2008 Progress Report: Database and Tools for Investigation of Climate-Mediated Human Disease

EPA Grant Number: R832753
Title: Database and Tools for Investigation of Climate-Mediated Human Disease
Investigators: Smith, Mark S. , Feied, Craig , Gillam, Michael , Handler, Jonathan
Institution: Washington Hospital Center
EPA Project Officer: Chung, Serena
Project Period: August 1, 2006 through July 31, 2010
Project Period Covered by this Report: August 1, 2007 through July 31,2008
Project Amount: $443,420
RFA: Decision Support Systems Involving Climate Change and Public Health (2005) RFA Text |  Recipients Lists
Research Category: Global Climate Change , Health Effects , Health , Climate Change


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:

Accomplishments over the last year included completion of the initial software agent creation and testing.  Progress was also made on the initial correlation analysis for existing locales in the registry. A Matlab based data-mining software system was written that can pull data to the Project Climate Query data core and perform specified statistical analyses in a semi-automated way. 

Currently, the Matlab tool is being interfaced to the S+ statistical package.  (This phase is currently delayed due to personnel turn-over but will resume December 1, 2008).  The Project will utilize 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 has been successfully instantiated using the GAMs methodology. The article is: 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.

Generalized Additive Models (see website below for more information) were chosen as a primary statistical methodology for the two studies to be undertaken.  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.

Multiple regression assumes linear relationships, is limited to one dependent variable and cannot provide solutions when the predictor variables are not linearly independent. General Linear Models provide an extended framework to overcome these limitations and thus allow for multivariate regression (i.e. multiple observations of multiple dependent variables), linear combinations of dependent variables (important e.g. for significance tests) as well as linear combinations of the predictor variables that are also allowed to be non-continuous (categorical).

Generalized Linear Models (GLZs) further extend General Linear Models by allowing for non-continuous and skewed distributions of dependent variables, as well as non-linear effect of the predictor on the outcome variables through the introduction of a link function.

GAMs represent the most advanced approach in this hierarchy of models by combining the notion of a generalized linear model with that of an additive model. This approach allows for accurate and statistically sound modeling of complex associations, with a minimum of prior assumptions.

Because of their inherent flexibility, GAMs are widely used to model complex dependencies in a variety of contexts ranging from environmental studies to bioinformatics. In particular, we closely examined temperature-mortality association studies that utilize GAMs, and used publicly available datasets to test our own instantiations of GAM-solving software against published results.  

Departure of a key member of the staff. led to delays in progress for year 2.  With additional negotiation and increased commitment from WHC, the Project Climate Query team was able to re-recruit the original lost team member.

Journal Articles:

No journal articles submitted with this report: View all 3 publications for this project

Supplemental 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, human exposure, climate models, demographics, regional climate model, Global Climate Change, human health risk

Relevant Websites:

Generalized Additive Model Slides: http://www.projectclimatequery.org/resources/gam_overview_2008/

EPA Presentation Slides:  http://www.projectclimatequery.org/resources/epa_philadelphia_april2008/

Progress and Final Reports:

Original Abstract
  • 2007 Progress Report
  • 2009 Progress Report
  • Final Report