Authors:Abdulrahman M El-Sayed; Peter Scarborough; Lars Seemann; Sandro Galea First page: 1 Abstract: Abstract The past five years have seen a growth in the interest in systems approaches in epidemiologic research. These approaches may be particularly appropriate for social epidemiology. Social network analysis and agent-based models (ABMs) are two approaches that have been used in the epidemiologic literature. Social network analysis involves the characterization of social networks to yield inference about how network structures may influence risk exposures among those in the network. ABMs can promote population-level inference from explicitly programmed, micro-level rules in simulated populations over time and space. In this paper, we discuss the implementation of these models in social epidemiologic research, highlighting the strengths and weaknesses of each approach. Network analysis may be ideal for understanding social contagion, as well as the influences of social interaction on population health. However, network analysis requires network data, which may sacrifice generalizability, and causal inference from current network analytic methods is limited. ABMs are uniquely suited for the assessment of health determinants at multiple levels of influence that may couple with social interaction to produce population health. ABMs allow for the exploration of feedback and reciprocity between exposures and outcomes in the etiology of complex diseases. They may also provide the opportunity for counterfactual simulation. However, appropriate implementation of ABMs requires a balance between mechanistic rigor and model parsimony, and the precision of output from complex models is limited. Social network and agent-based approaches are promising in social epidemiology, but continued development of each approach is needed. PubDate: 2012-02-01 DOI: 10.1186/1742-5573-9-1 Issue No:Vol. 9, No. 1 (2012)
Authors:Mike Davern; Lynn A Blewett; Brian Lee; Michel Boudreaux; Miriam L King First page: 2 Abstract: Abstract The Integrated Health Interview Series (IHIS) is a public data repository that harmonizes four decades of the National Health Interview Survey (NHIS). The NHIS is the premier source of information on the health of the U.S. population. Since 1957 the survey has collected information on health behaviors, health conditions, and health care access. The long running time series of the NHIS is a powerful tool for health research. However, efforts to fully utilize its time span are obstructed by difficult documentation, unstable variable and coding definitions, and non-ignorable sample re-designs. To overcome these hurdles the IHIS, a freely available and web-accessible resource, provides harmonized NHIS data from 1969-2010. This paper describes the challenges of working with the NHIS and how the IHIS reduces such burdens. To demonstrate one potential use of the IHIS we examine utilization patterns in the U.S. from 1972-2008. PubDate: 2012-03-30 DOI: 10.1186/1742-5573-9-2 Issue No:Vol. 9, No. 1 (2012)
Authors:Sharon Schwartz; Nicolle M Gatto; Ulka B Campbell First page: 3 Abstract: Abstract Causal inference requires an understanding of the conditions under which association equals causation. The exchangeability or no confounding assumption is well known and well understood as central to this task. More recently the epidemiologic literature has described additional assumptions related to the stability of causal effects. In this paper we extend the Sufficient Component Cause Model to represent one expression of this stability assumption--the Stable Unit Treatment Value Assumption. Approaching SUTVA from an SCC model helps clarify what SUTVA is and reinforces the connections between interaction and SUTVA. PubDate: 2012-04-03 DOI: 10.1186/1742-5573-9-3 Issue No:Vol. 9, No. 1 (2012)
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