Environmental Epidemiology最新文献

Background: Industrially contaminated areas (ICA) are areas hosting or that have hosted industrial human activities (including industrial agriculture and mining) that have produced or might produce, directly or indirectly, chemical contamination of soil, surface or groundwater, air, or food chains, resulting in ecosystem and/or human health impacts.Pollution in ICA can derive from multiple sources and be closely interrelated with social and economic deprivation of affected communities. Furthermore, these populations may experience health criticalities, limited access to health care and ecosystem resources, and can be considered overburdened or environmental justice (EJ) communities.

Objectives: This commentary stems from the discussion at the symposium "Assessing Environmental Justice for Communities Living in Industrially Contaminated Areas: Sharing Approaches and Experiences" (36th Conference of the International Society for Environmental Epidemiology-Santiago de Chile, 2024). The objective being to propose an international network to share research experiences on EJ in ICA in Europe and in the Latin American regions to enable more consistent knowledge and better policy responses to address the objectives of the 2030 United Nations Sustainable and Development Goals Agenda, specifically reducing inequality within and among countries.

Discussion and policy implication: EJ associated with ICA has been observed in different countries, as investigated through single-site or nationwide assessments. Procedural aspects of EJ include misrecognition of the rights of affected communities to be informed through inclusive communication. This implies the marginalization of the communities in the decision-making process addressing the health impacts of ICA. We propose an international effort based on collaborative work to identify experiences in diversified contexts, research needs, and intervention priorities.

Background: The maldistribution of greenspaces across Black, Hispanic, and low-income communities can contribute to health disparities. It is unclear whether the interaction of race/ethnicity and socioeconomic status may explain the maldistribution of greenspace, or whether the maldistribution varies by type of greenspace.

Methods: Applying deep learning algorithms to street-view images, we calculated percentages of specific types of residential greenspace (i.e., %Trees, %Grass) for each Multi-Ethnic Study of Atherosclerosis participant (N = 5,858; 2000-2002). We used multilevel analysis of individual heterogeneity and discriminatory accuracy to quantify inequities in greenspace type by intersecting stratum of race/ethnicity (Black, Chinese American, Hispanic, and White), education (high school, some college, and bachelor's degree), and neighborhood socioeconomic status (NSES; low, moderate, and high). Models adjusted for age, sex, individual income, and study site.

Results: The mean %Trees was 19.0 (SD 8.8) and the mean %Grass was 5.1 (4.6). Distribution of %Trees varied across strata, for example, 13.1% (95% confidence interval [CI] = 9.1, 23.8) for Hispanic participants in the lowest education and NSES group versus 20.5% (14.0, 30.4) for Hispanic participants in the highest education and NSES group. Patterns were similar among corresponding strata of Black and Chinese American participants. However, the lowest %Trees among White participants was in the highest NSES and education stratum (20.6, 95% CI = 14.8, 31.5). About 16% of the variability of %Trees and 11% of the variability of %Grass was explained by intersecting stratum of race/ethnicity, education, and NSES.

Conclusion: Maldistribution of greenspace types may be explained by combinations of race/ethnicity, education, and NSES subgroups, as opposed to each factor alone.

This commentary highlights the need for actionable and context-appropriate research on air pollution and health that will continue to drive policies to reduce exposures and disease burden. Research on air pollution and health has been substantial in high-income countries (HIC), leading to causal conclusions on the adverse effects of air pollution. Despite bearing the greatest disease burden from air pollution, low- and middle-income countries (LMICs) have had scant research funding, a trend that may well be aggravated due to changing political priorities in some HICs. High-quality data from LMICs is urgently needed to help motivate local, subnational, and national policies to raise awareness and identify priority actions to improve health. The new evidence will also provide a more complete understanding of air pollution and health globally. We highlight a framework for moving from research to action and address how this framework differs in HIC and LMIC contexts. We propose a hierarchy of research needs that begins with having the necessary air pollution monitoring and health data, and the capacity to use the data for informative analytics, risk assessment, valuation, and policy formulation. Building technical capacity may be needed for this purpose, as will development of a functioning regulatory system in parallel. We call for greater emphasis on surveillance studies to demonstrate the benefits of action and address barriers to action. The global community would benefit from a broad research agenda with priorities and adequate funding dedicated to building evidence that leads to positive policy change. We urge priority for advancing actionable research and improving research capacity in LMICs, including investments in routine collection of relevant data, emphasizing the foundation of risk monitoring and health data systems, and building a cadre of researchers and informed policy-makers.

