搜索结果：Journal of exposure science & environmental epidemiology

Exposure to nitrogen dioxide (NO2), ozone (O3), fine particulate matter (PM2.5), and heat has previously been associated with preterm birth. However, individuals are often exposed to a mixture of pollutants, which may exacerbate health effects. Unsupervised neural networks are highly suitable for characterizing high-dimensional exposures, but few studies of environmental mixtures have utilized these algorithms. To implement a novel epidemiologic machine learning framework for linking high-dimensional mixture exposures and health outcomes, with an application in preterm birth and residential exposure to PM2.5, O3, NO2, and temperature. Data comes from a retrospective cohort of 44,874 individuals living in Utah who gave birth for the first time between 2013 and 2016. Fine-scale air pollution estimates were used to create a high-dimensional self-organizing map (a type of unsupervised neural network) of exposure mixtures. We used cluster analysis to group similar weekly exposure mixtures. Pregnancies were linked to the clusters, and Bayesian mixed-effects logistic regression was used to estimate odds ratios at each gestational week with false discovery rate (FDR) multiple comparison adjustments. Exposure to certain mixtures at critical windows was associated with a higher risk of preterm birth. In particular, maternal exposure to Cluster 10 (a high O3 and PM2.5 mixture) in weeks 9-14 was associated with up to 53% greater risk of preterm birth, peaking in week 11 (ORweek11: 1.53, 95% BCI [1.12, 2.08], Cr = 99.7%). Repeated exposure for this entire period (weeks 9-14) was associated with 2.8-times greater risk of preterm birth (OR: 2.81, 95% BCI [1.99, 3.96], Cr = 100%). Using a novel machine learning approach, we identified several patterns of exposure to mixtures, primarily composed of O3 and PM2.5, which may be associated with preterm birth at critical windows. The proposed framework reduces complexity while preserving time-varying exposures. Exposure to air pollution mixtures at critical times during pregnancy may have synergistic effects on the risk of preterm birth, and few studies have examined these relationships using neural networks. We propose an epidemiologic machine learning framework for dimension reduction, which assigns exposure types to individuals over time and allows for estimation of odds ratios of the outcome of interest. Using this method, we describe patterns of exposure to mixtures of NO2, O3, PM2.5, and heat and estimate their effects on preterm birth.

The development of smoke fine particulate matter (PM2.5) exposure surfaces for estimating air pollution trends and associated health effects has advanced considerably. Currently available smoke exposure products rely on various data sources and modeling techniques, as there is no gold standard method for modeling wildfire smoke PM2.5. This study compares multiple daily smoke PM2.5 data sets developed using diverse methodologies spanning 2008-2018. Incorporating metrics for short- and long-term exposure, we compare four data sets at the census tract level in California: one using the U.S. Environmental Protection Agency's chemical transport model (CTM), the Community Multiscale Air Quality Modeling System (CMAQ); two using statistical methods, also referred to as machine learning (ML) techniques; and one combining these approaches to develop an ML-calibrated CTM-based exposure surface. Our analysis highlights differences between the data sets in terms of long-term exposure metrics, with the CTM data set estimating the highest concentrations overall, and considerable differences between estimates produced by the two ML models. An analysis of six case studies of large fires across the state finds that even data sets with similar inputs and methods produced estimates that varied several-fold, with additional differences by region and over time. Our findings have important implications for quantifying smoke PM2.5 exposures for use in population health impact studies, which rely on exposure estimates to accurately estimate health burden from pollution exposure. Over the last several decades, wildfires in the western U.S. have become more frequent and severe, exposing millions of people to toxic smoke pollution, including fine particulate matter (PM2.5). Several methods have emerged for modeling population exposure to this pollution. In this study, we compare four different smoke PM2.5 pollution models for California from 2008 to 2018. Our findings show significant differences in estimated smoke pollution exposure in both the short‐ and long‐term, even between similar modeling approaches. Many studies calculate the health burden of wildfire smoke exposure by comparing short‐ and long‐term health effects to short‐ and long‐term smoke pollution. Our findings suggest that health impact studies using smoke exposure estimates as a primary input may calculate differing health burden results based on the choice of smoke pollution data set.

