Abstract-In 2004, the first American Heart Association scientific statement on "Air Pollution and Cardiovascular Disease" concluded that exposure to particulate matter (PM) air pollution contributes to cardiovascular morbidity and mortality. In the interim, numerous studies have expanded our understanding of this association and further elucidated the physiological and molecular mechanisms involved. The main objective of this updated American Heart Association scientific statement is to provide a comprehensive review of the new evidence linking PM exposure with cardiovascular disease, with a specific focus on highlighting the clinical implications for researchers and healthcare providers. The writing group also sought to provide expert consensus opinions on many aspects of the current state of science and updated suggestions for areas of future research. On the basis of the findings of this review, several new conclusions were reached, including the following: Exposure to PM Ͻ2.5 m in diameter (PM 2.5 ) over a few hours to weeks can trigger cardiovascular disease-related mortality and nonfatal events; longer-term exposure (eg, a few years) increases the risk for cardiovascular mortality to an even greater extent than exposures over a few days and reduces life expectancy within more highly exposed segments of the population by several months to a few years; reductions in PM levels are associated with decreases in cardiovascular mortality within a time frame as short as a few years; and many credible pathological mechanisms have been elucidated that lend biological plausibility to these findings. It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM 2.5 exposure and cardiovascular morbidity and mortality. This body of evidence has grown and been strengthened substantially since the first American Heart Association scientific statement was published. Finally, PM 2.5 exposure is deemed a modifiable factor that contributes to cardiovascular morbidity and mortality. (Circulation. 2010;121:2331-2378.)

Epidemiology, sociology, and geography have been successful in re-establishing interest in the role of place in shaping health and health inequalities. However, some of the relevant empirical research has relied on rather conventional conceptions of space and place and focused on isolating the "independent" contribution of place-level and individual-level factors. This approach may have resulted in an underestimate of the contribution of 'place' to disease risk. In this paper we argue the case for extensive (quantitative) as well as intensive (qualitative) empirical, as well as theoretical, research on health variation that incorporates 'relational', views of space and place. Specifically, we argue that research in place and health should avoid the false dualism of context and composition by recognising that there is a mutually reinforcing and reciprocal relationship between people and place. We explore in the discussion how these theoretical perspectives are beginning to influence empirical research. We argue that these approaches to understanding how place relates to health are important in order to deliver effective, 'contextually sensitive' policy interventions.

Key Words methods, contextual effects, random effects, social epidemiology, ecologic s Abstract Over the past few years there has been growing interest in considering factors defined at multiple levels in public health research. Multilevel analysis has emerged as one analytical strategy that may partly address this need, by allowing the simultaneous examination of group-level and individual-level factors. This paper reviews the rationale for using multilevel analysis in public health research, summarizes the statistical methodology, and highlights some of the research questions that have been addressed using these methods. The advantages and disadvantages of multilevel analysis compared with standard methods are reviewed. The use of multilevel analysis raises theoretical and methodological issues related to the theoretical model being tested, the conceptual distinction between group-and individual-level variables, the ability to differentiate "independent" effects, the reciprocal relationships between factors at different levels, and the increased complexity that these models imply. The potentialities and limitations of multilevel analysis, within the broader context of understanding the role of factors defined at multiple levels in shaping health outcomes, are discussed.

A large portion of current epidemiologic research is based on methodologic individualism: the notion that the distribution of health and disease in populations can be explained exclusively in terms of the characteristics of individuals. The present paper discusses the need to include group- or macro-level variables in epidemiologic studies, thus incorporating multiple levels of determination in the study of health outcomes. These types of analyses, which have been called contextual or multi-level analyses, challenge epidemiologists to develop theoretical models of disease causation that extend across levels and explain how group-level and individual-level variables interact in shaping health and disease. They also raise a series of methodological issues, including the need to select the appropriate contextual unit and contextual variables, to correctly specify the individual-level model, and, in some cases, to account for residual correlation between individuals within contexts. Despite its complexities, multilevel analysis holds potential for reemphasizing the role of macro-level variables in shaping health and disease in populations.

Medicine and epidemiology currently dominate the study of the strong association between socioeconomic status and mortality. Socioeconomic status typically is viewed as a causally irrelevant "confounding variable" or as a less critical variable marking only the beginning of a causal chain in which intervening risk factors are given prominence. Yet the association between socioeconomic status and mortality has persisted despite radical changes in the diseases and risk factors that are presumed to explain it. This suggests that the effect of socioeconomic status on mortality essentially cannot be understood by reductive explanations that focus on current mechanisms. Accordingly, Link and Phelan (1995) proposed that socioeconomic status is a "fundamental cause" of mortality disparities-that socioeconomic disparities endure despite changing mechanisms because socioeconomic status embodies an array of resources, such as money, knowledge, prestige, power, and beneficial social connections, that protect health no matter what mechanisms are relevant at any given time. We identified a situation in which resources should be less helpful in prolonging life, and derived the following prediction from the theory: For less preventable causes of death (for which we know little about prevention or treatment), socioeconomic status will be less strongly associated with mortality than for more preventable causes. We tested this hypothesis with the National Longitudinal Mortality Study, which followed Current Population Survey respondents (N = 370,930) for mortality for nine years. Our hypothesis was supported, lending support to the theory of fundamental causes and more generally to the importance of a sociological approach to the study of socioeconomic disparities in mortality.