237393 Spatially explicit non-linear models for explaining the occurrence of infectious zoonotic diseases

Tuesday, November 1, 2011: 12:35 PM

Stephen Jones, PhD , Medical Informatics, BlueCross BlueShield of Tennessee, Chattanooga, TN
William Conner, PhD , Dept of Forest Resources, Clemson University, Georgetown, SC
Soyal Momin, MS, MBA , Medical Informatics, BlueCross BlueShield of Tennessee, Chattanooga, TN
Inga Himelright, MD, MBA , Medical Management & Operation, BlueCross BlueShield of TN, Chattanooga, TN
Sherri Zink, BS , Medical Informatics, BlueCross BlueShield of TN, Chattanooga, TN
Zoonotic diseases can be transmitted via an arthropod vector and disease risk maps are often based on underlying associative factors within the surrounding landscape of known occurrences. A major limitation is the ability to track disease incidence at a meaningful geographic scale and traditional linear modeling approaches may not always be appropriate. The objective is to create meaningful geospatial risk models at the ZIP code level describing the occurrence of 2 tick-borne zoonotic diseases (Lyme disease [LD] and Rocky Mountain spotted fever [RMSF]) known to occur in Tennessee. Case claims with ICD-9 diagnosis codes for LD (088.81) and RMSF (082.0) were extracted from a managed care organization data warehouse. Four separate modeling techniques were constructed (logistic regression, classification and regression tree [CART], gradient boosted tree [GBT], neural network [NNET]) and compared for accuracy. Post-analysis predictive risk maps were created using a geographic information system (GIS) and compared to simple incidence risk maps. Areas higher in disease prevalence were not necessarily the same areas having high predicted disease risk. Non-linear modeling provided better results than traditional regression-based approaches. GBT explained LD incidence (misclassification rate: 0.232; ROC: 0.789) and NNET explained RMSF incidence (misclassification rate: 0.288; ROC: 0.696). Covariates explaining disease risk included forested/non-forested wetland area, urbanization and median income levels. Administrative medical claims data is not being leveraged in surveillance efforts, but is an inexpensive volume rich dataset for mapping disease risk and studying zoonotic infections. We recommend integrating administrative data with state registries to improve geospatial analytical capabilities.

Learning Areas:
Biostatistics, economics
Public health biology

Learning Objectives:
1.Discuss the benefits of using administrative medical claims data in zoonotic disease surveillance and geospatial analytics surrounding zoonotic infections 2.Compare linear and non-linear spatial predictive modeling techniques

Keywords: Zoonoses, Biostatistics

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have been a researcher for over 13 years, and am a PhD candidate for this presented work. I lead a team of epidemiologists in the company R&D program, and lead all geospatial analytics for BlueCross of TN.
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.