Xingyou Zhang

Modeling spatial accessibility to parks: a national study

BackgroundParks provide ideal open spaces for leisure-time physical activity and important venues to promote physical activity. The spatial configuration of parks, the number of parks and their spatial distribution across neighborhood areas or local regions, represents the basic park access potential for their residential populations. A new measure of spatial access to parks, population-weighted distance (PWD) to parks, combines the advantages of current park access approaches and incorporates the information processing theory and probability access surface model to more accurately quantify residential populations potential spatial access to parks.ResultsThe PWD was constructed at the basic level of US census geography - blocks - using US park and population data. This new measure of population park accessibility was aggregated to census tract, county, state and national levels. On average, US residential populations are expected to travel 6.7 miles to access their local neighborhood parks. There are significant differences in the PWD to local parks among states. The District of Columbia and Connecticut have the best access to local neighborhood parks with PWD of 0.6 miles and 1.8 miles, respectively. Alaska, Montana, and Wyoming have the largest PWDs of 62.0, 37.4, and 32.8 miles, respectively. Rural states in the western and Midwestern US have lower neighborhood park access, while urban states have relatively higher park access.ConclusionsThe PWD to parks provides a consistent platform for evaluating spatial equity of park access and linking with population health outcomes. It could be an informative evaluation tool for health professionals and policy makers. This new method could be applied to quantify geographic accessibility of other types of services or destinations, such as food, alcohol, and tobacco outlets.

Spatial Disparities in the Distribution of Parks and Green Spaces in the USA

BackgroundLittle national evidence is available on spatial disparities in distributions of parks and green spaces in the USA.PurposeThis study examines ecological associations of spatial access to parks and green spaces with percentages of black, Hispanic, and low-income residents across the urban–rural continuum in the conterminous USA.MethodsCensus tract-level park and green space data were linked with data from the 2010 U.S. Census and 2006–2010 American Community Surveys. Linear mixed regression models were performed to examine these associations.ResultsPoverty levels were negatively associated with distances to parks and percentages of green spaces in urban/suburban areas while positively associated in rural areas. Percentages of blacks and Hispanics were in general negatively linked to distances to parks and green space coverage along the urban–rural spectrum.ConclusionsPlace-based race–ethnicity and poverty are important correlates of spatial access to parks and green spaces, but the associations vary across the urbanization levels.

Multilevel Regression and Poststratification for Small-Area Estimation of Population Health Outcomes: A Case Study of Chronic Obstructive Pulmonary Disease Prevalence Using the Behavioral Risk Factor Surveillance System

A variety of small-area statistical models have been developed for health surveys, but none are sufficiently flexible to generate small-area estimates (SAEs) to meet data needs at different geographic levels. We developed a multilevel logistic model with both state- and nested county-level random effects for chronic obstructive pulmonary disease (COPD) using 2011 data from the Behavioral Risk Factor Surveillance System. We applied poststratification with the (decennial) US Census 2010 counts of census-block population to generate census-block-level SAEs of COPD prevalence which could be conveniently aggregated to all other census geographic units, such as census tracts, counties, and congressional districts. The model-based SAEs and direct survey estimates of COPD prevalence were quite consistent at both the county and state levels. The Pearson correlation coefficient was 0.99 at the state level and ranged from 0.88 to 0.95 at the county level. Our extended multilevel regression modeling and poststratification approach could be adapted for other geocoded national health surveys to generate reliable SAEs for population health outcomes at all administrative and legislative geographic levels of interest in a scalable framework.

Validation of Multilevel Regression and Poststratification Methodology for Small Area Estimation of Health Indicators From the Behavioral Risk Factor Surveillance System

Small area estimation is a statistical technique used to produce reliable estimates for smaller geographic areas than those for which the original surveys were designed. Such small area estimates (SAEs) often lack rigorous external validation. In this study, we validated our multilevel regression and poststratification SAEs from 2011 Behavioral Risk Factor Surveillance System data using direct estimates from 2011 Missouri County-Level Study and American Community Survey data at both the state and county levels. Coefficients for correlation between model-based SAEs and Missouri County-Level Study direct estimates for 115 counties in Missouri were all significantly positive (0.28 for obesity and no health-care coverage, 0.40 for current smoking, 0.51 for diabetes, and 0.69 for chronic obstructive pulmonary disease). Coefficients for correlation between model-based SAEs and American Community Survey direct estimates of no health-care coverage were 0.85 at the county level (811 counties) and 0.95 at the state level. Unweighted and weighted model-based SAEs were compared with direct estimates; unweighted models performed better. External validation results suggest that multilevel regression and poststratification model-based SAEs using single-year Behavioral Risk Factor Surveillance System data are valid and could be used to characterize geographic variations in health indictors at local levels (such as counties) when high-quality local survey data are not available.

