http://www.epi-perspectives.com The most accessed research articles published by Epidemiologic Perspectives & Innovations 2012-03-30T00:00:00Z Austin Bradford Hill's landmark 1965 paper contains several important lessons for the current conduct of epidemiology. Unfortunately, it is almost exclusively cited as the source of the "Bradford-Hill criteria" for inferring causation when association is observed, despite Hill's explicit statement that cause-effect decisions cannot be based on a set of rules. Overlooked are Hill's important lessons about how to make decisions based on epidemiologic evidence. He advised epidemiologists to avoid over-emphasizing statistical significance testing, given the observation that systematic error is often greater than random error. His compelling and intuitive examples point out the need to consider costs and benefits when making decisions about health-promoting interventions. These lessons, which offer ways to dramatically increase the contribution of health science to decision making, are as needed today as they were when Hill presented them. http://www.epi-perspectives.com/content/1/1/3 Carl Phillips Karen Goodman Epidemiologic Perspectives & Innovations 2004, null:3 2004-10-04T00:00:00Z doi:10.1186/1742-5573-1-3 /content/figures/1742-5573-1-3-toc.gif Epidemiologic Perspectives & Innovations 1742-5573 ${item.volume} 3 2004-10-04T00:00:00Z XML 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. http://www.epi-perspectives.com/content/9/1/1 Abdulrahman El-Sayed Peter Scarborough Lars Seemann Sandro Galea Epidemiologic Perspectives & Innovations 2012, null:1 2012-02-01T00:00:00Z doi:10.1186/1742-5573-9-1 /content/figures/1742-5573-9-1-toc.gif Epidemiologic Perspectives & Innovations 1742-5573 ${item.volume} 1 2012-02-01T00:00:00Z XML Background: The WINPEPI computer programs for epidemiologists are designed for use in practice and research in the health field and as learning or teaching aids. The programs are free, and can be downloaded from the Internet. Numerous additions have been made in recent years.ImplementationThere are now seven WINPEPI programs: DESCRIBE, for use in descriptive epidemiology; COMPARE2, for use in comparisons of two independent groups or samples; PAIRSetc, for use in comparisons of paired and other matched observations; LOGISTIC, for logistic regression analysis; POISSON, for Poisson regression analysis; WHATIS, a "ready reckoner" utility program; and ETCETERA, for miscellaneous other procedures. The programs now contain 122 modules, each of which provides a number, sometimes a large number, of statistical procedures. The programs are accompanied by a Finder that indicates which modules are appropriate for different purposes. The manuals explain the uses, limitations and applicability of the procedures, and furnish formulae and references. Conclusions: WINPEPI is a handy resource for a wide variety of statistical routines used by epidemiologists. Because of its ready availability, portability, ease of use, and versatility, WINPEPI has a considerable potential as a learning and teaching aid, both with respect to practical procedures in the planning and analysis of epidemiological studies, and with respect to important epidemiological concepts. It can also be used as an aid in the teaching of general basic statistics. http://www.epi-perspectives.com/content/8/1/1 Joseph Abramson Epidemiologic Perspectives & Innovations 2011, null:1 2011-02-02T00:00:00Z doi:10.1186/1742-5573-8-1 /content/figures/1742-5573-8-1-toc.gif Epidemiologic Perspectives & Innovations 1742-5573 ${item.volume} 1 2011-02-02T00:00:00Z XML Background: The WINPEPI (PEPI-for-Windows) computer programs for epidemiologists are designed for use in practice and research in the health field and as learning or teaching aids. They aim to complement other statistics packages. The programs are free, and can be downloaded from the Internet.ImplementationThere are at present four WINPEPI programs: DESCRIBE, for use in descriptive epidemiology, COMPARE2, for use in comparisons of two independent groups or samples, PAIRSetc, for use in comparisons of paired and other matched observations, and WHATIS, a "ready reckoner" utility program. The programs contain 75 modules, each of which provides a number, sometimes a large number, of statistical procedures. The manuals explain the uses, limitations