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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. [ABSTRACT FROM AUTHOR]

Little is known about environmental determinants of type 2 diabetes. The authors hypothesized that insulin resistance is positively related to distance to a wealthy area and to local neighborhood poverty. Data were derived from The Multi-Ethnic Study of Atherosclerosis, a study of adults aged 45–84 years in six US locales, and the 2000 US Census. The homeostasis model assessment (HOMA) index was used to measure insulin resistance. Linear regression was used to estimate associations between area characteristics and insulin resistance after adjustment for age, sex, income, education, and race/ethnicity and for the potential mediators diet, physical activity, and body mass index (n = 4,821). Among persons not treated for diabetes, distance to a wealthy area was associated with HOMA independent of local poverty and person-level covariates: per 4.4-km change, the relative increase in HOMA was 13% (95% confidence interval: 7%, 19%), similar to the effect of a body mass index increase of 1.7 kg/m2 on HOMA. This association was reduced after adjustment for physical activity, diet, and body mass index (relative increase = 9%, 95% confidence interval: 3%, 15%). Local neighborhood poverty was also positively, but more weakly associated with insulin resistance, with no association after adjustment for race/ethnicity. This study shows that proximity to resources in high-income areas is related to insulin resistance. [ABSTRACT FROM AUTHOR]

The majority of studies examining the relation between neighborhood environments and health have used census-based indicators to characterize neighborhoods. These studies have shown that neighborhood socioeconomic characteristics are associated with a range of health outcomes. Establishing if these associations reflect causal relations requires testing hypotheses regarding how specific features of neighborhoods are related to specific health outcomes. However, there is little information on the reliability of neighborhood measures. The purpose of this study was to estimate the reliability of a questionnaire measuring various self-reported measures of the neighborhood environment of possible relevance to cardiovascular disease. The study consisted of a face-to-face and telephone interview administered twice to 48 participants over a 2-week period. The face-to-face and telephone portions of the interview lasted an average of 5 and 11 minutes, respectively. The questionnaire was piloted among a largely Latino and African American study sample recruited from a public hospital setting in New York City. Scales were used to assess six neighborhood domains: aesthetic quality, walking/ exercise environment, safety from crime, violence, access to healthy foods, and social cohesion. Cronbach’s α’s ranged from .77 to .94 for the scales corresponding to these domains, with test–retest correlations ranging from 0.78 to 0.91. In addition, neighborhood indices for presence of recreational facilities, quality of recreational facilities, neighborhood participation, and neighborhood problems were examined. Test–retest reliability measures for these indices ranged from 0.73 to 0.91. The results from this study suggested that self-reported neighborhood characteristics can be reliably measured. [ABSTRACT FROM AUTHOR]

Background This study investigates the relationship between neighbourhood characteristics and mortality (all-cause, cardiovascular disease [CVD], and cancer) in the Atherosclerosis Risk in Communities Study (ARIC).Methods Analysis was limited to African-American and white participants 45–64 years of age at baseline whose records were linked to census data. Deaths ascertained through 31 December 1999 were included in the analysis. Individual-level characteristics were obtained from the baseline interview. A composite index was used to characterize the neighbourhood socioeconomic environment. Proportional hazards regression was used to estimate the effect of neighbourhood socioeconomic status (SES) index and family income on the survival time.Results The rate of mortality adjusted for age and gender was highest among those who lived in disadvantaged neighbourhoods and were of lower SES. In general, all-cause and CVD mortality rates decreased with increasing neighbourhood SES advantage and family income in all race-gender groups. Although this pattern generally persisted after adjustment for individual socioeconomic factors, statistically significant associations persisted for CVD mortality in whites only (hazard ratio = 1.4, 95% CI: 1.0, 2.0) for most disadvantaged versus most advantaged tertile). When compared with the most affluent participants living in the most advantaged neighbourhoods, the increased risk of all-cause and CVD mortality associated with being poor and living in the most disadvantaged neighbourhoods was equivalent to being 11 and 13 years older at baseline for whites and African Americans, respectively.Conclusion Our findings indicate that neighbourhood socioeconomic characteristics are associated with modest increases in CVD mortality in white adults. The lack of neighbourhood effects in African Americans needs to be interpreted with caution due to the limited range in the characteristics of the neighbourhood from which these participants were drawn. [ABSTRACT FROM AUTHOR]

The article begins with a discussion of the rationale for studying the relationship between residential environments and cardiovascular health. Existing empirical research relating residential environments to cardiovascular outcomes and risk factors is summarized. The research areas discussed include neighborhood socioeconomic characteristics and cardiovascular disease, the effects of residential environments on physical activity, and the effects of residential environments on diet. Other mechanisms through which residential environments may affect cardiovascular health are also briefly noted. Key challenges in investigating the relationship between residential environments and health are discussed. These challenges include characterizing environments (including definition and geographic scale as well as conceptualization and measurement of relevant features), the limitations of observational studies, and the need to evaluate the health impact of interventions or "naturally" occurring changes in local environments. The need for interdisciplinary work is emphasized. [ABSTRACT FROM AUTHOR]

