Socioeconomic Disparities and Air Pollution Exposure: a Global Review

Risk Factors for Developing Perthes Disease: A Comprehensive National Analysis Spanning 2 Decades

BACKGROUND

Perthes disease is an uncommon pediatric condition affecting the hip joint, causing varying degrees of femoral head necrosis. The underlying cause of Perthes remains unknown, thus it is crucial to identify risk factors associated with its development to aid in early diagnosis and intervention. This study aimed to analyze risk factors associated with Perthes in a large cohort.

METHODS

A case-control study was conducted using data from a U.S. national database from 2003 to 2023. Patients diagnosed with Perthes were compared with those without the disease. Variables potentially associated with Perthes were analyzed using multivariable logit models, and adjusted odds ratios (AOR) with 95% CI were calculated. Statistical significance was determined, and a P -value <0.05 was considered significant.

RESULTS

The study included 4034 patients with Perthes and 3,483,745 age-matched controls. The mean age of patients with Perthes was 8.38 years, compared with 8.35 years in the control group ( P =0.27). Significant risk factors identified included male sex (AOR: 3.14, P <0.001), white race (AOR: 2.16, P <0.001), and obesity (AOR: 2.21, P <0.001). Conversely, Black (AOR: 0.26, P <0.001), Hispanic (AOR: 0.53, P <0.001), and Asian (AOR: 0.55, P <0.001) races were associated with lower odds of developing Perthes. Additional significant risk factors included tobacco exposure (AOR: 1.25, P =0.02), hypertension (AOR: 1.64, P <0.001), and thrombophilia (AOR: 9.17, P <0.001).

CONCLUSIONS

This study is the largest study on Perthes disease in literature, identifying several independent risk factors, including male sex, white race, obesity, tobacco exposure, hypertension, obesity, and thrombophilia. Among children with Perthes, thrombophilia exhibited the highest adjusted odds ratio, despite its rarity. These findings offer valuable insights for further research aimed at elucidating the underlying etiology of Perthes disease, particularly with regards to the roles of vascular and metabolic pathways.

LEVEL OF EVIDENCE

Level III-prognostic case-control study.

Socioeconomic Inequalities in PM2.5 Exposure and Local Source Contributions at Community Scales Using Hyper-Localized Taxi-Based Mobile Monitoring in Xi'an, China

The relationship between the socioeconomic status (SES) and PM2.5 exposure is rather inconclusive. We employed taxi-based measurements with 30 m resolution to characterize PM2.5 exposure with local source contribution (PM2.5 adjusted concentration) discerned for 2019 winter and 2020 summer, in Xi'an. A big data set comprising ∼6 × 106 hourly PM2.5 measurements and SES data from ∼5000 communities was utilized to examine the socioeconomic inequalities in community-level PM2.5 exposure. Our results indicate that the inhabitants with lower SES are more likely to be disproportionately exposed compared to those with higher SES. At least 92% of disproportionately exposed inhabitants in rural regions reside in low SES areas, whereas a relatively smaller proportion (69-78%) reside in urban regions. The local source has a more profound impact on PM2.5 exposure during summer than winter. The inhabitants in polluted areas and low PM2.5 adjusted concentration areas accounted for 22% and 26% of total PM2.5 exposure during the winter. However, inhabitants residing in low-concentration areas contributed only 12% of total exposure during summer while those polluted areas contributed 30%. These findings provide valuable insights into the relationship between community-level PM2.5 exposure and SES, highlighting the need for more sophisticated air quality policies to alleviate socioeconomic inequalities in PM2.5 exposure.

Ultrafine particles and late-life cognitive function: Influence of stationary mobile monitoring design on health inferences

Growing evidence links ultrafine particles (UFP) to neurotoxicity, but human studies remain limited. Various mobile monitoring approaches have been used to develop air pollution exposure models. However, whether design choices impact epidemiology, including for UFP and cognitive function, remains unclear. We evaluated the adjusted association between 5-year average UFP number concentration (PNC) and late-life cognitive function (Cognitive Abilities Screening Instrument - Item Response Theory [CASI-IRT]) in the Adult Changes in Thought cohort (N=5,283) by leveraging an extensive roadside mobile monitoring campaign specifically designed for epidemiology. To assess the impact of reduced monitoring approaches on this association, we repeatedly subsampled UFP measures from the campaign, developed exposure models, and evaluated the degree to which associations were impacted. In the primary analysis, each 1,900 pt/cm3 increment in PNC was associated with an adjusted mean baseline CASI-IRT score that was 0.002 (95% CI: -0.016, 0.020) higher, which was not statistically significant. Point estimates were consistent across sampling designs with fewer visits per site (≤12), fewer seasons (1-3), and unbalanced visit frequency across sites. Sampling designs restricted to rush hours were more similar (median point estimate 0.002, IQR of point estimates: 0.000, 0.003) than business hour designs (0.006, IQR: 0.005, 0.007), but the opposite was true when temporal adjustments were applied (rush: -0.003, IQR: -0.005, -0.001; business: 0.002, IQR: 0.001, 0.004). We observed similar results in sensitivity and secondary analyses. We did not find evidence of an association between UFP and cognitive function in fully adjusted models. Monitoring design had minimal impact on the inferential results in this setting, which may have been caused by the lack of association. Secondary analyses in a reduced model that is potentially confounded suggest that monitoring design might have a greater impact in other datasets. Further research is needed, particularly in contexts with robust statistically significant health associations.

A machine learning approach for type 2 diabetes diagnosis and prognosis using tailored heterogeneous feature subsets

Type 2 diabetes (T2D) is becoming one of the leading health problems in Western societies, diminishing quality of life and consuming a significant share of healthcare resources. This study presents machine learning models for T2D diagnosis and prognosis, developed using heterogeneous data from a Spanish population dataset (Di@bet.es study). The models were trained exclusively on individuals classified as controls and undiagnosed diabetics, ensuring that the results are not influenced by treatment effects or behavioral changes due to disease awareness. Two data domains are considered: environmental (patient lifestyle questionnaires and measurements) and clinical (biochemical and anthropometric measurements). The preprocessing pipeline consists of four key steps: geospatial data extraction, feature engineering, missing data imputation, and quasi-constancy filtering. Two working scenarios (Environmental and Healthcare) are defined based on the features used, and applied to two targets (diagnosis and prognosis), resulting in four distinct models. The feature subsets that best predict the target have been identified based on permutation importance and sequential backward selection, reducing the number of features and, consequently, the cost of predictions. In the Environmental scenario, models achieved an AUROC of 0.86 for diagnosis and 0.82 for prognosis. The Healthcare scenario performed better, with an AUROC of 0.96 for diagnosis and 0.88 for prognosis. A partial dependence analysis of the most relevant features is also presented. An online demo page showcasing the Environmental and Healthcare T2D prognosis models is available upon request.

High spatial resolution environmental noise assessment and its associations with risks of cardiovascular diseases based on digital healthcare data in China

Environmental noise pollution is increasing, while risks of cardiovascular diseases (CVD) associated with environmental noise in low- and middle-income countries (LMICs) still remain under-recognized. In this study, we utilized land-use regression (LUR) models at high spatial resolution to assess environmental noise and linked the exposure to digital healthcare data from the Yinzhou Regional Health Information Platform (YRHIP) in China to assess the associations of environmental noise with risks of CVD. Among 533,512 participants, mean (SD) age was 53.66 (15.52) years with females constituting 52.8 %. The mean environmental noise level was 59.34 dB[A]. A total of 177,111 (33.20 %), 18,030 (3.38 %), 15,912 (2.98 %), 9,601 (1.80 %), and 161,889 (30.34 %) participants were diagnosed with CVD, cerebrovascular diseases, ischemic heart diseases (IHD), stroke, and hypertension, respectively. Multivariable modified Poisson regression models incorporating community as a random-effect term were used to evaluate associations of environmental noise with the prevalent risk of CVD and its major subtypes. Individual covariates (age, sex, marital status, education, lifestyles), area-level covariates (urbanicity, population density, GDP), and PM2.5 were adjusted. We observed positive associations between residential environmental noise exposure and CVD (prevalence ratio (PR) 1.06, 95 % CI: 1.02-1.09 per 5 dB[A]), IHD (PR 1.14, 95 % CI: 1.07-1.21 per 5 dB[A]), and hypertension (PR 1.06, 95 % CI: 1.03-1.09 per 5 dB[A]), while no association was found for cerebrovascular diseases (PR 1.01, 95 % CI: 0.96-1.07 per 5 dB[A]) and stroke (PR 0.97, 95 % CI: 0.92-1.03 per 5 dB[A]). The risks of CVD and hypertension associated with environmental noise were higher among males and in the 50-60 years age group (P for interaction < 0.001). The associations between environmental noise and IHD were stronger in the ≥ 70 age group and among the participants with lower education levels (P for interaction < 0.05). The study fills the gap of knowledge about the associations between environmental noise, estimated using high spatial resolution LUR models, and CVD prevalent risk in LMICs. The evidence would provide significant implications for policy-making in terms of alleviating the surging disease burden of CVD related to environmental noise in LMICs.

