Tag Archives: health

Heterogeneities as an under-explored dimension of complexity theory (with examples from health, epidemiology, life course development)

This poster = taxonomy of heterogeneities + illustrations, raising issues about addressing or suppressing heterogeneity in social epidemiology and life course development, TbHPoster or http://bit.ly/TbHFeb14: “Troubled by Heterogeneity?: Control, Infrastructure and Participation in Social Epidemiology and Life Course Development”

It will be displayed at Complex Systems, Health Disparities, and Population Health: Building Bridges, February 24-25 in Bethesda, MD.

This post will be updated in light of any comments received.  Comments welcome in advance.


Health effects of income inequalities

Wilkinson summarizes his group’s research on the health effects of inequality in a TED talk at http://www.ted.com/talks/lang/en/richard_wilkinson.html.  Among developed countries, those with greatest inequality have worst average outcomes on many, many measures of health and other social measures (such as % of population in prison).  The same association holds among states within the USA.

Three things that are interesting to me are that:
Continue reading

Health: What’s Race got to do with it?

Take Home (from a presentation at Cambridge Science Festival, April 2012)
Many Knowledge claims in this area invite:

  • Consideration of Actions that follow from the Knowledge: What change could people pursue if they accept the Knowledge claim?
  • Questions for further Inquiry: What more do you want to know in order to—
      * clarify what people could do
      * clarify which people are interested in that action
      * understand more

Suppose you are a doctor seeing a patient with hypertension.  The patient is a 54 year-old black man.  What should you do?  What more do you want to know before deciding what to do?

K: The patient is a 54 year-old black man.  A: What should you do?  Q: What more do you want to know before deciding what to do?

K: Perhaps you already know more along the following lines:

blacks were less likely than whites to achieve treatment success with atenolol (P = .02) or prazosin (P = .03) and more likely with diltiazem (P = .05).  (Cushman et al., 2000)


(Atenolol is a beta blocker; prazosin lowers blood pressure by relaxing blood vessels ; diltiazem is a calcium channel blocker).

[Audience participation]

A: You might say to yourself, “I know that not all black men with hypertension do better with calcium channel blocker and worse with beta blockers, but on average they do.  So I’ll prescribe diltiazem and see what happens.  If it doesn’t work well, I’ll change the medication.”

Q: But you might also say, “I want to know more about this black man and learn to make decisions not simply based on averages.”

If so, let me trace two paths of learning more that you might follow, first around racism and second around ancestry.

Racism path

K: “This is a 54 y.o. black man, or, at least, that is how he appears to me…”

K->Q: You may know that not every man that looks black identifies as African-American, so you might first ask if he does.

K->Q: Even if he doesn’t self-identify that way, you might think, “Given his skin color, he may have experienced racial discrimination.  Let me ask him whether he has.”

K->Q: You may know that self-reports of experience of racism are conditioned by class, so you might look for and use a survey that is better at probing beyond self-reports.

K, A: You might, however, think, “Having someone answer questions on a survey and then interpreting the results takes time and the insurance company doesn’t reimburse me for that time.  What would I do with what I learn, anyway?”

Q: Yet, if enough doctors collected the survey data—or even the self-reports of experiences of discrimination—then researchers could assess whether there was an association between experiences of discrimination and the subjects’ responses to the various hypertension medications.

K: After all, it’s implausible that experiences of discrimination have no physical effect on our bodies and it’s plausible that these effects might add up over time to produce differences in ways that bodies are hypertensive.

K, Q, A: “OK,” you might say, “I’m interested in this line of inquiry, but I won’t start asking my patients about experiences of racial discrimination until researchers have shown clear associations between experiences of discrimination and the subjects’ responses to the various hypertension medications so that I know what anti-hypertension medicine would work best.”

K, A: Understandable that you say this, but instead of waiting for such results, you might do some reading after hours about the positive effects of acknowledging the topic of discrimination and, conversely, of pride in one’s identity.

Ancestry path

Now let’s shift to the ancestry line of inquiry:

K: “This is a 54 y.o. black man, or, at least, that is how he appears to me…”

K->Q: You may know that not every man that looks black identifies as African-American, so you might first ask that.

K’: You might discover that he is an immigrant from Africa, not a descendant of slaves in the United States.

K->Q: Either way, you might know that genomic studies are starting to identify some genetic variants of biomedical significance that are more common in people of specific areas of Africa (e.g., Genovese etal. 2010).   You might ask, “Does your ancestry trace back to region X?”

