Grantee Research Project Results
2009 Progress Report: Database and Tools for Investigation of Climate-Mediated Human Disease
EPA Grant Number: R832753Title: Database and Tools for Investigation of Climate-Mediated Human Disease
Investigators: Smith, Mark S. , Feied, Craig , Handler, Jonathan , Gillam, Michael
Institution: Washington Hospital Center
EPA Project Officer: Chung, Serena
Project Period: August 1, 2006 through July 31, 2010
Project Period Covered by this Report: September 25, 2008 through September 25,2009
Project Amount: $443,420
RFA: Decision Support Systems Involving Climate Change and Public Health (2005) RFA Text | Recipients Lists
Research Category: Climate Change
Objective:
Create a database to support research and decision making related to human disease that is caused, triggered, modified or predicted by changes in climatologic conditions. This project will enhance understanding of the relation between climate change and human health.
Progress Summary:
The main technical objectives of the grant are complete or close to completion. The major focus for the remainder of the grant will be on public availability and usage of the created tools in a research setting.
The project has been developed in several stages. First, a data core has been created with parsers that provide a daily feed containing both patient and climate data. This data core is integrated with the existing Amalga infrastructure and is accessible via SQL server queries for further analysis.
As a next step, a Matlab based data-mining software system was written to query the data core, perform specified statistical analyses in an automated way and report the resulting findings in user-definable html-templates. In addition to the built-in Matlab analytic and visual functionality, this tool has been interfaced to the S+ statistical package.
As a first test, this tool was used to implement the statistical methodology known as Generalized Additive Models (GAMs) as the primary analytical tool to evaluate the relationship between prognostic meteorological variables and health-related outcomes. A test article (Curriero FC, Heiner KS, Samet JM, et al. Temperature and mortality in eleven cities of the eastern United States. Am J Epidemiology, 2002;155:80–7) has been successfully instantiated using the GAMs methodology.
To facilitate access to the data core to a wider community and provide a more seamless integration between data, analysis and report generation, we next developed a framework that implements and extends the functionality of the Matlab toolset as a PHP class library. Graphical user interfaces for interactive analysis and data exploration have also been implemented.
Matlab framework
A Matlab based data-mining software system was written to query the data core, perform specified statistical analyses in an automated way and report the resulting findings in user-definable html-templates. In addition to the built-in Matlab analytic and visual functionality, this tool has been interfaced to the S+ statistical package.
Generalized Additive Models
Generalized Additive Models (GAMs) were chosen as the statistical methodology to implement in the framework of the Matlab tool. GAMs are an advanced statistical tool to identify and characterize the effect of prognostic factors on one or several outcomes. They represent the most generalized and flexible methodology in a class of statistical models that start from multiple regression as the most basic approach, and through successive steps of extensions include General Linear Models, Generalized Linear Models and finally GAMs. Because of their inherent flexibility, GAMs are widely used to model complex dependencies in a variety of contexts ranging from environmental studies to bioinformatics. Further details on this methodology are given in last year’s annual report.
PHP framework
In addition to the data retrieval and analysis, the Matlab framework offers a document creation system where html-templates are automatically populated with numerical or visual results from the analysis. To facilitate this process and extend the reach of our toolset to a larger community, we constructed a library of PHP classes that allow for the easy access, analysis and document creation within a unified and intuitive framework that at the same time is grounded in the widespread and documented standard of the PHP language.
To date, four main classes have been written: the climateQuery class provides access to the data without knowledge of the underlying database structure; the pcqRenderer class allows for the creation of tables and dynamic charts within the embedding html document using simple function calls; the splusConnect class provided a function interface to the SPlus statistical package (for Regression analysis, GAMs, time series, etc.) and the pcqParser class which provides an extra layer of separation between document design and code that is not normally provided in the PHP language.
Together, these classes combine easy access to comprehensive and complex data (patient and climate) with statistical analysis, visualization and reporting in the same framework. In addition to the practical utility of these tools to researchers, the integration of research articles with their underlying analysis into a unified and dynamic scientific document should also be of substantial conceptual interest as a modern publication form for scientific work.
Graphical User Interface
As a complement to the code-based toolset and to facilitate interactive data exploration and visualization, we have implemented graphical user interfaces (GUIs) for the PHP classes. To date, two different GUIs have been implemented on the basis of time-series and histogram-based representations.
Availability and Web-Platform
Both the code-based classes and the GUIs are currently available to project members through VPN. We are in the process of setting up a web platform and a user management system with the aim of providing public access to these tools for the research community in the near future
Journal Articles:
No journal articles submitted with this report: View all 3 publications for this projectSupplemental Keywords:
Climate, Climate Mediated Disease,, RFA, Health, Scientific Discipline, Air, Health Risk Assessment, climate change, Air Pollution Effects, Risk Assessments, Environmental Monitoring, Ecological Risk Assessment, Atmosphere, air quality modeling, ecosystem models, decision making database tool, climatic influence, Project Sentinel, modeling, climate models, demographics, human exposure, regional climate model, ambient air pollution, Global Climate ChangeProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.