Intersecting Processes | complexity & change in environment, biomedicine & society

January 24, 2011
by peter.taylor
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Heterogeneity within populations and subgroups in epidemiology

Idea: How people respond to treatment may vary from one subgroup to another–When is this a matter of chance or of undetected additional variables? How do we delineate the boundaries between subgroups?

Lagakos provides a statistician’s cautions about the significance of results derived from subgroups of the whole population, especially if the subgroups were only defined after exploring the data.
The opposite caution is that treating everyone as if they were from the same population (for good statistical reasons) distracts our attention from the clues that might lead us to seeing that the population is not one uniform whole, but is a mixture of types. This can have significant health care implications — see case studies about different kinds of breast cancer (Regan) and aspirin resistance.

Additional angles on heterogeneity are evident in Eikelboom 2003, Gum 2003, Kahn 2007, and Nelson 2005.

(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].)

References

Eikelboom, J. W. and G. J. Hankey (2003). “Aspirin resistance: a new independent predictor of vascular events?” Journal of the American College of Cardiology 41: 966-968.
Gum, P. A., K. Kottke-Marchant, et al. (2003). “A prospective, blinded determination of the natural history of aspirin resistance among stable patients with cardiovascular disease.” Journal of the American College of Cardiology 41: 961-965.
Kahn, J. (2007). “Race in a Bottle.” Scientific American(July 15).
Lagakos, S. W. (2006). “The challenge of subgroup analysis–Reporting without distorting.” New England Journal of Medicine 354: 1667-1669.
Nelson, M. R., D. Liew, et al. (2005). “Epidemiological modelling of routine use of low dose aspirin for the primary prevention of coronary heart disease and stroke in those aged >=70.” British Medical Journal 330: 1306-1311.
Regan, M. M. and R. D. Gelber (2005). “Predicting response to systematic treatments: Learning from the past to plan for the future.” The Breast 14: 582-593.

January 23, 2011
by peter.taylor
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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.

References

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.

January 22, 2011
by peter.taylor
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Confounders and conditioning of analyses

Idea: Statistical associations between any two variables generally vary depending on the values taken by other “confounding” variables. We need to take this dependency (or conditionality) into account when using our analyses to make predictions or hypothesize about causes, but how do we decide which variables are relevant and real confounders?

In cases such as the following, a range of ways can be seen for adjusting for confounding variables (which includes age-standardization). The questions for students explore that as well as controversies or discordant views about how to do adjustment and eliminate confounding.

Immunization levels (Egede): Note the conclusion about racial/ethnic inequality even after adjusting for other variables thought to correlate with race/ethnicity. Do you agree with the three implications p. 326ff) drawn from the results?

SES gradients in disease (Krieger): The abstract states that “for virtually all outcomes, risk increased with CT [census tract] poverty, and when we adjusted for CT poverty, racial/ethnic disparities were substantially reduced.” Where can the result of adjustment be seen in the paper?

Hormone replacement therapy (Prentice vs. Petitti): Notice the adjustments used by the first paper that bring the clinical component of the WHI hormone replacement trial into line with the observational component. Do Pettiti acknowledge and rebut this in concluding that it was wrong to think that hormone therapy prevents CV disease?

Birth weight and blood pressure (Huxley vs. Davies): Along with Huxley et al’s general argument that the birthweight-adult blood pressure association may well be an artifact of selective publication of studies with small sample size, they criticise the adjustment of the association for adult weight. (In other words, the association holds for people in the same stratum or slice of weight.) Try to form an opinion about whether you agree or disagree with such an adjustment. Davies et al. provide counter-evidence to Huxley et al. — how does their study differ in methods, results, and interpretation?

Control at work and mortality (Davey-Smith 1997): This simple study shows that “control at work” is not the cause of SES gradients in health outcomes. What method(s) do they use to undermine previous claims about control at work?

Mendelian randomization to analyze environmental exposures (Davey-Smith & Ebrahim 2007): The approach introduced in this paper is cutting edge “epidemiology in the age of genomics” and has led to funding of a major new Research Center under Davey-Smith at Bristol. I suggest that you summarize for yourself the logic of this approach so you can explain it to someone who’s never heard of it.

