I’ve been working on the COVID-19 epidemic in rural U.S. counties, and have now posted a paper on SocArXiv, here: https://osf.io/preprints/socarxiv/pnqrd/. Here’s the abstract, then some figures below:
Having first reached epidemic proportions in coastal metropolitan areas, COVID-19 has spread around the country. Reported case rates vary across counties from zero to 125 per thousand population (around a state prison in the rural county of Trousdale, Tennessee). Overall, rural counties are underrepresented relative to their share of the population, but a growing proportion of all daily cases and deaths have been reported in rural counties. This analysis uses daily reports for all counties to present the trends and distribution of COVID-19 cases and deaths in rural counties, from late March to May 16, 2020. I describe the relationship between population density and case rates in rural and non-rural counties. Then I focus on noteworthy outbreaks linked to prisons, meat and poultry plants, and nursing homes, many of which are linked to high concentrations of Hispanic, American Indian, and Black populations. The growing epidemic in rural counties is apparently driven by outbreaks concentrated in these institutional settings, which are conducive to transmission. The impact of the epidemic in rural areas may be heightening due to their weaker health infrastructure and more vulnerable populations, especially due to age, socioeconomic status, and health conditions. As a result, the epidemic may contribute to the ongoing decline of health, economic, and social conditions in rural areas.
Here are COVID-19 cases in rural counties across the country. Note that the South, Mid-Atlantic, Michigan, and New England have the most (fewer in West and upper Midwest). When you look at cases per capita, you see the concentration in the South and isolated others.
COVID is still underrepresented in rural counties, but their share of the national burden is increasing, as they keep adding more than 2,000 cases and just under 100 deaths per day.
Transmission dynamics are different in rural counties. They show a weaker relationship between pop density and cases. This suggests to me that there are more idiosyncratic factors at work (prisons, meat plants, nursing homes), which are high concentrations of vulnerable people.
These are the rural outbreak cases I identified, for which I could find obvious epidemic centers in institutions: Prisons, meatpacking and poultry plants, and nursing homes. These 28 select counties account for 15% of the rural burden.
In addition to the institutional concentration, these outbreak cases also show distinct overrepresentation of Hispanic, American Indian, and Black populations. Here are some of the outbreak cases plotted against minority concentrations.
And here’s a table of those selected cases:
Lots more to be done, obviously. It’s a strong limitation to be restricted to case and death counts at the county level. Someone could go get lists of prisons and meatpacking plants and nursing homes and run them through this, etc. But I wanted to raise this issue substantively. By posting the paper on SocArXiv, without peer review, I’m offering it up for comment and criticism. Also, I’m sharing the code (which links to the data, all public): https://osf.io/wd2n6/. Messy but usable.
A related thought on writing a paper about COVID19 right now: The lit review is daunting. There are thousands of papers, most on preprint servers. Is this bad? No. I use various tools to decide what’s reliable to learn from. If it’s outside my area, I’m more likely to rely on peer-reviewed journals, or those that are widely citied or reported. But the vast quantity available still helps me see what people are working on, what terms, and types of data they use. I learned a tremendous amount. Much respect to the thousands of researchers who are doing what they can to respond to this global crisis.
This is a sociological reflection about life history. It’s about me because I’m the person I know best, and I have permission to reveal details of my life.
I was born in August 1967, making me 50 years old this month. But life experience is better thought of in cohort terms. Where was I and what was I doing, with whom, at different ages and stages of development? Today I’m thinking of these intersections of biography and history in terms of technology, music, and health.
Tech
We had a TV in my household growing up, it just didn’t have a remote control or cable service, or color. We had two phones, they just shared one line and were connected by wires. (After I moved out my parents got an answering machine.) When my mother, a neurobiologist, was working on her dissertation (completed when I was 10) in the study my parents shared, she used a programmable calculator and graph paper to plot the results of her experiments with pencil. My father, a topologist, drew his figures with colored pencils (I can’t describe the sound of his pencils drawing across the hollow wooden door he used for a desktop, but I can still hear it, along with the buzz of his fluorescent lamp). A couple of my friends had personal computers by the time I started high school, in 1981 (one TRS-80 and one Apple II), but I brought a portable electric typewriter to college in 1988. I first got a cell phone in graduate school, after I was married.
The first portable electronic device I had (besides a flashlight) was a Sony Walkman, in about 1983, when I was 16. At the time nothing mattered to me more than music. Music consumed a large part of my imagination and formed the scaffolding of most socializing. The logistics of finding out about, finding, buying, copying, and listening to music played an outsized role in my daily life. From about 1980 to 1984, most of the money I made at my bagel store job went to stereo equipment, concerts, records, blank tapes for making copies, and eventually drums (as well video games). I subscribed to magazines (Rolling Stone, Modern Drummer), hitchhiked across town to visit the record store, pooled money with friends to buy blank tapes, spent hours copying records and labeling tapes with my friends, and made road trips to concerts across upstate New York (clockwise from Ithaca: Geneva, Buffalo, Rochester, Syracuse, Saratoga, Binghamton, New York City, Elmira).
As I’m writing this, I thought, “I haven’t listened to Long Distance Voyager in ages,” tapped it into Apple Music on my phone, and started streaming it on my Sonos player in a matter of seconds, which doesn’t impress you at all – but the sensory memories it invokes are shockingly vivid (like an acid flashback, honestly) – and having the power to evoke that so easily is awesome, in the old sense of that word.
Some of us worked at the Cornell student radio station (I eventually spent a while in the news department), whose album-oriented rock playlist heavily influenced the categories and relative status of the music we listened to. The radio station also determined what music stayed in the rotation – what eventually became known by the then-nonexistent term classic rock – and what would be allowed to slip away; it was history written in real time.
