Danelle T. Lobdell

Methodological issues in studies of air pollution and reproductive health

In the past decade there have been an increasing number of scientific studies describing possible effects of air pollution on perinatal health. These papers have mostly focused on commonly monitored air pollutants, primarily ozone (O(3)), particulate matter (PM), sulfur dioxide (SO(2)), carbon monoxide (CO), and nitrogen dioxide (NO(2)), and various indices of perinatal health, including fetal growth, pregnancy duration, and infant mortality. While most published studies have found some marker of air pollution related to some types of perinatal outcomes, variability exists in the nature of the pollutants and outcomes associated. Synthesis of the findings has been difficult for various reasons, including differences in study design and analysis. A workshop was held in September 2007 to discuss methodological differences in the published studies as a basis for understanding differences in study findings and to identify priorities for future research, including novel approaches for existing data. Four broad topic areas were considered: confounding and effect modification, spatial and temporal exposure variations, vulnerable windows of exposure, and multiple pollutants. Here we present a synopsis of the methodological issues and challenges in each area and make recommendations for future study. Two key recommendations include: (1) parallel analyses of existing data sets using a standardized methodological approach to disentangle true differences in associations from methodological differences among studies; and (2) identification of animal studies to inform important mechanistic research gaps. This work is of critical public health importance because of widespread exposure and because perinatal outcomes are important markers of future child and adult health.

Combining Regional-and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies

Abstract Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including air pollution concentrations; human activity patterns, such as the amount of time spent outdoors versus indoors, commuting, walking, and indoors at home; microenvironmental infiltration rates; and pollutant removal rates in indoor environments. Typically, exposure models rely upon ambient air concentration inputs from a sparse network of monitoring stations. Here we present a unique methodology for combining multiple types of air quality models (the Community Multi-Scale Air Quality [CMAQ] chemical transport model added to the AERMOD dispersion model) and linking the resulting hourly concentrations to population exposure models (the Hazardous Air Pollutant Exposure Model [HAPEM] or the Stochastic Human Exposure and Dose Simulation [SHEDS] model) to enhance estimates of air pollution exposures that vary temporally (annual and seasonal) and spatially (at census-block-group resolution) in an urban area. The results indicate that there is a strong spatial gradient in the predicted mean exposure concentrations near roadways and industrial facilities that can vary by almost a factor of 2 across the urban area studied. At the high end of the exposure distribution (95th percentile), exposures are higher in the central district than in the suburbs. This is mostly due to the importance of personal mobility factors whereby individuals living in the central area often move between microenvironments with high concentrations, as opposed to individuals residing at the outskirts of the city. Also, our results indicate 20–30% differences due to commuting patterns and almost a factor of 2 difference because of near-roadway effects. These differences are smaller for the median exposures, indicating the highly variable nature of the reflected ambient concentrations. In conjunction with local data on emission sources, microenvironmental factors, and behavioral and socioeconomic characteristics, the combined source-to-exposure modeling methodology presented in this paper can improve the assessment of exposures in future community air pollution health studies.

Maternal drinking water arsenic exposure and perinatal outcomes in Inner Mongolia, China

Background Bayingnormen is a region located in western Inner Mongolia China, with a population that is exposed to a wide range of drinking water arsenic concentrations. The relationship between maternal drinking water arsenic exposure and perinatal endpoints (term birth weight, preterm birth, stillbirth and neonatal death) in this region was evaluated in this study. Methods An analysis was conducted of all singleton deliveries in a defined geographical area of Inner Mongolia from December 1996 to December 1999 (n=9890). Outcome and covariate data were abstracted from prenatal care records. Exposure was based on well-water measures for the maternal subvillage. Mean birth weight at term was compared across four arsenic categories using analysis of covariance. ORs for stillbirth, preterm birth and neonatal death were estimated by logistic regression with arsenic exposure dichotomised at 50 μg/l. Results Term birth weight was 0.05 kg higher (95% CI 0.02 to 0.08) in the highest exposure category (>100 μg/l) compared to the reference (below limit of detection to 20 μg/l). Arsenic >50 μg/l was associated with an increased risk of neonatal death (OR 2.01, 95% CI 1.12 to 3.59). No relationship was found between maternal arsenic exposure and preterm or stillbirth delivery. Conclusions At the levels observed in our study, arsenic does not appear to contribute to adverse birth outcomes. Exposure may play a role in neonatal death; however, the neonatal death rate in this population was low and this potential association merits further research.

