Final Report: Research Project A: Mapping Disparities in Birth Outcomes

EPA Grant Number: R833293C001
Subproject: this is subproject number 001 , established and managed by the Center Director under grant R833293
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).

Center: Southern Center on Environmentally Driven Disparities in Birth Outcomes
Center Director: Miranda , Marie Lynn
Title: Research Project A: Mapping Disparities in Birth Outcomes
Investigators: Miranda , Marie Lynn , Gelfand, Alan , James, Sherman , Maxson, Pamela , Swamy, Geeta
Institution: Duke University
EPA Project Officer: Callan, Richard
Project Period: May 1, 2007 through April 30, 2012 (Extended to April 30, 2014)
RFA: Centers for Children’s Environmental Health and Disease Prevention Research (2005) RFA Text |  Recipients Lists
Research Category: Health Effects , Children's Health , Health

Objective:

Objectives of the Southern Center on Environmentally-Driven Disparities in Birth Outcomes (SCEDDBO)

The central mission of the Southern Center on Environmentally-Driven Disparities in Birth Outcomes was to determine how environmental, social, and host factors jointly contribute to health disparities.  Specific aims of the Center were:

  1. To develop and operate an interdisciplinary children’s health research center with a focus on understanding how biological, physiological, environmental, and social aspects of vulnerability contribute to health disparities;
  2. To enhance research in children’s health at Duke by promoting research interactions among programs in biomedicine, pediatric and obstetric care, environmental health, and the social sciences and establishing an infrastructure to support and extend interdisciplinary research;
  3. To develop new methodologies for incorporating innovative statistical analysis into children’s environmental health research and policy practice, with a particular emphasis on spatial, genetic and proteomic analysis;
  4. To serve as a technical and educational resource to the local community, region, the nation, and to international agencies in the area of children’s health and health disparities; and,
  5. To translate the results of the Center into direct interventions in clinical care and practice.

SCEDDBO leveraged and promoted active partnerships among the Nicholas School of the Environment at Duke University, the School of Natural Resources and Environment at the University of Michigan, the Duke University Medical Center, Trinity College of Arts and Sciences, and Duke’s Children's Environmental Health Initiative, as well as the Durham County Health Department (DCHD), and Lincoln Community Health Center (LCHC), Durham’s federally qualified health center.   The Center brought together the expertise of obstetricians, pediatricians, genetic epidemiologists, spatial statisticians, environmental scientists, social epidemiologists, social psychologists, geographers, and community organizations.  SCEDDBO capitalized on substantial ongoing commitments by Duke University to foster strong interdisciplinary research programs in environmental health sciences.

During the project period, SCEDDBO was characterized by significant synergies across center components. For example, our Community Assessment Project (CAP), conducted by the SCEDDBO COTC, assessed built environment variables for over 17,000 residential tax parcels in 2008 and for over 30,700 parcels in 2011, including the home addresses of over 65 percent of the 1800 participants in Project B’s pregnancy cohort study and 4279 women from the Project A data architecture (with the latter number increasing as additional years of birth record data become available). Findings from analyses of these datasets led to the decision to incorporate a nest deprivation model in our animal studies, which originally focused on air pollution exposures only. Over the course of the project period, we have published 80 papers resulting directly from the work of SCEDDBO.

Central to SCEDDBO’s mission is to determine how environmental, social, and host factors (see Figure 1) jointly contribute to health disparities was the development of an underlying, multi-sourced, detailed data architecture

 

Figure 1. M.L. Miranda, P. Maxon, S. Edwards. 2009. "Environmental contributors to disparities in pregnancy outcomes." Epidemiologic Reviews, 31:67-83. PMID:19846592.

Project A utilized the conceptual framework of the “weathering hypothesis,” which posits that chronic and persistent stressors lead to accelerated biological aging of women, which in turn accounts for adverse birth outcomes among certain subpopulations. The central objective was to determine whether and to what extent joint exposures to socioeconomic and environmental stressors contribute to racial and ethnic health disparities in fetal growth restriction.
 
