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2019
Wang, Q, Benmarhnia T, Li CC, Knibbs LD, Bao JZ, Ren M, Zhang HH, Wang SH, Zhang YW, Zhao QG, Huang CR.  2019.  Seasonal analyses of the association between prenatal ambient air pollution exposure and birth weight for gestational age in Guangzhou, China. Science of the Total Environment. 649:526-534.   10.1016/j.scitotenv.2018.08.303   AbstractWebsite

Ambient air pollution has been linked to small for gestational age (SGA); however, the relationship with large for gestational age (ILA) is unclear and very few studies have investigated seasonal effects on the association between air pollution and SGA or LGA. Using birth registry data of 506,000 singleton live births from 11 districts in Guangzhou, China between January 2015 and July 2017, we examined associations between ambient air pollutants (PM2.5, PM10, NO2, SO2, and O-3) and SGA/LGA, and further assessed the modification effect of season. Daily concentrations of air pollutants from 11 monitoring stations were used to estimate district-specific exposures for each participant based on their district of residence during pregnancy. Two-level binary logistic regression models were used to evaluate associations between air pollution and SGA/LGA. Stratified analyses by season and a Cochran Q test were performed to assess the modification of season. Exposure to PM2.5, NO2, SO2, and O-3 was significantly associated with increased risk of SGA, especially for exposure during the second and trimester. For an interquartile range (IQR) increase in PM2.5(6.5 mu g/m(3)), NO2 (12.7 mu g/m(3)), SO2 (2.8 mu g/m(3)) and O-3 (20.8 mu g/m(3)) during the entire pregnancy, SGA risk increased by 2% (OR - 1.02, 95% CI: 1.00-1.04), 8% (OR = 1.08, 95% CI: 1.04-1.12), 2% (OR - 1.02, 95% CI: 1.01-1.03), and 14% (1.14, 1.11-1.17), respectively. A decreased risk of WA was found for PM2.5, PM10, SO2, and O-3 during the first trimester or entire pregnancy. When examined by season, significant associations between air pollutants and SGA were observed for women who conceived during summer or fall, and the patterns were consistent for all pollutants. Our study suggests that conception during different seasons might modify the association between ambient air pollution and SGA. (C) 2018 Elsevier B.V. All rights reserved.

2018
Wang, Q, Benmarhnia T, Zhang HH, Knibbs LD, Sheridan P, Li CC, Bao JZ, Ren M, Wang SH, He YL, Zhang YW, Zhao QG, Huang CR.  2018.  Identifying windows of susceptibility for maternal exposure to ambient air pollution and preterm birth. Environment International. 121:317-324.   10.1016/j.envint.2018.09.021   AbstractWebsite

Maternal exposure to ambient air pollution has been associated with preterm birth (PTB), however, entire pregnancy or trimester-specific associations were generally reported, which may not sufficiently identify windows of susceptibility. Using birth registry data from Guangzhou, a megacity of southern China (population -14.5 million), including 469,975 singleton live births between January 2015 and July 2017, we assessed the association between weekly air pollution exposure and PTB in a retrospective cohort study. Daily average concentrations of PM2.5, PM10, NO2, SO2, and O-3 from 11 monitoring stations were used to estimate district-specific exposures for each participant based on their district residency during pregnancy. Distributed lag models (DLMs) incorporating Cox proportional hazard models were applied to estimate the association between weekly maternal exposure to air pollutant and PTB risk (as a time-to-event outcome), after controlling for temperature, seasonally, and individual-level covariates. We also considered moderate PTB (32-36 gestational weeks) and very PTB (28-31 gestational weeks) as outcomes of interest. Hazard ratios (HRs) and 95% confidential intervals (95% CIs) were calculated for an interquartile range (IQR) increase in air pollutants during the study period. An IQR increase in PM2.5 exposure during the 20th to 28th gestational weeks (27.0 mu g/m(3)) was significantly associated with PTB risk, with the strongest effect in the 25th week (HR = 1.034, 95% CI:1.010-1.059). The significant exposure windows were the 19th-28th weeks for PM10, the 18th-31st weeks for NO2, and the 23rd-31A weeks for O-3, respectively. The strongest associations were observed in the 25th week for PM10 (IQR = 37.0 mu g/m(3); HR = 1.048, 95% CI:1.034-1.062), the 26th week for NO2 (IQR = 29.0 mu g/m(3); HR = 1.060, 95% CI:1.028-1.094), and in the 28th week for O-3 (IQR = 90.0 mu g/m(3); HR = 1.063, 95% CI:1.046-1.081). Similar patterns were observed for moderate PTB (32-36 gestational weeks) and very PTB (28-31 gestational weeks) for PM2.5, PM10, NO2 exposure, but the effects were greater for very PTB. We did not observe any association between pregnancy SO2 exposure and the risk of PTB. Our results suggest that middle to late pregnancy is the most susceptible air pollution exposure window for air pollution and PTB among women in Guangzhou, China.

