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PurposeInflammatory breast cancer (IBC) is an aggressive variant characterized by erythema, edema, and “peau d’orange” of the skin progressing within 6 months. We assessed the incidence and survival of IBC in the US over four decades.MethodsUsing SEER*Stat, a case list of IBC patients diagnosed between 1973 and 2015 (n = 29,718) was extracted from SEER 18 registries by using a combination of morphology, stage, and extent of disease criteria. M1 and M0 patients were included. Age-adjusted incidence rates, relative survival rates, and mean survival time were calculated. Significance was determined as non-overlapping 95% confidence intervals.ResultsThe overall incidence of IBC from 1973 to 2015 is 2.76 (2.73, 2.79) cases per 100,000 people, with white patients having an incidence rate of 2.63 (2.60, 2.67), black patients 4.52 (4.39, 4.65), and patients of other race 1.84 (1.76, 1.93). The overall IBC relative 5-year survival rate is 40.5% (39.0%, 42.0%), 42.5% (40.7%, 44.3%), and 29.9% (26.6%, 33.3%) for white patients and black patients, respectively. Patients diagnosed in 1978–1982 have a mean survival time of 62.3 (52.0, 72.6) months, while those diagnosed in 2008–2012 have mean survival time of 99.4 (96.4, 102.4) months. There is no significant difference in survival time between T4D patients and patients with other T staging and extent of disease coding consistent with clinical IBC presentation.ConclusionsIBC survival has increased over four decades. Despite the improvement in survival for all racial groups, a persistent survival disparity that has not narrowed over two decades remains between white and black patients.

Diverse toxicological mechanisms may mediate the impact of environmental toxicants (phthalates, phenols, polycyclic aromatic hydrocarbons, and metals) on pregnancy outcomes. In this study, we introduce an analytical framework for multivariate mediation analysis to identify mediation pathways ( q  = 61 mediators) in the relationship between environmental toxicants ( p  = 38 analytes) and gestational age at delivery. Our analytical framework includes: (1) conducting pairwise mediation for unique exposure-mediator combinations, (2) exposure dimension reduction by estimating environmental risk scores, and (3) multivariate mediator analysis using either Bayesian shrinkage mediation analysis, population value decomposition, or mediation pathway penalization. Dimension reduction demonstrates that a one-unit increase in phthalate risk score is associated with a total effect of 1.07 lower gestational age (in weeks) at delivery (95% confidence interval: 0.48–1.67) and eicosanoids from the cytochrome p450 pathway mediated 26% of this effect (95% confidence interval: 4–63%). Eicosanoid products derived from the cytochrome p450 pathway may be important mediators of phthalate toxicity. Diverse toxicological mechanisms may mediate the impact of environmental toxicants on pregnancy outcomes. In this study the authors introduce an analytical framework for multivariate mediation analysis to identify mediation pathways in the relationship between environmental toxicants and gestational age at delivery.

Background There is growing concern of health effects of exposure to pollutant mixtures. We initially proposed an Environmental Risk Score (ERS) as a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research considering only pollutant main effects. We expand the ERS by consideration of pollutant-pollutant interactions using modern machine learning methods. We illustrate the multi-pollutant approaches to predicting a marker of oxidative stress (gamma-glutamyl transferase (GGT)), a common disease pathway linking environmental exposure and numerous health endpoints. Methods We examined 20 metal biomarkers measured in urine or whole blood from 6 cycles of the National Health and Nutrition Examination Survey (NHANES 2003–2004 to 2013–2014, n  = 9664). We randomly split the data evenly into training and testing sets and constructed ERS’s of metal mixtures for GGT using adaptive elastic-net with main effects and pairwise interactions (AENET-I), Bayesian additive regression tree (BART), Bayesian kernel machine regression (BKMR), and Super Learner in the training set and evaluated their performances in the testing set. We also evaluated the associations between GGT-ERS and cardiovascular endpoints. Results ERS based on AENET-I performed better than other approaches in terms of prediction errors in the testing set. Important metals identified in relation to GGT include cadmium (urine), dimethylarsonic acid, monomethylarsonic acid, cobalt, and barium. All ERS’s showed significant associations with systolic and diastolic blood pressure and hypertension. For hypertension, one SD increase in each ERS from AENET-I, BART and SuperLearner were associated with odds ratios of 1.26 (95% CI, 1.15, 1.38), 1.17 (1.09, 1.25), and 1.30 (1.20, 1.40), respectively. ERS’s showed non-significant positive associations with mortality outcomes. Conclusions ERS is a useful tool for characterizing cumulative risk from pollutant mixtures, with accounting for statistical challenges such as high degrees of correlations and pollutant-pollutant interactions. ERS constructed for an intermediate marker like GGT is predictive of related disease endpoints.

