Category Archives: Health

Testing, Testing, Testing

Health, Social policy

If we’re not willing to remain sheltered in place indefinitely, and if we’re not willing to lose up to a million lives to the coronavirus, the alternative is massive testing followed by contact tracing. Nobel Prize-winning economist Dr. Paul Romer of NYU claims to have done the math to determine how much testing we must do to bring the virus under control and keep it there. He is interviewed by Dr. Aaron Carroll for his weekly podcast, Healthcare Triage.

In the interview, they refer to R0 (“R zero”), which refers to the rate of transmission of the disease. If R0 equals 1, each person with the virus infects exactly one other person. If R0 is greater than 1, the disease spreads exponentially. If R0 is less than 1, the disease eventually dies out. Romer believes he has determined how much testing we need to do to keep R0 below 1.

You may have noticed that in my last post, I referred to the possibility of losing up to 2 million lives in order to achieve herd immunity.  This was assuming a mortality rate of 1%.  Romer assumes a mortality rate of .5%; hence he arrives at a figure of 1 million deaths.  Of course, the true mortality rate is unknown.

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“There’ll Be More Death”

“There’ll Be More Death”

Health, Politics,

The American oligarchy has spoken. For wealthy Americans, the cure is worse than the disease. We will restart the economy, regardless of how many lives are lost. Donald Trump is deliberately implementing a policy that he knows will result in hundreds of thousands of additional deaths.

From President Trump:

There’ll be more death. The virus will pass, with or without a vaccine. And I think we’re doing very well on the vaccines but, with or without a vaccine, it’s going to pass and we’re going to be back to normal.

We can’t keep our country closed. We have to open our country. . . . Will some people be affected? Yes. Will some people be affected badly? Yes. But we have to get our country open.

I used to say 65 thousand, and now I’m saying 80 or 90. And it goes up, and it goes up rapidly.

And look, we’re going to lose anywhere from 75, 80 to 100 thousand people.

From Governor Greg Abbott, as he announced the reopening of Texas businesses:

Listen, the fact of the matter is pretty much every scientific and medical report shows that when you have a reopening—whether you want to call it a reopening of businesses or just a reopening of the economy—in the aftermath of something like this, it will actually lead to an increase and spread.

From former New Jersey Governor Chris Christie:

The American people have gone through significant death before [in World Wars I and II] . . . and we’ve survived it. We sacrificed those lives.

Christie added that the sacrifice was necessary “to stand up for the American way of life.” When asked whether the American people would be willing to tolerate this many deaths, he replied, “They’re gonna have to.”

Drawing on a military analogy, Trump and Christie have referred to those who are about to die as “warriors,” hoping we will see them as having sacrificed their lives for their country. In fact, Trump is not making war on the coronavirus but surrendering to it in order to achieve herd immunity. As Vox columnist David Roberts noted, rather than referring to workers, the elderly and the sick as “warriors,” a more appropriate term might be “cannon fodder.”

How many Americans will die? On May 4, the New York Times leaked a report from the Centers for Disease Control and Prevention predicting that, if we reopen the economy, we will have 200,000 new cases and 3000 deaths per day by June 1. This is up from the current 25,000 cases and 1700 deaths per day. Epidemiologists predict that, assuming a mortality rate of 1%, allowing deaths to continue until we achieve herd immunity will result in about 2 million American deaths.

As usual, as Trump and his surrogates were making these grim announcements, the corporate media were obediently obscuring their importance by distracting us with trivial “issues” such Trump’s decision to tour a face mask manufacturing facility without wearing a face mask.

Letting the virus run its course conveniently coincides with Trump’s reelection strategy of hoping that a majority of Americans care more about their pocketbooks than the lives of their fellow citizens. Despite unanimous recommendations from experts that we need more COVID-19 testing, Trump rejected their advice, saying that “by doing all this testing, we make ourselves look bad.”  Does “ourselves” refer to the American people, or just the Trump administration?

It’s easy to dismiss Trump as an obvious sociopath, but he speaks for the American financial and political oligarchy that is quietly but ruthlessly taking pages out of the class warfare playbook. They began by passing trillions of dollars in bailouts, and ensuring that the majority of the funds would go to those corporations and individuals who are least in need of the money. (For details, see this article.) Needless to say, these bipartisan corporate welfare bills passed Congress almost unanimously.