Loizeau, M, Buteau S, Chaix B, McElroy S, Counil E, Benmarhnia T.  2018.  Does the air pollution model influence the evidence of socio-economic disparities in exposure and susceptibility? Environmental Research. 167:650-661.   10.1016/j.envres.2018.08.002   AbstractWebsite

Studies assessing socio-economic disparities in air pollution exposure and susceptibility are usually based on a single air pollution model. A time stratified case-crossover study was designed to assess the impact of the type of model on differential exposure and on the differential susceptibility in the relationship between ozone exposure and daily mortality by socio-economic strata (SES) in Montreal. Non-accidental deaths along with deaths from cardiovascular and respiratory causes on the island of Montreal for the period 1991-2002 were included as cases. Daily ozone concentration estimates at partictaipants' residence were obtained from the five following air pollution models: Average value (AV), Nearest station model (NS), Inverse-distance weighting interpolation (IDW), Land-use regression model with back-extrapolation (LUR-BE) and Bayesian maximum entropy model combined with a land-use regression (BME-LUR). The prevalence of a low household income ( < 20,000/year) was used as socio-economic variable, divided into two categories as a proxy for deprivation. Multivariable conditional logistic regressions were used considering 3-day average concentrations. Multiplicative and additive interactions (using Relative Excess Risk due to Interaction) as well as Cochran's tests were calculated and results were compared across the different air pollution models. Heterogeneity of susceptibility and exposure according to socio-economic status (SES) were found. Ratio of exposure across SES groups means ranged from 0.75 [0.74-0.76] to 1.01 [1.00-1.02], respectively for the LUR-BE and the BME-LUR models. Ratio of mortality odds ratios ranged from 1.01 [0.96-1.05] to 1.02 [0.97-1.08], respectively for the IDW and LUR-BE models. Cochran's test of heterogeneity between the air pollution models showed important heterogeneity regarding the differential exposure by SES, but the air pollution model was not found to influence heterogeneity regarding the differential susceptibility. The study showed air pollution models can influence the assessment of disparities in exposure according to SES in Montreal but not that of disparities in susceptibility.

Benmarhnia, T, Delpla I, Schwarz L, Rodriguez MJ, Levallois P.  2018.  Heterogeneity in the relationship between disinfection by-products in drinking water and cancer: A systematic review. International Journal of Environmental Research and Public Health. 15   10.3390/ijerph15050979   AbstractWebsite

The epidemiological evidence demonstrating the effect of disinfection by-products (DBPs) from drinking water on colon and rectal cancers is well documented. However, no systematic assessment has been conducted to assess the potential effect measure modification (EMM) in the relationship between DBPs and cancer. The objective of this paper is to conduct a systematic literature review to determine the extent to which EMM has been assessed in the relationship between DBPs in drinking water in past epidemiological studies. Selected articles (n = 19) were reviewed, and effect estimates and covariates that could have been used in an EMM assessment were gathered. Approximately half of the studies assess EMM (n = 10), but the majority of studies only estimate it relative to sex subgroups (n = 6 for bladder cancer and n = 2 both for rectal and colon cancers). Although EMM is rarely assessed, several variables that could have a potential modification effect are routinely collected in these studies, such as socioeconomic status or age. The role of environmental exposures through drinking water can play an important role and contribute to cancer disparities. We encourage a systematic use of subgroup analysis to understand which populations or territories are more vulnerable to the health impacts of DBPs.

Schinasi, LH, Benmarhnia T, De Roos AJ.  2018.  Modification of the association between high ambient temperature and health by urban microclimate indicators: A systematic review and meta-analysis. Environmental Research. 161:168-180.   10.1016/j.envres.2017.11.004   AbstractWebsite

Background: Landscape characteristics, including vegetation and impervious surfaces, influence urban micro climates and may lead to within-city differences in the adverse health effects of high ambient temperatures. Objective: Our objective was to quantitatively summarize the epidemiologic literature that assessed microclimate indicators as effect measure modifiers (EMM) of the association between ambient temperature and mortality or morbidity. Methods: We systematically identified papers and abstracted relative risk estimates for hot and cool micro climate indicator strata. We calculated the ratio of the relative risks (RRR) and 95% confidence intervals (95% CI) to assess differences in health effects across strata, and pooled the RRR estimates using random effects meta analyses. Results: Eleven papers were retained. In the pooled analyses, people living in hotter areas within cities (based on land surface temperature or modeled estimates of air temperature) had 6% higher risk of mortality/morbidity compared to those in cooler areas (95% CI: 1.03-1.09). Those living in less vegetated areas had 5% higher risk compared to those living in more vegetated areas (95% CI: 1.00-1.11). Discussion: There is epidemiologic evidence that those living in hotter, and less vegetated areas of cities have higher risk of morbidity or mortality from higher ambient temperature. Further research with improved assessment of landscape characteristics and investigation of the joint effects of physiologic adaptation and landscape will advance the current understanding. Conclusion: This review provides quantitative evidence that intra-urban differences in landscape characteristics and micro-urban heat islands contribute to within-city variability in the health effects of high ambient temperatures.