Air Pollution and Autism Spectrum Disorder in Israel: A Negative Control Analysis.
Journal
Epidemiology (Cambridge, Mass.)
ISSN: 1531-5487
Titre abrégé: Epidemiology
Pays: United States
ID NLM: 9009644
Informations de publication
Date de publication:
01 11 2021
01 11 2021
Historique:
pubmed:
5
8
2021
medline:
26
10
2021
entrez:
4
8
2021
Statut:
ppublish
Résumé
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. 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. 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. Our results further support a hypothesized causal link with ASD that is specific to postnatal exposures to traffic-related pollution.
Sections du résumé
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.
Identifiants
pubmed: 34347685
doi: 10.1097/EDE.0000000000001407
pii: 00001648-202111000-00002
pmc: PMC8478838
mid: NIHMS1728194
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
773-780Subventions
Organisme : NIEHS NIH HHS
ID : P30 ES000002
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG065276
Pays : United States
Organisme : NIEHS NIH HHS
ID : R21 ES026900
Pays : United States
Informations de copyright
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors report no conflicts of interest.
Références
American Psychological Association (APA). Diagnostic and Statistical Manual of Mental Disorders: Neurodevelopmental Disorders. 5th Edn. American Psychiatric Publishing; 2013.
Lyall K, Schmidt RJ, Hertz-Picciotto I. Environmental factors in the preconception and prenatal periods in relation to risk for ASD. In: Handbook of Autism and Pervasive Developmental Disorders, Volume 2, Assessment, Interventions, and Policy, 4th Edition. Wiley; 2014:424–456.
Kim YS, Leventhal BL. Genetic epidemiology and insights into interactive genetic and environmental effects in autism spectrum disorders. Biol Psychiatry. 2015;77:66–74.
Balestrieri E, Arpino C, Matteucci C, et al. HERVs expression in Autism Spectrum Disorders. PLoS One. 2012;7:e48831.
Sealey LA, Hughes BW, Sriskanda AN, et al. Environmental factors in the development of autism spectrum disorders. Environ Int. 2016;88:288–298.
Narayan KM, Ali MK, Koplan JP. Global noncommunicable diseases—where worlds meet. N Engl J Med. 2010;363:1196–1198.
Chen H, Goldberg MS, Villeneuve PJ. A systematic review of the relation between long-term exposure to ambient air pollution and chronic diseases. Rev Environ Health. 2008;23:243–297.
Chun H, Leung C, Wen SW, McDonald J, Shin HH. Maternal exposure to air pollution and risk of autism in children: a systematic review and meta-analysis. Environ Pollut. 2020;256:113307.
Flores-Pajot MC, Ofner M, Do MT, Lavigne E, Villeneuve PJ. Childhood autism spectrum disorders and exposure to nitrogen dioxide, and particulate matter air pollution: a review and meta-analysis. Environ Res. 2016;151:763–776.
Lam J, Sutton P, Kalkbrenner A, et al. A systematic review and meta-analysis of multiple airborne pollutants and autism spectrum disorder. PLoS One. 2016;11:e0161851.
Fujiwara T, Morisaki N, Honda Y, Sampei M, Tani Y. Chemicals, nutrition, and autism spectrum disorder: a mini-review. 2016;10:1–7.
Yang C, Zhao W, Deng K, Zhou V, Zhou X, Hou Y. The association between air pollutants and autism spectrum disorders. Environ Sci Pollut Res Int. 2017;24:15949–15958.
Lyall K, Schmidt RJ, Hertz-Picciotto I. Maternal lifestyle and environmental risk factors for autism spectrum disorders. Int J Epidemiol. 2014;43:443–464.
Suades-González E, Gascon M, Guxens M, Sunyer J. Air pollution and neuropsychological development: a review of the latest evidence. Endocrinology. 2015;156:3473–3482.
Bölte S, Girdler S, Marschik PB. The contribution of environmental exposure to the etiology of autism spectrum disorder. Cell Mol Life Sci. 2019;76:1275–1297.
Alonso-Gonzalez A, Rodriguez-Fontenla C, Carracedo A. De novo mutations (DNMs) in autism spectrum disorder (ASD): pathway and network analysis. Front Genet. 2018;9:406.
Boulanger-Bertolus J, Pancaro C, Mashour GA. Increasing role of maternal immune activation in neurodevelopmental disorders. Front Behav Neurosci. 2018;12:230.
Costa LG, Cole TB, Dao K, Chang YC, Coburn J, Garrick J. Neurotoxicity of air pollution: Role of neuroinflammation. In: Advances in Neurotoxicology Volume 3, 2019. Vol 3. Academic Press; 2019:195–221.
Brockmeyer S, D’Angiulli A. How air pollution alters brain development: the role of neuroinflammation. Transl Neurosci. 2016;7:24–30.
Weisskopf MG, Kioumourtzoglou MA, Roberts AL. Air pollution and autism spectrum disorders: causal or confounded? Curr Environ Health Rep. 2015;2:430–439.
Ritz B, Liew Z, Yan Q, et al. Air pollution and autism in denmark. Environ Epidemiol. 2018;2:e028.