However, in order to restart the economy, the corporate class still faces two problems. First, in order to reopen businesses, they must persuade workers (and sometimes consumers) to risk their lives and those of their families. This is to be accomplished through economic blackmail. Although figures are hard to come by, a high percentage—perhaps a majority—of working Americans are either ineligible for unemployment, or have not received it yet due to a bureaucratic system designed primarily to prevent fraud. Many of these same people have lost their health insurance. These workers will have to choose between risking death from COVID-19 and starvation.  (The weakest link in Trump’s plan may be the fact that consumers will usually not have to make this choice.)

Trump issued an executive order directing meat packing plants to remain open during the pandemic in spite of unsafe conditions. Republican governors of three states, Iowa, Oklahoma and Texas, have announced that workers who refuse to return to work when their workplace reopens will be ineligible for unemployment benefits. Denying benefits to people who have turned down a job is apparently legal and is likely to spread.

A second possible problem for corporations is that, should they fail to provide safe working conditions, they might be held legally responsible for the deaths or illnesses of their workers. Senator Mitch McConnell has announced that one of his conditions for approving any future coronavirus relief is that Congress grant employers immunity against any lawsuits from employees or their survivors.  Trump’s Justice Department has stated that they intend to take the side of meat-packing companies should they be sued by their workers for not providing a safe environment.

Whenever we turn on TV, we are bombarded by insipid messages from corporate America claiming “we’re all in this together” (and presumably all equally in need of the sponsor’s product). This message becomes a form of black humor in a country where not everyone has been rescued by the government and not everyone will be protected from harm.

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Did Ebola Influence the 2014 Elections (Revisited)?

The Changing Demographics of COVID-19

Health, Politics, , ,

The media have given us a stereotype of the Americans most likely to have contracted the coronavirus. You probably think of COVID-19 as a disease primarily affecting the country’s urban poor. You have probably also read the news that African-Americans, and possibly Latinos, have been stricken at a rate higher than their percentage of the population. These generalizations are accurate, but things are changing.

In a brief paper, Dr. William Frey of the Brookings Institution analyzed date compiled by the New York Times in order to compare the demographic characteristics of those counties hardest hit by the virus at different points in time.

In the above chart, the second bar from the left shows the characteristics of those counties with an infection rate of 100 or more per 100,000 population as of March 29. The bar at the left shows the population baselines. As you can see, the hardest hit counties were more likely to be in the Northeast, more urban, and more likely to have voted for Hillary Clinton in the 2016 election.

The next three bars show the characteristics of the new counties that reached the 100/100,000 rate during each of the next three weeks. These counties are increasingly located in the West, South and Midwest, they are more suburban and rural, and they are more likely than the early counties to have voted for Trump. In other words, the counties that are most affected by the coronavirus are gradually coming to resemble the demographics of the country as a whole.

This table shows a similar picture. Majority-white counties are catching up with counties with more minorities. The newly-affected counties are less likely to have a large immigrant population. The income data, however, are less consistent with the media stereotype, since the early counties contain more higher income people. I assume this is because the virus first took hold in cities with high income inequality like New York and Seattle. Over time, however, the income distribution is starting to resemble the baseline for the country.

These demographic shifts seem likely to have political implications. At the very least, white rural Republicans are not going to be able to dismiss the pandemic as somebody else’s problem. Frey suggests that they will become less receptive to Trump’s attempts to reopen American businesses. Fear of mortality will spread. In the past, such external threats have tended to help conservative candidates, but the situation is far too volatile to make a one-sided prediction. Will some people who voted for Trump in 2016 blame him for not keeping the danger away from their community?

You may also be interested in reading:

Did Ebola Influence the 2014 Elections (Revisited)?

Documenting the Flint Effect

Environment, Health, , ,

In April 2014, Flint, MI’s state-appointed Emergency Manager Darnell Earley made a decision to save $5 million by switching Flint, MI’s water source from Lake Huron to the heavily polluted Flint River. High acidity in the river eroded the protective coating on the city’s lead water pipes, introducing lead into the water supply. Lead is associated with a variety of health and behavioral problems, including impaired growth, kidney damage, high blood pressure, lower intelligence and criminal behavior. Emails show that Michigan Governor Rick Snyder, a former venture capitalist with an estimated net worth of $200 million, attempted to cover up the crisis for several months. It is difficult to determine what role the racial and socioeconomic composition of Flint—as opposed to Republican business values—played in the origin of the crisis or the delay in addressing it. Flint is 53% Black and 45% of its residents live below the poverty line.

