Tag Archives: health disparities

The COVID-19 epidemic in rural U.S. counties

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.

F1 rural county cases maps

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.

F2 new cases and deaths

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.

F3 population density and cases

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.

F4 rural county selected cases

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.

F5 rural county minority scatters

And here’s a table of those selected cases:

crt2

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): 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.

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Health disparities & COVID-19 lecture

For Social Problems, an introductory level sociological course, I gave a lecture that combines an introduction to health disparities and some issues of disparate impacts of the COVID-19 pandemic. It’s 23 minutes. Some slides and links below.

The first half describes the theory of fundamental causes (as I understand it), and has some basic health disparities examples. Here are some graphs:

Then I apply some of the ideas to what we know about COVID-19 impacts, and likely problem areas. Here is some of that:

The PowerPoint slides, with references in the notes, is up here: https://osf.io/d4ym3/.

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Maternal age and infant mortality paper forthcoming

Update: The paper is now published here. 

The working paper I wrote about here has now been accepted for publication in Sociological Science. Although the results haven’t changed substantially, I revised it since the last post, so you should use this copy instead. Here’s the abstract:

Maternal Age and Infant Mortality for White, Black, and Mexican Mothers in the United States

This paper assesses the pattern of infant mortality by maternal age for White, Black, and Mexican mothers, using 2013 Period Linked Birth/Infant Death Public Use File from the Centers for Disease Control. The results are consistent with the “weathering” hypothesis, which suggests that White women benefit from delayed childbearing while for Black women early childbearing is adaptive because of deteriorating health status through the childbearing years. For White women, the risk (adjusted for covariates) of infant death is U-shaped – lowest in the early thirties – while for Black women the risk increases linearly with age. Mexican-origin women show a J-shape, with highest risk at the oldest ages. The results underscore the need for understanding the relationship between maternal age and infant mortality in the context of unequal health unequal health experiences across race/ethnic groups in the U.S.

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Weathering and delayed births, get your norms off my body edition

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:

motherswdisab

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.

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. Data source: 2013 Period Linked Birth/Infant Death Public Use File, Centers for Disease Control.

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.

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Certain death? Black-White death dispersions

New research report, after rumination.

Knowing the exact moment of death is a common fantasy. How would it change your life? Here’s a concrete example: when I got a usually-incurable form of cancer, and the oncologist told me the median survival for my condition was 10 to 20 years, I treated myself to the notion that at least I wasn’t going to the dentist anymore (6 years later, with no detectable cancer, I’m almost ready to give up another precious hour to dentistry).

I assume most people don’t want to die at a young age, but is that because it makes life shorter or because it makes them think about death sooner? When a child discovers a fear of death, isn’t it tempting to say, “don’t worry: you’re not going to die for a long, long time”? The reasonable certainty of long life changes a lot about how we think and interact (one of the many reasons you can’t understand modernity without knowing some basic demography). I wrote in that cancer post, “Nothing aggravates the modern identity like incalculable risk.” I don’t know that’s literally true, but I’m sure there’s some connection between incalculability and aggravation.

Consider people who have to decide whether to get tested for the genetic mutation that causes Huntington’s disease. It’s incurable and strikes in what should be “mid”-life. Among people with a family history of Huntington’s disease, Amy Harmon reported in the New York Times, the younger generation increasingly wants to know:

More informed about the genetics of the disease than any previous generation, they are convinced that they would rather know how many healthy years they have left than wake up one day to find the illness upon them.

The subject of Harmon’s story set to calculating (among other things) whether she’d finish paying off her student loans before her first symptoms appeared.

The personal is demographic

So what is the difference between two populations, one of which has a greater variance in age at death than the other? (In practice, greater variance usually means more early deaths, and the risk of a super long life probably isn’t as disturbing as fear of early death.) Researchers call the prevalence of early death — as distinct from a lower average age at death — “life disparity,” and it probably has a corrosive effect on social life:

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. (source)

That’s why reducing life disparity may be as important socially as increasing life expectancy (the two are highly, but not perfectly, correlated).

New research

Consider a new paper in Demography by Glenn Firebaugh and colleagues, “Why Lifespans Are More Variable Among Blacks Than Among Whites in the United States.”

I previously reported on the greater life disparity and lower life expectancy among Blacks than among Whites. Here is Firebaugh et al’s representation of the pattern (the distribution of 100,000 deaths for each group):

bwdeaths

Black deaths are earlier, on average, but also more dispersed. The innovation of the paper is that they decompose the difference in dispersion according to the causes of death and the timing of death for each cause. The difference in death timing results from some combination of three patterns. Here’s their figure explaining that (to which I added colors and descriptions, as practice for teaching myself to use an illustration program — click to enlarge):

bw death disparities

The overall difference in death timing can result from the same causes of death, with different variance in timing for each around the same mean (spread); different causes of death, but with the same age pattern of death for each cause (allocation); and the same causes of death, but different average age at death for each (timing). Above I said greater variability in life expectancy usually means more early deaths, but with specific causes that’s not necessarily the case. For example, one group might have most of its accidental deaths at young ages, while another has them more spread over the life course.

Overall, the spread effect matters most. They conclude that even if Blacks and Whites died from the same causes, 87% of the difference in death timing would persist because of the greater variance in age at death for every major cause. There are differences in causes, but those mostly offset. Especially dramatic are greater variance in the timing of heart disease (especially for women), cancer, and asthma (presumably more early deaths), The offsetting causes are higher Black rates of homicide (for men) and HIV/AIDS deaths, versus high rates of suicide and accidental deaths among White men (especially drug overdoses).

The higher variance in causes of death seems consistent with problems of disease prevention and disparities in treatment access and quality. (I’m not expert on this stuff, so please don’t take it exclusively from me — read the paywalled paper or check with the authors if you want to pursue this.)

Are these differences in death timing enough to create differences in social life and outlook, or health-related behavior, between these two groups? I don’t know, but it’s worth considering.

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Life expectancy update, disparity edition

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:

Life Expectancy, Life Disparity

Reposted from July 23, 2013

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.

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:

expectancydisparity

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.

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Immigrant health paradox update

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).

epipara

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.

These articles are a good place to start on this topic: lots of references to fill in the background and previous research on this paradox, which goes back at least to the 1980s. This is a fascinating and important research area, dealing with such questions as health behaviorintergenerational change, thorny puzzles about different immigrant groups, child development and lots more.

*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.

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Are White women high school dropouts getting sicker?

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.)

poorhealthThat looks terrible, and it is, of course. But look at the size of the total group (all health statuses) over the same period:

dropoutsSo, 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:

drophealthSo, 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.

 

 

 

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Life expectancy, life disparity

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:

seeyourfolksThis 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.

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:

expectancydisparity

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.

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