Associations of social cohesion and quality of life with objective and perceived built environments: a latent profile analysis among seniors.

BACKGROUND: Healthy aging requires support from local built and social environments. Using latent profile analysis, this study captured the multidimensionality of the built environment and examined relations between objective and perceived built environment profiles, neighborhood social cohesion and quality of life among seniors. METHODS: In total, 693 participants aged 66-97 were sampled from two US locales in 2005-2008 as part of the Senior Neighborhood Quality of Life Study (SNQLS). Perceived social cohesion and quality of life were assessed using validated surveys. Six objective (geographic information system (GIS)-based) and seven perceived built environment latent profiles generated in previous SNQLS publications were used for analyses. Mixed-effects models estimated social cohesion and quality of life separately as a function of the built environment profiles. RESULTS: More walkable and destination-rich perceived built environment profiles were associated with higher social cohesion and quality of life. Objective built environment profiles were not associated with social cohesion and only positively associated with quality of life in only one locale (Baltimore/DC). CONCLUSIONS: Latent profile analysis offered a comprehensive approach to assessing the built environment. Seniors who perceived their neighborhoods to be highly walkable and recreationally dense experienced higher neighborhood social cohesion and quality of life, which may set the stage for healthier aging.

Transit Use and Health Care Costs: A Cross-sectional Analysis.

INTRODUCTION: Greater transit use is associated with higher levels of physical activity, which is associated with lower health risks and better health outcomes. However, there is scant evidence about whether health care costs differ based on level of transit ridership. METHODS: A sample (n=947) of members of Kaiser Permanente in the Portland, Oregon area were surveyed in 2015 about their typical use of various modes of travel including transit. Electronic medical record-derived health care costs were obtained among these members for the prior three years. Analysis examined proportional costs between High transit users (3+ days/week), Low transit users (1-2 days/week), and Non-users adjusting for age and sex, and then individually (base models) and together for demographic and health status variables. RESULTS: In separate base models across individual covariates, High transit users had lower total health care costs (59-69% of Non-user's costs) and medication costs (31-37% of Non-users' costs) than Non-users. Low transit users also had lower total health care (69%-76% of Non-users' costs) and medication costs (43-57% transit of Non-user's costs) than Non-users. High transit users' outpatient costs were also lower (77-82% of Non-users). In fully-adjusted models, total health care and medication costs were lower among High transit users' (67% and 39%) and Low transit users' (75% and 48%) compared to Non-users, but outpatient costs did not differ by transit use. CONCLUSIONS: Findings have implications for the potential cost benefit of encouraging and supporting more transit use, although controlled longitudinal and experimental evidence is needed to confirm findings and understand mechanisms.

Parental factors in children's active transport to school.

OBJECTIVE: Identify non-distance factors related to children's active transport (AT) to school, including parental, home, and environment characteristics. Understanding the factors related to children's AT to school, beyond distance to school, could inform interventions to increase AT and children's overall physical activity. STUDY DESIGN: Participants were in the Neighborhood Impact on Kids Study, a longitudinal, observational cohort study of children aged 6-11 and their parents in King County, WA and San Diego County, CA between 2007 and 2009. Parents reported frequency and mode of child transport to school, perceived neighbourhood, home and family environments, parental travel behaviours, and sociodemographics. METHODS: Children living less than a 20 minute walk to school were in this analysis. Children classified as active transporters (walked/bicycled to or from school at least once per week) were compared with those not using AT as often. RESULTS: Children using AT were older and had parents who reported themselves using active transport. Having a family rule that restricts the child to stay within sight of the parent or home and more parent working hours were related to lower odds of a child using AT. CONCLUSIONS: Children's AT to school is associated with parental AT to work and other locations. Interventions should be considered that enable whole family AT, ameliorate safety concerns and decrease the need for parental supervision, such as walking school buses.

The development of a walkability index: application to the Neighborhood Quality of Life Study.

Emerging evidence supports a link between neighbourhood built environment and physical activity. Systematic methodologies for characterising neighbourhood built environment are needed that take advantage of available population information such as census-level demographics. Based on transportation and urban planning literatures, an integrated index for operationalising walkability using parcel-level information is proposed. Validity of the walkability index is examined through travel surveys among areas examined in the Neighborhood Quality of Life Study (NQLS), a study investigating built environment correlates of adults' physical activity.

The development of a data model for research on the environmental correlates of physical inactivity and obesity.

Development of information systems to support research on environmental correlates of physical activity and nutrition has to date been largely ad hoc and driven by single research project requirements. In this rapidly growing field, research databases are becoming increasingly complex as researchers attempt to model the impact of multiple aspects of the environment such as neighborhood characteristics, site and building design, and nutritional environments on both aggregate and individual level measures of physical activity and weight. The presentation reports on the initial implementation of a logical data model in the context of an ongoing research program that is exploring the relation of neighborhood physical and demographic characteristics on physical activity levels measured in time and space.