New research demonstrates some of the results of lead exposure for Flint’s citizens. A paper by Drs. Daniel Grossman of the University of West Virginia and David Slusky of the University of Kansas looked at its consequences for fertility and fetal death rates. Dr. Marc Edwards of Virginia Tech—who played an important role in documenting the lead levels in Flint—had previously found decreases in fertility and increases in fetal deaths as a result of lead exposure through drinking water.

The Flint water crisis can be seen as a natural experiment with tragic consequences. Their analysis is an interrupted time series design with multiple comparison groups. The interruption occurred in April 2014, when Flint’s water supply was contaminated. The researchers examined changes in several variables of interest in Flint from before to after that date, using Michigan’s 15 other largest cities as comparison groups. Racial, socioeconomic and other demographic characteristics of the parents and children were statistically controlled. Here are the highlights:

  • The fertility rate after April 2014 was 8.5 births per 1000 women lower in Flint than in the comparison cities. This is a 12% decline in fertility and amounts to between 198 and 276 fewer children born in Flint during the time of the study due to the water crisis. Here are the trend lines.
  • There was a “horrifyingly large” 58% increase in the fetal death rate—defined as pregnancies of more than 20 weeks that do not result in a live birth—compared to other Michigan cities. This explains some, but not all, of the decline in fertility.
  • After April 2014, the overall health of Flint’s babies was not as good as those born in the other cities. They were born half a week sooner, were 150 grams lighter at birth, and gained 5 grams per week less than babies in the comparison groups. They also contained a .74% higher percentage of females. This is explained by the fact that male fetuses are more susceptible to prenatal damage.

Alternative explanations for an interrupted time series design focus on the possibility that something else happened in Flint in April 2014 that did not happen in Detroit’s other cities that affected its fertility rate. Maybe the change in the smell or taste of the water was sufficiently alarming to Flint residents to cause them to have less sex, or at least less unprotected sex.

Even if Flint residents avoided pregnancy during the water crisis, this does not explain the increase in fetal deaths or the differences in the health of newborns.

Dr. Slusky discusses the results of their study in this video.

It is likely that the residents of Flint will be dealing with social problems due to the lead crisis for decades, possibly even for generations. The Michigan Attorney General has filed indictments against 15 individuals for their roles in the crisis, but experience suggests that they are unlikely to be held accountable in any meaningful way. Meanwhile, experts are suggesting that residents of many other U. S. cities are being poisoned by lead. Of course, if we continue to defund the Environmental Protection Agency, we are less likely to be aware of the seriousness of the problem.

You may also be interested in reading:

Get the Lead Out, Part 1

Get the Lead Out, Part 2

Heavy Traffic

What We Can Learn From Denmark

Health, Social policy, ,

When we think about the current situation in Washington, it’s hard to believe that government can ever provide efficiently for the needs of the majority of our citizens. Yet, obviously, it doesn’t have to be this way. Other countries seem to manage. For example, a July 2017 study by the Commonwealth Fund compared the United States health care system to ten other high-income countries.

This chart plots health care spending (left to right) in relation to health care performance (top to bottom), an index which combines five dimensions—care process, access, administrative efficiency, equity, and health care outcomes. As you can see, we spend far more on health care that the other countries, yet we have poorer health outcomes. While life expectancy in the U. S. had been improving for several decades, it is now declining in some populations, in part due to the opioid crisis.

As an illustration of how things could be different, I recommend taking six minutes to watch this video by Joshua Holland, with animation by Rob Pybus, comparing life in Denmark, the second happiest country in the world, to life in the United States, the 15th happiest.

You can find the text of the video here. If you’d like to compare economic and social outcomes in the U. S. and Denmark more closely, check out the 17 charts in this article.

You may have noticed that this post has the same theme as Michael Moore’s 2015 documentary film, Where to Invade Next. For a longer (and funnier) look at what we can learn from the rest of the world, I highly recommend it.