Using a geographically based nested study design moving from analysis of births for the entire state of North Carolina to six demographically and geographically distinct counties to a single health center and state-of-the-art Geographic Information Systems applications with Bayesian spatial hierarchical modeling and other advanced spatial statistical approaches, the specific aims were to:
  1. Spatially link detailed birth record, fetal death certificates, socioeconomic, environmental, tax assessor, community-based, and clinical obstetric data at highly resolved scales for the state of North Carolina from 1990-2003;
  2. Refine the concept of fetal growth restriction by (a) developing a joint distribution for birthweight and gestation using bivariate modeling for live births and fetal deaths both separately and jointly, and (b) defining it in terms of fetal and infant mortality, rather than percentile cut points; and
  3. Determine whether and to what extent differential exposures to both environmental and social stressors help explain health disparities in fetal growth restriction among (a) African-American women compared to Non-Hispanic white and Hispanic women, (b) Older African-American women compared to younger African-American women, (c) Hispanic women compared to Non-Hispanic white and African-American women, and (d) Foreign born Hispanic women compared to U.S. born Hispanic women.
This project evaluated a large number of factors in diverse populations, providing broad relevance for birth outcomes across time, space, and demography. Identifying social and environmental factors contributing to fetal growth restriction helped improve our understanding of disease etiology and explain the racial disparity in disease incidence.

Summary/Accomplishments (Outputs/Outcomes):

Central to SCEDDBO’s mission to determine how environmental, social, and host factors jointly contribute to health disparities was the development of an underlying, multi-sourced, detailed data architecture.  During the project period, we constructed a large-scale, spatially referenced data warehouse, linking birth record data to social and environmental exposures data. We have shown that among women in our sample population, exposure to air pollutants, including PM2.5 and PM10, and using proximity to roadways as a proxy variable, is associated with poor pregnancy outcomes, as measured by birth weight, preterm birth, and maternal medical complications, controlling for maternal-level risk factors and neighborhood characteristics (Chang et al., 2012; Gray et al., 2010; Vinikoor-Imler et al., 2012; Gray et al., 2013; See Figure 2). 

Figure 2. S. Gray, E.S. Edwards, and M.L. Miranda. 2013. "Race, socioeconomic status, and air pollution exposure in North Carolina." Environmental Research. 126:152-158. PMID: 23850144.

Our work has investigated how to define meaningful exposure metrics for air pollution in terms of the impact of geographic scale of aggregation and how to incorporate uncertainty in personal exposure estimates derived from monitoring station measurements (Gray et al., 2011).  To do so, we constructed a stochastic simulator to directly make predictions of individual-level exposure that we related to birth weight (Berrocal et al., 2011).  Additional work on air pollution has identified windows of vulnerability for expectant mothers (Chang et al, 2012; Gray et al., 2010).  We re-coFnceptualized the analysis for binary pregnancy outcomes like preterm birth as a time-to-event analysis instead of simple logistic regression (Chang et al., 2012).  With the goal of linking pregnancy outcomes to improved estimates of air pollution exposure, we have constructed spatial downscalers, which fuse monitoring station data with computer model output to better assess environmental exposure at point level spatial resolution (Berrocal et al., 2010a; Berrocal,  et al., 2010b; Berrocal et al, 2011).   

Compared to non-Hispanic whites (NHW), non-Hispanic blacks (NHB) are more likely to live in neighborhoods characterized by poverty, elevated levels of environmental contaminants, and poor quality housing (Miranda et al., 2009).  In related work on racial residential segregation (which is associated with higher environmental exposures), we observed a detrimental association between a spatial measure of neighborhood level racial residential segregation and pregnancy outcomes (Anthopolos et al., 2011; See Figure 3). Racial residential segregation may impact health through the built environment. Using the Community Assessment Project data, we found that higher levels of housing damage, property disorder, tenure, and vacancy are associated with increased likelihood of preterm birth and low birth weight. Linking these two lines of research, we then showed in a formal mediation framework that poor quality built environment accounts for approximately 35% of the total effect of racial residential segregation on preterm birth (Anthopolos et al., 2014).

Figure 3. R. Anthopolos, S.A. James, A.E. Gelfand, and M.L. Miranda. 2011. "A spatial measure of neighborhood-level racial isolation applied to low birthweight, preterm birth, and birthweight in North Carolina." Spatial and Apatio Temporal Epidemiologh. 2(4):235-246. PMID: 27748223.

