Confounding by Socioeconomic Status in Epidemiological Studies of Air Pollution and Health: Challenges and Opportunities

A comparison of PM2.5 exposure estimates from different estimation methods and their associations with cognitive testing and brain MRI outcomes

BACKGROUND

Reported associations between particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) and cognitive outcomes remain mixed. Differences in exposure estimation method may contribute to this heterogeneity.

OBJECTIVES

To assess agreement between PM2.5 exposure concentrations across 11 exposure estimation methods and to compare resulting associations between PM2.5 and cognitive or MRI outcomes.

METHODS

We used Visit 5 (2011-2013) cognitive testing and brain MRI data from the Atherosclerosis Risk in Communities (ARIC) Study. We derived address-linked average 2000-2007 PM2.5 exposure concentrations in areas immediately surrounding the four ARIC recruitment sites (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; Washington County, MD) using 11 estimation methods. We assessed agreement between method-specific PM2.5 concentrations using descriptive statistics and plots, overall and by site. We used adjusted linear regression to estimate associations of method-specific PM2.5 exposure estimates with cognitive scores (n = 4678) and MRI outcomes (n = 1518) stratified by study site and combined site-specific estimates using meta-analyses to derive overall estimates. We explored the potential impact of unmeasured confounding by spatially patterned factors.

RESULTS

Exposure estimates from most methods had high agreement across sites, but low agreement within sites. Within-site exposure variation was limited for some methods. Consistently null findings for the PM2.5-cognitive outcome associations regardless of method precluded empirical conclusions about the potential impact of method on study findings in contexts where positive associations are observed. Not accounting for study site led to consistent, adverse associations, regardless of exposure estimation method, suggesting the potential for substantial bias due to residual confounding by spatially patterned factors.

DISCUSSION

PM2.5 estimation methods agreed across sites but not within sites. Choice of estimation method may impact findings when participants are concentrated in small geographic areas. Understanding unmeasured confounding by factors that are spatially patterned may be particularly important in studies of air pollution and cognitive or brain health.

Do social economic status modify the association between air pollution and depressive or anxiety symptoms? A big sample cross-sectional study from the rural areas of Central China

BACKGROUND

Fewer studies have examined the relationship between air pollution and depressive or anxiety symptoms in rural residents. Social economic status (SES), as an important indicator of the current state of socioeconomic development, we know little about how it modifies the relationship between air pollution and symptoms of depression or anxiety.

METHODS

The patient health questionnaire (PHQ-2) and generalized anxiety scale (GAD-2) were used to learn about the prevalence of depressive and anxiety symptoms, the social economic status of the participants was categorized into two levels: lower and higher, and a binary logistic regression model was used to analyze the relationship between air pollution and residents' symptoms of depression or anxiety.

RESULTS

A total of 10,670 adults were enrolled in this study, of which a total of 1292 participants suffered from depressive symptoms and 860 suffered from anxiety symptoms. Short-term exposure to PM2.5 and O3, singly or in combination, may be associated with the onset of depression symptoms, and there was a significant interaction between SES and exposure to PM2.5 or O3. Residents of areas with higher SES may have a lower risk of suffering from anxiety symptoms after O3 exposure compared to participants living in lower SES.

LIMITATIONS

The study was a cross-sectional study, which may have lowered the quality level of the evidence.

CONCLUSIONS

Short-term PM2.5 and O3 exposure may be associated with an increased prevalence risk of depressive symptoms. Higher levels of SES may reduce the adverse effects of air pollution on depressive or anxiety symptoms.

The mediating role of air pollutants in the association between education and lung function among the elderly, the HAPIEE study

BACKGROUND

Chronic exposure to air pollutants harms human health, and at a geographical level, concentrations of air pollutants are often associated with socioeconomic disadvantage.

OBJECTIVES

The aim of this study was to investigate the effects of educational attainment and air pollution on lung function in older adults, and whether air pollution may mediate the effect of education.

METHODS

The study included 6381 individuals (mean age 58.24 ± 7.14 years) who participated in the Czech HAPPIE (Health, Alcohol, and Psychosocial Factors in Eastern Europe) study. Participants' residential addresses were linked to air pollution data, including mean exposures to PM10 (particulate matter of aerodynamic diameter below 10 μm) and NO2 (nitrogen dioxide). We used path analysis to link educational attainment and air pollutants to a standardized measure of the Forced Expiratory Volume in the first second (FEV1).

RESULTS

Higher levels of participants' education were associated with lower exposures to PM10 and NO2. Individuals with tertiary education had higher standardized FEV1 than individuals with primary education (88 % vs 95 %). Path analysis revealed a direct positive effect of education on FEV1, while about 12 % of the relationship between education and lung function was mediated by PM10 and NO2. CONCLUSIONS: Education (typically completed at young ages) appeared to have a protective effect on lung function later in life, and a small part of this effect was mediated by air pollution.

The association between PM2.5 components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models

BACKGROUND

PM2.5 is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develop an analytical methodology combining traditional and novel machine learning models to evaluate PM2.5's combined effects on blood pressure (BP) and identify the most toxic components.

METHODS

We measured late-pregnancy BP of 1138 women from the Heshan cohort while simultaneously analyzing 31 PM2.5 components. We utilized multiple linear regression modeling to establish the relationship between PM2.5 components and late-pregnancy BP and applied Random Forest (RF) and generalized Weighted Quantile Sum (gWQS) regression to identify the most toxic components contributing to elevated BP and to quantitatively evaluate the cumulative effect of the PM2.5 component mixtures.

RESULTS

The results revealed that 16 PM2.5 components, such as EC, OC, Ti, Fe, Mn, Cu, Cd, Mg, K, Pb, Se, Na+, K+, Cl-, NO3-, and F-, contributed to elevated systolic blood pressure (SBP), while 26 components, including two carbon components (EC, OC), fourteen metallics (Ti, Fe, Mn, Cr, Mo, Co, Cu, Zn, Cd, Na, Mg, Al, K, Pb), one metalloid (Se), and nine water-soluble ions (Na+, K+, Mg2+, Ca2+, NH4+, Cl-, NO3-, SO42-, F-), contributed to elevated diastolic blood pressure (DBP). Mn and Cr were the most toxic components for elevated SBP and DBP, respectively, as analyzed by RF and gWQS models and verified against each other. Exposure to PM2.5 component mixtures increased SBP by 1.04 mmHg (95% CI: 0.33-1.76) and DBP by 1.13 mmHg (95% CI: 0.47-1.78).

CONCLUSIONS

Our study highlights the effectiveness of combining traditional and novel models as an analytical strategy to quantify the health effects of PM2.5 constituent mixtures.

Airborne Exposure to Pollutants and Mental Health: A Review with Implications for United States Veterans

PURPOSE OF REVIEW

Inhalation of airborne pollutants in the natural and built environment is ubiquitous; yet, exposures are different across a lifespan and unique to individuals. Here, we reviewed the connections between mental health outcomes from airborne pollutant exposures, the biological inflammatory mechanisms, and provide future directions for researchers and policy makers. The current state of knowledge is discussed on associations between mental health outcomes and Clean Air Act criteria pollutants, traffic-related air pollutants, pesticides, heavy metals, jet fuel, and burn pits.

RECENT FINDINGS

Although associations between airborne pollutants and negative physical health outcomes have been a topic of previous investigations, work highlighting associations between exposures and psychological health is only starting to emerge. Research on criteria pollutants and mental health outcomes has the most robust results to date, followed by traffic-related air pollutants, and then pesticides. In contrast, scarce mental health research has been conducted on exposure to heavy metals, jet fuel, and burn pits. Specific cohorts of individuals, such as United States military members and in-turn, Veterans, often have unique histories of exposures, including service-related exposures to aircraft (e.g. jet fuels) and burn pits. Research focused on Veterans and other individuals with an increased likelihood of exposure and higher vulnerability to negative mental health outcomes is needed. Future research will facilitate knowledge aimed at both prevention and intervention to improve physical and mental health among military personnel, Veterans, and other at-risk individuals.

Cumulative Exposures to Environmental and Socioeconomic Risk Factors in Milwaukee County, Wisconsin

The environmental justice literature demonstrates consistently that low-income and minority communities are disproportionately exposed to environmental hazards. In this case study, we examined cumulative multipollutant, multidomain, and multimatrix environmental exposures in Milwaukee County, Wisconsin for the year 2015. We identified spatial hot spots in Milwaukee County both individually (using local Moran's I) and through clusters (using K-means clustering) across a profile of environmental pollutants that span regulatory domains and matrices of exposure, as well as socioeconomic indicators. The cluster with the highest exposures within the urban area was largely characterized by low socioeconomic status and an overrepresentation of the Non-Hispanic Black population relative to the county as a whole. In this cluster, average pollutant concentrations were equivalent to the 78th percentile in county-level blood lead levels, 67th percentile in county-level NO2, 79th percentile in county-level CO, and 78th percentile in county-level air toxics. Simultaneously, this cluster had an average equivalent to the 62nd percentile in county-level unemployment, 70th percentile in county-level population rate lacking a high school diploma, 73rd percentile in county-level poverty rate, and 28th percentile in county-level median household income. The spatial patterns of pollutant exposure and SES indicators suggested that these disparities were not random but were instead structured by socioeconomic and racial factors. Our case study, which combines environmental pollutant exposures, sociodemographic data, and clustering analysis, provides a roadmap to identify and target overburdened communities for interventions that reduce environmental exposures and consequently improve public health.

