The Legacy of Redlining: Increasing Childhood Asthma Disparities Through Neighborhood Poverty.

RATIONALE: Identifying the root causes of racial disparities in childhood asthma is critical for health equity. OBJECTIVES: To determine if the 1930's racist policy of redlining led to present-day disparities in childhood asthma by increasing community-level poverty and decreasing neighborhood socioeconomic position (SEP). METHODS: We categorized census tracts at birth of participants from the Children's Respiratory and Environmental Workgroup birth cohort consortium into A, B, C, or D categories as defined by the Home Owners Loan Corporation (HOLC), with D being the highest perceived risk. Surrogates of present-day neighborhood-level SEP were determined for each tract including the percentage of low-income households, the CDC's social vulnerability index (SVI), and other tract-level variables. We performed causal mediation analysis, which, under the assumption of no unmeasured confounding, estimates the direct and mediated pathways by which redlining may cause asthma disparities through census tract-level mediators adjusting for individual-level covariates. MEASUREMENTS AND MAIN RESULTS: Of 4,849 children, the cumulative incidence of asthma through age 11 was 26.6% and 13.2% resided in census tracts with a HOLC grade of D. In mediation analyses, residing in grade D tracts (aOR = 1.03 [95%CI 1.01,1.05]) was significantly associated with childhood asthma, with 79% of this increased risk mediated by percentage of low-income households; results were similar for SVI and other tract-level variables. CONCLUSIONS: The historical structural racist policy of redlining led to present-day asthma disparities in part through decreased neighborhood SEP. Policies aimed at reversing the effects of structural racism should be considered to create more just, equitable, and healthy communities.

Cotinine concentrations in maternal serum and amniotic fluid during pregnancy and risk of testicular germ cell cancer in the offspring: A prospective nested case-control study.

Maternal smoking in pregnancy may increase the risk of testicular germ cell cancer (TGCC) in offspring, but current evidence remains inconclusive. We performed a nested case-control study using cotinine measurements in maternal serum and amniotic fluid as a biomarker for tobacco exposure during pregnancy. A total of 654 males with maternal serum (n = 359, ncases/controls = 71/288) and/or amniotic fluid (n = 295, ncases/controls = 66/229) samples were included. Data on TGCC diagnoses and relevant covariates were derived from nationwide Danish health registries. Cotinine was quantified by liquid chromatography tandem mass spectrometry. An adapted cox regression model estimated the risk of TGCC considering active and inactive tobacco use defined according to cotinine concentrations of <, ≥15 ng/ml. Overall, the concentrations of cotinine were comparable in maternal serum and amniotic fluid (medianserum/amniotic fluid : 2.1/2.6 ng/ml). A strong statistically significant correlation was detected in 14 paired samples (Spearman rho: 0.85). Based on maternal serum cotinine concentrations, exposure to active tobacco use was not associated with risk of TGCC in offspring (HR 0.88, 95% CI 0.51; 1.52). Similarly, based on amniotic fluid cotinine concentrations, exposure to active tobacco use was not associated with risk of TGCC (HR 1.11, 95% CI 0.64; 1.95). However, different risks were observed for seminomas and nonseminomas in both matrices, but none were statistically significant. Our findings did not provide convincing evidence supporting that exposure to tobacco during pregnancy is associated with TGCC.

Multivariate Cluster Point Process to Quantify and Explore Multi-Entity Configurations: Application to Biofilm Image Data.

