Assessing secondhand and thirdhand tobacco smoke exposure in Canadian infants using questionnaires, biomarkers, and machine learning.

BACKGROUND: As smoking prevalence has decreased in Canada, particularly during pregnancy and around children, and technological improvements have lowered detection limits, the use of traditional tobacco smoke biomarkers in infant populations requires re-evaluation. OBJECTIVE: We evaluated concentrations of urinary nicotine biomarkers, cotinine and trans-3'-hydroxycotinine (3HC), and questionnaire responses. We used machine learning and prediction modeling to understand sources of tobacco smoke exposure for infants from the CHILD Cohort Study. METHODS: Multivariable linear regression models, chosen through a combination of conceptual and data-driven strategies including random forest regression, assessed the ability of questionnaires to predict variation in urinary cotinine and 3HC concentrations of 2017 3-month-old infants. RESULTS: Although only 2% of mothers reported smoking prior to and throughout their pregnancy, cotinine and 3HC were detected in 76 and 89% of the infants' urine (n = 2017). Questionnaire-based models explained 31 and 41% of the variance in cotinine and 3HC levels, respectively. Observed concentrations suggest 0.25 and 0.50 ng/mL as cut-points in cotinine and 3HC to characterize SHS exposure. This cut-point suggests that 23.5% of infants had moderate or regular smoke exposure. SIGNIFICANCE: Though most people make efforts to reduce exposure to their infants, parents do not appear to consider the pervasiveness and persistence of secondhand and thirdhand smoke. More than half of the variation in urinary cotinine and 3HC in infants could not be predicted with modeling. The pervasiveness of thirdhand smoke, the potential for dermal and oral routes of nicotine exposure, along with changes in public perceptions of smoking exposure and risk warrant further exploration.

Respiratory hospital admissions in young children living near metal smelters, pulp mills and oil refineries in two Canadian provinces.

BACKGROUND: Industrial plants emit air pollutants like fine particles (PM2.5), sulfur dioxide (SO2) and nitrogen dioxide (NO2) that may affect the health of individuals living nearby. OBJECTIVE: To assess the effects of community exposure to air emissions of PM2.5, SO2, and NO2 from pulp mills, oil refineries, metal smelters, on respiratory hospital admissions in young children in Quebec (QC) and British Columbia (BC), Canada. METHODS: We assessed QC, BC and pooled associations between the following estimates of exposure and hospital admissions for asthma and bronchiolitis in children aged 2-4years for the years 2002-2010: i) Crude emission exposures at the residential postal codes of children, calculated by multiplying estimated daily emissions of PM2.5, SO2, or NO2 from all nearby (<7.5km) pulp mills, oil refineries, metal smelters emitting yearly ≥50t and their total emissions, by the percent of the day each postal code was downwind; ii) Daily levels of these pollutants at central ambient monitoring stations nearby the industries and the children's residences. RESULTS: Seventy-one major industries were selected between QC and BC, with a total of 2868 cases included in our analyses. More cases were exposed to emissions from major industries in QC than in BC (e.g. 2505 admissions near SO2 industrial emitters in QC vs 334 in BC), although air pollutant levels were similar. Odds ratios (ORs) for crude refinery and smelter emissions were positive in QC but more variable in BC. For example with PM2.5 in QC, ORs were 1.13 per 0.15t/day (95% CI: 1.00-1.27) and 1.03 (95% CI: 0.99-1.07) for refinery and smelter emissions, respectively. Pooled results of QC and BC for crude total SO2 emissions from all sources indicated a 1% increase (0-3%) in odds of hospital admissions per 1.50t/day increase in exposure. Associations with measured pollutant levels were only seen in BC, with SO2 and NO2. CONCLUSION: Hospital admissions for wheezing diseases in young children were associated with community exposure to industrial air pollutant emissions. Future work is needed to better assess the risk of exposure to complex mixture of air pollutants from multiple industrial sources.