Raz R, Levine H, Pinto O, Broday DM, Yuval Weisskopf MG. Traffic-related air pollution and autism spectrum disorder: a population-based nested case-control study in Israel. Am J Epidemiol. 2018;187:717–725.
Nørgaard M, Ehrenstein V, Vandenbroucke JP. Confounding in observational studies based on large health care databases: problems and potential solutions - a primer for the clinician. Clin Epidemiol. 2017;9:185–193.
USEPA USEPA. Integrated Science Assessment for Particulate Matter. U.S. Environmental Protection Agency; 2019.
Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology. 2010;21:383–388.
Weisskopf MG, Tchetgen EJT, Raz R. On the use of imperfect negative control exposures in epidemiologic studies. Epidemiology. 2016;27:365–367.
Flanders WD, Klein M, Darrow LA, et al. A method for detection of residual confounding in time-series and other observational studies. Epidemiology. 2011;22:59–67.
Flanders WD, Klein M, Darrow LA, et al. A method to detect residual confounding in spatial and other observational studies. Epidemiology. 2011;22:823–826.
Raz R, Weisskopf MG, Davidovitch M, Pinto O, Levine H. Differences in autism spectrum disorders incidence by sub-populations in Israel 1992-2009: a total population study. J Autism Dev Disord. 2015;45:1062–1069.
Kloog I, Sorek-Hamer M, Lyapustin A, et al. Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data. Atmos Environ (1994). 2015;122:409–416.
Yuval BS, Broday DM. Data-driven nonlinear optimisation of a simple air pollution dispersion model generating high resolution spatiotemporal exposure. Atmos Environ. 2013;79:261–270.
Yuval LI, Broday DM. Improving modeled air pollution concentration maps by residual interpolation. Sci Total Environ . 2017;598:780–788.
Yuval CS, Broday DM. A new modeling approach for assessing the contribution of industrial and traffic emissions to ambient NOx concentrations. Atmos Environ. 2018;173:173–184.
Rodier PM, Ingram JL, Tisdale B, Nelson S, Romano J. Embryological origin for autism: developmental anomalies of the cranial nerve motor nuclei. J Comp Neurol. 1996;370:247–261.
Rice D, Barone S Jr. Critical periods of vulnerability for the developing nervous system: evidence from humans and animal models. Environ Health Perspect. 2000;108 Suppl 3:511–533.
Conti E, Calderoni S, Marchi V, Muratori F, Cioni G, Guzzetta A. The first 1000 days of the autistic brain: a systematic review of diffusion imaging studies. Front Hum Neurosci. 2015;9:159.
Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37–48.
Pearl J. Causality: Models, Reasoning, and Inference. Cambridge University Press; 2011.
Israel Central Bureau of Statistics. CBS Socio-Economic Index. 2013. Available at: http://www.cbs.gov.il/publications17/socio_eco13_1694/pdf/intro_e.pdf . Accessed 21 July 2021.
R Foundation. R: A Language and Environment for Statistical Computing. 2018. Available at: https://www.r-project.org/ . Accessed 21 July 2021.
Schisterman EF, Perkins NJ, Mumford SL, Ahrens KA, Mitchell EM. Collinearity and causal diagrams: a lesson on the importance of model specification. Epidemiology. 2017;28:47–53.
Raz R, Kioumourtzoglou MA, Weisskopf MG. Live-birth bias and observed associations between air pollution and autism. Am J Epidemiol. 2018;187:2292–2296.
Krasnov H, Katra I, Friger M. Increase in dust storm related PM10 concentrations: a time series analysis of 2001-2015. Environ Pollut. 2016;213:36–42.
Abdeen Z, Qasrawi R, Heo J, et al. Spatial and temporal variation in fine particulate matter mass and chemical composition: the Middle East Consortium for Aerosol Research Study. ScientificWorldJournal. 2014;2014:878704.
Yuval TT, Raz R, Levi Y, Levy I, Broday DM. Emissions vs. turbulence and atmospheric stability: a study of their relative importance in determining air pollutant concentrations. Sci Total Environ. 2020;733:139300.
Lelieveld J, Evans JS, Fnais M, Giannadaki D, Pozzer A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature. 2015;525:367–371.
Minakova E, Warner BB. Maternal immune activation, central nervous system development and behavioral phenotypes. Birth Defects Res. 2018;110:1539–1550.
Woodward N, Finch CE, Morgan TE. Traffic-related air pollution and brain development. AIMS Environ Sci. 2015;2:353–373.
Block ML, Calderón-Garcidueñas L. Air pollution: mechanisms of neuroinflammation and CNS disease. Trends Neurosci. 2009;32:506–516.
Costa LG, Cole TB, Coburn J, Chang YC, Dao K, Roque P. Neurotoxicants are in the air: convergence of human, animal, and in vitro studies on the effects of air pollution on the brain. Biomed Res Int. 2014;2014:736385.
Sunyer J. The neurological effects of air pollution in children. 2008;32:535–7.
Weisskopf MG, Webster TF. Trade-offs of Personal Versus More Proxy Exposure Measures in Environmental Epidemiology. Epidemiology. 2017;28:635–643.