A Segmented Regression Analysis of Household Income and Recurrent Falls Among Adults in a National Cohort Study.

Falls can have life-altering consequences for older adults, including extended recovery periods and compromised independence. Higher household income may mitigate the risk of falls by providing financial resources for mobility tools, remediation of environmental hazards, and needed supports, or it may buffer the impact of an initial fall on subsequent risk through improved assistance and care. Household income has not had a consistently observed association with falls in older adults; however, a segmented association may exist such that associations are attenuated above a certain income threshold. In this study, we utilized segmented negative binomial regression analysis to examine the association between household income and recurrent falls among 2,302 participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study recruited between 2003 and 2007. Income-fall association segments separated by changes in slope were considered. Model results indicated a 2-segment association between household income and recurrent falls in the past year. In the range below the breakpoint, household income was negatively associated with the rate of recurrent falls across all age groups examined; in a higher income range (from $20,000-$49,999 to ≥$150,000), the association was attenuated (weaker negative trend) or reversed (positive trend). These findings point to potential benefits of ensuring that incomes for lower-income adults exceed the threshold needed to confer a reduced risk of recurrent falls.

Cumulative Experience of Neighborhood Walkability and Change in Weight and Waist Circumference in REGARDS.

Neighborhood walkability-features of the built environment that promote pedestrian activity-has been associated with greater physical activity and lower body mass index (BMI; calculated as weight (kg)/height (m)2) among neighborhood residents. However, much of the literature has been cross-sectional and only a few cohort studies have assessed neighborhood features throughout follow-up. Using data from the Reasons for Geographic and Racial Differences in Stroke Study (2003-2016) and a neighborhood walkability index (NWI) measured annually during follow-up, we assessed whether the cumulative experience of neighborhood walkability (NWI-years) predicted BMI and waist circumference after approximately 10 years of follow-up, controlling for these anthropometric measures at enrollment. Analyses were adjusted for individual-level sociodemographic covariates and the cumulative experience of neighborhood poverty rate and neighborhood greenspace coverage. Almost a third (29%) of participants changed address at least once during follow-up. The first change of residence, on average, brought the participants to neighborhoods with higher home values and lower NWI scores than their originating neighborhoods. Compared with those having experienced the lowest quartile of cumulative NWI-years, those who experienced the highest quartile had 0.83 lower BMI (95% confidence interval, -1.5, -0.16) and 1.07-cm smaller waist circumference (95% confidence interval, -1.96, -0.19) at follow-up. These analyses provide additional longitudinal evidence that residential neighborhood features that support pedestrian activity are associated with lower adiposity.

Healthy Community Design, Anti-displacement, and Equity Strategies in the USA: A Scoping Review.

Recent investments in built environment infrastructure to create healthy communities have highlighted the need for equity and environmental justice. Although the benefits of healthy community design (e.g., connecting transportation systems and land use changes) are well established, some reports suggest that these changes may increase property values. These increases can raise the risk of displacement for people with low incomes and/or who are from racial and ethnic minority groups, who would then miss out on benefits from changes in community design. This review scanned the literature for displacement mitigation and prevention measures, with the goal of providing a compilation of available strategies for a wide range of audiences including public health practitioners. A CDC librarian searched the Medline, EbscoHost, Scopus, and ProQuest Central databases, and we identified grey literature using Google and Google Scholar searches. The indexed literature search identified 6 articles, and the grey literature scan added 18 articles. From these 24 total articles, we identified 141 mitigation and prevention strategies for displacement and thematically characterized each by domain using an adapted existing typology. This work provides a well-categorized inventory for practitioners and sets the stage for future evaluation research on the implementation of strategies and practices to reduce displacement.

National trends and disparities in retail food environments in the USA between 1990 and 2014.

