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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]

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. [ABSTRACT FROM AUTHOR]

As a supplement to our lead editorial, the editors of the new journal, Epidemiologic Perspectives & Innovations, provide a partial list of specific analyses and topic areas they would like to see submitted to the journal. [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]

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 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]

This editorial introduces the use of article-processing charges at Epidemiologic Perspectives & Innovations and reviews that advantages of publishing in an Open Access journal. In addition, it introduces a new type of article the journal hopes to publish, detailed reports of study design or data analysis methods that have been used in health science research. The new type of article is intended to supplement the woefully constrained methods sections in standard research report articles, providing information that better fulfills the goals of scientific publishing. [ABSTRACT FROM AUTHOR]

This editorial introduces the new online, open-access, peer-reviewed journal, Epidemiologic Perspectives & Innovations. Epidemiology (which we define broadly, to include clinical research and various approaches to studying the health of populations) is a critically important field in informing decisions about the health of individuals and populations. But the desire for new information means that the health science literature is overwhelmingly devoted to reporting new findings, leaving little opportunity to improve the quality of the science. By creating a journal dedicated to all topics of and about epidemiology, except standard research reports, we hope to encourage authors to write more on the neglected aspects of the field. The journal will publish articles that analyze policy implications of health research, present new research methods and better communicate existing methods, reassess previous results and dogma, and provide other innovations in and perspectives on the field. Online publishing will permit articles of whatever length is required for the work, speed the time to publication and allow free access to the full content. [ABSTRACT FROM AUTHOR]

The article presents the author's experience with teacher Len Syme in his study of epidemiology. The author relates that Syme was stimulating, challenging, and encouraging. It is stated that he encouraged interdisciplinary work and taught the importance of asking the right question. Lastly, he relates that Syme's enduring contribution has been as an advocate for the idea that social processes may be as much causes of illness as are biological processes.

This is an account of the early days of research on social determinants as I experienced them. I describe my time as one of four Fellows in a new training program in Medical Sociology at Yale University and how I came to be the first Sociologist employed in the U.S. Public Health Service. I then became the first Executive Secretary of a new Study Section at NIH dealing with a small number of research grant proposals in the field of Epidemiology. My account deals with some of my experiences in this developing field, culminating with my appointment as the first Sociologist to become a Professor of Epidemiology in a School of Public Health. [ABSTRACT FROM AUTHOR]

The article presents the author's views on an article by C. Poole on the limit of epidemiology. He opines that examination of redundancy shows that even in simplest and most unrealistic circumstances redundancy can influence our effect estimates. He believes that the more complicated the sufficient component cause (SCC) model, the more redundant individuals there will be, larger will be the discrepancy between the etiologic and excess effects.

This commentary intends to instigate discussions about upcoming epidemiologic research, and its interpretation, into putative links between shift work, involving circadian disruption or chronodisruption [CD], and the development of internal cancers. In 2007, the International Agency for Research on Cancer (IARC) convened an expert group to examine the carcinogenicity of shift work, inter alia characterized by light exposures at unusual times. After a critical review of published data, the following was stated: "There is sufficient evidence in experimental animals for the carcinogenicity of light during the daily dark period (biological night)". However, in view of limited epidemiological evidence, it was overall concluded: "Shiftwork that involves circadian disruption is probably carcinogenic to humans (Group 2A)". Remarkably, the scenario around shift work, CD and internal cancers provides a unique case for "white-box" epidemiology: Research at many levels - from sub-cellular biochemistry, to whole cells, to organs, to organisms, including animals and humans - has suggested a series of quite precise and partly related causal mechanisms. This is in stark contrast to instances of "black box" or "stabs in the dark" epidemiology where causal mechanisms are neither known nor hypothesized or only poorly defined. The overriding theme that an adequate chronobiological organization of physiology can be critical for the protection against cancer builds the cornerstone of biological plausibility in this case. We can now benefit from biological plausibility in two ways: First, epidemiology should use biologically plausible insights into putative chains of causation between shift work and cancer to design future investigations. Second, when significant new data were to become available in coming years, IARC will re-evaluate cancer hazards associated with shift work. Biological plausibility may then be a key viewpoint to consider and, ultimately, to decide whether (or not) to pass from statistical associations, possibly detected in observational studies by then, to a verdict of causation. In the meantime, biological plausibility should not be invoked to facilitate publication of epidemiological research of inappropriate quality. Specific recommendations as to how to design, report and interpret epidemiological research into biologically plausible links between shift work and cancer are provided. [ABSTRACT FROM AUTHOR]

