Mortality Among High-School Educated Whites in the U.S.: An Anthropological View

Fig. 1. All-cause mortality, ages 45–54 for US White non-Hispanics (USW), US Hispanics (USH), and six comparison countries: France (FRA), Germany (GER), the United Kingdom (UK), Canada (CAN), Australia (AUS), and Sweden (SWE).

Every so often a piece of research comes along that is a real game-changer—it literally shakes the earth under your feet.  I had that experience about a year ago when Anne Case and Angus Deaton, two economists, published an analysis of recent mortality trends in the United States.  If you electronically search “mortality trends” for the U.S., you will see that, overall, mortality rates are declining, as they are for Canada and Western Europe.  What Case and Deaton did was to separate out mortality rates for non-Hispanic Whites.  Starting about the year 2000, rather than continuing to decline like everyone else’s, mortality for this group bucked the trend and started to climb.  When causes of mortality were examined, deaths from lung cancer were declining, from diabetes were stable, and for three causes were climbing, dramatically.  These were chronic liver disease, suicide, and what Case and Deaton refer to, by default since that’s what the feds say, “poisonings” (read: unintentional drug overdose).

I’ve taught epidemiology off-and-on for a long time, and mortality rates just don’t jump around like this, unless, that is, something catastrophic is happening.  An example of a catastrophic event leading to high mortality rates was the fall of the former Soviet Union.  In the decade following that political upheaval mortality—especially male mortality—climbed, fueled by a potent combination of vodka and cigarette smoke.

A further component to their findings was that the changes in mortality rates were highest among non-Hispanic Whites who had a high-school education or less.  In 1999, rates of death from “poisonings” were 4 times higher for people with a high-school education or less than they were for people with a college degree.  In 2013 those death rates were 7.2 times higher for the less-well educated versus the well-educated.

I felt so compelled by this evidence that I dropped what I was doing in my classes—one on cognitive anthropology and one on the history of anthropological theory—and taught the Case and Deaton paper.  Even though it caught a lot of attention in the national press, at least for awhile, I was afraid that it would escape the notice of many of my students and, furthermore, that they might not really appreciate the magnitude of the results.

The pattern of results suggests that non-college educated Whites are experiencing some kind of profound stress and that in response they are self-medicating with alcohol (hence chronic liver disease) or with prescription opioid pain medication (with its attendant risks of overdosing), and that they are responding with major depression and the associated risk of suicide.  In their interpretation, Case and Deaton emphasized the stress of economic insecurity for working class Whites, noting that widening inequality might account for the trend.  This would seem to me to affect other population groups—like African Americans—even more than non-college educated Whites, yet the trend toward higher mortality from these causes is not observed in other population groups.  Case and Deaton also suggest that it might be specifically the transition in retirement programs from guaranteed benefit plans to defined-contribution plans, with their associated stock market risk.  In this interpretation, looking forward to an uncertain and possibly impoverished future is the source of stress.

For obvious reasons—and I’m talking about the election season in which we find ourselves—I have continued to think about this research, given the prominent place that Whites with a high school or lower education seem to be playing in support of one of the major candidates.  Is it, to quote a political strategist from a past campaign associated with the other major candidate, “the economy, stupid!”  Or, is it that, and something more?

As I considered the findings, I was reminded of an old paper by James P. Henry and John C. Cassel from the American Journal of Epidemiology in 1969.  They examined cross-cultural data on age and blood pressure, noting that, while many physicians believed the rise of blood pressure with age to be “natural,” it was in fact “cultural.”  In many communities around the world, especially those that had yet to be drawn very closely into capitalist, market economies, there was little evidence of an increase of blood pressure with age.  In what were called (back in the day) “modern” communities, blood pressure rose with age.

To explain these results, they drew on a process that Cassel had been thinking about for some time, namely, the inconsistencies and incongruities that can accompany profound culture change.  Cassel’s preferred research strategy had been to follow migrants into a new setting, where he predicted that the incongruity between the culture they arrived with, and the culture of their majority host community, created a period of stressful and taxing adaptation, as the migrants tried to adjust to their new setting.  The end result of this stressful adjustment, especially if it was not especially successful, was an increased risk of disease.  Henry and Cassel suggested that the same process could be occurring across the life-span of an individual, arguing that in the modern world, with the ever increasing pace of social change, an individual is born into and socialized in one culture, yet ends up living in another, as the world changes around him or her.

This strikes me as an eminently plausible interpretation for the Case and Deaton findings.  Non-college educated Whites are indeed facing economic stresses, but the broader cultural changes they are experiencing are even more profound.  And what can more effectively and graphically communicate to them that the world around them has changed than the fact that they will shortly trade their first African American president for their first female president?  (I trust Sam Wang and his Princeton Election Consortium.)

Cassel drew heavily on culture theory for his insightful interpretation of epidemiologic data.  Case and Deaton’s findings suggest that those insights are still relevant.


Biocultural Systematics is written by members of the University of Alabama Biocultural Medical Anthropology program.

Bill Dressler, a professor in the department, has conducted research in social epidemiology in Brazil and the U.S.

