IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i23p8805-d451938.html
   My bibliography  Save this article

Estimating the Combined Effects of Natural and Built Environmental Exposures on Birthweight among Urban Residents in Massachusetts

Author

Listed:
  • Maayan Yitshak-Sade

    (Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA)

  • M. Patricia Fabian

    (Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA)

  • Kevin J. Lane

    (Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA)

  • Jaime E. Hart

    (Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
    Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA)

  • Joel D. Schwartz

    (Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
    Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA)

  • Francine Laden

    (Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
    Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA)

  • Peter James

    (Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
    Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA)

  • Kelvin C. Fong

    (Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
    School of the Environment, Yale University, New Haven, MA 06511, USA)

  • Itai Kloog

    (Department of Geography and Environmental Development, Faculty of Humanities and Social Sciences, Ben-Gurion University, Beer-Sheva 84105, Israel)

  • Antonella Zanobetti

    (Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA)

Abstract

Intrauterine growth has health implications both in childhood and adulthood. Birthweight is partially determined by prenatal environmental exposures. We aim to identify important predictors of birthweight out of a set of environmental, built environment exposures, and socioeconomic environment variables during pregnancy (i.e., fine particulate matter (PM 2.5 ), temperature, greenness, walkability, noise, and economic indices). We included all singleton live births of mothers who resided in urban census block-groups and delivered in Massachusetts between 2001 and 2011 ( n = 640,659). We used an elastic-net model to select important predictors of birthweight and constructed a multivariate model including the selected predictors, with adjustment for confounders. We additionally used a weighted quantile sum regression to assess the contribution of each exposure to differences in birthweight. All exposures were selected as important predictors of birthweight. In the multivariate model, lower birthweight was significantly associated with lower greenness and with higher temperature, walkability, noise, and segregation of the “high income” group. Treating the exposures individually, nighttime noise had the highest weight in its contribution to lower birthweight. In conclusion, after accounting for individual confounders, maternal environmental exposures, built environment exposures, and socioeconomic environment during pregnancy were important predictors of birthweight, emphasizing the role of these exposures in fetal growth and development.

Suggested Citation

  • Maayan Yitshak-Sade & M. Patricia Fabian & Kevin J. Lane & Jaime E. Hart & Joel D. Schwartz & Francine Laden & Peter James & Kelvin C. Fong & Itai Kloog & Antonella Zanobetti, 2020. "Estimating the Combined Effects of Natural and Built Environmental Exposures on Birthweight among Urban Residents in Massachusetts," IJERPH, MDPI, vol. 17(23), pages 1-16, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:23:p:8805-:d:451938
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/23/8805/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/23/8805/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:aph:ajpbhl:10.2105/ajph.2017.303713_7 is not listed on IDEAS
    2. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    3. Leeann Kuehn & Sabrina McCormick, 2017. "Heat Exposure and Maternal Health in the Face of Climate Change," IJERPH, MDPI, vol. 14(8), pages 1-13, July.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    6. Kelvin C. Fong & Itai Kloog & Brent A. Coull & Petros Koutrakis & Francine Laden & Joel D. Schwartz & Peter James, 2018. "Residential Greenness and Birthweight in the State of Massachusetts, USA," IJERPH, MDPI, vol. 15(6), pages 1-15, June.
    7. Krieger, N. & Waterman, P.D. & Batra, N. & Murphy, J.S. & Dooley, D.P. & Shah, S.N., 2017. "Measures of local segregation for monitoring health inequities by local health departments," American Journal of Public Health, American Public Health Association, vol. 107(6), pages 903-906.
    8. Bell, Janice F. & Zimmerman, Frederick J. & Almgren, Gunnar R. & Mayer, Jonathan D. & Huebner, Colleen E., 2006. "Birth outcomes among urban African-American women: A multilevel analysis of the role of racial residential segregation," Social Science & Medicine, Elsevier, vol. 63(12), pages 3030-3045, December.
    9. Roberts, E.M., 1997. "Neighborhood social environments and the distribution of low birthweight in Chicago," American Journal of Public Health, American Public Health Association, vol. 87(4), pages 597-603.
    10. Joan A. Casey & Peter James & Kara E. Rudolph & Chih-Da Wu & Brian S. Schwartz, 2016. "Greenness and Birth Outcomes in a Range of Pennsylvania Communities," IJERPH, MDPI, vol. 13(3), pages 1-15, March.
    11. Debbink, M.P. & Bader, M.D.M., 2011. "Racial residential segregation and low birth weight in Michigan's metropolitan areas," American Journal of Public Health, American Public Health Association, vol. 101(9), pages 1714-1720.
    12. Mark J. Nieuwenhuijsen & Gordana Ristovska & Payam Dadvand, 2017. "WHO Environmental Noise Guidelines for the European Region: A Systematic Review on Environmental Noise and Adverse Birth Outcomes," IJERPH, MDPI, vol. 14(10), pages 1-16, October.
    13. Michelle C. Kondo & Jaime M. Fluehr & Thomas McKeon & Charles C. Branas, 2018. "Urban Green Space and Its Impact on Human Health," IJERPH, MDPI, vol. 15(3), pages 1-28, March.
    14. Vinikoor, Lisa C. & Kaufman, Jay S. & MacLehose, Richard F. & Laraia, Barbara A., 2008. "Effects of racial density and income incongruity on pregnancy outcomes in less segregated communities," Social Science & Medicine, Elsevier, vol. 66(2), pages 255-259, January.
    15. Gordana Ristovska & Helga Elvira Laszlo & Anna L. Hansell, 2014. "Reproductive Outcomes Associated with Noise Exposure — A Systematic Review of the Literature," IJERPH, MDPI, vol. 11(8), pages 1-22, August.
    Full references (including those not matched with items on IDEAS)

