Maternal Exposure to Particulate Air Pollution and Term Birth Weight: A Multi-Country Evaluation of Effect and Heterogeneity

Abstract

Background: A growing body of evidence has associated maternal exposure to air pollution with adverse effects on fetal growth; however, the existing literature is inconsistent.Objectives: We aimed to quantify the association between maternal exposure to particulate air pollution and term birth weight and low birth weight (LBW) across 14 centers from 9 countries, and to explore the influence of site characteristics and exposure assessment methods on between-center heterogeneity in this association.Methods: Using a common analytical protocol, International Collaboration on Air Pollution and Pregnancy Outcomes (ICAPPO) centers generated effect estimates for term LBW and continuous birth weight associated with PM10 and PM2.5 (particulate matter ≤ 10 and 2.5 µm). We used meta-analysis to combine the estimates of effect across centers (~ 3 million births) and used meta-regression to evaluate the influence of center characteristics and exposure assessment methods on between-center heterogeneity in reported effect estimates.Results: In random-effects meta-analyses, term LBW was positively associated with a 10-μg/m3 increase in PM10 [odds ratio (OR) = 1.03; 95% CI: 1.01, 1.05] and PM2.5 (OR = 1.10; 95% CI: 1.03, 1.18) exposure during the entire pregnancy, adjusted for maternal socioeconomic status. A 10-μg/m3 increase in PM10 exposure was also negatively associated with term birth weight as a continuous outcome in the fully adjusted random-effects meta-analyses (–8.9 g; 95% CI: –13.2, –4.6 g). Meta-regressions revealed that centers with higher median PM2.5 levels and PM2.5:PM10 ratios, and centers that used a temporal exposure assessment (compared with spatiotemporal), tended to report stronger associations.Conclusion: Maternal exposure to particulate pollution was associated with LBW at term across study populations. We detected three site characteristics and aspects of exposure assessment methodology that appeared to contribute to the variation in associations reported by centers.

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures

“…Area-level socioeconomic status is controlled by census-tract median household income using data from the United States Census Bureau of 2000 for each census tract in Massachusetts. These covariates are consistent with the published literature on birthweight and particulate matter (Dadvand and others, 2013). Some studies also adjust for co-pollutant exposures, such as ozone, although the need for this may vary by region, where studies in the northeast have found similar effect sizes after this adjustment (Bell and others, 2007).…”

Air pollution as a risk factor in health impact assessments of a travel mode shift towards cycling

“…*Published emission factors but not reported in text. **Emission factors reported explicitly in text ᴬ Age groups according to health outcomes; ᴯ Such as ≥18 years or ≥30 years; C Environmental Benefits Mapping and Analysis Program using the concentration response function from chronic bronchitis [63], acute bronchitis [64], all-cause mortality [65,104], COPD hospitalization (Moolgavgkar 2000a, 2003) [66], asthma emergency room visits [67], work loss days [68], asthma (symptoms) [69], minor-restricted activity days [70], acute MI [71], respiratory disease [72], lower respiratory symptoms [73], and cough among asthmatic children [74]; D Probable, but not specified explicitly in the text; ᴱ Health And Air Pollution Study in New Zealand to estimate the morbidity and mortality health costs associated with traffic emissions [82]; F CVD admission >64 years: [75]; ᴳ Mortality: <75 and >75 years, respiratory disease (65 years) [76], and lung cancer [104] Morbidity: CVD, respiratory disease [76], and lung cancer [104]; H Method of transport emission estimation is quite vague in determination of emission factors; I External cost of energy to estimate the automotive pollution impact on health in Europe [81]; J Cerebrovascular disease and lower respiratory tract infection [77], preterm weight [78], low term weight [79], and CVD (Mustafic 2012) [80]; K Value of a Life Year: calculation of monetary benefits of mortality reduction using a life tables approach. …”

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PM2.5: Particulate matter less than 2.5 µm; PM10: particulate matter less than 10 µm; BS: black soot; CP: cardiopulmonary; RM: respiratory mortality. ᴬ Age groups according to health outcomes; ᴯ Such as ≥18 years or ≥30 years; C Probable, but not specified explicitly in the text; D External cost of energy to estimate the automotive pollution impact on health in Europe [81]; E Calculation based on the actual number of participants who changed mode from car to bicycle; F Estimated for hypothetical individuals who changed transport mode from car to bicycle; G Cerebrovascular disease and lower respiratory tract infection [77], preterm weight [78], low term weight [79], and CVD (Mustafic 2012) [80]; H Value of a Life Year: calculation of monetary benefits of mortality reduction using a life tables approach.

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Estimating the global abundance of ground level presence of particulate matter (PM2.5)

“…In recent years, researchers are finding it worthy to investigate potential links between PM 2.5 exposure and adverse birth outcomes (Slama et al, 2008;Dadvand et al, 2013), epigenetic alteration (Baccarelli et al, 2008;Salam et al, 2012;Byun et al, 2013;Hou et al, 2013) infant mortality (Woodruff et al, 1997;Lipfert et al, 2000;Dales et al, 2004;Glinianaia et al, 2004) atherosclerosis (Araujo et al, 2008;Araujo, 2011;Kaufman, 2011), stroke (Brook, 2008;Brook andRajagopalan, 2009, 2012;Maheswaran et al, 2010Maheswaran et al, , 2012, rheumatic autoimmune disease (Zeft et Table 1. Particulate matter and health outcomes for PM 10 , PM 2.5 and ultrafine particles (UFPs).…”