Tag Archives: public health

Rural COVID-19 paper peer reviewed. OK?

Twelve days ago I posted my paper on the COVID-19 epidemic in rural US counties. I put it on the blog, and on the SocArXiv paper server. At this writing the blog post has been shared on Facebook 69 times, the paper has been downloaded 149 times, and tweeted about by a handful of people. No one has told me it’s wrong yet, but not one has formally endorsed it yet, either.

Until now, that is. The paper, which I then submitted to the European Journal of Environment and Public Health, has now been peer reviewed and accepted. I’ve updated the SocArXiv version to the journal page proofs. Satisfied?

It’s a good question. We’ll come back to it.

Preprints

The other day (I think, not good at counting days anymore) a group of scholars published — or should I say posted — a paper titled, “Preprinting a pandemic: the role of preprints in the COVID-19 pandemic,” which reported that there have already been 16,000 scientific articles published about COVID-19, of which 6,000 were posted on preprint servers. That is, they weren’t peer-reviewed before being shared with the research community and the public. Some of these preprints are great and important, some are wrong and terrible, some are pretty rough, and some just aren’t important. This figure from the paper shows the preprint explosion:

F1.large

All this rapid scientific response to a worldwide crisis is extremely heartening. You can see the little sliver that SocArXiv (which I direct) represents in all that — about 100 papers so far (this link takes you to a search for the covid-19 tag), on subjects ranging from political attitudes to mortality rates to traffic patterns, from many countries around the world. I’m thrilled to be contributing to that, and really enjoy my shifts on the moderation desk these days.

On the other hand some bad papers have gotten out there. Most notoriously, an erroneous paper comparing COVID-19 to HIV stoked conspiracy theories that the virus was deliberately created by evil scientists. It was quickly “withdrawn,” meaning no longer endorsed by the authors, but it remains available to read. More subtly, a study (by more famous researchers) done in Santa Clara County, California, claimed to find a very high rate of infection in the general population, implying COVID-19 has a very low death rate (good news!), but it was riddled with design and execution errors (oh well), and accusations of bias and corruption. And some others.

Less remarked upon has been the widespread reporting by major news organizations on preprints that aren’t as controversial but have become part of the knowledge base of the crisis. For example, the New York Times ran a report on this preprint on page 1, under the headline, “Lockdown Delays Cost at Least 36,000 Lives, Data Show” (which looks reasonable in my opinion, although the interpretation is debatable), and the Washington Post led with, “U.S. Deaths Soared in Early Weeks of Pandemic, Far Exceeding Number Attributed to Covid-19,” based on this preprint. These media organizations offer a kind of endorsement, too. How could you not find this credible?

postpreprint

Peer review

To help sort out the veracity or truthiness of rapid publications, the administrators of the bioRxiv and medRxiv preprint servers (who are working together) have added this disclaimer in red to the top of their pages:

Caution: Preprints are preliminary reports of work that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

That’s reasonable. You don’t want people jumping the gun on clinical decisions, or news reports. Unless they should, of course. And, on the other hand, lots of peer reviewed research is wrong, too. I’m not compiling examples of this, but you can always consult the Retraction Watch database, which, for example, lists 130 papers published in Elsevier journals in 2019 that have been retracted for reasons ranging from plagiarism to “fake peer review” to forged authorship to simple errors. The database lists a few peer-reviewed COVID-19 papers that have already been retracted as well.

This comparison suggests that the standard of truthiness cannot be down to the simple dichotomy of peer reviewed or not. We need signals, but they don’t have to be that crude. In real life, we use a variety of signals for credibility that help determine how much to trust a piece of research. These include:

  • The reputation of the authors (their degrees, awards, twitter following, media presence)
  • The institutions that employ them (everyone loves to refer to these when they are fancy universities reporting results they favor, e.g., “the Columbia study showed…”)
  • Who published it (a journal, an association, a book publisher), which implies a whole secondary layer of endorsements (e.g., the editor of the journal, the assumed expertise of the reviewers, the prestige or impact factor of the journal as a whole, etc.)
  • Perceived conflicts of interest among the authors or publishers
  • The transparency of the research (e.g., are the data and materials available for inspection and replication)
  • Informal endorsements, from, e.g., people we respect on social media, or people using the Plaudit button (which is great and you should definitely use if you’re a researcher)
  • And finally, of course, our own assessment of the quality of the work, if it’s something we believe ourselves qualified to assess

As with the debate over the SAT/GRE for admissions, the quiet indicators sometimes do a lot of the work. Call something a “Harvard study” or a “New York Times report,” and people don’t often pry into the details of the peer review process.

