Abstract
The resurgence of interest in the effect of neighborhood contexts on health outcomes, motivated by advances in social epidemiology, multilevel theories and sophisticated statistical models, too often fails to confront the enormous methodological problems associated with causal inference. This paper employs the counterfactual causal framework to illuminate fundamental obstacles in the identification, explanation, and usefulness of multilevel neighborhood effect studies. We show that identifying useful independent neighborhood effect parameters, as currently conceptualized with observational data, to be impossible. Along with the development of a dependency-based methodology and theories of social interaction, randomized community trials are advocated as a superior research strategy, one that may help social epidemiology answer the causal questions necessary for remediating disparities and otherwise improving the public's health.
Original language | English (US) |
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Pages (from-to) | 1929-1952 |
Number of pages | 24 |
Journal | Social Science and Medicine |
Volume | 58 |
Issue number | 10 |
DOIs | |
State | Published - May 2004 |
Bibliographical note
Funding Information:This paper was supported by grant HL61573 from the National Heart, Lung and Blood Institute (NHLBI/NIH). Beyond the helpful recommendations of two anonymous reviewers, the comments and criticisms of several colleagues improved this paper. Thanks to Andre Araujo, Henry Blackburn, Heather R. Britt, Henry A. Feldman, David A. Freedman, Pamela Jo Johnson, Jay S. Kaufman, Ichiro Kawachi, David M. Murray, Stephen W. Raudenbush, Peter H. Rossi, Ruth N. Lopez Turley, and members of the Social Epi Workgroup, University of Minnesota. Special thanks to Ken Kleinman for abundant assistance on this and related endeavors. Formal research for this paper began whilst the author was at the New England Research Institutes, Watertown, MA, USA. The usual caveats apply.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
Keywords
- Assignment mechanism
- Cluster trial
- Community trial
- Counterfactual
- HLM
- Mixed model
- Propensity score