How to Build Your Own Public Health Crisis: Just Add Numbers


Because I’m that guy, I took a poll at the recent family barbecue.

“Heart disease—who has it worse? Men or women?” I asked. The answers came quickly. My mother-in-law and sister-in-law said, “Women.” My father-in-law, arms crossed, said confidently, “Men.”

My mother-in-law remembered hearing about how heart disease affected women more than men during the February American Heart Association (AHA) “Go Red for Women” campaign. Apparently, the message wasn’t heard by the men at this family gathering. They were moved by stories of men—fathers, brothers, friends—they knew who died from heart disease. We are taught that facts should trump feelings, evidence should trump anecdotes, and at first glance it would appear the men are too in touch with their feelings.

It is the mission of advocacy organizations like the AHA to raise awareness. Charts like this one are widely disseminated and used in countless presentations on the topic:

Figure 1

The graph demonstrates that over the last few decades the number of women dying from heart disease has been significantly higher than men dying from heart disease. In the year 2000 alone the gap is the most impressive, with 70,000 more women dying than men. The problem with this chart is that it is completely misleading.

Mortality in this case is best judged by death rates that take into account age and the population at risk rather than the crude number of deaths. The following table assembled from the CDC database for heart disease deaths by gender and age group for the year 2000 paints a more descriptive picture. The number of men dying from heart disease exceeds women in almost every age group. It’s not even close.

Figure 2

The absolute number of men dying in the prime of their lives is staggering. Between the ages of 35 and 64, 92,000 men die every year, which is twice as many men as women, and equivalent to those who died in the Korean War and Vietnam War combined. Even after the age of 75 when more women die than men in total, men die at higher rates because there simply are far fewer men left alive. The gap in numeric deaths in 2000 only exists because of the over-85 age group, where many more women than men die. Put another way, when elderly women eventually die, they die of heart disease.

A better way of representing mortality data to avoid this type of misinterpretation is to use the age adjusted mortality rate—a weighted average that takes into account mortality within age groups and the important denominator of people at risk of dying.

The following chart plotting the age adjusted mortality rate by sex is more accurate, though less politically convenient. Men have been dying at higher rates than women over time, and the gap appears to be constant.

Figure 3

To be clear, heart disease in women is well-deserving of attention. While the large portion of deaths do occur in women in later years, cardiovascular disease remains an important contributor to female mortality even at younger ages. As noted in the chart and graph below, cardiac disease is the second leading cause of death even among women aged 35 to 64, though again, at absolute rates far lower than their male counterparts. The wider public certainly needs to be aware that men have no particular ownership of cardiovascular disease, but the messaging to try to raise awareness of cardiac disease in women is misleading.

Figure 4

A good clinician chooses a diagnostic path based on an understanding of underlying risk. The 20-year-old with chest pain gets a different workup than the 55-year-old with chest pain. If the risk of death between men and women varies by a factor of two in the 35 to 64 age group, is it cause for alarm if men are worked up for heart disease at different rates by their doctors?

This wouldn’t be some sort of controversial sexist departure from standard practice. Every day, cardiologists use risk calculators with the difference in cardiac disease rates by sex baked into the algorithms to help decide if patients may be candidates for drug therapy like lipid lowering statins. The higher the risk of a heart attack, the higher the yield of drug treatment.

As an example, the commonly used cardiac risk calculator below was used to calculate risk on two patients. The only difference between the two patients is their sex. The man meets the threshold for therapy with a statin, the woman does not. Is the algorithm sexist?

Figure 5

Since it is the mission of professional organizations like the American College of Cardiology and the AHA to promote the importance of the disease they represent, messaging campaigns to identify lower risk groups that should be screened less aggressively are likely not to be on the menu in the foreseeable future. But the peculiar trend as reflected in remarks by prominent cardiologists is to specifically accentuate both screening for cardiac disease in women, and the possibility that the care women receive is inferior to men.

Examining a few of the pillars this claim rests on is revealing. One commonly held notion is that women may not have the hallmark symptoms of a heart attack—chest pain or discomfort—that has typically defined the disease. The theory goes that approaching women’s complaints with a typical “male-derived” framework of expected symptoms of heart disease may result in delays and subsequently poorer outcomes. But an attempt to source the frequency of atypical presentations by sex suggests that the complaints of women and men when it comes to heart attacks may not be that different.