Neighborhood deprivation and iodine levels influence air pollution effects on maternal thyroid function in early pregnancy

Exposure to air pollution during pregnancy that disrupts thyroid function can lead to adverse health outcomes in mother and child. We evaluated the overall effect and critical exposure window of residential ambient air pollution exposures on thyroid function in the MADRES pregnancy cohort. We also investigated whether these associations varied by iodine deficiency status and neighborhood deprivation. Early pregnancy (6-20 weeks) serum thyroid stimulating hormone (TSH) and free thyroxine (FT4) were measured for 217 mothers. Daily residential ambient air pollution exposures (PM2.5, PM10, NO2 and O3 8hr max) were estimated using inverse-distance squared spatial interpolation from regulatory monitors. We used linear regression to assess effects of single ambient air pollutants on thyroid function, including exploration of effect modification by iodine deficiency and neighborhood deprivation (Area Deprivation Index and Gini Index of income inequality, dichotomized at the median). Distributed lag models (DLM) were used to assess critical windows of exposure for ambient air pollutants from 12 weeks preconception to first trimester. We found that one SD increase in PM2.5 (2.4 μg/m3) and PM10 (5.8 μg/m3) were associated with 18.9 % (95 % CI: 2.7, 37.8 %) and 16.8 % (95 % CI: 0.7, 35.6 %) higher TSH levels, respectively, with significant windows of susceptibility in the first trimester (GW 5-8 or 6-8). These associations were also modified by neighborhood deprivation, and iodine status. Our findings indicate that relatively low levels of PM exposures in early pregnancy are associated with increased TSH levels particularly among women with replete iodine levels and women living in neighborhoods with greater deprivation.

County socioeconomic status and premature mortality from cancer in the United States

INTRODUCTION

There are consistent data demonstrating socioeconomic status (SES) is associated with cancer survivorship among older adults, but research on the relationship between area-level SES and risk of premature mortality from cancer remains not well understood. This study investigated the association between county-level SES and premature mortality from cancer.

METHODS

Demographic characteristics and causes of death were ascertained from the national death certificate data for years 2016-2020. Premature cancer death was defined as cancer mortality between ages 25-64. County SES was calculated using the Yost Index and categorized into distribution-based quintiles (1 =lowest SES, 5=highest SES). To calculate the mortality-adjusted rate ratios (aRR) and corresponding 95 % confidence intervals (95 %CI) for the associations between county SES and cancer, we performed multivariable linear mixed models, adjusting for confounders.

RESULTS

A total of 3143 counties were included. The age-adjusted mortality rates of all cancers combined were 107.6, 98.4, 88.6, 81.1, and 66.7 per 100,000 population for the 5 SES quintiles, respectively. Compared with high SES counties, low SES counties had a 58 % greater premature cancer mortality rate (aRRquintile 1 vs.5 =1.58, 95 %CI: 1.55-1.60). Similar associations were observed when stratified by sex, though risk was greatest among men ([aRRwomen=1.48, 95 %CI: 1.45-1.52]; [aRRmen=1.66, 95 %CI: 1.62-1.70]). Among leading cancer types, the association was greatest for lung cancer mortality for the lowest SES counties (aRR=2.03; 95 %CI: 1.98-2.08).

CONCLUSION

Our findings demonstrate that lower SES counties are at greater risk of premature mortality from cancer. Place-based interventions should target the socioeconomic environment across the cancer control continuum.

Association between socioeconomic position and lung cancer incidence in 16 countries: a prospective cohort consortium study

Background

Studies have reported higher lung cancer incidence among groups with lower socioeconomic position (SEP). However, it is not known how this difference in lung cancer incidence between SEP groups varies across different geographical settings. Furthermore, most prior studies that assessed the association between SEP and lung cancer incidence were conducted without detailed adjustment for smoking. Therefore, we aimed to assess this relationship across world regions.

Methods

In this international prospective cohort consortium study, we used data from the Lung Cancer Cohort Consortium (LC3), which includes 20 prospective population cohorts from 16 countries in North America, Europe, Asia, and Australia. Participants were enrolled between 1985 and 2010 and followed for cancer outcomes using registry linkages and/or active follow-up. We estimated hazard ratios (HRs) for the association between educational level (our primary measure of SEP, in 4 categories) and incident lung cancer using Cox proportional hazards models separately for participants with and without a smoking history. The models were adjusted for age, sex, cohort (when multiple cohorts were included), smoking duration, cigarettes per day, and time since cessation.

Findings

Among 2,487,511 participants, 53,830 developed lung cancer during a 13.5-year median follow-up (IQR = 6.5-15.0 years). Among participants with a smoking history, higher education was associated with decreased lung cancer incidence in nearly every cohort after detailed smoking adjustment. By world region, this association was observed in North America (HR per one-category increase in education [HRtrend] = 0.88, 95% CI = 0.87-0.89), Europe (HRtrend = 0.89, 95% CI = 0.88-0.91), and Asia (HRtrend = 0.91, 95% CI = 0.86-0.96), but not in the Australian study (HRtrend = 1.02, 95% CI = 0.95-1.09). By histological subtype, education associated most strongly with squamous cell carcinoma and more weakly with adenocarcinoma (p-heterogeneity < 0.0001). Among participants who never smoked, there was no association between education and lung cancer incidence in any cohort (all p-trend > 0.05), except the USA Southern Community Cohort Study (HRtrend = 0.75, 95% CI = 0.62-0.90).

Interpretation

Based on longitudinal data from 2.5 million participants from 16 countries, our findings suggest that higher educational attainment was associated with lower lung cancer risk among participants with a smoking history, but not among participants who never smoked. Limitations of our study include that cohort participants cannot fully represent the general populations of the geographical regions included, and education was the only measure of SEP consistently available across our consortium.

Funding

This study was supported in part by the National Cancer Institute (NCI), the Lung Cancer Research Foundation (LCRF), and the World Cancer Research Fund (WCRF).

Annual Research Review: Exposure to environmental chemicals and psychosocial stress and the development of children's learning difficulties

Although awareness of the role of environmental exposures in children's cognitive development is increasing, learning difficulties have not yet been a major focus of environmental health science. Learning difficulties disproportionately affect children living in economic disadvantage, yielding an 'achievement gap.' Studies examining the neurobiology of reading and math have mostly included economically advantaged youth, leaving a great deal unknown about the neural underpinnings of reading and math difficulties in youth living in disadvantaged contexts. Critically, due to environmental injustice, these youth are disproportionately exposed to environmental neurotoxicants. Herein, we review literature supporting a theoretical framework of environmentally associated phenotypes of learning difficulties. We propose that prenatal exposure to neurotoxicants and early-life exposure to psychosocial stressors increases risk for learning difficulties via effects on neural circuits that support cognitive processes which, in addition to literacy and numeracy, are integral to acquiring and performing academic skills. We describe models in which (1) prenatal exposure to air pollution has a main effect on learning via brain structure and function or associated domain-general cognitive processes and (2) a joint 'two-hit' pathway in which prenatal air pollution exposure followed by early life stress-when combined and sequential-increases risk for learning difficulties also via effects on brain structure, function, and/or associated cognitive processes. We review a select literature documenting effects of exposure to pollutants and early life stress on relevant neural circuits and associated cognitive processes in animal models and parallel findings in human epidemiologic studies. We advocate for team science in which researchers, practitioners, and policymakers collaborate to increase health literacy about environmentally associated phenotypes of learning difficulties and support the development of precision-oriented instructional and environmental intervention methods for youth living in economic disadvantage.

Environmental justice implications of hydraulic fracturing: A spatio-temporal analysis in Texas

This study contributes to the field of environmental justice by addressing two key questions: (1) To what extent are racial and ethnic composition, socio-demographic characteristics, and housing-related factors associated with the siting of hydraulic fracturing wells in the U.S.? and (2) How have these associations evolved over time, and have demographic changes contributed to increased environmental justice concerns? Using a panel dataset from FracFocus and the American Community Survey at the census-tract level between 2011 and 2023, the findings reveal that hydraulic fracturing wells are disproportionately located near census tracts with higher percentages of Black and Hispanic populations, indicating environmental injustice towards racial minorities. These findings remain consistent across alternative proximity measures, including different distance buffers and population exposure estimates using area interpolation. Moreover, the longitudinal analysis suggests a growing trend in well placement near marginalized communities in recent years. This pattern may be attributed to post-siting demographic change, where minority populations, particularly Hispanic residents, are increasingly concentrated in areas near fracking sites, while White populations gradually relocate farther away. These findings underscore our contribution to new empirical evidence that can inform communities and policymakers about the distributional impacts of fracking activities.

Particulate matter, socioeconomic status, and cognitive function among older adults in China

BACKGROUND

Both air pollution and low socioeconomic status (SES) are associated with worse cognitive function. The extent to which low SES may compound the adverse effect of air pollution on cognitive function remains unclear.

METHODS

7,087 older adults aged 65 and above were included from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and followed up in 4 waves during 2008-2018. Cognitive function was measured repeatedly at each wave using the modified Chinese Mini-Mental State Examination (MMSE). Concentrations of particulate matter (PM1, PM2.5, and PM10) were evaluated using satellite-based spatiotemporal models. SES was measured based on five components and categorized into three levels (low, middle, and high). Generalized estimating equation models were used to estimate the association of PM and SES with cognitive function. Stratified analyses and effect modification by SES levels were further conducted.

RESULTS

Each 10 µg/m3 increase in PM1, PM2.5, and PM10 was associated with a 0.43 (95 % CI: -0.58, -0.27), 0.29 (95% CI: -0.37, -0.20), and 0.17 (95 % CI: -0.22, -0.13) unit decrease in MMSE scores, respectively. Lower SES was associated with worse cognitive function. Significant effect modifications were observed by SES, with the corresponding association of PM exposure being more pronounced among participants with a lower SES (p-interaction = 0.006, 0.001, and 0.006 for PM1, PM2.5, and PM10, respectively).

CONCLUSIONS

SES is an important effect modifier, and lower SES may compound the detrimental effect of PM on cognitive health. This finding may have implications for identifying vulnerable populations and targeted interventions against air pollution.

