Multilevel Thinking and Analysis in the Social Determinants of Health
Multilevel Thinking and Analysis in the Social Determinants of Health A core tenet of a ‘social determinants of health’ perspective is that causal drivers of population health and health disparities exist at multiple levels, including individuals, families, neighborhoods, and nation-states. Mirroring this multilevel perspective on disease distribution and causation, many public health datasets are similarly ‘multi-level’ with observations on individuals nested within clusters; clusters may be defined by residential community, hospital, school, state, or other grouping. Multileveled data present two challenges for public health analysts. First, clustered observations violate statistical assumptions of independence, producing biased estimates. Second, the causal questions of interest often focus on the relative importance of and interaction between contextual levels (e.g., neighborhood) and individual levels. Multilevel regression is a toolset for addressing each of these concerns. In this workshop, we will develop conceptual understanding of multilevel processes in health, and introduce and apply analytic methods in SAS for the analysis of multilevel data INSTRUCTOR: Dr. Michael Kramer is an Associate Professor in the Rollins School of Public Health Department of Epidemiology. Refund Policy: If enrollment is not large enough, we reserve the right to cancel this class by April 15, 2019. People who are enrolled will have their tuition fully reimbursed. Accomodations: Accommodation information can be accessed here. For questions related to the course, please contact Jessica Seiler; firstname.lastname@example.org
201 Dowman Drive
Atlanta, GA 30322
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