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It is perhaps ironic that an epidemiologist who has been working in the field of social epidemiology for over a quarter of a century, and who directs a center focused on social epidemiology, should coin a title suggesting that there is something “wrong” with social epidemiology. Perhaps it is even inopportune, as it could provide ammunition to those who believe that the practice of social epidemiology is misguided, unscientific, ideological, or too overreaching (1–3). However, this title was chosen purposely with the hope that identifying some of the critical intellectual, methodological, and empirical lacunae and challenges in social epidemiology might promote continuing development of a social epidemiology that is both scientifically enlightening and useful, productive, and contributory to the public’s health. Indeed, the hope is that the “social” in epidemiology will become so integral a part of epidemiology that the term can be dropped altogether. To assert that all epidemiology is social is not an attempt at intellectual hegemony—that the problems of disease and the distribution of disease in populations over time and space can be understood from a social perspective only or that such information is in some sense more fundamental that other types of information about disease determinants. In the same way that our understanding of the etiology of chronic and infectious diseases benefits from knowledge of the pathobiologic processes involved in such diseases, increased understanding of social factors, broadly considered, may shed light on processes every bit as integral to our understanding of the etiology of those diseases. There is no question that social epidemiology has come of age and that the term “social epidemiology” is being increasingly used to describe examination of the role of a broad array of social factors in the development and progression of many important health problems, and in the natural history of the risk factors for those diseases and conditions. While not all may agree with Geoffrey Rose’s assertion that “the primary determinants of disease are mainly economic and social, and therefore its remedies must also be economic and social” (4, p. 129), there is no question that there has been enormous growth in the study of these economic and social forces on health and disease. Figure 1, which plots growth since 1966 in use of the term “social epidemiology” in article titles, abstracts, and keywords, graphically illustrates this increased interest. Beginning in the early 1980s, growth of such publications increased rapidly, well fit by an exponential curve. In fact, this figure is likely to dramatically underrepresent the growth of social epidemiologists’ interest in the matter; a similar exponential growth pattern has been seen when studies examining socioeconomic position and health (5) and social capital and social relationships (6) were similarly totaled. Other contributions in this volume of Epidemiologic Reviews take up the wide variety of topics that social epidemiologists study. In what follows, I instead touch on a series of issues that highlight some of the critical problems with which social epidemiology must grapple. Many of the issues discussed are not restricted to social epidemiology and have their analogs in other areas of epidemiology. Thus, while the focus of this review is on social epidemiology, it would be misleading to suppose that similar criticisms do not apply to other lines of research. In addition, I want to make it clear that many of the criticisms raised apply to my own work as well as to others’. Because this is an attempt at a form of self-criticism of the field, I do not focus on as extensive citation as other reviews do. Where I do cite the work of others, it should be considered illustrative, not as singling out a particular piece of work. The lessons to be learned hopefully apply to the many. Figures 2, 3, 4, and 5 present a few of the many diagrammatic models that have been used to illustrate recent social epidemiologic approaches to understanding the social determinants of health and health disparities (e.g., Kaplan et al. (7), Marmot (8), House (9), Lynch (10)). These models have many features in common, the most prominent being an emphasis on layered, multilevel understandings; a multiplicity of pathways; and possibilities for reciprocal influences. Such models serve as important metaphors, attempting to portray the component parts of complex processes, their interrelations, and the temporal relations between components. They act as an important caution against the potentially misleading oversimplification that comes from focusing on one level of influence, often the one most proximal to the outcome, and not the flow of information and influence represented by the totality of processes and relations. Similar to a good cartoon, they remove extraneous factors and draw our attention to what is believed to be most critical. While the heuristic utility of such models can be substantial, they do both too much and too little. Such models are common in science, but the span of factors considered in models of this type in social epidemiology—sometimes linking the most macro- and microlevel phenomena—is considerably broader than that found in many scientific pursuits (e.g., figure 6; Kaplan and Lynch (11)), creating considerable problems regarding data availability and analytical methods. Information is seldom available at the multitude of levels portrayed in such models and, where available, is often measured cross-sectionally, making the temporal influences that we consider so important in the assessment of causality opaque. Against the backdrop of these models of complex longitudinal processes, it is not unusual to attempt to use various standard multivariate statistical techniques to examine the relative contribution of one social determinant versus another to the incidence or progression of disease. Thus, the relative impact of income and race on an outcome may be compared; the independent effect of income, education, and occupation may be estimated; or the strength of the association between job control and occupational social class and some outcome may be estimated. The critical problem is that simultaneous measurement and use of these variables in multivariate models belies the historical, life-course, and temporally ordered social stratification processes that they reflect. Use of the standard toolbox of multivariate regression techniques to investigate these complex social epidemiologic models becomes even more difficult absent information related to measurement error (12); changes in exposure over time (13); the reciprocal effects of behavioral and social factors, for example, on each other over time (14); and deeper issues related to the very identifiability of certain kinds of causal effects (15). Caught between this rock of inadequate data and the hard place of analytical limitations, are