Sociodemographic inequities in food allergy: Insights on food allergy from birth cohorts

A large and growing corpus of epidemiologic studies suggests that the population-level burden of pediatric FA is not equitably distributed across major sociodemographic groups, including race, ethnicity, household income, parental educational attainment, and sex. As is the case for more extensively studied allergic disease states such as asthma and atopic dermatitis epidemiologic data suggest that FA may be more prevalent among certain populations experiencing lower socioeconomic status (SES), particularly those with specific racial and ethnic minority backgrounds living in highly urbanized regions. Emerging data also indicate that these patients may also experience more severe FA-related physical health, psychosocial, and economic outcomes relating to chronic disease management. However, many studies that have identified sociodemographic inequities in FA burden are limited by cross-sectional designs that are subject to numerous biases. Compared with cross-sectional study designs or cohorts established later in life, birth cohorts offer advantages relative to other study designs when investigators seek to understand causal relationships between exposures occurring during the prenatal or postnatal period and the atopic disease status of individuals later in life. Numerous birth cohorts have been established across recent decades, which include evaluation of food allergy-related outcomes, and a subset of these also have measured sociodemographic variables that, together, have the potential to shed light on the existence and possible etiology of sociodemographic inequities in food allergy. This manuscript reports the findings of a comprehensive survey of the current state of this birth cohort literature and draws insights into what is currently known, and what further information can potentially be gleaned from thoughtful examination and further follow-up of ongoing birth cohorts across the globe.

Area-level Measures of the Social Environment: Operationalization, Pitfalls, and Ways Forward

People's mental health is intertwined with the social environment in which they reside. This chapter explores approaches for quantifying the area-level social environment, focusing specifically on socioeconomic deprivation and social fragmentation. We discuss census data and administrative units, egocentric and ecometric approaches, neighborhood audits, social media data, and street view-based assessments. We close the chapter by discussing possible paths forward from associations between social environments and health to establishing causality, including longitudinal research designs and time-series social environmental indices.

Association of prenatal exposure to PM2.5 and NO2 with gestational diabetes in Western New York

BACKGROUND

Although many studies have examined the association between prenatal air pollution exposure and gestational diabetes (GDM), the relevant exposure windows remain inconclusive. We aim to examine the association between preconception and trimester-specific exposure to PM2.5 and NO2 and GDM risk and explore modifying effects of maternal age, pre-pregnancy body mass index (BMI), smoking, exercise during pregnancy, race and ethnicity, and neighborhood disadvantage.

METHODS

Analyses included 192,508 birth records of singletons born to women without pre-existing diabetes in Western New York, 2004-2016. Daily PM2.5 and NO2 at 1-km2 grids were estimated from ensemble-based models. We assigned each birth with exposures averaged in preconception and each trimester based on residential zip-codes. We used logistic regression to examine the associations and distributed lag models (DLMs) to explore the sensitive windows by month. Relative excess risk due to interaction (RERI) and multiplicative interaction terms were calculated.

RESULTS

GDM was associated with PM2.5 averaged in the first two trimesters (per 2.5 μg/m3: OR = 1.08, 95% CI: 1.01, 1.14) or from preconception to the second trimester (per 2.5 μg/m3: OR = 1.10, 95% CI: 1.03, 1.18). NO2 exposure during each averaging period was associated with GDM risk (per 10 ppb, preconception: OR = 1.10, 95% CI: 1.06, 1.14; first trimester: OR = 1.12, 95% CI: 1.08, 1.16; second trimester: OR = 1.10, 95% CI: 1.06, 1.14). In DLMs, sensitive windows were identified in the 5th and 6th gestational months for PM2.5 and one month before and three months after conception for NO2. Evidence of interaction was identified for pre-pregnancy BMI with PM2.5 (P-for-interaction = 0.023; RERI = 0.21, 95% CI: 0.10, 0.33) and with NO2 (P-for-interaction = 0.164; RERI = 0.16, 95% CI: 0.04, 0.27).

CONCLUSION

PM2.5 and NO2 exposure may increase GDM risk, and sensitive windows may be the late second trimester for PM2.5 and periconception for NO2. Women with higher pre-pregnancy BMI may be more susceptible to exposure effects.

The Gap of Health Inequalities Amongst Lung Cancer Patients of Different Socioeconomic Status: A Brief Reference to the Greek Reality

Lung cancer treatment and patient care are constantly improving, but it remains doubtful whether this applies equally to all socioeconomic groups. It is nowadays well established that there are socioeconomic inequalities regarding lung cancer incidence, screening, effective treatment, overall survival, and prognosis. One of the key contributing factors to low socioeconomic status is low education. Low educational level is correlated with several factors, such as smoking habits, bad lifestyle behaviors, lower paid and unhealthier occupations, polluted neighborhoods, and genetic-familial risk, that lead to increased lung cancer incidence. The disparities regarding lung cancer care are further enhanced by stigma. On this basis and inspired by the gap in health equality among the Greek population, the Greek Society of Lung Cancer initiated a campaign, "MIND THE GAP", to help increase awareness and minimize the gap associated with lung cancer, both in Greece and across Europe. The aim of this review is to explore the gap of health inequalities regarding lung cancer incidence and prognosis between patients of different SES and its root of causality. Key pivotal actions towards bridging this gap are reviewed as well.

Effect of In utero Exposure to Air Pollution on Adulthood Hospitalizations

Empirical analyses have demonstrated that individuals exposed to severe air pollution in utero have worse health outcomes during childhood. However, there is little evidence on the long-term health impacts of air pollution exposure. The objective of this paper is to estimate the effect of in utero exposure to the Great London Smog of 1952 (GLS) on five health outcomes identified through a scoping review to be those most likely affected: respiratory, circulatory, neoplasms, mental health, and nervous system conditions. We use the GLS, an extreme air pollution event in December 1952, as a quasi-natural experiment to estimate the effect of exposure to air pollution in utero on adulthood health. Data from the UK Biobank is analysed for a cohort of participants born from December 1952 to July 1956. Differences in health outcomes between adults exposed and not exposed to the GLS due to their birth dates, born inside and outside London, were explored. Our primary focus is hospitalization events between 1997 and 2020 (corresponding to ages 40 to 69), as recorded in linked administrative data from the National Health Service (NHS). Specifically, the five primary outcomes are binary variables indicating that the individual had at least one hospitalization where the main cause of hospitalization is related to respiratory, circulatory, neoplasms, mental health, or nervous system conditions. The analytical sample comprised 36,281 individuals. A positive effect on adulthood hospitalizations due to respiratory conditions was observed. If exposed to the GLS in utero, the probability of at least one respiratory health-related hospitalization between 1997 and 2020 increased by 2.58 percentage points (95% CI 0.08, 4.30, p = 0.03), a 23% increase relative to the sample mean. Small effects were found for all other outcomes, suggesting that these conditions were not affected by the GLS. We do not find heterogeneous effects by sex or childhood socioeconomic status. This study found that a 5-day pollution exposure event while in utero significantly increased respiratory-related hospitalizations at ages 40 to 69 but had no impact on hospitalizations due to circulatory, neoplasms, mental health, and nervous system conditions.

Air pollution and pregnancy

Increased fossil fuel usage and extreme climate change events have led to global increases in greenhouse gases and particulate matter with 99% of the world's population now breathing polluted air that exceeds the World Health Organization's recommended limits. Pregnant women and neonates with exposure to high levels of air pollutants are at increased risk of adverse health outcomes such as maternal hypertensive disorders, postpartum depression, placental abruption, low birth weight, preterm birth, infant mortality, and adverse lung and respiratory effects. While the exact mechanism by which air pollution exerts adverse health effects is unknown, oxidative stress as well as epigenetic and immune mechanisms are thought to play roles. Comprehensive, global efforts are urgently required to tackle the health challenges posed by air pollution through policies and action for reducing air pollution as well as finding ways to protect the health of vulnerable populations in the face of increasing air pollution.

Acute air pollution and temperature exposure as independent and joint triggers of spontaneous preterm birth in New South Wales, Australia: a time-to-event analysis

Introduction

Exposure to high ambient temperatures and air pollution has been shown to increase the risk of spontaneous preterm birth (sPTB). Less clear are the effects of cold and the joint effects of air pollution and temperature.

Methods

Using a Cox proportional hazard regression model, we assessed the risk of independent and combined short-term exposure to ambient daily mean temperature and PM2.5 associated with sPTB in the last week before delivery on overall sPTB (weeks 23-36) and three subtypes: extremely sPTB, very sPTB, and moderate-to-late sPTB for a birth cohort of 1,318,570 births from Australia (Jan 2001-Dec 2019), while controlling for chronic exposure (i.e., throughout pregnancy except the last week before delivery) to PM2.5 and temperature. The temperature was modeled as a natural cubic spline, PM2.5 as a linear term, and the interaction effect was estimated using a multiplicative term. For short-term exposure to temperature hazard ratios reported are relative to the median temperature (18.1°C).

Results

Hazard ratios at low temperature [5th percentile(11.5°C)] were 0.95 (95% CI: 0.90, 1.00), 1.08 (95% CI: 0.84, 1.4), 0.87 (95% CI: 0.71, 1.06), and 1.00 (95% CI: 0.94, 1.06) and greater for high temperature [95th percentile (24.5°C)]: 1.22 (95% CI: 1.16, 1.28), 1.27 (95% CI: 1.03, 1.57), and 1.26 (95% CI: 1.05, 1.5) and 1.05 (1.00, 1.11), respectively, for overall, extremely, very, and moderate-to-late sPTBs. While chronic exposure to PM2.5 had adverse effects on sPTB, short-term exposure to PM2.5 appeared to have a negative association with all types of sPTB, with hazard ratios ranging from 0.86 (95th CI: 0.80, 0.94) to 0.98 (95th CI: 0.97, 1.00) per 5 μg/m3 increase in PM2.5.