It’s like 1967, in 1981
You could think of the birth cohort of 1967 as the people who entered the world at the time of “race riots,” the Vietnam and Six Day wars, the Summer of Love, the 25th Amendment (you’re welcome!), Monterey Pop, Sgt. Peppper’s, and Loving v. Virginia. Or you could flip through Wikipedia’s list of celebrities born in 1967 to see how impressive (and good looking) we became, people like Benicio del Toro, Kurt Cobain, Paul Giamatti, Nicole Kidman, Pamela Anderson, Will Ferrell, Vin Diesel, Phillip Seymour Hoffman, Matt LeBlanc, Michael Johnson, Liev Schreiber, Julia Roberts, Jimmy Kimmel, Mark Ruffalo, and Jamie Foxx.
But maybe it makes more sense to think of us as the people who were 14 when John Lennon made his great commercial comeback, with an album no one took seriously – only after being murdered. The experiences at age 14, in 1981, define me more than what was happening at the moment of my birth. Those 1981 hits from album-oriented rock mean more to me than the Doors’ debut in 1967. My sense of the world changing in that year was acute – because it was 1981, or because I was 14? In music, old artists like the Moody Blues and the Rolling Stones released albums that seemed like complete departures, and more solo albums – by people like Stevie Nicks and Phil Collins – felt like stakes through the heart of history itself (I liked them, actually, but they were also impostors).
One moment that felt at the time like a historical turning point was the weekend of September 19, 1981. My family went to Washington for the Solidarity Day rally, at which a quarter million people demonstrated against President Reagan and for organized labor, a protest fueled by the new president’s firing of the PATCO air traffic controllers the previous month (and inspired by the Solidarity union in Poland, too). Besides hating Reagan, we also feared a nuclear war that would end humanity – I mean really feared it, real nightmare fear.
A piece of radio news copy I wrote and read at WVBR, probably 1983. The slashes are where I’m going to take a breath. “Local AQX” is the name of the tape cartridge with the sound bite (“actuality”) from Alfred Kahn, and “OQ:worse” means that’s the last word coming out of the clip.On the same day as Solidarity, while we were in D.C., was Simon and Garfunkel’s Concert in Central Park. They were all of 40 (literally my mother’s age), tired old people with a glorious past (I’m sure I ignored the rave reviews). As I look back on these events – Reagan, the Cold War, sell-out music – in the context of what I thought of as my emerging adulthood, they seemed to herald a dark future, in which loss of freedom and individuality, the rise of the machines, and runaway capitalism was reflected in the decline of rock music. (I am now embarrassed to admit that I even hated disco for a while, maybe even while I listened 20 times, dumbstruck, to an Earth, Wind, and Fire album I checked out of the library.)
I don’t want to overdramatize the drama of 1981; I was basically fine. I came out with a penchant for Camus, a taste for art rock, and leftism, which were hardly catastrophic traits. Still, those events, and their timing, probably left a mark of cynicism, sometimes nihilism, which I carry today.
About 1984, with Daniel Besman (who later died) in Ithaca. Photo by Linda Galgani.Data aside
Maybe one reason 1981 felt like a musical watershed to me is because it really was, because pop music just got worse in the 1980s compared to the 1970s. To test (I mean prove, really) that hypothesis, I fielded a little survey (more like a game) that asked people to rate the same artists in both decades. I chose 58 artists by flipping through album charts from 1975-1984 and finding those that charted in both decades; then I added some suggestions from early respondents. To keep the task from being too onerous, as it required scoring bands twice from 1 (terrible) to 5 (great), once for each period, and some people found it difficult, I set the survey to serve each person just 10 artists at random (a couple of people did it more than once). The participants were 3/4 men, 3/4 over 40, and 3/4 White and US-born; found on Facebook, Twitter, and Reddit. The average artist was rated 11 times in each period (range 5 to 19). (Feel free to play along or share this link; I’ll update it if more come in.)
The results look very bad for the 1980s. The average change was a drop of .59, and only three acts showed noticeable improvement: Pat Benatar, Michael Jackson, and Prince (and maybe Talking Heads and the lowly Bryan Adams). Here is the full set (click to enlarge):
Technology and survival
I don’t think I would have, at age 14, given much weight to the idea that my life would repeatedly be saved by medical technology, but now that seems like business as usual, to me anyway. I guess as long as there’s been technology there have been people who owe their lives to it (and of course we’re more likely to hear from them than from those who didn’t make it). But the details are cohort-specific. These days we’re a diverse club of privileged people, our conditions, or their remnants, often hidden like pebbles wedged under the balls of our aging feet, gnawing reminders of our bodily precarity.
Family lore says I was born with a bad case of jaundice, probably something like Rh incompatibility, and needed a blood transfusion. I don’t know what would have happened without it, but I’m probably better off now for that intervention.
Sometime in my late teens I reported to a doctor that I had periodic episodes of racing heartbeat. After a brief exam I was sent home with no tests, but advised to keep an eye on it; maybe mitral valve prolapse, he said. I usually controlled it by holding my breath and exhaling slowly. We found out later, in 2001 – after several hours in the emergency room at about 200 very irregular beats per minute – that it was actually a potentially much more serious condition called Wolff-Parkinson-White syndrome. The condition is easily diagnosed nowadays, as software can identify the tell-tale “delta wave” on the ECG, and the condition is listed right there in the test report.
Two lucky things combined: (a) I wasn’t diagnosed properly in the 1980s (which might have led to open-heart surgery or a lifetime of unpleasant medication), and; (b) I didn’t drop dead before it was finally diagnosed in 2001. They fixed it with a low-risk radiofrequency ablation, just running a few wires up through my arteries to my heart, where they lit up to burn off the errant nerve ending, all done while I was almost awake, watching the action on an x-ray image and – I believed, anyway – feeling the warmth spread through my chest as the doctor typed commands into his keyboard.