Figure 6

A summary of a number of large trials that included the clinical presentation of heart attacks by sex demonstrates that 28 percent of men and 37 percent of women presented without chest pain. A difference to be sure, but not enough to recommend a different and more aggressive screening strategy for women when the underlying disease being screened for appears to be more indolent in women. Curiously, the same crowd that finger wags about overtesting and overdiagnosis is silent here. The hallmark of this genre of research seems to be that the message matters more than the evidence.

Honing the Evidence to Match the Message

Consider a recent article published in the Journal of the American Heart Associationthat seeks to study differences in the treatment between men and women. The article focuses on two important times—the time from symptom onset to a blocked artery being opened, and the time from arriving at the hospital to opening the problem artery. The article is presented as more proof of a sex-based difference in treatment of cardiac disease because it finds a statistically significant delay in time to treatment women.

The path to statistical significance in this case is courtesy of log transformation of the raw data that is notable only because its need arises from the treatment times between men and women being clustered in such close proximity to one another.

It’s no surprise then, that the statistically significant delay in the time between hitting the doors of the hospital to opening the blocked artery amounted to just seven minutes. The time between symptom onset to therapy was longer, approaching 30 minutes for women. The study implies that delays for women result in worse outcomes. But while these times are statistically significantly different, no correlation between these time delays and poorer outcomes was found, arguing against the delays being clinically meaningful. Only in passing does the study mention that the delays are not correlated with poorer outcomes, a fact that would suggest other mechanisms account for the gender outcome gap. For example, women presenting with heart attacks are certainly older and may be sicker in ways that can’t completely be adjusted for.

There are also two types of heart attacks (STEMI/NSTEMI) that were studied—the more serious kind identified by electrocardiogram (STEMI), and the other kind typically identified by cardiac enzyme markers found in the blood (NSTEMI). Interestingly, only the more serious type of heart attack observed a delay in treatment by gender. If gender really does have such a powerful impact on delays in getting appropriate care, it seems strange that the effect is not seen with the less serious type of heart attack, especially as there’s more room for variation in the treatment of these heart attacks.

The paper, like many in this genre, is a masterpiece of confirming bias while glossing over the fact that, in the three years of study, more than three times as many men presented with heart attacks. And perhaps the most important information in the paper also gets lost: only 65 percent of women and 69 percent of men were treated within the widely accepted 90-minute target time for the opening of an artery. Instead of a call to action to improve these numbers for all, the authors choose to focus on the “statistically significant” four percent difference between men and women.

In a twist of black comedy, despite the fact that one reason proffered for the longer time for women between symptom onset and a health system encounter is that women live alone at higher rates without a companion to activate emergency services, there is no mention that a potential solution might be to try and keep more male companions alive. This is not a message likely to find currency in a paper meant to highlight female disadvantage.

Politicians and salesmen have long understood persuasion is the art of bending facts to support a side. The surprise here is that this pablum is coming from within the academic class, a venerated and trusted group. The facts don’t stand a chance against those who see the world through a lens of patriarchy and male privilege. At the very least, one would expect the intelligent designers of this one-note world to recognize a failure of the male patriarchy when it occurs in as spectacular a fashion as happens with men and heart disease. In what can only be described as a landslide victory for the matriarchy, men turning 35 are half as likely to make it to 45 as their female counterparts. The same dismal statistic awaits men who make it to 45, and those who make it to 55. Inexplicably the messaging on cardiovascular disease somehow white-washes all of this.

The story of historical female systemic disadvantage is a very real one but the presence of a long list of bonafide grievances gives no license for the creation of new grievances based on bad analysis. Men die of heart disease at rates that are well beyond women.  Full stop. The roots of this gap are likely rooted in biology; controlling for traditional cardiac risk factors like serum lipid levels, blood pressure and smoking seem to have no impact on the poorer outcomes seen in men.