Analogy: People who want to eat only kosher food need something to go on in daily life, and so they have erected a set of institutional devices that deliver such a seal (in fact, there are competing seal brands, but they all offer the same service: a yes/no endorsement by an organization one decides to trust). The seals cost money, which is added to the cost of the food; if people like it, they’re willing to pay. But, as God would presumably tell you, the seal should not always substitute for your own good judgment because even rabbis or honest food producers can make mistakes. And in the absence of a good kosher inspection to rely on altogether, you still have to eat — you just have to reason things through to the best of your ability. (In a pinch, maybe follow the guy with the big hat and see what he eats.) Finally, crucially for the analogy, anyone who tells you to ignore the evidence before you and always trust the authority that’s selling the dichotomous indicator is probably serving their own interests as least as much as they’re serving yours.

In the case of peer review, giant corporations, major institutions, and millions of careers depend on people believing that peer review is what you need to decide what to trust. And they also happen to be selling peer review services.

My COVID-19 paper

So should you trust my paper? Looking back at our list, you can see that I have degrees and some minor awards, some previous publications, some twitter followers, and some journalists who trust me. I work at a public research university that has its own reputation to protect. I have no apparent way of profiting from you believing one thing or another about COVID-19 in rural areas (I declared no conflicts of interest on the SocArXiv submission form). I made my data and code available (even if no one checks it, the fact that it’s there should increase your confidence). And of course you can read it.

And then I submitted it to the European Journal of Environment and Public Health, which, after peer review, endorsed its quality and agreed to publish it. The journal is published by Veritas Publications in the UK with the support of Tsinghua University in China. It’s an open access journal that has been publishing for only three years. It’s not indexed by Web of Science or listed in the Directory of Open Access Journals. It is, in short, a low-status journal. On the plus side, it has an editorial board of real researchers, albeit mostly at lower status institutions. It publishes real papers, and (at least for now) it doesn’t charge authors any  publication fee, it does a little peer review, and it is fast. My paper was accepted in four days with essentially no revisions, after one reviewer read it (based on the summary, I believe they did read it). It’s open access, and I kept my copyright. I chose it partly because one of the papers I found on Google Scholar during my literature search was published there and it seemed OK.

So, now it’s peer reviewed.

Here’s a lesson: when you set a dichotomous standard like peer-reviewed yes/no and tell the public to trust it, you create the incentive for people to do the least they can to just barely get over that bar. This is why we have a giant industry of tens of thousands of academic journals producing products all branded as peer reviewed. Half a century ago, some academics declared themselves the gatekeepers of quality, and called their system peer review. To protect the authority of their expertise (and probably because they believed they knew best), they insisted it was the standard that mattered. But they couldn’t prevent other people from doing it, too. And so we have a constant struggle over what gets to be counted, and an effort to disqualify some journals with labels like “predatory,” even though it’s the billion-dollar corporations at the top of this system that are preying on us the most (along with lots of smaller scam purveyors).

In the case of my paper, I wouldn’t tell you to trust it much more because it’s in EJEPH, although I don’t think the journal is a scam. It’s just one indicator. But I can say it’s peer reviewed now and you can’t stop me.

Aside on service and reciprocity: Immediately after I submitted my paper, the EJEPH editors sent me a paper to review, which I respect. I declined because it wasn’t qualified, and then they sent me another. This assignment I accepted. The paper was definitely outside my areas of expertise, but it was a small study quite transparently done, in Nigeria. I was able to verify important details — like the relevance of the question asked (from cited literature), the nature of the study site (from Google maps and directories), the standards of measurement used (from other studies), the type of the instruments used (widely available), and the statistical analysis. I suggested some improvements to the contextualization of the write-up and recommended publication. I see no reason why this paper shouldn’t be published with the peer review seal of approval. If it turns out to be important, great. If not, fine. Like my paper, honestly. I have to say, it was a refreshing peer review experience on both ends.