Identifying diet-disease relationships in nutritional cohort studies is plagued by the measurement error in self-reported intakes. The authors propose using biomarkers known to be correlated with dietary intake, so as to strengthen analyses of diet-disease hypotheses. The authors consider combining self-reported intakes and biomarker levels using principal components, Howe's method, or a joint statistical test of effects in a bivariate model. They compared the statistical power of these methods with that of conventional univariate analyses of self-reported intake or of biomarker level. They used computer simulation of different disease risk models, with input parameters based on data from the literature on the relationship between lutein intake and age-related macular degeneration. The results showed that if the dietary effect on disease was fully mediated through the biomarker level, then the univariate analysis of the biomarker was the most powerful approach. However, combination methods, particularly principal components and Howe's method, were not greatly inferior in this situation, and were as good as, or better than, univariate biomarker analysis if mediation was only partial or non-existent. In some circumstances sample size requirements were reduced to 20-50% of those required for conventional analyses of self-reported intake. The authors conclude that (i) including biomarker data in addition to the usual dietary data in a cohort could greatly strengthen the investigation of diet-disease relationships, and (ii) when the extent of mediation through the biomarker is unknown, use of principal components or Howe's method appears a good strategy. [ABSTRACT FROM AUTHOR]

Objectives: Although research has found that childhood socioeconomic status (SES) is associated with physical and mental health in mid- and later life, most of these studies used conventional, single dimension SES measures for the childhood period such as household income or educational attainment of parents. Life course and health disparities research would benefit from identification and measurement of a variety of childhood social and economic experiences and opportunities that might affect health in later life. Design: This study utilized qualitative research methods to identify key dimensions of childhood experiences related to SES. We conducted in-depth interviews with 25 adults age 55 to 80 years from diverse economic and ethnic backgrounds. Topics included home, neighborhood, school, and work experiences during early childhood and adolescence. Interviews were audio-taped and transcripts were coded to identify thematic domains. Results: We identified eight thematic domains, many of which had clear subdomains: home and family circumstances, neighborhood, work and money, potential for advancement through schooling, school quality and content, discrimination, influence and support of adults, and leisure activities. These domains highlight individual characteristics and experiences and also economic and educational opportunities. Conclusion: These domains of childhood social and economic circumstances add breadth and depth to conventional conceptualization of childhood SES. When the domains are translated into a measurement tool, it will allow for the possibility of classifying people along multiple dimensions, such as from a low economic circumstance with high levels of adult support. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Epidemiologic studies have identified a number of lifestyle factors, e.g. diet, obesity, and use of certain medications, which affect risk of colon cancer. However, the magnitude and significance of risk factor-disease associations differ among studies. We propose that population trends of changing prevalence of risk factors explains some of the variability between studies when factors that change prevalence also modify the effect of other risk factors. We used data collected from population-based control who were selected as study participants for two time periods, 1991-1994 and 1997-2000, along with data from the literature, to examine changes in the population prevalence of aspirin and non-steroidal anti-inflammatory medication (NSAID) use, obesity, and hormone replacement therapy (HRT) over time. Data from a population-based colon cancer case-control study were used to estimate effect-measurement modification among these factors. Sizeable changes in aspirin use, HRT use, and the proportion of the population that is obese were observed between the 1980s and 2000. Use of NSAIDs interacted with BMI and HRT; HRT use interacted with body mass index (BMI). We estimate that as the prevalence of NSAIDs use changed from 10% to almost 50%, the colon cancer relative risk associated with BMI >30 would change from 1.3 to 1.9 because of the modifying effect of NSAIDs. Similarly, the relative risk estimated for BMI would increase as the prevalence of use of HRT among post-menopausal women increased. In conclusion, as population characteristics change over time, these changes may have an influence on relative risk estimates for colon cancer for other exposures because of effect-measure modification. The impact of population changes on comparability between epidemiologic studies can be kept to a minimum if investigators assess exposure-disease associations within strata of other exposures, and present results in a manner that allows comparisons across studies. Effect-measure modification is an important component of data analysis that should be evaluated to obtain a complete understanding of disease etiology. [ABSTRACT FROM AUTHOR]