Clusters of similar or dissimilar objects are encountered in many fields. Frequently used approaches treat each cluster's central object as latent. Yet, often objects of one or more types cluster around objects of another type. Such arrangements are common in biomedical images of cells, in which nearby cell types likely interact. Quantifying spatial relationships may elucidate biological mechanisms. Parent-offspring statistical frameworks can be usefully applied even when central objects ("parents") differ from peripheral ones ("offspring"). We propose the novel multivariate cluster point process (MCPP) to quantify multi-object (e.g., multi-cellular) arrangements. Unlike commonly used approaches, the MCPP exploits locations of the central parent object in clusters. It accounts for possibly multilayered, multivariate clustering. The model formulation requires specification of which object types function as cluster centers and which reside peripherally. If such information is unknown, the relative roles of object types may be explored by comparing fit of different models via the deviance information criterion (DIC). In simulated data, we compared a series of models' DIC; the MCPP correctly identified simulated relationships. It also produced more accurate and precise parameter estimates than the classical univariate Neyman-Scott process model. We also used the MCPP to quantify proposed configurations and explore new ones in human dental plaque biofilm image data. MCPP models quantified simultaneous clustering of Streptococcus and Porphyromonas around Corynebacterium and of Pasteurellaceae around Streptococcus and successfully captured hypothesized structures for all taxa. Further exploration suggested the presence of clustering between Fusobacterium and Leptotrichia, a previously unreported relationship.

Radon decay product particle radioactivity and oxidative stress biomarkers in patients with COPD.

Radon decay products include α-radiation emitting radionuclides that attach to airborne particles that have potential to promote oxidative tissue damage after inhalation. To assess associations between α-particle radioactivity (α-PR) with urinary biomarkers of oxidative tissue damage, 140 patients with chronic obstructive pulmonary disease (COPD) had up to four 1-week seasonal assessments (N = 413) of indoor (home) and ambient (central site) PM2.5 and black carbon (BC). Following environmental sampling, urine samples were analyzed for total and free malondialdehyde (MDA), biomarkers of lipid oxidation, and 8-hydroxyl-2'-deoxyguanosine (8-OHdG), a biomarker of DNA oxidative damage. Particle radioactivity was measured as α-activity on PM2.5 filter samples. Linear mixed-effects regression models adjusted for urinary creatinine and other personal characteristics were used to assess associations. Indoor α-PR was associated with an increase in 8-OhdG (8.53%; 95% CI: 3.12, 14.23); total MDA (5.59%; 95% CI: 0.20, 11.71); and free MDA (2.17%; 95% CI: 2.75, 7.35) per interquartile range (IQR) of α-PR [median 1.25 mBq/m3; IQR 0.64], similar adjusting for PM2.5 or BC. The ratio of indoor/ambient α-PR was positively associated with each biomarker and associations with ambient α-PR were positive but weaker than with indoor concentrations. These findings are consistent with a contribution of radon decay products as measured by α-PR to oxidative stress in patients with COPD, with a greater contribution of indoor radon decay products.

Prenatal exposure to air pollution and BWGA Z-score: Modifying effects of placenta leukocyte telomere length and infant sex.

BACKGROUND: Air pollutants, such as fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3), have been associated with adverse birth outcomes, including low birth weight, often exhibiting sex-specific effects. However, the modifying effect of placental telomere length (TL), reflecting cumulative lifetime oxidative stress in mothers, remains unexplored. METHOD: Using data from a Northeastern U.S. birth cohort (n = 306), we employed linear regression and weighted quantile sum models to assess trimester-average air pollution exposures and birth weight for gestational age (BWGA) z-scores. Placental TL, categorized by median split, was considered as an effect modifier. Interactions among air pollutants, placental TL, infant sex, and BWGA z-score were evaluated. RESULTS: Without placental TL as a modifier, only 1st trimester O3 was significantly associated with BWGA z-scores (coefficient: 0.33, 95% CI: 0.03, 0.63). In models considering TL interactions, a significant modifying effect was observed between 3rd trimester NO2 and BWGA z-scores (interaction p-value = 0.02). Specifically, a one interquartile range (1-IQR) increase in 3rd trimester NO2 was linked to a 0.28 (95% CI: 0.06, 0.52) change in BWGA z-score among shorter placental TL group, with no significant association among longer TL group. Among male infants, there were significant associations between 3rd trimester PM2.5 exposure and BWGA z-scores in the longer TL group (coefficient: -0.34, 95% CI: -0.61, -0.02), and between 1st trimester O3 exposure and BWGA z-scores among males in the shorter TL group (coefficient: 0.59, 95% CI: 0.06, 1.08). For females, only a negative association in 2nd trimester mixture model was observed within the longer TL group (coefficient: -0.10, 95% CI: -0.21, -0.01). CONCLUSION: These findings highlight the need to consider the complex interactions among prenatal air pollutant exposures, placental TL, and fetal sex to better elucidate those at greatest risk for adverse birth outcomes.

