Exposure Measurement Error in Air Pollution Studies: The Impact of Shared, Multiplicative Measurement Error on Epidemiological Health Risk Estimates.

Spatiotemporal air pollution models are increasingly being used to estimate health effects in epidemiological studies. Although such exposure prediction models typically result in improved spatial and temporal resolution of air pollution predictions, they remain subject to shared measurement error, a type of measurement error common in spatiotemporal exposure models which occurs when measurement error is not independent of exposures. A fundamental challenge of exposure measurement error in air pollution assessment is the strong correlation and sometimes identical (shared) error of exposure estimates across geographic space and time. When exposure estimates with shared measurement error are used to estimate health risk in epidemiological analyses, complex errors are potentially introduced, resulting in biased epidemiological conclusions. We demonstrate the influence of using a three-stage spatiotemporal exposure prediction model and introduce formal methods of shared, multiplicative measurement error (SMME) correction of epidemiological health risk estimates. Using our three-stage, ensemble learning based nitrogen oxides (NOx) exposure prediction model, we quantified SMME. We conducted an epidemiological analysis of wheeze risk in relation to NOx exposure among school-aged children. To demonstrate the incremental influence of exposure modeling stage, we iteratively estimated the health risk using assigned exposure predictions from each stage of the NOx model. We then determined the impact of SMME on the variance of the health risk estimates under various scenarios. Depending on the stage of the spatiotemporal exposure model used, we found that wheeze odds ratio ranged from 1.16 to 1.28 for an interquartile range increase in NOx. With each additional stage of exposure modeling, the health effect estimate moved further away from the null (OR=1). When corrected for observed SMME, the health effects confidence intervals slightly lengthened, but our epidemiological conclusions were not altered. When the variance estimate was corrected for the potential "worst case scenario" of SMME, the standard error further increased, having a meaningful influence on epidemiological conclusions. Our framework can be expanded and used to understand the implications of using exposure predictions subject to shared measurement error in future health investigations.

Study Design, Protocol and Profile of the Maternal And Developmental Risks from Environmental and Social Stressors (MADRES) Pregnancy Cohort: a Prospective Cohort Study in Predominantly Low-Income Hispanic Women in Urban Los Angeles.

BACKGROUND: The burden of childhood and adult obesity disproportionally affects Hispanic and African-American populations in the US, and these groups as well as populations with lower income and education levels are disproportionately affected by environmental pollution. Pregnancy is a critical developmental period where maternal exposures may have significant impacts on infant and childhood growth as well as the future health of the mother. We initiated the "Maternal And Developmental Risks from Environmental and Social Stressors (MADRES)" cohort study to address critical gaps in understanding the increased risk for childhood obesity and maternal obesity outcomes among minority and low-income women in urban Los Angeles. METHODS: The MADRES cohort is specifically examining whether pre- and postpartum environmental exposures, in addition to exposures to psychosocial and built environment stressors, lead to excessive gestational weight gain and postpartum weight retention in women and to perturbed infant growth trajectories and increased childhood obesity risk through altered psychological, behavioral and/or metabolic responses. The ongoing MADRES study is a prospective pregnancy cohort of 1000 predominantly lower-income, Hispanic women in Los Angeles, CA. Enrollment in the MADRES cohort is initiated prior to 30 weeks gestation from partner community health clinics in Los Angeles. Cohort participants are followed through their pregnancies, at birth, and during the infant's first year of life through a series of in-person visits with interviewer-administered questionnaires, anthropometric measurements and biospecimen collection as well as telephone interviews conducted with the mother. DISCUSSION: In this paper, we outline the study rationale and data collection protocol for the MADRES cohort, and we present a profile of demographic, health and exposure characteristics for 291 participants who have delivered their infants, out of 523 participants enrolled in the study from November 2015 to October 2018 from four community health clinics in Los Angeles. Results from the MADRES cohort could provide a powerful rationale for regulation of targeted chemical environmental components, better transportation and urban design policies, and clinical recommendations for stress-coping strategies and behavior to reduce lifelong obesity risk.

Exposure measurement error in air pollution studies: A framework for assessing shared, multiplicative measurement error in ensemble learning estimates of nitrogen oxides.