Many pre-pregnancy and gestational exposures, such as maternal physical health, behavioral factors, psychosocial adversities, and environmental conditions during pregnancy, influence birth outcomes. These risk factors can co-occur, particularly among populations that experience health disparities, yet much of past researchers tend to examine them in isolation, which may lead inadvertently to some underestimation or mis-specification of risk. Therefore, this study examined the joint contribution of several maternal physical health, behaviors, psychosocial adversities, and environmental conditions during pregnancy on birth outcomes, and whether disparities were evident according to infant sex, maternal race/ethnicity, and education attainment. Participants were from the Albany Infant and Mother Study, a prospective observational cohort of socioeconomic and racial/ethnic diverse pregnant women and their infants (n&#x2009;=&#x2009;291). Latent Class Analysis was employed to identify discrete groups with different levels and combinations of the following prenatal exposures: maternal health (pre-pregnancy body mass index, pregnancy conditions), behaviors (smoking, recreational drug use, western diet), psychosocial (depression, adverse childhood experiences, social support), and physical environment (lead exposure, indoor environmental contaminants). Linear regression analysis tested associations between exposure classes and birth outcomes. Three latent classes were identified: Class 1 (47.7%; low-risk in all domains), Class 2 (42.9%; high-risk across all domains), and Class 3 (9.4%; high-risk in behavioral and environmental domains, but lower health and psychosocial risks). Compared to the low-risk referent Class 1, Class 2 was associated with lower birth weight, length, head circumference, more restricted fetal growth, and earlier gestational age at delivery (all p&#x2009;<&#x2009;0.05). Class 3 infants also had marginally lower birth weight and increased intrauterine growth restriction (all p&#x2009;<&#x2009;0.10). Associations were strongest among male infants, and participants from racial/ethnic and lower education backgrounds. This comprehensive examination of the combined effects of multiple exposures-encompassing physical health, behavioral, psychosocial and environmental domains- indicated that different patterns of multiple perinatal risks work in combination to influence birth outcomes and disparities at the earliest time in the life course. Research and practice focused on inequalities in maternal and perinatal health should consider the coordinated impacts of multiple risks simultaneously.

Age-specific patterns of malaria are well-established for children aged &#xa0;< 5 years. Less understood is the epidemiology of malaria in older children and adults, and the influence of granular environmental risk. We analyzed data from SchistoTrack, a community-based cohort in rural Uganda. We studied 4308 participants aged 5 to 90 years from 52 villages across three lakeside districts of Mayuge, Buliisa, and Pakwach, with enrollment between January 2022 to February 2024. The primary outcome was malaria infection status by rapid diagnostic test (RDT). Secondary outcomes included microscopy-confirmed infection with parasite density quantification and self-reported fever within the past month. We fitted a generalized additive mixed model (GAMM) with adaptive age smoothing, adjusting for sociodemographic factors, household characteristics, healthcare access, and environmental exposures. Environmental exposure was quantified using the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 satellite imagery (10 m resolution), processed through hexagonal aggregation with Gaussian neighborhood smoothing and validated against field malacology surveys and participatory community mapping. Overall RDT prevalence was 41.2% (1776/4308), with microscopy prevalence at 32.3% (1363/4219), which was predominantly Plasmodium falciparum (83.1%; 1133/1363). Most infections were low-density (&#xa0;< 999 parasites/&#x3bc;L; 71.6%; 976/1363). Malaria prevalence showed non-linear age patterns, peaking at 10 to 11 years then declining through adolescence before stabilizing in adulthood. Among RDT-positive individuals, fever prevalence decreased with age from 30.8% in children (aged 5 to 10 years) to 11.2% in adults (aged &#x2265;20 years). Dense vegetation (per unit NDVI increase: Odds Ratio (OR) 3.25, 95% Confidence Interval (CI) 1.33-7.96) and greater distance from government health centers (per log-km: OR 1.87, 95% CI 1.34-2.59) increased the odds of infection. Proximity to vegetated water bodies increased the odds of infection compared to beaches: ponds/swamps (OR 1.65, 95% CI 1.19-2.28), river/river marsh (OR 1.63, 95% CI 1.16-2.31), lake marsh (OR 1.40, 95% CI 1.07-1.83). Malaria prevalence remains high in older children and adults, though with fewer febrile cases, and is influenced by the local environment. Our findings support age-specific interventions targeting school-aged children while maintaining adult surveillance, and using validated environmental indices to guide sub-district resource allocation in high-risk areas.

