Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction

Prenatal metal(loid) mixtures and birth weight for gestational age: A pooled analysis of three cohorts participating in the ECHO program

Background: A growing number of studies have identified both toxic and essential metals which influence fetal growth. However, most studies have conducted single-cohort analyses, which are often limited by narrow exposure ranges, and evaluated metals individually. The objective of the current study was to conduct an environmental mixture analysis of metal impacts on fetal growth, pooling data from three geographically and demographically diverse cohorts in the United States participating in the Environmental Influences on Child Health Outcomes program. Methods: The pooled sample (N = 1,002) included participants from the MADRES, NHBCS, and PROTECT cohorts. Associations between seven metals (antimony, cadmium, cobalt, mercury, molybdenum, nickel, tin) measured in maternal urine samples collected during pregnancy (median: 16.0 weeks gestation) and birth weight for gestational age z-scores (BW for GA) were investigated using Bayesian Kernel Machine Regression (BKMR). Models were also stratified by cohort and infant sex to investigate possible heterogeneity. Chromium and uranium concentrations fell below the limits of detection for most participants and were evaluated separately as binary variables using pooled linear regression models. Results: In the pooled BKMR analysis, antimony, mercury, and tin were inversely and linearly associated with BW for GA, while a positive linear association was identified for nickel. The inverse association between antimony and BW for GA was observed in both males and females and for all three cohorts but was strongest for MADRES, a predominantly low-income Hispanic cohort in Los Angeles. A reverse j-shaped association was identified between cobalt and BW for GA, which was driven by female infants. Pooled associations were null for cadmium, chromium, molybdenum, and uranium, and BKMR did not identify potential interactions between metal pairs. Conclusions: Findings suggest that antimony, an understudied metalloid, may adversely impact fetal growth. Cohort- and/or sex-dependent associations were identified for many of the metals, which merit additional investigation

Opportunities for understanding the COVID-19 pandemic and child health in the United States: the Environmental influences on Child Health Outcomes (ECHO) program

Objective Ongoing pediatric cohort studies offer opportunities to investigate the impact of the COVID-19 pandemic on children's health. With well-characterized data from tens of thousands of US children, the Environmental influences on Child Health Outcomes (ECHO) Program offers such an opportunity. Methods ECHO enrolled children and their caregivers from community- and clinic-based pediatric cohort studies. Extant data from each of the cohorts were pooled and harmonized. In 2019, cohorts began collecting data under a common protocol, and data collection is ongoing with a focus on early life environmental exposures and five child health domains: birth outcomes, neurodevelopment, obesity, respiratory, and positive health. In April of 2020, ECHO began collecting a questionnaire designed to assess COVID-19 infection and the pandemic's impact on families. We describe and summarize the characteristics of children who participated in the ECHO Program during the COVID-19 pandemic and novel opportunities for scientific advancement. Results This sample (n = 13,725) was diverse by child age (31% early childhood, 41% middle childhood, and 16% adolescence up to age 21), sex (49% female), race (64% White, 15% Black, 3% Asian, 2% American Indian or Alaska Native, <1% Native Hawaiian or Pacific Islander, 10% Multiple race and 2% Other race), Hispanic ethnicity (22% Hispanic), and were similarly distributed across the four United States Census regions and Puerto Rico. Conclusion ECHO data collected during the pandemic can be used to conduct solution-oriented research to inform the development of programs and policies to support child health during the pandemic and in the post-pandemic era

Sociodemographic Differences in COVID-19 Pandemic Experiences Among Families in the United States