In our statistical methods development, we emphasized joint modeling of related outcomes that can borrow strength from each other, such as birthweight and gestational age, which will often help with causal inference interpretation and illuminate subpopulations with differential risk for adverse joint birth outcomes (Schwartz et al., 2010; See Figure 4.).  For the case of binary outcomes (e.g., low birth weight and preterm birth) and normal outcomes, we have introduced additional borrowing of information at the contextual level with spatially correlated random effects (Neelon, Anthopolos, & Miranda, 2014; Neelon, Gelfand, & Miranda, 2014). Given the critical nature of health outcomes defined by distribution tails, such as low birth weight and preterm birth, in addition to the potential for differential covariate effects over the response distribution, we developed a new method to apply quantile regression in a spatial context (i.e., relaxing the assumption that observations in adjacent neighborhoods are independent).  Applying this method, we observed differential effects of standard risk factors of birth weight, such as infant sex and birth order, along the birth weight response distribution (Lum & Gelfand, 2012).

Figure 4. S.Schwartz, A. Gelfand, and M.L. Miranda. 2010. "Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models." Statistics in Medicine 20. 20(16):1710-1723.

In addition, we have completed work on the impact of maternal age and birth order on birth weight (Swamy et al., 2012), on modeling ordinal categorical data using Gaussian processes (Heaton et al, 2012), the etiology of racial disparities in maternal hypertensive disorders (Miranda et al., 2010), racial differences in seasonality patterns of poor pregnancy outcomes (Miranda et al., 2011), the advantages of spatial analysis in environmental health research (Miranda and Edwards, 2011), the relationship between early childhood lead exposure and later school performance and exceptionality designations (Miranda et al., 2007; Miranda et al., 2009; Miranda et al., 2010), on flexible Bayesian modeling techniques for functional and longitudinal data (Montagna S. et al, 2012), on joint spatial modeling of areal multivariate categorical data (Tassone et al., 2010; see Figure 5), on optimal spatial designs for environmental health data, on the association between the built environment and child BMI (Miranda et al., 2012), and on synthesizing categorical data sets from different sampling designs (Berrocal et al., 2013).

Figure 5. E.Tassone, M.L.Miranda, and A. Gelfand. 2010. "Disaggregated spatial modeling for areal unit categorical data." Journal of the Royal Statistical Society: Series C (Applied Statistics) 59, Part, pp. 175-190. PMCID: PMC2999915.

Maternal Exposure to Air pollution. Over the project period, we have done much work on air pollution exposure and pregnancy outcomes. A continuing goal was the linking of the detailed birth record data to USEPA PM10, PM2.5, and ozone monitoring data in order to study the impact of maternal exposure to air pollution on birth weight.  To this end, in year 6 we were invited to write a review of air pollution effects on birth outcomes (Edwards et al., forthcoming).  We were especially focused on incorporating refined exposure metrics to most effectively characterize meaningful exposures, as well as to capture any windows of vulnerability.  Significant progress was made on the relationship between birth outcomes and exposure to particulate matter and ozone separately, and the current focus is determining how to characterize joint exposure to both particulate matter and ozone.  A manuscript on this work appeared in the Journal of Exposure Science and Environmental Epidemiology (Gray et al., 2010).  A critical issue in this work is addressing the misalignment between where monitoring stations are and where pregnant women live.  Two approaches have been explored.  One considers buffers of varying radii around monitoring sites to see how the exposure signal is affected by increasing distance from the site.  The other attaches more uncertainty to the putative exposure as the distance from the monitoring site to the residence increases.  Again, various exposure windows and metrics are considered.  This work appeared in Statistics and Medicine (Gray et al, 2011).   Time-to-event investigation of the effect of particulate matter and birth outcomes appeared in Chang et al. (2012a, 2012b).

As part of our larger efforts exploring the relationship of air pollution exposure and pregnancy outcomes, we sought to consider a relatively simple metric for assessing risk of exposure to air pollution, specifically traffic-related air pollution which includes particulate matter and diesel exhaust, both of which were investigated within Project C. We utilized the statewide GIS layer of street-geocoded 2005-2007 births to calculate the proximity of each geocoded birth to the nearest primary and secondary roadway.  While controlling for all standard covariates, we incorporated measures of air pollution exposure as dichotomous variables indicating residence within 500, 250, 150, 100, or 50m of a primary or secondary roadway into models for birthweight, LBW, VLBW, PTB, VPTB, and any hypertensive disorder.  Our findings indicate a significant dose-response relationship between proximity to a primary or secondary road and the adverse outcomes of PTB, VPTB and hypertension—for example, the probability of hypertension is increased by living within 500m of a primary or secondary roadway, with this probability being even higher at 250m, and still higher at each of 150, 100, and 50m (Miranda et al., 2013).  In addition, much of our statistical development work, described below, used air pollution as the application.

