Bikeshare Use in Urban Communities: Individual and Neighborhood Factors.

Objective: Bicycling is an affordable way to increase access to employment, schooling, and services and an effective measure against obesity. Bikeshare programs can make bicycling accessible to diverse populations, but little evidence exists on their adoption in low-resource neighborhoods. Our study examined factors associated with bikeshare use in a metropolitan area in the southern United States. Methods: We performed a retrospective cross-sectional analysis of a database of clients (N=815) who rented a bicycle from Zyp Bikeshare in Birmingham, Alabama between October 2015 and November 2016. Individual-level variables included bike use frequency, average speed, total miles traveled, total minutes ridden, bike type (traditional vs electricity-assisted pedelec), membership type, sex, and age. Area-level data aggregated to Census tracts, proxies for neighborhoods, were obtained from the 2010 US Census after geocoding clients' billing addresses. Using exploratory factor analysis, a neighborhood socioeconomic disadvantage index (SDI) was constructed. Bikeshare station presence in a tract was included as a covariate. Multivariate linear regression models, adjusted for clustering on Census tracts, were estimated to determine predictors of bikeshare use. Results: In a multivariate regression model of individual and neighborhood characteristics adjusted for clustering, each decile increase in the SDI was associated with a 9% increase in bikeshare use (P<.001). Bikeshare use was also positively associated with speed (.1, P<.001), total miles (.008, P<.001), and pedelec use (1.02, P<.01). Conclusion: Higher neighborhood socioeconomic disadvantage is associated with higher bikeshare use. Bikeshare is a viable transportation option in low-resource neighborhoods and may be an effective tool to improve the connectivity, livability, and health of urban communities.

Social Determinants of Adherence to Pulmonary Rehabilitation for Chronic Obstructive Pulmonary Disease.

Adherence to pulmonary rehabilitation (PR) is low. Previous studies have focused on clinical predictors of PR completion. We aimed to identify social determinants of adherence to PR. A cross-sectional analysis of a database of COPD patients (N = 455) in an outpatient PR program was performed. Adherence, a ratio of attended-to-prescribed sessions, was coded as low (<35%), moderate (35-85%), and high (>85%). Individual-level measures included age, sex, race, BMI, smoking status, pack-years, baseline 6-minute walk distance (6MWD: <150, 150-249, ≥250), co-morbidities, depression, and prescribed PR sessions (≤20, 21-30, >30). Fifteen area-level measures aggregated to Census tracts were obtained from the U.S. Census after geocoding patients' addresses. Using exploratory factor analysis, a neighborhood socioeconomic disadvantage index was constructed, which included variables with factor loading >0.5: poverty, public assistance, households without vehicles, cost burden, unemployment, and minority population. Multivariate regression models were adjusted for clustering on Census tracts. Twenty-six percent of patients had low adherence, 23% were moderately adherent, 51% were highly adherent. In the best fitted full model, each decile increase in neighborhood socioeconomic disadvantage increased the risk of moderate vs high adherence by 14% (p < 0.01). Smoking tripled the relative risk of low adherence (p < 0.01), while each increase in 6MWD category decreased that risk by 72% (p < 0.01) and 84% (p < 0.001), respectively. These findings show that, relative to high adherence, low adherence is associated with limited functional capacity and current smoking, while moderate adherence is associated with socioeconomic disadvantage. The distinction highlights different pathways to suboptimal adherence and calls for tailored intervention approaches.

The Social Determinants of Chronic Disease.

This review article addresses the concept of the social determinants of health (SDH), selected theories, and its application in studies of chronic disease. Once ignored or regarded only as distant or secondary influences on health and disease, social determinants have been increasingly acknowledged as fundamental causes of health afflictions. For the purposes of this discussion, SDH refers to SDH variables directly relevant to chronic diseases and, in some circumstances, obesity, in the research agenda of the Mid-South Transdisciplinary Collaborative Center for Health Disparities Research. The health effects of SDH are initially discussed with respect to smoking and the social gradient in mortality. Next, four leading SDH theories-life course, fundamental cause, social capital, and health lifestyle theory-are reviewed with supporting studies. The article concludes with an examination of neighborhood disadvantage, social networks, and perceived discrimination in SDH research.

A Comparison of Black and White Racial Differences in Health Lifestyles and Cardiovascular Disease.

INTRODUCTION: This study examines the health lifestyles of a cohort of blacks and whites in relation to cardiovascular disease (CVD). The link between health lifestyles and CVD is well established, but most of the focus has been on SES and more research is needed on racial differences. METHODS: Data were from the Coronary Artery Risk Development in Young Adults study of black (n=2,451) and white (n=2,351) men and women. Data were analyzed from baseline examinations in 1985-1986 when the participants were aged 18-30 years and any fatal or nonfatal CVD event that occurred over approximately the next 28 years (until August 2013). The first stage of the analysis used latent class models to identify distinct health lifestyles on the basis of race. The second stage used multinomial logit regression models to analyze specific characteristics in relation to the health lifestyles classes, followed by the third stage in which Cox proportional hazards models analyzed associations of the lifestyle classes with CVD risk. RESULTS: Four separate health lifestyle patterns for blacks and four for whites were identified, with the "unhealthy" lifestyle among blacks (hazard ratio, 1.60) and "most unhealthy" lifestyle among whites (hazard ratio, 3.12) showing an elevated risk of CVD. An important difference is that, in every lifestyle class, blacks showed a higher probability of excessive energy intake than whites-indicative of the potential for obesity. CONCLUSIONS: Health lifestyles differ by race and support the exploratory hypothesis that distinct classes of healthy-unhealthy lifestyles exist within each racial group.