Environmental Health Perspectives最新文献

Background: Acute exposure to high ambient temperature and heat waves during the warm season has been linked with psychiatric disorders. Emerging research has shown that pregnant people, due to physiological and psychological changes, may be more sensitive to extreme heat, and acute exposure has been linked to increased risk of pregnancy complications; however, few studies have examined psychiatric complications.

Objective: Our objective was to examine the association between acute exposure to warm ambient temperatures and emergency department (ED) visits for mental disorders during pregnancy.

Methods: A time-stratified case-crossover design with conditional logistic regression was performed on $\sim \mathrm{206,000}$ psychiatric ED visits for pregnant patients in North Carolina, from May to September 2016 to 2019. Daily average ambient temperature was the main exposure and was linked to daily visits by maternal zip code of residence for prenatal mood and anxiety disorders (PMAD), severe mental illness (SMI), mental disorder of pregnancy (MDP), suicidal thoughts (SUIC), and any psychiatric disorder (Any). Effect modification by trimester, residential segregation, economic segregation, urbanicity, and availability of greenspace was also investigated.

Results: Each $5°C$ increase in same-day exposure to warm ambient temperature on case days was associated with an increase in incidence rate ratio (IRR) for any psychiatric disorder [IRR = 1.07; 95% confidence interval (CI): 1.01, 1.14] including anxiety (IRR = 1.14; 95% CI: 1.00, 1.30), bipolar disorder (IRR = 1.28; 95% CI: 0.98, 1.67), and suicidal thoughts (IRR = 1.28; 95% CI: 1.00, 1.65) compared to control days. In general, the associations were strongest for warm season temperatures on the same day of exposure or for temperatures averaged over the 3 or 6 d preceding the ED visit. The greatest risk of an incident ED admission for PMAD (RR = 1.20; 95% CI: 1.04, 1.39), particularly for anxiety (RR = 1.30; 95% CI: 1.07, 1.59), and any psychiatric disorder (RR = 1.17; 95% CI: 1.07, 1.28) occurred following cumulative exposure to hot temperatures the week before admission. Higher psychiatric burden from temperature was observed in urban areas and on extreme heat days.

Conclusions: For this pregnant population in the southeastern United States, short-term exposure to high ambient temperatures during the warm season was associated with a greater risk of ED visits for an array of psychiatric disorders. Findings show that climate-related increases in ambient temperature may contribute to psychiatric morbidity in pregnant people. https://doi.org/10.1289/EHP13293.

Background: Maternal cigarette smoking during pregnancy (MSDP) is associated with numerous adverse health outcomes in infants and children with potential lifelong consequences. Negative effects of MSDP on placental DNA methylation (DNAm), placental structure, and function are well established.

Objective: Our aim was to develop biomarkers of MSDP using DNAm measured in placentas ($N=96$), collected as part of the Vitamin C to Decrease the Effects of Smoking in Pregnancy on Infant Lung Function double-blind, placebo-controlled randomized clinical trial conducted between 2012 and 2016. We also aimed to develop a digital polymerase chain reaction (PCR) assay for the top ranking cytosine-guanine dinucleotide (CpG) so that large numbers of samples can be screened for exposure at low cost.

Methods: We compared the ability of four machine learning methods [logistic least absolute shrinkage and selection operator (LASSO) regression, logistic elastic net regression, random forest, and gradient boosting machine] to classify MSDP based on placental DNAm signatures. We developed separate models using the complete EPIC array dataset and on the subset of probes also found on the 450K array so that models exist for both platforms. For comparison, we developed a model using CpGs previously associated with MSDP in placenta. For each final model, we used model coefficients and normalized beta values to calculate placental smoking index (PSI) scores for each sample. Final models were validated in two external datasets: the Extremely Low Gestational Age Newborn observational study, $N=426$; and the Rhode Island Children's Health Study, $N=237$.

Results: Logistic LASSO regression demonstrated the highest performance in cross-validation testing with the lowest number of input CpGs. Accuracy was greatest in external datasets when using models developed for the same platform. PSI scores in smokers only ($n=72$) were moderately correlated with maternal plasma cotinine levels. One CpG (cg27402634), with the largest coefficient in two models, was measured accurately by digital PCR compared with measurement by EPIC array ($R^2=0.98$).

Discussion: To our knowledge, we have developed the first placental DNAm-based biomarkers of MSDP with broad utility to studies of prenatal disease origins. https://doi.org/10.1289/EHP13838.