Nulliparous Women. We explored the observed association between parity and risk of adverse birth outcomes (i.e., women having their first child are at increased risk of adverse outcomes compared to women who have already had at least one child).  We linked births in the North Carolina Detailed Birth Record 1990-2007 with previous and subsequent births to the same mother using deterministic techniques that evaluated various combinations of maternal identifying variables to link births, including full name, maiden name, date and state of birth, parity, and date of last birth.  We employed statistical and modeling-based analyses to estimate first birth outcome rate differences between nulliparas who did have a subsequent pregnancy versus those who did not. Among nulliparas that were not linked to a second birth, maternal-age-adjusted rates of multiple measures of adverse outcomes, including maternal medical complications, were almost all statistically higher compared to rates for linked women.  This work suggests that the observed differences in rates of adverse outcomes between nulliparas and multiparas are partly attributable to higher risk women not having a subsequent pregnancy (either by choice or due to fecundity differences). See Miranda, Edwards, and Myers, 2011. 

Racial Residential Segregation.  Our project on racial residential segregation enables quantification of racial exposure/isolation at finer spatial scales within SMSA’s.  Such a measure can be connected to measures of social and economic disadvantage at these scales to gain insight into how racial residential segregation has manifested itself across urban landscapes.  In turn, this promises to reveal key insights into how to think about the spatial aspects of the social factors influencing health disparities.  We are working to determine which facets of segregation best characterize the way community-level racial residential segregation acts to promote health disparities in birth outcomes.  Although our initial efforts were statewide, we eventually decided that, given the significantly more detailed data available for Durham County, we would focus on this area to determine what variables are most important to characterizing racial residential segregation in terms of its health consequences (Anthopolos et al., 2011; Anthopolos et al., 2014; See Figure 6).

Figure 6. R. Anthopolos, J.S. Kaufman, L.C. Messer, and M.L.Miranda. 2014. "Racial residential segregation and preterm birth: built environment as a mediator." Epiedmiology. 25(3):397-405. doi: 10.1097/EDE.000000000000079. PMDI: 24681575

Community Assessment Project/Built Environment. Built environment data was collected under the Community Assessment Project (described under COTC) and preliminary analysis focused on spatial layers capturing four primary attributes of the built environment - housing damage, property disorder, tenure, and vacancy.  Connection was made to pregnancy outcomes.  Resultant work examining a bi-probit regression model as well as marginal logistic regressions has appeared (Miranda et al. 2012). Other work connecting the built environment with adverse birth outcomes appears in Miranda, Messer, & Kroeger, 2012.  Other manuscripts are described in Project B. 

Seasonality.  We have examined the relationship between seasonality and pregnancy outcomes.  Our initial aspatial models indicated that the effect of season was most apparent among non-Hispanic white women (Miranda, Anthopolos, & Edwards, 2011).  We utilized spatial models to better understand what factors of season of conception or birth are influencing pregnancy outcomes.

Racial Disparities in Maternal Hypertensive Disorders.  We analyzed data from North Carolina to determine how the pattern of maternal hypertensive disorders differs among non-Hispanic white, non-Hispanic black, and Hispanic women across the range of maternal ages.  In addition we explored whether rates of poor birth outcomes, including low birth-weight and preterm birth, among hypertensive women differed by race (Miranda et al., 2010;  Neelon et al. 2011, Vinikoor-Imler, 2012)

Developmental Outcomes. Having linked the North Carolina statewide detailed birth record and educational record databases, we examined the impact of pregnancy-related events and exposures on neurodevelopmental outcomes in early childhood. Two manuscripts have been published. The first, published in JAMA Pediatrics, (See Gregory et al. 2013), investigates whether induction and/or augmentation during labor may be associated with autism diagnosis in children in grades 3-8. In this work, we used logistic regression modeling for rare events data to first establish an association between labor induction/augmentation and autism diagnosis and then examine whether the association is robust to controlling for successive sets of potential confounders related to maternal demographics, maternal health conditions, and events of labor and delivery, as recorded in the detailed birth record. The second is in Pediatric and Perinatal Epidemiology, examining the joint effect of birth outcomes and maternal prenatal smoking on educational test scores in reading and math (Anthopolos et al. 2013). This study finds that maternal prenatal smoking may interact with birth outcomes on reading and mathematics test scores, particularly among non-Hispanic white children. Additionally, improvements in birth outcomes, even within the clinically normal range, may be associated with improved academic performance.