Prenatal exposure to polycyclic aromatic hydrocarbons, reduced hippocampal subfield volumes, and word reading

Reading difficulties and exposure to air pollution are both disproportionately high among youth living in economically disadvantaged contexts. Critically, variance in reading skills in youth living in higher socioeconomic status (SES) contexts largely derives from genetic factors, whereas environmental factors explain more of the variance in reading skills among youth living in lower SES contexts. Although reading research has focused closely on the psychosocial environment, little focus has been paid to the effects of the chemical environment. In this study, we measured prenatal exposure to a common air pollutant, polycyclic aromatic hydrocarbons (PAH), via the presence (versus absence) of PAH-DNA adducts in maternal blood during the third trimester of pregnancy. We examined the impact of prenatal PAH exposure on adolescent hippocampal subfield volume and on word reading in a sample of youth followed prospectively since birth (N = 165). Compared to those without prenatal exposure, those with detectable PAH-DNA adducts (N = 63) exhibited significantly smaller hippocampal volumes (CA2/3 subfield, t = -2.413, p < .05), which was associated with worse pseudoword reading (t = 2.346, p < .05). Exploratory mediation analyses showed a significant effect of PAH on pseudoword reading through CA2/3 vol (p = .028), suggesting that prenatal PAH exposure affects hippocampal volume with downstream effects on reading ability.

Association of ambient air pollution exposure with psychological distress in mid and later adulthood: A 26-year prospective cohort study

BACKGROUND

Existing evidence on associations between exposure to air pollution and psychological distress from middle to older age is limited by consideration of short exposure periods, poor historical covariates, exposures and outcomes, and cross-sectional study designs. We aimed to examine this association over a 26-year period between ages 43 and 69.

METHODS

We utilised data from the Medical Research Council National Survey of Health and Development Study (the 1946 British birth cohort). Land-use regression models estimated exposure to specific air pollutants using household addresses for 1991 (NO2), 2001 (PM10, NO2), and 2010 (NO2, NOx, PM10, PM2.5, PMcoarse, PM2.5abs). These were linked to the closest data collection wave at ages 43, 53 and 60-64, respectively. Psychological distress was assessed through the 28-item version of the General Health Questionnaire (GHQ-28), at ages 53, 60-64 and 69. Associations between each of the pollutants with psychological distress were analysed using generalised linear mixed models, adjusted for pollution exposure before age 43, assigned sex, social class, smoking status, neighbourhood deprivation, and previous mental health problems. We also examined effect modification by social class.

RESULTS

At age 69, 2125 participants completed the GHQ-28. In fully adjusted models, higher NO2 exposure was associated with higher GHQ-28 scores across a 26-year period (β=0.023, 95%CI:0.005, 0.040 per interquartile range increase in exposure), whereas higher exposure to PM10 was associated with lower GHQ-28 scores across a 16-year period (β=-0.021, 95%CI:-0.037, -0.006). There was no evidence of associations between exposure to other pollutants at age 60-64 and GHQ-28 at age 69. We found no effect modification by social class.

CONCLUSIONS

In this cohort there was some evidence of an association between higher cumulative exposure to NO2 and higher psychological distress, but mixed associations with other exposures. Policies to reduce pollutant exposure may help improve psychological symptoms in middle to late adulthood.

The Impact of Socioeconomic Factors on Long-Term Mortality Associated With Exposure to PM2.5: A Systematic Literature Review and Meta-Analysis

Objectives

Socioeconomic status (SES) is in many cases related to air pollution exposure, but less is known about its effects on susceptibility to air pollution. The main aim of this study was to analyse the impact of SES on health effects associated with exposure to fine particles (PM2.5).

Methods

Firstly, a systematic literature review of studies analysing the impacts of SES on health effects related to air pollution exposure was carried out. Secondly, a meta-analysis was performed by analysing studies on long-term mortality associated with exposure to PM2.5 divided into different SES groups.

Results

The meta-analysis showed that the relative risk (RR) for all-cause mortality associated with PM2.5 did not depend on individual education or income. It also revealed that adjustment for individual lifestyle factors (such as smoking, alcohol intake, physical activity, eating behaviours, and body mass index), in addition to adjustment for SES, did not significantly change the RR.

Conclusion

The association between all-cause mortality and PM2.5 did not depend on education or individual income. Due to the high heterogeneity observed, further studies are required to draw firm conclusions.

Long-term exposure to air pollutants, meteorological factors, and mental health status: a nationwide population-based study with multilevel regression analysis

BACKGROUND

Air pollutants and meteorological conditions have shown significant adverse effects on human health; however, their impact on mental health remains inconclusive and underexplored. Thus, this study aimed to investigate the association between long-term exposure to air pollutants (PM2.5 and PM10), meteorological factors, and depression and anxiety.

METHODS

We selected 10,076 participants aged 15-49 from the Nepal Demographic and Health Survey (NDHS) 2022, who had lived in their current domiciles for over five years. The Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) scale were used to quantify the presence of depression and anxiety. The mean levels of air pollutants (PM2.5 and PM10), temperature, and relative humidity between August 2021 and July 2022 were obtained from the national air quality monitoring center and the meteorological department. Adjusted linear and polynomial logistic regression models were used to estimate the risk of depression and anxiety.

RESULTS

The prevalence of depression and anxiety among participants was 3.8% and 16.9%, respectively. Increased PM2.5 and PM10 concentrations were significantly associated with increased PHQ-9 (PM2.5: β, 0.015; PM10: β, 0.011) and GAD-7 (PM2.5: β, 0.024; PM10: β, 0.011) scores. Exposure to higher PM2.5 and PM10 concentrations increased the risk of depression {OR, 95% CI (PM2.5: 1.05, 1.03-1.08); (PM10: 1.04, 1.01-1.05)} and anxiety {OR, 95% CI (PM2.5: 1.06, 1.04-1.10); (PM10: 1.03, 1.01-1.04)}, whereas higher temperatures and higher humidity showed a protective effect (p < 0.05).

CONCLUSION

This study demonstrates the substantial impact of air pollutants and meteorological factors on mental health status. Findings suggest that exposure to air pollutants may serve as an independent risk factor for depression and anxiety. Therefore, further robust investigations including large epidemiological cohorts and longitudinal observational studies are needed to elucidate these associations.

CLINICAL TRIAL NUMBER

Not applicable.

Cardiovascular benefit of statin use against air pollutant exposure in older adults

AIMS

Little is known about the cardiovascular benefit of statin use against ambient air pollution among older adults who are at higher risk of cardiovascular disease (CVD) potentially owing to age-related declines in cardiovascular functions along with other risk factors.

METHODS AND RESULTS

This retrospective, population-based cohort study consisted of adults aged 60 years and older free of CVD at baseline identified from the National Health Insurance Service database linked to the National Ambient Air Monitoring Information System for average daily exposure to PM10 and PM2.5 in 2015 in the major metropolitan areas in the Republic of Korea. The follow-up period began on 1 January 2016 and lasted until 31 December 2021. The Cox proportional hazards model was used to evaluate the association of cardiovascular benefit with statin use against different levels of air pollutant exposure. Of 1 229 444 participants aged 60 years and older (mean age, 67.4; 37.7% male), 377 076 (30.7%) were identified as statin users. During 11 963 322 person-years (PYs) of follow-up, a total of 86 018 incident stroke events occurred (719.0 events per 100 000 PYs). Compared to statin non-users exposed to high levels of PM10 (>50 µg/m3) and PM2.5 (>25 µg/m3), statin users had 20% [adjusted hazard ratio (HR), 0.80; 95% confidence intervals (CI), 0.75-0.85] and 17% (adjusted HR, 0.80; 95% CI, 0.80-0.86) lower adjusted risk of incident stroke for PM10 and PM2.5, respectively. A similar risk reduction for incident CVD was also found among statin users exposed to low or moderate levels of PM10 (≤50 µg/m3) and PM2.5 (≤25 µg/m3) exposure.

CONCLUSION

Among adults aged 60 years and older with high and low or moderate levels of exposure to PM10 and PM2.5, statin use was associated with a significantly lower risk of stroke.

Examining the social distributions in neighbourhood black carbon and ultrafine particles in Montreal and Toronto, Canada

BACKGROUND

Socioeconomic inequities in outdoor ultrafine particles (UFP) and black carbon (BC) are understudied in Canada, where metropoles like Montreal and Toronto feature distinct sociodemographic diversity and urban characteristics compared to U.S. cities.

METHODS

We collected vulnerability indicators, including social, economic, household composition, and immigration status, at the dissemination area level for Montreal and Toronto using data from the 2006 and 2021 Canadian Census of Population. Areas were classified as disadvantaged, intermediate, or advantaged following K-means clustering analysis. We aggregated and calculated population-weighted average concentrations of BC and UFP, and UFP size at the dissemination area and cluster levels using high-resolution exposure surfaces, derived from year-long mobile monitoring campaigns conducted in each city during 2020-2021. Final exposure surfaces were generated by integrating predictions from land-use regression models and deep convolutional neural network models.

FINDINGS

We observed high within-city variations in aggregated air pollutant levels, with higher outdoor BC and UFP concentrations and smaller UFP sizes in areas near local sources such as major roads, railways, airports, and densely populated regions. Advantaged areas experienced the lowest median UFP concentrations in both Montreal (10,707 pt/cm3) and Toronto (10,988 pt/cm3), as well as the lowest BC concentrations (650 ng/m3) in Montreal. The highest median UFP concentrations were observed in intermediate areas in Montreal (15,709 pt/cm3) and disadvantaged areas in Toronto (12,228 pt/cm3). Conversely, the highest BC concentrations were observed in disadvantaged and intermediate areas in Montreal (805-811 ng/m3), and disadvantaged and advantaged areas in Toronto (1,228-1,252 ng/m3). Notably, high priority areas for the double burden of vulnerability and high BC and UFP concentrations were located near air pollutants local emission sources.

INTERPRETATION

Our findings highlight the importance of prioritizing exposure mitigation for populations residing near local sources and to understand contextual factors influencing inequities across cities and pollutants.

