This project (data Mining & Neonatal Outcomes) is designed to explore the potential relations between chemical/ environmental influences and maternal/infant health: a major knowledge gap to date. Using a unique and novel method based on spatial data mining we will examine the relationships among co-location of sources of industrial toxic emissions, socio- economic index, and adverse birth outcomes (ABO) in Canada.

Research objectives (as developed by team members and knowledge users):

1. To develop a novel spatial data mining approach to analyze multiple variables that contributes to ABO.

2. To identify potential areas where there is non-trivial co-location/spatial association of ABO with multiple combinations of environmental and other known risk factors.

3. To identify potential patterns of interactions/correlations between chemical emissions, wind patterns, SEV and ABO, providing insights for postulating new hypotheses in public health.

4. To assess the feasibility of our novel DM methods for broader application in assessing the role-played by environmental factors in commonly occurring health outcomes.

5. To assess the utility of existing databases to investigate the contribution of environmental factors, specifically industrial emissions, to ABO.

6. Train a new generation of interdisciplinary high quality personnel in environmental health.

Research Hypothesis: there are areas where increased ABO, industrial emissions and social environmental factors co-locate. These geographical areas may reflect co-location of pairs of variables or even more complicated combination of variables, which can be discovered by advanced spatial DM techniques.

(Supported by CIHR/NSERC)


A. Osornio Vargas 2015