Diverticulitis is also pretty easily diagnosed nowadays, once they fire up the CT scanner, and usually successfully treated by antibiotics, though sometimes you have to remove some of your colon. Just one of those things people don’t die from as much anymore (though it’s also more common than it used to be, maybe just because we don’t die from other things as much). I didn’t feel like much like surviving when it was happening, but I suppose I might have made it even without the antibiotics. Who knows?
More interesting was the case of follicular lymphoma I discovered at age 40 (I wrote about it here). There is a reasonable chance I’d still be alive today if we had never biopsied the swollen lymph node in my thigh, but that’s hard to say, too. Median survival from diagnosis is supposed to be 10 years, but I had a good case (a rare stage I), and with all the great new treatments coming online the confidence in that estimate is fuzzy. Anyway, since the cancer was never identified anywhere else in my body, the treatment was just removing the lymph node and a little radiation (18 visits to the radiation place, a couple of tattoos for aiming the beams, all in the summer with no work days off). We have no way (with current technology) to tell if I still “have” it or whether it will come “back,” so I can’t yet say technology saved my life from this one (though if I’m lucky enough to die from something else — and only then — feel free to call me a cancer “survivor”).
It turns out that all this life saving also bequeaths a profound uncertainty, which leaves one with an uneasy feeling and a craving for antianxiety medication. I guess you have to learn to love the uncertainty, or die trying. That’s why I cherish this piece of a note from my oncologist, written as he sent me out of the office with instructions never to return: “Your chance for cure is reasonable. ‘Pretest probability’ is low.”
From my oncologist’s farewell note.Time travel
It’s hard to imagine what I would have thought if someone told my 14-year-old self this story: One day you will, during a Skype call from a hotel room in Hangzhou, where you are vacationing with your wife and two daughters from China, decide to sue President Donald Trump for blocking you on Twitter. On the other hand, I don’t know if it’s possible to know today what it was really like to be me at age 14.
In the classic time travel knot, a visitor from the future changes the future by going back and changing the past. The cool thing about mucking around with your narrative like I’m doing in this essay (as Walidah Imarisha has said) is that it by altering our perception of the past, we do change the future. So time travel is real. Just like it’s funny to think of my 14-year-old self having thoughts about the past, I’m sure my 14-year-old self would have laughed at the idea that my 50-year-old self would think about the future. But I do!
You can skip down to the new data and analysis — or go straight to my new working paper — if you don’t need the preamble diatribe.
I have complained recently about the edict from above that poor (implying Black) women should delay their births until they are “financially ready” — especially in light of the evidence on their odds of marriage during the childbearing years. And then we saw what seemed like a friendly suggestion that poor women use more birth control lead to some nut on Fox News telling Rebecca Vallas, who spoke up for raising the minimum wage:
A family of three is not supposed to be living on the minimum wage. If you’re making minimum wage you shouldn’t be having children and trying to raise a family on it.
As if minimum wage is just a phase poor people can expect to pass through only briefly, on their way to middle class stability — provided they don’t piss it away by having children they can’t “afford.” This was a wonderful illustration of the point Arline Geronimus makes in this excellent (paywalled) paper from 2003, aptly titled, “Damned if you do: culture, identity, privilege, and teenage childbearing in the United States.” Geronimus has been pointing out for several decades that Black women face increased health risks and other problems when they delay their childbearing, even as White women have the best health outcomes when they delay theirs. This has been termed “the weathering hypothesis.” In that 2003 paper, she explores the cultural dynamic of dominance and subordination that this debate over birth timing entails. Here’s a free passage (where dominant is White and marginal is Black):
In sum, a danger of social inequality is that dominant groups will be motivated to promote their own cultural goals, at least in part, by holding aspects of the behavior of specific marginal groups in public contempt. This is especially true when this behavior is viewed as antithetical or threatening to social control messages aimed at the youth in the dominant group. An acknowledgment that teen childbearing might have benefits for some groups undermines social control messages intended to convince dominant group youth to postpone childbearing by extolling the absolute hazards of early fertility. Moreover, to acknowledge cultural variability in the costs and consequences of early childbearing requires public admission of structural inequality and the benefits members of dominant groups derive from socially excluding others. One cannot explain why the benefits of early childbearing may outweigh the costs for many African Americans without noting that African American youth do not enjoy the same access to advanced education or career security enjoyed by most Americans; that their parents are compelled to be more focused on imperatives of survival and subsistence than on encouraging their children to engage in extended and expensive preparation for the competitive labor market; indeed, that African Americans cannot even take their health or longevity for granted through middle age (Geronimus, 1994; Geronimus et al., 2001). And one cannot explain why these social and health inequalities exist without recognizing that structural barriers to full participation in American society impede the success of marginalized groups (Dressler, 1995; Geronimus, 2000; James, 1994). To acknowledge these circumstances would be to contradict the broader societal ethic that denies the existence of social inequality and is conflicted about cultural diversity. And it would undermine the ability the dominant group currently enjoys to interpret their privilege as earned, the just reward for their exercise of personal responsibility.
But the failure to acknowledge these circumstances results in a disastrous misunderstanding. As a society, we have become caught in an endless loop that rationalizes, perhaps guarantees, the continued marginalization of urban African Americans. In the case at hand, by misunderstanding the motivation, context, and outcomes of early childbearing among African Americans, and by implementing social welfare and public health policies that follow from this misunderstanding, the dominant European American culture reinforces material hardship for and stigmatization of African Americans. Faced with these hardships, early fertility timing will continue to be adaptive practice for African Americans. And, reliably, these fertility and related family “behaviors” will again be unfairly derided as antisocial. And so on.
