Marriage rates among people with disabilities (save the data edition)

Cross posted on the Families as They Really Are blog.

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.

Disabilities

The ACS asks five disability questions (I put the shorthand label after each):

1. Is this person deaf or does he/she have serious difficulty hearing? (Hearing)
2. Is this person blind or does he/she have serious difficulty seeing even when wearing glasses? (Vision)
3. Because of a physical, mental, or emotional condition, does this person have serious difficulty concentrating, remembering, or making decisions? (Cognitive)
4. Does this person have serious difficulty walking or climbing stairs? (Ambulatory)
5. 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.

First-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 first-marriage rate for 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:

• Divorce generally
• Divorce for “millennials” in 25 cities
• Divorce for Asian Americans from different national origins

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.

The information about the planned cuts to the American Community Survey is here: https://www.federalregister.gov/articles/2014/10/31/2014-25912/proposed-information-collection-comment-request-the-american-community-survey-content-review-results:

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.

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.

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.

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).

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 behavior, intergenerational 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.

Home birth is more dangerous. Discuss.

How dangerous is too dangerous?

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 Times Well 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.

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.)

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.

 

 

 

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:

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.

Off topic: My 5-year cancerversary

This is not what this blog is about.

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.