OBJECTIVE: To describe national disparities in retail food environments by neighbourhood composition (race/ethnicity and socio-economic status) across time and space. DESIGN: We examined built food environments (retail outlets) between 1990 and 2014 for census tracts in the contiguous USA (n 71 547). We measured retail food environment as counts of all food stores, all unhealthy food sources (including fast food, convenience stores, bakeries and ice cream) and healthy food stores (including supermarkets, fruit and vegetable markets) from National Establishment Time Series business data. Changes in food environment were mapped to display spatial patterns. Multi-level Poisson models, clustered by tract, estimated time trends in counts of food stores with a land area offset and independent variables population density, racial composition (categorised as predominantly one race/ethnicity (>60 %) or mixed), and inflation-adjusted income tertile. SETTING: The contiguous USA between 1990 and 2014. PARTICIPANTS: All census tracts (n 71 547). RESULTS: All food stores and unhealthy food sources increased, while the subcategory healthy food remained relatively stable. In models adjusting for population density, predominantly non-Hispanic Black, Hispanic, Asian and mixed tracts had significantly more destinations of all food categories than predominantly non-Hispanic White tracts. This disparity increased over time, predominantly driven by larger increases in unhealthy food sources for tracts which were not predominantly non-Hispanic White. Income and food store access were inversely related, although disparities narrowed over time. CONCLUSIONS: Our findings illustrate a national food landscape with both persistent and shifting spatial patterns in the availability of establishments across neighbourhoods with different racial/ethnic and socio-economic compositions.

Association between Gentrification and Health and Healthcare Utilization.

There is tremendous interest in understanding how neighborhoods impact health by linking extant social and environmental drivers of health (SDOH) data with electronic health record (EHR) data. Studies quantifying such associations often use static neighborhood measures. Little research examines the impact of gentrification-a measure of neighborhood change-on the health of long-term neighborhood residents using EHR data, which may have a more generalizable population than traditional approaches. We quantified associations between gentrification and health and healthcare utilization by linking longitudinal socioeconomic data from the American Community Survey with EHR data across two health systems accessed by long-term residents of Durham County, NC, from 2007 to 2017. Census block group-level neighborhoods were eligible to be gentrified if they had low socioeconomic status relative to the county average. Gentrification was defined using socioeconomic data from 2006 to 2010 and 2011-2015, with the Steinmetz-Wood definition. Multivariable logistic and Poisson regression models estimated associations between gentrification and development of health indicators (cardiovascular disease, hypertension, diabetes, obesity, asthma, depression) or healthcare encounters (emergency department [ED], inpatient, or outpatient). Sensitivity analyses examined two alternative gentrification measures. Of the 99 block groups within the city of Durham, 28 were eligible (N = 10,807; median age = 42; 83% Black; 55% female) and 5 gentrified. Individuals in gentrifying neighborhoods had lower odds of obesity (odds ratio [OR] = 0.89; 95% confidence interval [CI]: 0.81-0.99), higher odds of an ED encounter (OR = 1.10; 95% CI: 1.01-1.20), and lower risk for outpatient encounters (incidence rate ratio = 0.93; 95% CI: 0.87-1.00) compared with non-gentrifying neighborhoods. The association between gentrification and health and healthcare utilization was sensitive to gentrification definition.

Comparing effects of Euclidean buffers and network buffers on associations between built environment and transport walking: the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Transport walking has drawn growing interest due to its potential to increase levels of physical activities and reduce reliance on vehicles. While existing studies have compared built environment-health associations between Euclidean buffers and network buffers, no studies have systematically quantified the extent of bias in health effect estimates when exposures are measured in different buffers. Further, prior studies have done the comparisons focusing on only one or two geographic regions, limiting generalizability and restricting ability to test whether direction or magnitude of bias are different by context. This study aimed to quantify the degree of bias in associations between built environment exposures and transport walking when exposures were operationalized using Euclidean buffers rather than network buffers in diverse contexts. METHODS: We performed a simulations study to systematically evaluate the degree of bias in associations between built environment exposures in Euclidean buffers and network buffers and transport walking, assuming network buffers more accurately captured true exposures. Additionally, we used empirical data from a multi-ethnic, multi-site cohort to compare associations between built environment amenities and walking for transport where built environment exposures were derived using Euclidean buffers versus network buffers. RESULTS: Simulation results found that the bias induced by using Euclidean buffer models was consistently negative across the six study sites (ranging from -80% to -20%), suggesting built environment exposures measured using Euclidean buffers underestimate health effects on transport walking. Percent bias was uniformly smaller for the larger 5 km scale than the 1 km and 0.25 km spatial scales, independent of site or built environment categories. Empirical findings aligned with the simulation results: built environment-health associations were stronger for built environment exposures operationalized using network buffers than using Euclidean buffers. CONCLUSION: This study is the first to quantify the extent of bias in the magnitude of the associations between built environment exposures and transport walking when the former are measured in Euclidean buffers vs. network buffers, informing future research to carefully conceptualize appropriate distance-based buffer metrics in order to better approximate real geographic contexts. It also helps contextualize existing research in the field that used Euclidean buffers when that were the only option. Further, this study provides an example of the uncertain geographic context problem.