Background: Published studies of geocoding accuracy often focus on a single geographic area, address source or vendor, do not adjust accuracy measures for address characteristics, and do not examine effects of inaccuracy on exposure measures. We addressed these issues in a Women's Health Initiative ancillary study, the Environmental Epidemiology of Arrhythmogenesis in WHI. Results: Addresses in 49 U.S. states (n = 3,615) with established coordinates were geocoded by four vendors (A-D). There were important differences among vendors in address match rate (98%; 82%; 81%; 30%), concordance between established and vendor-assigned census tracts (85%; 88%; 87%; 98%) and distance between established and vendor-assigned coordinates (mean ρ [meters]: 1809; 748; 704; 228). Mean ρ was lowest among street-matched, complete, zip-coded, unedited and urban addresses, and addresses with North American Datum of 1983 or World Geodetic System of 1984 coordinates. In mixed models restricted to vendors with minimally acceptable match rates (A-C) and adjusted for address characteristics, within-address correlation, and amongvendor heteroscedasticity of ρ, differences in mean ρ were small for street-type matches (280; 268; 275), i.e. likely to bias results relying on them about equally for most applications. In contrast, differences between centroid-type matches were substantial in some vendor contrasts, but not others (5497; 4303; 4210) pinteraction < 10-4, i.e. more likely to bias results differently in many applications. The adjusted odds of an address match was higher for vendor A versus C (odds ratio = 66, 95% confidence interval: 47, 93), but not B versus C (OR = 1.1, 95% CI: 0.9, 1.3). That of census tract concordance was no higher for vendor A versus C (OR = 1.0, 95% CI: 0.9, 1.2) or B versus C (OR = 1.1, 95% CI: 0.9, 1.3). Misclassification of a related exposure measure - distance to the nearest highway - increased with mean ? and in the absence of confounding, non-differential misclassification of this distance biased its hypothetical association with coronary heart disease mortality toward the null. Conclusion: Geocoding error depends on measures used to evaluate it, address characteristics and vendor. Vendor selection presents a trade-off between potential for missing data and error in estimating spatially defined attributes. Informed selection is needed to control the trade-off and adjust analyses for its effects. [ABSTRACT FROM AUTHOR]

The author reflects on the assessment and communication of causal associations in epidemiology. He sympathized that the assessment is a source of continual frustration for epidemiologists. He argued that this finding is rigour in asking good questions on testable hypotheses, in study design and execution, and in the description of results. However, there are also remarkable points on which he disagreed. He noted that the term pragmatic suggests some form of compromise between objective realism.