BACKGROUND: Increasingly ensemble learning-based spatiotemporal models are being used to estimate residential air pollution exposures in epidemiological studies. While these machine learning models typically have improved performance, they suffer from exposure measurement error that is inherent in all models. Our objective is to develop a framework to formally assess shared, multiplicative measurement error (SMME) in our previously published three-stage, ensemble learning-based nitrogen oxides (NOx) model to identify its spatial and temporal patterns and predictors. METHODS: By treating the ensembles as an external dosimetry system, we quantified shared and unshared, multiplicative and additive (SUMA) measurement error components in our exposure model. We used generalized additive models (GAMs) with a smooth term for location to identify geographic locations with significantly elevated SMME and explain their spatial and temporal determinants. RESULTS: We found evidence of significant shared and unshared multiplicative error (p < 0.0001) in our ensemble-learning based spatiotemporal NOx model predictions. Unshared multiplicative error was 26 times larger than SMME. We observed significant geographic (p < 0.0001) and temporal variation in SMME with the majority (43%) of predictions with elevated SMME occurring in the earliest time-period (1992-2000). Densely populated urban prediction regions with complex air pollution sources generally exhibited highest odds of elevated SMME. CONCLUSIONS: We developed a novel statistical framework to formally evaluate the magnitude and drivers of SMME in ensemble learning-based exposure models. Our framework can be used to inform building future improved exposure models.

Association of Prenatal Exposure to Ambient and Traffic-Related Air Pollution With Newborn Thyroid Function: Findings From the Children's Health Study.

Importance: Thyroid hormones are critical for fetal growth and development. Prenatal particulate matter (PM) air pollution exposure has been associated with altered newborn thyroid function, but other air pollutants have not been evaluated, and critical windows of exposure are unknown. Objectives: To investigate the association of prenatal exposure to ambient and traffic-related air pollutants with newborn thyroid function and identify critical windows of exposure. Design, Setting, and Participants: This cohort study used data from 2050 participants in the Children's Health Study. Statistical analyses were conducted from 2017 to 2018 using pregnancy and birth data from 1994 to 1997 for a subset of participants recruited from schools in 13 southern California communities in 2002 to 2003 when participants were 5 to 7 years of age. Participants were included in statistical analyses if they could be linked to their newborn blood spot and had complete monthly exposure measures for at least 1 air pollutant across pregnancy. Exposures: Prenatal monthly averages of ambient (PM diameter <2.5 µm [PM2.5] or <10 µm [PM10], nitrogen dioxide, and ozone) and traffic-related (freeway, nonfreeway, and total nitrogen oxides) air pollutant exposures were determined using inverse distance-squared weighting of central monitoring data and the California Line Source Dispersion model, respectively. Main Outcomes and Measures: Newborn heel-stick blood spot total thyroxine (TT4) measures were acquired retrospectively from the California Department of Public Health. Results: Participants included 2050 newborns (50.5% male), with a median (interquartile range) age of 20 (15-29) hours. The majority of newborns were Hispanic white (1202 [58.6%]) or non-Hispanic white (638 [31.1%]). Sixty-six (3.2%) were black and 144 (7.0%) were from other racial/ethnic groups. The mean (SD) newborn TT4 measure was 16.2 (4.3) µg/dL. A 2-SD increase in prenatal PM2.5 (16.3 µg/m3) and PM10 (22.2 µg/m3) was associated with a 1.2-µg/dL (95% CI, 0.5-1.8 µg/dL) and 1.5-µg/dL (95% CI, 0.9-2.1 µg/dL) higher TT4 measure, respectively, in covariate-adjusted linear regression models. Other pollutants were not consistently associated with newborn TT4. Distributed lag models revealed that PM2.5 exposure during months 3 to 7 of pregnancy and PM10 exposure during months 1 to 8 of pregnancy were associated with significantly higher newborn TT4 concentrations (P < .05). Conclusions and Relevance: Prenatal PM exposure, particularly in early pregnancy and midpregnancy, is associated with higher newborn TT4 concentrations. Future studies should assess the health implications of PM-associated differences in newborn TT4 concentrations.

Exposure to acute air pollution and risk of bronchiolitis and otitis media for preterm and term infants.