The aim of this systematic overview was to summarize and synthesize systematic reviews (SRs) with meta-analyses of the association between occupational mechanical exposures and low-back and lower-body musculoskeletal disorders (MSDs). A protocol was registered in PROSPERO and a systematic literature search was conducted in PubMed, Web of Science, Embase, and CINAHL. Systematic reviews were considered eligible if they included occupational mechanical exposures and provided weighted measures of association in relation to specific MSDs. Data were extracted from each SR, including databases searched, exposure, outcome, effect estimates, and quality of evidence of the association. The methodological quality of each SR was assessed using AMSTAR 2. The screening of 1,756 articles resulted in the inclusion of 17 SRs with meta-analyses covering 7 MSDs: chronic low back pain (n = 1), lumbar spine degeneration (n = 2), lumbosacral radiculopathy (n = 1), hip osteoarthritis (n = 7), inguinal hernia (n = 1), meniscal lesions (n = 1), knee osteoarthritis (n = 4), and knee and/or hip osteoarthritis (n = 1). Methodological quality assessment using the AMSTAR 2 tool revealed high confidence in 5 SRs, while 4 were rated as low and 8 as critically low confidence. The weighted odds ratios (OR) from the meta-analyses generally ranged between 1.3 and 2.8, although substantial heterogeneity was observed. Among the included SRs, 50% graded the certainty of evidence, and among these 60% graded it as low or very low. This overview demonstrates that most occupational mechanical exposures show directionally consistent associations (OR >1) with an increased risk of developing lower-body MSDs. However, as the quality of evidence was generally graded as low, the true effect may be substantially different from the reported estimates. Future research should prioritize prospective studies and objective exposure assessments to strengthen the quality of evidence.

Gestational environmental chemical exposures are widespread. Some chemicals are known to adversely affect birth outcomes, but many remain understudied. To evaluate associations of gestational exposure to a priori identified chemicals in 10 classes with birth outcomes in a large, diverse US cohort. In the prospective Environmental influences on Child Health Outcomes Cohort study, 5318 mother-child pairs were enrolled from January 1, 2000, to December 31, 2021, with data on gestational urinary chemical concentrations, gestational age at birth, and birth weight. Statistical analysis was performed from January 2024 to February 2026. In single, midgestation (median, 25 weeks [IQR, 21-30 weeks]) urine samples, concentrations of 113 analytes (chemicals or their metabolites) from 10 chemical classes were simultaneously measured: fungicides and herbicides (n&#x2009;=&#x2009;11), insecticides (n&#x2009;=&#x2009;20), halogenated phenols (n&#x2009;=&#x2009;5), organophosphate esters (n&#x2009;=&#x2009;10), benzophenones (n&#x2009;=&#x2009;6), bisphenols (n&#x2009;=&#x2009;14), parabens (n&#x2009;=&#x2009;6), antimicrobials (n&#x2009;=&#x2009;2), phthalates or alternative plasticizers (n&#x2009;=&#x2009;32), and polycyclic aromatic hydrocarbons (PAHs) (n&#x2009;=&#x2009;7). Linear mixed-effects regression models with a random effect for site were used to estimate covariate-adjusted differences in gestational age at birth (days) and birth weight-for-gestational age (BW-GA) z scores per IQR increase in urinary analyte concentrations. In secondary analyses, odds ratios (ORs) for preterm birth and small for gestational age (SGA) were estimated. In the sample of 5318 mother-child pairs, most infants (2667 female [50%]; median gestational age at birth, 39.0 weeks [IQR, 38.0-40.0 weeks]) were born to college-educated (67% [3218 of 4785]), parous (56% [2815 of 5007]) mothers (median age at delivery, 30.7 years [IQR, 26.1-34.3 years]). A total of 43 of 113 analytes (38%) were detected in 50% or more of samples. Multiple phthalates or alternative plasticizers were associated with younger gestational age at birth or lower BW-GA z scores; for example, summed diisononyl phthalate metabolites were associated with a 0.6-day (95% CI, -1.0 to -0.1 days) younger gestational age (preterm birth OR, 1.16 [95% CI, 1.01-1.34]), and summed phthalate or alternative plasticizers were associated with a 0.06 (95% CI, -0.11 to -0.02) lower BW-GA z score (SGA OR, 1.09 [95% CI, 0.93-1.27]). Two halogenated phenols, benzophenone 8, bisphenol F, and several PAHs were associated with lower BW-GA z scores; for example, 1- and 9-hydroxphenanthrene were associated with a 0.04 (95% CI, -0.08 to -0.01) lower BW-GA z score (SGA OR, 1.13 [95% CI, 1.01-1.27]). This large cohort study of diverse US pregnancies found widespread exposure to 10 classes of environmental chemicals, many of which were associated with differences in gestational age at birth or lower BW-GA z scores. These findings indicate that reducing gestational exposure to chemicals, particularly phthalates or alternative plasticizers and PAHs, could promote healthy deliveries and better child outcomes.