Environmental Contributions to Disparities in Pregnancy Outcomes.  We published an invited review article on social and environmental contributors to disparities in birth outcomes based on both national and North Carolina data, as a way of compiling the many literatures we have accessed throughout our work on Project A.  The manuscript, published in Epidemiologic Reviews, reviews research on how environmental exposures affect pregnancy outcomes and how these exposures may be embedded within a context of significant social and host factor stress.

Statistical Methods Development. Our work was highly focused on statistical methods development, particularly in the areas of leveraging Bayesian hierarchical spatial modeling and developing multivariate modeling approaches to examining correlated outcomes.

Out of efforts to develop new spatial methodologies for addressing health disparities, we did additional methodological work on disaggregated spatial modeling for areal unit categorical data. This work used innovative statistical methodology that extends spatial disease mapping techniques to model subgroups within areal units using a spatially smoothed, multilevel loglinear model.  This work appeared in the Journal of the Royal Statistical Society, Series C (Tassone, et al., 2010).  An attractive feature of this methodology for public health applications is the possibility to elucidate health disparities across space, across subgroups, and space-subgroup interactions.

Another completed manuscript builds joint models for birthweight and gestational age using bivariate normal mixtures.  Such joint modeling adjusts for maternal risk factors and provides mixture analysis of the residuals to help illuminate further subpopulations with differential risk for adverse joint birth outcomes.  Modeling of the mixture components is done through gestational age and then birthweight given gestational age.  Joint modeling eliminates potential causal inference concerns.  Follow-on work extended this effort to incorporate spatial structure, introducing spatial random effects in the regression modeling for both outcomes.

We also examined quantile regression methodology in explaining the effect of exposure on pregnancy outcomes.  Rather than explaining mean birthweight as in customary regression models, we explaini quantiles for birthweight.  For instance, it would be of interest to explain the 10th percentile of birthweight since this is the threshold for declaring small for gestational age.  Our work demonstrated that risk factors and environmental exposure affect different quantiles differently. See Lum and Gelfand 2012.

In addition, we developed a flexible Bayesian spatial discrete-time survival model to estimate the effect of environmental exposure on the risk of preterm birth. We view gestational age as time-to-event data where each pregnancy enters the risk set at a pre-specified time (e.g. the 32nd week). The pregnancy is then followed until either: (1) a birth occurs before the 37th week (preterm); or (2) it reaches the 37th week and a full-term birth is expected. As preliminary analysis, the methodology was applied to a dataset of geo-coded births in North Carolina from 2002. We estimated the risk of preterm birth associated with short-term exposure to fine particulate matter using air quality metrics derived from the EPA’s Statistically Fused Air Pollution Database. We also conducted a simulation study and compared the proposed approach to the standard case-control and time series design.  See Chang et al., 2012 and Chang et al., 2013.

Related work has studied the use of a PM2.5 exposure simulator to explain birthweight.  In a published paper, a template is developed for using an environmental dose simulator to connect ambient exposure to personal exposure.  Then, using various exposure metrics, calculated form these personal exposures, which are clinically plausible over the course of a pregnancy, linkage is built to adverse birth outcomes (Berrocal et al., 2011).

Another component of our work focused on building spatial downscalers.  Such modeling strategies enable the fusion of monitoring station data with computer model output to better assess environmental exposure at point level spatial resolution.  Such downscalers can be dynamic, enabling the tracking of exposure through time.  With improved estimation of local exposure, we can better examine linkage between exposure and adverse birth outcomes.  Three papers on this methodology have been published. The first, for the univariate case, appeared in the Journal of Agricultural, Biological and Environmental Statistics (Berrocal et al, 2010). The second considers the bivariate problem, looking at downscaling two exposures (ozone and PM2.5), borrowing strength in the joint modeling (Berrocal et al. 2010).  The third focused on measurement error associated with downscaling.  Such error is attributable both to misalignment between monitoring sites and model grids as well as to effects of neighboring grids on local monitoring site levels (Berrocal et al., 2012).

