Online Program

332404
Validating the Dasymetric Areal Interpolation Method to Inform Health Policy


Tuesday, November 3, 2015 : 3:10 p.m. - 3:30 p.m.

Chieko Maene, MS, MLIS, Social Sciences Computing Services, University of Chicago, Chicago, IL
Roderick Jones, PhD, MPH, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
Emily Laflamme, MPH, Chicago Department of Public Health, Chicago, IL
Adam Pah, PhD, Northwestern University, Chicago, IL
Nyahne Bergeron, MPH, Department of Medicine, Section of General Internal Medicine, University of Chicago, Chicago, IL
Elbert S. Huang, MD, MPH, Biological Sciences Division, Medicine, General Internal Medicine, University of Chicago, Chicago, IL
Monica E. Peek, MD, MPH, Department of Medicine, Section of General Internal Medicine, The University of Chicago, Chicago, IL
Background: Hospitalization rates, particularly for preventable conditions such as diabetes-related limb amputations, are important measures of community health and can directly inform health policy. However, hospital data is not always aggregated in spatial units that are meaningful to policy makers. In Chicago, hospital discharge data are aggregated at the five-digit ZIP Code level, while most policy data are tabulated for census tracts aggregates which uniquely identify 77 neighborhoods in Chicago. Reconciling information tabulated at various geographic levels is a known challenge, and represents potential sources of error in data interpretation.

Methods: To overcome inconsistent spatial units between hospital discharge data and health policy needs in Chicago, we developed and evaluated a dasymetric areal interpolation method, which disaggregates and allocates incidence rates from ZIP Code to Chicago community area. Community level rates of diabetes-related hospitalizations were estimated by allocating the number of hospital discharges associated with diabetes (diagnostic ICD-9 codes 250.00-250.93) of a given ZIP Code to overlapping communities based on proportions within the population. Ancillary data census block data (i.e. age, gender, race) were used to generate population estimates. To evaluate the method’s accuracy, we obtained raw hospital discharges from a single academic medical center in Chicago and compared the actual discharge rates to the estimated discharge rates from the dasymetric areal interpolation method for 77 communities in the city. Statistical significance was measured using a one-tailed Chi-square test with a p-value of 0.05.

Results: A total of 6,534 diabetes-related hospitalizations occurred during the study period (1/1/09 – 12/31/11). The average patient age was 61 years, and 60.5% were female. An estimated 6,544 hospitalizations were calculated using the dasymetric method, for a difference of 10 persons. Variations between the actual and estimated hospital discharge rates by neighborhoods were not statistically significant, X2(76, N=6,534) = 54, p=0.97.

Conclusions: This study shows that the dasymetric areal interpolation method can be a valid means of converting spatial units (e.g. zip codes to neighborhoods) that can make existing health data meaningful to community-level policy makers.

Learning Areas:

Epidemiology
Public health or related public policy
Public health or related research

Learning Objectives:
Define the dasymetric areal interpolation method. Discuss how dasymetric areal interpolation can be used to convert health data between spatial units and directly inform community-level health policy.

Keyword(s): Geographic Information Systems (GIS)

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have been a GIS research technician in academia since 2000. My main job is to introduce spatial dimension to research. I also look for new methods/tools to increase productivity. In my current position I am responsible for managing GIS/spatial analysis tasks, GIS instruction and GIS data management. I also provide GIS software support and technical/analytical knowledge regarding GIS to research teams, perform spatial analysis, and develop custom applications/interfaces for GIS data retrieval and analysis.
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.