Whoever said demography isn’t theoretical and political?
A simple illustration
In Geronimus’s classic weathering work, she documented disparities in healthy life expectancy, which is the expectation of healthy, or disability-free, years of life ahead. When a poor 18-year-old Black woman considers whether or not to have a child, she might take into account her expectation of healthy life expectancy — how long can she count on remaining healthy and active? — as well as, and this is crucial, that of her 40-year-old mother, who is expected to help out with the child-rearing (they’re poor, remember). Here’s a simple illustration: the percentage of Black and White mothers (women living in their own households, with their own children) who have a work-limiting disability, by age and education:
Not too many disabilities at age 20, but race and class kick in hard over these parenting years, till by their 50s one-in-five Black mothers with high school education or less has a disability, compared with one-in-twenty White mothers who’ve gone on to more education. That looming health trajectory is enough — Geronimus reasonably argues — to affect women’s decisions on whether or not to have a child (or go through with an accidental pregnancy). But for the group (say, Whites who aren’t that poor) who have a reasonable chance of getting higher education, and making it through their intensive parenting years disability-free, the economic consequence of an early birth weighs much more heavily.
Some new analysis
As I was thinking about all this the other day, I went to check on the latest infant mortality statistics, since that’s where Geronimus started this thread — with the observation that White women’s chance of a baby dying decline with age, while Black women’s don’t. And I noticed there is a new Period Linked Birth-Infant Death Data File for 2013. This is a giant database of all the births — with information from their birth certificates — linked to all the infant deaths from the same year. These records have been used for analyzing infant mortality dozens of times, including in pursuit of the weathering hypothesis, but I didn’t see any new analyses of the 2013 files, except the basic report the National Center for Health Statistics put out. The outcome is now a working paper at the Maryland Population Research Center.
The gist of the result is, to me, kind of shocking. Once you control for some basic health, birth, and socioeconomic conditions (plurality, parity, prenatal care, education, health insurance type, and smoking during pregnancy), the risk of infant mortality for Black mothers increases linearly with age: the longer they wait, the greater the risk. For White women the risk follows the familiar (and culturally lionized) U-shape, with the lowest risk in the early 30s. Mexican women (the largest Hispanic group I could include) are somewhere in between, with a sharp rise in risk at older ages, but no real advantage to waiting from 18 to 30.
I’ll show you (and these rates will differ a little from official rates for various technical reasons). First, the unadjusted infant mortality rates by maternal age:
Infant Death Rates, by Maternal Age: White, Black, and Mexican Mothers, U.S., 2013. Infant death rates per 1,000 live births for non-Hispanic white (N = 1,925,847), non-Hispanic black (N = 533,341), and Mexican origin (N = 501,390) mothers. Data source: 2013 Period Linked Birth/Infant Death Public Use File, Centers for Disease Control.These raw rates show the big health benefit to delay for White women, a smaller benefit for Mexican mothers, and no benefit for Black mothers. But when you control for those factors I mentioned, the infant mortality rates for young Black and Mexican mothers are lower — those are the mothers with low education and bad health care. Controlling for those things sort of simulates the decisions women face: given these things about me, what is the health effect of delay? (Of course, delaying could contribute to improving things, which is also part of the calculus.) Here are the adjusted age patterns:
Adjusted Probability of Infant Death, by Maternal Age: White, Black, and Mexican Mothers, U.S., 2013. Predicted probabilities of infant death generated by Stata margins command, adjusted for plurality, birth order, maternal education, prenatal care, payment source, and cigarette smoking during pregnancy; models estimated separately for white (A), black (B), and Mexican (C) mothers (see Tab. 1). Error bars are 95% confidence intervals. (A separate test showed the linear trend for Black women is statistically significant.) Data source: 2013 Period Linked Birth/Infant Death Public Use File, Centers for Disease Control.My jaw kind of dropped. Infant mortality is mostly a measure of mothers’ health. Early childbearing looks a lot crazier for White women than for Black and Mexican women, and you can see why the messaging around delaying till your “ready” seems so out of tune to the less privileged (and that really means race more than class, in this case). Why wait? If women knew they had higher education, a good job, and decent health care awaiting them throughout their childbearing years, I think the decision tree would look a lot different.
Of course, I have often said that delayed marriage is good for women. And delayed childbearing would be — should be — too, as long as it doesn’t put the health of the mother and her children at risk (and squander the healthy rearing years of their grandparents).
Please check out the working paper for more background and references, and details about my analysis.
Disability is a very broad concept, representing a wide array of conditions that are not easily captured in a simple demographic survey. However, disabilities are very prevalent, especially in an aging society, and the people who experience disabilities differ in important ways from those who do not. Previously I reported — in a preliminary way — that people with disabilities are much more likely to divorce than those without. Here I present some numbers on marriage rates.
This isn’t the kind of thorough, probing analysis this subject requires. But I have two reasons to do it now. First is that I hope to motivate other people to pursue this issue in greater depth. And second, I want to highlight the importance of the data I’m using — the American Community Survey (ACS) — because it might be not available for much longer. These questions have been slated for demolition by the U.S. Census Bureau on cost-saving grounds. I put details about this issue — and how to register your opinion with the federal government — at the end of the post.
Is this person deaf or does he/she have serious difficulty hearing? (Hearing)
Is this person blind or does he/she have serious difficulty seeing even when wearing glasses? (Vision)
Because of a physical, mental, or emotional condition, does this person have serious difficulty concentrating, remembering, or making decisions? (Cognitive)
Does this person have serious difficulty walking or climbing stairs? (Ambulatory)
Does this person have difficulty dressing or bathing? (Independent living)
These aren’t perfect questions, but they cover a lot of ground, and the ACS — which involves about 3 million households — can’t get into too much detail.