An Epidemiologic Anthropology: Considerations when Employing Mixed Methods

Anthropology versus Epidemiology

Author, Kathryn Oths
Author, Kathryn Oths

Anthropologists and epidemiologists have contributed vital knowledge to understanding public health problems such as low birth weight, reemerging disease, mental health, and more. Lively and enduring dialogue on the potential for collaboration between the disciplines was sparked in the ‘80s by Janes et al.’s (1986) Anthropology and Epidemiology and True’s (1990) chapter “Epidemiology and medical anthropology.”  The discourse continues to the present, well-summarized in the works of Dein and Bhui (2013), Hersch-Martínez (2013), Inhorn (1995), and Trostle (2005).

In contrast to early literature, later writing—from both camps—implies that what anthropology most offers epidemiology is its qualitative sensibility (e.g., Ragone and Willis 2000; Scammell 2010). While clearly one of anthropology’s great strengths, sensitivity to qualitative dimensions is not all we have to offer. Rigorous, contextualized mixed-methodology is more likely to be persuasive to other disciplines than mere entrained awareness (Prussing 2014). In fact, by incorporating epi techniques into anthropological designs, we can employ a holistic paradigm on our own—what Inhorn calls synthetic or wearing both hats. (The reverse, training health professionals in anthropology, has also been suggested [O’Mara et al. 2015]).

Kathy's epi anth model
Kathryn’s Epi Anth Model

Anthropological orientations in health research might be glossed as follows: Anthropologists of Suffering record the pain and distress of a people, striving to understand meaning surrounding health problems. Anthropologists of Sickness, in addition to searching for meaning, use structured surveys emerging from ethnographic observation to systematically ferret out factors contributing to dis-ease and illness. The first approach interrogates the meaning of critical life events, while the second investigates how socially and culturally constructed meanings themselves shape risk of morbidity and mortality. As Trostle and Sommerfeld (1996) state, “data can be used to create emotional responses in the reader, or to explain relationships.” Both approaches are vital and mutually enhancing, but less has been written about the latter.

For example, most anthropologists of reproduction interpret the clinical interactions that oppress and mystify women’s knowledge and autonomy, as well as women’s resistance to these controlling forces. They study the technologizing of natural processes and the hegemony of biomedical over self-knowledge. This research is an important corrective to years of neglect of reproductive work (Rapp 2001). The focus of others, including myself, has been more outcome-driven, a systematic explanatory study of the conditions not of clinical but rather daily lifelike workplace organization and intimate relationshipsthat shape women and babies’ health (Oths et al. 2001; Dunn & Oths 2004).

A Word on Publishing

While epidemiology and anthropology share the common goal of improving human health, each field has its own prerogatives. Those who blend qualitative and quantitative methods in the pursuit of an Epidemiological Anthropology of Sickness may face problems getting published in the public health literature. I’ll make three points regarding disciplinary differences of opinion on the accurate specification of analytic models:

   1. Anthropological methods are not self-explanatory. 

Anthropological methods essential to getting results are detailed, iterative, and not necessarily self-explanatory. However, there is no space to discuss these vital tools in standard public health journal articles. Be forewarned: Public health expects very brief methods sections!

   2. What’s reliable to others may not be valid to us.

Other fields are more strict than ours in insisting that survey items be tested for reliability before use. Reliability, or insuring that an instrument gives the same results with repeated use, is a good thing. However, a scale, once published, should not be changed. (A survey instrument you construct yourself? Even more suspect.) Yet without local contextualization, an instrument’s validityactually measuring what said instrument claims to measure—may be compromised. This is a constant issue when we employ scales that have been normed to populations other than the one we will survey. For epidemiologists, patterns of association are of greater concern than measurement issues. Categories they work with are believed to be fixed in nature, race being a prime example. For us, they are anything but fixed. Anthropologists insist on emic construct validity of categories—categories should make sense in the cultures we’re measuring them in. Rule of thumb: Take care of validity, and reliability will follow.

Rule of thumb: Take care of the validity, and reliability will follow.

   3. We lack authority to critique normative methods.

Some journals, such as American Journal of Public Health (AJPH), recommend use of specific statistics, such as logistic rather than ordinary least squares regression. They insist every dependent outcome variable be broken into two discrete categories instead of having the generally continuous, tough-to-define, but more precise character of real life. However, they don’t insist on power analyses, which determine if a given study’s sample size is sufficient to make a statistical test valid.  An example from my birth weight study illustrates this: None of six previous studies using a model developed by Karasek found a direct association between job strain and birth outcomes. Four had low power for their logistic regression, which may have resulted in undetected effects. And instead of using the full range of values—500 to 4500 grams for birth weight—logistic regression uses only ‘low’ or ‘normal’ as outcomes, which results in a loss of variability and, thus, information. We would’ve needed twice the sample size in our study to achieve sufficient power using logistic regression. When my colleagues and I demonstrated that least squares regression detects an effect while logistic regression does not, the editor of AJPH was not impressed.

Why the one model fits all assumption regardless of whether it’s the best one? It fits with naturalized categories, like disease and race, which are seen as binary oppositions: yes/no, black/white.  This implicit model of the world is simply too rigid for anthropological sensibilities (Dressler, Oths, and Gravlee 2005). Newsflash: The world isn’t always best modeled by dichotomies.

In summary, when we strive to measure more accurately, we may meet with resistance from the gatekeepers of public health journals. Perhaps my outline of some common pitfalls of writing for an interdisciplinary audience will help reduce the frustration of others who attempt the same.

This was originally posted in Anthropology News‘ August 2015 “Knowledge Exchange.”