Weekly prenatal PM2.5 and NO2 exposures in preterm, early term, and full term infants: Decrements in birth weight and critical windows of susceptibility.

BACKGROUND: Previous studies have observed associations between birth weight and prenatal air pollution exposure, but there is not consensus on timing of critical windows of susceptibility. OBJECTIVE: We estimated the difference in birth weight among preterm, early term and full term births associated with weekly exposure to PM2.5 and NO2 throughout gestation. METHODS: We included all singleton live births in the Lower Peninsula of Michigan (United States) between 2007 and 2012 occurring at or after 32 weeks gestational age (n = 497,897). Weekly ambient PM2.5 and NO2 concentrations were estimated at maternal residences using 1-km gridded data from ensemble-based models. We utilized a distributed lag nonlinear model to estimate the difference in birth weight associated with weekly exposures from the last menstrual period (week 0) through 31 weeks gestation for preterm births; through 36 weeks gestation for early term births; and through 38 weeks gestation for full term births. RESULTS: In single-pollutant models, a 5 µg/m3 increase in PM2.5 exposure was associated with a reduction in birth weight among preterm births (-37.1 g [95% confidence interval [CI]: 60.8 g, -13.5 g]); early term births (-13.5 g [95% CI: 26.2 g, -0.67 g]); and full term births (-8.23 g [95% CI: 15.8 g, -0.68 g])]. In single-pollutant models, a 10 ppb increase in NO2 exposure was associated with a -11.7 g (95% CI: 14.46 g, -8.92 g) decrement in birth weight among full term births only. In models co-adjusted for PM2.5 and NO2, PM2.5 exposure was associated with reduced birth weight among preterm births (-36.9 g [95% CI: 61.9 g, -11.8 g]) and NO2 exposure was associated with reduced birth weight among full term births (-11.8 g [95% CI: 14.7 g, -8.94 g]). The largest decrements in birth weight were associated with PM2.5 exposure between approximately 10 and 26 weeks of pregnancy; for NO2 exposure, the largest decrements in birth weight in full term births were associated with exposure between weeks 6-18. CONCLUSION: We observed the largest and most persistent adverse associations between PM2.5 exposure and birth weight in preterm infants, and between NO2 exposure and birth weight in full term infants. Exposure during the first half of pregnancy had a greater impact on birthweight.

Segregation and Childhood Blood Lead Levels in North Carolina.

BACKGROUND AND OBJECTIVES: Using a local measure of racial residential segregation, estimate the association between racial residential segregation and childhood blood lead levels between the early 1990s and 2015 in North Carolina. METHODS: This population-based observational study uses individual-level blood lead testing records obtained from the NC Department of Health and Human Services for 320 916 children aged <7 years who were tested between 1992 and 1996 or 2013 and 2015. NC childhood blood lead levels were georeferenced to the census tract. Neighborhood racial residential segregation, assessed using a local, spatial measure of the racial isolation of non-Hispanic Blacks (RINHB), was calculated at the census tract level. RESULTS: From 1990 to 2015, RINHB increased in 50% of 2195 NC census tracts, although the degree of change varied by geographic region. In 1992 to 1996 blood lead testing data, a 1-standard-deviation increase in tract-level RINHB was associated with a 2.86% (95% confidence interval: 0.96%-4.81%) and 2.44% (1.34%-3.56%) increase in BLL among non-Hispanic Black and non-Hispanic White children, respectively. In 2013 to 2015 blood lead testing data, this association was attenuated but persisted with a 1-standard-deviation increase in tract-level RINHB associated with a 1.59% (0.50%-2.70%) and 0.76% (0.08%-1.45%) increase in BLL among non-Hispanic Black and non-Hispanic White children, respectively. In the supplemental information, we show the change in racial residential segregation across the entire United States, demonstrating that RINHB increased in 69% of 72 899 US census tracts. CONCLUSIONS: Racially isolated neighborhoods are associated with higher childhood lead levels, demonstrating the disproportionate environmental burdens borne by segregated communities and warranting attention to providing whole child health care.

Mortality After Exposure to a Hurricane Among Older Adults Living With Dementia.

This cohort study examines mortality after hurricane exposure in older adults living with Alzheimer disease and other related dementias.