Association between gestational exposure to solar activity and pregnancy loss using live births from a Massachusetts-based medical center.

BACKGROUND: Solar activity has been linked to biological mechanisms important to pregnancy, including folate and melatonin levels and inflammatory markers. Thus, we aimed to investigate the association between gestational solar activity and pregnancy loss. METHODS: Our study included 71,963 singleton births conceived in 2002-2016 and delivered at an academic medical center in Eastern Massachusetts. We studied several solar activity metrics, including sunspot number, Kp index, and ultraviolet radiation, with data from the NASA Goddard Space Flight Center and European Centre for Medium-Range Weather Forecasts. We used a novel time series analytic approach to investigate associations between each metric from conception through 24 weeks of gestation and the number of live birth-identified conceptions (LBICs) -the total number of conceptions in each week that result in a live birth. This approach fits distributed lag models to data on LBICs, adjusted for time trends, and allows us to infer associations between pregnancy exposure and pregnancy loss. RESULTS: Overall, the association between solar activity during pregnancy and pregnancy loss varied by exposure metric. For sunspot number, we found that an interquartile range increase in sunspot number (78·7 sunspots) in all of the first 24 weeks of pregnancy was associated with 14·0 (95% CI: 6·5, 21·3) more pregnancy losses out of the average 92 LBICs in a week, and exposure in weeks ten through thirteen was identified as a critical window. Although not statistically significant, higher exposure to Kp index and to UV radiation across all 24 weeks of pregnancy was associated with more and less pregnancy losses, respectively. CONCLUSION: While exposure to certain metrics of solar activity (i.e., sunspot number) throughout the first 24 weeks of pregnancy may be associated with pregnancy losses, exposure to other metrics were not. Solar activity is a complex phenomenon, and more studies are needed to clarify underlying pathways.

Relative humidity, temperature, and hypertensive disorders of pregnancy: Findings from the Project Viva cohort.

BACKGROUND: Preeclampsia is a multi-system hypertensive disorder of pregnancy that is a leading cause of maternal and fetal morbidity and mortality. Prior studies disagree on the cause and even the presence of seasonal patterns in its incidence. Using unsuitable time windows for seasonal exposures can bias model results, potentially explaining these inconsistencies. OBJECTIVES: We aimed to investigate humidity and temperature as possible causes for seasonal trends in preeclampsia in Project Viva, a prebirth cohort in Boston, Massachusetts, considering only exposure windows that precede disease onset. METHODS: Using the Parameter-elevation Relationships on Independent Slopes Model (PRISM) Climate Dataset, we estimated daily residential temperature and relative humidity (RH) exposures during pregnancy. Our primary multinomial regression adjusted for person-level covariates and season. Secondary analyses included distributed lag models (DLMs) and adjusted for ambient air pollutants including fine particulates (PM2.5). We used Generalized Additive Mixed Models (GAMMs) for systolic blood pressure (SBP) trajectories across hypertensive disorder statuses to confirm exposure timing. RESULTS: While preeclampsia is typically diagnosed late in pregnancy, GAMM-fitted SBP trajectories for preeclamptic and non-preeclamptic women began to diverge at around 20 weeks' gestation, confirming the need to only consider early exposures. In the primary analysis with 1776 women, RH in the early second trimester, weeks 14-20, was associated with significantly higher odds of preeclampsia (OR per IQR increase: 1.81, 95% CI: 1.10, 2.97). The DLM corroborated this window, finding a positive association from weeks 12-20. There were no other significant associations between RH or temperature and preeclampsia or gestational hypertension in any other time period. DISCUSSION: The association between preeclampsia and RH in the early second trimester was robust to model choice, suggesting that RH may contribute to seasonal trends in preeclampsia incidence. Differences between these results and those of prior studies could be attributable to exposure timing differences.