In the end, the decision on testing and therapy is a tricky one. I recently took care of a physician’s wife who had developed exertional dyspnea and fatigue. Despite negative initial testing pointing away from her heart being the culprit, the knowledge she belonged to a relatively lower risk group didn’t stop her from getting a coronary CT scan that was prompted in part by an anxious and loving husband who knew all too well about women and their atypical presentation. I ordered it with some misgivings about the cascade of further diagnostics and treatment that carry their own very real harms when one proceeds down a path of testing low risk patients. Luckily, the odds being in her favor, the test was clean.

But played out on a larger scale with an anxiety ridden populace and their physicians searching furiously to stamp out the epidemic that isn’t, we may very well end up doing more harm than good. In the battle between biology and created “truths” that arise from the perceived grievances peddled by a coterie of motivated academics, I have a simple recommendation: Choose biology. It’s the best thing for men. And women.

Dr. Anish Koka is a board-certified internist and cardiologist practicing in Philadelphia. He can be followed on Twitter @anish_koka.

This post was first published at The Quillette.

13 thoughts on “How to Build Your Own Public Health Crisis: Just Add Numbers

  1. This has become a fascinating group, so I’m going to tell you something I might not ordinarily share. It takes some context, however, so you’ll have to bear with me while I tell my story.

    As you may or may not know, I am 78 and have a full time job as VP of Comms in a tech startup. This is paradigm-shifting because tech is known to be sexist and ageist. However, one visionary entrepreneur believes in the value of experience and hired me.

    I consider this a gift. I was doing private consulting, so I wasn’t without meaning and purpose. I was helping entrepreneurs. But it had been 25+ years since I’d been in a startup (my own) and I’m sure now that my skills were rusty.

    I love and appreciate my job. OK. Enough context.

    A few weeks ago I was walking down the street in New York City with my daughter, on the way to a play for which we had been given house seats. It was hot in NY, I had consumed stronger coffee than usual about three hours before, and I began to feel lightheaded and weak.

    My daughter and I stepped into a hotel lobby, and I looked at my Apple Watch. Heart rate? 183. I sat still, but couldn’t get it to come down. At that time, we made a terrible mistake. I called 911. When you call 911 in New York, it inconveniences hundreds of people.

    While waiting for the paramedics I called a good friend, a retired physician, and he suggested the valsalva maneuver. To do this, you hold your breath and bear down like you were having a baby. I did it. My heart rate went down to 108 immediately.

    I tried to cancel 911 but it was too late. When the paramedics examined me I was fine, but they insisted on taking me to the ER. My daughter and I should have trusted my investment in health and refused, but we chickened out.

    In the ER at NYU Langone, they determined I had not had a heart attack, but an arrhythmia. I already knew this. Most arrhythmias are not fatal, although they do increase your chances of having a stroke.

    Once you get into an ER, because of the way hospitals are compensated, they try to keep you there for “observation.” They did the requisite tests — cardiogram, chest X-ray, echocardiogram, blood — and found a minor elevation in one enzyme, troponin. Later reading told me the elevation, under .2, was insignificant. But they kept me there anyway, all night, in the ER, because they didn’t have a bed,

    The next day, they still kept me, because they finally HAD found a bed. So I went into the “real” hospital, where they didn’t even hook me up to any monitors because they had monitored me all night with “unremarkable” results.

    I asked to be discharged, but no one on that floor had the power to sign the papers. So I cooled my heels there all day until a doctor finally came to the floor at about 5 PM, and discharged me, telling me I might have AFIB and to follow up with my cardiologist at home.

    In the hospital, I never even saw a cardiologist, just residents and a hospitalist. I was kidding with the doctor on the way out of the hospital. “Well,” I said,”at least I didn’t have a heart attack.” He replied, “you did, just not the kind you see on TV.”

    Did I mention that I feel fine?

    When I got home I went to my cardiologist. I showed him the ECG I had taken with my Apple Watch and he and his electrophysiologist had a conversation about whether it indicated atrial flutter or AFIB. He recommended I go to the electrophysiologist, so I did.

    He said I should schedule a procedure where they run a wire up your groin while you are under anesthesia and try to figure out what nerve is misfiring. He said I am at high risk for stroke because of my co-morbidities (high blood pressure), sex, and age. In other words, older women are at risk for stroke. Duh. He gave me drugs to keep my heart rate slow and thin my blood.

    Remember, I’ve felt fine since. I kept trying to tell everyone caffeine has been a trigger for heart palpitations all my life and I had had an unusual amount of strong coffee that day on top of flying from Phoenix to NY. To no avail.