Phthalate exposure occurs readily in the environment and has been associated with an array of health end points, including adverse birth outcomes. Some of these may be mediated by oxidative stress, a proposed mechanism for phthalate action. In the present study, we explored the associations between phthalate metabolites and biomarkers of oxidative stress measured in urine samples from multiple time points during pregnancy. Women were participants in a nested case-control study of preterm birth (n = 130 cases, n = 352 controls). Each was recruited early in pregnancy and followed until delivery, providing urine samples at up to four visits. Nine phthalate metabolites were measured to assess exposure, and 8-hydroxydeoxyguanosine and 8-isoprostane were also measured in urine as markers of oxidative stress. Associations were assessed using linear mixed models to account for intraindividual correlation, with inverse selection probability weightings based on case status to allow for greater generalizability. Interquartile range increases in phthalate metabolites were associated with significantly higher concentrations of both biomarkers. Estimated differences were greater in association with monobenzyl phthalate (MBzP), mono-n-butyl phthalate (MBP), and monoisobutyl phthalate (MiBP), compared with di(2-ethylhexyl) phthalate (DEHP) metabolites. Urinary phthalate metabolites were associated with increased oxidative stress biomarkers in our study population of pregnant women. These relationships may be particularly relevant to the study of birth outcomes linked to phthalate exposure. Although replication is necessary in other populations, these results may also be of great importance for a range of other health outcomes associated with phthalates.

Despite the importance of human behavior in containing a disease outbreak, formal quantitative analyses examining the relationship between measures of trust and COVID-19 outcomes remain limited. We use data from Wave 7 (2017–2022) of the World Values Survey to assess the country-level relationship between trust and COVID-19 outcomes across 61 countries via clustering and regression. After adjusting for country-level confounders, our findings indicate that countries with low trust have significantly greater numbers of COVID-19 deaths (1200.6 more COVID-19 deaths per million, 95% CI [510.92, 1890.3]), significantly greater excess death (2289.1 more excess deaths per million, 95% CI [971.1, 3607.2]), and a lower vaccination rate (16.6 fewer people vaccinated per 100, 95% CI [−27.7, −5.6]) than high trust countries, suggesting a tangible impact of trust on country-level COVID-19 outcomes. We discuss differences between interpersonal and institutional trust and advocate for incorporating trust in disease modeling to better predict country-level outcomes.

Standard prospective logistic regression analysis of case-control data often leads to very imprecise estimates of gene-environment interactions due to small numbers of cases or controls in cells of crossing genotype and exposure. In contrast, under the assumption of gene-environment independence, modern \"retrospective\" methods, including the \"case-only\" approach, can estimate the interaction parameters much more precisely, but they can be seriously biased when the underlying assumption of gene-environment independence is violated. In this article, we propose a novel empirical Bayes-type shrinkage estimator to analyze case-control data that can relax the gene-environment independence assumption in a data-adaptive fashion. In the special case, involving a binary gene and a binary exposure, the method leads to an estimator of the interaction log odds ratio parameter in a simple closed form that corresponds to an weighted average of the standard case-only and case-control estimators. We also describe a general approach for deriving the new shrinkage estimator and its variance within the retrospective maximum-likelihood framework developed by Chatterjee and Carroll (2005, Biometrika92, 399-418). Both simulated and real data examples suggest that the proposed estimator strikes a balance between bias and efficiency depending on the true nature of the gene-environment association and the sample size for a given study.

