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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. [ABSTRACT FROM AUTHOR]

Sufficient causes of disease are redundant when an individual acquires the components of two or more sufficient causes. In this circumstance, the individual still would have become diseased even if one of the sufficient causes had not been acquired. In the context of a study, when any individuals acquire components of more than one sufficient cause over the observation period, the etiologic effect of the exposure (defined as the absolute or relative difference between the proportion of the exposed who develop the disease by the end of the study period and the proportion of those individuals who would have developed the disease at the moment they did even in the absence of the exposure) may be underestimated. Even in the absence of confounding and bias, the observed effect estimate represents only a subset of the etiologic effect. This underestimation occurs regardless of the measure of effect used. To some extent, redundancy of sufficient causes is always present, and under some circumstances, it may make a true cause of disease appear to be not causal. This problem is particularly relevant when the researcher's goal is to characterize the universe of sufficient causes of the disease, identify risk factors for targeted interventions, or construct causal diagrams. In this paper, we use the sufficient component cause model and the disease response type framework to show how redundant causation arises and the factors that determine the extent of its impact on epidemiologic effect measures. [ABSTRACT FROM AUTHOR]

One possibility for the statistical evaluation of trends in epidemiological exposure studies is the use of a trend test for data organized in a 2 × k contingency table. Commonly, the exposure data are naturally grouped or continuous exposure data are appropriately categorized. The trend test should be sensitive to any shape of the exposure-response relationship. Commonly, a global trend test only determines whether there is a trend or not. Once a trend is seen it is important to identify the likely shape of the exposure-response relationship. This paper introduces a best contrast approach and an alternative approach based on order-restricted information criteria for the model selection of a particular exposure-response relationship. For the simple change point alternative H1 : π1 = …= πq < πq+1 = … = πk an appropriate approach for the identification of a global trend as well as for the most likely shape of that exposure-response relationship is characterized by simulation and demonstrated for real data examples. Power and simultaneous confidence intervals can be estimated as well. If the conditions are fulfilled to transform the exposure-response data into a 2 × k table, a simple approach for identification of a global trend and its elementary shape is available for epidemiologists. [ABSTRACT FROM AUTHOR]

While chronic exposure to secondhand smoke has been well recognized as a cause of heart disease in nonsmokers, there has been recent speculation about the potential acute cardiovascular effects of transient exposure to secondhand smoke among nonsmokers; in particular, the possibility that such exposure could increase the risk of acute myocardial infarction even in an otherwise healthy nonsmoker. This paper reviews the claims being made by a number of anti-smoking and public health groups regarding the acute cardiovascular effects of secondhand smoke exposure among otherwise healthy adults, analyzes the validity of these claims based on a review of the scientific evidence, and discusses the implications of the findings for tobacco control and public health practice. Based on the analysis, it appears that a large number of anti-smoking organizations are making inaccurate claims that a single, acute, transient exposure to secondhand smoke can cause severe and even fatal cardiovascular events in healthy nonsmokers. The dissemination of inaccurate information by anti-smoking groups to the public in support of smoking bans is unfortunate because it may harm the tobacco control movement by undermining its credibility, reputation, and effectiveness. Disseminating inaccurate information also represents a violation of basic ethical principles that are a core value of public health practice that cannot and should not be sacrificed, even for a noble end such as protecting nonsmokers from secondhand smoke exposure. How the tobacco control movement responds to this crisis of credibility will go a long way towards determining the future effectiveness of the movement and its ability to continue to save lives and protect the public's health. [ABSTRACT FROM AUTHOR]

This analysis presents a detailed defense of my epidemiologic research in the May 17, 2003 British Medical Journal that found no significant relationship between environmental tobacco smoke (ETS) and tobacco-related mortality. In order to defend the honesty and scientific integrity of my research, I have identified and addressed in a detailed manner several unethical and erroneous attacks on this research. Specifically, I have demonstrated that this research is not "fatally flawed," that I have not made "inappropriate use" of the underlying database, and that my findings agree with other United States results on this relationship. My research suggests, contrary to popular claims, that there is not a causal relationship between ETS and mortality in the U.S. responsible for 50,000 excess annual deaths, but rather there is a weak and inconsistent relationship. The popular claims tend to damage the credibility of epidemiology. In addition, I address the omission of my research from the 2006 Surgeon General's Report on Involuntary Smoking and the inclusion of it in a massive U.S. Department of Justice racketeering lawsuit. I refute erroneous statements made by powerful U.S. epidemiologists and activists about me and my research and I defend the funding used to conduct this research. Finally, I compare many aspect of ETS epidemiology in the U.S. with pseudoscience in the Soviet Union during the period of Trofim Denisovich Lysenko. Overall, this paper is intended to defend legitimate research against illegitimate criticism by those who have attempted to suppress and discredit it because it does not support their ideological and political agendas. Hopefully, this defense will help other scientists defend their legitimate research and combat "Lysenko pseudoscience." [ABSTRACT FROM AUTHOR]

This commentary accompanies two articles submitted to Epidemiologic Perspectives & Innovations in response to a call for papers about threats to epidemiology or epidemiologists from organized political interests. Contrary to our expectations, we received no submissions that described threats from industry or government; all were about threats from anti-tobacco activists. The two we published, by James E. Enstrom and Michael Siegel, both deal with the issue of environmental tobacco smoke. This commentary adds a third story of attacks on legitimate science by antitobacco activists, the author's own experience. These stories suggest a willingness of influential anti-tobacco activists, including academics, to hurt legitimate scientists and turn epidemiology into junk science in order to further their agendas. The willingness of epidemiologists to embrace such anti-scientific influences bodes ill for the field's reputation as a legitimate science. [ABSTRACT FROM AUTHOR]

When reporting incidence rate estimates for relatively rare health conditions, associated case counts are often assumed to follow a Poisson distribution. Case counts obtained from large-scale electronic surveillance systems are often inflated by the presence of false positives, however, and adjusted case counts based on the results of a validation sample will have variances which are hyper-Poisson. This paper presents a simple method for constructing interval estimates for incidence rates based on case counts that are adjusted downward using an estimate of the predictive value positive of the surveillance case definition. [ABSTRACT FROM AUTHOR]