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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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COVID lecture for Social Problems class

On March 2, I opened up my Social Problems class to questions on the emerging coronavirus epidemic. One of the things I did was show them a graph of worldwide cases on a log scale, and told them that it implied the world would have a million cases a month later. We hit that number to the day two days ago. Here are my notes from that day:

A month later, with school indeed canceled (which I had given only a 10-20% on March 2, I recorded this 28-minute lecture for them as an update. Feel free to use any part of it any way you like*:


* Two notes, having watched it over myself and gotten some feedback:

  1. At 4:40 I said of the graph shown: “The number of new cases confirmed by testing, every day, in the country, since February.” I should have said, “in the world” (as the figure is labeled).
  2. It’s been pointed out that social distancing and other responses to the outbreak are not the only thing that differentiate trajectories of the different outbreaks around the country. Also relevant is the demography of the area, including age, as well as health status and healthcare infrastructure. Those factors will emerge as the pandemic matures.

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Ruth Sidel, appreciated

wccc

Some of Victor Sidel’s photos from Women and Child Care in China

I just learned that sociologist Ruth Sidel has died. These are a few scattered notes on the influence of some of her work. I always wanted to meet her but never did. I read her early work on China as a student, and I used her later work on poverty and welfare in the U.S. in my teaching.

She had a great influence on American leftists (and me) initially because of her writing on China, especially Women and Child Care in China, which came from a trip she took in 1971, during the Cultural Revolution, with her husband, Victor Sidel (and one of the founders of Physicians for Social Responsibility). At the time of that trip she was a social worker, having written only a masters thesis, invited in her role as wife, but found their hosts willing to open up their visit (which was supposed to be about medical care) to the issues of women’s liberation and education. She remembered in an oral history interview:

They integrated what I was interested in into every single thing we did. It was just remarkable. … Half way through the trip I said to Vic, “There is a book here.”… He said “How can you have a book after two weeks?” And I said “Trust me, there is a book here.” …and I did and wrote a book called “Women and Childcare in China” which was really like successful. People really wanted to read about it. They wanted to read about mutual aid. They wanted to read about how the communist government was trying to take care of children and women. We went to preschools and how the children were taught to help each other, love each other and take care of each other according to the words of Chairman Mao, literally, I’m quoting. The book went into paperback and sold even more copies and I spoke everywhere. I’d never given a speech before in my life, ever. Terrified but I had to do it because I knew stuff that other people didn’t know and I had to communicate it.

One book led to the next book about neighborhood organization [Families of Fengsheng: Urban Life in China] and I helped Vic finish his book on healthcare. The whole 70s I was writing about China and lecturing about China all over the country and in many parts of Europe. We were invited—I mean it was just unbelievable. It was a total life change and thrilling.

I am awed by that spirit of adventure, the confidence to seize that moment, and the commitment to doing social science for the public interest.

Later she got a PhD in sociology and went on to write on poverty and welfare, the work she was known for after the 1990s (see books listed below).

Although writing books promoting the Cultural Revolution in the early 1970s is not a fast-track ticket to respectability nowadays, if you go back to those books you will also see how close her observations are, and how incisive. The macro-political context of course is important (and she wrote about that), but that was not her primary contribution. In addition to what she learned (or didn’t) from official documents and statements, she did see some things with her own eyes. One of the key insights she brought back from China was the value of deprofessionalization, the role of non-professionals to improving community health and education. This was essential to the dramatic improvements in public health achieved in that period in China (which I wrote about in a remembrance on another China-inspired American feminist, Janet Salaff.) This was a radical-democratic view of public health in particular. From Families of Fengsheng:

Health care, perhaps better than any other single facet of Chinese society, vividly illustrates some of the principles that guide life in China today: a strong belief in mass involvement; recruitment of health workers from among those who live in the community to be served; short periods of training to minimize alienation from the community; a minimum of social distance between the helper and the helped; attempts to demystify as much of medicine as possible; decentralization; and motivating people through altruism rather than through prestige or material incentives.