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Don’t Worry, Be Happy

Reforms as Experiments

The Stress of Technology

Health, Media, Social psychology, , ,

The American Psychological Association has released Part 2 of its August 2016 survey of Stress in America dealing with technology and social media. Please see this previous post for basic information about how the survey was conducted.

According to this survey, 99% of Americans own at least one electronic device (which includes radio, television and telephones), 86% own a computer, and 74% own an internet-connected smart phone. The latter two figures seem suspiciously high to me. This may be related to the fact that it was an online survey. (Their methodology section notes that the data were weighted “to adjust for respondents’ propensity to be online,” but it doesn’t mention how people who have no internet connection were contacted.)

The Pew Research Center reported that the percentage of Americans using social media increased from 7% in 2005 to 65% in 2015. Among young adults aged 18 through 29, it was 12% in 2005 and 90% in 2015.

The APA survey finds that 18% of Americans say that technology is a very or somewhat significant source of stress in their lives. To put this in perspective, 61% report money as a very or somewhat significant source of stress, and 57% say the same for the current political climate.

Forty-three percent of Americans report that they constantly check their emails, texts or social media accounts, and another 43% check them often. Here is the breakdown of constant and frequent checkers on work and non-work days.

The constant checkers report a higher overall level of stress–5.3 on a 10-point scale, compared to 4.4 for everyone else. For employed Americans who check their work email constantly on non-work days, the overall stress level is 6.0. Of course, they may be people with more stressful jobs, one symptom of which is that they are expected to check their email on non-work days.

Constant checkers were also more likely to see technology as a very or somewhat significant source of stress.

These findings are generally consistent with a 2013 study which found that the more often their participants used Facebook, the lower their moment-to-moment self-ratings of happiness and the lower their overall satisfaction with their lives.

Not surprisingly, millennials (aged 18 to 37) report greater dependence on social media.

They are also more worried about their negative effects.

 

It is predictable that the negative aspects of this survey will be exaggerated by the mainstream media. For example, Bloomberg News ran an article about it this morning with the understated headline “Social Media Are Driving Americans Insane.”

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The Stress of Politics

Finding the Sweet Spot

The Stress of Politics

Health, Politics, Social psychology, , ,

Since 2007, the American Psychological Association (APA) has contracted with the Harris Poll to conduct an annual survey of Stress in America. Respondents are asked to rate their typical level of stress on a 10-point scale, where 1 = little or no stress and 10 = a great deal of stress. They are also asked to rate a variety of sources of stress as either very significant, somewhat significant, not very significant or not significant.

Until now, the APA survey has been a lackluster affair, with average stress levels remaining pretty much the same from year to year, and the most significant sources of stress being money, work and the economy. But that changed with the 2016 survey, due to the addition of some questions about politics.

The 2016 survey was conducted in August, with a sample of 3511 U. S. adults aged 18 or older. Because so many respondents (52%) reported that the 2016 presidential campaign was a very or somewhat significant source of stress, APA did a followup in January 2017 to see if the political climate had cooled off. January’s survey had a reduced sample size of 1,109—still a respectable number. Unless otherwise specified, the data reported below are from this most recent survey.

The overall stress level increased between August and January, from 4.8 to 5.1 on the 10-point scale. While that may not sound like much of a change, this was the first time in the history of the survey that there was a statistically significant increase in stress between consecutive samples. The percentage of respondents reporting physical symptoms of stress also increased, from 71% in August to 80% in January. The most commonly-reported symptoms were headaches (34%), feeling overwhelmed (33%), feeling nervous or anxious (33%), and feeling depressed or sad (32%).

As in previous years, economic and job-related sources of stress were among the the most important. Sixty-one percent reported that money was a very or somewhat significant source of stress; 58% said the same for their work; and 50% for the nation’s economy. However, these numbers were rivaled by three stressors related to politics.

Not suprisingly, responses to two of these questions were influenced by political partisanship. Democrats were more likely than Republicans to be stressed by the election outcome (72% vs. 26%), and by concern about the future of the country (76% vs. 59%).

Stress about the election outcome was influenced by several demographic variables. It varied by race.

It also varied with age.