Few conditions in reproductive medicine rival polycystic ovary syndrome (PCOS) in terms of clinical breadth and global impact. Affecting roughly 6-21% of women of childbearing age depending on which diagnostic criteria are applied PCOS sits at the intersection of endocrinology, metabolism, and gynecology, making it difficult to capture within any single disciplinary lens. Its hallmarks are well rehearsed: excess androgens, disrupted ovulation, and the characteristic follicular architecture seen on pelvic ultrasound. Yet what makes PCOS genuinely challenging is the degree to which these reproductive features overlap with far-reaching metabolic consequences, including insulin resistance, type 2 diabetes, lipid abnormalities, and a meaningfully elevated cardiovascular risk profile that persists well beyond the fertile years. Despite several decades of sustained investigation, the origins of PCOS remain imperfectly understood. Genetic susceptibility, epigenetic programming, environmental chemical exposures, and modern dietary habits all appear to play contributory roles, though no single culprit has emerged. The molecular picture is equally layered: aberrant insulin signaling feeds androgen overproduction, gonadotropin secretion goes out of balance, inflammatory cytokines accumulate, oxidative injury mounts, and more recently the gut microbial community has been implicated as an additional participant in this cascade. Diagnosis is further complicated by the phenotypic variability of the syndrome, with different criteria yielding meaningfully different patient populations. Treatment, in turn, requires individualization; lifestyle change, hormonal therapies, insulin sensitizers, and an expanding repertoire of repurposed drugs and plant-based agents each address different facets of a fundamentally heterogeneous disorder. This review provides a comprehensive and integrated account of PCOS across its full biological and clinical spectrum. It covers epidemiology, clinical presentation, risk factors, and current diagnostic frameworks. Pathophysiological mechanisms are examined in depth. A central and distinctive focus of this review is the experimental preclinical landscape. Established animal induction models, letrozole, dehydroepiandrosterone (DHEA), testosterone/dihydrotestosterone propionate, and high-fat diet protocols are critically evaluated for their translational relevance. Drawing on these models, we comprehensively catalogue protective agents across four systematic tables, encompassing both repurposed pharmaceuticals (metformin, GLP-1 receptor agonists, SGLT-2 inhibitors, statins, melatonin) and bioactive natural compounds (curcumin, berberine, quercetin, fisetin, myricetin, apigenin, and others), detailing their induction models, mechanistic pathways, and therapeutic outcomes. Together, this review aims to serve as a single, authoritative reference bridging basic science, translational pharmacology, and clinical practice in PCOS, while identifying the most promising avenues for future research and personalized therapeutic development.

The impact of polycyclic aromatic hydrocarbons (PAHs) and metals on aging biomarkers-telomere length (TL) and mitochondrial DNA copy number (mtDNAcn)- has been investigated separately. However, their combined effects, particularly in highly exposed occupational populations, remain unexamined, despite their frequent co-occurrence in environmental settings. This study measured baseline urinary and blood samples from 867 coke oven workers at baseline for 33 chemical pollutants and mtDNAcn, and assessed leukocyte TL at both baseline and follow-up. The combined effect of metals and PAHs on aging biomarkers was evaluated using weighted quantum sum (WQS) regression and Bayesian kernel machine regression (BKMR), and mediation analyses were also conducted. Multiple linear regression incorporating 33 pollutants and least absolute shrinkage and selection operator (LASSO) regression were used to identify individual exposures with significant effects, while restricted cubic splines were applied to explore potential nonlinear dose-response relationships. We observed that both the WQS and BKMR indicated that 33 chemical mixtures exposure was significantly negatively associated with the TL ratio (follow-up/baseline) and positively associated with baseline mtDNAcn. Urinary 1-hydroxypyrene, molybdenum, selenium (Se), and barium were identified as the major contributors. Furthermore, oxidative stress and inflammation mediated the link of major individual pollutants as well as the chemical mixture to biological aging. Network toxicology analysis identified HSP90AA1 and HSP90AB1 as core molecular targets underlying the effect of Se on TL regulation. To our knowledge, this is the first study to investigate the joint effects and underlying mechanisms of co-exposure to PAHs and metals on biomarkers of biological aging.