Wouldn’t that be something!

I hope there will be more comprehensive remembrances from people who knew Ruth Sidel and her work more fully. This note is just to register my own deep appreciation.


Some books by Ruth Sidel:

  • Sidel, Ruth. 1972. Women and Child Care in China; a Firsthand Report. New York: Hill and Wang.
  • Sidel, Ruth. 1974. Families of Fengsheng : Urban Life in China. Baltimore: Penguin Books.
  • Sidel, Ruth. 1978. Urban Survival : The World of Working-Class Women. Boston: Beacon Press.
  • Sidel, Ruth. 1986. Women and Children Last : The Plight of Poor Women in Affluent America. New York: Viking.
  • Sidel, Ruth. 1990. On Her Own : Growing up in the Shadow of the American Dream. New York, N.Y., U.S.A.: Viking.
  • Sidel, Ruth. 1996. Keeping Women and Children Last : America’s War on the Poor. New York, N.Y.: Penguin Books.
  • Sidel, Ruth. 2006. Unsung Heroines : Single Mothers and the American Dream. Berkeley: University of California Press.

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Charter, private, and wealthy schools lead California vaccine exemptions

We need to know who’s driving this epidemic of non-vaccination so we can decide what to do about it. One key element of the pattern is that it’s a practice of groups, not (just) individuals. And one way such groups are organized and maintained may be by interacting in and around schools — hotspots for parenting fads and identity performance, as well as (one hopes) useful information.

Kieran Healy the other day posted some visualizations of California vaccine exemption rates across schools and counties — then dug deeper on school type here. While he was writing his second post I followed his links to the data and did some more descriptive work, adding some more information on school type and now poverty levels.

I used this California Department of Education data source for the free-lunch eligibility rates (2013-2014), and this California Department of Public Health data source for the vaccine exemption rates for kindergartners (2014-2015). The measure of vaccine exemption is the “Personal Belief Exemption (PBE), whereby a parent requests exemption from the immunization requirements for school entry.” Some of them got supposedly got counseling before making the request, while others further requested a religious exemption from the counseling — those two groups are combined here in the PBE rate. I weighted the analyses by the number of kindergartners enrolled in each school, so the rates shown are for students, not schools (this also helps with the outliers, which are mostly very small schools).

1. Runaway vaccine exemptions are problems of the private and charter schools

I don’t know what percentage of kids need to be immunized, for which diseases, for the proper level of community protection. But these distributions are very skewed, so it makes sense to look at the extremes. Here are three measures, for just under 7,000 schools: the mean percent PBE (the average percent exempt among each kids’ classmates), the percentage of kids in a school with no exempt kindergartners, and the percentage of kids attending schools with more than 5% exempt.

graph.xlsx

The average charter school kindergartner goes to school with classmates almost 5-times more likely to be non-vaccinated; and charter school kids are more than 3-times as likely to be in class with 5% or more kids exempt.

2. Schools with lots of poor kids have much lower exemption rates

The relationship between exemption rates and percentage of children eligible for free lunch is negative and very strong. Because there are so many schools with no PBEs, I used a tobit regression to predict exemption rates (I also excluded the top 1% outliers). Note the private schools are excluded here because the free lunch data was missing for them. Here is the output:

tobit

Just in case the zeros are data errors, I reran the regression excluding the zero cases (logged and not logged), and got weaker but still very strong results.

I illustrate the relationship in the next section.

3. The poverty-exemption relationship is stronger in charter schools

Charter schools have fewer kids eligible for free-lunch than regular public schools (43% versus 55%). However, although the relationship between poverty and exemptions is strong in both charter and regular public schools, it is stronger in the charter schools (the interaction term is almost 7-times its standard error). Here they are, showing the steeper free-lunch slope for charter schools (with a logged y-axis):

charter-lunch

Rich charter schools on average have the highest exemption rates, while poor schools — charter or not — are heavily clustered around zero (note it’s jittered so you can see how many cases are at zero).