Few population-based studies in the US collected individual-level data from families during the COVID-19 pandemic.To examine differences in COVID-19 pandemic–related experiences in a large sociodemographically diverse sample of children and caregivers.The Environmental influences on Child Health Outcomes (ECHO) multi-cohort consortium is an ongoing study that brings together 64 individual cohorts with participants (24 757 children and 31 700 caregivers in this study) in all 50 US states and Puerto Rico. Participants who completed the ECHO COVID-19 survey between April 2020 and March 2022 were included in this cross-sectional analysis. Data were analyzed from July 2021 to September 2022.Exposures of interest were caregiver education level, child life stage (infant, preschool, middle childhood, and adolescent), and urban or rural (population &lt;50 000) residence. Dependent variables included COVID-19 infection status and testing; disruptions to school, child care, and health care; financial hardships; and remote work. Outcomes were examined separately in logistic regression models mutually adjusted for exposures of interest and race, ethnicity, US Census division, sex, and survey administration date.Analyses included 14 646 children (mean [SD] age, 7.1 [4.4] years; 7120 [49%] female) and 13 644 caregivers (mean [SD] age, 37.6 [7.2] years; 13 381 [98%] female). Caregivers were racially (3% Asian; 16% Black; 12% multiple race; 63% White) and ethnically (19% Hispanic) diverse and comparable with the US population. Less than high school education (vs master’s degree or more) was associated with more challenges accessing COVID-19 tests (adjusted odds ratio [aOR], 1.88; 95% CI, 1.06-1.58), lower odds of working remotely (aOR, 0.04; 95% CI, 0.03-0.07), and more food access concerns (aOR, 4.14; 95% CI, 3.20-5.36). Compared with other age groups, young children (age 1 to 5 years) were least likely to receive support from schools during school closures, and their caregivers were most likely to have challenges arranging childcare and concerns about work impacts. Rural caregivers were less likely to rank health concerns (aOR, 0.77; 95% CI, 0.69-0.86) and social distancing (aOR, 0.82; 95% CI, 0.73-0.91) as top stressors compared with urban caregivers.Findings in this cohort study of US families highlighted pandemic-related burdens faced by families with lower socioeconomic status and young children. Populations more vulnerable to public health crises should be prioritized in recovery efforts and future planning

Perceptions and experiences of environmental health and risks among Latina mothers in urban Los Angeles, California, USA

Abstract Background Environmental exposures during pregnancy and early childhood can have acute and chronic adverse health impacts. As minoritized populations are more likely to reside in areas with greater pollution, it is important to understand their views and lived experiences to inform action. The purpose of this community-driven qualitative research study was to understand how urban Latina mothers in Los Angeles County, California perceived environmental health and risks. Methods We conducted semi-structured individual interviews with Latina pregnant women and mothers of young children, recruited through existing collaborations with community organizations. Interviews conducted in either English or Spanish and were coded inductively according to a modified grounded theory approach. Results Thirty-six Latina mothers completed interviews between August–October 2016. Participants lived primarily in low-income communities of South-Central Los Angeles and East Los Angeles. We identified three major themes based on the participants’ responses during interviews: Defining the Environment, Environment & Health Risks, and Social & Political Responsibility. Women defined their environment in terms of both “nature” and “hazards.” They consistently identified foul odors, dirtiness, noise, trash, bugs, smoke, and other visible blights as indicators of household and neighborhood environmental hazards. They expressed fear and uncertainty about how their environment could affect their health and that of their children, as well as specific concerns about respiratory health, asthma, allergies, cancer, and adverse pregnancy outcomes. Mothers often changed individual behaviors around diet and cleaning during pregnancy but were frustrated by power imbalances that left them unable to change their home or neighborhood environments, despite their desire to do so. Discussion Our study is among the first to describe how urban Latina mothers perceive and experience environmental health risks during pregnancy and early childhood. Our research suggests additional attention is needed by public health professionals and researchers to address the environmental health risks that matter most to urban Latina mothers. They also highlight the tension that many urban Latina mothers feel between wanting to protect their families’ health and well-being and feeling powerless to change their environment. Broad policy changes, rather than additional individual recommendations, are needed to address the concerns of this vulnerable population

Urinary metals and maternal circulating extracellular vesicle microRNA in the MADRES pregnancy cohort