Coal-fired power plant emissions have been associated with exacerbation of respiratory conditions and mortality, and unconventional natural gas development (UNGD) sites have gained attention as potential sources of soil contamination and air and water pollution. Growing evidence links environmental exposures, particularly air pollution, to the development and exacerbation of childhood asthma. Recent studies show that environmental exposures alter miRNA transcription, supporting their use as biomarkers of childhood asthma risk and pathogenesis. We collected saliva from 12 children with asthma and 41 age-matched controls without asthma in Pennsylvania. We estimated residential exposure to coal-fired PM2.5 power plant emissions using HYSPLIT Average Dispersion Scores (HyADS), while proximity-based metrics were used to characterize UNGD exposure. We assessed exposures over one-month, three-month, and one-year periods prior to saliva collection. Salivary miRNA was isolated and sequenced using HTG EdgeSeq. We performed a transcriptome-wide association study to evaluate associations between exposures, asthma status, and salivary miRNA expression. Nineteen salivary extracellular miRNAs were associated with asthma, and four were associated with coal PM2.5 emissions, particularly over the one- to three-month exposure windows. Several miRNAs associated with asthma and coal-related PM2.5 exposure clustered within functional families, including miR-29 and miR-101-3p. No significant associations were observed for distance to nearest UNGD. This study shows a salivary miRNA signature of both asthma and fossil fuel-related air pollution in children. These findings highlight the potential of salivary miRNAs as non-invasive biomarkers for respiratory diseases, underscoring the need for future studies to validate and extend these observations in larger, population-based cohorts.

Pregnancy complications pose a major hazard to maternal and fetal health and increase the global burden of disease. Phthalates are extensively encountered by pregnant women. To estimate associations of individual phthalate or mixture exposure in early pregnancy with pregnancy complications, a nested case-control study was conducted, in which cases with gestational diabetes mellitus (GDM), gestational hypertension (PIH), premature rupture of membranes (PROM), placental abnormality (PA), intrauterine distress (IUD), insufficient amniotic fluid (IAF), anemia and preterm birth (PB) were matched with controls by fetal sex, maternal age, sampling year and season, and measured urinary phthalate metabolites in the first trimester. Conditional logistic regression and quantile g-computation models were employed to assess associations of individual phthalate and mixture exposure with pregnancy complications. The conditional logistic regression revealed that certain phthalate exposure in early pregnancy was notably associated with higher risks of pregnancy complications, especially GDM, PIH, and IAF. For example, compared with the 1st tertile, the 2nd and 3rd tertiles of exposure were significantly associated with elevated odds of GDM, PIH, and IAF, with ORs of 1.40-1.76 and 1.38-2.98, 1.41-1.67 and 1.36-2.05, and 2.17-3.17 and 1.97-2.54, respectively. Moreover, these associations varied by fetal sex. Additionally, quantile g-computation models showed that phthalate mixture exposure was significantly associated with elevated ORs of GDM (OR = 1.32; 95% CI: 1.09, 1.74), PIH (OR = 1.49; 95% CI: 1.04, 1.78), and IAF (OR = 1.73; 1.06, 2.47). MBP and DEHP metabolites were likely the major contributors to the joint effects. Prenatal phthalate exposure during early pregnancy may serve as a potential risk factor for pregnancy complications. Further studies are essential to validate these findings.

Occupational and environmental epidemiology play a vital role in efforts to reduce preventable forms of disease and premature mortality worldwide by evaluating disease risk in relation to exposure sources, routes, and levels in a variety of industrial and community settings. There are considerable challenges in measuring these exposures due to wide variations in intensity, frequency, and duration. Because exposure levels and conditions often vary by site, comparing results across studies is also difficult. In this paper, we address these challenges and provide suggestions pertaining to the design, analysis, and reporting of individual studies as well as systemic reviews and meta-analyses. Key recommendations include reporting specific exposure levels within a study population and including quantitative bias assessment to address the impact of exposure measurement error on results within and across studies. These recommendations can improve the reporting quality and utility of occupational and environmental epidemiology, thereby strengthening its role in risk assessment.

This systematic review aims to provide evidence from current observational studies on whether Secondhand smoke (SHS) exposure is associated with increased risk of atrial fibrillation (AF) risk, so to identify potential high-risk subgroup to inform both mechanistic hypotheses and prevention strategies. A systematic screening of the PubMed, EMBASE and Cochrane Central databases was conducted until September 2025 to identify observational studies focused on the association between SHS and AF. Six observational studies involving more than 420,000 participants were included. Early-life SHS exposure, including gestational and childhood household exposure, was consistently associated with a higher risk of AF in adulthood. Evidence for adulthood SHS exposure was more heterogeneous; however, studies incorporating objective biomarkers or dose&#x2013;response assessments demonstrated clearer associations. Several studies reported stronger associations among women, Black individuals, never-smokers, and those with elevated genetic susceptibility. Notably, a large population-based cohort demonstrated a graded relationship between SHS exposure and incident AF, with evidence of gene&#x2013;environment interaction. Available evidence suggests that SHS exposure, particularly during early life, may be associated with an increased risk of AF. The observed heterogeneity by exposure window, assessment method, and population subgroup underscores the importance of life-course and equity-focused approaches to AF prevention. Reducing SHS exposure may represent a modifiable and underrecognized strategy for long-term AF risk reduction. The online version contains supplementary material available at 10.1186/s12889-026-26895-3.