Discussion

The risk of sPTB was found to increase following acute exposure to hot and cold ambient temperatures. Earlier sPTB subtypes seemed to be the most vulnerable. This study adds to the evidence that short-term exposure to ambient cold and heat and longer term gestational exposure to ambient PM2.5 are associated with an elevated risk of sPTB.

Health impact assessment for air pollution in the presence of regional variation in effect sizes: The implications of using different meta-analytic approaches

The estimated health effects of air pollution vary between studies, and this variation is caused by factors associated with the study location, hereafter termed regional heterogeneity. This heterogeneity raises a methodological question as to which studies should be used to estimate risks in a specific region in a health impact assessment. Should one use all studies across the world, or only those in the region of interest? The current study provides novel insight into this question in two ways. Firstly, it presents an up-to-date analysis examining the magnitude of continent-level regional heterogeneity in the short-term health effects of air pollution, using a database of studies collected by Orellano et al. (2020). Secondly, it provides in-depth simulation analyses examining whether existing meta-analyses are likely to be underpowered to identify statistically significant regional heterogeneity, as well as evaluating which meta-analytic technique is best for estimating region-specific estimates. The techniques considered include global and continent-specific (sub-group) random effects meta-analysis and meta-regression, with omnibus statistical tests used to quantify regional heterogeneity. We find statistically significant regional heterogeneity for 4 of the 8 pollutant-outcome pairs considered, comprising NO2, O3 and PM2.5 with all-cause mortality, and PM2.5 with cardiovascular mortality. From the simulation analysis statistically significant regional heterogeneity is more likely to be identified as the number of studies increases (between 3 and 30 in each region were considered), between region heterogeneity increases and within region heterogeneity decreases. Finally, while a sub-group analysis using Cochran's Q test has a higher median power (0.71) than a test based on the moderators' coefficients from meta-regression (0.59) to identify regional heterogeneity, it also has an inflated type-1 error leading to more false positives (median errors of 0.15 compared to 0.09).

Association of Antepartum and Postpartum Air Pollution Exposure With Postpartum Depression in Southern California

Importance

Women are especially vulnerable to mental health matters post partum because of biological, emotional, and social changes during this period. However, epidemiologic evidence of an association between air pollution exposure and postpartum depression (PPD) is limited.

Objective

To examine the associations between antepartum and postpartum maternal air pollution exposure and PPD.

Design, Setting, and Participants

This retrospective cohort study used data from Kaiser Permanente Southern California (KPSC) electronic health records and included women who had singleton live births at KPSC facilities between January 1, 2008, and December 31, 2016. Data were analyzed between January 1 and May 10, 2023.

Exposures

Ambient air pollution exposures were assessed based on maternal residential addresses using monthly averages of particulate matter less than or equal to 2.5 μm (PM2.5), particulate matter less than or equal to 10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) from spatial interpolation of monitoring station measurements. Constituents of PM2.5 (sulfate, nitrate, ammonium, organic matter, and black carbon) were obtained from fine-resolution geoscience-derived models based on satellite, ground-based monitor, and chemical transport modeling data.

Main Outcomes and Measures

Participants with an Edinburgh Postnatal Depression Scale score of 10 or higher during the 6 months after giving birth were referred to a clinical interview for further assessment and diagnosis. Ascertainment of PPD was defined using a combination of diagnostic codes and prescription medications.

Results

The study included 340 679 participants (mean [SD] age, 30.05 [5.81] years), with 25 674 having PPD (7.54%). Increased risks for PPD were observed to be associated with per-IQR increases in antepartum and postpartum exposures to O3 (adjusted odds ratio [AOR], 1.09; 95% CI, 1.06-1.12), PM10 (AOR, 1.02; 95% CI, 1.00-1.04), and PM2.5 (AOR, 1.02; 95% CI, 1. 00-1.03) but not with NO2; PPD risks were mainly associated with PM2.5 organic matter and black carbon. Overall, a higher risk of PPD was associated with O3 during the entire pregnancy and postpartum periods and with PM exposure during the late pregnancy and postpartum periods.

Conclusions and Relevance

The study findings suggest that long-term exposure to antepartum and postpartum air pollution was associated with higher PPD risks. Identifying the modifiable environmental risk factors and developing interventions are important public health issues to improve maternal mental health and alleviate the disease burden of PPD.

Integrating the social environment with an equity perspective into the exposome paradigm: A new conceptual framework of the Social Exposome

The importance of the social environment and social inequalities in disease etiology is well-known due to the profound research and conceptual framework on social determinants of health. For a long period, in exposome research with its classical orientation towards detrimental health effects of biological, chemical, and physical exposures, this knowledge remained underrepresented. But currently it gains great awareness and calls for innovations in rethinking the role of social environmental health determinants. To fill this gap that exists in terms of the social domain within exposome research, we propose a novel conceptual framework of the Social Exposome, to integrate the social environment in conjunction with the physical environment into the exposome concept. The iterative development process of the Social Exposome was based on a systematic compilation of social exposures in order to achieve a holistic portrayal of the human social environment - including social, psychosocial, socioeconomic, sociodemographic, local, regional, and cultural aspects, at individual and contextual levels. In order to move the Social Exposome beyond a mere compilation of exposures, three core principles are emphasized that underly the interplay of the multitude of exposures: Multidimensionality, Reciprocity, and Timing and continuity. The key focus of the conceptual framework of the Social Exposome is on understanding the underlying mechanisms that translate social exposures into health outcomes. In particular, insights from research on health equity and environmental justice have been incorporated to uncover how social inequalities in health emerge, are maintained, and systematically drive health outcomes. Three transmission pathways are presented: Embodiment, Resilience and Susceptibility or Vulnerability, and Empowerment. The Social Exposome conceptual framework may serve as a strategic map for, both, research and intervention planning, aiming to further explore the impact of the complex social environment and to alter transmission pathways to minimize health risks and health inequalities and to foster equity in health.

Longitudinal associations of neighbourhood environmental exposures with mental health problems during adolescence: Findings from the TRAILS study

BACKGROUND

Cross-sectional studies have found associations between neighbourhood environments and adolescent mental health, but the few longitudinal studies mainly focused on single exposure-based analyses and rarely assessed the mental health associations with environmental changes.

OBJECTIVES

We assessed longitudinal within- and between-person associations of multiple neighbourhood time-varying physical and social environmental exposures with externalising and internalising problems throughout adolescence.

METHODS

We used four waves of TRAILS (Tracking Adolescents' Individual Lives Survey) data on self-reported externalising and internalising problems at ages 11, 13, 16, and 19 among 2,135 adolescents in the Netherlands. We measured residence-based time-varying environmental exposures, including green space, air pollution (fine particulate matter (PM2.5)), noise, deprivation, and social fragmentation. We fitted random-effect within-between regression models to assess the environment-mental health associations.

RESULTS

At the within-person level, an interquartile range (IQR) increase in PM2.5 was associated with a 0.056 IQR (95% CI: 0.014, 0.099) increase in externalising problems, while an IQR social fragmentation increase was associated with a 0.010 IQR (95% CI: -0.020, -0.001) decrease in externalising problems. Stratification revealed that the association with PM2.5 was significant only for movers, whereas the association with social fragmentation remained only for non-movers. At the between-person level, an IQR higher noise was associated with a 0.100 IQR (95% CI: 0.031, 0.169) more externalising problems, while higher deprivation (β = 0.080; 95% CI: 0.022, 0.138) and lower fragmentation (β = -0.073; 95% CI: -0.128, -0.018) were associated with more internalising problems. We also observed positive between-person associations between PM2.5, noise, and internalising problems, but both associations were unstable due to the high PM2.5-noise correlation. Further, we observed a non-linear between-person PM2.5-externalising problems association turning positive when PM2.5 > 15 µg/m3. Null associations were found for green space.

CONCLUSION

Our findings suggested that air pollution, noise, and neighbourhood deprivation are risk factors for adolescent mental health. Not only exposure levels but also exposure changes matter for adolescent mental health.

Disparities in greenspace associated with sleep duration among adolescent children in Southern California

More than half of adolescent children do not get the recommended 8 hours of sleep necessary for optimal growth and development. In adults, several studies have evaluated effects of urban stressors including lack of greenspace, air pollution, noise, nighttime light, and psychosocial stress on sleep duration. Little is known about these effects in adolescents, however, it is known that these exposures vary by socioeconomic status (SES). We evaluated the association between several environmental exposures and sleep in adolescent children in Southern California.

Methods

In 2010, a total of 1476 Southern California Children's Health Study (CHS) participants in grades 9 and 10 (mean age, 13.4 years; SD, 0.6) completed a questionnaire including topics on sleep and psychosocial stress. Exposures to greenspace, artificial light at night (ALAN), nighttime noise, and air pollution were estimated at each child's residential address, and SES was characterized by maternal education. Odds ratios and 95% confidence intervals (95% CIs) for sleep outcomes were estimated by environmental exposure, adjusting for age, sex, race/ethnicity, home secondhand smoke, and SES.

Results

An interquartile range (IQR) increase in greenspace decreased the odds of not sleeping at least 8 hours (odds ratio [OR], 0.86 [95% CI, 0.71, 1.05]). This association was significantly protective in low SES participants (OR, 0.77 [95% CI, 0.60, 0.98]) but not for those with high SES (OR, 1.16 [95%CI, 0.80, 1.70]), interaction P = 0.03. Stress mediated 18.4% of the association among low SES participants.