and applicability of specific procedures, and furnish formulae and references. Conclusions: WINPEPI provides a wide variety of statistical routines commonly used by epidemiologists, and is a handy resource for many procedures that are not very commonly used or easily found. The programs are in general user-friendly, although some users may be confused by the large numbers of options and results provided. The main limitations are the inability to read data files and the fact that only one of the programs presents graphic results. WINPEPI has a considerable potential as a learning and teaching aid. http://www.epi-perspectives.com/content/1/1/6 Joseph Abramson Epidemiologic Perspectives & Innovations 2004, null:6 2004-12-17T00:00:00Z doi:10.1186/1742-5573-1-6 /content/figures/1742-5573-1-6-toc.gif Epidemiologic Perspectives & Innovations 1742-5573 ${item.volume} 6 2004-12-17T00:00:00Z XML 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." http://www.epi-perspectives.com/content/4/1/11 James Enstrom Epidemiologic Perspectives & Innovations 2007, null:11 2007-10-10T00:00:00Z doi:10.1186/1742-5573-4-11 /content/figures/1742-5573-4-11-toc.gif Epidemiologic Perspectives & Innovations 1742-5573 ${item.volume} 11 2007-10-10T00:00:00Z XML Interaction measured on the additive scale has been argued to be better correlated with biologic interaction than when measured on the multiplicative scale. Measures of interaction on the additive scale have been developed using risk ratios. However, in studies that use odds ratios as the sole measure of effect, the calculation of these measures of additive interaction is usually performed by directly substituting odds ratios for risk ratios. Yet assessing additive interaction based on replacing risk ratios by odds ratios in formulas that were derived using the former may be erroneous. In this paper, we evaluate the extent to which three measures of additive interaction – the interaction contrast ratio (ICR), the attributable proportion due to interaction (AP), and the synergy index (S), estimated using odds ratios versus using risk ratios differ as the incidence of the outcome of interest increases in the source population and/or as the magnitude of interaction increases. Our analysis shows that the difference between the two depends on the measure of interaction used, the type of interaction present, and the baseline incidence of the outcome. Substituting odds ratios for risk ratios, when calculating measures of additive interaction, may result in misleading conclusions. Of the three measures, AP appears to be the most robust to this direct substitution. Formulas that use stratum specific odds and odds ratios to accurately calculate measures of additive interaction are presented. http://www.epi-perspectives.com/content/3/1/5 Linda Kalilani Julius Atashili Epidemiologic Perspectives & Innovations 2006, null:5 2006-04-18T00:00:00Z doi:10.1186/1742-5573-3-5 /content/figures/1742-5573-3-5-toc.gif Epidemiologic Perspectives & Innovations 1742-5573 ${item.volume} 5 2006-04-18T00:00:00Z XML As noted by Wesley Salmon and many others, causal concepts are ubiquitous in every branch of theoretical science, in the practical disciplines and in everyday life. In the theoretical and practical sciences especially, people often base claims about causal relations on applications of statistical methods to data. However, the source and type of data place important constraints on the choice of statistical methods as well as on the warrant attributed to the causal claims based on the use of such methods. For example, much of the data used by people interested in making causal claims come from non-experimental, observational studies in which random allocations to treatment and control groups are not present. Thus, one of the most important problems in the social and health sciences concerns making justified causal inferences using non-experimental, observational data. In this paper, I examine one method of justifying such inferences that is especially widespread in epidemiology and the health sciences generally – the use of causal criteria. I argue that while the use of causal criteria is not appropriate for either deductive or inductive inferences, they do have an important role to play in inferences to the best explanation. As such, causal criteria, exemplified by what Bradford Hill