IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i7p2572-d343238.html
   My bibliography  Save this article

Maternal Exposure to Ambient Air Pollution and Pregnancy Complications in Victoria, Australia

Author

Listed:
  • Shannon M. Melody

    (Menzies Institute for Medical Research, University of Tasmania, Private Bag 23, Hobart, TAS 7001, Australia)

  • Karen Wills

    (Menzies Institute for Medical Research, University of Tasmania, Private Bag 23, Hobart, TAS 7001, Australia)

  • Luke D. Knibbs

    (School of Public Health, The University of Queensland, Herston, QLD 4006, Australia)

  • Jane Ford

    (Clinical and Population Perinatal Health Research, Kolling Institute, Northern Sydney Local Health District, St Leonards, NSW 2065, Australia)

  • Alison Venn

    (Menzies Institute for Medical Research, University of Tasmania, Private Bag 23, Hobart, TAS 7001, Australia)

  • Fay Johnston

    (Menzies Institute for Medical Research, University of Tasmania, Private Bag 23, Hobart, TAS 7001, Australia)

Abstract

The relationship between maternal exposure to ambient air pollution and pregnancy complications is not well characterized. We aimed to explore the relationship between maternal exposure to ambient nitrogen dioxide (NO 2 ) and fine particulate matter (PM 2.5 ) and hypertensive disorders of pregnancy, gestational diabetes mellitus (GDM) and placental abruption. Using administrative data, we defined a state-wide cohort of singleton pregnancies born between 1 March 2012 and 31 December 2015 in Victoria, Australia. Annual average NO 2 and PM 2.5 was assigned to maternal residence at the time of birth. 285,594 singleton pregnancies were included. An IQR increase in NO 2 (3.9 ppb) was associated with reduced likelihood of hypertensive disorders of pregnancy (RR 0.89; 95%CI 0.86, 0.91), GDM (RR 0.92; 95%CI 0.90, 0.94) and placental abruption (RR 0.81; 95%CI 0.69, 0.95). Mixed observations and smaller effect sizes were observed for IQR increases in PM 2.5 (1.3 µg/m 3 ) and pregnancy complications; reduced likelihood of hypertensive disorders of pregnancy (RR 0.95; 95%CI 0.93, 0.97), increased likelihood of GDM (RR 1.02; 95%CI 1.00, 1.03) and no relationship for placental abruption. In this exploratory study using an annual metric of exposure, findings were largely inconsistent with a priori expectations and further research involving temporally resolved exposure estimates are required.

Suggested Citation

  • Shannon M. Melody & Karen Wills & Luke D. Knibbs & Jane Ford & Alison Venn & Fay Johnston, 2020. "Maternal Exposure to Ambient Air Pollution and Pregnancy Complications in Victoria, Australia," IJERPH, MDPI, vol. 17(7), pages 1-12, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2572-:d:343238
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/7/2572/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/7/2572/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Monika Bączkowska & Katarzyna Kosińska-Kaczyńska & Magdalena Zgliczyńska & Robert Brawura-Biskupski-Samaha & Beata Rebizant & Michał Ciebiera, 2022. "Epidemiology, Risk Factors, and Perinatal Outcomes of Placental Abruption—Detailed Annual Data and Clinical Perspectives from Polish Tertiary Center," IJERPH, MDPI, vol. 19(9), pages 1-14, April.
    2. Gabriela Martins Costa Gomes & Wilfried Karmaus & Vanessa E. Murphy & Peter G. Gibson & Elizabeth Percival & Philip M. Hansbro & Malcolm R. Starkey & Joerg Mattes & Adam M. Collison, 2021. "Environmental Air Pollutants Inhaled during Pregnancy Are Associated with Altered Cord Blood Immune Cell Profiles," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    3. Amal Rammah & Kristina W. Whitworth & Christopher I. Amos & Marisa Estarlich & Mònica Guxens & Jesús Ibarluzea & Carmen Iñiguez & Mikel Subiza-Pérez & Martine Vrijheid & Elaine Symanski, 2021. "Air Pollution, Residential Greenness and Metabolic Dysfunction during Early Pregnancy in the INfancia y Medio Ambiente (INMA) Cohort," IJERPH, MDPI, vol. 18(17), pages 1-12, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    3. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    4. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    5. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    6. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    7. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    8. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    9. Georges Steffgen & Philipp E. Sischka & Martha Fernandez de Henestrosa, 2020. "The Quality of Work Index and the Quality of Employment Index: A Multidimensional Approach of Job Quality and Its Links to Well-Being at Work," IJERPH, MDPI, vol. 17(21), pages 1-31, October.
    10. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    11. Esef Hakan Toytok & Sungur Gürel, 2019. "Does Project Children’s University Increase Academic Self-Efficacy in 6th Graders? A Weak Experimental Design," Sustainability, MDPI, vol. 11(3), pages 1-12, February.
    12. J M van Niekerk & M C Vos & A Stein & L M A Braakman-Jansen & A F Voor in ‘t holt & J E W C van Gemert-Pijnen, 2020. "Risk factors for surgical site infections using a data-driven approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    13. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    14. Lara Jehi & Xinge Ji & Alex Milinovich & Serpil Erzurum & Amy Merlino & Steve Gordon & James B Young & Michael W Kattan, 2020. "Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    15. Matthew Carli & Mary H. Ward & Catherine Metayer & David C. Wheeler, 2022. "Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    16. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    17. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    18. Debra Javeline & Tracy Kijewski-Correa & Angela Chesler, 2019. "Does it matter if you “believe” in climate change? Not for coastal home vulnerability," Climatic Change, Springer, vol. 155(4), pages 511-532, August.
    19. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2018. "Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs," Journal of Educational and Behavioral Statistics, , vol. 43(3), pages 316-353, June.
    20. Svenja Meyn & Simon Blaschke & Filip Mess, 2022. "Food Literacy and Dietary Intake in German Office Workers: A Longitudinal Intervention Study," IJERPH, MDPI, vol. 19(24), pages 1-17, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2572-:d:343238. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.