    Once you are in the system, you realize the system is their system not yours. The will find something wrong with my heart. I’ve been plant-based since 2011, and they keep testing me for things anyway.

    But when you are older, the chart is not the patient. It is important to work on the patient, not the chart, and certainly not the chart of potential compensation for procedures.

    As I sit here, I ponder whether western medicine does any good at all for people my age. After all, we’re all “incurable” after a while. Why subject us to all this proceduring. (Yes, I made that word up).

    I am now reading a book I’d highly recommend to everyone. It’s called “The Art of Dying Well” and it lays out what you should do while you are capable and still strong to prepare that what happened to me doesn’t happen to you. I guarantee I won’t do that again.

    The most dangerous place for someone on Medicare is the hospital. The old saw is true, “hospitals are places where they kill people.”

    Take care of yourself. You’ll be better off. Modern medicine is for the young. It can kill anyone old enough to afford it. I’m beginning to feel that’s by design. They invent procedures and practice on us.

    And yes, I’m exaggerating. But not by much.

  2. Francine, your story matches mine. My mom is 93, physically healthy but significantly demented, and has a live-in caretaker in her home. I’m 200 miles away and, over 15 years, experienced managing the situation you describe at least four times.

    One thing I can add is that my parents, as they were going into their 80’s were managed by either a superb academic geriatric practice at a top Manhattan hospital or at a small local non-name docs-in-a-box in New Jersey. The financial incentives are such that the process and outcome in the geriatric population you discuss are the same at both extremes.

    Yes, these are anecdotes. I wonder if anyone on this list has any relevant data. Our system seems to avoid transparency of costs or outcomes as much as it can.

    1. I’m looking. I don’t want to be endangered by health care. I’m doing just fine give or take what happened in NY

    1. You make my point. No chance any of my doctors really know if I have a fib and if so if it is dangerous. Nobody remembers how well I eat and how long I’ve exercised.

      1. The smartest person in this story was you. The second smartest was the retired doctor.
        Everyone else was just deaf to your story.

        I could have accomplished the exact same basic exam and evaluation in my office for $100 AND you might have still made your show.

        Enjoy the book. It is about dying well.

      2. The book was terrific Niran. I don’t have Afib. I just know I don’t. A lifetime of yoga, plant based eating, exercise and paying attention to my own body tell me what happened. Also I monitor with the Apple Watch.

      3. That’s my point. You know yourself. The best doctors listen to their patients.

      4. Many specialists are not fond of my patients. They think my patients are too smart. I prep my patients ahead of time with the three questions the specialist needs to answer. 😂😂😂 I literally get phone calls from the specialist saying “your patient told me I needed to call you with these answers. “

        Patients know themselves, better yet, they know their children too. I can’t tell you how many times I have no idea what is going on with the child but the mother knows somethings wrong. My patients teach me far beyond what medical school equipped me with.

        The lay public needs to understand they are their own best advocates. Good luck with your writing.

      5. ^^^ This is the best thing I’ve read all year. I am going to start doing the same.

        Niran when are you writing some of this down? – a ‘Manual Of Good Doctoring’ is so badly needed at this time of multiple crises in medicine

  3. Thanks all for these engaging and insightful comments. I attach here a book review essay on the invention of risk factors. It’s a stunning story.

    -Some highlights:

    In the 1950s, Many doctors claimed that rich white men were thought to have more heart attacks because they bore the nobel burden of being heros of business and capitalism…. leading the nation and the economy. Their widows were told that they died to make America great.

    In the 1950s, the AMA was fighting a push for “nationalized medicine,” and the tobacco industry was fighting to hide information that smoking caused cancer. They formed an alliance: tobacco would help doctors fight nationalized medicine if the doctors were quite about the dangers of tobacco. The compromise is that both groups would fund a campaign to fight “smoking in bed.” Those of us old enough will remember those advts.

    Insurance companies developed some of the earliest metrics for mortality: the wt chart on those scales were called the “Met-Life” wt chart. Insurance companies also pushed urinalysis for diabetes, and many other tests — to predict risk rates.

