Assessing Measurement Invariance of a Land Use Environment Construct Across Levels of Urbanicity.

Variation in the land use environment (LUE) impacts the continuum of walkability to car dependency, which has been shown to have effects on health outcomes. Existing objective measures of the LUE do not consider whether the measurement of the construct varies across different types of communities along the rural/urban spectrum. To help meet the goals of the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, we developed a national, census tract-level LUE measure which evaluates the road network and land development. We tested for measurement invariance by LEAD community type (higher density urban, lower density urban, suburban/small town, and rural) using multiple group confirmatory factor analysis. We determined that metric invariance does not exist; thus, measurement of the LUE does vary across community type with average block length, average block size, and percent developed land driving most shared variability in rural tracts and with intersection density, street connectivity, household density, and commercial establishment density driving most shared variability in higher density urban tracts. As a result, epidemiologic studies need to consider community type when assessing the LUE to minimize place-based confounding.

Comparing Global and Spatial Composite Measures of Neighborhood Socioeconomic Status Across US Counties.

Area-level neighborhood socioeconomic status (NSES) is often measured without consideration of spatial autocorrelation and variation. In this paper, we compared a non-spatial NSES measure to a spatial NSES measure for counties in the USA using principal component analysis and geographically weighted principal component analysis (GWPCA), respectively. We assessed spatial variation in the loadings using a Monte Carlo randomization test. The results indicated that there was statistically significant variation (p = 0.004) in the loadings of the spatial index. The variability of the census variables explained by the spatial index ranged from 60 to 90%. We found that the first geographically weighted principal component explained the most variability in the census variables in counties in the Northeast and the West, and the least variability in counties in the Midwest. We also tested the two measures by assessing the associations with county-level diabetes prevalence using data from the CDC's US Diabetes Surveillance System. While associations of the two NSES measures with diabetes did not differ for this application, the descriptive results suggest that it might be important to consider a spatial index over a global index when constructing national county measures of NSES. The spatial approach may be useful in identifying what factors drive the socioeconomic status of a county and how they vary across counties. Furthermore, we offer suggestions on how a GWPCA-based NSES index may be replicated for smaller geographic scopes.

The association between neighborhood social and economic environment and prevalent diabetes in urban and rural communities: The Reasons for Geographic and Racial Differences in Stroke (REGARDS) study.

Objective: The association between neighborhood disadvantage and health is well-documented. However, whether these associations may differ across rural and urban areas is unclear. This study examines the association between a multi-item neighborhood social and economic environment (NSEE) measure and diabetes prevalence across urban and rural communities in the US. Methods: This study included 27,159 Black and White participants aged ≥45 years at baseline (2003-2007) from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. Each participant's residential address was geocoded. NSEE was calculated as the sum of z-scores for six US Census tract variables (% of adults with less than high school education; % of adults unemployed; % of households earning <$30,000 per year; % of households in poverty; % of households on public assistance; and % of households with no car) and within strata of community type (higher density urban, lower density urban, suburban/small town, and rural). NSEE was categorized as quartiles, with higher NSEE quartiles reflecting more disadvantage. Prevalent diabetes was defined as fasting blood glucose ≥126 mg/dL or random blood glucose ≥200 mg/dL or use of diabetes medication at baseline. Multivariable adjusted Poisson regression models were used to estimate prevalence ratios (PR) and 95% confidence intervals (CI) for the association between NSEE and prevalent diabetes across community types. Results: The mean age was 64.8 (SD=9.4) years, 55% were women, 40.7% were non-Hispanic Black adults. The overall prevalence of diabetes was 21% at baseline and was greatest for participants living in higher density urban areas (24.5%) and lowest for those in suburban/small town areas (18.5%). Compared with participants living in the most advantaged neighborhood (NSEE quartile 1, reference group), those living in the most disadvantaged neighborhoods (NSEE quartile 4) had higher diabetes prevalence in crude models. After adjustment for sociodemographic factors, the association remained statistically significant for moderate density community types (lower density urban quartile 4 PR=1.50, 95% CI=1.29, 1.75; suburban/small town quartile 4 PR=1.54, 95% CI=1.24, 1.92). These associations were also attenuated and of smaller magnitude for those living in higher density urban and rural communities. Conclusion: Participants living in the most disadvantaged neighborhoods had a higher diabetes prevalence in each urban/rural community type and these associations were only partly explained by individual-level sociodemographic factors. In addition to addressing individual-level factors, identifying neighborhood characteristics and how they operate across urban and rural settings may be helpful for informing interventions that target chronic health conditions.