Conclusions

Residing in urban neighborhoods of greater greenness was associated with improved sleep duration among children of low SES but not higher SES. These findings support the importance of widely reported disparities in exposure and access to greenspace in socioeconomically disadvantaged populations.

Socioeconomic and racial disparities in source-apportioned PM2.5 levels across urban areas in the contiguous US, 2010

Fine particulate matter (PM2.5) air pollution exposure is associated with short and long-term health effects. Several studies found differences in PM2.5 exposure associated with neighborhood racial and socioeconomic composition. However, most focused on total PM2.5 mass rather than its chemical components and their sources. In this study, we describe the ZIP code characteristics that drive the disparities in exposure to PM2.5 chemical components attributed to source categories both nationally and regionally. We obtained annual mean predictions of PM2.5 and fourteen of its chemical components from spatiotemporal models and socioeconomic and racial predictor variables from the 2010 US Census, and the American Community Survey 5-year estimates. We used non-negative matrix factorization to attribute the chemical components to five source categories. We fit generalized nonlinear models to assess the associations between the neighborhood predictors and each PM2.5 source category in urban areas in the United States in 2010 (n=25,790 zip codes). We observed higher PM2.5 levels in ZIP codes with higher proportions of Black individuals and lower socioeconomic status. Racial exposure disparities were mainly attributed to Heavy Fuel, Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Economic disparities were mainly attributed to Soil and Crustal Dust, Heavy Fuel Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Upon further analysis through stratifying by regions within the United States, we found that the associations between ZIP code characteristics and source-attributed PM2.5 levels were generally greater in Western states. In conclusion, racial, socioeconomic, and geographic inequalities in exposure to PM2.5 and its components are driven by systematic differences in component sources that can inform air quality improvement strategies.

Assessing socioeconomic bias of exposure to urban air pollution: an autopsy-based study in São Paulo, Brazil

Background

The characterisation of individual exposure to air pollution in urban scenarios is a challenge in environmental epidemiological studies. We investigated if the city's pollution monitoring stations over or underestimate the exposure of individuals depending on their socioeconomic conditions and daily commuting times.

Methods

The amount of black carbon accumulated in the lungs of 604 deceased who underwent autopsy in São Paulo was considered as a proxy for PM₁₀. The concentrations of PM₁₀ in the residence of the deceased were estimated by interpolating an ordinary kriging model. These two-exposure metrics allowed us to construct an environmental exposure misclassification index ranging from -1 to 1. The association between the index and daily commuting, socioeconomic context index (GeoSES), and street density as predictors was assessed by means of a multilevel linear regression model.

Findings

With a decrease of 0^.1 units in GeoSES, the index increases, on average, by 0^.028 units and with an increase of 1 h in daily commuting, the index increases, on average, by 0^.022 units indicating that individual exposure to air pollution is underestimated in the lower GeoSES and in people with many hours spent in daily commuting.

Interpretation

Reduction of health consequences of air pollution demands not only alternative fuel and more efficient mobility strategies, but also should include profound rethink of cities.

Funding

São Paulo Research Foundation (FAPESP-13/21728-2) and National Council for Scientific and Technological Development (CNPq-304126/2015-2, 401825/2020-5).

Greenness modifies the association between ambient air pollution and cognitive function in Australian adolescents, but not in mid-life adults

Exposure to ambient air pollution has been associated with reduced cognitive function in childhood and later life, with too few mid-life studies to draw conclusions. In contrast, residential greenness has been associated with enhanced cognitive function throughout the lifecourse. Here we examine the extent to which (1) ambient air pollution and residential greenness predict later cognitive function in adolescence and mid-life, and (2) greenness modifies air pollution-cognitive function associations.

PARTICIPANTS

6220 adolescents (51% male) and 2623 mid-life adults (96% mothers) from the Longitudinal Study of Australian Children.

MEASURES

Exposures: Annual average particulate matter <2.5 μm (PM_2.5), nitrogen dioxide (NO₂) and greenness (Normalised Difference Vegetation Index) for residential addresses from validated land-use regression models over a 10-13-year period.

OUTCOMES

Cognitive function from CogState tests of attention, working memory and executive function, dichotomised into poorer (worst quartile) versus not poor.

ANALYSES

Adjusted mixed-effects generalised linear models with residential greenness assessed as an effect modifier (high vs. low divided at median). The annual mean for PM_2.5 and NO₂ across exposure windows was 6.3-6.8 μg/m³, and 5.5-7.1 ppb, respectively. For adolescents, an IQR increment of NO₂ was associated with 19-24% increased odds of having poorer executive function across all time windows, while associations weren't observed between air pollution and other outcomes. For adults, high NO₂ predicted poorer cognitive function across all outcomes, while high PM_2.5 predicted poorer attention only. There was little evidence of associations between greenness and cognitive function in adjusted models for both generations. Interactions were found between residential greenness, air pollutants and cognitive function in adolescents, but not adults. The magnitude of effects was similar across generations and exposure windows. Findings highlight the potential benefits of cognitive health associated with the regulation of air pollution and urban planning strategies for increasing green spaces and vegetation.

Racial/ethnic and socioeconomic differences in breast cancer surgery performed and delayed treatment: mediating impact on mortality

OBJECTIVE

Although Socioeconomic status (SES), race/ethnicity, surgical type, and treatment delays are associated with breast cancer mortality outcomes, studies on these associations have been contrasting. This study examined the racial/ethnic and SES differences in surgical treatment types and treatment delays. Also, we quantified the extent to which these differences explained the racial/ethnic disparities in breast cancer mortality.

METHODS

We studied 290,066 women 40 + years old diagnosed with breast cancer between 2010 and 2017 identified from the Surveillance, Epidemiology, and End Results database. We performed logistic regression models to examine the association of SES and race/ethnicity with surgical treatment type and treatment delays. We performed mediation analysis models to quantify the extent to which mortality differences were mediated by treatment, sociodemographic, and clinicopathologic factors.

RESULTS

Non-Hispanic (NH) Black [Odds ratio (OR) = 1.16, 95% CI 1.13-1.19] and Hispanic women [OR = 1.27, 95% CI 1.24-1.31] were significantly more likely to undergo mastectomy compared to NH White women. Similarly, NH Black and Hispanic women had higher odds of delayed treatment than NH Whites. Patients in the highest SES quintile, compared to those in lowest the lowest, were less likely to experience breast cancer-specific mortality (BCSM). Variations in treatment, SES, and clinicopathological factors significantly explained 70% of the excess BCSM among NH Blacks compared to their NH White counterparts.

CONCLUSIONS

Bridging the gap of access to adequate healthcare services for all to diminish the disproportionate burden of breast cancer would require a multifactorial approach that addresses several biological and social factors that cause these differences.

State of the science on outdoor air pollution exposure and liver cancer risk

Background

There is emerging evidence that air pollution exposure increases the risk of developing liver cancer. To date, there have been four epidemiologic studies conducted in the United States, Taiwan, and Europe showing generally consistent positive associations between ambient exposure to air pollutants, including particulate matter <2.5 μm in aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2), and liver cancer risk. There are several research gaps and thus valuable opportunities for future work to continue building on this expanding body of literature. The objectives of this paper are to narratively synthesize existing epidemiologic literature on the association between air pollution exposure and liver cancer incidence and describe future research directions to advance the science of understanding the role of air pollution exposure in liver cancer development.

Future research directions

include 1) accounting for potential confounding by established risk factors for the predominant histological subtype, hepatocellular carcinoma (HCC); 2) examination of incident primary liver cancer outcomes with consideration of potential differential associations according to histology; 3) air pollution exposure assessments considering early-life and/or historical exposures, residential histories, residual confounding from other sources of air pollution (e.g., tobacco smoking), and integration of geospatial ambient exposure modeling with novel biomarker technologies; 4) examination of air pollution mixtures experienced in the exposome; 5) consideration of increased opportunities for exposure to outdoor air pollution due to climate change (e.g., wildfires); and 6) consideration of modifying factors for air pollution exposure, such as socioeconomic status, that may contribute to disparities in liver cancer incidence.

Conclusions

In light of mounting evidence demonstrating that higher levels of air pollution exposure increase the risk for developing liver cancer, methodological considerations primarily concerning residual confounding and improved exposure assessment are warranted to robustly demonstrate an independent association for air pollution as a hepatocarcinogen.

Are air quality perception and PM2.5 exposure differently associated with cardiovascular and respiratory disease mortality in Brussels? Findings from a census-based study

BACKGROUND

There is ample evidence that air pollution increases mortality risk, but most studies are based on modelled estimates of air pollution, while the subjective perception of air quality is scarcely assessed. We aimed to compare the effects of objective and subjective exposure to air pollution on cardiorespiratory mortality in Brussels, Belgium.

METHODS

Data consisted of the 2001 Belgian census linked to registry-based mortality data for the follow-up period 2001-2014. We included individuals aged >30 years of age residing in Brussels at baseline (2001). Air pollution exposure was assessed with objective (modelled annual mean concentrations of PM_2.5 in micrograms per cubic metre, μg/m³) and subjective indicators (poor self-reported air quality perception in the census). We used Cox Proportional Hazard models with age as the underlying time scale to evaluate associations with cardiovascular disease (CVD) and respiratory disease mortality, and separately, ischaemic heart disease (IHD), cerebrovascular disease, and COPD excluding asthma mortality. We specified single- and two-exposure models and evaluated effect modification by neighbourhood unemployment rate.