Significant progress was made on the relationships between air pollution exposures, socioeconomic status, and birth outcomes. We extended our methodological work with spatial downscalers to conduct an applied analysis on racial and socioeconomic disparities in exposure to air pollution across the State of North Carolina (see Gray et al. 2010). While previous studies of the environmental justice dimensions of air pollution limit analysis of populations living near air quality monitoring stations, we used space-time downscaling methods that we previously developed to output predictive surfaces of ozone (O3) and particulate matter < 2.5 μm in aerodynamic diameter (PM2.5) at the census-tract level covering all of North Carolina. This analysis sought to provide a better understanding of the environmental justice dimension of air pollution exposure across the entire North Carolina population. Moreover, in additional work, we linked the downscaled output to the detailed birth record in order to examine the joint effects of socioeconomic status and air pollution on birth outcomes, using the highly resolved estimated pollution exposures. The downscaled output allowed us to estimate the association between air pollution exposure and birth outcomes for times and locations where exposure data were otherwise unavailable.

We also continued building joint models in order to examine correlated outcomes. Joint modeling eliminates potential causal inference concerns (Schwartz et al., 2010). In work under preparation, we examine the association between features of the built environment with the bivariate outcome of preterm birth and low birthweight (MacLehose et al, in preparation). Additionally, we have developed multivariate spatial modeling to accommodate correlated continuous outcomes (Neelon et al, 2014.). This latter work incorporates correlation not only between jointly modeled outcomes but also among mothers living in nearby neighborhood units.

We furthered methodological work on expected performance accruing to synthesizing categorical datasets, with the objective of enhancing inference deals with a collection of datasets of varying sizes that are all relevant to a particular scientific question, but which include different subsets of the relevant variables, with some overlap (see Berrocal et al., 2012). We synthesized cross-classified categorical datasets drawn from a common population where many of the sets are incomplete (i.e., one or more of the classification variables is unobserved), but at least one is completely observed. The method is expected to reduce uncertainty about the cell probabilities in the associated multi-way contingency table.

We have synthesized our hypotheses concerning the adverse effects of racial residential segregation on the one hand, and poor quality built environment on poor birth outcomes on the other. Using advanced mediation models as the basis for our analytical approach, we examined whether poor quality built environment acts as a mediator in the relationship between racial residential segregation and preterm birth. This work developed a novel method to maintain an additive scale in estimating natural direct and indirect effects from non-linear models (e.g., logistic regression). Additivity is required for interpreting the proportion of the total effect (i.e., the effect of the exposure, racial residential segregation, on the outcome) explained by the mediator (i.e., the poor quality built environment) in a causal framework. In addition, we developed a summary poor built environment index to avoid violating assumptions of no unmeasured confounding in the mediation model (Anthopolos et al, 2014). 