K: There is a good chance that African-Americans won’t know this, or won’t know their places of ancestry with enough precision to be helpful, or will have multiple places of origin in Africa.  K: Moreover, if he is a descendant of slaves, you know he has probably has a lot of European ancestry.

Q: “OK, we need to do a genetic test to see if he has the genetic variant in question.”

K: But, on second thoughts, doing the test is expensive and the insurance company doesn’t reimburse me for that.

A->Q: In any case, what would you do with what you learned from the test?  Has any genetic variant been associated with one anti-hypertensive medication being more effective than the others?  Or is it simply telling us that the population of African-American men as a whole (meaning the population on average) has higher risk when they possess that variant?”

Q: Yet, if researchers collected more data about ancestry (which, given people’s imperfect knowledge of their ancestors, would probably require genetic markers) then researchers could assess whether there was an association between ancestry and responses to the different hypertension medications.

Q: But you might ask, “how plausible is it that such associations will be useful for clinical decisions, or even for further research?”

K->A?: You might ask this because you know that Genome-Wide Association studies “have not found common genes with a big impact on heart health” (Couzin-Frankel 2010).  There is heated debate about whether to “hope that the low-effect genes they are finding will help identify pathways and drug targets.”  (“Low effect” here means much smaller relative risk than between modifiable aspects of diet and behavior.)

Q: “OK,” you might say, “I wonder if I should be interested in this line of inquiry?  Let me see if anyone can show me what considerations I’m overlooking.”

K: Now, let me note one thing that has been put aside in this exposition of the two paths.  The Cushman study showing differential response to medication between blacks and whites was actually a study of differential responses to medication inside versus outside the “stroke belt,” that is, the “12 states with stroke mortality rates more than 10% above the mean rate for the rest of the United States: 10 of these states are in the south- eastern region.”  The authors conclude:

Hypertension in patients residing inside the Stroke Belt responded less to the use of several antihypertensive medications [even after controlling for race] and important differences were shown in a number of characteristics that may affect the control of blood pressure, compared with patients residing outside the Stroke belt.

Q: Perhaps the first questions a doctor might ask is “Do you come from the Stroke belt? Do your parents?”

K: In their spare time the doctor might learn about the competing theories for the existence of this belt (e.g., Howard et al. 2010, Kuzawa and Sweet 2009).


Couzin-Frankel , J.: 2010, Major Heart Disease Genes Prove Elusive. Science 328(5983),1220-1221.

Cushman, W; D.J. Reda; H. M. Perry; D. Williams; M. Abdellatif; B. J. Materson, Regional and Racial Differences in Response to Antihypertensive Medication Use in a Randomized Controlled Trial of Men With Hypertension in the United States, Arch Intern Med. 2000;160:825-831.

Genovese, G. et al. (2010), Association of Trypanolytic ApoL1 Variants with Kidney Disease in African Americans, Science 13 August 2010: 329 (5993), 841-845.

Howard, V. et al. (2010) Prevalence of hypertension by duration and age at exposure to the stroke belt, J Am Soc Hypertens. 2010 Jan–Feb; 4(1): 32–41.

Kuzawa C. and E. Sweet (2009), Epigenetics and the Embodiment of Race: Developmental Origins of US Racial Disparities in Cardiovascular Health. AMERICAN JOURNAL OF HUMAN BIOLOGY, 21(1); 2-15.

Variations in health care (by place, race, class, gender)

Idea: Inequalities in people’s health and how they are treated are associated with place, race, class, gender, even after conditioning on other relevant variables.

The issues here are not only variations or disparities, but also how to measure, track, and talk about those variations.
Krieger et al. started the the Public Health Disparities Geocoding Project because socioeconomic data is often lacking in US public health surveillance systems. Socioeconomic deprivation contributes to racial/ethnic health disparities in more than half of the cases studied.
Davey Smith advises against using ethnicity as a proxy for socioeconomic position and advocates for incorporating both in quantitative models.
Alter et al. conclude that despite Canada’s Universal Health Care System a individual’s socioeconomic status affected access to cardiac services and increased the prevalence of mortality.
Gawande describes how medical costs can be high even in poor areas; this results from the overuse of medicine from over-treating patients and over-prescribing tests and procedures.
Marmot and Wilkinson argue that researchers should look beyond material privation to examine psychosocial effects on variation in health outcomes, particularly relative deprivation concerning individual agency and control.
Wright et al.’s study of asthma among children in low-income urban settings found a correlation between asthma, stress, and exposure to violence that suggests the need for addressing these intervening variables. However, smoking was not found to be associated with asthma attack incidence.