The Diabetes Location, Environmental Attributes, and Disparities Network: Protocol for Nested Case Control and Cohort Studies, Rationale, and Baseline Characteristics.

BACKGROUND: Diabetes prevalence and incidence vary by neighborhood socioeconomic environment (NSEE) and geographic region in the United States. Identifying modifiable community factors driving type 2 diabetes disparities is essential to inform policy interventions that reduce the risk of type 2 diabetes. OBJECTIVE: This paper aims to describe the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, a group funded by the Centers for Disease Control and Prevention to apply harmonized epidemiologic approaches across unique and geographically expansive data to identify community factors that contribute to type 2 diabetes risk. METHODS: The Diabetes LEAD Network is a collaboration of 3 study sites and a data coordinating center (Drexel University). The Geisinger and Johns Hopkins University study population includes 578,485 individuals receiving primary care at Geisinger, a health system serving a population representative of 37 counties in Pennsylvania. The New York University School of Medicine study population is a baseline cohort of 6,082,146 veterans who do not have diabetes and are receiving primary care through Veterans Affairs from every US county. The University of Alabama at Birmingham study population includes 11,199 participants who did not have diabetes at baseline from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a cohort study with oversampling of participants from the Stroke Belt region. RESULTS: The Network has established a shared set of aims: evaluate mediation of the association of the NSEE with type 2 diabetes onset, evaluate effect modification of the association of NSEE with type 2 diabetes onset, assess the differential item functioning of community measures by geographic region and community type, and evaluate the impact of the spatial scale used to measure community factors. The Network has developed standardized approaches for measurement. CONCLUSIONS: The Network will provide insight into the community factors driving geographical disparities in type 2 diabetes risk and disseminate findings to stakeholders, providing guidance on policies to ameliorate geographic disparities in type 2 diabetes in the United States. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21377.

The legacy of slavery and contemporary declines in heart disease mortality in the U.S. South.

BACKGROUND: This study aims to characterize the role of county-specific legacy of slavery in patterning temporal (i.e., 1968-2014), and geographic (i.e., Southern counties) declines in heart disease mortality. In this context, the U.S. has witnessed dramatic declines in heart disease mortality since the 1960's, which have benefitted place and race groups unevenly, with slower declines in the South, especially for the Black population. METHODS: Age-adjusted race- and county-specific mortality rates from 1968-2014 for all diseases of the heart were calculated for all Southern U.S. counties. Candidate confounding and mediating covariates from 1860, 1930, and 1970, were combined with mortality data in multivariable regression models to estimate the ecological association between the concentration of slavery in1860 and declines in heart disease mortality from 1968-2014. RESULTS: Black populations, in counties with a history of highest versus lowest concentration of slavery, experienced a 17% slower decline in heart disease mortality. The association for Black populations varied by region (stronger in Deep South than Upper South states) and was partially explained by intervening socioeconomic factors. In models accounting for spatial autocorrelation, there was no association between slave concentration and heart disease mortality decline for Whites. CONCLUSIONS: Nearly 50 years of declining heart disease mortality is a major public health success, but one marked by uneven progress by place and race. At the county level, progress in heart disease mortality reduction among Blacks is associated with place-based historical legacy of slavery. Effective and equitable public health prevention efforts should consider the historical context of place and the social and economic institutions that may play a role in facilitating or impeding diffusion of prevention efforts thereby producing heart healthy places and populations. Graphical abstract.

Racial segregation and maternal smoking during pregnancy: a multilevel analysis using the racial segregation interaction index.

Drawing from both the place stratification and ethnic enclave perspectives, we use multilevel modeling to investigate the relationships between women's race/ethnicity (i.e., non-Hispanic white, non-Hispanic black, Asian, and Hispanic) and maternal smoking during pregnancy, and examine if these relationships are moderated by racial segregation in the continental United States. The results show that increased interaction with whites is associated with increased probability of maternal smoking during pregnancy, and racial segregation moderates the relationships between race/ethnicity and maternal smoking. Specifically, living in a less racially segregated area is related to a lower probability of smoking during pregnancy for black women, but it could double and almost triple the probability of smoking for Asian women and Hispanic women, respectively. Our findings provide empirical evidence for both the place stratification and ethnic enclave perspectives.