Firefighters may experience occupational exposure to per- and polyfluoroalkyl substances (PFAS). Volunteer firefighters make up 65% of the US fire service, but their serum PFAS profiles have not been well characterized. This study aims to (1) describe PFAS serum profiles among US volunteer firefighters from 9 states enrolled in the Firefighter Cancer Assessment and Prevention Study (CAPS), and (2) identify predictors of their serum PFAS concentrations. Twelve PFAS were quantified in serum of 550 active volunteer firefighters collected at enrollment into CAPS between 2020 and 2023. Fire service, occupational, sociodemographic, and health predictors of interest were captured in the CAPS enrollment survey. PFAS detection frequencies, average concentrations, and distributions were characterized overall and by regional groups. Associations between serum PFAS and predictors of interest were assessed using multivariable generalized estimating equation models for all participants and by sex in those <&#x2009;50 years old. CAPS participants' serum PFAS profiles varied by geography, age, and sex. Firefighting, occupational, sociodemographic, and health factors were associated with serum PFAS in the full cohort. In sex-stratified models, some associations differed between males and females and from the full cohort. Stronger associations between sociodemographic and health factors with serum PFAS, with less strong associations between serum PFAS and occupational and fire service factors highlight the likely multifaceted exposure experience of volunteer firefighters and the relative importance of community factors. Sex and age differences in serum profiles and associations with predictors may imply different exposure and elimination pathways among male and female volunteer firefighters.

Indoor dampness and mould exposure are associated with respiratory health, but there is limited evidence on long-term effects in adults. Indoor dampness and mould, and the onset of respiratory symptoms, asthma, and rhinitis, were studied in the Respiratory Health in Northern Europe (RHINE) cohort, in which 8407 adults participated in both the 20-year and 30-year follow-up (RHINE III and IV, respectively). Participants completed a questionnaire on indoor dampness and mould at home and at the workplace, and on respiratory health. In RHINE IV, the prevalence of dampness or mould exposure in the past 10 years was 28.6%, while 15.1% of participants reported a 20-year cumulative exposure. Home exposure of dampness or mould was linked to increased onset of nocturnal chest tightness (OR = 1.80), nocturnal breathlessness (OR = 1.76), productive cough (OR = 1.41), and allergic rhinitis (OR = 1.27) and work exposure of dampness or mould with increased onset of wheeze (OR = 1.58), nocturnal chest tightness (OR = 1.53), doctor-diagnosed asthma (OR = 1.68), and rhinitis (OR = 1.35). Combined home and work exposure showed stronger associations for nocturnal chest tightness (OR = 1.99), doctor-diagnosed asthma (OR = 1.90), and rhinitis (OR = 2.24). Cumulative 20-year exposure accentuated the association with respiratory symptoms. Long-term exposure to dampness or mould at home or at work increased the risk of developing respiratory symptoms, asthma, and rhinitis. Combined exposure at home and work, and a prolonged exposure period, may play a significant role in the development of adverse respiratory outcomes in adults.

Exposure to ambient fine particulate matter (PM2.5) varies by structural determinants of health, through mechanisms such as racism and material deprivation. These disparities are well documented in the US across individual and community-level race and ethnicity (RE) and socioeconomic status (SES). Since 2000, California air quality has generally improved, and disparities have narrowed, tentatively attributed by previous studies to air regulations. In parallel, wildfires became major contributors to ambient PM2.5, with different exposure patterns from traditional emission sources. To explore wildfires' contribution to exposure disparities, we tracked the temporal trend in total ambient PM2.5 exposure disparities in California from 2008 to 2006 and disentangled the role of wildfire smoke. We evaluated the population-weighted and rank-ordered temporal change in total, wildfire, and non-wildfire PM2.5 exposure across California census tracts and by RE and SES groups. We confirmed an absolute decrease in total PM2.5 over time and fluctuations in wildfire PM2.5 with peaks in 2008 and 2018. Census tracts with historically high total PM2.5 exposure in 2006 were less exposed in 2018, but this rank-ordered temporal change was mostly driven by increased wildfire PM2.5 in some tracts. Across the study period, community disparities in total PM2.5 existed among RE and SES groups, with higher exposure among socioeconomically disadvantaged and non-Hispanic Black and Hispanic populations. Community disparities in total PM2.5 narrowed from 2006 to 2018, yet these reductions were 9.4% to 59.5% attributable to increased wildfire PM2.5 exposure among socioeconomically affluent or non-Hispanic populations. In summary, wildfire PM2.5 has exaggerated the progress in reducing inequities in traditional sources of PM2.5, especially across racial groups and in years with severe wildfire like 2018. Additional targeted efforts are needed to reduce persistent inequities in PM2.5 exposure from traditional sources, which can be masked by increases in wildfire PM2.5 in an era of climate change.