One great thing about having these questions on the giant ACS is you can use the data to get all the way down to the local level, or into small race/ethnic groups. And with the marital events questions, you can combine disability information and marriage information.
Marriage rates*
Using marital events (did you get married in the last year), marital history (how many times have you been married), detailed race and ethnicity breakdowns, and the disability questions above, I produced the following figure. This uses the combined 2008-2012 ACS data because these are small groups, but even with five years of data these groups get quite small. There are about 90,000 non-Hispanic Whites with a cognitive disability in my sample, but only 356 people who are both White and American Indian with a hearing disability (the smallest group I included). This sample is people ages 18-49 who have never been married (or just got married).
The overall marriage rate for never-married people ages 18-49 is 71.8 per 1,000. For people with disabilities it’s 41.1 (shown by the blue line). So that’s much lower than for the general population. But there is a very wide variation across these groups, from 15.5 per thousand for Blacks with disabilities in independent living all the way up to above the national average for Whites and White/American Indians with hearing disabilities. (For every condition, Blacks with disabilities have the lowest marriage rates.)
I don’t draw any conclusions here, except that this is an important subject and I hope more people will study it. Also, we need data like this.
In previous posts demonstrating the value of this data source, I wrote about:
Whether you are a researcher or some other member of the concerned public, I hope you will consider dropping the government a line about this before the end of the year.
Direct all written comments to Jennifer Jessup, Departmental Paperwork Clearance Officer, Department of Commerce, Room 6616, 14th and Constitution Avenue NW., Washington, DC 20230 (or via the Internet at jjessup@doc.gov).
Comments will be accepted until December 30.
* Erratum: In the original post I described this as a “first-marriage rate.” However, in checking over the code I used, I see that I used all marriages in the nominator, and never-married people in the denominator. Therefore, this is more accurately described as “marriages per 1000 never-married people.” It would have made more sense to just put first marriages in the numerator. For reference, the first-marriage rates were 54.0 for the total never-married 18-49 population, and 24.5 for those with a disability. I regret the error.
The good news is that U.S. life expectancy is at a record high, 78.8 as of 2012.
What about life disparity — the inequality in life expectancy? With the economic crisis and rise in income inequality, it would be great to know. However, the National Center for Health Statistics hasn’t released detailed life tables with data more recent than 2008, so I can’t yet update the data for the analysis I did last year, so here it is reposted instead:
In 2008 the life expectancy at birth in the U.S. was 78.1. That means that if a group children born in 2008 lived every year of their lives exposed to the risks of death observed in 2008, their average lifespan would be 78.1 years. But those who made it to age 60 would live an average of 22.7 more years, for a total of 82.7. And those who live to age 99 would live an average of 2.4 more years, for an average of 101.4.
So “life expectancy” as commonly used is not a prediction of how long today’s babies will live — since we hope the future is better than living 2008 over and over — and it’s not a prediction of how long your elderly loved ones will live.
Life disparity
Life expectancy — for any age — is a measure of central tendency: the average number of years of life remaining. And so there is a dispersion around that mean. That dispersion is inequality. A very nice article in the open-access journal BMJ Open, by James Vaupel, Zhen Zhang and Alyson A van Raalte, describes the measure of life disparity. It’s complicated, but a neat tool.
Life disparity is the average number of years people are expected to live when they die. For example, in the U.S. in 2008 an infant who died on the first day of life died 78.1 years early. And a 78-year-old who died, counterintuitively, died 10 years early (since the life expectancy at 78 is 10). To understand what this measure means, consider that if everyone died at exactly 78.1 years of age, life expectancy would be unchanged but life disparity would be 0. On the other hand, the greatest life disparity would occur if all early occurred at age 0.
Life disparity and life expectancy usually go together. That’s because reducing early deaths has the biggest effect on both measures. Here is the cool figure from that paper:
The association between life disparity in a specific year and life expectancy in that year for males in 40 countries and regions, 1840–2009. The black triangle represents the USA in 2007; the USA had a male life expectancy 3.78 years lower than the international record in 2007 and a life disparity 2.8 years greater. The brown points denote years after 1950, the orange points 1900–1949 and the yellow points 1840–1900. The light blue triangles represent countries with the lowest life disparity but with a life expectancy below the international record in the specific year; the dark blue triangles indicate the life expectancy leaders in a given year, with life disparities greater than the most egalitarian country in that year. The black point at (0,0) marks countries with the lowest life disparity and the highest life expectancy. During the 170 years from 1840 to 2009, 89 holders of record life expectancy also enjoyed the lowest life disparity.
Countries at the bottom left (0,0) have both the world’s highest life expectancy and the lowest life disparity in the world for that year, which occurred 89 times over 170 years. Countries below the diagonal have relatively low life disparity given their life expectancy; those above the diagonal (like the U.S.) have higher-than-expected life disparity for their level of life expectancy. In our case that reflects the fact that we do a pretty good job keeping old people alive, but let too many young people die.
U.S. improvement
The good news is that life expectancy is increasing in the U.S. (and most other places), and that the inequality between Blacks and Whites is getting smaller, as reported by the National Center for Health Statistics. That is, the Black-White inequality in average expectation of life at birth has shrunk.