Addressing patient's unmet social needs: disparities in access to social services in the United States from 1990 to 2014, a national times series study.

BACKGROUND: To address patient's unmet social needs and improve health outcomes, health systems have developed programs to refer patients in need to social service agencies. However, the capacity to respond to patient referrals varies tremendously across communities. This study assesses the emergence of disparities in spatial access to social services from 1990 to 2014. METHODS: Social service providers in the lower 48 continental U.S. states were identified annually from 1990 to 2014 from the National Establishment Times Series (NETS) database. The addresses of providers were linked in each year to 2010 US Census tract geometries. Time series analyses of annual counts of services per Km2 were conducted using Generalized Estimating Equations with tracts stratified into tertiles of 1990 population density, quartiles of 1990 poverty rate and quartiles of 1990 to 2010 change in median household income. RESULTS: Throughout the period, social service agencies/Km2 increased across tracts. For high population density tracts, in the top quartile of 1990 poverty rate, compared to tracts that experienced the steepest declines in median household income from 1990 to 2010, tracts that experienced the largest increases in income had more services (+ 1.53/Km2, 95% CI 1.23, 1.83) in 1990 and also experienced the steepest increases in services from 1990 to 2010: a 0.09 services/Km2/year greater increase (95% CI 0.07, 0.11). Similar results were observed for high poverty tracts in the middle third of population density, but not in tracts in the lowest third of population density, where there were very few providers. CONCLUSION: From 1990 to 2014 a spatial mismatch emerged between the availability of social services and the expected need for social services as the population characteristics of neighborhoods changed. High poverty tracts that experienced further economic decline from 1990 to 2010, began the period with the lowest access to services and experienced the smallest increases in access to services. Access was highest and grew the fastest in high poverty tracts that experienced the largest increases in median household income. We theorize that agglomeration benefits and the marketization of welfare may explain the emergence of this spatial mismatch.

Exploring Residents' Perceptions of Neighborhood Development and Revitalization for Active Living Opportunities.

INTRODUCTION: Community fears of gentrification have created concerns about building active living infrastructure in neighborhoods with low-income populations. However, little empirical research exists related to these concerns. This work describes characteristics of residents who reported 1) concerns about increased cost of living caused by neighborhood development and 2) support for infrastructural improvements even if the changes lead to a higher cost of living. METHODS: Data on concerns about or support for transportation-related and land use-related improvements and sociodemographic characteristics were obtained from the 2018 SummerStyles survey, an online panel survey conducted on a nationwide sample of US adults (n = 3,782). Descriptive statistics characterized the sample, and χ2 tests examined associations among variables. RESULTS: Overall, 19.1% of study respondents agreed that development had caused concerns about higher cost of living. Approximately half (50.7%) supported neighborhood changes for active living opportunities even if they lead to higher costs of living. Prevalences of both concern and support were higher among respondents who were younger and who had higher levels of education than their counterparts. Support did not differ between racial or ethnic groups, but concern was reported more often by Hispanic/Latino (28.9%) and other non-Hispanic (including multiracial) respondents (25.5%) than by non-Hispanic White respondents (15.6%). Respondents who reported concerns were more likely to express support (65.3%) than respondents who did not report concerns (47.3%). CONCLUSION: The study showed that that low-income, racial, or ethnic minority populations support environmental changes to improve active living despite cost of living concerns associated with community revitalization.

Business Data Categorization and Refinement for Application in Longitudinal Neighborhood Health Research: a Methodology.

