Geostatistical software for spatial and multi-dimensional joinpoint regression analysis of time series of health outcomes - 7. Project Summary/Abstract
Analyzing temporal trends in cancer incidence and mortality rates can provide a more comprehensive picture
of the burden of the disease and generate new insights about the impact of various interventions. Joinpoint
regression developed by NCI Surveillance Research Program is increasingly used to identify the timing and
extent of changes in time series of health outcomes and to project future cancer burden through the prediction
of the future number of new cancer cases or deaths. The analysis of temporal trends outside a spatial
framework is however unsatisfactory, since it has long been recognized that there is significant variation
among U.S. counties and states with regard to the incidence of cancer. It is thus critical to implement joinpoint
regression within Geographical Information Systems (GIS), and develop interfaces offering user-friendly tools
for pre-processing, modeling, visualizing and summarizing large ensembles of time series of health outcomes.
This SBIR project is developing the first commercial software to offer tools for the geostatistical modeling and
joinpoint regression analysis of time series of health outcomes. The research product will be a stand-alone
module into the desktop space-time visualization core developed by BioMedware, an Esri partner. This
software package will provide a comprehensive suite for: 1) the computation and geostatistical noise-filtering
(kriging) of time series of health outcomes at various spatial scales (e.g. ZIP codes, counties), 2) the
visualization of how the parameters of the regression model (e.g. joinpoint years, Average Annual Percent
Change) change in space and across spatial scales, and 3) the analysis of similarities among time series and
their aggregation through multi-dimensional scaling and clustering analysis. These tools will be suited for the
analysis of data outside health sciences, such as in crime mapping, fish stock assessment or climate change,
broadening significantly the commercial market for the end product. This project will accomplish four aims:
Expand the statistical methodology developed in Phase I through: 1) spatial generalization of the recent
approach developed at NCI for clustering of trend data using joinpoint regression models and simulation-
based comparison with alternative clustering approaches, and 2) development of dissimilarity metrics
between any two time series and comparison of their impact on clustering and visualization.
Build a fully functional and tested Time Trend Analysis & Visualization Module ready for commercialization.
Conduct a usability study to evaluate the design of the prototype based on NIH usability protocols.
Apply the software to demonstrate the approach and its unique benefits in several epidemiological studies,
covering a broad range of health outcomes (e.g., prostate and breast cancer, adverse birth outcomes).
These technologic, scientific and commercial innovations will revolutionize our ability to detect changes in
cancer incidence and mortality across space and through time, bringing important information and knowledge
that will benefit substantially cancer epidemiology, control and surveillance and help reducing these disparities.
