gradient along the socioeconomic ladder. That is, middle class communities were healthier than poorer communities, but less healthy than the most affluent communities. (Much more to come on this "gradient" in future posts.)
• Counties with greater insurance coverage were healthier than those lacking such coverage. But coverage rates did not fully explain the health gap: counties with similar rates of insurance coverage and numbers of doctors and hospitals still had different levels of health (with richer counties on average experiencing better health). 

Further evidence highlights the reality that ethnic group and minority status arises as a predictor of health: 

• A study comparing the health of African American and Caucasian residents in 256 U.S. metropolitan areas. found that African Americans had 81% higher premature death rates on average. 
• A nationwide survey conducted by the CDC analyzed rates of disability and asked people  to "rate" their own health. (Such "self-ratings", by the way, are actually good predictors of objective health outcomes like death and disease rates). The survey found a distinct pattern by race: almost one-and-a-half-times the number of African-Americans and triple the number of Hispanic adults reported their health as "fair" or "poor" compared to white adults. 
• In that same study, almost three times as many Native Americans experienced disabilities or mental health problems compared to Asian-Americans.

The issue of ethnicity, race and health could merit an entire blog by itself - and scores of books cover this topic.(And much more will follow in this blog about the patterns above.) But for now, a preview of why the ethnic link to health is a fundamentally unnatural and illogical phenomenon.  

 Researchers who have dedicated their lives to the subject conclude that there exists no basic biological or genetic explanation for why health should differ so markedly based on skin color. Genes certainly play a role in some diseases. There are lists of genetic diseases known to concentrate in particular ethnic groups (lists which medical students nationwide are currently cramming to pass medical school): Tay Sachs disease in Ashkenazi Jews; sickle-cell anemia in African-Americans;  hypokalemic periodic paralysis in Asian-Americans.  And, there is even evidence that some ethnic groups respond uniquely to commonly prescribed medications. (This is the motivation for physicians who admit to the health benefits of  racial profiling in medical practice.) 

But the vast ethnic health differences in death and disease  don't arise from genetic culprits like Tay Sachs disease or sickle cell anemia. They come from controllable things like smoking, homicide, heart disease. 

 Moreover, a fact of science is that simply having a gene does not guarantee its expression. (Twins with exactly the same genes, for example, have different health outcomes when placed in different environments. The study of how genes are expressed - epigenetics - is a whole, mysterious and fascinating field in itself.)

Repeatedly, studies confirm that biology and genes alone do not explain the variation in health by race, community or gender. In short, differences in people of the same ethnicity far exceeds the genetic variation between groups (one expert categorically notes, "race does not account for human genetic variation, which is continuous, complexly structured, constantly changing, and predominantly within races.") Some geneticists argue that skin color is a flimsy facade of difference - that there is, in fact, little real biologic basis for "race." 

So it's not genes or biology, but social structures and community environments - the social determinants of health -  that explain the patterns of health detailed above: the "Eight Americas", the ethnicity-health link, the zip-code predictor of lifespan. 

I became motivated to make a difference in health by tackling these root causes, and that motivation led me to the Kaiser Permanente Community Medicine Fellowship. It's an amazing experience; every day, I tackle systems-based issues that undermine community health, while also delivering direct clinical care to patients in need.

 But most rewarding is the group I work with: a talented, inspired and passionate group of fellows who work tirelessly to boost the health of the community they serve. Together, we work to improve the health context of our patients, transforming their vision for health and sustaining the work we do in the clinic setting. 

In the entries to come, Community Medicine Fellows will share their knowledge and experiences: pictures, reflections, questions, dilemmas, and practical insights. This is a forum for interaction, discussion and learning - helping us to stay connected but also to teach each other and grow in the process of becoming compassionate physicians.

It's going to be a great year.  Here's to making a difference.