A key component in any investigation of association and/or cause-effect relationships between the environment (e.g. air pollution, heat waves) and health outcomes (e.g. asthma, heart disease, cancer) is the availability of accurate models of exposure at the same geographical scale and temporal resolution as the health outcomes. The computation of human exposure is particularly challenging for cancers since they may take years or decades to develop, especially in presence of low level of contaminants. In this situation pollutant levels are rarely available for every location and time interval visited by the subjects;therefore data gaps need to be filled-in through space-time (ST) interpolation. Surprisingly, there is currently no commercial software for the geostatistical treatment of space-time data, including the interpolation at unmonitored times and locations. This SBIR project is developing the first commercial software to offer tools for geostatistical ST interpolation and modeling of uncertainty. 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 advisor-guided modeling of space-time covariance functions, 2) the ST interpolation and stochastic modeling of exposure data at the same scale as health outcomes (i.e. individual-level or aggregated) and using any secondary information available (e.g. remote sensing, land-use regression model, air dispersion model), and 3) the quantification and Monte-Carlo based propagation of uncertainty attached to estimates through exposure reconstruction. These tools will be suited for the analysis of data outside health sciences, such as in remote sensing, nuclear environmental engineering or climate change, broadening significantly the commercial market for the end product. This project will accomplish three aims: ? Compare the performance (i.e. prediction accuracy, impact on exposure-response assessment) and user- friendliness (i.e. ease of inference, potential for automatic implementation in software) of two classes of ST covariance models that encompass the main hypotheses of stationarity, full symmetry, separability and supported compactness. ? Develop and test a prototype module that will guide non-expert users through the selection and optimal fitting of space-time covariance models, followed by the interpolation of space-time data based on BioMedware's space-time visualization and analysis technology. ? Conduct a usability study and identify additional methods and tools to consider in Phase II. These technologic, scientific and commercial innovations will revolutionize our ability to model geostatistically space-time phenomena and compute estimates and the associated uncertainty at the scale (e.g. point location, census-tract level) the most relevant for environmental epidemiological studies.
A key component in any investigation of association and/or cause-effect relationships between the environment (e.g. air pollution, heat waves) and health outcomes (e.g. asthma, heart disease, cancer) is the availability of accurate models of exposure at the same geographical scale and temporal resolution as the health outcomes. The computation of human exposure is particularly challenging for cancers since they may take years or decades to develop, especially in presence of low level of contaminants, increasing the likelihood of data gaps that need to be filled-in through space-time (ST) interpolation. Thus, many public health issues would greatly benefit from improved tools for estimation of environmental exposure data for every location and time interval visited by the patients under study.
| Goovaerts, Pierre; Xiao, Hong; Gwede, Clement K et al. (2015) Impact of Age, Race and Socio-economic Status on Temporal Trends in Late-Stage Prostate Cancer Diagnosis in Florida. Spat Stat 14:321-337 |