Per- and polyfluoroalkyl substances (PFAS) are associated with less favorable blood lipid profiles in epidemiological studies. However, little is known about the potential role of PFAS in longitudinal changes in lipids among midlife women even though women become more susceptible to metabolic alterations during the menopausal transition. To examine associations of serum PFAS concentrations with longitudinal trajectories of blood total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides in midlife women undergoing menopausal transition. The sample included 1,130 women from the Study of Women's Health Across the Nation 45-56 y of age at baseline (1999-2000). We measured serum PFAS concentrations including linear perfluorooctanoic acid (n-PFOA), perfluorononanoic acid (PFNA), linear and branched perfluorooctanesulfonic acid (n-PFOS and Sm-PFOS, respectively), and perfluorohexanesulfonic acid (PFHxS) at baseline. We used -means clustering to identify subgroups with different patterns of PFAS mixture. Blood lipids were measured annually or biannually through 2016 with an average follow-up of 14.8 y. We identified longitudinal trajectories of each lipid using latent class growth models. We used multinomial log-linear models adjusted for covariates to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of lipid trajectory classes by PFAS and their mixtures. Three distinct trajectories (low, middle, high) of total, LDL, and HDL cholesterol and two distinct trajectories (low and high) of triglycerides were identified. n-PFOS, Sm-PFOS, and PFHxS were positively associated with total and LDL cholesterol trajectories. n-PFOS was inversely associated with triglycerides trajectories. PFAS mixtures (high vs. low) showed positive associations with total and LDL cholesterol trajectories (high vs. low), showing ORs (95% CIs) of 1.69 (95% CI: 1.36, 2.12) and 1.79 (95% CI: 1.44, 2.22), respectively. Concentrations of serum PFAS were positively associated with trajectories of total and LDL cholesterol, providing a line of evidence supporting adverse effects of PFAS on lipid homeostasis. https://doi.org/10.1289/EHP12351.

Susceptible-Exposed-Infected-Removed (SEIR)-type epidemiologic models, modeling unascertained infections latently, can predict unreported cases and deaths assuming perfect testing. We apply a method we developed to account for the high false negative rates of diagnostic RT-PCR tests for detecting an active SARS-CoV-2 infection in a classic SEIR model. The number of unascertained cases and false negatives being unobservable in a real study, population-based serosurveys can help validate model projections. Applying our method to training data from Delhi, India, during March 15–June 30, 2020, we estimate the underreporting factor for cases at 34–53 (deaths: 8–13) on July 10, 2020, largely consistent with the findings of the first round of serosurveys for Delhi (done during June 27–July 10, 2020) with an estimated 22.86% IgG antibody prevalence, yielding estimated underreporting factors of 30–42 for cases. Together, these imply approximately 96–98% cases in Delhi remained unreported (July 10, 2020). Updated calculations using training data during March 15-December 31, 2020 yield estimated underreporting factor for cases at 13–22 (deaths: 3–7) on January 23, 2021, which are again consistent with the latest (fifth) round of serosurveys for Delhi (done during January 15–23, 2021) with an estimated 56.13% IgG antibody prevalence, yielding an estimated range for the underreporting factor for cases at 17–21. Together, these updated estimates imply approximately 92–96% cases in Delhi remained unreported (January 23, 2021). Such model-based estimates, updated with latest data, provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths and gauging the true extent of the pandemic.