referred to as "aspects of [statistical] associations", have an indispensible part to play in the goal of making justified causal claims. http://www.epi-perspectives.com/content/6/1/2 Andrew Ward Epidemiologic Perspectives & Innovations 2009, null:2 2009-06-17T00:00:00Z doi:10.1186/1742-5573-6-2 /content/figures/1742-5573-6-2-toc.gif Epidemiologic Perspectives & Innovations 1742-5573 ${item.volume} 2 2009-06-17T00:00:00Z XML When carefully planned and analysed, the case-cohort design is a powerful choice for follow-up studies with multiple event types of interest. While the literature is rich with analysis methods for case-cohort data, little is written about the designing of a case-cohort study. Our experiences in designing, coordinating and analysing the MORGAM case-cohort study are potentially useful for other studies with similar characteristics. The motivation for using the case-cohort design in the MORGAM genetic study is discussed and issues relevant to its planning and analysis are studied. We propose solutions for appending the earlier case-cohort selection after an extension of the follow-up period and for achieving maximum overlap between earlier designs and the case-cohort design. Approaches for statistical analysis are studied in a simulation example based on the MORGAM data. http://www.epi-perspectives.com/content/4/1/15 Sangita Kulathinal Juha Karvanen Olli Saarela Kari Kuulasmaa for the MORGAM Project Epidemiologic Perspectives & Innovations 2007, null:15 2007-12-04T00:00:00Z doi:10.1186/1742-5573-4-15 /content/figures/1742-5573-4-15-toc.gif Epidemiologic Perspectives & Innovations 1742-5573 ${item.volume} 15 2007-12-04T00:00:00Z XML ObjectiveTo examine the generalizability of two National Institutes of Health (NIH)-funded double-blind randomized placebo-controlled clinical trials in patients with chronic Lyme disease and to determine whether selection factors resulted in the unfavorable outcomes.DesignEpidemiologic review of the generalizability of two trials conducted by Klempner et al. This paper considers whether the study group was representative of the general chronic Lyme disease population. Results: In their article in The New England Journal of Medicine, Klempner et al. failed to discuss the limitations of their clinical trials. This epidemiologic review argues that their results are not generalizable to the overall Lyme disease population. The treatment failure reported by the authors may be the result of enrolling patients who remained ill after an average of 4.7 years and an average of 3 previous courses of treatment. The poor outcome cited in these trials may be explained by having selected patients who had undergone delayed treatment or multiple treatments unsuccessfully. These selection factors were not addressed by the studies' authors, nor have they been discussed by reviewers. The trials have been over-interpreted by the NIH and widely publicized in a press release. The results have been extrapolated to other groups of Lyme disease patients by commentators, by a case discussant in an influential medical journal, and by health insurance companies to deny antibiotic treatment. Conclusion: The Klempner et al. trials are assumed to be internally valid based on a Randomized Control Trial (RCT) design. However, this review argues that the trials have limited generalizability beyond the select group of patients with characteristics like those in the trial. Applying the findings to target populations with characteristics that differ from those included in these trials is inappropriate and may limit options for chronic Lyme disease patients who might benefit from antibiotic treatment. http://www.epi-perspectives.com/content/3/1/12 Daniel Cameron Epidemiologic Perspectives & Innovations 2006, null:12 2006-10-17T00:00:00Z doi:10.1186/1742-5573-3-12 /content/figures/1742-5573-3-12-toc.gif Epidemiologic Perspectives & Innovations 1742-5573 ${item.volume} 12 2006-10-17T00:00:00Z XML 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. http://www.epi-perspectives.com/content/9/1/2 Michael Davern Lynn Blewett Brian Lee Michel Boudreaux Miriam King Epidemiologic Perspectives & Innovations 2012, null:2 2012-03-30T00:00:00Z doi:10.1186/1742-5573-9-2 /content/figures/1742-5573-9-2-toc.gif Epidemiologic Perspectives & Innovations 1742-5573 ${item.volume} 2 2012-03-30T00:00:00Z XML