The mixed news is that life disparity is much higher for Blacks than Whites — but that gap is falling as well. Here are those numbers for 1998 and 2008 (I did the life disparity calculations from this and this, and will happily share the spreadsheet). Click to enlarge:
So Black deaths are more dispersed than White deaths: 14 and 13 for males and females, compared with 12 and 11. For comparison, the Swedish female life disparity is 9. What does a higher disparity mean? Generally, a larger share of early deaths. That’s why the race gap in life expectancy at birth is greater than the race gap in life expectancy at older ages — average 65-year-old Whites and Blacks have more similar life expectancies than do infants.
Why is life disparity more interesting than life expectancy alone, and how does this help explain Black-White inequality in the U.S.? For one thing, high life disparity indicates either relatively unhealthy or dangerous living conditions at younger ages. So it’s partly a measure of the quality of life. Vaupel et al. add:
Reducing early-life disparities helps people plan their less-uncertain lifetimes. A higher likelihood of surviving to old age makes savings more worthwhile, raises the value of individual and public investments in education and training, and increases the prevalence of long-term relationships. Hence, healthy longevity is a prime driver of a country’s wealth and well-being. While some degree of income inequality might create incentives to work harder, premature deaths bring little benefit and impose major costs. Moreover, equity in the capability to maintain good health is central to any larger concept of societal justice.
I think what they say about differences between countries would apply to differences between groups within a society as well.
I wrote a few years ago about the surprisingly low infant mortality rates among immigrants, especially Mexican immigrants, given their relative socioeconomic status. As poor as they other, in other words, we would expect higher infant mortality rates than they have. This has been called the epidemiological paradox. Here is an update, which includes some text from the previous post.
In almost every race/ethnic group, immigrants are healthier.* Here’s the pattern for infant mortality, now updated with 2010 infant mortality rates from federal vital statistics records (click to enlarge).
For Latinos in particular, their health is surprisingly good given their economic conditions. Robert Hummer and colleagues, in a 2007 article, offered a succinct description:
…the relatively low levels of education, income, and health insurance coverage among Hispanics compared with non-Hispanic whites is thought to place the former at higher risk for negative health outcomes. However, it is well documented that some Hispanic groups exhibit similar observed death rates compared with the non-Hispanic white population and much lower death rates than the non-Hispanic black population, whom they closely resemble with respect to socioeconomic characteristics. The greatest enigma is exhibited by the Mexican-origin population of the United States. This Hispanic subgroup is characterized by low educational attainment; low health insurance coverage rates; mortality rates similar to non-Hispanic whites; and much more favorable mortality rates than those of non-Hispanic blacks across most of the life course.
In a 2013 revisiting of the paradox, Daniel Powers confirms the basic pattern, but adds an important wrinkle for Mexican mothers: the foreign-born advantage disappears for older mothers. Thus, children born to older Mexican immigrants have similar risks as those who mothers are born in the U.S. He concludes, in part:
Given the association between infant survival and maternal health, differential infant survival within the Mexican-origin population suggests that longer exposure to social conditions in the U.S. undermines the health of mothers who, in general, seem to have more favorable health endowments than their non-Hispanic white counterparts as evidenced by the relatively lower rates of infant mortality at younger ages.
Immigrants are often healthier than the average people in the countries they came from, which explains some of the paradox. However, our ability to accurately assess the relative health of immigrants versus the populations they left behind is limited by available data. Further, in the case of Mexico, the situation is complicated by cyclical movements of immigration and emigration. In a recent paper, Georgiana Bostean reviews this problem, and compares the health of immigrants, non-migrants, and return migrants to Mexico. And — It’s complicated. She concludes:
…there is no simple explanation for Latinos’ perplexing health outcomes, such as simply that healthier people migrate. Rather, migrants are positively selected in some health aspects, negatively selected in others, and in yet other health outcomes, there is no selection effect. In sum, selective migration plays a role in explaining some of U.S. Latinos’ health outcomes, but is not the only explanation and does not account for the Paradox.
*Because Puerto Rico is part of the U.S. (albeit not a free part), people born in Puerto Rico who move to the states are not immigrants, just migrants. In the figure I used the terms “US Born” and “Foreign born,” but this is just shorthand, and not strictly accurate for Puerto Ricans.
We don’t prohibit all dangerous behavior, or even behavior that endangers others, including people’s own children.
Question: Is the limit of acceptable risks to which we may subject our own children determined by absolute risks or relative risks?
Case for consideration: Home birth.
Let’s say planning to have your birth at home doubles the risk of some serious complications. Does that mean no one should do it, or be allowed to do it? Other policy options: do nothing, discourage home birth, promote it, regulate it, or educate people about the risks and let them do what they want.
Here is the most recent result from a large study reported on the New York TimesWell blog, which looks to me like it was done properly, from the American Journal of Obstetrics & Gynecology. Researchers analyzed about 2 million birth records of live, term (37-43 weeks), singleton, vertex (head-first) births, including 12,000 planned home births (that is, not including those where the home birth was accidental). They also excluded those at freestanding birthing centers.
The planned-home birth mothers were generally relatively privileged, more likely to be White and non-Hispanic, college-educated, married, and not having their first child. However, they were also more likely to be older than 34 and to have waited to see a doctor until their second trimester.
On three measures of birth outcomes, the home-birth infants were more likely to have bad results: low Apgar scores and neonatal seizures. Apgar is the standard for measuring an infant’s wellbeing within 5 minutes of birth, assessing breathing, heart rate, muscle tone, reflex irritability and circulation (blue skin). With up to 2 points on each indicator, the maximum score is 10, but 7 or more is considered normal and under 4 is serious trouble. Low scores are usually caused by some difficulty in the birth process, and babies with low scores usually require medical attention. The score is a good indicator of risk for infant mortality.