Our aim is to estimate associations between acute increases in particulate matter with diameter of 2.5 µm or less (PM2.5) concentrations and risk of infant bronchiolitis and otitis media among Massachusetts births born 2001 through 2008.Our case-crossover study included 20,017 infant bronchiolitis and 42,336 otitis media clinical encounter visits. PM2.5 was modeled using satellite, remote sensing, meteorological and land use data. We applied conditional logistic regression to estimate odds ratios (ORs) and confidence intervals (CIs) per 10-µg/m3 increase in PM2.5. We assessed effect modification to determine the most susceptible subgroups. Infant bronchiolitis risk was elevated for PM2.5 exposure 1 day (OR = 1.07, 95% CI = 1.03-1.11) and 4 days (OR = 1.04, 95% CI = 0.99-1.08) prior to clinical encounter, but not 7 days. Non-significant associations with otitis media varied depending on lag. Preterm infants were at substantially increased risk of bronchiolitis 1 day prior to clinical encounter (OR = 1.17, 95% CI = 1.08-1.28) and otitis media 4 and 7 days prior to clinical encounter (OR = 1.09, 95% CI = 1.02-1.16 and OR = 1.08, 95% CI = 1.02-1.15, respectively). In conclusion, preterm infants are most susceptible to infant bronchiolitis and otitis media associated with acute PM2.5 exposures.

Chronic PM2.5 exposure and risk of infant bronchiolitis and otitis media clinical encounters.

Chronic particulate matter less than 2.5µm in diameter (PM2.5) exposure can leave infants more susceptible to illness. Our objective is to estimate associations of the chronic PM2.5 exposure with infant bronchiolitis and otitis media (OM) clinical encounters. We obtained all first time bronchiolitis (n=18,029) and OM (n=40,042) clinical encounters among children less than 12 and 36 months of age, respectively, diagnosed from 2001 to 2009 and two controls per case matched on birthdate and gestational age from the Pregnancy to Early Life Longitudinal data linkage system in Massachusetts. We applied conditional logistic regression to estimate odds ratios (OR) and confidence intervals (CI) per 2-µg/m3 increase in lifetime average satellite based PM2.5 exposure. Effect modification was assessed by age, gestational age, frequency of clinical encounter, and income. We examined associations between residential distance to roadways, traffic density, and infant bronchiolitis and OM risk. PM2.5 was not associated with infant bronchiolitis (OR=1.02, 95% CI=1.00, 1.04) and inversely associated with OM (OR=0.97, 95% CI=0.95, 0.99). There was no evidence of effect modification. Compared to infants living near low traffic density, infants residing in high traffic density had elevated risk of bronchiolitis (OR=1.23, 95% CI=1.14, 1.31) but not OM (OR=0.98, 95% CI=0.93, 1.02) clinical encounter. We did not find strong evidence to support an association between early-life long-term PM2.5 exposure and infant bronchiolitis or OM. Bronchiolitis risk was increased among infants living near high traffic density.

Maternal exposure to traffic-related air pollution and birth defects in Massachusetts.

Exposures to particulate matter with diameter of 2.5µm or less (PM2.5) may influence risk of birth defects. We estimated associations between maternal exposure to prenatal traffic-related air pollution and risk of cardiac, orofacial, and neural tube defects among Massachusetts births conceived 2001 through 2008. Our analyses included 2729 cardiac, 255 neural tube, and 729 orofacial defects. We used satellite remote sensing, meteorological and land use data to assess PM2.5 and traffic-related exposures (distance to roads and traffic density) at geocoded birth addresses. We calculated adjusted odds ratios (OR) and confidence intervals (CI) using logistic regression models. Generalized additive models were used to assess spatial patterns of birth defect risk. There were positive but non-significant associations for a 10µg/m(3) increase in PM2.5 and perimembranous ventricular septal defects (OR=1.34, 95% CI: 0.98, 1.83), patent foramen ovale (OR=1.19, 95% CI: 0.92, 1.54) and patent ductus arteriosus (OR=1.20, 95% CI: 0.95, 1.62). There was a non-significant inverse association between PM2.5 and cleft lip with or without palate (OR=0.76, 95% CI: 0.50, 1.10), cleft palate only (OR=0.89, 95% CI: 0.54, 1.46) and neural tube defects (OR=0.77, 95% CI: 0.46, 1.05). Results for traffic related exposure were similar. Only ostium secundum atrial septal defects displayed significant spatial variation after accounting for known risk factors.