And it varied by place of residence.

Education also made a difference, with 53% of those with more than a high school education being stressed out by the election outcome, compared to 38% with a high school education or less.

Some stressors that were presidential campaign issues increased in importance since the last survey. Those saying that terrorism was a very or somewhat significant source of stress went from 51% in August to 59% in January. Those concerned about police violence toward minorities went from 36% to 44%. And the rate of concern over one’s own personal safety increased from 29% to 34%.

Here’s the breakdown of concern about police violence by race. Black respondents appeared to show a ceiling effect. Their stress level didn’t increase very much because it was quite high to begin with.

Americans are usually described as apathetic about politics.  Partisan political conflict usually declines after a presidential campaign is over, but that hasn’t happened this year. Stress over the election outcome is almost as high (49%) as stress over the campaign itself was (52%). It is tempting to attribute this to a growing awareness among Americans that they have elected a man who is unfit to be president, or to the fact that Republicans seem determined to proceed with a political agenda most of which is not supported by a majority of citizens. Unfortunately, we don’t have historical data with which to compare stress over this election outcome to the same question after the 2000 and 2008 elections.

We also can’t be certain whether the rhetoric of the presidential campaign increased concern over terrorism, police violence and our personal safety, since perceptions of those stressors may have been influenced by real events that occurred between August and January, i.e., actual acts of terrorism or police violence. However, it seems obvious that Donald Trump tried to elevate anxiety about terrorism and personal safety to an unrealistically high level. The APA survey suggest that he may have been successful. Whether Hillary Clinton’s campaign raised concerns about police violence is less clear, since she typically called for greater respect for the police as well as clearer use of force guidelines.

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So Far, It Looks Like It Was the Racism

Why the Minority Rules

Framing the Debates

Finding the Sweet Spot

Health, Media, ,

Our lives are filled with linear relationships. Pedaling your bike harder makes you go faster in direct proportion to how hard you pedal. If you always tip 15%, then the amount of your tip will be a linear function of the amount of the bill. But in nature, relationships are not always linear. For example, if your body temperature deviates too much from 98.6° in either direction you’ll be sick. You could say that 98.6° is the sweet spot which you should try to maintain.

An example of a sweet spot from psychology is the Yerkes-Dodson law which describes the inverted U-shaped relationship between motivation and performance. Increased motivational arousal improves performance up to a point; you perform better if you are energized. However, if you are under too much pressure, you get anxious and your performance suffers. There is an optimal level of arousal—a sweet spot—but its exact location varies with the individual, the nature of the task, etc.

Sometimes good social policy is a matter of finding the sweet spot. For example, how much should the government pay in unemployment insurance, so that the unemployed don’t become impoverished but are still motivated to look for work.

The average amount of time adolescents in Great Britain spent online increased from 8 hours per week in 2005 to 19 hours per week in 2015. Is this good or bad for their mental health? Most social critics suggest that the effect is negative. They propose some form of displacement hypothesis—that time spent online displaces other activites that are potentially more valuable, such as studying, exercising or socializing with friends. However, evidence for it is weak. Przybylski and Weinstein note that online activity also teaches valuable social skills. They suggest that there is an inverted U-shaped relationship between time spent online and mental well-being. The call it the “Goldilocks hypothesis,” since, like the temperature of porridge, there is an amount of time spent online that is “just right.” Their research is an attempt to find this sweet spot.

The participants in their survey were slightly over 120,000 15-year-old British young people, recruited from the database of the U. K. Department of Education. They were asked how many hours they spent per day, separately for weekdays and weekends, engaging in these four activites: (A) watching TV and movies, (B) playing video games, (C) using computers, and (D) using smartphones. They were also asked to complete the Warwick-Edinburgh Mental Well-Being Scale, a 14-item self-report scale measuring “happiness, life-satisfaction, psychological functioning and social functioning.” Here it is. It could just as easily be described as a measure of optimism or self-esteem.

Here are the average amounts of time boys and girls reported spending on each of the four activities on weekdays (top) and weekends (bottom). Our gender stereotypes are confirmed. Boys spent more time playing video games, and girls spent more time on each of the other three, but especially the telephone.

