Invited Commentary: Beyond Individuals—Area Poverty and Health, or the Search for an Impactful Epidemiology

Av, Diez Roux

doi:10.1093/aje/kwx084

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Recent literature calls for social epidemiologists to incorporate analyses at multiple units of analysis, e.g., district-level policies’ impacts on individuals’ health (Diez Roux, 2017; O’Campo, 2003). While multilevel analyses improve rigor to support causal inference, using them in concert with ecological analyses can inform inference related to implications for entire communities.…”

Section: Methodsmentioning

confidence: 99%

The legacy of redlining in the effect of foreclosures on Detroit residents’ self-rated health

et al. 2019

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Decades of historical practices like housing discrimination in Detroit have lasting impacts on communities. Perhaps the most explicit example is the practice of redlining in the 1930s, whereby lenders outlined financially undesirable neighborhoods, populated by minority families, on maps and prevented residents from moving to better resourced neighborhoods. Awareness of historical housing discrimination may improve research assessing the impacts of current neighborhood characteristics on health. Using the Detroit Neighborhood Health Study (DNHS), we assessed the association between two-year changes in home foreclosure rates following the 2007-2008 Great Recession, and residents’ five-year self-rated health trajectories (2008-2013); and estimated the confounding bias introduced by ignoring historical redlining practices in the city. We used both ecological and multilevel models to make inference about person- and community-level processes. In a neighborhood-level linear regression adjusted for confounders (including percent redlined); a 10 percentage-point slower foreclosure rate recovery was associated with an increase in prevalence of poor self-rated health of 0.31 (95% CI: −0.02-0.64). At the individual level, it was associated with a within-person increase in probability of poor health of 0.45 (95% CI: 0.15-0.72). Removing redlining from the model biased the estimated effect upward to 0.38 (95% CI: 0.07-0.69) and 0.56 (95% CI: 0.21-0.84) in the neighborhood and individual-level models, respectively. Stratum-specific foreclosure recovery effects indicate stronger influence in neighborhoods with a greater proportion of residents identifying as white and a greater degree of historic redlining. These findings support theory that structural discrimination has lasting influences on current neighborhood health effects, and suggests that historical redlining specifically may increase vulnerability to contemporary neighborhood foreclosures. Community interventions should consider historical discrimination in conjunction with current place-based indicators to more equitably improve population health.

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Section: Methodsmentioning

confidence: 99%

The legacy of redlining in the effect of foreclosures on Detroit residents’ self-rated health

et al. 2019

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A geographic identifier assignment algorithm with Bayesian variable selection to identify neighborhood factors associated with emergency department visit disparities for asthma

et al. 2020

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Background: Ecologic health studies often rely on outcomes from health service utilization data that are limited by relatively coarse spatial resolutions and missing geographic information, particularly neighborhood level identifiers. When fine-scale geographic data are missing, the ramifications and strategies for addressing them are not well researched or developed. This study illustrates a novel spatio-temporal framework that combines a geographic identifier assignment (i.e., geographic imputation) algorithm with predictive Bayesian variable selection to identify neighborhood factors associated with disparities in emergency department (ED) visits for asthma. Methods: ED visit records with missing fine-scale spatial identifiers (~ 20%) were geocoded using information from known, coarser, misaligned spatial units using an innovative geographic identifier assignment algorithm. We then employed systematic variable selection in a spatio-temporal Bayesian hierarchical model (BHM) predictive framework within the NIMBLE package in R. Our novel methodology is illustrated in an ecologic case study aimed at identifying neighborhood-level predictors of asthma ED visits in South Carolina, United States, from 1999 to 2015. The health outcome was annual ED visit counts in small areas (i.e., census tracts) with primary diagnoses of asthma (ICD9 codes 493.XX) among children ages 5 to 19 years. Results: We maintained 96% of ED visit records for this analysis. When the algorithm used areal proportions as probabilities for assignment, which addressed differential missingness of census tract identifiers in rural areas, variable selection consistently identified significant neighborhood-level predictors of asthma ED visit risk including pharmacy proximity, average household size, and carbon monoxide interactions. Contrasted with common solutions of removing geographically incomplete records or scaling up analyses, our methodology identified critical differences in parameters estimated, predictors selected, and inferences. We posit that the differences were attributable to improved data resolution, resulting in greater power and less bias. Importantly, without this methodology, we would have inaccurately identified predictors of risk for asthma ED visits, particularly in rural areas.

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Invited Commentary: Beyond Individuals—Area Poverty and Health, or the Search for an Impactful Epidemiology

Cited by 2 publications

References 6 publications

The legacy of redlining in the effect of foreclosures on Detroit residents’ self-rated health

The legacy of redlining in the effect of foreclosures on Detroit residents’ self-rated health

A geographic identifier assignment algorithm with Bayesian variable selection to identify neighborhood factors associated with emergency department visit disparities for asthma

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