Online Program

An exploratory geospatial and statistical analysis of the relationship among community characteristics and hospital readmission rates in the DC-metro area

Tuesday, November 3, 2015

Maurice Johnson Jr., MPH, Westat, Rockville, MD

Hospital readmissions have been recognized as an indicator of poor health system coordination. Since 2009, the Centers for Medicare and Medicaid Services has been publicly reporting 30-day readmission rates for heart attack, heart failure, and pneumonia. With the passing of the Affordable Care Act (ACA), CMS began linking payments to hospitals to how well they perform on these measures. One of the concerns with the ACA’s strategy of addressing readmission is that it may be penalizing hospitals for factors more attributable to the patients they treat as well as the communities in which they operate. 


This study was an exploratory analysis of whether hospitals located in areas with disparate socioeconomic and structural conditions are at a disadvantage under the Hospital Readmissions Reduction Program (HRRP). 


This was accomplished by using geospatial software to develop various dot-density and choropleth maps.  This spatial analysis was used to identify potential relationships among community level characteristics and hospital readmissions within the DC metro area.  Upon identification of potential relationships, ordinary least square regression models applying robust techniques to adjust for heterogeneity and lack of normality was conducted.  The primary hypothesis was hospital readmission rates had a relationship with income related community characteristics.   


Results from this analysis were statistically inconclusive, but suggested dual-eligible populations may serve as a proxy for income related characteristics, and have a positive relationship with hospital readmissions, particularly for heart failure.

Discussion and Conclusion:

Despite the limitations of the study, it suggests a preliminary policy alternative to incorporating socioeconomic factors into the adjusted readmission calculation rates, by focusing the adjustments on dual-eligible populations.  Future research should look to obtain patient claims data to properly assess where hospital patients reside as well as obtain community characteristics from the census tract level to develop stronger regression models.

Learning Areas:

Biostatistics, economics
Conduct evaluation related to programs, research, and other areas of practice
Diversity and culture
Planning of health education strategies, interventions, and programs
Public health or related organizational policy, standards, or other guidelines

Learning Objectives:
Assess implications of the Hospital Readmissions Reduction Program on hospitals; Identify data sources available for geospatial analysis of hospitals and various community characteristics; List software available for conducting geospatial and statistical analysis; and Assess geospatial and statistical techniques available for determining relationships among community level characteristics and hospital readmissions.

Keyword(s): Geographic Information Systems (GIS), Health Disparities/Inequities

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: Maurice Johnson, Jr., MPH is a research analyst with 7 years of experience supporting data collection and management and literature reviews for health-related research. His work has been focused on primary care, care coordination, hospital readmission, and health disparities. Mr. Johnson is also a 3rd year doctoral student at George Mason University School of Public Policy, focusing his work on the Affordable Care Act, health disparities, and hospital re-admissions.
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.