Background: Ovarian cancer is a leading cause of gynecologic cancer mortality, with Black women experiencing 5-year survival rates of only 41%. Disproportionate air pollution exposure may impact survival. We evaluated associations of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) exposure with survival among Black women with epithelial ovarian cancer using data from the California Cancer Registry (CCR, n = 540) and the multi-state African American Cancer Epidemiology Study (AACES, n = 766).

Methods: Annual PM_2.5 and NO₂ levels were estimated at a 1 km resolution using well-validated ensemble-based prediction models derived from the Socioeconomic Data and Application Center and assigned to the participants' residential addresses per their year of diagnosis (2004-2016). Weibull accelerated failure time models with participant-level frailty were used to assess air pollutant exposure associations with overall survival.

Results: Average PM_2.5 and NO₂ exposures were 11.3 μg/m³ and 25.8 ppb in CCR and 9.7 μg/m³ and 17.5 ppb in AACES. There was little evidence of an association between air pollution exposures and survival, with event time ratios (> 1 indicate longer survival) in CCR of 1.08 (95% CI = 0.97, 1.20) per 1 μg/m³ PM_2.5 and 1.07 (95% CI = 0.99, 1.15) per 10 ppb NO₂, and in AACES of 1.00 (95% CI = 0.93, 1.07) per 1 μg/m³ PM_2.5 and 1.04 (95% CI = 0.91, 1.19) per 10 ppb NO₂.

Conclusions: Findings were modest and consistent across both cohorts and sensitivity analyses, supported by the use of advanced exposure modeling. Future research should use time-varying, long-term exposure data and examine interactions with occupation, physical activity, and neighborhood stressors.

Background: In 2024, the US Environmental Protection Agency tightened the Mercury and Air Toxics Standards (MATS) for emissions of filterable particulate matter (fPM) from coal-fired power plants to 0.010 lb/MMBtu. In April 2025, a presidential proclamation stated that 47 specific power plant companies received a 2-year exemption from the new requirements. The proclamation provided no estimates of the resulting health impacts.

Methods: Our approach applies conventional risk-assessment calculations for mortality from inhalation of filterable PM_2.5 (fPM_2.5) emissions, for "with" versus "without" the exemption, across four steps: (1) calculate fPM_2.5 emissions, based on government databases; (2) calculate the change in ambient PM_2.5 concentrations, using the Intervention Model for Air Pollution (InMAP) source-receptor matrix (ISRM); (3) calculate mortality impacts from inhalation of PM_2.5, using the Orellano et al., 2024 concentration-response function (CRF; relative risk (RR) per 10 μg/m³: 1.095, 95% confidence interval (CI) = 1.064, 1.127; in sensitivity analyses, we employ other CRFs); (4) aggregate results (e.g., by US state).

Results: Most (83%) of the exempted power plant facilities already have sufficient control technology installed that they operate below the new MATS limit, indicating that much of that fleet already adopted cleaner technologies. For the remaining 17% of facilities, the proclamation will increase total fPM_2.5 emissions to ~6,900 tons, from ~4,400 tons. We estimate that the additional ~2,500 tons emitted will lead to 32 (95% CI = 22, 43) deaths. The highest mortality is in St. Louis, Missouri, (population: 2.2 million) with an estimated 14 (95% CI = 10,19) deaths. The increased mortality is, for some states (e.g., Missouri, and Pennsylvania), caused by mostly in-state emissions; for other states (e.g., Illinois, Maryland, New Jersey, and Virginia), the cause is out-of-state emissions.