Trends in adult current asthma prevalence and contributing risk factors in the United States by state: 2000–2009

BackgroundCurrent asthma prevalence among adults in the United States has reached historically high levels. Although national-level estimates indicate that asthma prevalence among adults increased by 33% from 2000 to 2009, state-specific temporal trends of current asthma prevalence and their contributing risk factors have not been explored.MethodsWe used 2000–2009 Behavioral Risk Factor Surveillance System data from all 50 states and the District of Columbia (D.C.) to estimate state-specific current asthma prevalence by 2-year periods (2000–2001, 2002–2003, 2004–2005, 2006–2007, 2008–2009). We fitted a series of four logistic-regression models for each state to evaluate whether there was a statistically significant linear change in the current asthma prevalence over time, accounting for sociodemographic factors, smoking status, and weight status (using body mass index as the indicator).ResultsDuring 2000–2009, current asthma prevalence increased in all 50 states and D.C., with significant increases in 46/50 (92%) states and D.C. After accounting for weight status in the model series with sociodemographic factors, and smoking status, 10 states (AR, AZ, IA, IL, KS, ME, MT, UT, WV, and WY) that had previously shown a significant increase did not show a significant increase in current asthma prevalence.ConclusionsThere was a significant increasing trend in state-specific current asthma prevalence among adults from 2000 to 2009 in most states in the United States. Obesity prevalence appears to contribute to increased current asthma prevalence in some states.

Predicting Periodontitis at State and Local Levels in the United States

The objective of the study was to estimate the prevalence of periodontitis at state and local levels across the United States by using a novel, small area estimation (SAE) method. Extended multilevel regression and poststratification analyses were used to estimate the prevalence of periodontitis among adults aged 30 to 79 y at state, county, congressional district, and census tract levels by using periodontal data from the National Health and Nutrition Examination Survey (NHANES) 2009–2012, population counts from the 2010 US census, and smoking status estimates from the Behavioral Risk Factor Surveillance System in 2012. The SAE method used age, race, gender, smoking, and poverty variables to estimate the prevalence of periodontitis as defined by the Centers for Disease Control and Prevention/American Academy of Periodontology case definitions at the census block levels and aggregated to larger administrative and geographic areas of interest. Model-based SAEs were validated against national estimates directly from NHANES 2009–2012. Estimated prevalence of periodontitis ranged from 37.7% in Utah to 52.8% in New Mexico among the states (mean, 45.1%; median, 44.9%) and from 33.7% to 68% among counties (mean, 46.6%; median, 45.9%). Severe periodontitis ranged from 7.27% in New Hampshire to 10.26% in Louisiana among the states (mean, 8.9%; median, 8.8%) and from 5.2% to 17.9% among counties (mean, 9.2%; median, 8.8%). Overall, the predicted prevalence of periodontitis was highest for southeastern and southwestern states and for geographic areas in the Southeast along the Mississippi Delta, as well as along the US and Mexico border. Aggregated model-based SAEs were consistent with national prevalence estimates from NHANES 2009–2012. This study is the first-ever estimation of periodontitis prevalence at state and local levels in the United States, and this modeling approach complements public health surveillance efforts to identify areas with a high burden of periodontitis.

Neighborhood commuting environment and obesity in the United States: an urban-rural stratified multilevel analysis.

OBJECTIVE Automobile dependency and longer commuting are associated with current obesity epidemic. We aimed to examine the urban-rural differential effects of neighborhood commuting environment on obesity in the US METHODS: The 1997-2005 National Health Interview Survey (NHIS) were linked to 2000 US Census data to assess the effects of neighborhood commuting environment: census tract-level automobile dependency and commuting time, on individual obesity status. RESULTS Higher neighborhood automobile dependency was associated with increased obesity risk in urbanized areas (large central metro (OR 1.11[1.09, 1.12]), large fringe metro (OR 1.17[1.13, 1.22]), medium metro (OR 1.22[1.16, 1.29]), small metro (OR 1.11[1.04, 1.19]), and micropolitan (OR 1.09[1.00, 1.19])), but not in non-core rural areas (OR 1.00[0.92, 1.08]). Longer neighborhood commuting time was associated with increased obesity risk in large central metro (OR 1.09[1.04, 1.13]), and less urbanized areas (small metro (OR 1.08[1.01, 1.16]), micropolitan (OR 1.06[1.01, 1.12]), and non-core rural areas (OR 1.08[1.01, 1.17])), but not in (large fringe metro (OR 1.05[1.00, 1.11]), and medium metro (OR 1.04[0.98, 1.10])). CONCLUSION The link between commuting environment and obesity differed across the regional urbanization levels. Urban and regional planning policies may improve current commuting environment and better support healthy behaviors and healthy community development.