    Met life’s leaders also introduced “visiting nurses” to the USA. (They were previously available in the UK). And philanthropists developed milk distribution centers–because giving milk to poor mothers didn’t work b/c they lacked refrigeration and they diluted it with dirty water.

    NYC made millions by creating and selling the antitoxin for diphtheria. The drug companies refused to touch it b/c it was made with horse blood.

    NYC was also in the lead in identifying TB symptoms.

    At one point, most doctors in the USA were paid by insurance companies to perform physicals on life insurance applicants.

    I’m attaching my essay. It was in the major sociology book review journal about 15 years ago.
    Public Health and the Risk Factor: A History of an Uneven Medical Revolution, by William G. Rothstein. Rochester, NY: University of Rochester Press, 2003. 480 pp. $95.00 cloth. ISBN: 1-58046-127-1.

    Ross Koppel
    University of Pennsylvania

    Remember the weight-height tables? They were printed on scales, in medical pamphlets, in newspapers, and on charts in your doctor’s office. Sometimes they were identified as the “Met-Life” tables. Those tables reflect an essential and fascinating but long-ignored story of social history, medicine, economics, governmental action, politics, sociology, and culture, brilliantly told in William Rothstein’s new book, Public Health and the Risk Factor.

    This book is ostensibly about the evolution of the idea of “risk factors”: how behavior, environment, and genetics affect the probability of developing a disease. But that sentence ludicrously underrepresents this book’s range and power. This book is about the role of the insurance industry in generating statistics about disease and dying (hint: they had the data and motive); the development of probability and statistics in general and as applied to healthcare; the promotion of public health efforts in America; the concept of healthy lifestyles; the importance of weight and build to health (the old image was skinny = TB, fat = healthy); the link between heart disease and lifestyle (officially presented as affecting important businessmen crippled by heart disease from the pressures of critical business decisions); unhealthy alliances (The American Medical Association and big tobacco’s joint fight against national health insurance, Medicare, and restrictions on smoking); how an insurance company invented many of the nation’s best public health programs; the construction of the government’s nutrition guidelines (written, in part, by paid consultants to egg-producers and other food companies); and the history of our understanding of cholesterol and blood pressure.
    Rothstein also provides more remarkable facts and intellectual gems per page than any book I can remember. But a sampling of those bonbons must await the meat and potatoes of this review.

    Part I. Development of the Study of Health Care. Rothstein begins with a historical discussion of the understanding of disease and risk factors. Acceptance of the concept of risk factors required a change in the perception of disease, shifting from a nineteenth century belief that each disease had a specific causative pathogen to a perspective that a combination of behaviors, environmental conditions, and genetics could cause or at least predict disease. The shift also required seven developments: (1.) Enough people had to live long enough to die from lifestyle-related illnesses; (2.) This was made possible by improvements in food availability and infection/bacteriological control sufficient to allow people to suffer and die from chronic and other diseases like cardiovascular illness; (3.) Development and use of probability and statistics to quantify risks of disease and dying, along with acceptance of multifactorial, probabilistic, and complex disease etiology; (4.) Belief in the benefits of treatment and public health measures for the targeted illnesses; (5.) Recognition that healthy lifestyles could produce better health and longer lives; (6.) Use of public health educational campaigns to influence behavior; (7.) Focus on a dreaded and pervasive threat, such as coronary heart disease, that could be affected by public health campaigns and lifestyle change.
    An insight Rothstein, a sociologist, does not offer but seems noteworthy here, is that the precursors for these epidemiological observations and interventions are remarkably similar to the precursors of modern sociology: Increasingly reliable data on society and on people’s lives, increasingly theoretical and methodological sophistication, an appreciation of statistics and probabilistic models, and, pace our founding fathers, a belief that we could do something useful about society’s ills.

    The book then reviews the development of statistics and of societies’ efforts at census taking and enumerating mortality and disease rates for populations and for groupings by age, sex, location, and so on. Here, the work of the French-Belgian social scientist Adolphe Quetelet (1796-1874) is critical. Quetelet developed concepts of the “average man,” many statistical techniques including use of the normal curve, and what we think of as Lazersfeldian comparisons. He also pioneered work on the environment and illness, and on the role of the aforementioned normal curve in understanding human growth and characteristics.
    Rothstein outlines the methodological hurdles faced by public health officials as statistics, measurement and disease trends all change simultaneously. In the case of coronary heart disease in the early twentieth century, he points out that the classification of the disease and the way it was listed on death certificates changed continually, along with the expertise of the certifying physician.

