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Individual and collective bodies: using measures of variance and association in contextual epidemiology
  1. Juan Merlo1,
  2. Henrik Ohlsson1,
  3. Kristian F Lynch1,
  4. Basile Chaix2,
  5. S V Subramanian3
  1. 1 Unit for Social Epidemiology, Faculty of Medicine, Lund University, Sweden;
  2. 2 UMR-S 707 Inserm, Université Pierre et Marie Curie, Faculté de Médecine Saint-Antoine, Paris, Sweden;
  3. 3 Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, Sweden
  1. * Corresponding author; email: juan.merlo{at}med.lu.se

Abstract

Background: Social epidemiology investigates both individuals and their collectives. While the limits that define the individual bodies are very apparent, the collective body's geographical or cultural limits (e.g., “neighbourhood”) are more difficult to discern. Also, epidemiologists normally investigate causation as changes in group means. However, many variables of interest in epidemiology may cause a change in the variance of the distribution of the dependent variable. In spite of that, variance is normally considered a measure of uncertainty or a nuisance rather than a source of substantive information. This reasoning is also true in many multilevel investigations, whereas understanding the distribution of variance across levels should be fundamental. This means-centric reductionism is mostly concerned with risk factors and creates a paradoxical situation, since social medicine is not only interested in increasing the (mean) health of the population, but also in understanding and decreasing inappropriate health and health care inequalities (variance).

Methods: Critical essay and literature review.

Results: The present essay promotes (a) the application of measures of variance and clustering to evaluate the boundaries one uses in defining collective levels of analysis (e.g., neighbourhoods), (b) the combined use of measures of variance and means-centric measures of association, and (c) the investigation of causes of health variation (variance-altering causation).

Conclusions: Both measures of variance and means-centric measures of association need to be included when performing contextual analyses. The variance approach, a new aspect of contextual analysis that cannot be interpreted in means-centric terms, allows us to expand our perspectives.

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