Economic and mental health impacts of multiple adverse events: Hurricane Harvey, other flooding events, and the COVID-19 pandemic.

OBJECTIVES: To assess the economic and mental health impacts of COVID-19 in the presence of previous exposure to flooding events. METHODS: Starting in April 2018, the Texas Flood Registry (TFR) invited residents to complete an online survey regarding their experiences with Hurricane Harvey and subsequent flooding events. Starting in April 2020, participants nationwide were invited to complete a brief online survey on their experiences during the pandemic. This study includes participants in the TFR (N = 20,754) and the COVID-19 Registry (N = 8568) through October 2020 (joint N = 2929). Logistic regression and generalized estimating equations were used to examine the relationship between exposure to flooding events and the economic and mental health impacts of COVID-19. RESULTS: Among COVID-19 registrants, 21% experienced moderate to severe anxiety during the pandemic, and 7% and 12% of households had difficulty paying rent and bills, respectively. Approximately 17% of Black and 15% of Hispanic households had difficulty paying rent, compared to 5% of non-Hispanic white households. The odds of COVID-19 income loss are 1.20 (1.02, 1.40) times higher for those who previously had storm-related home damage compared to those who did not and 3.84 (3.25-4.55) times higher for those who experienced Harvey income loss compared to those who did not. For registrants for whom Harvey was a severe impact event, the odds of having more severe anxiety during the pandemic are 5.14 (4.02, 6.58) times higher than among registrants for whom Harvey was a no meaningful impact event. CONCLUSIONS: Multiple crises can jointly and cumulatively shape health and wellbeing outcomes. This knowledge can help craft emergency preparation and intervention programs.

Racial residential segregation shapes the relationship between early childhood lead exposure and fourth-grade standardized test scores.

Racial/ethnic disparities in academic performance may result from a confluence of adverse exposures that arise from structural racism and accrue to specific subpopulations. This study investigates childhood lead exposure, racial residential segregation, and early educational outcomes. Geocoded North Carolina birth data is linked to blood lead surveillance data and fourth-grade standardized test scores (n = 25,699). We constructed a census tract-level measure of racial isolation (RI) of the non-Hispanic Black (NHB) population. We fit generalized additive models of reading and mathematics test scores regressed on individual-level blood lead level (BLL) and neighborhood RI of NHB (RINHB). Models included an interaction term between BLL and RINHB. BLL and RINHB were associated with lower reading scores; among NHB children, an interaction was observed between BLL and RINHB. Reading scores for NHB children with BLLs of 1 to 3 µg/dL were similar across the range of RINHB values. For NHB children with BLLs of 4 µg/dL, reading scores were similar to those of NHB children with BLLs of 1 to 3 µg/dL at lower RINHB values (less racial isolation/segregation). At higher RINHB levels (greater racial isolation/segregation), children with BLLs of 4 µg/dL had lower reading scores than children with BLLs of 1 to 3 µg/dL. This pattern becomes more marked at higher BLLs. Higher BLL was associated with lower mathematics test scores among NHB and non-Hispanic White (NHW) children, but there was no evidence of an interaction. In conclusion, NHB children with high BLLs residing in high RINHB neighborhoods had worse reading scores.

Symptomatic, Presymptomatic, and Asymptomatic Transmission of SARS-CoV-2 in a University Student Population, August-November 2020.

OBJECTIVES: The impact and risk of SARS-CoV-2 transmission from asymptomatic and presymptomatic hosts remains an open question. This study measured the secondary attack rates (SARs) and relative risk (RR) of SARS-CoV-2 transmission from asymptomatic and presymptomatic index cases as compared with symptomatic index cases. METHODS: We used COVID-19 test results, daily health check reports, and contact tracing data to measure SARs and corresponding RRs among close contacts of index cases in a cohort of 12 960 young adults at the University of Notre Dame in Indiana for 103 days, from August 10 to November 20, 2020. Further analysis included Fisher exact tests to determine the association between symptoms and COVID-19 infection and z tests to determine statistical differences between SARs. RESULTS: Asymptomatic rates of transmission of SARS-CoV-2 were higher (SAR = 0.19; 95% CI, 0.14-0.24) than was estimated in prior studies, producing an RR of 0.75 (95% CI, 0.54-1.07) when compared with symptomatic transmission. In addition, the transmission rate associated with presymptomatic cases (SAR = 0.25; 95% CI, 0.21-0.30) was approximately the same as that for symptomatic cases (SAR = 0.25; 95% CI, 0.19-0.31). Furthermore, different symptoms were associated with different transmission rates. CONCLUSIONS: Asymptomatic and presymptomatic hosts of SARS-CoV-2 are a risk for community spread of COVID-19, especially with new variants emerging. Moreover, typical symptom checks may easily miss people who are asymptomatic or presymptomatic but still infectious. Our study results may be used as a guide to analyze the spread of SARS-CoV-2 variants and help inform appropriate public health measures as they relate to asymptomatic and presymptomatic cases.

Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina.

Studies increasingly use output from the Environmental Protection Agency's Fused Air Quality Surface Downscaler ("downscaler") model, which provides spatial predictions of daily concentrations of fine particulate matter (PM2.5) and ozone (O3) at the census tract level, to study the health and societal impacts of exposure to air pollution. Downscaler outputs have been used to show that lower income and higher minority neighborhoods are exposed to higher levels of PM2.5 and lower levels of O3. However, the uncertainty of the downscaler estimates remains poorly characterized, and it is not known if all subpopulations are benefiting equally from reliable predictions. We examined how the percent errors (PEs) of daily concentrations of PM2.5 and O3 between 2002 and 2016 at the 2010 census tract centroids across North Carolina were associated with measures of racial and educational isolation, neighborhood disadvantage, and urbanicity. Results suggest that there were socioeconomic and demographic disparities in surface concentrations of PM2.5 and O3, as well as their prediction uncertainties. Neighborhoods characterized by less reliable downscaler predictions (i.e., higher PEPM2.5 and PEO3) exhibited greater levels of aerial deprivation as well as educational isolation, and were often non-urban areas (i.e., suburban, or rural). Between 2002 and 2016, predicted PM2.5 and O3 levels decreased and O3 predictions became more reliable. However, the predictive uncertainty for PM2.5 has increased since 2010. Substantial spatial variability was observed in the temporal changes in the predictive uncertainties; educational isolation and neighborhood deprivation levels were associated with smaller increases in predictive uncertainty of PM2.5. In contrast, racial isolation was associated with a greater decline in the reliability of PM2.5 predictions between 2002 and 2016; it was associated with a greater improvement in the predictive reliability of O3 within the same time frame.

Where Is Air Quality Improving, and Who Benefits? A Study of PM2.5 and Ozone Over 15 Years.

In the United States, concentrations of criteria air pollutants have declined in recent decades. Questions remain regarding whether improvements in air quality are equitably distributed across subpopulations. We assessed spatial variability and temporal trends in concentrations of particulate matter with an aerodynamic diameter ≤2.5 µm (PM2.5) and ozone (O3) across North Carolina from 2002-2016, and associations with community characteristics. Estimated daily PM2.5 and O3 concentrations at 2010 Census tracts were obtained from the Fused Air Quality Surface Using Downscaling archive and averaged to create tract-level annual PM2.5 and O3 estimates. We calculated tract-level measures of: racial isolation of non-Hispanic Black individuals, educational isolation of non-college educated individuals, the neighborhood deprivation index (NDI), and percentage of the population in urban areas. We fitted hierarchical Bayesian space-time models to estimate baseline concentrations of and time trends in PM2.5 and O3 for each tract, accounting for spatial between-tract correlation. Concentrations of PM2.5 and O3 declined by 6.4 µg/m3 and 13.5 ppb, respectively. Tracts with lower educational isolation and higher urbanicity had higher PM2.5 and more pronounced declines in PM2.5. Racial isolation was associated with higher PM2.5 but not with the rate of decline in PM2.5. Despite declines in pollutant concentrations, over time, disparities in exposure increased for racially and educationally isolated communities.

SARS-CoV-2 Variant Tracking and Mitigation During In-Person Learning at a Midwestern University in the 2020-2021 School Year.