A multilevel approach to estimating small area childhood obesity prevalence at the census block-group level.

Introduction Traditional survey methods for obtaining nationwide small-area estimates (SAEs) of childhood obesity are costly. This study applied a geocoded national health survey in a multilevel modeling framework to estimate prevalence of childhood obesity at the census block-group level. Methods We constructed a multilevel logistic regression model to evaluate the influence of individual demographic characteristics, zip code, county, and state on the childhood obesity measures from the 2007 National Survey of Children’s Health. The obesity risk for a child in each census block group was then estimated on the basis of this multilevel model. We compared direct survey and model-based SAEs to evaluate the model specification. Results Multilevel models in this study explained about 60% of state-level variances associated with childhood obesity, 82.8% to 86.5% of county-level, and 93.1% of zip code-level. The 95% confidence intervals of block- group level SAEs have a wide range (0.795-20.0), a low median of 2.02, and a mean of 2.12. The model-based SAEs of childhood obesity prevalence ranged from 2.3% to 54.7% with a median of 16.0% at the block-group level. Conclusion The geographic variances among census block groups, counties, and states demonstrate that locale may be as significant as individual characteristics such as race/ethnicity in the development of the childhood obesity epidemic. Our estimates provide data to identify priority areas for local health programs and to establish feasible local intervention goals. Model-based SAEs of population health outcomes could be a tool of public health assessment and surveillance.

Measuring the Structure of U.S. Metropolitan Areas, 1970–2000: Spatial Statistical Metrics and an Application to Commuting Behavior

Problem, research strategy, and findings: Metropolitan planning organizations attempt to shape urban form at the regional and metropolitan scale, including the pattern of suburban centers. How do these efforts change behavior? Our study informs that question by way of a new family of urban form metrics summarizing the polycentric structure of U.S. metropolitan areas. Using a spatial statistical approach, these measures are sensitive to the size, amount, and location of suburban centers. The article then tests the influence of these structures on commute times nationally from 1970 to 2000. Takeaway for practice: The influence of development densities on travel in sprawling regions is more complicated than previously understood or measured. While the level of both neighborhood density and regional density explain average commuting times, density also works relatively. The spatial variation of density, the density of suburban centers relative to the region, and the spatial distribution of high-density nodes each appear to play distinct roles in influencing travel. Research support: Centers for Disease Control and Prevention, and Institute for Bioscience and Biomedical Engineering.

Changes in density of on-premises alcohol outlets and impact on violent crime, Atlanta, Georgia, 1997-2007

Introduction Regulating alcohol outlet density is an evidence-based strategy for reducing excessive drinking. However, the effect of this strategy on violent crime has not been well characterized. A reduction in alcohol outlet density in the Buckhead neighborhood of Atlanta from 2003 through 2007 provided an opportunity to evaluate this effect. Methods We conducted a community-based longitudinal study to evaluate the impact of changes in alcohol outlet density on violent crime in Buckhead compared with 2 other cluster areas in Atlanta (Midtown and Downtown) with high densities of alcohol outlets, from 1997 through 2002 (preintervention) to 2003 through 2007 (postintervention). The relationship between exposures to on-premises retail alcohol outlets and violent crime were assessed by using annual spatially defined indices at the census block level. Multilevel regression models were used to evaluate the relationship between changes in exposure to on-premises alcohol outlets and violent crime while controlling for potential census block-level confounders. Results A 3% relative reduction in alcohol outlet density in Buckhead from 1997–2002 to 2003–2007 was associated with a 2-fold greater reduction in exposure to violent crime than occurred in Midtown or Downtown, where exposure to on-premises retail alcohol outlets increased. The magnitude of the association between exposure to alcohol outlets and violent crime was 2 to 5 times greater in Buckhead than in either Midtown or Downtown during the postintervention period. Conclusions A modest reduction in alcohol outlet density can substantially reduce exposure to violent crime in neighborhoods with high density of alcohol outlets. Routine monitoring of community exposure to alcohol outlets could also inform the regulation of alcohol outlet density, consistent with Guide to Community Preventive Services recommendations.