Online Program

Accounting for spatial autocorrelation in the association between preventable congestive heart failure hospitalizations and neighborhood measures in New York City

Wednesday, November 4, 2015 : 10:30 a.m. - 10:50 a.m.

Rachael Weiss Riley, MPH, DPH Program at the Graduate Center, City University of New York School of Public Health, New York, NY
Luisa N. Borrell, DDS, PhD, Lehman College, CUNY, Bronx, NY
Lance Waller, PhD, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA
Andrew Maroko, PhD, Lehman College, Department of Health Sciences, City University of New York (CUNY), Bronx, NY
Juliana A. Maantay, PhD, MUP, FRGS, Department of Earth, Enviromental, and Geospatial Science, Lehman College, City University of New York, Bronx, NY
background: Congestive heart failure (CHF) is the most common preventable hospitalization in the Unites States (US) yet, there is a dearth of research aimed at understanding how neighborhood attributes and spatial relationships among places impact hospitalization rates. We assessed the degree of spatial autocorrelation in CHF hospitalization rates and accounted for clustering to accurately describe associations with neighborhood compositional measures in New York City.

methods: Using 2007 inpatient discharge data (n= 23,058) from New York Statewide Planning and Research Cooperative System, CHF unique and readmission hospitalization rates among adults were calculated at the US Census block group level and examined for spatial autocorrelation.  Both ordinary least squares (OLS) and spatial autoregressive (SAR) error models were fit to determine the effect of sociodemographic area measures on CHF rates with and without accounting for spatial clustering.

results: Older age composition and greater proportions of non-Hispanic black residents, Hispanic residents, households in poverty, and adults without a high school degree were significant predictors of higher CHF hospitalization rates in both OLS and SAR models. However, the latter showed a better model fit and reduced spatial autocorrelation in residuals.  

conclusions: Our findings indicate that CHF discharge rates are impacted by neighborhood compositional measures underscoring the importance of population-level approaches to prevention. Furthermore, when evaluating associations between area effects and health, spatial autocorrelation should be assessed and accounted for in regression models.

Learning Areas:

Chronic disease management and prevention

Learning Objectives:
Describe the spatial patterning of preventable congestive heart failure (CHF) hospitalizations in NYC. Assess the association between CHF rates and neighborhood compositional measures. Explain the importance of accounting for spatial dependence in regression models.

Keyword(s): Heart Disease, Methodology

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a DPH candidate in epidemiology and this work is part of my dissertation. I have been working in public health epidemiology for 9 years.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.