The Effects of Air Pollution on Neurological Diseases: A Narrative Review on Causes and Mechanisms

Air pollution has well-documented adverse effects on human health; however, its impact on neurological diseases remains underrecognized. The mechanisms by which various components of air pollutants contribute to neurological disorders are not yet fully understood. This review focuses on key air pollutants, including particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and diesel exhaust particles (DEPs). This paper summarizes key findings on the effects of air pollution on neurological disorders, including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), and Parkinson's disease (PD). Although the precise biological mechanisms remain to be fully elucidated, evidence suggests that multiple pathways are involved, including blood-brain barrier disruption, oxidative stress, inflammation, and the activation of microglia and astrocytes. This review underscores the role of environmental pollutants as significant risk factors for various neurological diseases and explores their mechanisms of action. By advancing our understanding of these interactions, this work aims to inform new insights for mitigating the adverse effects of air pollution on neurological diseases, ultimately contributing to the establishment of a cleaner and healthier environment for future generations.

Bisphenol A Biomonitoring after Sealant Placement: A Prospective Cohort Study in Schoolchildren

INTRODUCTION

Bisphenol A (BPA), known for its endocrine-disrupting properties, is a concern when leaching from dental sealants. This study investigates the systemic BPA exposure following the placement of Bis-GMA-based dental sealants in Thai primary school students.

OBJECTIVES

The main objective was to evaluate the changes in urinary BPA concentrations after the application of Bis-GMA dental sealants in a cohort of Thai students.

METHODS

In a prospective cohort of Thai primary school students participating in a voluntary sealant placement program, urinary BPA concentrations were measured at 4 time points: immediately before and on days 1, 7, and 14 after sealant application. BPA levels were measured using high-performance liquid chromatography. Repeated-measures analysis using generalized estimating equations was performed to determine the correlation between BPA concentration and time. Beta coefficient was used to estimate the mean change in BPA concentration following sealant placement. The associated cofactors were evaluated by calculating the incidence rate ratio.

RESULTS

Sixty-seven children (50.7% boys) with a mean age of 9.9 ± 1.3 years and a body mass index of 17.7 ± 4.4 kg/m2 participated. On average, students had 2.9 ± 1.9 (range, 1-11) teeth with filled sealants. The median adjusted BPA levels before sealant placement and on days 1, 7, and 14 were 0.01, 0.03, 0.19, and 0.23 µg/g creatinine, respectively. The analysis showed a significant association between the number of sealed teeth and increased BPA levels at all visits.

CONCLUSION

The application of Bis-GMA-based dental sealants in children results in low-level systemic BPA exposure due to chemical leaching. Despite such a low level of BPA exposure, approved by international health authorities, it could be considered a silent, chronic, and persistent systemic event, the long-term implications of which are yet to be deciphered.Knowledge Transfer Statement:This research shows that Bis-GMA dental sealants cause minimal bisphenol A (BPA) exposure in children, within safe international limits. However, the long-term effects are unknown. This information should inform dental care practices and guide policymakers in assessing the chronic impacts of BPA exposure from dental materials.

A systematic review of air pollution exposure and brain structure and function during development

OBJECTIVES

Air pollutants are known neurotoxicants. In this updated systematic review, we evaluate new evidence since our 2019 systematic review on the effect of outdoor air pollution exposure on childhood and adolescent brain structure and function as measured by magnetic resonance imaging (MRI).

METHODS

Using PubMed, Web of Science, and Scopus we conducted an updated literature search and systematic review of articles published through January 2025, using key terms for air pollution and functional and/or structural MRI. Two raters independently screened all articles using Covidence and implemented the risk of bias instrument for systematic reviews used to inform the World Health Organization Global Air Quality Guidelines.

RESULTS

We identified 29 relevant papers, and 20 new studies met our inclusion criteria. Including six studies from our 2019 review, the 26 publications to date include study populations from the United States, Netherlands, Spain, and United Kingdom. Studies investigated exposure periods spanning pregnancy through early adolescence, and estimated air pollutant exposure levels via personal monitoring, geospatial residential estimates, or school courtyard monitors. Brain MRI occurred when children were on average 6-14.7 years old; however, one study assessed newborns. Several MRI modalities were leveraged, including structural morphology, diffusion tensor imaging, restriction spectrum imaging, arterial spin labeling, magnetic resonance spectroscopy, as well as resting-state and task-based functional MRI. Air pollutants were associated with widespread brain differences, although the magnitude and direction of findings are largely inconsistent, making it difficult to draw strong conclusions.

CONCLUSION

Prenatal and childhood exposure to outdoor air pollution is associated with structural and functional brain variations. Compared to our initial 2019 review comprised of only cross-sectional studies, the current literature now includes longitudinal studies and more advanced neuroimaging methods. Further research is needed to clarify the effects of developmental timing, along with the downstream implications of outdoor air pollution exposure on children's cognitive and mental health.

Environmental Risk Assessment of Trace Metal Pollution: A Statistical Perspective

Trace metal pollution is primarily driven by industrial, agricultural, and mining activities and presents complex environmental challenges with significant implications for ecological and human health. Traditional methods of environmental risk assessment (ERA) often fall short in addressing the intricate dynamics of trace metals, necessitating the adoption of advanced statistical techniques. This review focuses on integrating contemporary statistical methods, such as Bayesian modeling, machine learning, and geostatistics, into ERA frameworks to improve risk assessment precision, reliability, and interpretability. Using these innovative approaches, either alone or preferably in combination, provides a better understanding of the mechanisms of trace metal transport, bioavailability, and their ecological impacts can be achieved while also predicting future contamination patterns. The use of spatial and temporal analysis, coupled with uncertainty quantification, enhances the assessment of contamination hotspots and their associated risks. Integrating statistical models with ecotoxicology further strengthens the ability to evaluate ecological and human health risks, providing a broad framework for managing trace metal pollution. As new contaminants emerge and existing pollutants evolve in their behavior, the need for adaptable, data-driven ERA methodologies becomes ever more pressing. The advancement of statistical tools and interdisciplinary collaboration will be essential for developing more effective environmental management strategies and informing policy decisions. Ultimately, the future of ERA lies in integrating diverse data sources, advanced analytical techniques, and stakeholder engagement, ensuring a more resilient approach to mitigating trace metal pollution and protecting environmental and public health.

Air pollution and cortical myelin T1w/T2w ratio estimates in school-age children from the ABCD and NeuroSmog studies

Air pollution affects human health and may disrupt brain maturation, including axon myelination, critical for efficient neural signaling. Here, we assess the impact of prenatal and current long-term particulate matter (PM) and nitrogen dioxide (NO2) exposure on cortical T1w/T2w ratios - a proxy for myelin content - in school-age children from the Adolescent Brain Cognitive Development (ABCD) Study (United States; N = 2021) and NeuroSmog study (Poland; N = 577), using Siemens scanners. Across both samples, we found that NO2 and PM were not significantly associated with cortical T1w/T2w except for one association of PM10 with lower T1w/T2w in the precuneus in NeuroSmog. Superficially, ABCD Study analyses including data from all scanner types (Siemens, GE, Philips; N = 3089) revealed a negative association between NO₂ exposure and T1w/T2w ratios. However, this finding could be an artifact of between-site sociodemographic differences and large scanner-type-related measurement differences. While significant associations between air pollution and cortical myelin were largely absent, these findings do not rule out the possibility that air pollution affects cortical myelin during other exposure periods/stages of neurodevelopment. Future research should examine these relationships across diverse populations and developmental periods using unified analysis methods to better understand the potential neurotoxic effects of air pollution.

Climate change and multiple sclerosis: Clinical challenges and strategies

Climate change poses significant challenges for people with multiple sclerosis (PwMS), exacerbating symptoms such as heat sensitivity. Increasing levels of air pollution contribute to neuroinflammation and has been associated with symptom flares, and mobility impairments complicate resource access for PwMS during climate-related weather emergencies. This paper explores the broad implications of climate change on multiple sclerosis (MS) and offers strategies for clinicians to address these emerging challenges, as understanding the broad impacts of climate change on MS is crucial to provide effective care in a changing world.

Leveraging High-Resolution Satellite-Derived NO2 Estimates to Evaluate NO2 Exposure Representativeness and Socioeconomic Disparities

Research has typically estimated NO2 concentrations over several kilometers; thus, NO2 data at finer spatial resolution remain limited. This study used tropospheric NO2 data from the TROPOspheric Monitoring Instrument (TROPOMI) and traffic-related land use parameters to estimate long-term average NO2 concentrations at a spatial resolution of 500 m in South Korea from 2018 to 2022. Our satellite-land use hybrid regression model showed reasonably high predictability with a cross-validation R2 of 0.81, mean absolute error of 2.28 ppb and root mean squared error of 2.85 ppb. Leveraging these high-resolution data, we assessed the representativeness of ground monitors for population exposure by comparing population-weighted NO2 concentrations from estimated and measured data. Across 17 metropolitan cities and provinces, the ratios of population-weighted estimated to measured NO2 ranged from 0.62 to 1.12, with the ratio of 1 exhibiting the most representative monitoring networks. We further investigated disproportionate NO2 exposures based on socioeconomic status, revealing that NO2 exposures were consistently higher in local districts with higher socioeconomic status because of the unique historical backgrounds of rapid economic development and urban infrastructure design in South Korea. Using high-resolution NO2 data can lead to more comprehensive and precise exposure assessments, enhancing public health and regulatory applications.

Associations between residential segregation, ambient air pollution, and hippocampal features in recent trauma survivors

Background

Residential segregation is associated with differential exposure to air pollution. Hippocampus structure and function are highly susceptible to pollutants and associated with posttraumatic stress disorder (PTSD) development. Therefore, we investigated associations between residential segregation, air pollutants, hippocampal neurobiology, and PTSD in recent trauma survivors.

