Area variations in health: A spatial multilevel modeling approach

Abstract

Both space and membership in geographically-embedded administrative units can produce variations in health, resulting in geographic clusters of good and poor health. Despite important differences between these two types of dependence, one is easily mistaken for the other, and the possibility that both are at work is commonly ignored. We fit a series of hierarchical and spatially-explicit multilevel models to a U.S. county-level life dataset of life expectancy in 1999 to demonstrate approaches for data analysis and interpretation when multiple sources of area-clustering are present. We demonstrate the methods to detect, interpret, and differentiate evidence of spatial and geographic membership effects and discuss key considerations for analyzing data with spatial or/and membership dimensions. We find evidence that life expectancy is driven by both within-state geographic process, and by spatial processes. We argue that considering spatial and membership processes simultaneously yields valuable insights into the patterning of area variations in health.

The Association of Minority Self-Rated Health with Black versus White Gentrification

“…[13][14][15][16] In spite of the potential ramifications that gentrification carries onto health, no study to date has sought to untangle the complex relationships between gentrification and health using this method. Through hierarchical modeling, we answer the question of how an individual's race/ethnicity moderates the effects of neighborhood gentrification on their health.…”

Spatial Random Slope Multilevel Modeling Using Multivariate Conditional Autoregressive Models: A Case Study of Subjective Travel Satisfaction in Beijing

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“…Regarding these methodological advances, spatial dependence is conceptualized as a simultaneous autoregressive (SAR) model following the spatial econometrics convention (e.g., Cliff and Ord 1981;Anselin 1988;LeSage and Pace 2009). In another type of hybrid model, spatial dependence (at the lower level) is represented by a conditional autoregressive (CAR; e.g., Besag, York, and Mollie 1991;Banerjee, Carlin, and Gelfand 2004) model in addition to another set of independent random effects at the higher level (Arcaya et al 2012). Similarly, Browne, Goldstein, and Rasbash (2001) employed a spatial multiple membership model to tackle spatial dependence effects in the sense that individuals are allowed to be influenced by both their immediate and neighboring contexts.…”

Epidemiology and Place: A Spatial Area for Health Analysis

“…In this regard, Arcaya et al (2012) argue that in contrast to the place perspective that uses geographic information to form groups, the use of the space perspective defines each observation according to their proximity with other observations, ignoring potential significant similarities that are usually shared with geographic and political boundaries.…”