Discussion: Results here quantify a portion of the health impacts but leave unquantified nonmortality impacts, impacts from hazardous air pollutant (HAP) exposures, and noninhalation pathways. The reduced computational demands of the air pollution model employed here allows for more timely investigation of government actions than would traditional air dispersion modeling. Sensitivity analyses yielded mortality results that ranged from 47% lower to 169% higher than the core findings.

Conclusions: We estimate that a 2-year delay in MATS emission reductions of fPM_2.5 at the exempted coal-fired power plants will lead to 32 (95% CI = 22, 43) additional deaths.

Background: Childhood obesity is a complex and multifaceted public health issue. Several studies have found that children living in greener neighborhoods have a lower body mass index (BMI); however, evidence on longitudinal exposure remains limited. This study examined the relationship between Enhanced Vegetation Index (EVI), green space, and children's weight status using linked environmental and national health data.

Methods: We derived annual EVI values from Landsat 8 satellite imagery (30 m resolution) within 300 m of a child's residence in Wales from 2008 to 2019. Mean EVI exposure was calculated for the 4 years preceding BMI measurement. We utilized 2017 Ordnance Survey Open Greenspace data to identify green spaces within 800 m of a child's residence. BMI obtained from the Child Measurement Programme for Wales (2012/13 to 2018/19) for children aged 4-5 years was used to define healthy versus overweight/obesity. We used logistic regression to evaluate associations between residential greenness, green spaces, and childhood weight status.

Results: The final cohort consisted of 200,237 children. A one-unit increase in EVI was associated with a 20% higher likelihood of being overweight or obese (OR = 1.20, 95% CI = 1.05, 1.37). For every additional green space within 800 m, the likelihood of having an unhealthy weight increased by 0.3%.

Conclusions: Our findings suggest that EVI and access to green spaces should be interpreted with care, as they may not capture how young children interact with nearby green environments. Future work investigating the impact of greenness and greenspace on child weight status should use measures tailored to more accurately represent age-specific behaviors.

Background: To mitigate the health impact of high temperatures, heat plans (HPs) have become widespread around the world. Our aim was to evaluate the temperature-mortality associations and estimate the temperature-related deaths in the Netherlands in the years before (2000-2009) and after (2010-2019) the first activation of the national HP.

Methods: We obtained data about daily all-cause mortality (2000-2019) for the entire Dutch population, and by age, sex, neighborhood socioeconomic status, and urbanization. We linked the daily maximum temperature based on 23 monitoring stations across the Netherlands. Time-series Poisson regression models with a distributed lag nonlinear model, adjusted for long-term and seasonal trends and day of the week, were used to assess relative risks (RRs, 95% confidence intervals [CIs]) in the warm months (May-September). Temperature-attributable mortality fractions for high-temperature exposures and potential HP days were calculated.

Results: We observed positive associations between daily maximum temperature and mortality in 2000-2009 and in 2010-2019. Associations of high temperatures (28.9 °C-95th percentile) were weaker in 2010-2019 (RR: 1.07, 95% CI: 1.05, 1.09) than in 2000-2009 (RR: 1.17, 95% CI: 1.15, 1.20). The attenuation in temperature-mortality risk was strongest for the elderly, women, and individuals living in low-socioeconomic status neighborhoods. The estimated mortality attributable fractions of high temperatures (≥28.9 °C) were lower in 2010-2019 (0.72, 95% CI: 0.60, 0.84) than in 2000-2009 (1.21%, 95% CI: 1.07, 1.33).

Conclusion: The impact of high temperatures on mortality attenuated in the Netherlands. This might be due to the implementation of the national HP, but other factors may have played a role as well.

Background: Prospective studies suggest that prenatal exposure to chemical neurotoxicants and maternal stress increase risk for psychiatric problems. However, most studies have focused on childhood outcomes, leaving adolescence-a critical period for the emergence or worsening of psychiatric symptoms-relatively understudied. The complexity of prenatal coexposures and adolescent psychiatric comorbidities, particularly among structurally marginalized populations with high exposure burdens, remains poorly understood.

Objectives: We aimed to investigate correlated features in prenatal chemical and psychosocial exposures and in adolescent behavioral outcomes in a sample of economically disadvantaged urban youth.