Psychological resilience predicting cardiometabolic conditions in adulthood in the Midlife in the United States Study.

Early adversity is associated with poor cardiometabolic health, potentially via psychological distress. However, not everyone exposed to adversity develops significant distress. Psychological resilience and positive psychological health despite adversity may protect against unfavorable cardiometabolic outcomes that are otherwise more likely. We examined early adversity, psychological resilience, and cardiometabolic risk among 3,254 adults in the Midlife in the United States Study. Psychological resilience was defined according to both early psychosocial adversity and adult psychological health (characterized by low distress and high wellbeing) at Wave 1 (1994 to 1995). Categorical resilience was derived by cross-classifying adversity (exposed versus unexposed) and psychological health (higher versus lower). We also assessed count of adversities experienced and psychological symptoms as separate variables. Incident cardiometabolic conditions (e.g., heart attack, stroke, and diabetes) were self-reported at Waves 2 (2004 to 2005) and 3 (2013 to 2014). Secondary analyses examined biological cardiometabolic risk using a composite of biomarkers available within a Wave-2 subsample. Logistic and Poisson regressions evaluated associations of resilience with cardiometabolic health across 20 follow-up y, adjusting for relevant covariates. In this initially healthy sample, nonresilient (adversity-exposed, lower psychological health) versus resilient (adversity-exposed, high psychological health) individuals had 43% higher odds of cardiometabolic conditions (95% CI 1.10 to 1.85). Odds of cardiometabolic conditions were similar among resilient versus unexposed, psychologically healthy individuals. More adversity experiences were associated with increased odds, while better psychological health with decreased odds of cardiometabolic conditions, and effects were largely independent. Patterns were similar for objectively assessed cardiometabolic risk. Psychological resilience in midlife may protect against negative cardiometabolic impacts of early adversity.

Residential radon decay products are associated with cough and phlegm in patients with COPD.

Radon decay products attach to particulate matter (referred to as particle radioactivity, PR) has been shown to be potential to promote airway damage after inhalation. In this study, we investigated associations between PR with respiratory symptoms and health-related quality of life (HRQL) in patients with COPD. 141 male patients with COPD, former smokers, completed the St. George's Respiratory Questionnaire (SGRQ) after up to four 1-week seasonal assessments (N=474) of indoor (home) and ambient (central site) particulate matter ≤ 2.5 µm in diameter (PM2.5) and black carbon (BC). Indoor PR was measured as α-activity (radiation) on PM2.5 filter samples. The ratio of indoor/ambient sulfur in PM2.5 (a ventilation surrogate) was used to estimate α-PR from indoor radon decay. SGRQ responses assessed frequent cough, phlegm, shortness of breath, wheeze, and chest attacks in the past 3 months. Multivariable linear regression with generalized estimating equations accounting for repeated measures was used to explore associations, adjusting for potential confounders. Median (IQR) indoor α-PR was 1.22 (0.62) mBq/m3. We found that there were positive associations between α-PR with cough and phlegm. The strongest associations were with estimated α-PR of indoor origin for cough (31.1 % increase/IQR, 95 %CI: 8.8 %, 57.8 %), and was suggestive for phlegm (13.0 % increase/IQR, 95 %CI: -2.5 %, 31.0 %), similar adjusting for indoor BC or PM2.5. α-PR of indoor origin was positively associated with an increase in SGRQ Symptoms score [1.2 units/IQR; 95 %CI: -0.3, 2.6] that did not meet conventional levels of statistical significance. Our results suggested that exposure to indoor radon decay products measured as particle radioactivity, a common indoor exposure, is associated with cough, and suggestively associated with phlegm and worse HRQL symptoms score in patients with COPD.

Bias Amplification and Variance Inflation in Distributed Lag Models Using Low-Spatial-Resolution Data.