Methods

Participants (N = 278; 34% non-Hispanic white, 46% Non-Hispanic Black, 16% Hispanic) completed multimodal neuroimaging two weeks after trauma. Yearly averages of air pollutants (PM2.5 and NO2) and racial/economic segregation (Index of Concentration at the Extremes) were derived from each participant's address. Linear models assessed if air pollutants mediated associations between segregation and hippocampal volume, threat reactivity, or parahippocampal cingulum fractional anisotropy (FA) after covarying for age, sex, income, and 2-week PTSD symptoms. Further models evaluated if pollutants or segregation prospectively predicted PTSD symptoms six months post-trauma.

Results

Non-Hispanic Black participants lived in neighborhoods with significantly greater segregation and air pollution compared to Hispanic and non-Hispanic white participants (ps<.001). There was a significant indirect effect of NO2 between segregation and FA values (β = 0.08, 95% CI[0.01, 0.15]), and an indirect effect of PM2.5 between segregation and threat reactivity (β = -0.08, 95% CI[-0.14, -0.01]). There was no direct effect of segregation on hippocampal features. Pollutants and segregation were not associated with PTSD symptoms .

Conclusion

Residential segregation is associated with greater air pollution exposure, which is in turn associated with variability in hippocampal features among recent trauma survivors. Further research is needed to assess relationships between other environmental factors and trauma and stress-related disorders.

Sleep duration and efficiency moderate the effects of prenatal and childhood ambient pollutant exposure on global white matter microstructural integrity in adolescence

Background

Air pollution is a ubiquitous neurotoxicant associated with alterations in structural connectivity. Good habitual sleep may be an important protective lifestyle factor due to its involvement in the brain waste clearance and its bidirectional relationship with immune function. Wearable multisensory devices may provide more objective measures of sleep quantity and quality. We investigated whether sleep duration and efficiency moderated the relationship between prenatal and childhood pollutant exposure and whole-brain white matter microstructural integrity at ages 10-13 years.

Methods

We used multi-shell diffusion-weighted imaging data collected on 3T MRI scanners and objective sleep data collected with Fitbit Charge 2 from the 2-year follow-up visit for 2178 subjects in the Adolescent Brain Cognitive Development Study®. White matter tracts were identified using a probabilistic atlas. Restriction spectrum imaging was performed to extract restricted normalized isotropic (RNI) and directional (RND) signal fraction parameters for all white matter tracts, then averaged to calculate global measures. Sleep duration was calculated by summing the time spent in each sleep stage; sleep efficiency was calculated by dividing sleep duration by time spent in bed. Using an ensemble-based modeling approach, air pollution concentrations of PM2.5, NO2, and O3 were assigned to each child's residential addresses during the prenatal period (9-month average before birthdate) as well as at ages 9-10 years. Multi-pollutant linear mixed effects models assessed the associations between global RNI and RND and sleep-by-pollutant interactions, adjusting for appropriate covariates.

Results

Sleep duration interacted with childhood NO2 exposure and sleep efficiency interacted with prenatal O3 exposure to affect RND at ages 10-13 years. Longer sleep duration and higher sleep efficiency in the context of higher pollutant exposure was associated with lower RND compared to those with similar pollutant exposure but shorter sleep duration and lower sleep efficiency.

Conclusions

Low-level air pollution poses a risk to brain health in youth, and healthy sleep duration and efficiency may increase resilience to its harmful effects on white matter microstructural integrity. Future studies should evaluate the generalizability of these results in more diverse cohorts as well as utilize longitudinal data to understand how sleep may impact brain health trajectories in the context of pollution over time.

Air pollution exposure disparities among ethnic groups in high-income countries: A scoping review

BACKGROUND

The adverse health effects of air pollution are well-established. Previous reviews have highlighted disparities in air pollution exposure between minoritized ethnic groups and majoritized ethnic groups. However, these reviews primarily focused on proximity to pollution sources rather than objectively measured concentrations. This scoping review aims to provide an overview of the extent of inequalities in objectively measured air pollution exposure among ethnic groups in high-income countries.

METHODS

We systematically searched PubMed, Scopus, Web of Science, and Google Scholar for studies published in English, that reported on objectively measured air pollution exposure stratified by ethnic groups in high-income countries. Data on study characteristics and air pollution exposure were extracted.

RESULTS

The majority of all 55 included studies were conducted in North America (n = 46), followed by Europe (n = 8), and Oceania (n = 1). Across studies, 25 ethnic groups were identified, with African American, Hispanic, Latino, and Asian populations being the most studied minoritized ethnic groups. PM2.5 was the most studied (n = 38) air pollutant. Eighteen studies statistically tested differences in exposure across ethnic groups. Thirteen studies reported significantly higher air pollution exposure among minoritized ethnic populations compared to the majoritized ethnic population, and five studies showed mixed evidence.

CONCLUSIONS

This review highlights the extent of disparities in air pollution exposure among ethnic groups in high-income countries. It underscores the need for further research to understand the contributing factors and potential interventions to address these disparities.

Global diversity in bipolar disorder: the role of cultural and social differences with a view to genomics

As global gene discovery efforts turn away from a historic Eurocentric focus and advance towards embracing more diverse populations, consideration of sociocultural aspects of bipolar disorder become critical to their success. Diversity can be leveraged to accelerate gene discovery, via different patterns of linkage disequilibrium that lead to greater resolution of mapping association signals, and convergence of genes and pathways implicated within and across diverse ancestral groups improving our understanding of the molecular underpinnings of disease. However it is not just the differences in linkage disequilibrium structure and allele frequency that drive differences in genomic signals between populations. This review focuses on the role of social, cultural and societal factors on bipolar disorder, and their potential impact on disease prevalence, clinical course and outcome, and disease burden. Social, cultural, and geographical differences in expression of symptoms, and frequency of clinical subtypes in bipolar disorder present both opportunities and challenges to the field. In this era of global multi-ancestry research, resources that facilitate the collection and harmonization of data from culturally and ancestrally-diverse population groups will enhance our ability to gain true biological understanding. Such resources are essential to disambiguate the genetic and environmental components of disease risk, as well as inform effective lifestyle interventions to improve outcome for global citizens living with bipolar disorder.

Neighborhood Environment, DNA Methylation, and Presence of Crown-Like Structures of the Breast

Importance

Inflammation impacts cancer risk and tumor biological processes, yet studies linking it to social and environmental risk factors are lacking.

Objective

To investigate the association of neighborhood deprivation and air pollution with breast adipose inflammation as well as the association between crown-like structures of the breast (CLS-B) and DNA methylation in Black and White women.

Design, Setting, and Participants

This cross-sectional study analyzed women with and without breast cancer participating in the National Cancer Institute-Maryland Breast Cancer Study, most of whom were recruited between January 1, 1993, and December 1, 2003, from the University of Maryland Medical Center and surrounding hospitals in the Baltimore, Maryland, area. A small subset of the sample was recruited between March 27, 2012, and November 27, 2017. Noncancerous breast tissue was collected from women who underwent reduction mammoplasty or breast cancer surgery. Statistical analyses were conducted between May and August 2024.

Exposures

Two socioenvironmental exposures were examined: air pollution (specifically, fine particulate matter less than 2.5 μm in diameter [PM2.5]) and neighborhood deprivation (measured with Neighborhood Deprivation Index [NDI]). Participant geocodes were linked to 2000 US Census data to calculate PM2.5 concentrations (total mass [μg/m3]) and NDI.

Main Outcomes and Measures

Breast tissues underwent immunohistochemical staining for pan-macrophage marker CD68 to detect 2 outcomes: CLS-B and adipose-associated macrophages. CLS-B and adipose-related macrophages were assessed by pathologists using artificial intelligence-assisted and manual approaches. Covariate-adjusted logistic regression models were used to ascertain associations between PM2.5 and NDI (exposures) and presence or absence of CLS-B (outcome); CD68-positive adipose macrophages were modeled as a dichotomous high or low variable. Covariate-adjusted linear regression was used to identify associations between CLS-B (exposure) and DNA methylation (outcome).

Results

The cohort included 205 participants (127 Black [62.0%], 78 White [38.0%] women; mean [SD] age, 48.7 [13.3] years). Women with vs without CLS-B had higher median (IQR) body mass index (calculated as weight in kilograms divided by height in meters squared; 35.5 [30.5-40.9] vs 31.8 [26.6-36.4]; P = .02). Higher levels of PM2.5 (odds ratio [OR], 2.32; 95% CI, 1.12-4.78; P = .02) and NDI (OR, 1.21; 95% CI, 1.02-1.43; P = .03) were associated with presence of CLS-B overall; findings were still significant among Black women (PM2.5: OR, 2.64 [95% CI, 1.10-6.33], P = .03; NDI: OR, 1.22 [95% CI, 1.01-1.48], P = .04) but were not statistically significant among White women (PM2.5: OR, 1.65 [95% CI, 0.45-5.99], P = .45; NDI: OR, 1.19 [95% CI, 0.83-1.70], P = .35). Higher PM2.5 concentration was associated with increased macrophage infiltration (OR, 2.11; 95% CI, 1.24-3.60; P = .006), with similar outcomes by race. The top 2 significant differentially methylated CpG sites by CLS-B status were SAR1B (β = 0.01; 95% CI, 0.01-0.02; P < .001) and IL2RB (β = -0.04; 95% CI, -0.05 to -0.02; P < .001). Significant interaction was observed between CLS-B status and race for IL2RB methylation levels (β = -0.03; 95% CI, -0.04 to -0.01; P for interaction <.001).

Conclusions and Relevance

This cross-sectional study uncovered an association between neighborhood-level social and environmental risk factors and breast tissue inflammation. The findings help inform efforts to reduce racial and socioeconomic disparities in breast cancer and improve health equity for socially vulnerable populations.

