Article

Identifying clusters of active transportation using spatial scan statistics.

Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA.
American journal of preventive medicine (Impact Factor: 4.24). 09/2009; 37(2):157-66. DOI: 10.1016/j.amepre.2009.04.021
Source: PubMed

ABSTRACT There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation.
Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007-2008.
Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC.
The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units.

0 Bookmarks
 · 
76 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Late antenatal care and smoking during pregnancy are two important factors that are amenable to intervention. Despite the adverse health impacts of smoking during pregnancy and the health benefits of early first antenatal visit on both the mother and the unborn child, substantial proportions of women still smoke during pregnancy or have their first antenatal visit after 10 weeks gestation. This study was undertaken to assess the usefulness of geospatial methods in identifying communities at high risk of smoking during pregnancy and timing of the first antenatal visit, for which targeted interventions may be warranted, and more importantly, feasible. The Perinatal Data Collection, from 1999 to 2008 for south-western Sydney, were obtained from the New South Wales Ministry of Health. Maternal addresses at the time of delivery were georeferenced. A spatial scan statistic implemented in SaTScan was then used to identify statistically significant spatial clusters of women who smoked during pregnancy or women whose first antenatal care visit occurred at or after 10 weeks of pregnancy. Four spatial clusters of maternal smoking during pregnancy and four spatial clusters of first antenatal visit occurring at or after 10 weeks were identified in our analyses. In the maternal smoking during pregnancy clusters, higher proportions of mothers, were aged less than 35 years, had their first antenatal visit at or after 10 weeks and a lower proportion of mothers were primiparous. For the clusters of increased risk of late first antenatal visit at or after 10 weeks of gestation, a higher proportion of mothers lived in the most disadvantaged areas and a lower proportion of mothers were primiparous. The application of spatial analyses provides a means to identify spatial clusters of antenatal risk factors and to investigate the associated socio-demographic characteristics of the clusters.
    International Journal of Health Geographics 10/2013; 12(1):46. · 2.62 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Identifying spatial clusters of chronic diseases has been conducted over the past several decades. More recently these approaches have been applied to physical activity and obesity. However, few studies have investigated built environment characteristics in relation to these spatial clusters. This study's aims were to detect spatial clusters of physical activity and obesity, examine whether the geographic distribution of covariates affects clusters, and compare built environment characteristics inside and outside clusters. In 2004, Nurses' Health Study participants from California, Massachusetts, and Pennsylvania completed survey items on physical activity (N = 22,599) and weight-status (N = 19,448). The spatial scan statistic was utilized to detect spatial clustering of higher and lower likelihood of obesity and meeting physical activity recommendations via walking. Clustering analyses and tests that adjusted for socio-demographic and health-related variables were conducted. Neighborhood built environment characteristics for participants inside and outside spatial clusters were compared. Seven clusters of physical activity were identified in California and Massachusetts. Two clusters of obesity were identified in Pennsylvania. Overall, adjusting for socio-demographic and health-related covariates had little effect on the size or location of clusters in the three states with a few exceptions. For instance, adjusting for husband's education fully accounted for physical activity clusters in California. In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters. In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters. In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters. Spatial clustering techniques can identify high and low risk areas for physical activity and obesity. Although covariates significantly differed inside and outside the clusters, patterns of differences were mostly inconsistent. The findings from these spatial analyses could eventually facilitate the design and implementation of more resource-efficient, geographically targeted interventions for both physical activity and obesity.
    BMC Public Health 12/2014; 14(1):1322. · 2.32 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: OBJECTIVES: The aim of this study was to investigate which GIS-based measures of walkability (density, land-use mix, connectivity and walkability indexes) in urban and suburban neighbourhoods are used in research and which of them are consistently associated with walking and cycling for transport, overall active transportation and weight-related measures in adults. METHODS: A systematic review of English publications using PubMed, Science Direct, Active Living Research Literature Database, the Transportation Research Information Service and reference lists was conducted. The search terms utilised were synonyms for GIS in combination with synonyms for the outcomes. RESULTS: Thirty-four publications based on 19 different studies were eligible. Walkability measures such as gross population density, intersection density and walkability indexes most consistently correlated with measures of physical activity for transport. Results on weight-related measures were inconsistent. CONCLUSIONS: More research is needed to determine whether walkability is an appropriate measure for predicting weight-related measures and overall active transportation. As most of the consistent correlates, gross population density, intersection density and the walkability indexes have the potential to be used in planning and monitoring.
    International Journal of Public Health 12/2012; · 1.99 Impact Factor

Full-text (2 Sources)

Download
57 Downloads
Available from
May 22, 2014

David G Stinchcomb