Update: Here is the same data, for all public schools, presented more simply:

graph.xlsx

One interpretation of this pattern goes like this: Charter schools do not per se promote vaccine exemption. But because they are more parent-driven, or targeted at certain types of parents, charter schools are more ideologically homogeneous. And because anti-vaccine ideology is concentrated among richer parents, charter schools provide them with a fertile breeding ground in which to generate and transmit anti-vaccine ideas. That’s why, although richer parents in general are driving vaccine denial, it’s especially concentrated in charter schools. This seems consistent with the general echo-chamber nature of information sharing in cultural niches, and the clusering/contagious nature of parenting fads. (The same may or may not hold for private schools.) This could be totally wrong — I’m open to other interpretations.

I’m also happy to share my data on request, but I’m not posting it yet because when I tinker with it more I might make corrections.

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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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Yo, how big is that yogurt bucket?

People don’t know how much they’re eating.

A recent experiment found that people eat more when the container is larger, even when the portion size is not. They gave Belgian college students a container of M&Ms and parked them in front of a TV, with some cover story. The students were randomly assigned to three groups, medium-portion/small-container, medium-portion/large-container, and large-portion/large-container. These were the results: The ones who got the large container ate more, whether it was full or not (the difference between the two wasn’t significant). These kinds of experiments continuously suggest that distractions, distortions and other apparently irrelevant information and events routinely have large effects on people’s eating practices (here’s an extensive review). One infamous study showed that even people served 14-day-stale popcorn at the movies ate 34% more when it was served in a large container. In an earlier popcorn study, researchers found that people given large containers not only ate more, but were less able to report how much they ate. They concluded:

When a food is eaten from a large container, it appears easy to lose track of how much one eats. Even if the food were to taste relatively unfavorable, eating it from a large container may cause one to overeat because they lose track of how much they have consumed.

About that yogurt tub All this occurred to me when I visited one of our many local Frozenyo franchise outlets. It’s a self-serve frozen yogurt place where you pay one price by weight no matter what you put in your bucket. The trick that impressed me is the bucket — there is only one size, and it’s very large. But you can’t judge how big it is because there’s nothing to compare it with — no sizes or prices on the wall, no mini cup for kids — just one stack of identical buckets. So the person who posted this picture on Yelp probably thought she had a reasonable size serving, since the thing is barely half full:

There are three possible ways to judge your self-served serving size. You can go by the tub (“I filled it half way”), you can go by the person next to you (“sheesh!”), or you can look at the cartoon penguins on the wall:

How much is the penguin eating? I took home one of the buckets, and measured the volume of water it holds: 18 ounces. In comparison, a standard kid-sized serving bowl, the kind some people use to give their kids ice cream at home, holds 12 ounces:

An innocent child used to half a bowl of ice cream — in the bowl on the left — might be pretty steamed if you served her this:

According to the serving size information on the back wall of Frozenyo, I think that’s about 1.5 servings, or 150 calories of the nonfat variety, before toppings. The penguin’s overflowing bowl is 5.0 servings. With no toppings that’s 500 calories. If you pile it with M&Ms, sprinkles, hot fudge, Captain Crunch, coconut topping and fresh kiwis, who knows. It’s not really that many calories to consume — the same number as a single slice of banana bread at Starbucks.

But the point is you don’t know how much you’re eating. One Yelp reviewer cautioned that you can get a stomach ache after eating at Frozenyo, because “your eyes are bigger than your stomach.” I think it’s because the dump-truck sized delivery vehicle you eat it out of is bigger than your stomach.

But most reviewers love it for the individual control over serving size and toppings, and the reasonable price ($.39 per ounce by weight, or $5-$6 for a typical load).* I think it’s a winning business model, with low labor costs, because all you need is one person to pour the mix into the machines and another to weigh the tubs and swipe credit cards. According to the company’s ambitious map, there are still 46 states with “territory available.”

If I were them, I would increase the bucket size by 5% per year. I doubt anyone would notice.

* Paging George Ritzer: it’s the irrationality of rationality.

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Incarceration’s contribution to infant mortality

A recent study in the journal Social Problems by sociologist Chistopher Wildeman shows that America’s practice of mass incarceration may be exacerbating both infant mortality in general and stubborn racial inequality in infant mortality in particular.