    The Insurance Industry: Insurance on marine trade goes back millennia. But life insurance first became popular as a tool of creditors to insure debtors. It expanded to gamblers who insured the lives of celebrities known to be ill (would they die before the insurance bet was over?). Betting on others’ lives, in fact, became so popular that most European countries outlawed it. In England, in 1774, however, a more socially acceptable policy was initiated: Those in a meaningful relationship (e.g., husband and wife) could hold policies on each other. Selling many policies across a wide geographic area allowed insurance companies to spread their risks and to use statistical analyses to predict loss rates. When life insurance came to America, it initially focused on specific groups (e.g., clergymen), which made actuarial tables easier to calculate. It quickly burgeoned to the general population, and just as quickly became corrupt and deceptive. Companies would refuse to pay widows and orphans on trumped up technicalities or would conveniently go out of business after collecting premiums. As with many industries, it was salvaged by government regulations, commencing with state regulators, specifically New York State, and eventually by federal statutes.

    Two types of policies: There were two types of insurance policies: (1.) those sold to the rich, with large but infrequent premiums; and (2.) those sold to the poor, with small but weekly or monthly premiums paid to agents who walked the neighborhoods (called a “debit”). These latter policies were called “industrial” insurance, and changed the way insurance was sold and administered. As we shall see, it is not an overstatement to say that much of modern medicine is an outgrowth of this mass marketing innovation.
    Selling insurance to a vast market required new statistical approaches to predicting and understanding chronic disease and mortality. Insurance companies wanted physicians conducting the application examination to be guided by valid data on the causes of early death. Public health organizations were focused on infectious disease and on enumerations of births and deaths. Few physicians had even basic statistical training. It fell to the insurance industry to determine the causes of death for large groups of Americans. This had both immediate and long-term implications. The immediate: In 1911, 80,000 of the 150,000 physicians in the United States and Canada were medical examiners of applicants for insurance policies. For more than half of the continent’s doctors, insurance company checklists and standardized “normal ranges” were often the medical profession’s only real systematic predictors of mortality and morbidity. Rothstein notes the irony of physicians as data gatherers and insurance actuaries as analysts of the causes and likelihood of death. Longer term, insurance companies invented and advanced the idea of risk factors and obliged physicians to offer probabilistic diagnoses, a practice that was heretofore discouraged in the profession. If companies were to bet on clients’ longevity, they wanted predictors of early mortality.
    Means and Motive: In the early 1900s, life insurance companies started to collect information on the 20-year life expectancies of accepted and rejected clients. The companies were eager to titrate their premiums to capture market share, sell to previously marginal populations (e.g., the somewhat sick), and to better guide their physicians in the insurance application medical exam. This research resulted in stunning findings: The emergence of “build” – what became the famous Met Life (Metropolitan Life Insurance) height-to-weight charts. In the 1920s, physicians erred in their understanding of the implications of body build because they more often saw people already sick with TB, the “wasting disease.” Also, because they lacked systematic population data, rotund build was viewed as healthy, which was also a partial legacy of the link between poverty and starvation. The findings from the insurance industry studies, however, showed that when analyzing all male policyholders between the ages of 40 and 49, the ones who were at least 25 percent overweight for their height had dramatically higher than average chances of premature death. Those who were 5 percent to 14 percent underweight had dramatically lower than average rates of premature death. Policyholders significantly underweight, not surprisingly, were often suffering with TB, and had low survival rates. The concept of build was a powerful and simple predictor insurance company doctors could use to predict morbidity and mortality. Others would follow.

    Urinalysis: Around the turn of the century, it became clear to insurance companies and to a limited number of physicians that urinalysis could demonstrate both diabetes mellitus and kidney disease. Most physicians, however, saw little value in the practice and refused to conduct the analysis. In 1905, the New York Life Insurance Company developed a technique for preserving the urine so it could be mailed back to the company’s offices for analysis. Although the insurance industry met with considerable resistance from the medical profession, the fact that insurance companies were paying for the examinations proved convincing. Rothstein calls the standard use of urinalysis one of the major health contributions of insurance companies in the late nineteenth century.

