Hadas Magen-Molho1,2, Marc G Weisskopf3, Daniel Nevo4, Alexandra Shtein5, Shimon Chen6, David Broday6, Itai Kloog5, Hagai Levine1, Ofir Pinto7, Raanan Raz1. 1. From the Braun School of Public Health and Community Medicine, The Hebrew University-Hadassah, Jerusalem, Israel. 2. The Advanced School for Environmental Studies, The Hebrew University, Jerusalem, Israel. 3. Department of Epidemiology and Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA. 4. Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel. 5. Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beersheba, Israel. 6. Department of Civil and Environmental Engineering, and Technion Center of Excellence in Exposure Science and Environmental Health (TCEEH), Technion, Israel Institute of Technology, Haifa, Israel. 7. The National Insurance Institute of Israel, Jerusalem, Israel.
Abstract
BACKGROUND: Residual confounding is a major concern for causal inference in observational studies on air pollution-autism spectrum disorder (ASD) associations. This study is aimed at assessing confounding in these associations using negative control exposures. METHODS: This nested case-control study included all children diagnosed with ASD (detected through 31 December 2016) born during 2007-2012 in Israel and residing in the study area (N = 3,843), and matched controls of the same age (N = 38,430). We assigned individual house-level exposure estimates for each child. We estimated associations using logistic regression models, mutually adjusted for all relevant exposure periods (prepregnancy, pregnancy, and postnatal). We assessed residual confounding using postoutcome negative control exposure at age 28-36 months. RESULTS: In mutually adjusted models, we observed positive associations with ASD for postnatal exposures to NOx (odds ratio per interquartile range, 95% confidence interval: 1.19, 1.02-1.38) and NO2 (1.20, 1.00-1.43), and gestational exposure to PM2.5-10 (1.08, 1.01-1.15). The result for the negative control period was 1.04, 0.99-1.10 for PM2.5, suggesting some residual confounding, but no associations for PM2.5-10 (0.98, 0.81-1.18), NOx (1.02, 0.84-1.25), or NO2 (0.98, 0.81-1.18), suggesting no residual confounding. CONCLUSIONS: Our results further support a hypothesized causal link with ASD that is specific to postnatal exposures to traffic-related pollution.
BACKGROUND: Residual confounding is a major concern for causal inference in observational studies on air pollution-autism spectrum disorder (ASD) associations. This study is aimed at assessing confounding in these associations using negative control exposures. METHODS: This nested case-control study included all children diagnosed with ASD (detected through 31 December 2016) born during 2007-2012 in Israel and residing in the study area (N = 3,843), and matched controls of the same age (N = 38,430). We assigned individual house-level exposure estimates for each child. We estimated associations using logistic regression models, mutually adjusted for all relevant exposure periods (prepregnancy, pregnancy, and postnatal). We assessed residual confounding using postoutcome negative control exposure at age 28-36 months. RESULTS: In mutually adjusted models, we observed positive associations with ASD for postnatal exposures to NOx (odds ratio per interquartile range, 95% confidence interval: 1.19, 1.02-1.38) and NO2 (1.20, 1.00-1.43), and gestational exposure to PM2.5-10 (1.08, 1.01-1.15). The result for the negative control period was 1.04, 0.99-1.10 for PM2.5, suggesting some residual confounding, but no associations for PM2.5-10 (0.98, 0.81-1.18), NOx (1.02, 0.84-1.25), or NO2 (0.98, 0.81-1.18), suggesting no residual confounding. CONCLUSIONS: Our results further support a hypothesized causal link with ASD that is specific to postnatal exposures to traffic-related pollution.
Authors: Marie-Claire Flores-Pajot; Marianna Ofner; Minh T Do; Eric Lavigne; Paul J Villeneuve Journal: Environ Res Date: 2016-09-05 Impact factor: 6.498
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