The correlations between time spent on the four activites and mental well-being are shown below, separately for weekdays and weekends. (A = TV and movies, B = video games, C = computers, and D = smartphones.) The data analyses statistically controlled for gender, race and socioeconomic status.

The hypothesis that there would be a non-linear relationship between time spent on these activities and mental health is supported. In all cases, doing some of the activity was better than doing none of it. The sweet spots tended to be down around one or two hours per day. Longer times spent at these activities were associated with better mental health when they occurred on weekends than on weekdays.

Although these relationships are statistically significant because of the large sample size, the authors note that the four activities each only accounted for 1% or less of the variability in their measure of mental well-being. This was only about one-third of the size of its association with eating breakfast regularly or getting a good night’s sleep.

Since these data are correlational, it is necessary to remember that correlation does not mean causation. The authors sometimes slip into the habit of thinking that too much online activity is a cause of poor mental health, for example, when they speak of “harmful effects” of online activity. However, the reverse causal order is possible. That is, if a teenager’s psychological or social functioning is poor, he or she may find more satisfaction in solitary pastimes.

It should also be noted that these are self-report measures, and self-report measures share sources of variability that may have little to do with the measures themselves. Consider social desirability bias—the tendency of people to answer questions in a way that they think others will view favorably. It’s usually considered socially desirable to claim to have good mental health. On the other hand, teenagers probably think it’s socially undesirable to admit spending too much time online. Therefore, the relationships found in this survey could be due in part to their joint association with social desirability bias.

The tentative bottom line is that there probably is a sweet spot for time spent in online activities and it is probably a fairly short time each day. However, time spent with electronic media is not strongly associated with mental health, at least as measured by this instrument.

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Longevity, By the Book

Porn Wars

Situation Alarming–But Not Serious

Invisible Inequality

Economics, Health, War and peace, ,

The people who benefit least from American capitalism are mostly likely to be killed or maimed defending it, according to a new paper entitled “Invisible Inequality: The Two Americas of Military Sacrifice” by political scientist Douglas Kriner and law professor Francis Shen. And it wasn’t always that way.

The centerpiece of their investigation is a study of the socioeconomic status of American soldiers killed or wounded in World War II, Korea, Vietnam, and the Iraq/Afghanistan wars. Of course, the Pentagon does not provide such data, but they do list the home towns of the dead and wounded. The authors determined the median family incomes in the home counties of each casualty. Obviously, this introduces “rounding error” into the data, but it gives valuable information about whether the dead and wounded come from richer or poorer parts of the country. Here are the data for fatalities, with the median incomes adjusted to reflect dollars from the year 2000.

study

Clearly, as the U.S. has come to rely less on the draft and more on other forms of recruitment, what was once shared sacrifice has become more unequal. The results for non-fatal casualties are quite similar.

The authors attribute these results to two processes. The selection mechanism refers to differential selection into the armed forces of young people whose economic opportunities are limited, making them responsive to financial incentives the military offers. The sorting mechanism refers to the assignment of lower socioeconomic status soldiers to higher risk positions in the military, since they lack the education or job skills that would make them more useful away from the front lines.

It has been noted that soldiers injured in Iraq and Afghanistan have a higher survival rate than in previous wars, but return home with more serious injuries. This means that inequality continues long after the war. The authors note several studies showing that social class is an important factor affecting the health outcomes of veterans. Veterans from poorer counties return to communities with fewer resources to help in their readjustment, and their injuries place an additional financial burden on those communities.

Kriner and Shen did a national survey showing that only about half of the public is aware of these inequalities. They asked the following question of a national sample: “Thinking about the American soldiers who have died fighting in Iraq and Afghanistan, what parts of the United States do you think they are coming from?” The alternatives were more from richer communities, more from poorer communities, or equally from richer and poorer communities. Forty-five percent believed that the sacrifice was shared equally, while 44% realized that poorer communities carried a larger part of the burden.

Finally, they did two web-based experiments measuring how Americans react to correct information about military inequality. In one of these, half the respondents were told that many more of the Iraq and Afghanistan fatalities came from socioeconomically disadvantaged communities, while those in the control group were not given this information. Fifty-six percent of those in the control group said the invasion of Iraq was a mistake, compared to 62% given information about inequality of sacrifice. A similar result was obtained in a second study measuring willingness to engage in future wars. As the authors state, “The invisibility of casaulty inequality artificially inflates public support for war and the leaders who wage it.”

