Interdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

The safety of Canadian rural maternity services: a multi-jurisdictional cohort analysis.

BACKGROUND: Small Canadian rural maternity services are struggling to maintain core staffing and remain open. Existing evidence states that having to travel to access maternity services is associated with adverse outcomes. The goal of this study is to systematically examine rural maternal and newborn outcomes across three Canadian provinces. METHODS: We analyzed maternal newborn outcomes data through provincial perinatal registries in British Columbia, Alberta and Nova Scotia for deliveries that occurred between April 1st 2003 and March 31st 2008. All births were allocated to maternity service catchments based on the residence of the mothers. Individual catchments were stratified to service levels based on distance to access intrapartum maternity services or the model of maternity services available in the community. The amalgamation of analyses from each jurisdiction involved comparison of logistic regression effect estimates. RESULTS: The number of singleton births included in the study is 150,797. Perinatal mortality is highest in communities that are greater than 4 h from maternity services overall. Rates of prematurity at less than 37 weeks gestation are higher for rural women without local access to services. Caesarean section rates are highest in communities served by general surgical models. CONCLUSION: Composite analysis of data from three Canadian provinces provides the strongest evidence to date demonstrating that we need to sustain small community maternity services with and without caesarean section capability.

Examining caesarean section rates in Canada using the Robson classification system.

OBJECTIVE: To determine the groups within the obstetric population contributing most substantially to the Caesarean section rate in five Canadian provinces. METHODS: Hospital births from five participating provinces were grouped into Robson's 10 mutually exclusive and totally inclusive classification categories. The relative contribution of each group to the overall CS rate, relative size of group, and CS rate were calculated for British Columbia, Alberta, Ontario, Nova Scotia, and Newfoundland and Labrador for the four-year period from 2007-2008 to 2010-2011. RESULTS: In all five provinces (accounting for approximately 64% of births in Canada), and for all years examined, the group making the largest relative contribution to the CS rate was women with at least one previous CS and a term, singleton, cephalic-presenting pregnancy (Robson Group 5). The CS rate for this group ranged from 76.1% in Alberta to 89.9% in Newfoundland and Labrador in 2010 to 2011, accounting for 11.3% of all deliveries. The rate of CS for Group 5 decreased slightly over the four years, except in Ontario. The next largest contributing group was nulliparous women with a term, singleton, cephalic-presenting pregnancy. Those with induced labour or Caesarean section before labour (Robson Group 2) had CS rates ranging from 34.4% in Nova Scotia to 44.6% in British Columbia (accounting for 13.1% of all deliveries), and those with spontaneous onset of labour (Robson Group 1) had CS rates of 14.5% to 20.3% in 2010 to 2011 (accounting for 23.6% of all deliveries). CONCLUSION: All hospitals and health authorities can use this standardized classification system as part of a quality improvement initiative to monitor Caesarean section rates. This classification system identifies relevant areas for interventions and resources to reduce rates of Caesarean section.