Retail environments, such as healthcare locations, food stores, and recreation facilities, may be relevant to many health behaviors and outcomes. However, minimal guidance on how to collect, process, aggregate, and link these data results in inconsistent or incomplete measurement that can introduce misclassification bias and limit replication of existing research. We describe the following steps to leverage business data for longitudinal neighborhood health research: re-geolocating establishment addresses, preliminary classification using standard industrial codes, systematic checks to refine classifications, incorporation and integration of complementary data sources, documentation of a flexible hierarchical classification system and variable naming conventions, and linking to neighborhoods and participant residences. We show results of this classification from a dataset of locations (over 77 million establishment locations) across the contiguous U.S. from 1990 to 2014. By incorporating complementary data sources, through manual spot checks in Google StreetView and word and name searches, we enhanced a basic classification using only standard industrial codes. Ultimately, providing these enhanced longitudinal data and supplying detailed methods for researchers to replicate our work promotes consistency, replicability, and new opportunities in neighborhood health research.

A method for estimating neighborhood characterization in studies of the association with availability of sit-down restaurants and supermarkets.

BACKGROUND: Although neighborhood-level access to food differs by sociodemographic factors, a majority of research on neighborhoods and food access has used a single construct of neighborhood context, such as income or race. Therefore, the many interrelated built environment and sociodemographic characteristics of neighborhoods obscure relationships between neighborhood factors and food access. METHODS: The objective of this study was to account for the many interrelated characteristics of food-related neighborhood environments and examine the association between neighborhood type and relative availability of sit-down restaurants and supermarkets. Using cluster analyses with multiple measures of neighborhood characteristics (e.g., population density, mix of land use, and sociodemographic factors) we identified six neighborhood types in 1993 in the Twin Cities Region, Minnesota. We then used mixed effects regression models to estimate differences in the relative availability of sit-down restaurants and supermarkets in 1993, 2001, and 2011 across the six neighborhood types. RESULTS: We defined six types of neighborhoods that existed in 1993, namely, urban core, inner city, urban, aging suburb, high-income suburb, and suburban edge. Between 1993 and 2011, inner city neighborhoods experienced a greater increase in the percent of sit-down restaurants compared with urban core, urban, and aging suburbs. Differences in the percent of sit-down restaurants between inner city and aging suburbs, high-income suburbs and suburban edge neighborhoods increased between 1993 and 2011. Similarly, aging suburb neighborhoods had a greater percent of supermarkets compared with urban and high-income suburb neighborhoods in 2001 and 2011, but not in 1993, suggesting a more varied distribution of food stores across neighborhoods over time. Thus, the classification of neighborhood type based on sociodemographic and built environment characteristics resulted in a complex and increasingly varied distribution of restaurants and food stores. CONCLUSIONS: The temporal increase in the relative availability of sit-down restaurants in inner cities after accounting for all restaurants might be partly related to a higher proportion of residents who eat-away-from-home, which is associated with higher calorie and fat intake.

Associations between neighborhood greenspace and brain imaging measures in non-demented older adults: the Cardiovascular Health Study.

PURPOSE: Greater neighborhood greenspace has been associated with brain health, including better cognition and lower odds of Alzheimer's disease in older adults. We investigated associations between neighborhood greenspace and brain-based magnetic resonance imaging (MRI) measures and potential effect modification by sex or apolipoprotein E genotype (APOE), a risk factor for Alzheimer's disease. METHODS: We obtained a sample of non-demented participants 65 years or older (n = 1125) from the longitudinal, population-based Cardiovascular Health Study (CHS). Greenspace data were derived from the National Land Cover Dataset. Adjusted multivariable linear regression estimated associations between neighborhood greenspace five years prior to the MRI and left and right hippocampal volume and 10-point grades of ventricular size and burden of white matter hyperintensity. Interaction terms tested effect modification by APOE genotype and sex. CHS data (1989-1999) were obtained/analyzed in 2020. RESULTS: Participants were on average 79 years old [standard deviation (SD) = 4], 58% were female, and 11% were non-white race. Mean neighborhood greenspace was 38% (SD = 28%). Greater proportion of greenspace in the neighborhood five years before MRI was borderline associated with lower ventricle grade (estimate: - 0.30; 95% confidence interval: - 0.61, 0.00). We observed no associations between greenspace and the other MRI outcome measures and no evidence of effect modification by APOE genotype and sex. CONCLUSION: This study suggests a possible association between greater greenspace and less ventricular enlargement, a measure reflecting global brain atrophy. If confirmed in other longitudinal cohort studies, interventions and policies to improve community greenspaces may help to maintain brain health in older age.