The Association Between Air Pollution and Lung Function in Sarcoidosis and Implications for Health Disparities

BACKGROUND

Sarcoidosis is a granulomatous disease with varying courses of disease progression. Environmental exposures are thought to be contributors to disease onset. Exposure to air pollutants such as fine particulate matter of 2.5 μm diameter or smaller (PM2.5) and nitrogen dioxide (NO2) have been identified as contributors to health disparities in lung diseases; little is known about these environmental exposures' associations with disease outcomes in sarcoidosis.

RESEARCH QUESTION

Is higher exposure to PM2.5 and NO2 associated with worse lung function in sarcoidosis?

STUDY DESIGN AND METHODS

We conducted a retrospective, cross-sectional study of individuals with pulmonary sarcoidosis seen from 2005 to 2015. Home addresses at the year of enrollment were geocoded, and exposure to PM2.5 and NO2 was modeled using high-resolution 1 km × 1 km annual surface exposure data during the year of enrollment. Racial and sex differences in exposure were determined. Multivariable linear regression models were used to examine the associations between PM2.5 and NO2 and the pulmonary function test measures FVC, FEV1, and diffusing capacity of the lungs for carbon monoxide (Dlco).

RESULTS

Among the 415 individuals in the analysis, Black individuals had significantly higher exposure to PM2.5 and NO2 compared with non-Hispanic White individuals, 12.2 μg/m3 (SD 2.4) vs 11 μg/m3 (SD 2.2) and 6.3 parts per billion (ppb) (SD 1.9) vs 5.0 ppb (SD 2.0), respectively. Every 1 μg/m3 higher exposure to PM2.5 was associated with 1.12% lower Dlco% predicted (95% CI, -1.83 to -0.41; P < .05). Every 1 ppb higher exposure to NO2 was associated with 1.04% lower Dlco% predicted (95% CI, -1.91 to -0.18; P < .05) in fully adjusted models. There were no significant associations between these pollutants and either FVC or FEV1% predicted.

INTERPRETATION

Higher exposure to PM2.5 and NO2 was associated with worse Dlco% predicted. Black individuals with sarcoidosis were exposed to higher PM2.5 and NO2 than non-Hispanic White individuals. Air pollution exposure may be a contributor to reported health disparities in sarcoidosis.

Disparities in demographic and health indicators among communities by frequency of hazardous waste sites in Indiana, USA: differences by Superfund vs. brownfield classification

Background

The location of hazardous sites is a known environmental justice issue, but impact of site classification (Superfund vs. brownfield) is less clear. The objective of this study is to determine whether demographic and health indicators are correlated with the prevalence of brownfield and/or Superfund sites in Indiana, USA.

Methods

This is a cross-sectional, ecologic study using publicly available data on hazardous waste sites, demographics, and self-reported health status. Complete data were available for 1252 census tracts, with 2251 brownfield and 2264 Superfund sites. Adjusted Poisson and linear regression models were used to determine associations between hazardous site frequency with demographic and health characteristics.

Results

Approximately 24% of census tracts had >3 Superfund or brownfield sites. Higher brownfield frequency was significantly associated with a higher nonwhite population (adjusted β=0.004, 95% confidence interval (CI): 0.002, 0.006); in contrast, more Superfund sites were significantly associated with a lower nonwhite population (adjβ=-0.006, 95% CI:-0.008, 0.004). Lower income was significantly associated with higher Superfund or brownfield site frequency. Results were consistent when limited to urban areas. Self-reported health measures were not associated with Superfund frequency. Census tracts with a higher number of brownfields were associated with a higher proportion of the population reporting poor general health (adjβ=0.057, 95% CI:0.010, 0.104) and poor mental health (adjβ=0.046, 95% CI:0.017, 0.075) compared to tracts without brownfield sites.

Conclusions

These results concur with existing evidence on disparities related to the location of hazardous site; they additionally suggest these disparities may extent to how hazardous sites are classified.

Racial residential segregation is associated with ambient air pollution exposure after adjustment for multilevel sociodemographic factors: Evidence from eight US-based cohorts

Objective

We examined if racial residential segregation (RRS) - a fundamental cause of disease - is independently associated with air pollution after accounting for other neighborhood and individual-level sociodemographic factors, to better understand its potential role as a confounder of air pollution-health studies.

Methods

We compiled data from eight large cohorts, restricting to non-Hispanic Black and White urban-residing participants observed at least once between 1999 and 2005. We used 2000 decennial census data to derive a spatial RRS measure (divergence index) and neighborhood socioeconomic status (NSES) index for participants' residing Census tracts, in addition to participant baseline data, to examine associations between RRS and sociodemographic factors (NSES, education, race) and residential exposure to spatiotemporal model-predicted PM2.5 and NO2 levels. We fit random-effects meta-analysis models to pool estimates across adjusted cohort-specific multilevel models.

Results

Analytic sample included eligible participants in CHS (N = 3,605), MESA (4,785), REGARDS (22,649), NHS (90,415), NHSII (91,654), HPFS (32,625), WHI-OS (77,680), and WHI-CT (56,639). In adjusted univariate models, a quartile higher RRS was associated with 3.73% higher PM2.5 exposure (95% CI: 2.14%, 5.32%), and an 11.53% higher (95% CI: 10.83%, 12.22%) NO2 exposure on average. In fully adjusted models, higher RRS was associated with 3.25% higher PM2.5 exposure (95% CI: 1.45%, 5.05%; P < 0.05) and 10.22% higher NO2 exposure (95% CI: 6.69%, 13.74%; P < 0.001) on average.

Conclusions

Our findings indicate that RRS is associated with the differential distribution of poor air quality independent of NSES or individual race, suggesting it may be a relevant confounder to be considered in future air pollution epidemiology studies.

Extreme Heat and Air Quality: Community Leader Perspectives on Information Barriers and Opportunities in Two Environmental Justice Communities

Extreme heat and air pollution exposure are leading causes of adverse cardiorespiratory health outcomes. Exposure reduction strategies are often focused at the local level. This study examined critical challenges community leaders face in understanding and sharing environmental exposure and health information. We conducted interviews with 19 community leaders of two urban environmental justice communities in Massachusetts, United States. Using directed content analysis, we examined air quality and heat perceptions, information and data resources, and barriers to understanding and communicating relevant local information. Participants shared concerns about both poor air quality and extreme heat. They also expressed the opinion that exposure risk information about these topics is siloed; heat and air quality data can be hard to access, interpret, and effectively communicate with community members. Solutions recommended by participants included community engagement, open-data portals, and creative science communication. Increasing sustainable collaborations among academic, government, healthcare, and nonprofit sectors is recommended.

Environmental justice mapping tools in the United States: A review of national and state tools

Environmental justice (EJ) mapping tools are geographic information system (GIS)-based digital maps that integrate environmental, socioeconomic, health, and demographic data to identify areas experiencing environmental injustices. These tools are increasingly used to guide investments toward disadvantaged communities. This review examines 25 EJ tools, describing their functionalities, coverage, and indicator types, ranging from biological susceptibilities to socioeconomic and environmental factors. We discuss the tools' resolutions, update frequencies, and data breadth, emphasizing their role in informing EJ interventions. However, gaps exist, particularly in the underrepresentation of U.S. territories and the limited inclusion of communicable diseases and climate impacts. This underscores the need for more comprehensive tools that consider diverse health risks and socio-environmental factors. Cumulative impact assessments should be integrated into EJ tools, incorporating a broad spectrum of indicators to capture the multifaceted nature of environmental injustices. Community engagement is also crucial in developing and updating EJ tools to ensure they accurately reflect community needs and conditions. By addressing these recommendations, EJ tools can better serve as effective instruments for highlighting and mitigating environmental disparities, supporting broader environmental justice and health equity goals.

Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality

Poor ambient air quality poses a substantial global health threat. However, accurate measurement remains challenging, particularly in countries such as India where ground monitors are scarce despite high expected exposure and health burdens. This lack of precise measurements impedes understanding of changes in pollution exposure over time and across populations. Here, we develop open-source daily fine particulate matter (PM2.5) datasets at a 10-kilometer resolution for India from 2005 to 2023 using a two-stage machine learning model validated on held-out monitor data. Analyzing long-term air quality trends, we find that PM2.5 concentrations increased across most of the country until around 2016 and then declined partly due to favorable meteorology in southern India. Recent reductions in PM2.5 were substantially larger in wealthier areas, highlighting the urgency of air quality control policies addressing all socioeconomic communities. To advance equitable air quality monitoring, we propose additional monitor locations in India and examine the adaptability of our method to other countries with scarce monitoring data.

Wildfire Smoke Exposure and Incident Dementia

Importance

Long-term exposure to total fine particulate matter (PM2.5) is a recognized dementia risk factor, but less is known about wildfire-generated PM2.5, an increasingly common PM2.5 source.

Objective

To assess the association between long-term wildfire and nonwildfire PM2.5 exposure and risk of incident dementia.

Design, Setting, and Participants

This open cohort study was conducted using January 2008 to December 2019 electronic health record (EHR) data among members of Kaiser Permanente Southern California (KPSC), which serves 4.7 million people across 10 California counties. KPSC members aged 60 years or older were eligible for inclusion. Members were excluded if they did not meet eligibility criteria, if they had a dementia diagnosis before cohort entry, or if EHR data lacked address information. Data analysis was conducted from May 2023 to May 2024.

Exposures

Three-year rolling mean wildfire and nonwildfire PM2.5 in member census tracts from January 2006 to December 2019, updated quarterly and estimated via monitoring and remote-sensing data and statistical techniques.

Main Outcome and Measures

The primary outcome was incident dementia, identified using diagnostic codes in the EHR. Odds of dementia diagnoses associated with 3-year mean wildfire and nonwildfire PM2.5 exposure were estimated using a discrete-time approach with pooled logistic regression. Models adjusted for age, sex, race and ethnicity (considered as a social construct rather than as a biological determinant), marital status, smoking status, calendar year, and census tract-level poverty and population density. Stratified models assessed effect measure modification by age, sex, race and ethnicity, and census tract-level poverty.