Journal Articles on this Report : 36 Displayed | Download in RIS Format

Other subproject views: All 66 publications 37 publications in selected types All 36 journal articles
Other center views: All 162 publications 76 publications in selected types All 75 journal articles
Type Citation Sub Project Document Sources
Journal Article Anthopolos R, James SA, Gelfand AE, Miranda ML. A spatial measure of neighborhood level racial isolation applied to low birthweight, preterm birth, and birthweight in North Carolina. Spatial and Spatio-temporal Epidemiology 2011;2(4):235-246. R833293 (2009)
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  • Journal Article Anthopolos R, Edwards SE, Miranda ML. Effects of maternal prenatal smoking and birth outcomes extending into the normal range on academic performance in fourth grade in North Carolina, USA. Paediatric and Perinatal Epidemiology 2013;27(6):564-574. R833293 (2012)
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  • Journal Article Anthopolos R, Kaufman JS, Messer LC, Miranda ML. Racial residential segregation and preterm birth: built environment as a mediator. Epidemiology 2014;25(3):397-405. R833293 (Final)
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  • Journal Article Berrocal VJ, Gelfand AE, Holland DM. A bivariate space-time downscaler under space and time misalignment. Annals of Applied Statistics 2010;4(4):1942-1975. R833293 (2009)
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  • Journal Article Berrocal VJ, Gelfand AE, Holland DM. A spatio-temporal downscaler for output from numerical models. Journal of Agricultural, Biological, and Environmental Statistics 2010;15(2):176-197. R833293 (2009)
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  • Journal Article Berrocal VJ, Gelfand AE, Holland DM, Burke J, Miranda ML. On the use of a PM2.5 exposure simulator to explain birthweight. Environmetrics 2011;22(4):553-571. R833293 (2009)
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  • Journal Article Berrocal VJ, Gelfand AE, Holland DM. Space-time data fusion under error in computer model output: an application to modeling air quality. Biometrics 2012;68(3):837-848. R833293 (2011)
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  • Journal Article Berrocal VJ, Miranda ML, Gelfand AE, Bhattacharya S. Synthesizing categorical datasets to enhance inference. Statistical Methodology 2013;15:25-45. R833293 (2007)
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  • Journal Article Chang HH, Reich BJ, Miranda ML. Time-to-event analysis of fine particle air pollution and preterm birth: results from North Carolina, 2001-2005. American Journal of Epidemiology 2012;175(2):91-98. R833293 (2010)
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  • Journal Article Chang HH, Reich BJ, Miranda ML. A spatial time-to-event approach for estimating associations between air pollution and preterm birth. Journal of the Royal Statistical Society--Series C (Applied Statistics) 2013;62(2):167-179. R833293 (2011)
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  • Journal Article Edwards SE, Strauss B, Miranda ML. Geocoding large population-level administrative datasets at highly resolved spatial scales. Transactions in GIS 2014;18(4):586-603. R833293 (Final)
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  • Journal Article Gray SC, Edwards SE, Miranda ML. Assessing exposure metrics for PM and birth weight models. Journal of Exposure Science and Environmental Epidemiology 2010;20(5):469-477. R833293 (2008)
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  • Journal Article Gray SC, Gelfand AE, Miranda ML. Hierarchical spatial modeling of uncertainty in air pollution and birth weight study. Statistics in Medicine 2011;30(17):2187-2198. R833293 (2010)
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  • Journal Article Gray SC, Edwards SE, Miranda ML. Race, socioeconomic status, and air pollution exposure in North Carolina. Environmental Research 2013;126:152-158. R833293 (2012)
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  • Journal Article Gregory SG, Anthopolos R, Osgood CE, Grotegut CA, Miranda ML. Association of autism with induced or augmented childbirth in North Carolina Birth Record (1990-1998) and Education Research (1997-2007) databases. JAMA Pediatrics 2013;167(10):959-966. R833293 (2012)
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  • Journal Article Heaton MJ, Gray SC, Gelfand AE. Process modeling for contingency tables with ordered categories. Statistical Modelling 2012;12(3):211-228. R833293 (Final)
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  • Journal Article Lum K, Gelfand AE. Spatial quantile multiple regression using the asymmetric Laplace process. Bayesian Analysis 2012;7(2):235-258. R833293 (2011)
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  • Abstract: Bayesian Analysis-Abstract
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  • Journal Article Miranda ML, Maxson P, Edwards S. Environmental contributions to disparities in pregnancy outcomes. Epidemiologic Reviews 2009;31(1):67-83. R833293 (2008)
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  • Journal Article Miranda ML, Swamy GK, Edwards S, Maxson P, Gelfand A, James S. Disparities in maternal hypertension and pregnancy outcomes: evidence from North Carolina, 1994-2003. Public Health Reports 2010;125(4):579-587. R833293 (2008)
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  • Journal Article Miranda ML, Maxson P, Kim D. Early childhood lead exposure and exceptionality designations for students. International Journal of Child Health and Human Development 2010;3(1):77-84. R833293 (2008)
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  • Abstract: International Journal of Child Health and Human Development-Abstract