These are the unadjusted low-Apgar and seizure rates:
These are big differences considering the home birth mothers are usually healthier. In the subsequent analysis, the researchers controlled for parity, maternal age, race/ethnicity, education, gestational age at delivery, number of prenatal care visits, cigarette smoking during pregnancy, and medical/obstetric conditions. With those controls, the odds ratios were 1.9 for Apgar<4, 2.4 for Apgar<7, and 3.1 for seizures. Pretty big effects.
Two years ago I wrote about a British study that found much higher rates of birth complications among home births when the mother was delivering her first child. This is my chart for their findings:
Again, those were the unadjusted rates, but the disparities held with a variety of important controls.
These birth complication rates are low by world historical standards. In New Delhi, India, in the 1980s 10% of 5-minute-olds had Apgar scores of 3 or less. So that’s many-times worse than American home births. On the other hand, a number of big European countries (Germany, France, Italy) have Apgar<7 rates of 1% or less, which is much better.
A large proportional increase on a low risk for a high-consequence event (like nuclear meltdown) can be very serious. A large absolute risk of a common low-consequence event (like having a hangover) can be completely acceptable. Birth complications are somewhere in between. But where?
Seems like a good topic for discussion, and having some real numbers helps. Let me know what you decide.
My Twitter feed lit up yesterday with this story about how life expectancy is falling for White women who have not finished high school. The story was called, “What’s Killing Poor White Women?“, by Monica Potts.
I have complete sympathy for poor people with health problems and high mortality rates. Things are killing them, and that’s bad. They should have better education, better jobs, better health care and more money.
White women without high school degrees have lost five years of life expectancy. Something must be getting worse. But I don’t quite think so. I could be wrong. But I think that as the category White women without high school degrees shrinks, it is the healthier people who are leaving (or never entering) the group. As a result, the group’s average health is declining.
The first thing to realize is that, according to the Census Bureau [spreadsheet link], 95% of non-Hispanic White women ages 25-29 have completed four years of high school or more. So we’re talking about a very (negatively) select population. And it’s getting more select – it was 92% 20 years ago. (Potts’s story revolves around a woman who died at 38.*)
The article doesn’t give any numbers to show that more people are dying, just that the life expectancy of the group has fallen. If this were a group, like race or gender, whose membership doesn’t change much over time, that would be enough to indicate their health status was getting worse. But an education group isn’t like that. It’s membership changes over time. Neither of the two academic articles Potts cites seem to consider this possibility (here and here).
One take
Here’s a try at it. Since 1996, the Current Population Survey has asked an excellent health status question, asking people to rate their own health as excellent, very good, good, fair, or poor. Let’s treat those whose health is “poor” as the group driving the mortality trend (which seems to fit the narrative in the story).
Here is the scary trend: A sharp rise in the proportion of non-Hispanic White women high school dropouts, ages 20-29, who rate their health as “poor.” (All the figures use three-year averages.)
That looks terrible, and it is, of course. But look at the size of the total group (all health statuses) over the same period:
So, the group has shrunk by about 18%, from about 850,000 to less than 700,000. And here is how the group’s population has changed according to health status, using the two endpoints of the trend, 1996-98 and 2010-12:
So, there has been, in effect, no change in the number of non-Hispanic White women high school dropouts ages 20-29 in poor health, for the last decade and a half (the numbers shown are population estimates based on a sample size of only a few hundred women in this category per year, so I discount small shifts). In contrast, there has been a decline of those in good health. Result: the average health of the group has declined, but there are not more sick women.
That’s good news, because in Potts’s telling their problems are very serious, and something should be done about it.
*I (or you) could redo this to include more ages. I used young people because, if they have high mortality rates, they’re going to disappear from the sample at relatively young ages and make the group look healthier.
This is a serious post about life expectancy and inequality. But first a short rant.
Quick: Life expectancy in the U.S. is 78.7 Your parents are 85. How much longer are they expected to live? If you were worried about how much time you had left to spend with them, and you asked the helpful site seeyourfolks.com, you would get this:
This app, and the Slate piece about it, managed to combined two of my pet peeves: the understandable difficulty with understanding life expectancy, and the inexcusable use of second-person reporting on social science findings, which does more to discredit than to disseminate important research.
The error here (apart from “you”) is the common notion that “life expectancy” is the average age at which people of any current age can expect to die. If we were more rigorous about using the phrase “life expectancy at birth” it would be easier to grasp.
In 2008 the life expectancy at birth in the U.S. was 78.1. That means that if a group children born in 2008 lived every year of their lives exposed to the risks of death observed in 2008, their average lifespan would be 78.1 years. But those who made it to age 60 would live an average of 22.7 more years, for a total of 82.7. And those who live to age 99 would live an average of 2.4 more years, for an average of 101.4.
So “life expectancy” as commonly used is not a prediction of how long today’s babies will live — since we hope the future is better than living 2008 over and over — and it’s not a prediction of how long your elderly loved ones will live.
Life disparity
Life expectancy — for any age — is a measure of central tendency: the average number of years of life remaining. And so there is a dispersion around that mean. That dispersion is inequality. A very nice article in the open-access journal BMJ Open, by James Vaupel, Zhen Zhang and Alyson A van Raalte, describes the measure of life disparity. It’s complicated, but a neat tool.
Life disparity is the average number of years people are expected to live when they die. For example, in the U.S. in 2008 an infant who died on the first day of life died 78.1 years early. And a 78-year-old who died, counterintuitively, died 10 years early (since the life expectancy at 78 is 10). To understand what this measure means, consider that if everyone died at exactly 78.1 years of age, life expectancy would be unchanged but life disparity would be 0. On the other hand, the greatest life disparity would occur if all early occurred at age 0.