RESULTS

437,340 individuals were included at baseline. During follow-up (2001-2014), 22,821 (5%) individuals had died from CVDs and 8572 (2%) from respiratory diseases. In single-exposure models, PM_2.5 was significantly associated with an increased risk in CVD and IHD mortality (e.g. for IHD, per 5 μg/m³ increase: Hazard Ratio, HR:1.22, 95%CI:1.08-1.37), and poor air quality perception with COPD excluding asthma mortality (HR:1.23, 95%CI:1.15-1.33). Associations remained significant in the two-exposure models, and additionally, perception was associated with respiratory disease mortality. Associations became gradually stronger with increasing neighbourhood unemployment rate [e.g. in the highest, Q3: PM_2.5 and cerebrovascular disease mortality (HR:1.53, 95%CI:1.04-2.24)].

CONCLUSION

Our findings suggest that objective and subjective exposure to air pollution increased the risk of dying from cardiovascular and respiratory diseases respectively in Brussels. These results encourage policies reducing pollution load in Brussels whilst considering socio-economic inequalities.

Effects of Sociodemographic Characteristics, Comorbidity, and Coexposures on the Association between Air Pollution and Type 2 Diabetes: A Nationwide Cohort Study

BACKGROUND

Exposure to air pollution has been associated with a higher risk of type 2 diabetes (T2D), but studies investigating whether deprived groups are more susceptible to the harmful effects of air pollution are inconsistent.

OBJECTIVES

We aimed to investigate whether the association between air pollution and T2D differed according to sociodemographic characteristics, comorbidity, and coexposures.

METHODS

We estimated residential exposure to PM2.5, ultrafine particles (UFP), elemental carbon, and NO2 for all persons living in Denmark in the period 2005-2017. In total, 1.8 million persons 50-80 y of age were included for main analyses of whom 113,985 developed T2D during follow-up. We conducted additional analyses on 1.3 million persons age 35-50 y. Using Cox proportional hazards model (relative risk) and Aalens additive hazard model (absolute risk), we calculated associations between 5-y time-weighted running means of air pollution and T2D in strata of sociodemographic variables, comorbidity, population density, road traffic noise, and green space proximity.

RESULTS

Air pollution was associated with T2D, especially among people age 50-80 y, with hazard ratios of 1.17 [95% confidence interval (CI): 1.13, 1.21] per 5 μg/m3 PM2.5 and 1.16 (95% CI: 1.13, 1.19) per 10,000  UFP/cm3. In the age 50-80 y population, we found higher associations between air pollution and T2D among men in comparison with women, people with lower education vs. individuals with high education, people with medium income vs. those with low or high income, people cohabiting vs. those living alone, and people with comorbidities vs. those without comorbidities. We observed no marked changes according to occupation, population density, road noise, or surrounding greenness. In the age 35-50 y population, similar tendencies were observed, except in relation to sex and occupation, where we observed associations with air pollution only among women and blue-collar workers.

DISCUSSION

We found stronger associations between air pollution and T2D among people with existing comorbidities and weaker associations among people with high socioeconomic status in comparison with those with lower socioeconomic status. https://doi.org/10.1289/EHP11347.

Associations of socioeconomic status with infectious diseases mediated by lifestyle, environmental pollution and chronic comorbidities: a comprehensive evaluation based on UK Biobank

BACKGROUND

Socioeconomic status (SES) inequity was recognized as a driver of some certain infectious diseases. However, few studies evaluated the association between SES and the burden of overall infections, and even fewer identified preventable mediators. This study aimed to assess the association between SES and overall infectious diseases burden, and the potential roles of factors including lifestyle, environmental pollution, chronic disease history.

METHODS

We included 401,009 participants from the UK Biobank (UKB) and defined the infection status for each participant according to their diagnosis records. Latent class analysis (LCA) was used to define SES for each participant. We further defined healthy lifestyle score, environment pollution score (EPS) and four types of chronic comorbidities. We used multivariate logistic regression to test the associations between the four above covariates and infectious diseases. Then, we performed the mediation and interaction analysis to explain the relationships between SES and other variables on infectious diseases. Finally, we employed seven types of sensitivity analyses, including considering the Townsend deprivation index as an area level SES variable, repeating our main analysis for some individual or composite factors and in some subgroups, as well as in an external data from the US National Health and Nutrition Examination Survey, to verify the main results.

RESULTS

In UKB, 60,771 (15.2%) participants were diagnosed with infectious diseases during follow-up. Lower SES [odds ratio (OR) = 1.5570] were associated with higher risk of overall infections. Lifestyle score mediated 2.9% of effects from SES, which ranged from 2.9 to 4.0% in different infection subtypes, while cardiovascular disease (CVD) mediated a proportion of 6.2% with a range from 2.1 to 6.8%. In addition, SES showed significant negative interaction with lifestyle score (OR = 0.8650) and a history of cancer (OR = 0.9096), while a significant synergy interaction was observed between SES and EPS (OR = 1.0024). In subgroup analysis, we found that males and African (AFR) with lower SES showed much higher infection risk. Results from sensitivity and validation analyses showed relative consistent with the main analysis.

CONCLUSIONS

Low SES is shown to be an important risk factor for infectious disease, part of which may be mediated by poor lifestyle and chronic comorbidities. Efforts to enhance health education and improve the quality of living environment may help reduce burden of infectious disease, especially for people with low SES.

Long-term exposure to nitrogen dioxide air pollution and breast cancer risk: A nested case-control within the French E3N cohort study

Nitrogen dioxide (NO₂) is an important air pollutant due to its adverse effects on human health. Yet, current evidence on the association between NO₂ and the risk of breast cancer lacks consistency. In this study, we investigated the association between long-term exposure to NO₂ and breast cancer risk in the French E3N cohort study. Association of breast cancer risk with NO₂ exposure was assessed in a nested case-control study within the French E3N cohort including 5222 breast cancer cases identified over the 1990-2011 follow-up period and 5222 matched controls. Annual mean concentrations of NO₂ at participants' residential addresses for each year from recruitment 1990 through 2011, were estimated using a land use regression (LUR) model. Multivariable conditional logistic regression models were used to compute odds ratios (ORs) and their 95% confidence intervals (CIs). Additional analyses were performed using NO₂ concentrations estimated by CHIMERE, a chemistry transport model. Overall, the mean NO₂ exposure was associated with an increased risk of breast cancer. In all women, for each interquartile range (IQR) increase in NO₂ levels (LUR: 17.8 μg/m³), the OR of the model adjusted for confounders was 1.09 (95% CI: 1.01-1.18). The corresponding OR in the fully adjusted model (additionally adjusted for established breast cancer risk factors) was 1.07 (95% CI: 0.98-1.15). By menopausal status, results for postmenopausal women were comparable to those for all women, while no association was observed among premenopausal women. By hormone receptor status, the OR of estrogen receptor positive breast cancer = 1.07 (95% CI: 0.97-1.19) in the fully adjusted model. Additional analyses using the CHIMERE model showed slight differences in ORs estimates. The results of this study indicate an increased risk of breast cancer associated with long-term exposure to NO₂ air pollution. Observing comparable effects of NO₂ exposure estimated by two different models, reinforces these findings.

Living near coasts is associated with higher suicide rates among females but not males: A register-based linkage study in the Netherlands

Exposure to blue spaces may promote psychological wellbeing and reduce mental distress. Whether these effects extend to suicide is unknown. We used register data from 14 million Dutch adults aged 18-64-years between 2007 and 2016 in a nested case-control study to estimate associations between blue space exposures and suicide risk. Each suicide case was matched to ten randomly selected controls. Two blue space exposures were assigned over a ten-year residential address history: distance to the closest inland blue space and distance to the coast. We fitted (gender-stratified) conditional logistic regressions to the data. Possible effect modifications by income were also examined. In total, our analyses included 9757 cases and 95,641 controls. Effect estimates for distance to the closest inland blue space in the total population showed that people living farthest away from inland blue space were at-risk. Suicide risk was lower among women who lived farther away from the coast; no significant effect was observed for men. No evidence was observed that income modified these associations. Our findings provide suggestive evidence that living close to the coast is associated with greater suicide risk for women, while living closer to inland blue spaces may add to the resilience against suicide in the total population. Past research shows that coastal proximity protects against milder forms of mental illness, but these protective effects do not appear to hold for suicide. Blue space interventions for women with severe mental illness or propensities to engage in self-harm should be approached with caution.

Joint exposure to air pollution, ambient temperature and residential greenness and their association with metabolic syndrome (MetS): A large population-based study among Chinese adults

Previous studies assessing adverse health have traditionally focused on a single environmental exposure, failing to reflect the reality of various exposures present simultaneously. Air pollution, ambient temperature and greenness have been proposed as critical environmental factors associated with metabolic syndrome (MetS). However, evidence exploring their joint relationships with MetS is needed for identifying interactive factors and developing more targeted public health interventions. The baseline data was obtained from China Multi-Ethnic Cohort (CMEC). Environmental data of air pollutants (PM_2.5, O₃) and NDVI for greenness was calculated from satellites data. Ambient temperature data were obtained from European Center for Medium-Range Weather Forecasts (ECMWF). MetS was classified based on National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) using anthropometric measures and biomarkers. Logistic regression models were utilized to examine the combined relationship of MetS with three-year exposure to air pollutants, temperature and NDVI. Relative excess risk due to interaction (RERI) was calculated to evaluate interaction on an additive scale. We found associations between prevalent MetS and interquartile range (IQR) increases in PM_2.5 (OR: 1.38; 95% confidence interval [95% CI]: 1.23, 1.55) and O₃ (OR: 1.15; 95% CI: 1.09, 1.22). Additive and multiplicative interactions were observed between air pollutants and temperature exposure. Compared to low-temperature level, the relationship between PM_2.5 and MetS attenuated (RERI: 0.22, 95% CI: 0.44, -0.04) at high-temperature level, while the relationship between O₃ and MetS enhanced (RERI: 0.05, 95% CI: 0.02, 0.11). At low NDVI 250 m, the association between PM_2.5 and MetS was stronger (RERI: 0.13, 95% CI: 0.05, 0.19) with high NDVI 250 m as the reference group. Our findings showed that ambient temperature and residential greenness could affect the relationship between air pollutants and MetS.

