Causation in epidemiology.

Causation is an essential concept in epidemiology, yet there is no single, clearly articulated definition for the discipline. From a systematic review of the literature, five categories can be delineated: production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic. Strengths and weaknesses of these categories are examined in terms of proposed characteristics of a useful scientific definition of causation: it must be specific enough to distinguish causation from mere correlation, but not so narrow as to eliminate apparent causal phenomena from consideration. Two categories-production and counterfactual-are present in any definition of causation but are not themselves sufficient as definitions. The necessary and sufficient cause definition assumes that all causes are deterministic. The sufficient-component cause definition attempts to explain probabilistic phenomena via unknown component causes. Thus, on both of these views, heavy smoking can be cited as a cause of lung cancer only when the existence of unknown deterministic variables is assumed. The probabilistic definition, however, avoids these assumptions and appears to best fit the characteristics of a useful definition of causation. It is also concluded that the probabilistic definition is consistent with scientific and public health goals of epidemiology. In debates in the literature over these goals, proponents of epidemiology as pure science tend to favour a narrower deterministic notion of causation models while proponents of epidemiology as public health tend to favour a probabilistic view. The authors argue that a single definition of causation for the discipline should be and is consistent with both of these aims. It is concluded that a counterfactually-based probabilistic definition is more amenable to the quantitative tools of epidemiology, is consistent with both deterministic and probabilistic phenomena, and serves equally well for the acquisition and the application of scientific knowledge.

Measuring the public health burden of cancer in the United States through lifetime and age-conditional risk estimates.

PURPOSE: Effects of an aging population in the United States on lifetime and age-conditional risk estimates of developing site-specific cancers are identified and the potential role these statistics play in monitoring disease burden discussed. METHODS: Risk estimates were derived by applying cross-sectional population-based incidence rates of cancer and mortality rates from other causes to a hypothetical cohort. The cohort was aged through a double decrement life table to determine the expected proportion of the population that would develop the disease. RESULTS: Despite black men having higher invasive cancer incidence rates than white men, and black and white women having similar rates, because of the better life expectancy among whites lifetime risk estimates of developing cancer are higher for whites than blacks: 45.5% in white men, 40.4% in black men, 39.2% in white women, and 32.4% in black women based on 1995-97 data. White men experience higher 10-year cancer risk than black men in only bladder cancer, non-Hodgkin's lymphomas (NHL), and leukemia. White women tended to show a greater risk than black women for cancers of the breast, corpus uteri, ovary, NHL, and leukemia. For both whites and blacks, the 10-year risk of lung cancer ranks first among men aged 40, ranks second to prostate cancer for men aged 50, 60, and 70, and ranks second to breast cancer for women aged 40, 50, 60, and 70. CONCLUSIONS: Lifetime and age-conditional risk measures reflect both changes in the disease incidence rates and age distribution over calendar time such that they are useful for monitoring the disease burden in the population. Even if cancer rates remain stable or fall, it is possible for the cancer burden, as reflected by lifetime and age-conditional risk estimates, to increase due to the aging population.

Comparison of information processing technologies.

OBJECTIVE: To examine the type of information obtainable from scientific papers, using three different methods for the extraction, organization, and preparation of literature reviews. DESIGN: A set of three review papers was identified, and the ideas represented by the authors of those papers were extracted. The 161 articles referenced in those three reviews were then analyzed using 1) a formalized data extraction approach, which uses a protocol-driven manual process to extract the variables, values, and statistical significance of the stated relationships; and 2) a computerized approach known as "Idea Analysis," which uses the abstracts of the original articles and processes them through a computer software program that reads the abstracts and organizes the ideas presented by the authors. The results were then compared. The literature focused on the human papillomavirus and its relationship to cervical cancer. RESULTS: Idea Analysis was able to identify 68.9 percent of the ideas considered by the authors of the three review papers to be of importance in describing the association between human papillomavirus and cervical cancer. The formalized data extraction identified 27 percent of the authors' ideas. The combination of the two approaches identified 74.3 percent of the ideas considered important in the relationship between human papillomavirus and cervical cancer, as reported by the authors of the three review articles. CONCLUSION: This research demonstrated that both a technically derived and a computer derived collection, categorization, and summarization of original articles and abstracts could provide a reliable, valid, and reproducible source of ideas duplicating, to a major degree, the ideas presented by subject specialists in review articles. As such, these tools may be useful to experts preparing literature reviews by eliminating many of the clerical-mechanical features associated with present-day scientific text processing.