Distributed lag models (DLMs) are often used to estimate lagged associations and identify critical exposure windows. In a simulation study of prenatal nitrogen dioxide (NO2) exposure and birth weight, we demonstrate that bias amplification and variance inflation can manifest under certain combinations of DLM estimation approaches and time-trend adjustment methods when using low-spatial-resolution exposures with extended lags. Our simulations showed that when using high-spatial-resolution exposure data, any time-trend adjustment method produced low bias and nominal coverage for the distributed lag estimator. When using either low- or no-spatial-resolution exposures, bias due to time trends was amplified for all adjustment methods. Variance inflation was higher in low- or no-spatial-resolution DLMs when using a long-term spline to adjust for seasonality and long-term trends due to concurvity between a distributed lag function and secular function of time. NO2-birth weight analyses in a Massachusetts-based cohort showed that associations were negative for exposures experienced in gestational weeks 15-30 when using high-spatial-resolution DLMs; however, associations were null and positive for DLMs with low- and no-spatial-resolution exposures, respectively, which is likely due to bias amplification. DLM analyses should jointly consider the spatial resolution of exposure data and the parameterizations of the time trend adjustment and lag constraints.

Traffic-Related Air Pollution and Ultrasound Parameters of Fetal Growth in Eastern Massachusetts.

Previous studies have examined the association between prenatal nitrogen dioxide (NO2)-a traffic emissions tracer-and fetal growth based on ultrasound measures. Yet, most have used exposure assessment methods with low temporal resolution, which limits the identification of critical exposure windows given that pregnancy is relatively short. Here, we used NO2 data from an ensemble model linked to residential addresses at birth to fit distributed lag models that estimated the association between NO2 exposure (resolved weekly) and ultrasound biometric parameters in a Massachusetts-based cohort of 9,446 singleton births from 2011-2016. Ultrasound biometric parameters examined included biparietal diameter (BPD), head circumference, femur length, and abdominal circumference. All models adjusted for sociodemographic characteristics, time trends, and temperature. We found that higher NO2 was negatively associated with all ultrasound parameters. The critical window differed depending on the parameter and when it was assessed. For example, for BPD measured after week 31, the critical exposure window appeared to be weeks 15-25; 10-parts-per-billion higher NO2 sustained from conception to the time of measurement was associated with a lower mean z score of -0.11 (95% CI: -0.17, -0.05). Our findings indicate that reducing traffic emissions is one potential avenue to improving fetal and offspring health.

Multiple exposure distributed lag models with variable selection.

Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time periods during which exposure to a pollutant adversely affects health outcomes. Recent studies have focused on estimating the health effects of a large number of environmental exposures, or an environmental mixture, on health outcomes. In such settings, it is important to understand which environmental exposures affect a particular outcome, while acknowledging the possibility that different exposures have different critical windows. Further, in studies of environmental mixtures, it is important to identify interactions among exposures and to account for the fact that this interaction may occur between two exposures having different critical windows. Exposure to one exposure early in time could cause an individual to be more or less susceptible to another exposure later in time. We propose a Bayesian model to estimate the temporal effects of a large number of exposures on an outcome. We use spike-and-slab priors and semiparametric distributed lag curves to identify important exposures and exposure interactions and discuss extensions with improved power to detect harmful exposures. We then apply these methods to estimate the effects of exposure to multiple air pollutants during pregnancy on birthweight from vital records in Colorado.

The role of body mass index at diagnosis of colorectal cancer on Black-White disparities in survival: a density regression mediation approach.

The study of racial/ethnic inequalities in health is important to reduce the uneven burden of disease. In the case of colorectal cancer (CRC), disparities in survival among non-Hispanic Whites and Blacks are well documented, and mechanisms leading to these disparities need to be studied formally. It has also been established that body mass index (BMI) is a risk factor for developing CRC, and recent literature shows BMI at diagnosis of CRC is associated with survival. Since BMI varies by racial/ethnic group, a question that arises is whether differences in BMI are partially responsible for observed racial/ethnic disparities in survival for CRC patients. This article presents new methodology to quantify the impact of the hypothetical intervention that matches the BMI distribution in the Black population to a potentially complex distributional form observed in the White population on racial/ethnic disparities in survival. Our density mediation approach can be utilized to estimate natural direct and indirect effects in the general causal mediation setting under stronger assumptions. We perform a simulation study that shows our proposed Bayesian density regression approach performs as well as or better than current methodology allowing for a shift in the mean of the distribution only, and that standard practice of categorizing BMI leads to large biases when BMI is a mediator variable. When applied to motivating data from the Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium, our approach suggests the proposed intervention is potentially beneficial for elderly and low-income Black patients, yet harmful for young or high-income Black populations.