Life disparity and life expectancy usually go together. That’s because reducing early deaths has the biggest effect on both measures. Here is the cool figure from that paper:
The association between life disparity in a specific year and life expectancy in that year for males in 40 countries and regions, 1840–2009. The black triangle represents the USA in 2007; the USA had a male life expectancy 3.78 years lower than the international record in 2007 and a life disparity 2.8 years greater. The brown points denote years after 1950, the orange points 1900–1949 and the yellow points 1840–1900. The light blue triangles represent countries with the lowest life disparity but with a life expectancy below the international record in the specific year; the dark blue triangles indicate the life expectancy leaders in a given year, with life disparities greater than the most egalitarian country in that year. The black point at (0,0) marks countries with the lowest life disparity and the highest life expectancy. During the 170 years from 1840 to 2009, 89 holders of record life expectancy also enjoyed the lowest life disparity.
Countries at the bottom left (0,0) have both the world’s highest life expectancy and the lowest life disparity in the world for that year, which occurred 89 times over 170 years. Countries below the diagonal have relatively low life disparity given their life expectancy; those above the diagonal (like the U.S.) have higher-than-expected life disparity for their level of life expectancy. In our case that reflects the fact that we do a pretty good job keeping old people alive, but let too many young people die.
U.S. improvement
The good news is that life expectancy is increasing in the U.S. (and most other places), and that the inequality between Blacks and Whites is getting smaller, as reported by the National Center for Health Statistics. That is, the Black-White inequality in average expectation of life at birth has shrunk.
The mixed news is that life disparity is much higher for Blacks than Whites — but that gap is falling as well. Here are those numbers for 1998 and 2008 (I did the life disparity calculations from this and this, and will happily share the spreadsheet). Click to enlarge:
So Black deaths are more dispersed than White deaths: 14 and 13 for males and females, compared with 12 and 11. For comparison, the Swedish female life disparity is 9. What does a higher disparity mean? Generally, a larger share of early deaths. That’s why the race gap in life expectancy at birth is greater than the race gap in life expectancy at older ages — average 65-year-old Whites and Blacks have more similar life expectancies than do infants.
Why is life disparity more interesting than life expectancy alone, and how does this help explain Black-White inequality in the U.S.? For one thing, high life disparity indicates either relatively unhealthy or dangerous living conditions at younger ages. So it’s partly a measure of the quality of life. Vaupel et al. add:
Reducing early-life disparities helps people plan their less-uncertain lifetimes. A higher likelihood of surviving to old age makes savings more worthwhile, raises the value of individual and public investments in education and training, and increases the prevalence of long-term relationships. Hence, healthy longevity is a prime driver of a country’s wealth and well-being. While some degree of income inequality might create incentives to work harder, premature deaths bring little benefit and impose major costs. Moreover, equity in the capability to maintain good health is central to any larger concept of societal justice.
I think what they say about differences between countries would apply to differences between groups within a society as well.
I didn’t even register it right away. Five years ago this Memorial Day I got my diagnosis of follicular lymphoma, a form of non-Hodgkin’s lymphoma. It was late on the Friday afternoon when the surgeon called with the biopsy results. He never said the word “cancer,” but recommended I see an oncologist. He was a very nice guy, and told me I was going to live to be an old man. Within 15 minutes I had read that follicular lymphoma is usually incurable. (The UpToDate database I used now puts it this way: “most cases of follicular lymphoma are not curable with currently available therapies.”) It was a long long weekend.
Usually follicular lymphoma – a blood cancer – is advanced before it’s first discovered. In the next few weeks, one oncologist told me the median survival was between 10 and 20 years. I was 40 with a wife and 4-year-old daughter. I asked her why she was an oncologist. She said she was interested in end-of-life issues. Also, the nicest people get cancer.
Eventually we determined that I had what apparently was a rare case of Stage I, which may be curable. I had 18 days of painless radiation and didn’t (physically) miss a day of work. Lucky is a funny word for this.
Five years later I don’t have an oncologist anymore. It’s the first line on my medical chart but not a to-do list item. When we moved away, my Bayesian-minded oncologist wrote in his farewell note, using his best handwriting: “Your chance for cure is reasonable: pre-test probability is low. Early detection is not helpful. If you get an enlarged lymph node, get biopsied.” Maybe that’s oncology speak for: “Relax, good luck!”
Anyway, there were lots of people I never told, including the chair of my department and some good friends and colleagues. Maybe that’s because it went from incurable (yikes, too much information) to possibly-cured (so stop complaining already) so quickly – before the start of the new semester – so I didn’t know how to bring it up or what to say.
For most people with this disease, the story is different. Thankfully, we’ve had a revolution in lymphoma treatment, and it’s usually a very long story. Most people live many years, and I’m told the new treatments usually aren’t that bad. (Easy for me to say.) Chance of surviving (that is, dying from something else) is pretty good. Experts debate whether the word “cure” should be used more.
Meanwhile, now there are two kinds of people in the world: people with a better prognosis, and people with a worse prognosis. Of course that’s always been true. But this experience sometimes makes me dwell on that, which increases my tendency to draw a sharp resentment/sympathy line according to this criterion. That isn’t healthy because it obscures the more important bases upon which to relentlessly judge people and compare myself to them.
I’m writing this because I remembered how lonely and scared I felt back then – when I didn’t even know where on the scale to put myself. Nothing aggravates the modern identity like incalculable risk. Fortunately, I had the greatest family and friend support – and medical care – anyone could ask for. Life got back to normal. We adopted another daughter. There are other risks to worry about.
But I’m thinking that somewhere someone with no idea what to do next is getting news like I did and Googling “follicular lymphoma.” If that’s someone you know, or it is you, maybe it will help to know about one more person who’s still living about as normal a life as I was before. Feel free to drop me a note.