Application of population screening principles to genetic screening for adult-onset conditions.

Recent advances in molecular genetics have highlighted the potential use of genetic testing to screen for adult-onset chronic diseases. Several issues must be addressed, however, before such tests can be recommended for population-based prevention programs. These issues include the adequacy of the scientific evidence, the balance of risks and benefits, the need for counseling and informed consent, and the costs and resources required. Ongoing assessment of the screening program and quality assurance of laboratory testing are also needed. This paper considers the application of general principles for mass screening to genetic testing for susceptibility to adult-onset chronic diseases. Evaluation of proposals for genetic screening in context of these principles reveals that needed evidence is often absent, particularly with respect to the predictive value of tests, efficacy of interventions, and social consequences of testing. The principles of population screening are developed into a framework for public health policy on genetic screening that has three stages: assessment of the screening test and interventions for those who test positive, including assessment of risks and costs, policy development, and program evaluation. Essential elements are identified, including evaluation of evidence and processes for consensus development and program evaluation. The proposed framework for public health policymaking outlined in this commentary, when combined with future efforts that involve an authoritative consensus process, may be useful for the evaluation and planning of genetic screening programs aimed at reducing morbidity and mortality from adult-onset chronic diseases.

Theory and practice in epidemiology.

Scientific and ethical theories are examined in terms of the practice of epidemiology, the study of the determinants and distributions of disease, and the application of the knowledge gained to prevent disease and improve the health of populations. Scientific theories provide explanations and predictions for epidemiologic studies of disease etiology and prevention. Causal theory is the key example for epidemiologic science, although theories of selection processes, theories of health, and theories of probability and statistics are also core. Theories of ethics provide principles, warrants, and methods for acting upon the knowledge gained in the scientific pursuits of epidemiology and for obtaining that knowledge appropriately. Ethical theories guide practitioners in making justified decisions about when and under what conditions public health interventions should be undertaken and how research participants should be treated. Theories of midlevel bioethical principles have received the most attention in epidemiology, but other theories, such as virtue theory and communitarian theory, are also relevant. Many theories matter to the practice of epidemiology: theories of biology, aging, evolution, and medicine; theories of history, religion, law, economics, and politics, as well as the theories of the physical, behavioral, and social sciences. These theories matter because epidemiologists study many different biological and social phenomena, and preventive interventions occur at many different levels of explanation. Development of theory is not a high priority in contemporary epidemiology. Identifying the responsibilities of the discipline to be public health intervention and rigorous science is a first step toward developing and applying theory in epidemiology.

Advocacy in epidemiology and demography.

This paper is a summary of a panel discussion at the Conference on Epidemiology and Demography held at Georgetown University, in Washington D.C. on February 8-9, 2001. The participants were Al Hermalin, Linda Martin, Mike Stoto, Robert Wallace, Douglas Weed, and Rose Li (who chaired the session). A list of questions similar to the section headings in this paper was prepared in advance of the conference, and each of the participants was asked to address specific issues, although the presentations typically covered a range of topics. This summary also includes comments from the floor.

Epidemiologic evidence and causal inference.

Preventing cancer depends on the ability to recognize and remove causal factors. In current practice, the methods used to judge cause from epidemiologic, clinical trials and biologic evidence include systematic narrative reviews, criteria-based inference methods, and meta-analysis. Subjectivity and values play a key role in the practice of causal inference, especially in selecting criteria and assigning rules of evidence to those criteria. Judging cause is a central concern of physicians, epidemiologists, and other public health professionals committed to cancer prevention.

Investigation of design and bias issues in case-control studies of cancer screening using microsimulation.

Using a microsimulation approach, the authors examined design and bias issues in case-control studies of cancer screening. Specifically, they looked at the impact on the odds ratio of the way in which exposure to screening is defined, the type of age matching, the time scale used, and the criteria used to determine control eligibility. The results showed that defining exposure as "ever/never" screened produced, as expected, a serious bias in favor of screening. Defining exposure as being screened no later than the time the case's cancer is diagnosed has a serious bias against screening. An alternative exposure definition--screening can occur no later than the time the case would have been clinically diagnosed--eliminates the bias against screening. Further, the results showed that the type of age matching and the time scale used can produce a bias against screening and that this bias can be quite strong when case-control studies are performed in populations with a periodic screening program that is the only source of screening. Finally, control eligibility criteria had little effect.