    Blood pressure: In the earliest decades of the twentieth century, physicians did not view blood pressure measurements as especially important. This unenthusiastic perspective was encouraged by unreliable measurement devices and inadequate training. Insurance companies, however, became aware that high blood pressure was an excellent predictor of kidney disease, arteriosclerosis, and premature mortality. They lobbied medical schools to teach proper measurement techniques, they insisted on blood pressure measures in medical examination reports, and they pushed the dissemination of better blood pressure measuring devices. Although they faced decades-long arguments with physicians about the use of the measurement, measurement techniques, and the specific ranges (i.e., category definitions of high blood pressure), the medical profession eventually accepted blood pressure as a standard diagnostic measure.

    Part II. Health Education. If the increasing knowledge of disease etiology, risk factors, and lifestyles were to be of any value, it had to be applied to society. Rothstein devotes several chapters to health education and public health intervention. These chapters are likewise full of wonderful insights and too-long-neglected research. He reviews the efforts to understand the cultural and environmental influences on urban mortality rates, health education and infant mortality in New York City, and the germ theory of disease in diphtheria and TB control. Each of these chapters is worth the cost of the book.

    Early public health efforts were often concentrated in cities, usually because they had higher rates of disease, greater scientific resources, more obvious poor, and because the higher rates of disease among the poor were less easy to ignore. The bacterial basis of diphtheria was discovered in the early 1890s by a lab created by New York City. By 1894, the lab developed the first effective antitoxin. They tried to get pharmaceutical companies to manufacture and sell it but were refused because it required the use of horse blood, with which the companies had little experience. So, the City of New York manufactured it for its population, and then also sold it to the rest of the world for many years thereafter, happily and substantially subsidizing the city’s public health budget

    New York City played another important role in understanding tuberculosis. The city developed a reporting system that, for the first time, collected measures of TB from middle class victims, overcoming reluctance among physicians to report middle class sufferers. Likewise, the city, in cooperation with the press, businesses, health professionals, and the neighborhood-based insurance agents of the Metropolitan Life Insurance Company, conducted a house-by-house canvas of actual disease prevalence. The effort resulted in a much better understanding of the disease’s purview, and in a broad public health education effort involving many languages, many means of communication, and local community groups, to name a few of the lessons.
    Milk in the City: The children of the New York City’s poor were often in horrific health, frequently suffering from malnutrition and disease associated with germs in milk. At first parents were told to observe and smell the milk — perhaps pour it through a filter to remove impurities. But filtering, while aesthetically pleasing, did little to control the germs, and the smell was soon disguised by chemicals used by diaries. In response, philanthropist Nathan Straus opened several milk distribution stations throughout the city, providing good milk gratis or at very low rates, depending on ability to pay. Unfortunately the tenements usually lacked refrigeration, so even if clean milk could be delivered, it was often spoiled by heat. The solution attempted was to dramatically expand the number and distribution of milk stations and to refrigerate all of them. This helped considerably. But mothers, often only recently transitioning from old world settings, were still found to contaminate the milk with dirty water from taps or the river. In response, the city installed a service used in England since the 1850s, the “visiting nurses.” Within a decade the city and private charities had a medium sized army of visiting nurses who would instruct new mothers, and help guide vaccinations and general medical care. The visiting nurses were found to be remarkably effective and became the model for programs throughout the nation.