We know from attribution theory that if the public believes that people in the armed forces freely chose to serve out of personal motives such as patriotism, rather than being driven by environmental forces such as economic necessity, they are more likely to be held responsible for the outcomes of their decisions. Thus, the invisibility of military inequality may contribute to tendencies to blame these vicitims for their deaths or injuries, since they “freely chose” to enlist.

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On Obama’s Speech

Whose Opinion Matters?

Longevity, By the Book

Cognitive psychology, Health, Media,

Here’s good news for readers. Book reading, sometimes maligned as a sedentary behavior that may harm your health, actually increases your life expectancy. This  according to a study by Avni Bavishi and two colleagues from the Yale University School of Public Health. Since this is a correlational study, and correlation does not imply causation, it’s worth looking at their methods in some detail.

The data came from 3635 participants in the University of Michigan’s Health and Retirement Study, a nationally representative sample of adults over 50. They were interviewed every other year between 2001 and 2012, during which time 27.4% of them died. Participants were asked how many hours they spent during the past week reading books. They were asked the same question regarding periodicals (magazines and newspapers). The average time spent reading books was 3.92 hours a week; for periodicals, it was 6.10 hours. The correlation between book and periodical reading was modest (r = .23).

The authors predicted that the effect of book reading on life expectancy would be mediated by cognitive engagement; that is, reading books causes you to think about them, which in turn increases your longevity. Cognitive engagement was measured by performance on eight mental tasks, including immediate and delayed recall, backward counting and object naming.

In a correlational study such as this, it is important to control for alternative explanations that might cause both reading and longevity. Three variables predicted greater book reading in their sample. Women read more than men, people with more education read more, and so did higher income people. The statistical analysis held these three variables constant, plus an impressive list of others: age, race, visual acuity, marital status, job status, depression, self-rated health, and the presence of seven health problems (cancer, heart disease, diabetes, etc.). The analysis also controlled for cognitive engagement scores at the beginning of the study.

The results showed that book reading increased longevity, and that the more time you spend reading, the greater the effect. The effect of reading books was greater than that of reading magazines and newspapers. By the end of the study, 27% of the book readers had died, compared to 33% of non-readers. Comparing book readers and non-readers at the time at which 20% of the participants had died, the readers had a survival advantage of 23 months.

fig-1-survival-advantage-associated-with-book-reading-unadjusted-survival-curves-jpgAs predicted, the effect of book reading on longevity was mediated by cognitive engagement. (See this earlier post for an explanation of mediational analysis.) The researchers suggested two ways in which reading books increases cognitive engagement. First of all, book reading is deep reading, meaning that the greater length of books encourages readers to ask questions as they go along and to draw connections between various parts of the book. Secondly, book reading promotes empathy with the persons you are reading about, which might lead to greater social intelligence.

Of course, it’s impossible to rule out all possible alternative explanations for these results. I’m troubled by the lack of control for the participants’ social capital—the sum total of people’s involvement in community life-–which is known to be related to good health and life expectancy. However, the relationship between social capital and reading is unclear. You could argue that people who are involved in the community have less time to read. On the other hand, community involvement may encourage reading. People may read books in order to discuss them with other people, who in turn may suggest new books to read.

If these findings are valid, they raise several interesting questions. For example, would listening to audiobooks produce the same survival advantage? That is, is it the act of reading that is beneficial, or is it the content, regardless of how it is accessed? Of course, content must have some effect, since periodicals were less beneficial than books. Future researchers might want to look at the differences between fiction and non-fiction, or between genres or topics. Mysteries, for example, would seem to encourage deep reading.

As the authors note, the average American over 65 spends 4.4 hours per day watching television. In a 2012 study similar to this one, Peter Meunnig and his colleagues found that TV viewing reduced longevity. Specifically, each hour of daily viewing cost their participants about 1.2 years of life expectancy. The effect was mediated by greater unhappiness, reduced social capital and lower confidence in social institutions. If people could be persuaded to spend some of that 4.4 hours reading instead, they might be doing themselves a favor in more ways than one.

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Don’t Worry, Be Happy?

Bullshit