The articles by Bassuk, Dunn, Egede, Roger raise additional perspectives.

(This post continues a series laying out a sequence of basic ideas in thinking like epidemiologists, especially epidemiologists who pay attention to possible social influences on the development and unequal distribution of diseases and behaviors in populations [see first post in series and contribute to open-source curriculum http://bit.ly/EpiContribute].)


Alter, D. A., C. D. Naylor, et al. (1999). “Effects of socioeconomic status on access to invasive cardiac procedures and on mortality after acute myocardial infarction.” New England Journal of Medicine 341: 1359-1367.
Bassuk, S. S., L. F. Berkman, et al. (2002). “Socioeconomic Status and Mortality among the Elderly: Findings from Four US Communities.” American Journal of Epidemiology 155: 520-533.
Davey-Smith, G. (2000). “Learning to live with complexity: Ethnicity, socioeconomic position, and health in Britain and the United States.” American Journal of Public Health 90: 1694-1698.
Dunn, J. R. and S. Cummins (2007). “Placing health in context.” Social Science & Medicine 65: 1821-1824
Egede, L. E. and D. Zheng (2003). “Racial/Ethnic Differences in Adult Vaccination Among Individuals With Diabetes.” American Journal of Public Health 93(2): 324-329.
Gawande, A. (2009). “The cost conundrum: What a Texas town can teach us about health care.” The New Yorker (1 June).
Krieger, N., J. T. Chen, et al. (2005). “Painting a truer picture of US socioeconomic and racial/ethnic health inequalities: The Public Health Disparities Geocoding Projec.” American Journal of Public Health 95: 312-323.
Marmot, M. and R. G. Wilkinson (2001). “Psychosocial and material pathways in the relation between income and health: a response to Lynch et al ” British Medical Journal 322: 1233-1236.
Roger, V. L., M. E. Farkouh, et al. (2000). “Sex Differences in Evaluation and Outcome of Unstable Angina.” Journal of the American Medical Association 283: 646-652.
Wright, R. J., H. Mitchell, et al. (2004). “Community Violence and Asthma Morbidity: The Inner-City Asthma Study ” American Journal of Public Health 94: 625-632.

Rehabilitating a biological notion of race? II

Sesardic (2010) makes the point that the fact that genetic variation within a group is of larger than variation between (the average of) the groups does not mean that the groups cannot be distinguished. This point is not, however, sufficient to rehabilitate a biological picture of race.  I continue from the previous post to sketch the issues we face once we delve deeper into the relevant scientific knowledge, concepts, methods, and questions for inquiry…

2. Putting point #1 aside, suppose we imagine an original human gene pool that dispersed at some point of time from its origins in Africa around the world and was not subject to subsequent breeding among widely dispersed parts of the pool. Cluster analysis techniques could be used on genetic data to divide humans into, say, N groups. Such clustering techniques are sensitive to assumptions that determine whether groups are of roughly equal size or are a mix of a few large groups. If we looked for groups that had similar within-group genetic variation, most of the N groups would be in Africa. In other words, the traditional subdivisions of human races would have to be reformulated. However, experience using cluster analysis in large agricultural data sets (Cooper and Hammer 1996) suggests that many individuals cannot be consistently be assigned to one group versus another; the grouping changes according to what traits (in our case, variants at genetic loci or SNPs) and how much each group is represented in the data.

3. Suppose we now add migration subsequent to the initial dispersal, but without cross-breeding among the groups. Picking up this last point in #2, if individuals from non-African groups outweigh those from African groups in, say, the United States, then how well could we recover from the U.S. data all the groups delineated in #2? That would be an empirical question, but the experience from agriculture warns us not to be optimistic.

4. Of course, #3 is only a thought-experiment. There has been considerable migration and cross-breeding subsequent to the initial dispersal from the place of human origin in Africa, including but not confined to the recent centuries of cross-Atlantic slavery and master-slave relations. How well could we recover from current individuals the one or more groups (as delineated in #2) that make up the individuals´ ancestry? Again, this is an empirical question. Biomedical researchers do not have to be politically biased to judge that research efforts might be more fruitfully directed along other avenues, such as those indicated by biomedical correlates of socially defined race (i.e., not the groups that would emerge from the cluster analyses in #2).

