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Environ Int. 2017 Oct;107:173-180. doi: 10.1016/j.envint.2017.07.012. Epub 2017 Jul 22.

Prenatal exposure to PM2.5 and birth weight: A pooled analysis from three North American longitudinal pregnancy cohort studies.

Author information

1
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: maria.rosa@mssm.edu.
2
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: ashley.pajak@mssm.edu.
3
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: allan.just@mssm.edu.
4
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: perry.sheffield@mssm.edu.
5
Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva, Israel. Electronic address: ikloog@bgu.ac.il.
6
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: jschwrtz@hsph.harvard.edu.
7
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: bcoull@hsph.harvard.edu.
8
Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: michelle.bosquet@childrens.harvard.edu.
9
Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA. Electronic address: andrea.baccarelli@columbia.edu.
10
Inova Translational Medicine Institute, Falls Church, VA, USA. Electronic address: kathi.huddleston@inova.org.
11
Inova Translational Medicine Institute, Falls Church, VA, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: john.niederhuber@inova.org.
12
Center for Nutrition and Health Research, National Institute of Public Health, Ministry of Health, Cuernavaca, Morelos, Mexico. Electronic address: mmtellez@insp.mx.
13
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: robert.wright@mssm.edu.
14
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: chris.gennings@mssm.edu.
15
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Kravis Children's Hospital, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: rosalind.wright@mssm.edu.

Abstract

A common practice when analyzing multi-site epidemiological data is to include a term for 'site' to account for unmeasured effects at each location. This practice should be carefully considered when site can have complex relationships with important demographic and exposure variables. We leverage data from three longitudinal North American pregnancy cohorts to demonstrate a novel method to assess study heterogeneity and potential combinability of studies for pooled analyses in order to better understand how to consider site in analyses. Results from linear regression and fixed effects meta-regression models run both prior to and following the proposed combinability analyses were compared. In order to exemplify this approach, we examined associations between prenatal exposure to particulate matter and birth weight. Analyses included mother-child dyads (N=1966) from the Asthma Coalition on Community Environment and Social Stress (ACCESS) Project and the PRogramming of Intergenerational Stress Mechanisms (PRISM) study in the northeastern United States, and the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study in Mexico City. Mothers' daily third trimester exposure to particulate matter≤2.5μm in diameter (PM2.5) was estimated using a validated satellite-based spatio-temporally resolved model in all studies. Fenton birth weight for gestational age z-scores were calculated. Linear regression analyses within each cohort separately did not find significant associations between PM2.5 averaged over the third trimester and Fenton z-scores. The initial meta-regression model also did not find significant associations between prenatal PM2.5 and birthweight. Next, propensity scores and log linear models were used to assess higher order interactions and determine if sites were comparable with regard to sociodemographics and other covariates; these analyses demonstrated that PROGRESS and ACCESS were combinable. Adjusted linear regression models including a 2-level site variable according to the pooling indicated by the log linear models (ACCESS and PROGRESS as one level and PRISM as another) revealed that a 5μg/m3 increase in PM2.5 was associated with a 0.075 decrease in Fenton z-score (p<0.0001); linear models including a 3-level site variable did not reveal significant associations. By assessing the combinability of heterogeneous populations prior to combining data using a method that more optimally accounts for underlying cohort differences, we were able to identify significant associations between prenatal PM2.5 exposure and birthweight that were not detected using standard methods.

KEYWORDS:

Air pollution; Birth weight; Propensity scores

PMID:
28738263
PMCID:
PMC5568041
DOI:
10.1016/j.envint.2017.07.012
[Indexed for MEDLINE]
Free PMC Article

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