Interdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes.
Jesus Serrano-Lomelin, Charlene C. Nielsen, M. Shazan M. Jabbar, Osnat Wine, Colin Bellinger, Paul J. Villeneuvee, Dave Stieb, Nancy Aelicks, Khalid Aziz, Irena Buka, Sue Chandra, Susan Crawford, Paul Demers, Anders C. Erickson, Perry Hystad, Manoj Kumar, Erica Phipps, Prakesh S. Shah, YanYuan, Osmar R. Zaiane, Alvaro R. Osornio-Vargas.
Environment International Volume 131, October 2019, https://doi.org/10.1016/j.envint.2019.104972
Adverse birth outcomes (ABO) such as prematurity and small for gestational age confer a high risk of mortality and morbidity. ABO have been linked to air pollution; however, relationships with mixtures of industrial emissions are poorly understood. The exploration of relationships between ABO and mixtures is complex when hundreds of chemicals are analyzed simultaneously, requiring the use of novel approaches.
We aimed to generate robust hypotheses spatially linking mixtures and the occurrence of ABO using a spatial data mining algorithm and subsequent geographical and statistical analysis. The spatial data mining approach aimed to reduce data dimensionality and efficiently identify spatial associations between multiple chemicals and ABO.
We discovered co-location patterns of mixtures and ABO in Alberta, Canada (2006–2012). An ad-hoc spatial data mining algorithm allowed the extraction of primary co-location patterns of 136 chemicals released into the air by 6279 industrial facilities (National Pollutant Release Inventory), wind-patterns from 182 stations, and 333,247 singleton live births at the maternal postal code at delivery (Alberta Perinatal Health Program), from which we identified cases of preterm birth, small for gestational age, and low birth weight at term. We selected secondary patterns using a lift ratio metric from ABO and non-ABO impacted by the same mixture. The relevance of the secondary patterns was estimated using logistic models (adjusted by socioeconomic status and ABO-related maternal factors) and a geographic-based assignment of maternal exposure to the mixtures as calculated by kernel density.
From 136 chemicals and three ABO, spatial data mining identified 1700 primary patterns from which five secondary patterns of three-chemical mixtures, including particulate matter, methyl-ethyl-ketone, xylene, carbon monoxide, 2-butoxyethanol, and n-butyl alcohol, were subsequently analyzed. The significance of the associations (odds ratio > 1) between the five mixtures and ABO provided statistical support for a new set of hypotheses.
This study demonstrated that, in complex research settings, spatial data mining followed by pattern selection and geographic and statistical analyses can catalyze future research on associations between air pollutant mixtures and adverse birth outcomes.