A major focus of recent work on the spatial patterning of health has been the study of how features of residential environments or neighborhoods may affect health. Place effects on health emerge from complex interdependent processes in which individuals interact with each other and their environment and in which both individuals and environments adapt and change over time. Traditional epidemiologic study designs and statistical regression approaches are unable to examine these dynamic processes. These limitations have constrained the types of questions asked, the answers received, and the hypotheses and theoretical explanations that are developed. Agent-based models and other systems-dynamics models may help to address some of these challenges. Agent-based models are computer representations of systems consisting of heterogeneous microentities that can interact and change/adapt over time in response to other agents and features of the environment. Using these models, one can observe how macroscale dynamics emerge from microscale interactions and adaptations. A number of challenges and limitations exist for agent-based modeling. Nevertheless, use of these dynamic models may complement traditional epidemiologic analyses and yield additional insights into the processes involved and the interventions that may be most useful. [ABSTRACT FROM AUTHOR]

A major focus of recent work on the spatial patterning of health has been the study of how features of residential environments or neighborhoods may affect health. Place effects on health emerge from complex interdependent processes in which individuals interact with each other and their environment and in which both individuals and environments adapt and change over time. Traditional epidemiologic study designs and statistical regression approaches are unable to examine these dynamic processes. These limitations have constrained the types of questions asked, the answers received, and the hypotheses and theoretical explanations that are developed. Agent-based models and other systems-dynamics models may help to address some of these challenges. Agent-based models are computer representations of systems consisting of heterogeneous microentities that can interact and change/adapt over time in response to other agents and features of the environment. Using these models, one can observe how macroscale dynamics emerge from microscale interactions and adaptations. A number of challenges and limitations exist for agent-based modeling. Nevertheless, use of these dynamic models may complement traditional epidemiologic analyses and yield additional insights into the processes involved and the interventions that may be most useful. [ABSTRACT FROM AUTHOR]

This study investigated associations between neighborhood physical and social environments and body mass index in 2,865 participants of the Multi-Ethnic Study of Atherosclerosis (MESA) aged 45–84 years and residing in Maryland, New York, and North Carolina. Neighborhood (census tract) environments were measured in non-MESA participants residing in MESA neighborhoods (2000–2002). The neighborhood physical environment score combined measures of a better walking environment and greater availability of healthy foods. The neighborhood social environment score combined measures of greater aesthetic quality, safety, and social cohesion and less violent crime. Marginal maximum likelihood was used to estimate associations between neighborhood environments and body mass index (kg/m2) before and after adjustment for individual-level covariates. MESA residents of neighborhoods with better physical environments had lower body mass index (mean difference per standard deviation higher neighborhood measure = −2.38 (95% confidence interval (CI): −3.38, −1.38) kg/m2 for women and −1.20 (95% CI: −1.84, −0.57) kg/m2 for men), independent of age, race/ethnicity, education, and income. Attenuation of these associations after adjustment for diet and physical activity suggests a mediating role of these behaviors. In men, the mean body mass index was higher in areas with better social environments (mean difference = 0.52 (95% CI: 0.07, 0.97) kg/m2). Improvement in the neighborhood physical environment should be considered for its contribution to reducing obesity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Exposure to airborne particulate matter has been linked to cardiovascular events. Whether this finding reflects an effect of particulate matter exposure on the triggering of events or development of atherosclerosis remains unknown. Using data from the Multi-Ethnic Study of Atherosclerosis collected at baseline (2000–2002), the authors investigated associations of 20-year exposures to particulate matter with measures of subclinical disease (coronary calcium, common carotid intimal-medial thickness, and ankle-brachial index) in 5,172 US adults without clinical cardiovascular disease. Particulate matter exposures for the 20 years prior to assessment of subclinical disease were obtained from a space-time model of Environmental Protection Agency monitor data linked to residential history data for each participant. Intimal-medial thickness was weakly, positively associated with exposures to particulate matter <10 μm in aerodynamic diameter and <2.5 μm in aerodynamic diameter after controlling for age, sex, race/ethnicity, socioeconomic factors, diet, smoking, physical activity, blood lipids, diabetes, hypertension, and body mass index (1–4% increase per 21-μg/m3 increase in particulate matter <10 μm in aerodynamic diameter or a 12.5-μg/m3 increase in particulate matter <2.5 μm in aerodynamic diameter). No consistent associations with other measures of atherosclerosis were observed. There was no evidence of effect modification by sociodemographic factors, lipid status, smoking, diabetes, body mass index, or site. Results are compatible with some effect of particulate matter exposures on development of carotid atherosclerosis. [ABSTRACT FROM AUTHOR]