Methods: Principal Component Pursuit, a pattern recognition technique, coupled with factor analysis identified profiles of prenatal chemical and psychosocial exposures and of adolescent behavioral outcomes. Linear regression tested associations between exposure and behavioral profiles, accounting for potential confounders.

Results: Three prenatal exposure profiles were identified: one combining exposure to air pollutants, polycyclic aromatic hydrocarbons-DNA adducts, and psychosocial stressors, and two others separating exposure to endocrine-disrupting chemicals by molecular weight. Three adolescent behavioral profiles reflected two phenotypes: one characterized by attention problems only; two others characterized by attention, thought, substance use, and self-control problems, differentiated by informant (self vs. parent). The prenatal exposure profile involving air pollution and stress was associated with the phenotype of self-reported attention, thought, substance use, and self-control problems (β = 0.21, 95% confidence interval = 0.02, 0.40).

Discussion: Principal Component Pursuit identified consistent patterns of multidimensional prenatal exposures and adolescent behavioral symptoms. Results support prior studies linking prenatal exposures associated with economic disadvantage to complex, self-reported transdiagnostic psychiatric problems in adolescence.

Background: Long-term exposure to air pollution may be related to neural atrophy or cerebrovascular pathology. A major source of air pollution is vehicle traffic, which is modifiable. In this study, we estimated associations between four traffic-related air pollutants and five neuroimaging biomarkers.

Methods: We analyzed data from a subset (N = 817) of participants in the Chicago Health and Aging Project (1993-2012) who underwent a structural magnetic resonance imaging (MRI) scan between 2002 and 2012. Using previously developed air pollution models, we predicted participant-level exposure to the tailpipe pollutants oxides of nitrogen (NO_X) and nitrogen dioxide (NO₂), plus the nontailpipe pollutants copper and zinc found in coarse particulate matter (PM_10-2.5,Cu and PM_10-2.5,Zn, respectively) over the 3 years before each participants' first MRI scan date. Using linear regression, we estimated cross-sectional, covariate-adjusted associations between each pollutant with total cerebral volume, total hippocampal volume, total lateral ventricle volume, total white matter hyperintensity volume, and cortical thickness. These models incorporated inverse probability weights to account for potential selection biases driven by differences between participants who did and did not undergo an MRI scan after being offered one.

Results: Exposure to NO_X and NO₂ was associated with less cortical thickness on average (-0.06 mm, 95% confidence interval [CI] = -0.09, -0.02 per 7.8 ppb NO_X; -0.04 mm, 95% CI = -0.07, -0.01 per 2.7 ppb NO₂). All other associations were consistent with no effect.

Conclusion: These results are not indicative of large adverse associations between traffic-related air pollution exposures and indicators of neural atrophy or cerebrovascular pathology.

Background: Recent reports indicate that the occurrence of appendicitis follows a seasonal pattern and that there is an association between increased incidence and warmer weather. It is noteworthy that a reduction in the incidence of appendicitis has been observed in the Northern Hemisphere. The objective of this study is to present the epidemiological profile of appendicitis at the national level in France.

Methods: This retrospective observational study, based on data from the French National Discharge Database, encompasses all hospitalizations for appendicitis diagnosis between 2013 and 2022. The progression of appendicitis over time was assessed through time-series models. The incidence of appendicitis was also compared depending on year, gender, age, and latitude.

Results: It is noteworthy that the incidence of appendectomy in France exhibited a significant decrease between 2013 and 2022, with an average reduction of 2.1% annually. There was a significant decrease in the number of appendectomies performed on patients ≤20, while there was an increase in those >60. Furthermore, there was a seasonal pattern in the incidence of appendicitis, with a peak during the summer months. The seasonality remained consistent over time. Furthermore, there was a south-north gradient, with a higher number of appendectomies performed in the south.

Conclusion: Seasonality (summer vs. winter) and latitude (south vs. north) could be considered as a proxy for temperature. However, temperature alone cannot explain the observed variations in appendicitis occurrence, since the latter decreases over time, in parallel with global warming. It is likely that other environmental and ecological parameters may be responsible for these variations.