Neighborhood characteristics as confounders and effect modifiers for the association between air pollution exposure and subjective cognitive functioning

BACKGROUND

Air pollution has been associated with cognitive function in the elderly. Previous studies have not evaluated the simultaneous effect of neighborhood-level socioeconomic status (N-SES), which can be an essential source of bias.

OBJECTIVES

We explored N-SES as a confounder and effect modifier in a cross-sectional study of air pollution and subjective cognitive function.

METHODS

We included 12,058 participants age 50+ years from the Emory Healthy Aging Study in Metro Atlanta using the Cognitive Function Instrument (CFI) score as our outcome, with higher scores representing worse subjective cognitive function. We estimated 9-year average ambient carbon monoxide (CO), nitrogen oxides (NOx), and fine particulate matter (PM2.5) concentrations at residential addresses using a fusion of dispersion and chemical transport models. We collected census-tract level N-SES indicators and created two composite measures via principal component analysis and k-means clustering. Associations between pollutants and CFI and effect modification by N-SES were estimated via linear regression models adjusted for age, education, race and N-SES.

RESULTS

N-SES confounded the association between air pollution and CFI, independent of individual characteristics. We found significant effect modifications by N-SES for the association between air pollution and CFI (p-values<0.001) suggesting that effects of air pollution differ depending on N-SES. Participants living in areas with low N-SES were most vulnerable to air pollution. In the lowest N-SES urban areas, interquartile range (IQR) increases in CO, NOx, and PM2.5 were associated with 5.4% (95%-confidence interval, -0.2,11.3), 4.9% (-0.4,10.4), and 9.8% (2.2,18.0) changes in CFI, respectively. In lowest N-SES suburban areas, IQR increases in CO, NOx, and PM2.5 were associated with higher changes in CFI, namely 13.0% (0.9,26.5), 13.0% (-0.1,27.8), and 17.3% (2.5,34.2), respectively.

DISCUSSION

N-SES is an important confounder and effect modifier in our study. This finding could have implications for studying health effects of air pollution and identifying susceptible populations.

Longitudinal associations between the neighborhood social, natural, and built environment and mental health: A systematic review with meta-analyses

This review aimed to assess the longitudinal associations between neighborhood social, natural, and built environments, and multiple mental health outcomes (i.e., depression, anxiety, common mental disorder, and pooled mental disorders). Of 6,785 records retrieved, 30 studies fulfilled the inclusion criteria. Meta-analytical results primarily obtained from developed country studies showed that composite neighborhood socioeconomic status was negatively associated with depression (p = 0.007) and pooled mental disorders (p = 0.002), while neighborhood urbanicity was positively associated with depression (p = 0.012) and pooled mental disorders (p = 0.005). Future longitudinal studies with similar designs and standardized exposure assessments are warranted.

Associations between short-term ambient ozone exposure and cause-specific mortality in rural and urban areas of Jiangsu, China

Most previous studies on the acute health effects of ozone are limited to urban areas, largely due to the paucity of air pollutant measurements in rural areas. We here estimated the county-specific daily maximum 8-h average ozone concentration in Jiangsu Province, China during 2015-2018, using a recently developed spatiotemporal machine learning model at a spatial resolution of 0.1° × 0.1° (∼11 × 11 km). Counties were equally divided into urban and rural groups based on the median of the percentage of urban residents across Jiangsu counties obtained from the National Population Census in 2010. We first conducted time-series analyses to estimate the county-specific effect of ozone using generalized linear models, then pooled the effect estimates by random-effects modeling. A 10 μg/m³ increase in the 4-day moving average (lag 0-3) of ambient ozone exposure was associated with increases of 0.66% (95% confidence interval [CI] 0.36%-0.95%) in daily nonaccidental mortality in rural areas and 0.42% in urban areas (95% CI, 0.27%-0.56%). Short-term ambient ozone exposure was associated with an increased risk of mortality caused by chronic obstructive pulmonary disease, hypertension, ischemic heart disease, and stroke. Our finding suggests that both urban and rural residents suffer adverse health effects from short-term ozone exposure.

Environmental exposures and lymphoma risk: a nested case-control study using the Golden Retriever Lifetime Study cohort

Lymphoma is the second most common cancer affecting Golden Retrievers and is hypothesized to arise through a complex interaction of genetic and environmental factors. The aim of this nested case–control study was to investigate the association between potential environmental pollutant sources and lymphoma risk among Golden Retrievers participating in the Golden Retriever Lifetime Study. Forty-nine Golden Retrievers with non-cutaneous lymphoma and 98 Golden Retrievers without a history of cancer matched by age, sex and neuter status were selected from the Golden Retriever Lifetime Study cohort. Geographic proximity between each dog’s primary residence and nine potential sources of environmental pollution was determined. In addition, the average annual ozone and airborne fine particulate matter levels for each dog’s county of residence and owner-reported secondhand smoke exposure were evaluated. Environmental pollution sources of interest included chemical plants, municipal dumps, manufacturing plants, incineration plants, railroad embankment tracks, landfills, coal plants, high-voltage transmission lines, and nuclear power plants. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for each exposure of interest. Subgroup analyses were conducted to evaluate whether associations differed among 1) dogs with multicentric lymphoma, 2) dogs with B-cell lymphoma, and 3) dogs with T-cell lymphoma. No variables reached statistical significance when evaluating all cases together. However, cumulative exposure burden (household proximity to 3 or more pollution sources) approached significance within the multicentric lymphoma subgroup (OR = 2.60, 95%CI 0.99–6.86, p-value = 0.053). Patterns emerged among B- and T-cell subgroups, but none reached statistical significance. Ongoing research is warranted to discern if different environmental mechanisms may be driving B- and T-cell lymphoma immunophenotypes, consistent with previously reported regional differences in subtype prevalence.

Lymphoma is a common cancer affecting dogs, particularly Golden Retrievers. By identifying risk factors for lymphoma, work can be done to reduce harmful exposures or increase monitoring among dogs at a higher risk of disease. Using a subset of dogs from the Golden Retriever Lifetime Study, we sought to investigate whether dogs with lymphoma were more likely to live near certain environmental pollutant sources than dogs without lymphoma.

Forty-nine Golden Retrievers with non-cutaneous lymphoma and 98 Golden Retrievers without a history of cancer were selected from the Golden Retriever Lifetime Study Cohort. We evaluated how close each dog lived to nine environmental pollutant sources: chemical plants, municipal dumps, manufacturing plants, incineration plants, railroad embankment tracks, landfills, coal plants, high-voltage transmission lines, and nuclear power plants. Additionally, we evaluated individual exposure to secondhand smoke, and average annual ozone and particulate matter exposure (as surrogate measures for air pollution) for each dog’s county of residence.

None of the exposures examined were associated with an increased lymphoma risk in this population. More research is needed, including direct biomonitoring, to determine whether specific environmental exposures are associated with lymphoma in the Golden Retriever breed.

Ambient ozone effects on respiratory outcomes among smokers modified by neighborhood poverty: An analysis of SPIROMICS AIR

BACKGROUND

Neighborhood poverty has been associated with poor health outcomes. Previous studies have also identified adverse respiratory effects of long-term ambient ozone. Factors associated with neighborhood poverty may accentuate the adverse impact of ozone on respiratory health.

OBJECTIVES

To evaluate whether neighborhood poverty modifies the association between ambient ozone exposure and respiratory morbidity including symptoms, exacerbation risk, and radiologic parameters, among participants of the SPIROMICS AIR cohort study.

METHODS

Spatiotemporal models incorporating cohort-specific monitoring estimated 10-year average outdoor ozone concentrations at participants' homes. Adjusted regression models were used to determine the association of ozone exposure with respiratory outcomes, accounting for demographic factors, education, individual income, body mass index (BMI), and study site. Neighborhood poverty rate was defined by percentage of families living below federal poverty level per census tract. Interaction terms for neighborhood poverty rate with ozone were included in covariate-adjusted models to evaluate for effect modification.

RESULTS

1874 participants were included in the analysis, with mean (± SD) age 64 (± 8.8) years and FEV₁ (forced expiratory volume in one second) 74.7% (±25.8) predicted. Participants resided in neighborhoods with mean poverty rate of 9.9% (±10.3) of families below the federal poverty level and mean 10-year ambient ozone concentration of 24.7 (±5.2) ppb. There was an interaction between neighborhood poverty rate and ozone concentration for numerous respiratory outcomes, including COPD Assessment Test score, modified Medical Research Council Dyspnea Scale, six-minute walk test, and odds of COPD exacerbation in the year prior to enrollment, such that adverse effects of ozone were greater among participants in higher poverty neighborhoods.

CONCLUSION

Individuals with COPD in high poverty neighborhoods have higher susceptibility to adverse respiratory effects of ambient ozone exposure, after adjusting for individual factors. These findings highlight the interaction between exposures associated with poverty and their effect on respiratory health.

Associations of Pre- and Postnatal Air Pollution Exposures with Child Behavioral Problems and Cognitive Performance: A U.S. Multi-Cohort Study

BACKGROUND

Population studies support the adverse associations of air pollution exposures with child behavioral functioning and cognitive performance, but few studies have used spatiotemporally resolved pollutant assessments.