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  • Journal Article Miranda ML, Edwards SE, Keating MH, Paul CJ. Making the environmental justice grade: the relative burden of air pollution exposure in the United States. International Journal of Environmental Research and Public Health 2011;8(6):1755-1771. R833293 (2011)
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  • Journal Article Miranda ML, Edwards SE. Use of spatial analysis to support environmental health research and practice. North Carolina Medical Journal 2011;72(2):132-135. R833293 (2011)
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  • Journal Article Miranda ML, Edwards SE, Myers ER. Adverse birth outcomes among nulliparous vs. multiparous women. Public Health Reports 2011;126(6):797-805. R833293 (2010)
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  • Abstract: Public Health Reports-Abstract
  • Journal Article Miranda ML, Anthopolos R, Edwards SE. Seasonality of poor pregnancy outcomes in North Carolina. North Carolina Medical Journal 2011;72(6):447-453. R833293 (2010)
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  • Full-text: NC Medical Journal-Full Text HTML
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  • Abstract: NC Medical Journal
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  • Other: NC Medical Journal-Full Text PDF
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  • Journal Article Miranda ML, Anthopolos R, Hastings D. A geospatial analysis of the effects of aviation gasoline on childhood blood lead levels. Environmental Health Perspectives 2011;119(10):1513-1516. R833293 (2011)
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  • Journal Article Miranda ML, Edwards SE, Anthopolos R, Dolinsky DH, Kemper AR. The built environment and childhood obesity in Durham, North Carolina. Clinical Pediatrics 2012;51(8):750-758. R833293 (2011)
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  • Journal Article Miranda ML, Messer LC, Kroeger GL. Associations between the quality of the residential built environment and pregnancy outcomes among women in North Carolina. Environmental Health Perspectives 2012;120(3):471-477. R833293 (2011)
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  • Journal Article Miranda ML, Edwards SE, Chang HH, Auten RL. Proximity to roadways and pregnancy outcomes. Journal of Exposure Science & Environmental Epidemiology 2013;23(1):32-38. R833293 (2011)
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  • Abstract: Nature-Abstract
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  • Journal Article Montagna S, Tokdar ST, Neelon B, Dunson DB. Bayesian latent factor regression for functional and longitudinal data. Biometrics 2012;68(4):1064-1073. R833293 (2011)
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  • Abstract: Wiley-Abstract
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  • Journal Article Neelon B, Swamy GK, Burgette LF, Miranda ML. A Bayesian growth mixture model to examine maternal hypertension and birth outcomes. Statistics in Medicine 2011;30(22):2721-2735. R833293 (2010)
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  • Journal Article Neelon B, Anthopolos R, Miranda ML. A spatial bivariate probit model for correlated binary data with application to adverse birth outcomes. Statistical Methods in Medical Research 2014;23(2):119-133. R833293 (Final)
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  • Journal Article Neelon B, Gelfand AE, Miranda ML. A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores. Journal of the Royal Statistical Societ--Series C (Applied Statistics) 2014;63(5):737-761. R833293 (Final)
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  • Other: University of Minnesota-Prepublication PDF
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  • Journal Article Schwartz SL, Gelfand AE, Miranda ML. Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models. Statistics in Medicine 2010;29(16):1710-1723. R833293 (2008)
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  • Journal Article Swamy GK, Edwards S, Gelfand A, James SA, Miranda ML. Maternal age, birth order, and race: differential effects on birthweight. Journal of Epidemiology and Community Health 2012;66(2):136-142. R833293 (2009)
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  • Journal Article Tassone EC, Miranda ML, Gelfand AE. Disaggregated spatial modelling for areal unit categorical data. Journal of the Royal Statistical Society--Series C (Applied Statistics) 2010;59(1):175-190. R833293 (2007)
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  • Journal Article Vinikoor-Imler LC, Gray SC, Edwards SE, Miranda ML. The effects of exposure to particulate matter and neighbourhood deprivation on gestational hypertension. Paediatric and Perinatal Epidemiology 2012;26(2):91-100. R833293 (2011)
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  • Supplemental Keywords:

    Data fusion, meta analysis, disparities, spatial disaggregation, spatial interpolation, spatial modeling, racial residential segregation, built environment, birth outcomes

    Progress and Final Reports:

    Original Abstract
  • 2007
  • 2008
  • 2009 Progress Report
  • 2010 Progress Report
  • 2011 Progress Report
  • 2012

  • Main Center Abstract and Reports:

    R833293    Southern Center on Environmentally Driven Disparities in Birth Outcomes

    Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R833293C001 Research Project A: Mapping Disparities in Birth Outcomes
    R833293C002 Research Project B: Healthy Pregnancy, Healthy Baby: Studying Racial Disparities in Birth Outcomes
    R833293C003 Research Project C: Perinatal Environmental Exposure Disparity and Neonatal Respiratory Health
    R833293C004 Community Outreach and Translation Core
    R833293C005 Geographic Information System and Statistical Analysis Core