Importance: The COVID-19 pandemic led many higher education institutions to close campuses during the 2020-2021 academic year. As campuses prepared for a return to in-person education, many institutions were mandating vaccines for students and considering the same for faculty and staff. Objective: To determine the association between vaccination coverage and the levels and spread of SARS-CoV-2, even in the presence of highly-transmissible variants and congregate living, at a midsized university in the US. Design, Setting, and Participants: This case series was conducted at a midsized Midwestern university during the spring 2021 semester. The university developed a saliva-based surveillance program capable of high-throughput SARS-CoV-2 polymerase chain reaction testing and genomic sequencing with the capacity to deliver results in less than 24 hours. On April 7, 2021, the university announced a vaccine requirement for all students for the fall 2021 semester and announced the same requirement for faculty and staff on May 20, 2021. The university hosted an onsite mass vaccination clinic using the 2-dose Pfizer-BioNTech vaccine during April 8 to 15 and April 29 to May 6, 2021. Data were analyzed for 14 894 individuals from the university population who were tested for COVID-19 on campus from January 6 to May 20, 2021. Main Outcomes and Measures: Positive SARS-CoV-2 diagnosis was confirmed by quantitative reverse transcription-polymerase chain reaction of saliva specimens, and variant identity was assessed by quantitative reverse transcription-polymerase chain reaction and next-generation sequencing of viral genomes. Results: Between January 6 and May 20, 2021, the university conducted 196 185 COVID-19 tests for 14 894 individuals and identified 1603 positive cases. Within those positive cases, 950 individuals (59.3%) were male, 644 (40.2%) were female, 1426 (89.0%) were students, and 1265 (78.9%) were aged 17 to 22 years. Among the 1603 positive cases, 687 were identified via polymerase chain reaction of saliva specimens. The Alpha (B.1.1.7) variant constituted 218 of the 446 total positives sequenced (48.9%). By May 20, 2021, 10 068 of 11 091 students (90.8%), 814 of 883 faculty (92.2%), and 2081 of 2890 staff (72.0%) were vaccinated. The 7-day rolling average of positive cases peaked at 37 cases on February 17 but declined to zero by May 14, 2021. The 7-day moving average of positive cases was inversely associated with cumulative vaccination coverage, with a statistically significant Pearson correlation coefficient of -0.57 (95% CI, -0.68 to -0.44). Conclusions and Relevance: This case series study elucidated the association of a robust vaccination program with a statistically significant decrease in positive COVID-19 cases among the study population even in the presence of highly transmissible variants and congregate living.

Concentrations of per- and polyfluoroalkyl substances (PFAS) in human placental tissues and associations with birth outcomes.

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous environmental contaminants commonly detected in human serum. Previous studies have observed associations between maternal serum PFAS and adverse pregnancy and birth outcomes such as lower birth weight or pre-eclampsia; however, few studies have explored these associations with birth outcomes and placental tissue PFAS concentration. The placenta is a vital contributor to a healthy pregnancy and may be involved in the mechanism of PFAS reproductive toxicity. Our goal was to measure placental PFAS concentrations and examine associations with birth outcomes (e.g., birth weight, gestational duration). Placenta samples (n = 120) were collected during delivery from women enrolled in the Healthy Pregnancy, Healthy Baby cohort (HPHB) in Durham, North Carolina. All placenta samples contained detectable PFAS, with perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and perfluorodecanoic acid (PFDA) being the most abundant and most frequently detected (all >96% detection frequency). While placental PFAS concentrations did not differ by infant sex, higher PFAS levels were observed in placenta from nulliparous women, suggesting that parity influences the accumulation of PFAS in the placenta. We used linear regression models to examine associations between placental PFAS and birth outcomes. After adjustment for parity, tobacco use, maternal age, and maternal race, we found that placental PFOS was associated with lower birth weight for gestational age in male infants and higher birth weight for gestational age in female infants. Similar findings were seen for PFNA for birth weight for gestational age. These differences in birth outcomes based on infant sex highlight a need to explore mechanistic differences in PFAS toxicity during gestation for male and female infants.

Data-driven testing program improves detection of COVID-19 cases and reduces community transmission.