OBJECTIVES

We investigated these associations using more refined exposure assessments in 1,967 mother-child dyads from three U.S. pregnancy cohorts in six cities in the ECHO-PATHWAYS Consortium.

METHODS

Pre- and postnatal nitrogen dioxide (NO2) and particulate matter (PM) ≤2.5μm in aerodynamic diameter (PM2.5) exposures were derived from an advanced spatiotemporal model. Child behavior was reported as Total Problems raw score using the Child Behavior Checklist at age 4-6 y. Child cognition was assessed using cohort-specific cognitive performance scales and quantified as the Full-Scale Intelligence Quotient (IQ). We fitted multivariate linear regression models that were adjusted for sociodemographic, behavioral, and psychological factors to estimate associations per 2-unit increase in pollutant in each exposure window and examined modification by child sex. Identified critical windows were further verified by distributed lag models (DLMs).

RESULTS

Mean NO2 and PM2.5 ranged from 8.4 to 9.0 ppb and 8.4 to 9.1 μg/m3, respectively, across pre- and postnatal windows. Average child Total Problems score and IQ were 22.7 [standard deviation (SD): 18.5] and 102.6 (SD: 15.3), respectively. Children with higher prenatal NO2 exposures were likely to have more behavioral problems [β: 1.24; 95% confidence interval (CI): 0.39, 2.08; per 2 ppb NO2], particularly NO2 in the first and second trimester. Each 2-μg/m3 increase in PM2.5 at age 2-4 y was associated with a 3.59 unit (95% CI: 0.35, 6.84) higher Total Problems score and a 2.63 point (95% CI: -5.08, -0.17) lower IQ. The associations between PM2.5 and Total Problems score were generally stronger in girls. Most predefined windows identified were not confirmed by DLMs.

DISCUSSION

Our study extends earlier findings that have raised concerns about impaired behavioral functioning and cognitive performance in children exposed to NO2 and PM2.5 in utero and in early life. https://doi.org/10.1289/EHP10248.

What Is "Socioeconomic Position (SEP)," and How Might It Modify Air Pollution-Health Associations? Cohering Findings, Identifying Challenges, and Disentangling Effects of SEP and Race in US City Settings

Purpose of Review

Environmental epidemiology has long considered socioeconomic position (SEP) to be an important confounder of pollution effects on health, given that, in the USA, lower-income and minority communities are often disproportionately exposed to pollution. In recent decades, a growing literature has revealed that lower-SEP communities may also be more susceptible to pollution. Given the vast number of material and psychosocial stressors that vary by SEP, however, it is unclear which specific aspects of SEP may underlie this susceptibility. As environmental epidemiology engages more rigorously with issues of differential susceptibility, it is pertinent to define SEP more clearly, to disentangle its many aspects, and to move towards identifying causal components. Myriad stressors and exposures vary with SEP, with effects accumulating and interacting over the lifecourse. Here, we ask: In the context of environmental epidemiology, how do we meaningfully characterize”SEP”?

Recent Findings

In answering this question, it is critical to acknowledge that SEP, stressors, and pollution are differentially distributed by race in US cities. These distributions have been shaped by neighborhood sorting and race-based residential segregation rooted in historical policies and processes (e.g., redlining), which have served to concentrate wealth and opportunities for education and employment in predominantly-white communities. As a result, it is now profoundly challenging to separate SEP from race in the urban US setting.

Summary

Here, we cohere evidence from our recent and on-going studies aimed at disentangling synergistic health effects among SEP-related stressors and pollutants. We consider an array of SEP-linked social stressors, and discuss persistent challenges in this epidemiology, many of which are related to spatial confounding among multiple pollutants and stressors. Combining quantitative results with insights from qualitative data on neighborhood perceptions and stress (including violence and police-community relations), we offer a lens towards unpacking the complex interplay among SEP, community stressors, race, and pollution in US cities.

Health Effects of Long-Term Exposure to Ambient PM2.5 in Asia-Pacific: a Systematic Review of Cohort Studies

Abstract

Purpose of Review

Health effects of long-term exposure to ambient PM_2.5 vary with regions, and 75% of the deaths attributable to PM_2.5 were estimated in Asia-Pacific in 2017. This systematic review aims to summarize the existing evidence from cohort studies on health effects of long-term exposure to ambient PM_2.5 in Asia-Pacific.

Recent Findings

In Asia-Pacific, 60 cohort studies were conducted in Australia, Mainland China, Hong Kong, Taiwan, and South Korea. They consistently supported associations of long-term exposure to PM_2.5 with increased all-cause/non-accidental and cardiovascular mortality as well as with incidence of cardiovascular diseases, type 2 diabetes mellitus, kidney diseases, and chronic obstructive pulmonary disease. Evidence for other health effects was limited. Inequalities were identified in PM_2.5-health associations.

Summary

To optimize air pollution control and public health prevention, further studies need to assess the health effects of long-term PM_2.5 exposure in understudied regions, the health effects of long-term PM_2.5 exposure on mortality and risk of type 2 diabetes mellitus, renal diseases, dementia and lung cancer, and inequalities in PM_2.5-health associations. Study design, especially exposure assessment methods, should be improved.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40572-022-00344-w.

Long-term exposure to air pollution and the blood lipid levels of healthy young men

BACKGROUND

There is insufficient evidence of an association between long-term exposure to air pollution and changes in blood lipid levels, and assessments may be influenced by residual confounding factors, such as socioeconomic status.

OBJECTIVES

To investigate the associations between long-term exposure to air pollution and blood lipid profiles while controlling for the risk of residual confounding factors.

METHODS

We conducted a study involving conscripted Korean soldiers to assess the associations between air pollution and blood lipid levels. The soldiers, who were randomly distributed among military units throughout the country, led homogenous lives and were subjected to health checkups 8-12 months post-enlistment. We analyzed data pertaining to those who enlisted and underwent health checkups in 2019 (n = 12,778) using linear mixed models. Additionally, we evaluated quantile-specific associations using quantile regression models. We also assessed interactions based on body mass index (BMI) at the time of enlistment (≥25.0 vs. < 25.0 kg/m²).

RESULTS

The linear mixed models revealed that a 10-µg/m³ increase in fine particulate matter ≤ 2.5 μm (PM_2.5) decreased high-density lipoprotein cholesterol (HDL-C) levels by -0.66% (95% confidence interval [CI]: -1.21, -0.10), and a 10-ppb increase in nitrogen dioxide (NO₂) increased total cholesterol (TC) levels by 1.04% (95% CI: 0.24, 1.84). In the quantile regression models, associations were also found at specific deciles. PM_2.5 exposure contributed to higher TC, NO₂ resulted in higher triglycerides and lower HDL-C, and ozone (O₃) led to lower HDL-C. The association between O₃ and TC differed according to BMI (p-value for interaction = 0.03); among those with a BMI ≥ 25.0 kg/m², a 10-ppb increase in O₃ increased TC by 1.09% (95% CI: 0.20, 1.09).

DISCUSSION

These results shed new light on the importance of controlling air pollution, which can contribute to abnormal blood lipid levels, an independent risk factor for cardiovascular disease.

The Exposome and Toxicology: A Win-Win Collaboration

The development of the exposome concept has been one of the hallmarks of environmental and health research for the last decade. The exposome encompasses the life course environmental exposures including lifestyle factors from the prenatal period onwards. It has inspired many research programs and is expected to influence environmental and health research, practices, and policies. Yet, the links bridging toxicology and the exposome concept have not been well developed. In this review, we describe how the exposome framework can interface with and influence the field of toxicology, as well as how the field of toxicology can help advance the exposome field by providing the needed mechanistic understanding of the exposome impacts on health. Indeed, exposome-informed toxicology is expected to emphasize several orientations including (1) developing approaches integrating multiple stressors, in particular chemical mixtures, as well as the interaction of chemicals with other stressors, (2) using mechanistic frameworks such as the adverse outcome pathways to link the different stressors with toxicity outcomes, (3) characterizing the mechanistic basis of long-term effects by distinguishing different patterns of exposures and further exploring the environment-DNA interface through genetic and epigenetic studies, and (4) improving the links between environmental and human health, in particular through a stronger connection between alterations in our ecosystems and human toxicology. The exposome concept provides the linkage between the complex environment and contemporary mechanistic toxicology. What toxicology can bring to exposome characterization is a needed framework for mechanistic understanding and regulatory outcomes in risk assessment.

Modeling annual elevated blood lead levels among children in Maryland in relation to neighborhood deprivation

Estimating environmental lead exposure using ecologic risk models is an inexpensive strategy to inform public health departments and to develop location-based intervention strategies such as targeted screening and mitigation. Importantly, studies in this area have not assessed temporal and spatio-temporal lead exposure risk trends. Due to lead abatement efforts and targeted screening efforts, it is anticipated that lead exposure risk has decreased over time. However, it is unknown if decreases have occurred, and if the decreases are evenly distributed across neighborhoods. Thus, the purpose of this study was to examine the association between neighborhood deprivation and risk of elevated blood lead levels (EBLLs) in both temporal and spatio-temporal contexts within the US state of Maryland in 2005-2015. To consider the temporal dimension of lead risk, we used a novel extension of Bayesian index models to estimate time-varying neighborhood deprivation indices along with time-varying index effects. The results showed that overall EBLL proportion decreased over time, from a high of 0.11 in 2006 to a low of 0.02 in 2015. The association between neighborhood deprivation and EBLL risk was positive and significant annually, but generally diminished over time. The most important variables in the neighborhood deprivation index were percent of houses built before 1940 and median household income. In summary, using Bayesian index models that can account for both temporal and spatio-temporal contexts is a promising approach to inform public health efforts to remediate lead and focus testing efforts and may be useful in studies in other geographic areas and times.