Constructing Within-City Neighborhood Health Rankings in Philadelphia by Using Data From the 500 Cities Project.

INTRODUCTION: Profound geographic disparities in health exist in many US cities. Most reporting on these disparities is based on predetermined administrative districts that may not reflect true neighborhoods. We undertook a ranking project to describe health at the neighborhood level and used Philadelphia, Pennsylvania, as our case study. METHODS: To create neighborhood health rankings, we first divided the city into neighborhoods according to groups of contiguous census tracts. Modeling our ranking methods and indicators on the Robert Wood Johnson Foundation County Health Rankings, we gathered census tract-level data from the Centers for Disease Control and Prevention's 500 Cities Project and local sources and aggregated these data, as needed, to each neighborhood. We assigned composite scores and rankings for both health outcomes and health factors to each neighborhood. RESULTS: Scores for health outcomes and health factors were highly correlated. We found clusters of neighborhoods with low rankings in Philadelphia's northern, lower northeastern, western, and southwestern regions. We disseminated information on rankings throughout the city, including through a comprehensive webpage, public communication, and a museum exhibit. CONCLUSION: The Philadelphia neighborhood health rankings were designed to be accessible to people unfamiliar with public health, facilitating education on drivers of health in communities. Our methods can be used as a model for other cities to create and communicate data on within-city geographic health disparities.

Health and Health-Related Resources in Newly Designated Federally Qualified Opportunity Zones: United States, 2012-2016.

Objectives. To characterize health and health-related resources in the new qualified opportunity zones (QOZs) relative to tracts not selected or not eligible for this federal investment incentive.Methods. We used tract-level data from the 498 largest cities in the contiguous United States (n = 24 409), categorized using designations from the Department of Treasury. We compiled data on population characteristics, health-related resources, and health from the American Community Survey, the National Establishment Time Series, the National Land Cover Dataset, and the US Small-Area Life Expectancy Estimates Project and the 500 Cities projects. We calculated means and SDs for ineligible, eligible (but not designated), and designated QOZ tracts.Results. In general, designated QOZ tracts had lower access to health care facilities, physical activity resources, and healthy food. They had a higher prevalence of unhealthy behaviors and worse health outcomes across most measures.Conclusions. By benchmarking conditions, we facilitate tracking and assessment of QOZ impacts.Public Health Implications. QOZ could spur unprecedented neighborhood change with substantial influence on health resources and outcomes. Public health collaboration and strategic local governance of QOZ will be crucial for yielding health benefits for existing residents.

Neighborhood Recreation Facilities and Facility Membership Are Jointly Associated with Objectively Measured Physical Activity.

Efforts to increase physical activity have traditionally included either individual-level interventions (e.g., educational campaigns) or neighborhood-level interventions (e.g., additional recreational facilities). Little work has addressed the interaction between spatial proximity and individual characteristics related to facility use. We aimed to better understand the synergistic impact of both physical activity environments and recreational facility membership on objectively measured physical activity. Using the New York City Physical Activity and Transit (PAT) survey (n = 644), we evaluated associations between counts of commercial physical activity facilities within 1 km of participants' home addresses with both facility membership and accelerometry-measured physical activity. Individuals living near more facilities were more likely to report membership (adjusted odds ratio for top versus bottom quartile of facility count: 3.77 (95% CI 1.54-9.20). Additionally, while amount of facilities within a neighborhood was associated with more physical activity, this association was stronger for individuals reporting gym membership. Interventions aiming to increase physical activity should consider both neighborhood amenities and potential barriers, including the financial and social barriers of membership. Evaluation of neighborhood opportunities must expand beyond physical presence to consider multiple dimensions of accessibility.

Freedom from the Station: Spatial Equity in Access to Dockless Bike Share.