    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. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Post-Print halshs-00917797, HAL.
    2. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    3. Lee Anthony & Caron Francois & Doucet Arnaud & Holmes Chris, 2012. "Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-31, January.
    4. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2016. "The lasso for high dimensional regression with a possible change point," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 193-210, January.
    5. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
    6. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
    7. Shaobo Jin & Irini Moustaki & Fan Yang-Wallentin, 2018. "Approximated Penalized Maximum Likelihood for Exploratory Factor Analysis: An Orthogonal Case," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 628-649, September.
    8. Daniel Kim & Adrianna Saada, 2013. "The Social Determinants of Infant Mortality and Birth Outcomes in Western Developed Nations: A Cross-Country Systematic Review," IJERPH, MDPI, vol. 10(6), pages 1-40, June.
    9. Niemesh, Gregory T. & Shester, Katharine L., 2020. "Racial residential segregation and black low birth weight, 1970–2010," Regional Science and Urban Economics, Elsevier, vol. 83(C).
    10. Yanfang Zhang & Chuanhua Wei & Xiaolin Liu, 2022. "Group Logistic Regression Models with l p,q Regularization," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
    11. Ncube, Collette N. & Enquobahrie, Daniel A. & Albert, Steven M. & Herrick, Amy L. & Burke, Jessica G., 2016. "Association of neighborhood context with offspring risk of preterm birth and low birthweight: A systematic review and meta-analysis of population-based studies," Social Science & Medicine, Elsevier, vol. 153(C), pages 156-164.
    12. Tanin Sirimongkolkasem & Reza Drikvandi, 2019. "On Regularisation Methods for Analysis of High Dimensional Data," Annals of Data Science, Springer, vol. 6(4), pages 737-763, December.
    13. van Erp, Sara & Oberski, Daniel L. & Mulder, Joris, 2018. "Shrinkage priors for Bayesian penalized regression," OSF Preprints cg8fq, Center for Open Science.
    14. Craig, Sarah J.C. & Kenney, Ana M. & Lin, Junli & Paul, Ian M. & Birch, Leann L. & Savage, Jennifer S. & Marini, Michele E. & Chiaromonte, Francesca & Reimherr, Matthew L. & Makova, Kateryna D., 2023. "Constructing a polygenic risk score for childhood obesity using functional data analysis," Econometrics and Statistics, Elsevier, vol. 25(C), pages 66-86.
    15. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Forecasting, MDPI, vol. 3(2), pages 1-44, May.
    16. JinXing Che & YouLong Yang, 2017. "Stochastic correlation coefficient ensembles for variable selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1721-1742, July.
    17. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-04250269, HAL.
    18. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
    19. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    20. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.

    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:23:p:8805-:d:451938. 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.