Utility of Recent Studies to Assess the National Research Council 2001 Estimates of Cancer Risk from Ingested Arsenic

Objective The purpose of this review is to evaluate the impact of recent epidemiologic literature on the National Research Council (NRC) assessment of the lung and bladder cancer risks from ingesting low concentrations (< 100 μg/L) of arsenic-contaminated water. Data sources, extraction, and synthesis PubMed was searched for epidemiologic studies pertinent to the lung and bladder cancer risk estimates from low-dose arsenic exposure. Articles published from 2001, the date of the NRC assessment, through September 2010 were included. Fourteen epidemiologic studies on lung and bladder cancer risk were identified as potentially useful for the analysis. Conclusions Recent epidemiologic studies that have investigated the risk of lung and bladder cancer from low arsenic exposure are limited in their ability to detect the NRC estimates of excess risk because of sample size and less than lifetime exposure. Although the ecologic nature of the Taiwanese studies on which the NRC estimates are based present certain limitations, the data from these studies have particular strengths in that they describe lung and bladder cancer risks resulting from lifetime exposure in a large population and remain the best data on which to conduct quantitative risk assessment. Continued follow-up of a population in northeastern Taiwan, however, offers the best opportunity to improve the cancer risk assessment for arsenic in drinking water. Future studies of arsenic < 100 μg/L in drinking water and lung and bladder cancer should consider adequacy of the sample size, the synergistic relationship of arsenic and smoking, duration of arsenic exposure, age when exposure began and ended, and histologic subtype.

Exposure to fine particulate matter during pregnancy and risk of preterm birth among women in New Jersey, Ohio, and Pennsylvania, 2000-2005.

Background: Particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5) has been variably associated with preterm birth (PTB). Objective: We classified PTB into four categories (20–27, 28–31, 32–34, and 35–36 weeks completed gestation) and estimated risk differences (RDs) for each category in association with a 1-μg/m3 increase in PM2.5 exposure during each week of gestation. Methods: We assembled a cohort of singleton pregnancies that completed ≥ 20 weeks of gestation during 2000–2005 using live birth certificate data from three states (Pennsylvania, Ohio, and New Jersey) (n = 1,940,213; 8% PTB). We estimated mean PM2.5 exposures for each week of gestation from monitor-corrected Community Multi-Scale Air Quality modeling data. RDs were estimated using modified Poisson linear regression and adjusted for maternal race/ethnicity, marital status, education, age, and ozone. Results: RD estimates varied by exposure window and outcome period. Average PM2.5 exposure during the fourth week of gestation was positively associated with all PTB outcomes, although magnitude varied by PTB category [e.g., for a 1-μg/m3 increase, RD = 11.8 (95% CI: –6, 29.2); RD = 46 (95% CI: 23.2, 68.9); RD = 61.1 (95% CI: 22.6, 99.7); and RD = 28.5 (95% CI: –39, 95.7) for preterm births during 20–27, 28–31, 32–34, and 35–36 weeks, respectively]. Exposures during the week of birth and the 2 weeks before birth also were positively associated with all PTB categories. Conclusions: Exposures beginning around the time of implantation and near birth appeared to be more strongly associated with PTB than exposures during other time periods. Because particulate matter exposure is ubiquitous, evidence of effects of PM2.5 exposure on PTB, even if small in magnitude, is cause for concern. Citation: Rappazzo KM, Daniels JL, Messer LC, Poole C, Lobdell DT. 2014. Exposure to fine particulate matter during pregnancy and risk of preterm birth among women in New Jersey, Ohio, and Pennsylvania, 2000–2005. Environ Health Perspect 122:992–997; http://dx.doi.org/10.1289/ehp.1307456

Anxiety affecting parkinsonian outcome and motor efficiency in adults of an Ohio community with environmental airborne manganese exposure.