Results

Among 1.64 million KPSC members aged 60 years or older during the study period, 1 223 107 members were eligible for inclusion in this study. The study population consisted of 644 766 female members (53.0%). In total, 319 521 members identified as Hispanic (26.0%), 601 334 members identified as non-Hispanic White (49.0%), and 80 993 members received a dementia diagnosis during follow-up (6.6%). In adjusted models, a 1-μg/m3 increase in the 3-year mean of wildfire PM2.5 exposure was associated with an 18% increase in the odds of dementia diagnosis (odds ratio [OR], 1.18; 95% CI, 1.03-1.34). In comparison, a 1-μg/m3 increase in nonwildfire PM2.5 exposure was associated with a 1% increase (OR, 1.01; 95% CI, 1.01-1.02). For wildfire PM2.5 exposure, associations were stronger among members less than 75 years old upon cohort entry, members from racially minoritized subgroups, and those living in high-poverty vs low-poverty census tracts.

Conclusions and Relevance

In this cohort study, after adjusting for measured confounders, long-term exposure to wildfire and nonwildfire PM2.5 over a 3-year period was associated with dementia diagnoses. As the climate changes, interventions focused on reducing wildfire PM2.5 exposure may reduce dementia diagnoses and related inequities.

Cardiovascular disease burden linked to particulate matter pollution in Latin America and the Caribbean: Insights from GBD 2021 and socio-demographic index analysis

OBJECTIVES

This study aimed to analyze the burden of cardiovascular disease (CVD) attributable to particulate matter (PM) pollution in Latin American and Caribbean (LAC) countries and territories, and its relationship with the Socio-Demographic Index (SDI).

STUDY DESIGN

A cross-sectional analysis was conducted.

METHODS

The disability-adjusted life years (DALYs) and the SDI data were obtained from the Global Burden of Disease Study 2021. Spearman's regression coefficients (rho) and 95 % confidence intervals (CI) were used to evaluate the relationship of interest.

RESULTS

We computed a total of 22,231,164 disability-adjusted life years DALYs due to CVD in 2021, with an age-standardized rate of 3742 DALYs per 100,000 population. Ischemic heart disease and ischemic stroke were predominant. PM pollution was responsible for 2,974,358 DALYs (12.1 % of total CVD DALYs), varying widely across regions, with the highest attributable fractions observed in Haiti, Honduras, and Guatemala. A negative linear relationship (rho = -0.73, 95 % CI -0.95 to -0.51) was observed between PM pollution-attributable CVD burden and SDI, indicating a higher burden in regions with lower SDI. Public health interventions targeting PM pollution could substantially mitigate the burden of CVD, particularly in vulnerable populations identified in this study.

CONCLUSSIONS

These findings underscore the critical importance of environmental policies and interventions aimed at reducing PM pollution, and underlying socio-economic disparities, to alleviate the health impact of CVD in LAC countries and territories.

Ambient air pollution, urban green space and childhood overweight and obesity: A health impact assessment for Barcelona, Spain

BACKGROUND

The burden of childhood overweight and obesity attributable to ambient air pollution and a lack of urban green spaces (UGS) remains unknown. This study aimed to estimate the attributable cases of childhood overweight and obesity due to air pollution and insufficient UGS exposure in Barcelona, Spain.

METHODS

We applied a quantitative health impact assessment approach. We collected childhood overweight and obesity prevalence levels and exposure data from 69 spatial basic health zones in Barcelona. We estimated particulate matter (PM2.5) and nitrogen dioxide (NO2) levels using land use regression models, normalized difference vegetation index (NDVI) levels using remote sensing and percentage of green area (%GA) using land use. We estimated relative risks, population attributable fractions, and preventable overweight/obesity cases in children under following scenarios: Compliance of World Health Organization (WHO) air quality guidelines (AQGs) for (1) PM2.5 and (2) NO2; achieving (3) city-target NDVI levels and (4) 25% green area (%GA) recommendations. The analyses were stratified by socioeconomic deprivation index (in quintiles). Uncertainty was quantified using Monte Carlos simulations.

RESULTS

Compliance of WHO AQGs could prevent 0.4% [253 (95%CI, -604; 1086)] and 4.2% [3000 (95%CI, 1009; 4943)] of childhood overweight/obesity cases due to excess PM2.5 and NO2 levels in Barcelona, respectively. Compliance of NDVI and %GA targeted levels could prevent 6% [4094 (95%CI, 1698; 6379)] and 10% [6853 (95%CI, 1440; 12779)] of childhood overweight/obesity cases respectively. The preventable burdens of childhood overweight/obesity cases were slightly higher in middle-class socioeconomic areas due to the higher adverse exposure levels at baseline (high air pollution, less UGS).

DISCUSSION

Compliance with WHO AQGs and achieving UGS targets can reduce childhood overweight and obesity levels in Barcelona, and potentially in other locations as well. This underscores the need for policies that foster healthier urban environments of high environmental quality in order to protect child health.

Sensitivity Analysis for Effects of Multiple Exposures in the Presence of Unmeasured Confounding: Non-Gaussian and Time-to-Event Outcomes

In epidemiological studies, evaluating the health impacts stemming from multiple exposures is one of the important goals. To analyze the effects of multiple exposures on discrete or time-to-event health outcomes, researchers often employ generalized linear models, Cox proportional hazards models, and machine learning methods. However, observational studies are prone to unmeasured confounding factors, which can introduce the potential for substantial bias in the multiple exposure effects. To address this issue, we propose a novel outcome model-based sensitivity analysis method for non-Gaussian and time-to-event outcomes with multiple exposures. All the proposed sensitivity analysis problems are formulated as linear programming problems with quadratic and linear constraints, which can be solved efficiently. Analytic solutions are provided for some optimization problems, and a numerical study is performed to examine how the proposed sensitivity analysis behaves in finite samples. We illustrate the proposed method using two real data examples.

Assessment of knowledge, attitudes, and practices regarding air pollution among traffic polices in Dhaka city, Bangladesh: a cross-sectional study

BACKGROUND

The increasing number of motor vehicles in Dhaka city is contributing to a rise in air pollution. Prolonged exposure to vehicle emissions has led to various health issues for everyone, but traffic policies might be particularly affected. This study aims to evaluate their knowledge, attitudes, and practices regarding air pollution, with the goal of raising awareness and promoting healthier practices to mitigate the adverse effects of pollution.

METHODOLOGY

A cross-sectional survey was carried out from January to March 2023 across five zones in Dhaka City, Bangladesh. Using a convenience sampling method, 401 male participants with a mean age = 39.24 ± 6.25 years were recruited. The survey employed a semi-structured questionnaire that covered informed consent, socio-demographic data, and included sections on knowledge (9 items), attitudes (6 items), and practices (7 items) related to air pollution and its health effects. Data analysis was conducted with Microsoft Excel (version 2019), SPSS (version 25.0), and STATA (version 15.0).

RESULT

The average scores for knowledge, attitudes, and practices were 7.62 out of 9, 11.06 out of 18, and 14.53 out of 21, respectively. Higher knowledge scores were associated with education (p < 0.001), higher monthly family income (p < 0.001), and having 15 or fewer years of work experience (p = 0.040). Attitudes were positively linked to education (p < 0.001), higher income (p < 0.001), working in the same area for over a year (p < 0.001), asthma (p = 0.042), cardiovascular diseases (p = 0.027), and coughing symptoms (p < 0.001). Practices were positively influenced by lower income (p = 0.031), absence of breathing problems/asthma (p = 0.023), and absence of coughing symptoms (p < 0.001).

CONCLUSION

This study suggests that education, income, and work experience are likely influential factors shaping knowledge, attitudes, and practices regarding air pollution. There appears to be a probable association between prolonged exposure to air pollution and an increased risk of respiratory, cardiovascular, and eye problems among Dhaka's traffic police. The authors anticipate that implementing stricter air quality regulations, promoting the use of protective gear, conducting regular health check-ups, and raising public awareness could significantly reduce these health risks.

Confounding effects of socioeconomic status on the association between long-term PM2.5 exposure and mortality in Korea

BACKGROUND

This study assesses the national distribution of ambient fine particulate matter (PM2.5) exposure across socioeconomic status (SES) and its confounding on long-term PM2.5 mortality in Korea, aiming to minimize SES influence.

METHODS

A nationwide cohort of 5% of Koreans, aged 30 or older, from 2007 to 2019, from the National Health Information Database, was analysed. PM2.5 exposure levels were estimated at the city level using the Community Multiscale Air Quality system. Mortality data were obtained from Statistics Korea. The study examined annual PM2.5 exposure by SES indicators and its confounding on mortality risks associated with PM2.5, using time-varying Cox proportional hazards models.

RESULTS

The study followed 1 453 036 individuals from 2007 to 2019, totalling 17 760 227 person-years (PYs). The non-accidental (A00-R99), cardiovascular (I00-I99) and respiratory (J00-J99) mortality rates per 1000 PY were 7.6, 1.9 and 0.8, respectively. We observed a trend of decreasing PM2.5 exposure levels but increased mortality among medical aid beneficiaries, those with lower household incomes and those residing in neighbourhoods with a higher area deprivation index. When adjusting for these SES covariates, the long-term mortality effects of PM2.5 shifted in the direction of increased risk [hazard ratio (HR) for cardiovascular mortality in the unadjusted model = 0.968 (95% CI: 0.909-0.959); HR in the fully adjusted model = 1.053 (95% CI: 1.004-1.105)].

CONCLUSION

In regions where SES and PM2.5 concentrations are positively correlated, as in Korea, it is crucial to rigorously control for SES confounding to avoid underestimating the mortality effects associated with PM2.5.