Abnormal uterine bleeding patterns determined through menstrual tracking among participants in the Apple Women's Health Study.

BACKGROUND: Use of menstrual tracking data to understand abnormal bleeding patterns has been limited because of lack of incorporation of key demographic and health characteristics and confirmation of menstrual tracking accuracy. OBJECTIVE: This study aimed to identify abnormal uterine bleeding patterns and their prevalence and confirm existing and expected associations between abnormal uterine bleeding patterns, demographics, and medical conditions. STUDY DESIGN: Apple Women's Health Study participants from November 2019 through July 2021 who contributed menstrual tracking data and did not report pregnancy, lactation, use of hormones, or menopause were included in the analysis. Four abnormal uterine bleeding patterns were evaluated: irregular menses, infrequent menses, prolonged menses, and irregular intermenstrual bleeding (spotting). Monthly tracking confirmation using survey responses was used to exclude inaccurate or incomplete digital records. We investigated the prevalence of abnormal uterine bleeding stratified by demographic characteristics and used logistic regression to evaluate the relationship of abnormal uterine bleeding to a number of self-reported medical conditions. RESULTS: There were 18,875 participants who met inclusion criteria, with a mean age of 33 (standard deviation, 8.2) years, mean body mass index of 29.3 (standard deviation, 8.0), and with 68.9% (95% confidence interval, 68.2-69.5) identifying as White, non-Hispanic. Abnormal uterine bleeding was found in 16.4% of participants (n=3103; 95% confidence interval, 15.9-17.0) after accurate tracking was confirmed; 2.9% had irregular menses (95% confidence interval, 2.7-3.1), 8.4% had infrequent menses (95% confidence interval, 8.0-8.8), 2.3% had prolonged menses (95% confidence interval, 2.1-2.5), and 6.1% had spotting (95% confidence interval, 5.7-6.4). Black participants had 33% higher prevalence (prevalence ratio, 1.33; 95% confidence interval, 1.09-1.61) of infrequent menses compared with White, non-Hispanic participants after controlling for age and body mass index. The prevalence of infrequent menses was increased in class 1, 2, and 3 obesity (class 1: body mass index, 30-34.9; prevalence ratio, 1.31; 95% confidence interval, 1.13-1.52; class 2: body mass index, 35-39.9; prevalence ratio, 1.25; 95% confidence interval, 1.05-1.49; class 3: body mass index, >40; prevalence ratio, 1.51; 95% confidence interval, 1.21-1.88) after controlling for age and race/ethnicity. Those with class 3 obesity had 18% higher prevalence of abnormal uterine bleeding compared with healthy-weight participants (prevalence ratio, 1.18; 95% confidence interval, 1.02-1.38). Participants with polycystic ovary syndrome had 19% higher prevalence of abnormal uterine bleeding compared with participants without this condition (prevalence ratio, 1.19; 95% confidence interval, 1.08-1.31). Participants with hyperthyroidism (prevalence ratio, 1.34; 95% confidence interval, 1.13-1.59) and hypothyroidism (prevalence ratio, 1.17; 95% confidence interval, 1.05-1.31) had a higher prevalence of abnormal uterine bleeding, as did those reporting endometriosis (prevalence ratio, 1.28; 95% confidence interval, 1.12-1.45), cervical dysplasia (prevalence ratio, 1.20; 95% confidence interval, 1.03-1.39), and fibroids (prevalence ratio, 1.14; 95% confidence interval, 1.00-1.30). CONCLUSION: In this cohort, abnormal uterine bleeding was present in 16.4% of those with confirmed menstrual tracking. Black or obese participants had increased prevalence of abnormal uterine bleeding. Participants reporting conditions such as polycystic ovary syndrome, thyroid disease, endometriosis, and cervical dysplasia had a higher prevalence of abnormal uterine bleeding.