References

Davey-Smith, G. and S. Harding (1997). “Is control at work the key to socioeconomic gradients in mortality?” Lancet 350: 1369-1370.

Davey-Smith, G. and S. Ebrahim (2007). “Mendelian randomization: Genetic variants as instruments for strengthening causal influences in observational studies. Pp 336-366 in Weinstein, M., Vaupel, J. W., Wachter, K.W. (eds) Biosocial Surveys. Washington, DC, National Academies Press.

Davies, A., G. Davey-Smith, et al. (2006). “Association between birth weight and blood pressure is robust, amplifies with age, and may be underestimated.” Hypertension 48: 431-436.

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.

Hernan, M. A. (2002). “Causal Knowledge as a Preequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology.” American Journal of Epidemiology 155: 176-184.

Huxley, R., A. Neil, et al. (2002). “Unravelling the fetal origins hypothesis: is there really an inverse association between birthweight and subsequent blood pressure?” Lancet 360(9334): 659-65.

Lawlor, D. A., G. Davey-Smith, et al. (2004). “Those confounded vitamins: what can we learn from the differences between observational versus randomised trial evidence?” The Lancet 363: 1724-1726.

Lynch, J. (2007) Relevant Risk, Revolution and Revisiting Rose – Causes of Population Levels and Social Inequalities in Health. http://cpheo4.sph.umn.edu/ramgen/vcontent/healthdisparities/lynch/lynch.smil

Petitti, D. B. and D. A. Freedman (2005). “Invited Commentary: How Far Can Epidemiologists Get with Statistical Adjustment?” American Journal of Epidemiology 162: 415-418.

Prentice, R. L., R. Langer, et al. (2005). “Combined Postmenopausal Hormone Therapy and Cardiovascular Disease: Toward Resolving the Discrepancy between Observational Studies and the Women’s Health Initiative Clinical Trial.” American Journal of Epidemiology 162(5): 404-414.

January 21, 2011
by peter.taylor
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Associations, Predictions, Causes, and Interventions

Idea: Relationships among associations, predictions, causes, and interventions run through all the cases and controversies in this course. The idea introduced in this session is that epidemiology has two faces: One from which the thinking about associations, predictions, causes, and interventions are allowed to cross-fertilize, and the other from which the distinctions among them are vigorously maintained, as in “Correlation is not causation!” The second face views Randomized Control Trial (RCTs) as the “gold-standard” for testing treatments in medicine. The first face recognizes that many hypotheses about treatment and other interventions emerge from observational studies and often such studies provide the only data we have to work with. What are the shortcomings of observational studies we need to pay attention to (e.g., systematic sampling errors leading to unmeasured confounders-see next post)?

Ridker et al. show that the conventional risk factors for heart disease in women (as combined in the Framingham score) identify many women as of intermediate risk who are higher or lower risk. The new Reynolds Risk Score does a much better job, primarily it seems by including the risk marker cReactive Protein. Both scores are based on observations not randomized trials. (But see Shunkert for recent assessment of the role of CRP.)

The case of hormone replacement therapy as a protection against heart disease (Stampfer 1990) is another, more significant instance of mismatch of observational results and RCTs — see Stampfer 2004 & Pettiti for analyses of the discrepancy. It is important to get a handle on the different kinds of explanation for this and other discrepancies, including physician bias in who gets prescribed a treatment, residual confounders, and reverse causation.

Jick presents evidence that statin treatment was associated with lowered risk of dementia but the Alzheimer Research Forum presents the more recent assessment (using RCTs) that statins are not protective against dementia. The discrepancy seems to be undetected bias in which patients get prescribed statins.

Davey-Smith & Ebrahim (2007, pp.2-8) provide a quick review of a number of cases.