Interpreting epidemiological evidence: how meta-analysis and causal inference methods are related.

Interpreting observational epidemiological evidence can involve both the quantitative method of meta-analysis and the qualitative criteria-based method of causal inference. The relationships between these two methods are examined in terms of the capacity of meta-analysis to contribute to causal claims, with special emphasis on the most commonly used causal criteria: consistency, strength of association, dose-response, and plausibility. Although meta-analysis alone is not sufficient for making causal claims, it can provide a reproducible weighted average of the estimate of effect that seems better than the rules-of-thumb (e.g. majority rules and all-or-none) often used to assess consistency. A finding of statistical heterogeneity, however, need not preclude a conclusion of consistency (e.g. consistently greater than 1.0). For the criteria of strength of association and dose-response, meta-analysis provides more precise estimates, but the causal relevance of these estimates remains a matter of judgement. Finally, meta-analysis may be used to summarize evidence from biological, clinical, and social levels of knowledge, but combining evidence across levels is beyond its current capacity. Meta-analysis has a real but limited role in causal inference, adding to an understanding of some causal criteria. Meta-analysis may also point to sources of confounding or bias in its assessment of heterogeneity.

Causal criteria in nutritional epidemiology.

Making nutrition recommendations involves complex judgments about the balance between benefits and risks associated with a nutrient or food. Causal criteria are central features of such judgments but are not sufficient. Other scientific considerations include study designs, statistical tests, bias, confounding, and measurement issues. At a minimum, the set of criteria includes consistency, strength of association, dose response, plausibility, and temporality. The current practice, methods, and theory of causal inference permit flexibility in the choice of criteria, their relative priority, and the rules of inference assigned to them. Our approach is as follows. Consistency across study designs is compelling when the studies are of high quality and are not subject to biases. A statistically significant risk estimate with a > 20% increase or decrease in risk is considered a positive finding. A statistically significant linear or otherwise regularly increasing trend reinforces the judgment in favor of a recommendation. A plausible hypothesis likewise reinforces a recommendation, although the rules of inference for biological evidence are highly variable and depend on the situation. Temporality is, for nutrition recommendations, more a consideration of the extent to which a dietary factor affects disease onset or progression. Evidence supporting these criteria provides a strong basis for making a nutrition recommendation, given due consideration of the balance between presumed benefits and presumed harms. Recommendations should make clear their breadth of application; a narrow recommendation involves a single disease or condition whereas a broad recommendation involves all relevant diseases or conditions.

New ethics guidelines for epidemiology: background and rationale.

In the past decade, at least four sets of ethics guidelines for epidemiologists have been prepared by various national and international organizations. None, however, have been officially adopted by the American College of Epidemiology (ACE). Recently, the ACE asked its Ethics and Standards of Practice (ESOP) Committee to produce ethics guidelines. In this paper, we explain the context and rationale for this effort, describe the purpose and content of ethics guidelines in epidemiology, and discuss their strengths and weaknesses. Three issues that are central to the mission of ACE-education, policy, and advocacy-are inadequately addressed in existing ethics guidelines. In addition, ethics guidelines are not static documents; they should reflect the changing role of epidemiologists in society, including issues arising in emerging subspecialty areas. New, more dynamic, guidelines that emphasize core values, obligations, and virtues, may help to further define and legitimize the profession of epidemiology and will provide a foundation for the discussion of specific ethical issues in the classroom and in professional practice. Guidelines however, do not provide the final word on ethical issues. Specific decisions in particular cases require judgments made upon reflection of the core values, obligations, and virtues described in the guidelines. From our review, we conclude that a new set of guidelines is reasonable and warranted.

Cancer knowledge and misconceptions among immigrant Salvadorean men in the Washington, D.C. area.

Although Salvadoreans are the fourth largest group of Hispanics in the United States, little is known about their cancer knowledge, attitudes, and practices. There are no publications assessing cancer knowledge among Salvadorean men. In this cross-sectional survey, information was gathered from 706 immigrant Salvadorean men in Washington, D.C. The majority of these men knew that smoking causes cancer and that some cancers can be cured if detected early. However, the men in this survey had inadequate knowledge about symptoms of cancer and early detection methods. The most important predictor of cancer screening among older participants was enrollment in health insurance plans. Our study suggests that Salvadorean men would participate in cancer screening efforts if they had access to medical care. Educational programs to increase awareness of cancer and availability of preventive services may help prevent cancer in this population.