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.

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.