References

Alzheimer_Research_Forum (2004). “Philadelphia: All Is Not Well with the Statin Story.” http://www.alzforum.org/new/detailprint.asp?id=1046.
Davey-Smith, G. and S. Ebrahim (2007). “Mendelian randomization: Genetic variants as instruments for strengthening causal influences in observational studies. Pp 336-366 in Weinstein, M., Vaupel, J. W., Wachter, K.W. (eds) Biosocial Surveys. Washington, DC, National Academies Press.
Jick, H., G. L. Zomberg, et al. (2000). “Statins and the risk of dementia.” Lancet 356: 1627-1631.
Petitti, D. B. and D. A. Freedman (2005). “Invited Commentary: How Far Can Epidemiologists Get with Statistical Adjustment?” American Journal of Epidemiology 162: 415-418.
Ridker, P. M., J. E. Buring, et al. (2007). “Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women: The Reynolds Risk Score.” Journal of the American Medical Association 297: 611-619.
Schunkert, H. and N. J. Samani (2008). “Elevated C-Reactive Protein in Atherosclerosis – Chicken or Egg?” New England Journal of Medicine 359(18): 1953-1955.
Stampfer, M. J. and G. A. Colditz (1991). “Estrogen replacement therapy and coronary heart disease: a quantitative assessment of the epidemiologic evidence.” Preventive Medicine 20: 47-63.
Stampfer, M. J.(2004) “Commentary: Hormones and heart disease: do trials and observational studies address different questions?” International Journal of Epidemiology 33: 545-455.

January 20, 2011
by peter.taylor
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Categories in epidemiology

Idea: Collecting and analyzing data requires categories: Have we omitted relevant categories or mixed different phenomena under one label? What basis do we have for subdividing a continuum into categories? How do we ensure correct diagnosis and assignment to categories? What meaning do we intend to give to data collected in our categories?

1. We can identify a chain of steps in scientific inquiry in which each step involves assumptions and is open for negotiation and wider influences (Taylor 2005, chapter 2).

All possible phenomena

(-> experimental manipulation)
- -> phenomenon deemed interesting
  - -> questions asked
    - -> categories demarcated
      - -> observations made
        
        -> data collected
        
        -> patterns perceived
        
        -> predictions made
        
        and/or hypotheses about causes
        
        -> actions supported

Decisions made at early steps influence outcomes at later steps. E.g., if schizophrenia is used as a category as defined by the DSM, it is harder for a clinician to pay attention to the contextual and life history information of patients (Poland 2004). This is not a one-way sequence. There is also the possibility that desired outcomes for the later stages (especially the actions the researcher favors in advance and would like to be supported by the inquiry) influence decisions made at earlier steps (as indicated in the following schema).

2. When reading a study, take note of:

a) where the categories demarcated seem to favor certain kinds of action over others (e.g., Galton only collected data about similarities among relatives so there was no way he could explore hypotheses about non-hereditary or environmental influences or illuminate action regarding those influences); and
b) what kinds of remedies you would propose whenever the categories seem limited (e.g., disaggregate the category “approve of Congress,” which includes Democrats who want the Democratic majority in the Senate not to accede to fillibustering Republicans and Republicans who don’t want Democrats to get their way).

Hymowitz (2007) [not a scholarly article] disaggregates divorce rates in the USA, which hide different phenomena and trends in different social classes.

Pickles and Angold (2003) review the debate about whether categories of psychopathology are best thought of as categorical (e.g., one has schizophrenia or doesn’t) or dimensional (e.g., there are degrees of schizophrenic behavior).

Poland (picking up on both points above) argues that the category “of schizophrenia and the associated received view [does not] have anything useful to add to clinical practice concerned with severe mental illness.”

George Brown (UK) and Bruce Dohrenwend (USA) have done research for decades on the relationship between mental illness and life events or difficulties. Brown (as described by Birley and Goldberg 2000) developed methods that tried to expose the meaning of an event for the person and was critical of the US emphasis on “objective” surveys (where the same event, e.g, death of a spouse, might have very different meanings and significance for the subject). Dohrenwend describes his group’s eventual realization of this issue, but they still wanted to measure events without having the context fused into the rating of the event.

Davey-Smith et al. (2000) consider comparative methods for studying socioeconomic position and health in different ethnic communities, e.g., — Does socio-economic status (SES) mean the same thing for different communities? If not, what is our proposed remedy?