Background: While limited studies have evaluated the health impacts of thunderstorms and power outages (POs) separately, few have assessed their joint effects. We aimed to investigate the individual and joint effects of both thunderstorms and POs on respiratory diseases, to identify disparities by demographics, and to examine the modifications and mediations by meteorological factors and air pollution.

Methods: Distributed lag nonlinear models were used to examine exposures during three periods (i.e., days with both thunderstorms and POs, thunderstorms only, and POs only) in relation to emergency department visits for respiratory diseases (2005-2018) compared to controls (no thunderstorm/no PO) in New York State (NYS) while controlling for confounders. Interactions between thunderstorms and weather factors or air pollutants on health were assessed. The disparities by demographics and seasons and the mediative effects by particulate matter with aerodynamic diameter $\le 2.5\mu m$ (${\mathrm{PM}}_{2.5}$) and relative humidity (RH) were also evaluated.

Results: Thunderstorms and POs were independently associated with total and six subtypes of respiratory diseases in NYS [highest risk ratio (RR) = 1.12; 95% confidence interval (CI): 1.08, 1.17], but the impact was stronger when they co-occurred (highest RR = 1.44; 95% CI: 1.22, 1.70), especially during grass weed, ragweed, and tree pollen seasons. The stronger thunderstorm/PO joint effects were observed on chronic obstructive pulmonary diseases, bronchitis, and asthma (lasted 0-10 d) and were higher among residents who lived in rural areas, were uninsured, were of Hispanic ethnicity, were 6-17 or over 65 years old, and during spring and summer. The number of comorbidities was significantly higher by 0.299-0.782/case. Extreme cold/heat, high RH, ${\mathrm{PM}}_{2.5}$, and ozone concentrations significantly modified the thunderstorm-health effect on both multiplicative and additive scales. Over 35% of the thunderstorm effects were mediated by ${\mathrm{PM}}_{2.5}$ and RH.

Conclusion: Thunderstorms accompanied by POs showed the strongest respiratory effects. There were large disparities in thunderstorm-health associations by demographics. Meteorological factors and air pollution levels modified and mediated the thunderstorm-health effects. https://doi.org/10.1289/EHP13237.

Background: The potential health benefits of exposure to vegetation, or greenness, are well documented, but there are few nationwide studies in Brazil, a country facing challenges related to land-use planning, deforestation, and environmental health risks.

Objectives: In this study, we investigated the association between greenness and hospitalizations for cardiorespiratory diseases in Brazil.

Methods: We accessed hospital admissions data from 967,771 postal codes (a total of 26,724,624 admissions) covering Brazil for the period between 2008 and 2018. We used Normalized Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to measure greenness at the postal-code level. First, we applied a quasi-Poisson regression model to estimate the association between greenness and hospitalizations for circulatory and respiratory diseases, adjusted for air pollution, weather variables, and area-level socioeconomic status. We stratified the analyzes by sex, age group, health outcome, and Brazilian regions. In the second stage, we performed a meta-analysis to estimate pooled effects across the Brazilian regions.

Results: The national meta-analysis for the whole population, incorporating both urban and nonurban areas, showed that higher levels of greenness were associated with a lower risk of hospitalizations for circulatory diseases. An interquartile range ($\text{IQR}=0.18$) increase in average NDVI was associated with a 17% (95% confidence interval: 8%, 27%) lower risk of cardiovascular admissions. In contrast, there was no association found between greenness and respiratory admissions. When specifically examining urban areas, the results remained consistent with the overall findings. However, the analyses of nonurban areas revealed divergent results, suggesting that higher levels of greenness in rural regions are associated with a lower risk of hospital admissions for both circulatory and respiratory diseases.

Discussion: The findings emphasize the importance of prioritizing the preservation and creation of green spaces in urban areas as a means of promoting cardiovascular health in Brazil. https://doi.org/10.1289/EHP13442.

Human cells and zebrafish coexposed to nanoplastics and the sunscreen ingredient homosalate showed more plastics in tissues, estrogenic activity, and relevant gene expression changes than they showed after either exposure alone.

Background: Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors.

Objectives: Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies.

Methods: Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies.

Results: Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in $5\text{-km}$ buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to $€\mathrm{300,000}$. The directions of associations were less consistent for walkability and share of single residents.

Discussion: Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393.