Aims/hypothesisDiabetogenic effects of per- and polyfluoroalkyl substances (PFAS) have been suggested. However, evidence based on prospective cohort studies is limited. We examined the association between serum PFAS concentrations and incident diabetes in the Study of Women’s Health Across the Nation Multi-Pollutant Study (SWAN-MPS).MethodsWe included 1237 diabetes-free women aged 45–56 years at baseline (1999–2000) who were followed up to 2017. At each follow-up visit, women with incident diabetes were identified by the presence of one or more of the following conditions: (1) use of a glucose-lowering medication at any visit; (2) fasting glucose ≥7 mmol/l on two consecutive visits while not on steroids; and (3) any two visits with self-reported diabetes and at least one visit with fasting blood glucose ≥7 mmol/l. Serum concentrations of 11 PFAS were quantified by online solid-phase extraction–HPLC–isotope dilution–tandem MS. Seven PFAS with high detection rates (>96%) (n-perfluorooctanoic acid [n-PFOA], perfluorononanoic acid [PFNA], perfluorohexane sulfonic acid [PFHxS], n-perfluorooctane sulfonic acid [n-PFOS], sum of perfluoromethylheptane sulfonic acid isomers [Sm-PFOS], 2-[N-methyl-perfluorooctane sulfonamido] acetic acid [MeFOSAA] and 2-[N-ethyl-perfluorooctane sulfonamido] acetic acid) were included in data analysis. Cox proportional hazards models were used to compute HRs and 95% CIs. Quantile-based g-computation was used to evaluate the joint effects of PFAS mixtures.ResultsAfter adjustment for race/ethnicity, site, education, smoking status, alcohol consumption, total energy intake, physical activity, menopausal status and BMI, the HR (95% CI) comparing the lowest with the highest tertile was 1.67 (1.21, 2.31) for n-PFOA (ptrend = 0.001), 1.58 (1.13, 2.21) for PFHxS (ptrend = 0.003), 1.36 (0.97, 1.90) for Sm-PFOS (ptrend = 0.05), 1.85 (1.28, 2.67) for MeFOSAA (ptrend = 0.0004) and 1.64 (1.17, 2.31) for the sum of four common PFAS (n-PFOA, PFNA, PFHxS and total PFOS) (ptrend = 0.002). Exposure to seven PFAS as mixtures was associated with an HR of 2.62 (95% CI 1.12, 6.20), comparing the top with the bottom tertiles for all seven PFAS.Conclusions/interpretationThis study suggests that PFAS may increase diabetes risk in midlife women. Reduced exposure to these ‘forever and everywhere chemicals’ may be an important preventative approach to lowering population-wide diabetes risk.

Preeclampsia represents a major cause of maternal mortality and morbidity worldwide. Although it is known that the placenta plays a central role in development of preeclampsia, investigation into the contribution of environmental toxicants to the risk of preeclampsia has been sparse. In the present study we examined the relationship between longitudinally measured urinary BPA and phthalate metabolite concentrations during gestation and preeclampsia. A nested case-control study of preterm birth was performed in 2011 from women enrolled in a prospective birth cohort study at Brigham and Women's Hospital in Boston. There were 50 cases of preeclampsia as part of this study. Urine samples were analyzed for concentrations of BPA and nine phthalate metabolites several times during pregnancy. Adjusted Cox proportional hazard models were used to calculate hazard ratios of preeclampsia in association with an interquartile range increase in BPA and phthalate concentrations and were weighted to reflect results generalizable to the base population. Adjusted hazard ratios indicated that an interquartile range increase of urinary concentrations of BPA (1.53; 95% CI: 1.04, 2.25) and MEP (monoethyl phthalate) (1.72; 95% CI: 1.28, 2.30) at 10 weeks gestation was associated with onset of preeclampsia, whereas significantly elevated hazard ratios were found across gestation for all DEHP [di(2-ethylhexyl) phthalate] metabolites. These relationships differed based on infant sex. Urinary concentrations of BPA and several phthalate metabolites were significantly associated with increased risk of preeclampsia. If validated, these results indicate an environmental contribution of endocrine-disrupting chemicals to preeclampsia and suggest a modifiable means to reduce the mortality and morbidity associated with this condition. Cantonwine DE, Meeker JD, Ferguson KK, Mukherjee B, Hauser R, McElrath TF. 2016. Urinary concentrations of bisphenol A and phthalate metabolites measured during pregnancy and risk of preeclampsia. Environ Health Perspect 124:1651-1655; http://dx.doi.org/10.1289/EHP188.