References

Birley, J. and D. Goldberg (2000). George Brown’s contribution to psychiatry: The effort after meaning. Where Inner and Outer Worlds Meet. T. Harris. London, Routledge: 55-60.

Brown, G. W. and T. O. Harris (1978). Sociology and the aetiology of depression; Depression and Loss; A Model of Depression; Summary and conclusions. Social Origins of Depression: a Study of Psychiatric Disorder in Women. New York, Free Press: 3-20; 233-293.

Davey-Smith, G. et al. (2000). Ethnicity, health and the meaning of socio-economic position Pp. 25-37 In Graham, H., Ed. Understanding health inequalities. Buckingham [England], Open University Press.

Dohrenwend, B. P., K. G. Raphael, et al. (1993). The structured event probe and narrative rating method for measuring stressful life events. Handbook of Stress: Theoretical and Clinical Aspects. L. Goldberg and S. Breznitz. New York, Free Press: 174-199.

Hymowitz, K. S. (2007). “Marriage and Caste in America: Separate and Unequal Families in a PostMarital Age.” Heritage Lecture #1005.

Pickles, A. and A. Angold (2003). “Natural categories or fundamental dimensions: On carving nature at the joints and the rearticulation of psychopathology.” Development and Psychopathology 15: 529-551.

Poland, J. (2004). “Bias and schizophrenia.” Pp. 149-161 in P. J. Caplan and L. Cosgrove, eds. Bias in Psychiatric Diagnosis. Lanham, MD: Rowman & Littlefield.

Taylor, P. J. (2005). Unruly Complexity: Ecology, Interpretation, Engagement. Chicago, University of Chicago Press.

January 19, 2011
by peter.taylor
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The scope and challenges of epidemiology II: Rose's population health emphasis

Idea: In advising on the most effective measures to be taken to improve the health of a population, epidemiologists may focus on different determinants of the disease than a doctor would when faced with sick or high-risk individuals.

Rose (1985) promotes the population health focus, but this is not universally accepted by healthcare practitioners and policy makers. If someone asks you the question Rose’s mentor posed, “Why did this patient get this disease at this time?,” how do you answer? Can you identify areas in your own life and/or work when you would take a population view and other areas where your focus would be individually-centered?

(Repeating text from a recent post): Road accidents and alcohol consumption may be a good illustration of Rose’s argument. Most of us know of getting home safely when we’ve drunk too much “risk factor,” but we also know that a substantial fraction of people in accidents have high alcohol levels. We also sense that some people are more susceptible to having their judgement and reaction times impaired by alcohol so we could imagine doing further epidemiological and biological research to develop multivariable risk factor formulas. Would a more refined knowledge of riskiness help us prioritize our risk-prevention efforts, or would that pale into insignificance relative to a Rosean drink-don’t-drive efforts?

Controversy over vaccination of girls for HPV, given the physical side effects (at a low rate — see http://www.usatoday.com/news/health/2009-08-31-hpv-gardasil_N.htm) and promiscuity-inducing side effects (no data for this). Question: What would Rose propose?

Question: Why isn’t a population an aggregation of individuals and thus population risk = sum of individual risks?
My response: 1. It is necessary to think of different meaning of “treatment.” A sick individual is treated by a physician to cure or reduce the effects of the disease. Population health policies do not treat a large group of sick people, but attempt to reduce the incidence in the next generation.

2. A physician treating sick individuals adjusts the treatment for individuals if it doesn’t work well for them. In contrast, public health measures usually discount the heterogeneity in the population and apply the same policy to all. Nevertherless, it is possible to imagine that knowledge of heterogeneous responses to treatment of individuals could lead to more effective population health policies (and reduce the kickback that occurs when some individuals claim to have suffered under the population health policy).

Reference

Rose, G. (1985). “Sick individuals and sick populations.” International Journal of Epidemiology 14: 32-38. Reprinted in IJE 30: 427-432 (2001)

January 18, 2011
by peter.taylor
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The scope and challenges of epidemiology I

Idea 1: The uses of epidemiology are many, but shift over time, and are subject to recurrent challenges from inside and outside the field.