COVID-19 remains a global threat in the face of emerging SARS-CoV-2 variants and gaps in vaccine administration and availability. In this study, we analyze a data-driven COVID-19 testing program implemented at a mid-sized university, which utilized two simple, diverse, and easily interpretable machine learning models to predict which students were at elevated risk and should be tested. The program produced a positivity rate of 0.53% (95% CI 0.34-0.77%) from 20,862 tests, with 1.49% (95% CI 1.15-1.89%) of students testing positive within five days of the initial test-a significant increase from the general surveillance baseline, which produced a positivity rate of 0.37% (95% CI 0.28-0.47%) with 0.67% (95% CI 0.55-0.81%) testing positive within five days. Close contacts who were predicted by the data-driven models were tested much more quickly on average (0.94 days from reported exposure; 95% CI 0.78-1.11) than those who were manually contact traced (1.92 days; 95% CI 1.81-2.02). We further discuss how other universities, business, and organizations could adopt similar strategies to help quickly identify positive cases and reduce community transmission.

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

A longitudinal study of exposure to fine particulate matter during pregnancy, small-for-gestational age births, and birthweight percentile for gestational age in a statewide birth cohort.

BACKGROUND: Previous studies observed associations between prenatal exposure to fine particulate matter (≤ 2.5 µm; PM2.5) and small-for-gestational-age (SGA) birth and lower birthweight percentile for gestational age. Few, if any, studies examine prenatal air pollution exposure and these pregnancy outcomes in neonates born to the same women. Here, we assess whether prenatal exposure to ambient fine particulate matter (PM2.5) is associated with small-for-gestational-age (SGA) birth or birthweight percentile for gestational age in a longitudinal setting. METHODS: Detailed birth record data were used to identify women who had singleton live births at least twice in North Carolina during 2002-2006 (n = 53,414 women, n = 109,929 births). Prenatal PM2.5 exposures were calculated using daily concentration estimates obtained from the US EPA Fused Air Quality Surface using Downscaling data archive. Associations between PM2.5 exposure and birthweight percentile and odds of SGA birth were calculated using linear and generalized mixed models, comparing successive pregnancies to the same woman. Odds ratios and associations were also estimated in models that did not account for siblings born to the same mother. RESULTS: Among NHW women, pregnancy-long PM2.5 exposure was associated with SGA (OR: 1.11 [1.06, 1.18]) and lower birthweight percentile (- 0.46 [- 0.74, - 0.17]). Trimester-specific PM2.5 was also associated with SGA and lower birthweight percentile. Among NHB women, statistically significant within-woman associations between PM2.5, SGA, and birthweight percentile were not observed. However, in models that did not account for births to the same mother, statistically significant associations were observed between some PM2.5 exposure windows and higher odds of SGA and lower birthweight percentile among NHB women. CONCLUSIONS: Findings suggest that a woman is at greater risk of delivering an SGA or low birthweight percentile neonate when she has been exposed to higher PM2.5 levels. The within-woman comparison implemented here better controls for factors that may differ between women and potentially confound the relationship between PM2.5 exposure and pregnancy outcomes. This adds to the evidence that PM2.5 exposure may be causally related to SGA and birthweight percentile, even at concentrations close to or below National Ambient Air Quality Standards.

Exposure to heat during pregnancy and preterm birth in North Carolina: Main effect and disparities by residential greenness, urbanicity, and socioeconomic status.

BACKGROUND: Although previous literature suggested that several factors may be associated with higher risk of adverse health outcomes related to heat, research is limited for birth outcomes. OBJECTIVES: We investigated associations between exposure to heat/heat waves during the last week of gestation and preterm birth (PTB) in North Carolina (NC) and evaluated effect modification by residential greenness, urbanicity, and socioeconomic status (SES). METHODS: We obtained individual-level NC birth certificate data for May-September 2003-2014. We estimated daily mean temperature at each maternal residential address using Parameter-elevation Regressions on Independent Slopes Model (PRISM) data. We created 3 definitions of heat waves (daily temperature ≥95th, 97th, 99th percentile for NC warm season temperature, for ≥2 consecutive days). Normalized Difference Vegetation Index (NDVI) was used to assess residential greenness. Community-level modifiers (e.g., income, urbanicity) were considered. We applied Cox proportional hazard models to estimate the association between exposure to heat/heat waves and PTB, controlling for covariates. Stratified analyses were conducted to evaluate whether the association between heat and PTB varied by several individual and community characteristics. RESULTS: Of the 546,441 births, 8% were preterm. Heat exposure during the last week before delivery was significantly associated with risk of PTB. The hazard ratio for a 1 °C increase in temperature during the last week before delivery was 1.01 (95% CI: 1.00, 1.02). Higher heat-PTB risk was associated with some characteristics (e.g., areas that were urbanized, low SES, or in the Coastal Plain). We also found significant PTB-heat risk in areas with low greenness for urbanized area. For heat waves, we did not find significantly positive associations with PTB. DISCUSSION: Findings provide evidence that exposure to heat during pregnancy increases risk of PTB and suggest disparities in these risks. Our results have implications for future studies of disparity in heat and birth outcomes associations.