BACKGROUND: Bike sharing systems have potential to substantially boost active transportation levels (and consequent physical and mental health) in urban populations. We explored equity of spatial access in a novel 'dockless' bike share system that does not that constrain bike pickup and drop-off locations to docking stations. METHODS: Starting in July 2017, Seattle, Washington piloted a dockless bike share system that made 10,000 bikes available. We merged data on resident sociodemographic and economic characteristics from the American Community Survey about 93 defined neighborhoods with data about bike locations, bike idle time, and which neighborhoods operators rebalanced bikes to. We used mapping and descriptive statistics to compare access between neighborhoods along sociodemographic and economic lines. RESULTS: With many bikes available, no neighborhood was consistently excluded from access. However, the average availability ranged from 3 bikes per day to 341 per day. Neighborhoods with more bikes had more college-educated residents (median 75% college-educated vs. 65%) and local community resources (median opportunity index score of 24 vs. 19), and higher incomes (median 83,202 vs. 71,296). Rebalancing destinations were strongly correlated with neighborhood demand (r=0.61). CONCLUSIONS: The overall scale of the dockless system ensured there was baseline access throughout Seattle. We observed modest inequities in access along sociodemographic lines, similar to prior findings in studies of docked bike share systems. Dockless bike share systems hold promise for offering equitable spatial access to bike sharing.

Associations Between the Built Environment and Objective Measures of Sleep: The Multi-Ethnic Study of Atherosclerosis.

Although dense neighborhood built environments support increased physical activity and lower obesity, these features may also disturb sleep. Therefore, we sought to understand the association between the built environment and objectively measured sleep. From 2010 to 2013, we analyzed data from examination 5 of the Multi-Ethnic Study of Atherosclerosis, a diverse population from 6 US cities. We fit multilevel models that assessed the association between the built environment (Street Smart Walk Score, social engagement destinations, street intersections, and population density) and sleep duration or efficiency from 1-week wrist actigraphy in 1,889 individuals. After adjustment for covariates, a 1-standard-deviation increase in Street Smart Walk Score was associated with 23% higher odds of short sleep duration (≤6 hours; odds ratio = 1.2, 95% confidence interval: 1.0, 1.4), as well as shorter average sleep duration (mean difference = -8.1 minutes, 95% confidence interval: -12.1, -4.2). Results were consistent across other built environment measures. Associations were attenuated after adjustment for survey-based measure of neighborhood noise. Dense neighborhood development may have multiple health consequence. In promoting denser neighborhoods to increase walkability, it is important to also implement strategies that reduce the adverse impacts of this development on sleep, such as noise reductions efforts.

Statistical Methods to Study Variation in Associations Between Food Store Availability and Body Mass in the Multi-Ethnic Study of Atherosclerosis.

Research linking characteristics of the neighborhood environment to health has relied on traditional regression methods where prespecified distances from participant's locations or areas are used to operationalize neighborhood-level measures. Because the relevant spatial scale of neighborhood environment measures may differ across places or individuals, using prespecified distances could result in biased association estimates or efficiency losses. We use novel hierarchical distributed lag models and data from the Multi-Ethnic Study of Atherosclerosis (MESA) to (1) examine whether and how the association between the availability of favorable food stores and body mass index (BMI) depends on continuous distance from participant locations (instead of traditional buffers), thus allowing us to indirectly infer the spatial scale at which this association operates; (2) examine if the spatial scale and magnitude of the association differs across six MESA sites, and (3) across individuals. As expected, we found that the association between higher availability of favorable food stores within closer distances from participant's residential location was stronger than at farther distances, and that the magnitude of the adjusted association declined quickly from zero to two miles. Furthermore, between-individual heterogeneity in the scale and magnitude of the association was present; the extent of this heterogeneity was different across the MESA sites. Individual heterogeneity was partially explained by sex. This study illustrated novel methods to examine how neighborhood environmental factors may be differentially associated with health at different scales, providing nuance to previous research that ignored the heterogeneity found across individuals and contexts.

Municipal investment in off-road trails and changes in bicycle commuting in Minneapolis, Minnesota over 10 years: a longitudinal repeated cross-sectional study.