Manganese (Mn) is a nutrient and neurotoxicant sometimes associated with mood, motor and neurological effects. Reports of health effects from occupational exposure to Mn are well known, but the reported links to environmental airborne Mn (Mn-Air) are less conclusive. Marietta, OH (USA) is a previously identified community with elevated Mn-Air from industrial emissions. Households were randomly selected in Marietta and the comparison town (Mount Vernon, OH). The responders were used to recruit on a voluntary basis 30- to 75-year-old residents, i.e. 100 in Marietta and 90 in Mount Vernon. They were administered the Unified Parkinsons Disease Rating Scale (UPDRS), motor efficiency, and mood tests, along with a comprehensive questionnaire including demographics, health and work history. Blood Mn (MnB), serum ferritin, and hepatic enzymes were measured. Results were compared with those of 90 residents from a demographically similar comparison town, Mount Vernon, OH, where Mn-Air from industrial emissions was not of concern. Mn-Air exposure indices were modeled for Marietta residents. The Mn-exposed participants resided on average 4.75 miles (range 1-11) from the Mn point source. Their modeled residential Mn-Air estimate ranged from 0.04 to 0.96 μg/m(3) and was on average 0.18 μg/m(3). The group means of MnB were similar for the Mn-exposed (9.65 μg/L) and comparison (9.48 μg/L) participants. The Marietta group reported more generalized anxiety on the Symptom Checklist-90-Revised (SCL-90-R) than the comparison group (p=0.035). Generalized anxiety in Marietta was related to a cumulative exposure index (p=0.002), based on modeled Mn-Air concentration and length of residence. Higher generalized anxiety scores were related to poorer performance on UPDRS tests [adjusted relative risk (95%CI): 2.18 (1.46-3.25) for motor-related activities of daily living, 3.44 (1.48-7.98) for bradykinesia, and 1.63 (1.06-2.53) for motor/movement]. Group differences in SCL-90-R generalized anxiety between the two towns and the observed relationship between exposure indices and generalized anxiety suggest an association between environmental Mn exposure and anxiety states. Whether this association is due to direct neurotoxic effects of Mn-Air or concern about the health effects of air pollution remains an open question. The results highlight the importance of measuring anxiety in relation to neuropsychological and neurological endpoints, and should be validated in other studies of Mn-exposed communities.

Feasibility of assessing public health impacts of air pollution reduction programs on a local scale: New Haven case study.

Background New approaches to link health surveillance data with environmental and population exposure information are needed to examine the health benefits of risk management decisions. Objective We examined the feasibility of conducting a local assessment of the public health impacts of cumulative air pollution reduction activities from federal, state, local, and voluntary actions in the City of New Haven, Connecticut (USA). Methods Using a hybrid modeling approach that combines regional and local-scale air quality data, we estimated ambient concentrations for multiple air pollutants [e.g., PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter), NOx (nitrogen oxides)] for baseline year 2001 and projected emissions for 2010, 2020, and 2030. We assessed the feasibility of detecting health improvements in relation to reductions in air pollution for 26 different pollutant–health outcome linkages using both sample size and exploratory epidemiological simulations to further inform decision-making needs. Results Model projections suggested decreases (~ 10–60%) in pollutant concentrations, mainly attributable to decreases in pollutants from local sources between 2001 and 2010. Models indicated considerable spatial variability in the concentrations of most pollutants. Sample size analyses supported the feasibility of identifying linkages between reductions in NOx and improvements in all-cause mortality, prevalence of asthma in children and adults, and cardiovascular and respiratory hospitalizations. Conclusion Substantial reductions in air pollution (e.g., ~ 60% for NOx) are needed to detect health impacts of environmental actions using traditional epidemiological study designs in small communities like New Haven. In contrast, exploratory epidemiological simulations suggest that it may be possible to demonstrate the health impacts of PM reductions by predicting intraurban pollution gradients within New Haven using coupled models.