While breast cancer risk from high-dose ionising radiation is known, uncertainties remain about risks at lower doses and risk-modifying factors. We conducted a systematic review and meta-analysis of publications on radiation-associated risk of breast cancer in women. We included studies published in 2005-2022 that assessed breast cancer incidence or mortality in women exposed to ionising radiation. Risk of bias was evaluated. Random-effects meta-analyses estimated excess relative risks per gray (ERR/Gy). Of the 3522 articles screened, 106 met the inclusion criteria; 40 studies provided 44 ERR/Gy estimates. Overall, radiation exposure was associated with increased breast cancer risk (ERR/Gy&#x2009;=&#x2009;0.56, 95% CI: 0.29-0.83). Between-study heterogeneity (I2&#x2009;=&#x2009;95%) was substantially reduced in subgroup analyses, reaching 5% in low-dose-rate studies. Higher summary ERRs were observed for high dose-rate exposures, moderate (1-5&#x2009;Gy) doses, childhood exposures, and attained age over 55. Lower but significantly increased risks were estimated for other subgroups and exposure scenarios. Radiation exposure was associated with a significantly increased risk of breast cancer among women, particularly following high dose-rates, moderate doses, childhood exposures, and older attained age. PROSPERO registration: CRD42021260610.

Asymptomatic malaria in pregnancy (AMiP) remains a major public health challenge with important maternal and neonatal consequences. Despite ongoing malaria control efforts, little is known about trimester-specific disparities in AMiP and the extent to which these differences are explained by socioeconomic, clinical, and environmental factors. Most existing studies have focused on overall malaria burden rather than explaining disparities across gestation. This study examined the determinants of trimester disparities in AMiP in the Kintampo South District of Ghana using Oaxaca-Blinder decomposition. A facility-based cross-sectional study was conducted among 462 pregnant women in the Kintampo South District of Ghana. Peripheral blood samples were screened for Plasmodium species using microscopy, while structured questionnaires captured sociodemographic, clinical, and environmental characteristics. Data were analysed with STATA 17 using Oaxaca-Blinder decomposition to partition trimester differences in AMiP into explained(characteristics) and unexplained (coefficients) components. The overall prevalence of AMiP was 13.4%, with a significantly higher burden in the second trimester (&#x3c7;2 = 10.96, p < 0.001). Plasmodium falciparum accounted for most infections. Oaxaca-Blinder decomposition showed that 73.7% of the trimester disparity in AMiP was explained by differences in observed characteristics (endowments). The major contributors to the explained disparity were suboptimal uptake of intermittent preventive treatment with sulfadoxine-pyrimethamine (IPTp-SP) (71.3%), frequent evening outdoor activity (58.6%), infrequent use of mosquito repellents (50.6%), proximity to overgrown vegetation (42.5%), &#x2264;2 malaria diagnostic tests during antenatal care (ANC) (24.1%), and infrequent iron-folate supplementation (18.4%). Conversely, ownership of long-lasting insecticidal nets (LLINs) reduced the unexplained disparity by 41.9%, highlighting its protective effect. Disparities in AMiP across trimesters are primarily driven by modifiable socioeconomic, programmatic, and environmental factors rather than inherent biological differences in gestation. These highlight the need to strengthen the prioritisation of early initiation of ANC and frequent exposure to provider-supervised IPTp-SP uptake. In addition, promoting sustained use of LLINs and repellents, alongside community-level environmental management, is critical to reducing exposure risk. Finally, embedding these targeted interventions within ANC services, supported by coordinated multisectoral efforts, will be essential to reduce trimester-specific inequities and improve maternal health outcomes.

