246139 Balancing data limitations and opportunities with geographically linked datasets

Sunday, October 30, 2011

Martha Priedeman Skiles, MPH , Department of Maternal and Child Health, UNC-Chapel Hill, Chapel Hill, NC
Sian L. Curtis, PhD , MEASURE Evaluation, Carolina Population Center, UNC-Chapel Hill, Chapel Hill, NC
John Spencer, MS , MEASURE Evaluation, Carolina Population Center, UNC-Chapel Hill, Chapel Hill, NC
Purpose: To demonstrate the benefits and limitations of linking independent datasets using geographic methods. Data: Individual- and household-level socio-demographic characteristics and health data from the 2005 Rwanda Demographic and Health Survey (RDHS) and facility data from Rwanda's 2005 Service Availability Mapping (SAM) census are used. Methods: RDHS clusters and SAM facilities are geographically linked in two simulations. A nationally representative sample of health facilities was selected from the full census and linked to household clusters based on proximity within the same administrative unit. The second simulation linked the census of government facilities to clusters by distance, irrespective of administrative borders. Spatial analytic techniques identify clusters that deviate from the national mean for select reproductive health indicators. Facility data describe the health service environment for these communities. Mapping the clusters alongside their proximate health facilities provides service context for the observed outcomes. Results: 436 facilities were mapped by SAM; 270 (62%) were selected for simulation one. The effects of facility sampling on data linking will be shown. Clusters deviating from the mean will be mapped for a selection of reproductive health outcomes and the linking will generate hypotheses about the influences of services on health outcomes. Additional analyses between individual outcomes and facility services will be explored. Implications: Linking independent datasets supports evaluation of service influences on health outcomes. It's important that linked data is used in a manner consistent with the component datasets' limitations. Understanding the added-value of spatial analyses to address limitations will inform data use and sampling strategies.

Learning Areas:
Conduct evaluation related to programs, research, and other areas of practice

Learning Objectives:
1. Identify the limitations of geographically linked independent datasets with different sampling strategies. 2. Describe potential spatial analytic techniques that can provide a richer understanding of the health service environment geographically related to individual health outcomes.

Keywords: Geographic Information Systems, International Reproductive Health

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a doctoral student working with MEASURE Evaluation on data linking and analysis for program planning and evaluation purposes.
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.