Environmental exposure to manganese in air: Associations with cognitive functions

Manganese (Mn), an essential element, can be neurotoxic in high doses. This cross-sectional study explored the cognitive function of adults residing in two towns (Marietta and East Liverpool, Ohio, USA) identified as having high levels of environmental airborne Mn from industrial sources. Air-Mn site surface emissions method modeling for total suspended particulate (TSP) ranged from 0.03 to 1.61 μg/m(3) in Marietta and 0.01-6.32 μg/m(3) in East Liverpool. A comprehensive screening test battery of cognitive function, including the domains of abstract thinking, attention/concentration, executive function and memory was administered. The mean age of the participants was 56 years (±10.8 years). Participants were mostly female (59.1) and primarily white (94.6%). Significant relationships (p<0.05) were found between Mn exposure and performance on working and visuospatial memory (e.g., Rey-O Immediate β=-0.19, Rey-O Delayed β=-0.16) and verbal skills (e.g., Similarities β=-0.19). Using extensive cognitive testing and computer modeling of 10-plus years of measured air monitoring data, this study suggests that long-term environmental exposure to high levels of air-Mn, the exposure metric of this paper, may result in mild deficits of cognitive function in adult populations.

Construction of an environmental quality index for public health research

BackgroundA more comprehensive estimate of environmental quality would improve our understanding of the relationship between environmental conditions and human health. An environmental quality index (EQI) for all counties in the U.S. was developed.MethodsThe EQI was developed in four parts: domain identification; data source acquisition; variable construction; and data reduction. Five environmental domains (air, water, land, built and sociodemographic) were recognized. Within each domain, data sources were identified; each was temporally (years 2000–2005) and geographically (county) restricted. Variables were constructed for each domain and assessed for missingness, collinearity, and normality. Domain-specific data reduction was accomplished using principal components analysis (PCA), resulting in domain-specific indices. Domain-specific indices were then combined into an overall EQI using PCA. In each PCA procedure, the first principal component was retained. Both domain-specific indices and overall EQI were stratified by four rural–urban continuum codes (RUCC). Higher values for each index were set to correspond to areas with poorer environmental quality.ResultsConcentrations of included variables differed across rural–urban strata, as did within-domain variable loadings, and domain index loadings for the EQI. In general, higher values of the air and sociodemographic indices were found in the more metropolitan areas and the most thinly populated areas have the lowest values of each of the domain indices. The less-urbanized counties (RUCC 3) demonstrated the greatest heterogeneity and range of EQI scores (−4.76, 3.57) while the thinly populated strata (RUCC 4) contained counties with the most positive scores (EQI score ranges from −5.86, 2.52).ConclusionThe EQI holds promise for improving our characterization of the overall environment for public health. The EQI describes the non-residential ambient county-level conditions to which residents are exposed and domain-specific EQI loadings indicate which of the environmental domains account for the largest portion of the variability in the EQI environment. The EQI was constructed for all counties in the United States, incorporating a variety of data to provide a broad picture of environmental conditions. We undertook a reproducible approach that primarily utilized publically-available data sources.

Data Sources for an Environmental Quality Index: Availability, Quality, and Utility

OBJECTIVES An environmental quality index (EQI) for all counties in the United States is under development to explore the relationship between environmental insults and human health. The EQI is potentially useful for investigators researching health disparities to account for other concurrent environmental conditions. This article focused on the identification and assessment of data sources used in developing the EQI. Data source strengths, limitations, and utility were addressed. METHODS Five domains were identified that contribute to environmental quality: air, water, land, built, and sociodemographic environments. An inventory of possible data sources was created. Data sources were evaluated for appropriate spatial and temporal coverage and data quality. RESULTS The overall data inventory identified multiple data sources for each domain. From the inventory (187 sources, 617 records), the air, water, land, built environment, and sociodemographic domains retained 2, 9, 7, 4, and 2 data sources for inclusion in the EQI, respectively. However, differences in data quality, geographic coverage, and data availability existed between the domains. CONCLUSIONS The data sources identified for use in the EQI may be useful to researchers, advocates, and communities to explore specific environmental quality questions.