Bayesian multiple index models for environmental mixtures.

An important goal of environmental health research is to assess the risk posed by mixtures of environmental exposures. Two popular classes of models for mixtures analyses are response-surface methods and exposure-index methods. Response-surface methods estimate high-dimensional surfaces and are thus highly flexible but difficult to interpret. In contrast, exposure-index methods decompose coefficients from a linear model into an overall mixture effect and individual index weights; these models yield easily interpretable effect estimates and efficient inferences when model assumptions hold, but, like most parsimonious models, incur bias when these assumptions do not hold. In this paper, we propose a Bayesian multiple index model framework that combines the strengths of each, allowing for non-linear and non-additive relationships between exposure indices and a health outcome, while reducing the dimensionality of the exposure vector and estimating index weights with variable selection. This framework contains response-surface and exposure-index models as special cases, thereby unifying the two analysis strategies. This unification increases the range of models possible for analysing environmental mixtures and health, allowing one to select an appropriate analysis from a spectrum of models varying in flexibility and interpretability. In an analysis of the association between telomere length and 18 organic pollutants in the National Health and Nutrition Examination Survey (NHANES), the proposed approach fits the data as well as more complex response-surface methods and yields more interpretable results.

Incorporating biological knowledge in analyses of environmental mixtures and health.

A key goal of environmental health research is to assess the risk posed by mixtures of pollutants. As epidemiologic studies of mixtures can be expensive to conduct, it behooves researchers to incorporate prior knowledge about mixtures into their analyses. This work extends the Bayesian multiple index model (BMIM), which assumes the exposure-response function is a nonparametric function of a set of linear combinations of pollutants formed with a set of exposure-specific weights. The framework is attractive because it combines the flexibility of response-surface methods with the interpretability of linear index models. We propose three strategies to incorporate prior toxicological knowledge into construction of indices in a BMIM: (a) imposing directional homogeneity constraints on the weights, (b) structuring index weights by exposure transformations, and (c) placing informative priors on the index weights. We propose a novel prior specification that combines spike-and-slab variable selection with an informative Dirichlet distribution based on relative potency factors often derived from previous toxicological studies. In simulations we show that the proposed priors improve inferences when prior information is correct and can protect against misspecification suffered by naïve toxicological models when prior information is incorrect. Moreover, different strategies may be mixed-and-matched for different indices to suit available information (or lack thereof). We demonstrate the proposed methods on an analysis of data from the National Health and Nutrition Examination Survey and incorporate prior information on relative chemical potencies obtained from toxic equivalency factors available in the literature.

The impact of correlated exposures and missing data on multiple informant models used to identify critical exposure windows.

There has been heightened interest in identifying critical windows of exposure for adverse health outcomes; that is, time points during which exposures have the greatest impact on a person's health. Multiple informant models implemented using generalized estimating equations (MIM GEEs) have been applied to address this research question because they enable statistical comparisons of differences in associations across exposure windows. As interest rises in using MIMs, the feasibility and appropriateness of their application under settings of correlated exposures and partially missing exposure measurements requires further examination. We evaluated the impact of correlation between exposure measurements and missing exposure data on the power and differences in association estimated by the MIM GEE and an inverse probability weighted extension to account for informatively missing exposures. We assessed these operating characteristics under a variety of correlation structures, sample sizes, and missing data mechanisms considering various exposure-outcome scenarios. We showed that applying MIM GEEs maintains higher power when there is a single critical window of exposure and exposure measures are not highly correlated, but may result in low power and bias under other settings. We applied these methods to a study of pregnant women living with HIV to explore differences in association between trimester-specific viral load and infant neurodevelopment.

HEPA filtration intervention in classrooms may improve some students' asthma.