5. Perhaps, we could ask less than we have in #4. Rather than full recovery of original ancestries, we might seek simply want to predict whether an individual patient has some major biomedically relevant genes that differed, on average, among the original groups. These predictions, necessarily probabilistic, would be limited in value given the recently-emerged consensus that most medically significant traits are associated with many genes of quite small effect (McCarthy et al. 2008). Moreover, given that the groups delineated in #2 would not match the traditional subdivisions of human races or those current in the U.S.—there would be several different groups of African origin—medical practitioners would need to disregard superficial assignments to racial groups. They might just as well test directly for the presence or absence of the biomedically relevant genes.

6. If we put #1-5 aside and imagine a world in which we were able to use genetic information to assign humans to original post-dispersal groups as reliably as in the statistics class we were able to assign individuals to male and female groups. What could we do with that knowledge that there is a difference between the average genetic profiles for groups A and B when there is large within-group variation for most genetic loci (at least, for those that vary within the human species)? Let me accentuate this question with using the IQ test score case Sesardic has paid considerable attention to (2005). Suppose we knew (which we do not) that only a certain small set of genes influenced IQ test scores. What could we do with the knowledge that there is a large difference between the average IQ test score for two groups and this difference is smaller than the within-group variation? (To visualize this situation, imagine one of the axes in the Figure is IQ test score.) I would not use my ability to assign humans to original post-dispersal groups based on genetic profiles as grounds for using an individual´s membership in a group to make educational or employment decisions for the individual. But I will not speak for Sesardic; I do not know what he thinks would follow if a biological view of race were to be rehabilitated along the lines he discusses.

7. There are clearly many issues to be delved deeply into before the relevant science about human genetic variation would support a biological notion of human ancestral groupings. A biological notion of socially and historically varying racial categories lies well outside the scope of “what the best contemporary science tells us about human genetic variation.”

Cooper M, Hammer GL (eds) (1996). Plant Adaptation and Crop Improvement. CAB International, Wallingford, UK.

McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JPA, Hirschhorn JN  (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics 9: 356-369.

Sesardic N (2010) Race: A Social Destruction of a Biological Concept.  Biology and Philosophy 25:143-162.

Rehabilitating a biological notion of race?

Neven Sesardic, a philosopher of science, is critical of positions accepted by liberal-left philosophers about heritability, genes, IQ test scores, and racial differences. His guiding theme is that philosphers need to delve more deeply into the science as they consider their arguments (2000, 2005). In his latest contribution in this spirit, Sesardic (2010) argues that ”the biological notion of race (is not at all inconsistent) with what the best contemporary science tells us about human genetic variation.” In particular, the fact that genetic variation within a group is of larger than variation between (the average of) the groups, does not mean that the groups cannot be distinguished.

Let me affirm this last point with an example from a course I once took in multivariate statistics. We could not say with confidence whether a student was male and female on the basis of their height—there was too much overlap of the ranges—or, for the same reason, on the basis of their hip circumference. Yet a simple linear function that subtracted hip from height was very reliable in discriminating male from female students. In Sesardic´s figure, rotated 90 degrees below, height would be the x-axis, hip the y-axis; the squares the males, the triangles the females.

This point of Sesardic is not, however, sufficient to rehabilitate a biological picture of race.  In this post and the next, I sketch the issues we face once we delve deeper into the relevant scientific knowledge, concepts, methods, and questions for inquiry.

  1. Biology is more than genetic variation.  For example, experience of racial discrimination by African-American women has been associated with higher risk of pre-term delivery of their babies even after controlling statistically for other factors that increase that risk (Mustillo et al. 2004).  Race can be linked with biology even if races cannot be distinguished on the basis of genetic differences.

to be continued


Mustillo SA, Krieger N, Gunderson EP, Sidney S, McCreath H, Kiefe CI (2004) The Association of Self-Reported Experiences of Racial Discrimination with Black/White Differences in Preterm Delivery and Low Birth Weight: The CARDIA Study. American Journal Public Health 94:2125-2131.

Sesardic N (2000) Philosophy of Science that Ignores Science: Race, IQ and Heritability. Philosophy of Science 67: 580-602.

Sesardic N (2005) Making Sense of Heritability. Cambridge University Press, Cambridge.

Sesardic N (2010) Race: A Social Destruction of a Biological Concept.  Biology and Philosophy 25:143-162.