TY - JOUR
T1 - Prenatal exposure to PM2.5 and birth weight
T2 - A pooled analysis from three North American longitudinal pregnancy cohort studies
AU - Rosa, Maria José
AU - Pajak, Ashley
AU - Just, Allan C.
AU - Sheffield, Perry E.
AU - Kloog, Itai
AU - Schwartz, Joel
AU - Coull, Brent
AU - Enlow, Michelle Bosquet
AU - Baccarelli, Andrea A.
AU - Huddleston, Kathi
AU - Niederhuber, John E.
AU - Rojo, Martha María Téllez
AU - Wright, Robert O.
AU - Gennings, Chris
AU - Wright, Rosalind J.
N1 - Funding Information:
This work was supported by the National Institutes of Health (grant UG3OD023337, T32 HD049311-09 supported MJR); the National Institute of Environmental Health Sciences (grants R01 ES010932, R01 ES013744, R21 ES021318, R01 ES021357, P30 ES023515; R00 ES023450 supported ACJ) and the National Heart Lung and Blood Institute (grants U01 HL072494, R01 HL080674, R01 HL095606) and U2CES026555. MBE was supported by the Program for Behavioral Science in the Department of Psychiatry at Boston Children's Hospital. This study was also supported and partially funded by the National Institute of Public Health/Ministry of Health of Mexico.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017
Y1 - 2017
N2 - 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 ≤ 2.5 μm 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 5 μg/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.
AB - 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 ≤ 2.5 μm 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 5 μg/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.
KW - Air pollution
KW - Birth weight
KW - Propensity scores
UR - http://www.scopus.com/inward/record.url?scp=85025154753&partnerID=8YFLogxK
U2 - 10.1016/j.envint.2017.07.012
DO - 10.1016/j.envint.2017.07.012
M3 - Article
C2 - 28738263
AN - SCOPUS:85025154753
SN - 0160-4120
VL - 107
SP - 173
EP - 180
JO - Environment international
JF - Environment international
ER -