Although epidemiology is necessarily involved with elucidating causal processes, we argue that there is little practical need, having described an epidemiological result, to then explicitly label it as causal (or not). Doing so is a convention which obscures the valuable core work of epidemiology as an important constituent of public health practice. We discuss another approach which emphasizes the public health "use value" of research findings in regard to prediction and intervention independent from explicit metaphysical causal claims. Examples are drawn from smoking and lung cancer, with particular focus on the original 1964 Surgeon General's report on smoking and the new version released in 2004. The intent is to help the epidemiologist focus on the pertinent implications of research, which, from a public health point of view, in large part entails the ability to predict and to intervene. Further discussion will center on the importance of differentiating between technical/practical uses of causal language, as might be used in structural equations or marginal structural modeling, and more foundational notions of cause. We show that statistical/epidemiological results, such as "smoking two packs a day increases risk of lung cancer by 10 times" are in themselves a kind of causal argument that are not in need of additional support from relatively ambiguous language such as "smoking causes lung cancer." We will show that the confusion stemming from the use of this latter statement is more than mere semantics. Our goal is to allow researchers to feel more confident in the power of their research to tell a convincing story without resorting to metaphysical/unsupportable notions of cause. [ABSTRACT FROM AUTHOR]

The Mantel-Haenszel estimate for the odds ratio (and its logarithm) in stratified case control studies lacked a generally acceptable variance estimate for many years. The Robins-Breslow-Greenland estimate has met this need, but standard textbooks still do not provide an explanation of how it is derived. This article provides an accessible derivation which demonstrates the link between the Robins-Breslow-Greenland estimate and the familiar Woolf estimate for the variance of the log odds ratio, and which could easily be included in Masters level courses in epidemiology. The relationships to the unconditional and conditional maximum likelihood estimates are also reviewed. [ABSTRACT FROM AUTHOR]

In teaching epidemiology, confounding is a difficult topic. The authors designed active learning objects (LO) based on manipulable three-dimensional (3D) plots to facilitate understanding of confounding. The 3D LOs help illustrate of how confounding can occur, how it generates bias and how to adjust for it. For the development of the LOs, guidelines were formulated based on epidemiology and theories of instructional design. These included integrating the conceptual and empirical aspects: the causal relationships believed to be operating in the study population (conceptual aspect) and data-oriented associations (empirical aspect). Other guidelines based on theories of instructional design included: actively engage the students, use visual methods when possible, and motivate the students about the importance of the topic. Students gave the method strong positive evaluations. Experts in epidemiology agreed that the 3D LOs apply generally accepted scientific views on confounding. Based on their experiences, the authors think that the 3D plots can be useful addition in the teaching of confounding. The article includes links and a downloadable file that provide a demonstration of the 3D LO-based teaching materials. [ABSTRACT FROM AUTHOR]

Excess Years of Life Lost due to exposure is an important measure of health impact complementary to rate or risk statistics. I show that the total excess Years of Life Lost due to exposure can be estimated unbiasedly by calculating the corresponding excess Years of Potential Life Lost given conditions that describe study validity (like exchangeability of exposed and unexposed) and assuming that exposure is never preventive. I further demonstrate that the excess Years of Life Lost conditional on age at death cannot be estimated unbiasedly by a calculation of conditional excess Years of Potential Life Lost without adopting speculative causal models that cannot be tested empirically. Furthermore, I point out by example that the excess Years of Life Lost for a specific cause of death, like lung cancer, cannot be identified from epidemiologic data without assuming non-testable assumptions about the causal mechanism as to how exposure produces death. Hence, excess Years of Life Lost estimated from life tables or regression models, as presented by some authors for lung cancer or after stratification for age, are potentially biased. These points were already made by Robins and Greenland 1991 reasoning on an abstract level. In addition, I demonstrate by adequate life table examples designed to critically discuss the Years of Potential Life Lost analysis published by Park et al. 2002 that the potential biases involved may be fairly extreme. Although statistics conveying information about the advancement of disease onset are helpful in exposure impact analysis and especially worthwhile in exposure impact communication, I believe that attention should be drawn to the difficulties involved and that epidemiologists should always be aware of these conceptual limits of the Years of Potential Life Lost method when applying it as a regular tool in cohort analysis. [ABSTRACT FROM AUTHOR]

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. Implementation: There 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. [ABSTRACT FROM AUTHOR]