    Enter Met Life, Haley Fiske, and Lee Frankel: Rothstein’s work on the insurance industry extends to its public health interventions and to their impact on other large corporations. Metropolitan Life Insurance Company (Met Life), driven by a progressive president, Haley Fiske, undertook a massive national campaign to improve the health of the American population. Fiske thought it was both good business and good public relations. The company established a free nursing care system for sick policyholders, educational and public programs to reduce infectious and chronic diseases, and adult- and child-based educational efforts to alter unhealthy behaviors. The Met Life programs reached more people than any other public or private health efforts, and the company became, de facto, the largest national public information department in the world. Frankel, a former chemist, was invited by Fiske in 1909 to expand the company’s research on causes of sickness, starting with its link to unemployment. Rothstein calls Frankel’s research the precursor to the National Institutes of Health, and his social action programs dwarfed many of the national health programs. By 1924, Met Life had its own visiting nurse program in four thousand cities and towns, and its nurses made 2.5 million visits in that year. Company PR insisted that faster access to healthcare lowered mortality rates and saved the company money. Fiske and Frankel claimed that the public spirited policies also encouraged client retention. A separate analysis revealed them to be right. The client loss ratio was about half the industry average and the company was consistently one of the most profitable in the industry.
    The model of altruism was, naturally, copied by other companies, often with Orwellian (but unintended) effects. Packaged food companies argued their food was more sanitary than that prepared at home. Cereal manufacturers hyped the health benefits of their products as they continued to reduce nutritive values and increase sugar. Soap manufacturers created classroom programs and coloring books to teach proper cleanliness and the benefits of soap (“The Cleanliness Institute” created by the Association of American Soap and Glycerine Producers).

    Part III. Heart Disease. Rothstein devotes several chapters to the struggles to understand and then effectively treat heart disease. As late as 1965, a presidential commission noted that many physicians did not accept streptococcus as the etiological factor of rheumatic fever, and that only 100,000 of the 1.3 million cases (7.7%) received rheumatic fever prophylaxis. On the other hand, physicians enthusiastically endorsed tonsillectomy as a preventive measure — which had been shown repeatedly to be useless.
    One of the most sociologically interesting (and humorous) chapters is on coronary heart disease and social position. Physicians and the press seemed to accept coronary heart disease as a burden of rich white men, “the disease of the intelligentsia.” A U.S. Senator who had just died of a heart disease was described as a “very typical example of the hard-working, high-tension dynamic individual who is ever attentive to the day’s work.” At the same time, in the 1930s, two Met Life studies found that the (poor) industrial policyholders had higher rates of heart disease than the richer policyholders, a finding repeated in 1954.

    Part IV. Risk Factors and Coronary Heart Disease. Rothstein examines the emerging understanding of smoking, diet, and exercise, in relation to heart disease. Here, as in the rest of the book, Rothstein fills each page with sufficient gems to keep you the star of any cocktail party as well as knowledgeable about health policy. For example, in 1964, the American Medical Association issued a pamphlet about smoking and health that emphasized the dangers of cigarette burns and fires. As noted above, the AMA and tobacco companies worked together to defeat both national health insurance and tobacco restrictions (in 1971). And as late as 1985, an AMA executive vice president warned the Journal of the American Medical Association to exercise sensitivity to the topics of tobacco and control of tobacco, nuclear war, and abortion. Later, of course, the AMA became an influential force against smoking.

    Similarly, Rothstein relates how doctors in the 1930s told the grieving families of those killed by heart diseased that they should be proud of their recently deceased because the disease manifested “his” societal importance, diligence and hard work. Also, medical authorities told the public that the increase in heart disease and chronic illnesses represented the triumph of modern medicine over infection, and should be a source of satisfaction (p 208).

    Rothstein’s discussion of the Framingham studies, the development of nutrition guidelines, secular trends in heart disease, the benefits of blood pressure treatment, and the debates about cholesterol and cholesterol treatment are equally cogent, thoughtful, and informative. His data argue that often steady development of understanding about these conditions were selectively interpreted and overgeneralized by professional associations. The common experience that medicine keeps “changing its mind” may well result not from conflicting data but from biased or premature recommendations by its representatives.

    Conclusion: This is an important book that compellingly pulls together many of the major elements of our modern understanding of health and of the current approaches to health care. It is a sophisticated analysis of the way health policy was and is created, amended, and circumvented. While maintaining good sociological skepticism about motives, it is open-minded about the role of industry and technology in helping to advance a healthier society.

    The writing is exceptionally clear and well organized. The individual and collective power of Rothstein’s facts and linkages is overwhelming and, at the same time, delightful. Rothstein maintains a coy deadpan while presenting remarkable and consequential information.

    My one regret is that he did not extend his analysis to modern health insurance companies who benefit by keeping us alive to the point where our healthcare is nationalized under Medicare. Perhaps that will be his next book?

    He may not be the most elegant writer in the discipline, but he has written one of the best books in the sociology of medicine in recent memory.

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