Risk Factors for Sudden Infant Death in North Carolina.

Background: Sudden infant death syndrome (SIDS) is the sudden, unexplained death of infants <1 year old. SIDS remains a leading cause of death in US infants. We aim to identify associations between SIDS and race/ethnicity, birth weight/gestational age, and socioeconomic/environmental factors in North Carolina (NC) to help identify infants at risk for SIDS. Methods and Results: In this IRB-approved study, infant mortality 2007-2016 and death certificate-linked natality 2007-2014 were obtained from the NC Department of Health and Human Services. General, NC natality statistics 2007-2016 were obtained from CDC Wonder. Association between SIDS/total infant death and covariates (below) were calculated. Total infant mortality decreased 2007-2016 by an average of 14 deaths/100,000 live births per year, while SIDS incidence remained constant. Risk ratios of SIDS/total infant deaths, standardized to Non-Hispanic White, were 1.76/2.41 for Non-Hispanic Black and 0.49/0.97 for Hispanic infants. Increased SIDS risk was significantly and independently associated with male infant sex, Non-Hispanic Black maternal race/ethnicity, young maternal age, low prenatal care, gestational age <39 weeks, birthweight <2500 g, low maternal education, and maternal tobacco use (p < 0.01). Maternal previous children now deceased also trended toward association with increased SIDS risk. Conclusions: A thorough SIDS risk assessment should include maternal, socioeconomic, and environmental risk factors as these are associated with SIDS in our population.

Assessing Disparity Using Measures of Racial and Educational Isolation.

We develop a local, spatial measure of educational isolation (EI) and characterize the relationship between EI and our previously developed measure of racial isolation (RI). EI measures the extent to which non-college educated individuals are exposed primarily to other non-college educated individuals. To characterize how the RI-EI relationship varies across space, we propose a novel measure of local correlation. Using birth records from the State of Michigan (2005-2012), we estimate associations between RI, EI, and birth outcomes. EI was lower in urban communities and higher in rural communities, while RI was highest in urban areas and parts of the southeastern United States (US). We observed greater heterogeneity in EI in low RI tracts, especially in non-urban tracts; residents of high RI tracts are likely to be both educationally and racially isolated. Associations were also observed between RI, EI, and gestational length (weeks) and preterm birth (PTB). For example, moving from the lowest to the highest quintile of RI was associated with a 1.11 (1.07, 1.15) and 1.16 (1.10, 1.22) increase in odds of PTB among NHB and NHW women, respectively. Moving from the lowest to the highest quintile of EI was associated with a 1.07 (1.02, 1.12) and 1.03 (1.00, 1.05) increase in odds of PTB among NHB and NHW women, respectively. This work provides three tools (RI, EI, and the local correlation measure) to researchers and policymakers interested in how residential isolation shapes disparate outcomes.

Exposure to concentrated animal feeding operations (CAFOs) and risk of mortality in North Carolina, USA.