The following articles provide a variety of historical perspectives and opinion statements on this idea:

Davey-Smith, G. (2001). “The uses of Uses of Epidemiology.” International Journal of Epidemiology 30: 1146-1155.

(See Davey-Smith’s conversation with Jerry Morris, the author of the Uses of Epidemiology, http://bit.ly/g9DDHz)

Brandt, A. M. and M. Gardner (2000). “Antagonism and accommodation: interpreting the relationship between public health and medicine in the United States during the 20th century.” American Journal of Public Health 90: 707-715 — the title conveys the point: physicians have often opposed an increasing role of public health. Epidemiology might be needed for quantitative assessment of new interventions and evaluating patient safety and healthcare quality, but its role beyond evaluation and assessment, especially in regards to social, cultural, and economic factors of diseases, is contested.

Caldwell, J. C. (2001). “Population health in transition.” Bulletin of the World Health Organization 79(2): 159-160.

Pearce, N. (1996). “Traditional epidemiology, modern epidemiology, and public health.” American Journal of Public Health 86: 678-683 — Pearce argues that modern epidemiologists have little concern for the socioeconomic factors that may affect health. He contrasts “bottom-up” and “top-down” approaches. The latter begins at the population level in order to determine the primary factors that effect health, and it uses a structural model of causation. The bottom-up approach, e.g., molecular epidemiology, begins on the individual level and aims to proceed upward toward the population level.

Schwartz, S., E. Susser, et al. (1999). “A Future for Epidemiology?” Annual Review of Public Health 20: 15-35.

January 17, 2011
by peter.taylor
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Phenomena in epidemiology: Exploring the "natural history" of disease

Idea: Detailed observation (like a naturalist) or detective work–albeit informed by theoretical ideas–may be needed before we can characterize what the phenomenon is we are studying, what questions we need to ask, and what categories we need for subsequent data collection and analysis.

1. The initial motivation for this class was to highlight that epidemiology does not necessarily begin with data sets to analyze. There may be exploratory, investigative, detective, anthropological, and naturalist inquiries before phenomena are even noticed, categories are defined, questions are framed. Good examples of this seemed to be provided by John Snow’s work on cholera, Barker’s (1971) research in Uganda, and on “clues from geography” of infant mortality and heart disease (1998), and the three Lancashire towns, and Oxford’s account of the conditions that provided a source for a global pandemic of the 1918 flu. (40 million died from flu, while 8.5m died from war.) Even Barker’s (1999) speculation about anomalous French cardiovascular disease rates looks like someone who is able to connect dots of diverse kinds and that are spread out in time.

2. Brody’s paper, in addition to drawing attention to the role of maps in this exploratory research, makes the Snow story more complicated and interesting. Snow had clear hypotheses that guided his mapping and his advocacy of stopping the water supply from the Broad St pump — he was certainly not simply noticing patterns in the data and hypothesizing about the causes. This account opens up broader questions in philosophy of science. E.g., where do hypotheses that get assessed by research come from in the first place?

References

Barker, D. J. P. (1971). “Buruli disease in a district of Uganda.” Journal of Tropical Medicine and Hygiene 74: 260-264.

Barker, D. J. P. (1998). Mothers, Babies, and Health in Later Life. Edinburgh, Churchill Livingstone, pp1-12, 167-172.

Barker, D. J. P. (1999). “Commentary: Intrauterine nutrition may be important.” British Medical Journal 318: 1471-1480. (http://www.bmj.com/cgi/content/full/318/7196/1471#resp2)

Brody, H., M. R. Rip, et al. (2000). “Map-making and myth-making in Broad Street: the London cholera epidemic, 1854.” The Lancet 356: 64-68.

Oxford, J. S., R. Lambkin, et al. (2005). ” A hypothesis: the conjunction of soldiers, gas, pigs, ducks, geese and horses in northern France during the Great War provided the conditions for the emergence of the “Spanish” influenza pandemic of 1918-1919.” Vaccine 23(7): 940-945.

PBS Home Video. (2004). “Killer flu”

January 16, 2011
by peter.taylor
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Learning community and reading strategies for epidemiological literacy

Ideas: Developing epidemiological literacy requires:

1) collaboration with others (of differing skills and interests) and reflection on personal and professional development.

