School of Public Health, University of Alberta, Edmonton Clinic Health Academy, 11405 87 Avenue, Edmonton, Alberta T6G 1C9, Canada; Department of Obstetrics & Gynecology, University of Alberta, Royal Alexandra Hospital, 10240 Kingsway Avenue, Edmonton, Alberta T5H 3V9, Canada.
Department of Pediatrics, University of Alberta, Edmonton Clinic Health Academy, 11405 87 Avenue, Edmonton, Alberta T6G 1C9, Canada; Department of Earth and Atmospheric Sciences, University of Alberta, 1-26 Earth Science Building, Edmonton, Alberta T6G 2E3, Canada.
Environ Int. 2019 Oct;131:104972. doi: 10.1016/j.envint.2019.104972. Epub 2019 Jul 9.
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.
不良出生结局（ABO），如早产和小于胎龄儿，会增加死亡率和发病率的风险。ABO 与空气污染有关；然而，与工业排放混合物的关系尚不清楚。当同时分析数百种化学物质时，探索 ABO 与混合物之间的关系非常复杂，需要使用新的方法。
我们旨在使用空间数据挖掘算法和随后的地理和统计分析，生成与 ABO 空间相关的混合物的稳健假设。空间数据挖掘方法旨在降低数据维度，并有效地识别多种化学物质与 ABO 之间的空间关联。
我们在加拿大艾伯塔省（2006-2012 年）发现了混合物和 ABO 的共定位模式。一个特定的空间数据挖掘算法允许提取 136 种化学物质的主要共定位模式，这些化学物质是由 6279 家工业设施（国家污染物释放清单）排放到空气中的，182 个风站的风向，以及 333247 个母性邮政编码处的单胎活产（艾伯塔围产期健康计划），从中我们确定了早产、小于胎龄儿和足月出生体重不足的病例。我们使用来自 ABO 和受同一混合物影响的非 ABO 的提升比指标选择次要模式。使用逻辑模型（通过社会经济地位和 ABO 相关的母亲因素进行调整）和基于地理的母体暴露分配（通过核密度计算）来估计次要模式的相关性。
从 136 种化学物质和三种 ABO 中，空间数据挖掘确定了 1700 种主要模式，从中选择了三种化学物质混合物的五个次要模式，包括颗粒物、甲基乙基酮、二甲苯、一氧化碳、2-丁氧基乙醇和正丁醇。五个混合物与 ABO 之间的关联（比值比>1）的显著性为一系列新的假设提供了统计支持。
这项研究表明，在复杂的研究环境中，空间数据挖掘，随后是模式选择以及地理和统计分析，可以促进未来关于空气污染物混合物与不良出生结局之间关系的研究。