BACKGROUND: We studied the effect of key development and expansion of an off-road multipurpose trail system in Minneapolis, Minnesota between 2000 and 2007 to understand whether infrastructure investments are associated with increases in commuting by bicycle. METHODS: We used repeated measures regression on tract-level (N = 116 tracts) data to examine changes in bicycle commuting between 2000 and 2008-2012. We investigated: 1) trail proximity measured as distance from the trail system and 2) trail potential use measured as the proportion of commuting trips to destinations that might traverse the trail system. All analyses (performed 2015-2016) adjusted for tract-level sociodemographic covariates and contemporaneous cycling infrastructure changes (e.g., bicycle lanes). RESULTS: Tracts that were both closer to the new trail system and had a higher proportion of trips to destinations across the trail system experienced greater 10-year increases in commuting by bicycle. CONCLUSIONS: Proximity to off-road infrastructure and travel patterns are relevant to increased bicycle commuting, an important contributor to overall physical activity. Municipal investment in bicycle facilities, especially off-road trails that connect a city's population and its employment centers, is likely to lead to increases in commuting by bicycle.

The influence of walkability on broader mobility for Canadian middle aged and older adults: An examination of Walk Score™ and the Mobility Over Varied Environments Scale (MOVES).

Neighborhood built environments may play an important role in shaping mobility and subsequent health outcomes. However, little work includes broader mobility considerations such as cognitive ability to be mobile, social connections with community, or transportation choices. We used a population-based sample of Canadian middle aged and older adults (aged 45 and older) from the Canadian Community Health Survey-Healthy Aging (CCHS-HA, 2008-2009) to create a holistic mobility measure: Mobility over Varied Environments Scale (MOVES). Data from CCHS-HA respondents from British Columbia with MOVES were linked with Street Smart Walk Score™ data by postal code (n=2046). Mean MOVES was estimated across sociodemographic and health characteristics. Linear regression, adjusted for relevant covariates, was used to estimate the association between Street Smart Walk Score™ and the MOVES. The mean MOVES was 30.67 (95% confidence interval (CI) 30.36, 30.99), 5th percentile 23.27 (CI 22.16, 24.38) and 95th percentile was 36.93 (CI 35.98, 37.87). MOVES was higher for those who were younger, married, higher socioeconomic status, and had better health. In unadjusted models, for every 10 point increase in Street Smart Walk Score™, MOVES increased 4.84 points (CI 4.52, 5.15). However, results attenuated after adjustment for sociodemographic covariates: each 10 point increase in Street Smart Walk Score™ was associated with a 0.10 (CI 0.00, 0.20) point increase in MOVES. The modest but important link we observed between walkability and mobility highlights the implication of neighborhood design on the health of middle aged and older adults.

Developing a comprehensive measure of mobility: mobility over varied environments scale (MOVES).

BACKGROUND: While recent work emphasizes the multi-dimensionality of mobility, no current measure incorporates multiple domains of mobility. Using existing conceptual frameworks we identified four domains of mobility (physical, cognitive, social, transportation) to create a "Mobility Over Varied Environments Scale" (MOVES). We then assessed expected patterns of MOVES in the Canadian population. METHODS: An expert panel identified survey items within each MOVES domain from the Canadian Community Health Survey- Healthy Aging Cycle (2008-2009) for 28,555 (weighted population n = 12,805,067) adults (≥45 years). We refined MOVES using principal components analysis and Cronbach's alpha and weighted items so each domain was 10 points. Expected mobility trends, as assessed by average MOVES, were examined by sociodemographic and health factors, and by province, using Analysis of Variance (ANOVA). RESULTS: MOVES ranged from 0 to 40, where 0 represents individuals who are immobile and 40 those who are fully mobile. Mean MOVES was 29.58 (95% confidence interval (CI) 29.49, 29.67) (10th percentile: 24.17 (95% CI 23.96, 24.38), 90th percentile: 34.70 (CI 34.55, 34.85)). MOVES scores were lower for older, female, and non-white Canadians with worse health and lower socioeconomic status. MOVES was also lower for those who live in less urban areas. CONCLUSIONS: MOVES is a holistic measure of mobility for characterizing older adult mobility across populations. Future work should examine individual or neighborhood predictors of MOVES and its relationship to broader health outcomes. MOVES holds utility for research, surveillance, evaluation, and interventions around the broad factors influencing mobility in older adults.