The Multi-Ethnic Study of Atherosclerosis was initiated in July 2000 to investigate the prevalence, correlates, and progression of subclinical cardiovascular disease (CVD) in a population-based sample of 6,500 men and women aged 45-84 years. The cohort will be selected from six US field centers. Approximately 38% of the cohort will be White, 28% African-American, 23% Hispanic, and 11% Asian (of Chinese descent). Baseline measurements will include measurement of coronary calcium using computed tomography; measurement of ventricular mass and function using cardiac magnetic resonance imaging; measurement of flow-mediated brachial artery endothelial vasodilation, carotid intimal-medial wall thickness, and distensibility of the carotid arteries using ultrasonography; measurement of peripheral vascular disease using ankle and brachial blood pressures; electrocardiography; and assessments of microalbuminuria, standard CVD risk factors, sociodemographic factors, life habits, and psychosocial factors. Blood samples will be assayed for putative biochemical risk factors and stored for use in nested case-control studies. DNA will be extracted and lymphocytes will be immortalized for genetic studies. Measurement of selected subclinical disease indicators and risk factors will be repeated for the study of progression over 7 years. Participants will be followed through 2008 for identification and characterization of CVD events, including acute myocardial infarction and other coronary heart disease, stroke, peripheral vascular disease, and congestive heart failure; therapeutic interventions for CVD; and mortality.

Features of neighborhoods or residential environments may affect health and contribute to social and race/ethnic inequalities in health. The study of neighborhood health effects has grown exponentially over the past 15 years. This chapter summarizes key work in this area with a particular focus on chronic disease outcomes (specifically obesity and related risk factors) and mental health (specifically depression and depressive symptoms). Empirical work is classified into two main eras: studies that use census proxies and studies that directly measure neighborhood attributes using a variety of approaches. Key conceptual and methodological challenges in studying neighborhood health effects are reviewed. Existing gaps in knowledge and promising new directions in the field are highlighted.

The past few years have witnessed an explosion of interest in neighborhood or area effects on health. Several types of empiric studies have been used to examine possible area or neighborhood effects, including ecologic studies relating area characteristics to morbidity and mortality rates, contextual and multilevel analyses relating area socioeconomic context to health outcomes, and studies comparing small numbers of well-defined neighborhoods. Strengthening inferences regarding the presence and magnitude of neighborhood effects will require addressing a series of conceptual and methodological issues. Many of these issues relate to the need to develop theory and specific hypotheses on the processes through which neighborhood and individual factors may jointly influence specific health outcomes. Important challenges include defining neighborhoods or relevant geographic areas, identifying significant area or neighborhood characteristics, specifying the role of individual-level variables, incorporating life-course and longitudinal dimensions, combining a variety of research designs, and avoiding reductionism in the way in which "neighborhood" factors are incorporated into models of disease causation and quantitative analyses.analyses.

Most studies examining the relation between residential environment and health have used census-derived measures of neighborhood socioeconomic position (SEP). There is a need to identify specific features of neighborhoods relevant to disease risk, but few measures of these features exist, and their measurement properties are understudied. In this paper, the authors 1) develop measures (scales) of neighborhood environment that are important in cardiovascular disease risk, 2) assess the psychometric and ecometric properties of these measures, and 3) examine individual- and neighborhood-level predictors of these measures. In 2004, data on neighborhood conditions were collected from a telephone survey of 5,988 residents at three US study sites (Baltimore, Maryland; Forsyth County, North Carolina; and New York, New York). Information collected covered seven dimensions of neighborhood environment (aesthetic quality, walking environment, availability of healthy foods, safety, violence, social cohesion, and activities with neighbors). Neighborhoods were defined as census tracts or census clusters. Cronbach's alpha coefficient ranged from 0.73 to 0.83, with test-retest reliabilities of 0.60-0.88. Intraneighborhood correlations were 0.28-0.51, and neighborhood reliabilities were 0.64-0.78 for census tracts for most scales. The neighborhood scales were strongly associated with neighborhood SEP but also provided information distinct from neighborhood SEP. These results illustrate a methodological approach for assessing the measurement properties of neighborhood-level constructs and show that these constructs can be measured reliably.

Local food environments vary substantially by neighborhood racial/ethnic and socioeconomic composition and may contribute to disparities in health.

Even after controlling for personal income, education, and occupation, we found that living in a disadvantaged neighborhood is associated with an increased incidence of coronary heart disease.

A review of published observational studies of neighbourhoods and depression/depressive symptoms was conducted to inform future directions for the field. Forty-five English-language cross-sectional and longitudinal studies that analysed the effect of at least one neighbourhood-level variable on either depression or depressive symptoms were analysed. Of the 45 studies, 37 reported associations of at least one neighbourhood characteristic with depression/depressive symptoms. Seven of the 10 longitudinal studies reported associations of at least one neighbourhood characteristic with incident depression. Socioeconomic composition was the most common neighbourhood characteristic investigated. The associations of depressive symptoms/depression with structural features (socioeconomic and racial composition, stability and built environment) were less consistent than with social processes (disorder, social interactions, violence). Among the structural features, measures of the built environment were the most consistently associated with depression but the number of studies was small. The extent to which these associations reflect causal processes remains to be determined. The large variability in studies across neighbourhood definitions and measures, adjustment variables and study populations makes it difficult to draw more than a few general qualitative conclusions. Improving the quality of observational work through improved measurement of neighbourhood attributes, more sophisticated consideration of spatial scale, longitudinal designs and evaluation of natural experiments will strengthen inferences regarding causal effects of area attributes on depression.

There is growing interest in understanding how food environments affect diet, but characterizing the food environment is challenging. The authors investigated the relation between global diet measures (an empirically derived "fats and processed meats" (FPM) dietary pattern and the Alternate Healthy Eating Index (AHEI)) and three complementary measures of the local food environment: 1) supermarket density, 2) participant-reported assessments, and 3) aggregated survey responses of independent informants. Data were derived from the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis, a US study of adults aged 45-84 years. A healthy diet was defined as scoring in the top or bottom quintile of AHEI or FPM, respectively. The probability of having a healthy diet was modeled by each environment measure using binomial regression. Participants with no supermarkets near their homes were 25-46% less likely to have a healthy diet than those with the most stores, after adjustment for age, sex, race/ethnicity, and socioeconomic indicators: The relative probability of a healthy diet for the lowest store density category versus the highest was 0.75 (95% confidence interval: 0.59, 0.95) for the AHEI and 0.54 (95% confidence interval: 0.42, 0.70) for FPM. Similarly, participants living in areas with the worst-ranked food environments (by participants or informants) were 22-35% less likely to have a healthy diet than those in the best-ranked food environments. Efforts to improve diet may benefit from combining individual and environmental approaches.