Mental ill-health often emerges during adolescence, and the environments in which children grow up may shape this risk. Still, evidence is limited to single environmental exposures, urban samples, and short follow-ups. We investigated how built, social and chemical environments across childhood relate to common mental illness in adolescence, and whether associations differ by population density. Data were drawn from the Millennium Cohort Study, a nationally representative cohort born in 2000/2002 in the UK. High-resolution environmental data was linked to home addresses at birth and ages 3, 5, 7, 11, 14 and 17. Psychological distress (Kessler-6) and doctor diagnosed depression or serious anxiety were assessed at age 17. Confounder-adjusted and weighted generalized linear mixed models were fitted for single and domain-specific exposures. At age 17, &#x223c;16% of participants had high psychological distress and&#xa0;&#x223c;&#xa0;10.5% had been diagnosed with depression or anxiety (n&#xa0;=&#xa0;7769-8374). Built and social environment from early childhood onwards (5&#xa0;year: OR&#xa0;=&#xa0;1.22 [95% CI: 1.07-1.39]; 7&#xa0;year: OR&#xa0;=&#xa0;1.16 [1.02, 1.33]; 11&#xa0;year: OR&#xa0;=&#xa0;1.15 [1.01-1.30]; 17&#xa0;year: OR&#xa0;=&#xa0;1.22 [1.05-1.39]; accumulation: OR&#xa0;=&#xa0;1.21 [1.05-1.39])-especially lower greenness, greater distance to green space, more grey space, higher area deprivation and crime-were associated with high psychological distress, and, to a lesser extent, with diagnosis. Living close to the sea was associated with higher likelihood of common mental disorder, particular their diagnosis. Findings on air pollution were inconclusive. Associations with built and social environments were stronger in rural areas. Built and social environments in childhood and adolescence were significant correlates of adolescent common mental disorders. Future studies and interventions should consider urban/rural differences and timing of exposure.

Glyphosate and its primary metabolite, aminomethylphosphonic acid (AMPA), are widespread environmental contaminants with potential endocrine-disrupting properties. Despite near-universal glyphosate and AMPA exposure, human studies evaluating their association with maternal hormonal regulation during gestation remain scarce. To assess associations between urinary glyphosate and AMPA concentrations and repeated measures of maternal hormone levels during pregnancy and explore effect modification by gestational timing and fetal sex. We analyzed data from 752 pregnancies in the PROTECT birth cohort in Puerto Rico. Glyphosate and AMPA were measured in up to three urine samples per participant collected at 18&#x2009;&#xb1;&#x2009;2-, 22&#x2009;&#xb1;&#x2009;2-, and 26&#x2009;&#xb1;&#x2009;2-weeks' gestation. Serum concentrations of sex steroid (estriol, progesterone, testosterone), thyroid (T3, T4, fT4, TSH), and stress-related (CRH) hormones were assessed at two periods. Linear mixed effects models estimated differences in serum hormone levels associated with interquartile range (IQR) increases in urinary glyphosate/AMPA, adjusting for demographic, biological, and technical covariates. Glyphosate and AMPA were associated with lower estriol concentrations across pregnancy. Each IQR increase in AMPA was associated with 10.6% lower estriol (95% CI: -18.1%, -2.4%), while glyphosate was associated with 8.3% lower estriol (95% CI: -14.6%, -1.4%). AMPA was associated with 4.0% higher T3 (95% CI: 0.9%, 7.0%). In sensitivity analyses, AMPA was linked to 7.9% higher TSH at 26&#x2009;&#xb1;&#x2009;2-weeks' gestation (95% CI: 0.5%, 15.9%), while glyphosate was associated with 9.5% higher CRH at this time point (95% CI: 1.5%, 18.2%). Stratified models revealed stronger associations among participants carrying female versus male fetuses. This study provides first evidence of associations between prenatal glyphosate and AMPA exposure and disruption of multiple maternal hormonal pathways during pregnancy. Findings suggest these contaminants may interfere with estrogenic, thyroid, and stress hormone systems critical for pregnancy maintenance and fetal development, with implications for understanding mechanisms underlying adverse birth outcomes. This study provides the first human evidence that glyphosate and AMPA, ubiquitous environmental contaminants detected in 75-95% of pregnant women, disrupt multiple maternal hormonal pathways during pregnancy. Among 752 participants in a Puerto Rico birth cohort, we demonstrate significant reductions in estriol (8-11% decreases) and alterations in thyroid and stress hormones associated with glyphosate exposure. These findings illuminate potential biological mechanisms underlying previously observed associations between glyphosate exposure and adverse birth outcomes, including preterm birth and altered fetal growth. Given the widespread use of glyphosate-based herbicides globally, our results have significant implications for public health policy and regulatory decision-making. The hormonal disruptions we identified suggest that pregnant populations may be particularly vulnerable to glyphosate exposure, supporting the need for enhanced protective measures and exposure reduction strategies during critical windows of fetal development.