Concentrated animal feeding operations (CAFOs) have emerged as an environmental justice issue due to disproportionate siting in low-income and minority communities. However, CAFOs' impact on health is not fully understood. We examined risk of cause-specific mortality associated with CAFOs in North Carolina (NC) for 2000-2017 and health disparities. We obtained data on individual-level cause-specific mortality and on permitted animal facilities. We estimated associations between exposure to CAFOs and cause-specific mortality using logistic regression, controlling for demographics (e.g., age) and area-level covariates. To estimate exposure to CAFOs, we considered (1) a binary indicator (presence or absence) of CAFOs within a buffer around individual residence based on several buffer sizes, and (2) four levels of exposure (no, low, medium, and high) based on the number of CAFOs within 15 km around each residence. We considered individual-level (sex, race/ethnicity, age, education) and community-level (median household income, urbanicity, and region) factors. Under all buffer sizes used to estimate CAFOs exposure, people living near CAFOs had significantly higher risk of cardiovascular mortality than other persons. Comparing those living near CAFOs to the no exposure group, odds ratios (ORs) for cardiovascular mortality were 1.01 (95% confidence interval (CI) 1.00, 1.03), 1.04 (1.03, 1.06), and 1.06 (1.05, 1.07) for low, medium, and high CAFOs exposure, respectively, indicating a trend of higher risk with higher exposure. Those in the high CAFOs exposure group had significantly higher risk of anemia and kidney disease mortality than those with no exposure. Results suggest higher mortality risk from CAFOs for some subpopulations, however differences were not statistically significant. Findings provide evidence of excess mortality risk from CAFOs in NC. These results have implications for future studies of environmental justice and CAFOs.

SPATIAL DISTRIBUTED LAG DATA FUSION FOR ESTIMATING AMBIENT AIR POLLUTION.

We introduce spatial (DLfuse) and spatiotemporal (DLfuseST) distributed lag data fusion methods for predicting point-level ambient air pollution concentrations, using, as input, gridded average pollution estimates from a deterministic numerical air quality model. The methods incorporate predictive information from grid cells surrounding the prediction location of interest and are shown to collapse to existing downscaling approaches when this information adds no benefit. The spatial lagged parameters are allowed to vary spatially/spatiotemporally to accommodate the setting where surrounding geographic information is useful in one area/time but not in another. We apply the new methods to predict ambient concentrations of eight-hour maximum ozone and 24-hour average PM2.5 at unobserved spatial locations and times, and compare the predictions with those from several state-of-the-art data fusion approaches. Results show that DLfuse and DLfuseST often provide improved model fit and predictive accuracy when the lagged information is shown to be beneficial. Code to apply the methods is available in the R package DLfuse.

Bayesian variable selection for understanding mixtures in environmental exposures.

Social and environmental stressors are crucial factors in child development. However, there exists a multitude of measurable social and environmental factors-the effects of which may be cumulative, interactive, or null. Using a comprehensive cohort of children in North Carolina, we study the impact of social and environmental variables on 4th end-of-grade exam scores in reading and mathematics. To identify the essential factors that predict these educational outcomes, we design new tools for Bayesian linear variable selection using decision analysis. We extract a predictive optimal subset of explanatory variables by coupling a loss function with a novel model-based penalization scheme, which leads to coherent Bayesian decision analysis and empirically improves variable selection, estimation, and prediction on simulated data. The Bayesian linear model propagates uncertainty quantification to all predictive evaluations, which is important for interpretable and robust model comparisons. These predictive comparisons are conducted out-of-sample with a customized approximation algorithm that avoids computationally intensive model refitting. We apply our variable selection techniques to identify the joint collection of social and environmental stressors-and their interactions-that offer clear and quantifiable improvements in prediction of reading and mathematics exam scores.

The Texas flood registry: a flexible tool for environmental and public health practitioners and researchers.

BACKGROUND: Making landfall in Rockport, Texas in August 2017, Hurricane Harvey resulted in unprecedented flooding, displacing tens of thousands of people, and creating environmental hazards and exposures for many more. OBJECTIVE: We describe a collaborative project to establish the Texas Flood Registry to track the health and housing impacts of major flooding events. METHODS: Those who enroll in the registry answer retrospective questions regarding the impact of storms on their health and housing status. We recruit both those who did and did not flood during storm events to enable key comparisons. We leverage partnerships with multiple local health departments, community groups, and media outlets to recruit broadly. We performed a preliminary analysis using multivariable logistic regression and a binomial Bayesian conditional autoregressive (CAR) spatial model. RESULTS: We find that those whose homes flooded, or who came into direct skin contact with flood water, are more likely to experience a series of self-reported health effects. Median household income is inversely related to adverse health effects, and spatial analysis provides important insights within the modeling approach. SIGNIFICANCE: Global climate change is likely to increase the number and intensity of rainfall events, resulting in additional health burdens. Population-level data on the health and housing impacts of major flooding events is imperative in preparing for our planet's future.