Exposure to metals increases risk for pregnancy complications. Extracellular vesicle (EV) miRNA contribute to maternal-foetal communication and are dysregulated in pregnancy complications. However, metal impacts on maternal circulating EV miRNA during pregnancy are unknown. Our objective was to investigate the impact of multiple metal exposures on EV miRNA in maternal circulation during pregnancy in the MADRES Study. Associations between urinary concentrations of nine metals and 106 EV miRNA in maternal plasma during pregnancy were investigated using robust linear regression (N = 231). Primary analyses focused on metal-miRNA associations in early pregnancy (median: 12.3 weeks gestation). In secondary analyses, we investigated associations with late pregnancy miRNA counts (median: 31.8 weeks gestation) in a subset of participants (N = 184) with paired measures. MiRNA associated with three or more metals (PFDR<0.05) were further investigated using Bayesian Kernel Machine Regression (BKMR), an environmental mixture method. Thirty-five miRNA were associated (PFDR<0.05) with at least one metal in early pregnancy. One association (an inverse association between cobalt and miR–150-5p) remained statistically significant when evaluating late pregnancy miRNA counts. Eight miRNA (miR–302b-3p, miR–199a-5p, miR–188-5p, miR–138-5p, miR–212-3p, miR–608, miR–1272, miR–19b-3p) were associated with three metals (barium, mercury, and thallium) in early pregnancy, and their predicted target genes were enriched in pathways important for placental development. Results were consistent when using BKMR. Early pregnancy exposure to barium, mercury, and thallium may have short-term impacts on a common set of EV miRNA which target pathways important for placental development

Prenatal Maternal Cortisol Levels and Infant Birth Weight in a Predominately Low-Income Hispanic Cohort

Infant birth weight influences numerous health outcomes throughout the life course including childhood obesity and metabolic morbidities. Maternal experience of stress, both before and during pregnancy, has been hypothesized to influence fetal growth and birth outcomes. However, these associations currently are not fully understood, due to conflicting results in the published literature. Salivary cortisol is often used as a biological biomarker to assess the diurnal pattern of the hypothalamic–pituitary–adrenal axis (HPA-axis) functioning. Cortisol metrics include both the total cortisol concentration secreted during waking hours, reflected by the area under the curve (AUC), and cortisol dynamics, which include the diurnal cortisol slope (DCS) and the cortisol awakening response (CAR). This study examined the association of these cortisol metrics measured during the third trimester of pregnancy and infant birth weight among 240 mother-infant dyads participating in the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) pregnancy cohort study, which is predominately comprised of Hispanic low-income women. There were no significant associations with the maternal biological stress response and infant birth weight in this study. More research is needed in larger studies to better understand how the biological stress response influences birth weight in populations facing health disparities

Bi-Directional Associations of Affective States and Diet among Low-Income Hispanic Pregnant Women Using Ecological Momentary Assessment

Affective states play a role in dietary behaviors. Yet, little research has studied within-subjects associations between affect and diet during pregnancy. We examined the acute bidirectional relationships between affect and food intake and moderation by pre-pregnancy body mass index (BMI) in low-income, Hispanic pregnant women using ecological momentary assessment (EMA). Women (N = 57) completed four days of EMA during their first trimester. Women responded to five random prompts per day about their current affect and past two-hour food intake. Higher positive affect (PA) or lower negative affect (NA) predicted greater likelihood of fruit/vegetable consumption in the next two hours in women with lower pre-pregnancy BMI and lower likelihood in women with higher pre-pregnancy BMI. Higher PA predicted less likelihood of fast food consumption in the next two hours in women with lower pre-pregnancy BMI and slightly higher likelihood in women with higher pre-pregnancy BMI. Women with lower pre-pregnancy BMI had higher PA when they reported consuming chips/fries in the past two hours, and women with higher pre-pregnancy BMI had lower PA when they reported consumption of chips/fries in the past two hours. Results showed differential relationships between affect and food intake as a function of pre-pregnancy BMI