Policies and changes to the built environment are promising targets for obesity prevention efforts and can be evaluated as "natural"-or "quasi"-experiments. This systematic review examined the use of natural-or quasi-experiments to evaluate the efficacy of policy and built environment changes on obesity-related outcomes (body mass index, diet, or physical activity). PubMed (Medline) was searched for studies published 2005-2013; 1,175 abstracts and 115 articles were reviewed. Of the 37 studies included, 18 studies evaluated impacts on nutrition/diet, 17 on physical activity, and 3 on body mass index. Nutrition-related studies found greater effects due to bans/restrictions on unhealthy foods, mandates offering healthier foods, and altering purchase/ payment rules on foods purchased using low-income food vouchers compared to other interventions (menu labeling, new supermarkets). Physical activity-related studies generally found stronger impacts when the intervention involved improvements to active transportation infrastructure, longer follow-up time, or measured process outcomes (e.g., cycling rather than total physical activity) compared to other studies. Only three studies directly assessed body mass index or weight, and only one (installing light-rail system) observed a significant effect. Studies varied widely in the strength of their design and studies with weaker designs were more likely to report associations in the positive direction.

BackgroundBlood pressure (BP) may be implicated in associations observed between ambient particulate matter and cardiovascular morbidity and mortality. This study examined cross-sectional associations between short-term ambient fine particles (particulate matter ≤ 2.5 μm in aerodynamic diameter; PM2.5) and BP: systolic (SBP), diastolic (DBP), mean arterial (MAP), and pulse pressure (PP).MethodsThe study sample included 5,112 persons 45–84 years of age, free of cardiovascular disease at the Multi-Ethnic Study of Atherosclerosis baseline examination (2000–2002). Data from U.S. Environmental Protection Agency monitors were used to estimate ambient PM2.5 exposures for the preceding 1, 2, 7, 30, and 60 days. Roadway data were used to estimate local exposures to traffic-related particles.ResultsResults from linear regression found PP and SBP positively associated with PM2.5. For example, a 10-μg/m3 increase in PM2.5 30-day mean was associated with 1.12 mmHg higher pulse pressure [95% confidence interval (CI), 0.28–1.97] and 0.99 mmHg higher systolic BP (95% CI, –0.15 to 2.13), adjusted for age, sex, race/ethnicity, income, education, body mass index, diabetes, cigarette smoking and environmental tobacco smoke, alcohol use, physical activity, medications, atmospheric pressure, and temperature. Results were much weaker and not statistically significant for MAP and DBP. Although traffic-related variables were not themselves associated with BP, the association between PM2.5 and BP was stronger in the presence of higher traffic exposure.ConclusionsHigher SBP and PP were associated with ambient levels of PM2.5 and the association was stronger in the presence of roadway traffic, suggesting that impairment of blood pressure regulation may play a role in response to air pollution.

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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.

Background Despite increasing interest in the extent to which features of residential environments contribute to incidence of type 2 diabetes mellitus, no multisite prospective studies have investigated this question. We hypothesized that neighborhood resources supporting physical activity and healthy diets are associated with a lower incidence of type 2 diabetes. Methods Person-level data came from 3 sites of the Multi-Ethnic Study of Atherosclerosis, a population-based, prospective study of adults aged 45 to 84 years at baseline. Neighborhood data were derived from a population-based residential survey. Type 2 diabetes was defined as a fasting glucose level of 126 mg/dL or higher (≥7 mmol/L) or taking insulin or oral hypoglycemic agents. We estimated the hazard ratio of type 2 diabetes incidence associated with neighborhood (US Census tract) resources. Results Among 2285 participants, 233 new type 2 diabetes cases occurred during a median of 5 follow-up years. Better neighborhood resources, determined by a combined score for physical activity and healthy foods, were associated with a 38% lower incidence of type 2 diabetes (hazard ratio corresponding to a difference between the 90th and 10th percentiles for resource distribution, 0.62; 95% confidence interval, 0.43–0.88 adjusted for age, sex, family history of diabetes, race/ethnicity, income, assets, educational level, alcohol use, and smoking status). The association remained statistically significant after further adjustment for individual dietary factors, physical activity level, and body mass index. Conclusion Better neighborhood resources were associated with lower incidence of type 2 diabetes, which suggests that improving environmental features may be a viable population-level strategy for addressing this disease.

Understanding the impact of place on health is a key element of epidemiologic investigation, and numerous tools are being employed for analysis of spatial health-related data. This review documents the huge growth in spatial epidemiology, summarizes the tools that have been employed, and provides in-depth discussion of several methods. Relevant research articles for 2000–2010 from seven epidemiology journals were included if the study utilized a spatial analysis method in primary analysis (n = 207). Results summarized frequency of spatial methods and substantive focus; graphs explored trends over time. The most common spatial methods were distance calculations, spatial aggregation, clustering, spatial smoothing and interpolation, and spatial regression. Proximity measures were predominant and were applied primarily to air quality and climate science and resource access studies. The review concludes by noting emerging areas that are likely to be important to future spatial analysis in public health.

Mobility restrictions in older adults are common and increase the likelihood of negative health outcomes and premature mortality. The effect of built environment on mobility in older populations, among whom environmental effects may be strongest, is the focus of a growing body of the literature. We reviewed recent research (1990–2010) that examined associations of objective measures of the built environment with mobility and disability in adults aged 60 years or older. Seventeen empirical articles were identified. The existing literature suggests that mobility is associated with higher street connectivity leading to shorter pedestrian distances, street and traffic conditions such as safety measures, and proximity to destinations such as retail establishments, parks, and green spaces. Existing research is limited by differences in exposure and outcome assessments and use of cross-sectional study designs. This research could lead to policy interventions that allow older adults to live more healthy and active lives in their communities.

Background: Although research has shown that low socioeconomic status (SES) and minority communities have higher exposure to air pollution, few studies have simultaneously investigated the associations of individual and neighborhood SES with pollutants across multiple sites.Objectives: We characterized the distribution of ambient air pollution by both individual and neighborhood SES using spatial regression methods.Methods: The study population comprised 6,140 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Year 2000 annual average ambient PM2.5 and NOx concentrations were calculated for each study participant’s home address at baseline examination. We investigated individual and neighborhood (2000 U.S. Census tract level) SES measures corresponding to the domains of income, wealth, education, and occupation. We used a spatial intrinsic conditional autoregressive model for multivariable analysis and examined pooled and metropolitan area–specific models.Results: A 1-unit increase in the z-score for family income was associated with 0.03-μg/m3 lower PM2.5 (95% CI: –0.05, –0.01) and 0.93% lower NOx (95% CI: –1.33, –0.53) after adjustment for covariates. A 1-SD–unit increase in the neighborhood’s percentage of persons with at least a high school degree was associated with 0.47-μg/m3 lower mean PM2.5 (95% CI: –0.55, –0.40) and 9.61% lower NOx (95% CI: –10.85, –8.37). Metropolitan area–specific results exhibited considerable heterogeneity. For example, in New York, high-SES neighborhoods were associated with higher concentrations of pollution.Conclusions: We found statistically significant associations of SES measures with predicted air pollutant concentrations, demonstrating the importance of accounting for neighborhood- and individual-level SES in air pollution health effects research.Citation: Hajat A, Diez-Roux AV, Adar SD, Auchincloss AH, Lovasi GS, O’Neill MS, Sheppard L, Kaufman JD. 2013. Air pollution and individual and neighborhood socioeconomic status: evidence from the Multi-Ethnic Study of Atherosclerosis (MESA). Environ Health Perspect 121:1325–1333; http://dx.doi.org/10.1289/ehp.1206337