2) establishing our own practices of learning from material we don’t fully grasp at first reading/hearing.

These two ideas are the first in a sequence of basic ideas in thinking like epidemiologists (see introduction).

To begin to address idea 1, students in the first class of my course are asked to identify personal, intellectual, professional interests in relation to central themes about inequality, pathways of development, and policy (worksheet, followed by spoken introductions, in which one member of a pair introduces the other [after the pairs have introduced themselves to each other]). The worksheets and introductions acknowledge that the students bring a particular set of interests, knowledge, experience into the learning activities and interactions ahead.

To begin to address idea 2, I pre-distribute a newspaper article [e.g., Rabin, R. (2009). “Rare Side Effect Is Seen in Long-Term Use of a Breast Cancer Drug,” New York Times (August 26)], asking students to

make notes on: what you learned; questions the article raised for you; and where you skimmed/skipped because you did not understand/appreciate the technical detail. Think about the specific steps you’d take –- other than waiting for the instructor to explain everything — to address the questions and to understand/appreciate more. In other words, the purpose of reading this article now is not to critically understand the research right away, but to get us thinking and talking about how we establish our own practices of learning from material we don’t fully grasp at first reading.

We compare our readings of the article, then discuss our reading/learning strategies.

Also on learning/reading strategies, we watch John Lynch’s video that relates to effective interventions, absolute versus relative risk, and how health disparities are conceived.

Start to watch the video at http://cpheo4.sph.umn.edu/ramgen/vcontent/healthdisparities/lynch/lynch.smil
(You should see John in the top corner, hear his voice, and see his powerpoints in the large box. If this is not the case, you’ll have to try another computer.)
Again, make notes on: what you learn; questions the video raises for you; and where you skim/skip/stall/rewind because you do not understand/appreciate the technical detail. Stop when you feel too lost or frustrated – overwhelming you is not the point of watching this video now. Rather, the point is as for the article (see above) and also to provide a benchmark against which you can appreciate how much you’ve learned as the semester progresses.

In class we watch some of the video, then discuss our listening/learning strategies.

As an alternative to the activities above, we might examine the progression of the translation of research for the general audience on a particular epidemiological finding (e.g., an activity based on Caspi et al.’s 2002 paper linking the MAOA Gene, Childhood Maltreatment, and Adult anti-social behavior).

January 15, 2011
by peter.taylor
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Epidemiological Literacy

In 2007 I started teaching a graduate course on Epidemiological Thinking for non-specialists, in which I, as a non-specialist myself, emphasized epidemiological literacy with a view to collaborating thoughtfully with specialists, not technical expertise (see http://bit.ly/epicourse). More people should be acquainted or even conversant with epidemiological thinking (in my opinion, but that’s what blogs are for) and teaching epidemiology as a statistical methods course pushes many people away (or leaves them with the “I hate stats attitude”).

The current course description reads:

Introduction to the concepts, methods, and problems involved in analyzing the biological and social influences on behaviors and diseases and in translating such analyses into population health policy and practice. Special attention given to social inequalities, changes over the life course, and heterogeneous pathways. Case studies and course projects are shaped to accommodate students with interests in diverse fields related to health and public policy. Students are assumed to have a statistical background, but the course emphasizes epidemiological literacy with a view to collaborating thoughtfully with specialists, not technical expertise.

The syllabus is organized around 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. As I prepare to teach it for the third time, I will use a series of posts to review that sequence of basic ideas, starting with “Phenomena: Exploring the ‘natural history’ of disease,” and ending with “Popular epidemiology and health-based social movements.”

Postscript, 19 Jan. ’11: The curriculum is now, Open Source-like, set up to take contributions, using http://bit.ly/EpiContribute, in the form of:

suggested revisions and additions to the ideas and their description;
additional or replacement readings related to any of the ideas; and
annotations of the current or suggested readings.

—

p.s. Shameless plug: The course will be offered again this spring (starting Jan. 26). It can be taken from a distance and welcomes a few additional registrants; see http://uc.umb.edu/dl/spring11/ppolg753l/