The Impact of Environmental Health Determinants in Surgical Oncology

Environmental determinants of health refer to external factors in our surroundings that influence health outcomes. It is estimated that healthier environments could prevent almost one-quarter of the global burden of disease. Additionally, environmental factors, including lifestyle factors, air pollution, chemical exposures, and natural exposures, are responsible for a significant incidence of cancers and premature cancer deaths. Minority populations, low-income populations, children, and older adults are at increased risk for oncologic risks secondary to environmental factors.

Sociodemographic inequalities in residential nighttime light pollution in urban Bulgaria: An environmental justice analysis

INTRODUCTION

Outdoor nighttime light (NTL) is a potential anthropogenic stressor in urban settings. While ecological studies have identified outdoor NTL exposure disparities, uncertainties remain about disparities in individual exposure levels, particularly in Europe.

AIM

To assess whether some populations are disproportionately affected by outdoor NTL at their residences in urban Bulgaria.

METHODS

We analyzed 2023 data from a representative cross-sectional survey of 4,270 adults from the five largest Bulgarian cities. Respondents' annual exposures to outdoor artificial nighttime luminance were measured using satellite imagery and assigned at their places of residence. We calculated the Gini coefficient as a descriptive NTL inequality measure. Associations between respondents' NTL exposure levels and sociodemographic characteristics were assessed by estimating quantile mixed regression models. Stratified regressions were fitted by gender and for each city.

RESULTS

We found moderate distributive NTL inequalities, as indicated by a Gini coefficient of 0.214. Regression analyses showed associations between greater NTL exposure and higher educational attainment. Respondents with incomes perceived as moderate experienced less NTL exposure at the 0.5 and 0.8 quantiles, while unemployed respondents experienced lower exposure at the 0.2 and 0.5 quantiles. We observed null associations for the elderly and non-Bulgarian ethnicities. Regardless of the quantile, greater population density was associated with higher NTL levels. Stratification by sex did not yield substantial differences in the associations. We observed notable city-specific heterogeneities in the associations, with differences in the magnitudes and directions of the associations and the NTL quantiles.

CONCLUSIONS

NTL exposures appeared to embody an environmental injustice dimension in Bulgaria. Our findings suggest that some sociodemographic populations experience higher exposure levels to NTL; however, those are not necessarily the underprivileged or marginalized. Identifying populations with high exposure levels is critical to influencing lighting policies to ease related health implications.

PM2.5 and hospitalizations through the emergency department in people with disabilities: a nationwide case-crossover study in South Korea

Background

Little is known about the impact of PM2.5 on people with disabilities. We aimed to explore the association between PM2.5 and hospitalization via the emergency department (ED admission) among people with disabilities, together with the attributable ED admission cases and costs.

Methods

We applied a time-stratified case-crossover design adjusting ozone, holiday, and temperature using seven years (2015-2021) of claim-based data on ED admissions from the Korean National Health Insurance Database. The analysis included all ED admission cases of beneficiaries with disabilities living in Korea (physical, intellectual, and mental disabilities; brain lesion disorders; blindness or vision loss; deafness or hearing loss; and autism) as well as selected controls without disabilities.

Findings

There were 900,311 ED admissions among the 3,624,590 people with disabilities. The odds ratios of ED admissions associated with a 10 μg/m3 increase in PM2.5 were 1.039 (95% CI: 1.036-1.042) in people with disabilities and 1.022 (95% CI: 1.019-1.025) in people without disabilities. Individuals with mental disability, intellectual disability, and brain lesion disorder showed higher risk estimates compared to other disabilities. The risk estimates of ED admissions for cardiovascular and genitourinary diseases were more prominent among people with disabilities than those without disabilities.

Interpretation

The impacts of PM2.5 on ED admissions was generally higher in the population with disabilities than those without disabilities, especially for certain causes of admission. These results could contribute to establishing targeted action plans including early warning system referring different threshold concentrations.

Funding

National Research Foundation.

Effect of particulate matter on cognitive function among middle-aged and older adults in China: An instrumental variable approach

Emerging studies have suggested the association of ambient air pollution with worse cognitive function; however, causal evidence remains scarce. We aimed to estimate the effect of particulate matter (PM) exposure on overall and domain-specific cognitive function. A total of 14,205 participants aged 45 years and above were drawn from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). Cognitive function was assessed using a standardized questionnaire consisting of two domains: episodic memory and mental status. Participants' exposure to city-level PM (PM1, PM2.5, and PM10) was evaluated using satellite-based spatiotemporal models. To avoid potential endogenous biases, an instrumental variable method with two-stage least squares estimation was employed to examine the effect of air pollution on overall and domain-specific cognitive function. Stratified analysis was further performed based on sociodemographic characteristics. We found PM exposure exertedsignificant adverse effects on overall and domain-specific cognitive function, with mental status being more adversely affected as compared to episodic memory. The effect of PM exposure was more pronounced with smaller particle sizes, with PM1 exhibiting the largest effect size. Moreover, participants with lower education attainment were more adversely affected by PM exposure. Our findings add additional evidence of the potential causal role of PM exposure in worsening overall and domain-specific cognitive function. Efforts to further reduce PM are needed to protect the cognitive health of the aging Chinese population.

Ear to the ground! socioeconomic status, environmental stress, and the neural substrate of selective attention

We reanalyzed data originally published by Berman and Friedman (1995), who recorded event related potentials (ERPs) while children and adults with low, medium, and high socioeconomic status (SES) detected oddball auditory targets (tones and consonant-vowel sequences) among distractors. The ERP differential measuring how much attention was allocated to the targets vs. distractors increased significantly with SES, independently of age. To explain these findings, we integrate the ear to the ground hypothesis and the socioenvironmental epigenetic stress approach. According to the ear to the ground hypothesis, frequent and prolonged environmental uncertainty and hazard induce low-SES children to learn adaptive over-vigilance, attending disproportionately to stimuli that are currently irrelevant but may quickly become relevant and thus require an immediate survival response. Socioenvironmental epigenetic stress refers to the bidirectional interaction between a stress-inducing environment and the impact and expression of transgenerational gene selections in low-SES contexts. Because low-SES individuals are historically under and misrepresented in research, the proposed framework contributes to increase our understanding of how socioeconomic and environmental conditions may affect neurocognitive development. This offers significant points of entry for future interventions and policies targeting macrosocial settings (i.e., education and the justice system) and microsocial ontogenetic settings (i.e., individuals and families).

Do poor psychosocial resources mediate health inequalities in type 2 diabetes mellitus? Findings from the Maastricht study

Low socioeconomic position (SEP) has been identified as a risk factor for type 2 diabetes mellitus (T2DM), and psychosocial resources might be on the pathway in this association. We examined two poor psychosocial resources, low control beliefs and inferiority beliefs, that might link low SEP with T2DM. 8292 participants aged 40-75 living in Southern Netherlands participated in The Maastricht Study starting from September 2010 to October 2020 and were followed up to 10 years with annual questionnaires. SEP (education, income, occupation), low control beliefs, inferiority beliefs, and (pre)diabetes by oral glucose tolerance test were measured at baseline. Incident T2DM was self-reported per annum. We analysed the mediating roles of poor psychosocial resources by using counterfactual mediation analysis. People with low SEP had more often prevalent and incident T2DM (e.g. low education: HR = 2.13, 95%CI: 1.53-2.97). Low control beliefs and high inferiority beliefs were more common among people with low SEP. Moreover, low control beliefs and high inferiority beliefs were risk factors for T2DM (e.g. low control beliefs: HR = 1.50, 95%CI: 1.08-2.09). The relationship between SEP and T2DM was partially mediated by control beliefs (8.0-13.6%) and inferiority beliefs (2.2-4.5%). We conclude that poor psychosocial resources are important in socioeconomic inequalities in diabetes. Researchers and practitioners should consider the psychosocial profile of people with lower SEP, as such a profile might interfere with the development, treatment, and prevention of T2DM. Further research should explore how poor psychosocial resources interact with chronic stress in relation to socioeconomic health inequalities.

Enhancing cardiovascular patients' knowledge of air pollution: a pilot study evaluating the impact of an educational intervention in cardiac rehabilitation

Introduction

Air pollution poses significant risks to cardiovascular health, yet patients often lack comprehensive knowledge about its impact and mitigation strategies. This study aims to evaluate the effectiveness of an educational intervention within a cardiac rehabilitation (CR) context in enhancing patients' understanding of air pollution and its cardiovascular effects.

Methods

A pre-post pilot study was conducted from February 2021 to June 2021 at the Gregorio Marañón University General Hospital, Madrid, Spain. A total of 43 patients with cardiovascular disease attending CR were enrolled. Participants received a 1-h educational intervention delivered either in-person or online, focusing on the effects of air pollution and strategies to reduce exposure. Pre- and post-intervention questionnaires assessed participants' knowledge and perceptions. Descriptive statistics were used to analyze changes in awareness and understanding.

Results

Initially, participants demonstrated a high level of awareness about the health impacts of air pollution, with 100% acknowledging its effects on health. However, detailed knowledge about specific concepts such as the Air Quality Index (AQI) and particulate matter (PM2.5) was limited. Post-intervention, there was a significant increase in knowledge, with familiarity with AQI rising from 61% to 81% (p = 0.02) and understanding of PM2.5 improving from 28% to 58% (p = 0.01). This indicates that the educational intervention effectively bridged gaps in understanding and reinforced the importance of environmental factors in cardiovascular health management.

Discussion

The pilot study highlights the critical role of targeted education in improving patient awareness and knowledge about air pollution. The significant improvement in understanding key concepts underscores the need for broader educational initiatives that extend beyond CR programs. Future research should explore the impact of such interventions on long-term health outcomes and consider expanding educational efforts to include healthcare providers and family members.