The Urban Built Environment and Mobility in Older Adults: A Comprehensive Review

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

A new tool for Epidemiology? The usefulness of dynamic agent models in understanding place effects on health

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.http://deepblue.lib.umich.edu/bitstream/2027.42/60335/1/A new tool for Epdiemiology The usefulness of dynamic agent models in understanding place effects on health.pd

An Actor-Based Model of Social Network Influence on Adolescent Body Size, Screen Time, and Playing Sports

Recent studies suggest that obesity may be “contagious” between individuals in social networks. Social contagion (influence), however, may not be identifiable using traditional statistical approaches because they cannot distinguish contagion from homophily (the propensity for individuals to select friends who are similar to themselves) or from shared environmental influences. In this paper, we apply the stochastic actor-based model (SABM) framework developed by Snijders and colleagues to data on adolescent body mass index (BMI), screen time, and playing active sports. Our primary hypothesis was that social influences on adolescent body size and related behaviors are independent of friend selection. Employing the SABM, we simultaneously modeled network dynamics (friendship selection based on homophily and structural characteristics of the network) and social influence. We focused on the 2 largest schools in the National Longitudinal Study of Adolescent Health (Add Health) and held the school environment constant by examining the 2 school networks separately (N = 624 and 1151). Results show support in both schools for homophily on BMI, but also for social influence on BMI. There was no evidence of homophily on screen time in either school, while only one of the schools showed homophily on playing active sports. There was, however, evidence of social influence on screen time in one of the schools, and playing active sports in both schools. These results suggest that both homophily and social influence are important in understanding patterns of adolescent obesity. Intervention efforts should take into consideration peers’ influence on one another, rather than treating “high risk” adolescents in isolation

Circadian rhythm of cortisol and neighborhood characteristics in a population-based sample: The Multi-Ethnic Study of Atherosclerosis

Although stress is often hypothesized to contribute to the effects of neighborhoods on health, very few studies have investigated associations of neighborhood characteristics with stress biomarkers. This study helps address the gap in the literature by examining whether neighborhood characteristics are associated with cortisol profiles. Analyses were based on data from the Multi-Ethnic Study of Atherosclerosis Stress study which collected multiple measures of salivary cortisol over three days on a population based sample of approximately 800 adults. Multilevel models with splines were used to examine associations of cortisol with neighborhood poverty, violence, disorder, and social cohesion. Neighborhood violence was significantly associated with lower cortisol values at wakeup and with a slower decline in cortisol over the earlier part of the day, after sociodemographic controls. Associations were weaker and less consistent for neighborhood poverty, social cohesion, and disorder. Results revealed suggestive, though limited, evidence linking neighborhood contexts to cortisol circadian rhythms

Neighborhood Resources for Physical Activity and Healthy Foods and Their Association with Insulin Resistance

OBJECTIVE:: Little is known about the influence of the built environment, and in particular neighborhood resources, on health. We hypothesized that neighborhood resources for physical activity and healthy foods are associated with insulin resistance. METHODS:: Person-level data (n = 2026) came from 3 sites of The Multi-Ethnic Study of Atherosclerosis, a study of adults aged 45-84 years. Area-level data were derived from a population-based residential survey. The homeostasis model assessment index was used as an insulin resistance measure among persons not treated for diabetes. We used linear regression to estimate associations between area features and insulin resistance. RESULTS:: Greater neighborhood physical activity resources consistently were associated with lower insulin resistance. Adjusted for age, sex, family history of diabetes, race/ethnicity, income and education, insulin resistance was reduced by 17% (95% confidence interval = -31% to -1%) for an increase from the 10th to 90th percentiles of resources. Greater healthy food resources were also inversely related to insulin resistance, although the association was not robust to adjustment for race/ethnicity. Analyses including diet, physical activity, and body mass index suggested that these variables partly mediated observed associations. Results were similar when impaired fasting glucose/diabetes was considered as the outcome variable. CONCLUSION:: Diabetes prevention efforts may need to consider features of residential environment.http://deepblue.lib.umich.edu/bitstream/2027.42/57885/1/Neighborhood Resources for Physical Activity and Healthy Foods and Their Association With Insulin Resistance.pd

Exploring walking differences by socioeconomic status using a spatial agent-based model

We use an exploratory agent-based model of adults’ walking behavior within a city to examine the possible impact of interventions on socioeconomic differences in walking. Simulated results show that for persons of low socioeconomic status, increases in walking resulting from increases in their positive attitude towards walking may diminish over time if other features of the environment are not conducive to walking. Similarly, improving the safety level for the lower SES neighborhoods may be effective in increasing walking, however, the magnitude of its effectiveness varies by levels of land use mix, such that effects of safety are greatest when persons live in areas with a large mix of uses

Neighborhood Resources for Physical Activity and Healthy Foods and Incidence of Type 2 Diabetes Mellitus

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 populationbased 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 yearsBetter 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.This research was supported by contracts R01 HL071759 and N01-HC-95159 through N01-HC-95165 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, National Institutes of Health.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64274/1/auchincloss_archiveinternalmedicine_oct2009.pd

Filling the gaps: spatial interpolation of residential survey data in the estimation of neighborhood characteristics

The measurement of area-level attributes remains a major challenge in studies of neighborhood health effects. Even when neighborhood survey data are collected, they necessarily have incomplete spatial coverage. We investigated whether interpolation of neighborhood survey data was aided by information on spatial dependencies and supplementary data. Neighborhood "availability of healthy foods" was measured in a population-based survey of 5186 persons in Baltimore, New York, and Forsyth County (North Carolina). The following supplementary data were compiled from Census 2000 and InfoUSA, Inc.: distance to supermarkets, density of supermarkets and fruit and vegetable stores, housing density, distance to a high-income area, and percent of households that do not own a vehicle. We compared 4 interpolation models (ordinary least squares, residual kriging, spatial error regression, and thin-plate splines) using error statistics and Pearson correlation coefficients (r) from repeated replications of cross-validations. There was positive spatial autocorrelation in neighborhood availability of healthy foods (by site, Moran coefficient range = 0.10-0.28; all P < 0.0001). Prediction performances were generally similar for the evaluated models (r [almost equal to] 0.35 for Baltimore and Forsyth; r [almost equal to] 0.54 for New York). Supplementary data accounted for much of the spatial autocorrelation and, thus, spatial modeling was only advantageous when spatial correlation was at least moderate. A variety of interpolation techniques will likely need to be utilized in order to increase the data available for examining health effects of residential environments. The most appropriate method will vary depending on the construct of interest, availability of relevant supplementary data, and types of observed spatial patterns.http://deepblue.lib.umich.edu/bitstream/2027.42/57780/1/Filling the gaps Spatail Interpolation of Residental Survey Data in the Estimation of Neighborhood Characteristics.pd

Home and work neighbourhood environments in relation to body mass index: the Multi-Ethnic Study of Atherosclerosis (MESA)

Little is known about neighborhood characteristics of workplaces, the extent to which they are independently and synergistically correlated with residential environments, and their impact on health