such models worthwhile? The ultimate answer will of course result from the extent to which both the heuristic use of such models and the analytical results based on them are illuminating. On the analytical side, new multilevel analytical techniques (e.g., Raudenbush and Bryk (16), Diez Roux (17)), extensions of recent advances in causal analysis and simulation (e.g., Greenland et al. (18), Wolfson (19)), and techniques borrowed from other fields (e.g., Zohoori and Savitz (20)) may prove useful. In my opinion, the recent substantively modest, but methodologically complex contribution by Adams et al. (21) and the responses to it by economists, epidemiologists, and others in the same publication suggest a less optimistic view. While methodological rigor is always to be applauded, one is left with the impression that a number of the authors believe that the analysis of causal relations in observational data is so flawed as to potentially threaten even the conclusion that smoking causes lung cancer. The fundamental need for better and more comprehensive data to “test” these models will not be solved by better statistical techniques, and there has been considerable lament regarding the ascendancy of technique over theory in epidemiology (22, 23) and its separation from basic foci of public health. Indeed, development of epidemiologic theory per se, separate from techniques for analyzing causal effects and partitioning sources of noise in data (24), may be required. However, even such developments will not substitute for better sources of data and for new methods to allow the stitching together of data from a variety of sources, levels, periods, and places. Quilts of such data stitched together by using such new techniques, and incorporating sensitivity analyses and other techniques, could considerably strengthen our ability to turn the complex models of social epidemiology into useful analytical models of disease processes in persons and populations. Perhaps nowhere is the need for social epidemiologic theory more apparent than in the study of “place” effects on health. While some have argued that it is methodologically difficult to identify the effects of context (25), and discussion of the role of context versus compositional effects continues (26), there is now an impressive array of studies from epidemiology, human development, sociology, and other disciplines suggesting an important role of place in a variety of health and developmental outcomes (27–30). For example, Haan et al. (31), using the Alameda County Study cohort, found that in a was with more than a increased risk of over the These were at the level by and and, in the Alameda County in the was with in over time and an increased incidence of this early there has been a in studies of a variety of health and effects with in areas as as and as as and use of multilevel statistical techniques in such studies has increased and methodological these are what it is about in a particular place that increased or In the of more it is much to come up with an to exposure of exposure of and Such areas of also one in the of a particular level or of For example, studies of exposure to would be more likely to focus on the of the and very to the in studies of would likely focus on much perhaps incorporating knowledge of and In the relative of such models of the level of influence of a particular social or economic on a particular health outcome, one is left with such as the income to health in which the from to the over which determinants of outcomes such as or disease or other is less some theory to suggest the in which the determinants of these outcomes are one is left with a of analyses of effects that more often by the level of data available, or other factors, than by of is not its it often to wide use of such as while they may may some are with to the processes that are health or There are some for example, work on the impact of social at the on the of there are social processes, which can be measured on a and a that these to the outcome in in most areas of epidemiologic social epidemiology use of observational with all of the methodological and analytical For the most advances in measurement theory and causal analysis have not social epidemiologic analyses very For example, the for social factors with a particular health outcome often use of more risk factors measured to considerable with the of most often by the data available in these are often to considerable over which may or may not be related to the social for them results in considerable of exposure over The for risk factors has been a in social epidemiology, and risk epidemiology has come from a number of is for much of epidemiology, to independent effects often issues of statistical that are at on measurement and with causal a not its In social epidemiology, this for independent effects may more of an attempt at of a risk something more Many analyses with a association between the risk factors and outcomes in question for risk While it could be argued that identifying new risk factors can the for new disease it can also result in a of new “social” risk factors that in an we them to be related to some outcome but identify the that this the very that the measurement and analysis issues discussed to misleading of of this problem come to For example, much of the early work an association between socioeconomic position and from disease or all causes for behavioral and and that for these factors not the increased risk with socioeconomic position work has to broad about these not being by such some analyses do not such a conclusion can to of potentially new the it can also an that on all Such an less useful than one the contribution of to the a the to the between and outcomes illustrates the of identifying the role of both behavioral and in this To some social epidemiology, similar to much of epidemiology, would be well by to analysis of and variables in causal as portrayed in in the over a quarter of a The out a of analysis based on a and and social understanding of the outcomes being not present in more While it to be seen recent work on and causal analysis for example, will take than this the about the of causal the focus on multilevel determinants of as in figure 2, new methodological and data challenges to social data the levels of information to empirical of such and the models are not well to of In analyses focused on such it is useful to on and For example, an understanding of the association between socioeconomic position and a particular outcome benefits from an understanding of the social, and that socioeconomic position and the However, another question is it that these are by socioeconomic such a Geoffrey might be the of the a a broad array of determinants from to public and perhaps even to difficult to it clear that an that on only the is a recent of that and from a of disciplines has the of reciprocal over time health and and social p. 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