OBJECTIVE: The School Inner-City Asthma Intervention Study 2 (SICAS 2) tested interventions to reduce exposures in classrooms of students with asthma. The objective of this post-hoc analysis was limited to evaluating the effect of high-efficiency particulate (HEPA) filtration interventions on mold levels as quantified using the Environmental Relative Moldiness Index (ERMI) and the possible improvement in the students' asthma, as quantified by spirometry testing. METHODS: Pre-intervention dust samples were collected at the beginning of the school year from classrooms and corresponding homes of students with asthma (n = 150). Follow-up dust samples were collected in the classrooms at the end of the HEPA or Sham intervention. For each dust sample, ERMI values and the Group 1 and Group 2 mold levels (components of the ERMI metric) were quantified. In addition, each student's lung function was evaluated by spirometry testing, specifically the percentage predicted forced expiratory volume at 1 sec (FEV1%), before and at the end of the intervention. RESULTS: For those students with a higher Group 1 mold level in their pre-intervention classroom than home (n = 94), the FEV1% results for those students was significantly (p < 0.05) inversely correlated with the Group 1 level in their classrooms. After the HEPA intervention, the average Group 1 and ERMI values were significantly lowered, and the average FEV1% test results significantly increased by an average of 4.22% for students in HEPA compared to Sham classrooms. CONCLUSIONS: HEPA intervention in classrooms reduced Group 1 and ERMI values, which corresponded to improvements in the students' FEV1% test results.

Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality.

BACKGROUND: Previous studies on the impact of measurement error for PM2.5 were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM2.5 concentration may vary by time and region. We aim to correct the measurement error of PM2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM2.5 and mortality in the Medicare Cohort. METHODS: The "gold-standard" measurements of PM2.5 were defined as daily monitoring data. We regressed daily monitoring PM2.5 on modeled PM2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM2.5 was calculated with stratum-specific calibration parameters ß0 (intercept) and ß1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models. RESULTS: Across 208 strata, the median of ß0 and ß1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM2.5 data, we estimated that each 10 µg/m3 increase in PM2.5 was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders. CONCLUSIONS: Regression calibration parameters of PM2.5 varied by time and region. Using error-prone measures of PM2.5 underestimated the association between PM2.5 and all-cause mortality. Modern exposure models produce relatively small bias.

Disparities in joint exposure to environmental and social stressors in urban households in Greater Boston.

OBJECTIVES: Understanding how environmental and social stressors cluster is critical to explaining and addressing health disparities. It remains unclear how these stressors cluster at fine spatial resolution in low to medium-income, urban households. We explored patterns of environmental and social exposures at the household-level and potential predictors of these joint exposures in two environmental justice communities in the Greater Boston area. METHODS: We recruited 150 households in Chelsea, MA and the Dorchester neighborhood of Boston, MA, between 2016 and 2019 and collected data on two domains: environmental and social stressor. For each domain, we fit Latent Class Analysis (LCA) models to exposure data to assess intra-domain variability, and cross-classified the resultant classes to identify joint exposure profiles. We compared differences in the distribution of these profiles by participants' demographic and household characteristics using χ2, Fisher's exact, Analysis of Variance, and Kruskal-Wallis tests. RESULTS: We identified two latent classes in each domain: High environmental (n = 90; 60.4%), Low environmental (n = 59; 39.6%), High Social (n = 31; 20.8%), and Low Social (n = 118; 79.2%). Cross-classification yielded four joint exposure profiles: Both Low (n = 46, 30.9%); Both High (n = 18, 12.1%); High environmental-Low Social (n = 72, 48.3%); and Low environmental-High Social (n = 13, 8.7%). Significant group differences were found by housing type (e.g., single-family vs. multi-family) (Fisher's exact p = 0.0016), housing tenure (p = 0.0007), and study site (p < 0.0001). We also observed differences by race/ethnicity, income, and education: households that were Hispanic/Latinx, below the poverty level, and with lower education were more likely to be in the Both High group. CONCLUSIONS: Our analyses confirmed that environmental and social stressors cluster in socially disadvantaged households. Housing type, housing tenure, and location of the residence were also strong predictors of cluster membership, with renter and multi-family residents at risk of high exposures to environmental and social stressors.