Drawing on recent literature by himself and others, Wildeman spells out the case for incarceration’s negative effect on family economies, including: lost earnings and financial contributions from fathers, the expensive burden of maintaining the relationship with an incarcerated parent, and the lost value of the incarcerated parent’s unpaid labor. All of those costs may take a toll on mothers’ health, which is the primary cause of infant mortality.

In addition, family members of incarcerated parents may contract infectious diseases, experience significant stress, and lose support networks — all taking an additional health toll.

Sure enough, his analysis of data from the Pregnancy Risk Assessment Monitoring System confirms that children born into families in which a parent has been incarcerated are more likely to die in the first year of life. The association may not be causal, but it holds with a lot of important control variables.

Does this increase racial inequality? Probably, because parental incarceration is so concentrated among Black families, as Wildeman and Bruce Western reported previously (my graph of their numbers):

To make the connection to racial inequality explicit, Wildeman moves to compare states over time, on the suspicion that incarceration could increase infant mortality rates, and racial inequality in infant mortality rates. That could be because concentrated incarceration undermines community support and income, people with felony records often are disenfranchised (so the political system can ignore their needs), and the costs of incarceration crowd out more beneficial spending that could improve community health.

The results of a lot of fancy statistical models comparing states show that:

the imprisonment rate is positively and significantly associated with the total infant mortality rate, the black infant mortality rate, and the black-white gap in the infant mortality rate.

It’s an impressive article on an important subject, one that thankfully is attracting more attention from good scholars.

I previously reported on Wildeman’s work on how the drug war affect families, here.

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Warning: What do smokers Google?

If I ran the Federal scary anti-smoking image warning program, I might show smokers the list of health-related terms that show up most in the states with the highest cigarette smoking rates.

The Google Correlate tool is showing the great potential for using Internet search activity to investigate layers of behavior and meaning behind other observable social phenomena, such as race/ethnic composition, health behavior, and family patterns. Today’s example is smoking.

If you take the smoking rates by state, and throw them into the Google Correlate hopper, you can see the 100 search terms that are most highly correlated with that reported smoking behavior. That is, the terms that are most likely to be used in high-smoking states and least likely to be used in the low-smoking states.

Is the result just a lot of noise? Maybe, but I don’t think so. Here are the smoking-related terms in the top 100:

  • camel no 9
  • cigarette coupon
  • cigarette coupons
  • marlboro coupons
  • my time to quit
  • safe cigarettes
  • stopping smoking
  • time to quit
  • fire safe cigarettes
  • ways to stop smoking

So that’s good for face validity — a list of random search terms isn’t likely to have all those smoking terms on it.

But after the smoking terms, the thing that jumps out is the health-related terms. We know from the Google flu tracker that people search for their symptoms. So these caught my eye.

Here is a screen shot of the first page of results:

I selected “stages of copd” as the term to map. The map on the left is the smoking rates; the one on the right is the relative frequency of searches for “stages of copd.” That is, chronic obstructive pulmonary disease, a nasty disease the most common cause of which is smoking.

Here is the complete list of health-related terms among the top-100 correlates with smoking rates:

Lymph node swelling, which is implicated in the jaw and neck searches, most often reflects infection — which smoking causes.

How strong are the connections? They’re not the strongest I’ve seen on Google Correlate. The “stages of copd” search is correlated with smoking rates at .77 on a scale of 0 to 1. It’s not uncommon to find correlations of .93 (which is the relationship between “quiche” and “volvo v70 xc”).

But considering the smoking rates come from a sample survey (the National Survey on Drug Use and Health) which includes random error, and states are somewhat arbitrary geographic units, that correlation seems pretty high to me. Here’s the scatterplot:

What is the correlation causality story here? I can’t say. But the simplest explanation is that these are the terms smokers (and maybe those who know or care for them) are most likely to Google relative to non-smokers — not that they are the most common searches smokers do, of course, but the searches that differentiate them from non-smokers. The simplest explanation is the best place to start.

I like this list of conditions because in my experience smokers sometimes have the attitude of “you have to die of something.” But it’s not just the chance of dying that smoking increases – it’s a lot of possible forms of suffering along the way.

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