Neighborhood greenspace exposure as a protective factor in dementia risk among U.S. adults 75 years or older: a cohort study

BACKGROUND

Research suggests that greenspace may confer neurocognitive benefits. This study examines whether residential greenspace is associated with risk of dementia among older adults.

METHODS

Greenspace exposure was computed for 3047 participants aged 75 years and older enrolled in the Gingko Evaluation of Memory Study (GEMS) across four U.S. sites that prospectively evaluated dementia and its subtypes, Alzheimer's disease (AD), vascular dementia (VaD), and mixed pathologies, using neuropsychiatric evaluations between 2000 and 2008. After geocoding participant residences at baseline, three greenspace metrics-Normalized Difference Vegetative Index, percent park overlap within a 2-km radius, and linear distance to nearest park-were combined to create a composite residential greenspace measure categorized into tertiles. Cox proportional hazards models estimated the associations between baseline greenspace and risk of incident all-cause dementia, AD, and Mixed/VaD.

RESULTS

Compared to low residential greenspace, high residential greenspace was associated with a reduced risk of dementia (HR = 0.76 95% CI: 0.59,0.98) in models adjusted for multiple covariates. After additional adjustment for behavioral characteristics, Apolipoprotein E ɛ4 status, and other covariates, the association was slightly attenuated (HR = 0.82; 95% CI:0.63,1.06). Those exposed to medium levels of greenspace also had 28% lower risk (HR = 0.72; CI: 0.55, 0.95) of dementia compared to those with low greenspace in adjusted models. Subtype associations between high residential greenspace and AD were not statistically significant. Greenspace was not found to be significantly associated with mixed/vascular pathologies.

CONCLUSIONS

This study showed evidence for an association between residential greenspace and all-cause dementia among older adults. Future research with larger sample size, precise characterization of different dementia subtypes, and assessment of residential greenspace earlier in life may help clarify the role between exposure to greenspace and dementia risk.

Exposure to air pollutant mixture and gestational diabetes mellitus in Southern California: Results from electronic health record data of a large pregnancy cohort

BACKGROUND

Epidemiological findings are inconsistent regarding the associations between air pollution exposure during pregnancy and gestational diabetes mellitus (GDM). Several limitations exist in previous studies, including potential outcome and exposure misclassification, unassessed confounding, and lack of simultaneous consideration of air pollution mixtures and particulate matter (PM) constituents.

OBJECTIVES

To assess the association between GDM and maternal residential exposure to air pollution, and the joint effect of the mixture of air pollutants and PM constituents.

METHODS

Detailed clinical data were obtained for 395,927 pregnancies in southern California (2008-2018) from Kaiser Permanente Southern California (KPSC) electronic health records. GDM diagnosis was based on KPSC laboratory tests. Monthly average concentrations of fine particulate matter < 2.5 μm (PM_2.5), <10 μm (PM₁₀), nitrogen dioxide (NO₂), and ozone (O₃) were estimated using kriging interpolation of Environmental Protection Agency's routine monitoring station data, while PM_2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon) were estimated using a fine-resolution geoscience-derived model. A multilevel logistic regression was used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of air pollution and PM component mixtures. Main analyses adjusted for maternal age, race/ethnicity, education, median family household income, pre-pregnancy BMI, smoking during pregnancy, insurance type, season of conception and year of delivery.

RESULTS

The incidence of GDM was 10.9% in the study population. In single-pollutant models, we observed an increased odds for GDM associated with exposures to PM_2.5, PM₁₀, NO₂ and PM_2.5 constituents. The association was strongest for NO₂ [adjusted odds ratio (OR) per interquartile range: 1.176, 95% confidence interval (CI): 1.147-1.205)]. In multi-pollutant models, increased ORs for GDM in association with one quartile increase in air pollution mixtures were found for both kriging-based regional air pollutants (NO₂, PM_2.5, and PM₁₀, OR = 1.095, 95% CI: 1.082-1.108) and PM_2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon, OR = 1.258, 95% CI: 1.206-1.314); NO₂ (78%) and black carbon (48%) contributed the most to the overall mixture effects among all krigged air pollutants and all PM_2.5 constituents, respectively. The risk of GDM associated with air pollution exposure were significantly higher among Hispanic mothers, and overweight/obese mothers.

CONCLUSION

This study found that exposure to a mixture of ambient PM_2.5, PM₁₀, NO₂, and PM_2.5 chemical constituents was associated with an increased risk of GDM. NO₂ and black carbon PM_2.5 contributed most to GDM risk.

Association of abnormal-glucose tolerance during pregnancy with exposure to PM2.5 components and sources

Maternal exposure to PM_2.5 has been associated with abnormal glucose tolerance during pregnancy, but little is known about which constituents and sources are most relevant to glycemic effects. We conducted a retrospective cohort study of 1148 pregnant women to investigate associations of PM_2.5 chemical components with gestational diabetes mellitus (GDM) and impaired glucose tolerance (IGT) and to identify the most harmful sources in Heshan, China from January 2015 to July 2016. We measured PM_2.5 using filter-based method and analyzed them for 28 constituents, including carbonaceous species, water-soluble ions and metal elements. Contributions of PM_2.5 sources were assessed by positive matrix factorization (PMF). Logistic regression model was used to estimate composition-specific and source-specific effects on GDM/IGT. Random forest algorithm was applied to evaluate the relative importance of components to GDM and IGT. PM_2.5 total mass and several chemical constituents were associated with GDM and IGT across the early to mid-gestation periods, as were the PM_2.5 sources fossil fuel/oil combustion, road dust, metal smelting, construction dust, electronic waster, vehicular emissions and industrial emissions. The trimester-specific associations differed among pollutants and sources. The third and highest quartile of elemental carbon, ammonium (NH₄⁺), iron (Fe) and manganese (Mn) across gestation were consistently associated with higher odds of GDM/IGT. Maternal exposures to zinc (Zn), titanium (Ti) and vehicular emissions during the first trimester, and vanadium (V), nickel (Ni), road dust and fossil fuel/oil combustion during the second trimester were more important for GDM/IGT. This study provides important new evidence that maternal exposure to PM_2.5 components and sources is significantly related to elevated risk for abnormal glucose tolerance during pregnancy.

Long term exposure to air pollution, mortality and morbidity in New Zealand: Cohort study

OBJECTIVES

To investigate associations between long-term exposure to PM_2.5, NO₂, mortality and morbidity in New Zealand, a country with low levels of exposure.

DESIGN

Retrospective cohort study.

SETTING

The New Zealand resident population.

METHOD

The main analyses included all adults aged 30 years and over with complete data on covariates: N = 2,223,507. People who died, or were admitted to hospital, (2013-2016) were linked anonymously to the 2013 census, and to estimates of ambient PM_2.5, and NO₂ concentration. We fitted Poisson regression models of mortality and morbidity in adults (≥30) for all natural causes of death, and by sub- group of major cause. Person-time of exposure, censored at the time of death, was included as an offset. We adjusted for confounding by age, sex, ethnicity, income, education, smoking status and ambient temperature. Further analyses stratified by ethnic group, and investigated respiratory hospital admissions in children.

RESULTS

There were statistically significant positive associations between pollutants and natural causes of death: RR (per 10 μg/m³) for PM_2.5 1.11 (1.07 to 1.15) and for NO₂ 1.10 (1.07 to 1.12). For morbidity, the strongest associations were for PM_2.5 and ischaemic heart disease in adults, RR: 1.29 (1.23 to 1.35) and for NO₂ and asthma in children, RR: 1.18 (1.09 to 1.28). In models restricted to specific ethnic groups, we found no consistent differences in any of the associations.

CONCLUSIONS

The results for NO₂ are higher than those published previously. Other studies have reported that the dose-response for PM_2.5 may be higher at low concentrations, but less is known about NO₂. It is possible NO₂ is acting as a proxy for other traffic-related pollutants that are causally related to health impacts. This study underlines the importance of controlling pollution caused by motor vehicles.

Long-term exposures to PM2.5, black carbon and NO2 and prevalence of current rhinitis in French adults: The Constances Cohort

BACKGROUND

Rhinitis is one of the most common disease worldwide with a high and increasing prevalence. There is limited knowledge on the link between long-term exposure to air pollution and rhinitis.

OBJECTIVES

We aim to study the associations between long-term exposure to air pollutants and self-reported current rhinitis among adults from Constances, a large French population-based cohort.

METHODS

Current rhinitis was defined at inclusion from questionnaire by the presence of sneezing, runny or blocked nose in the last 12 months without a cold or the flu. Annual concentrations of nitrogen dioxide (NO₂), particulate matter ≤ 2.5 µm (PM_2.5) and black carbon (BC) were estimated at the participants' residential address by European land-use regression models. Cross-sectional associations between annual exposure to each air pollutant and current rhinitis were estimated using logistic models adjusted for age, sex, smoking, education level and French deprivation index. The health prevention centers were taken into account by marginal models with generalized estimating equations. Several supplementary analyses were performed.

RESULTS

Analyses were performed on 127,108 participants (47 years old on average, 54% women, 19% current smokers). The prevalence of current rhinitis, allergic (AR) and non-allergic (NAR) rhinitis were 36%, 25% and 11% respectively. Adjusted ORs for current rhinitis were 1.13 (1.08, 1.17), 1.12 (1.07, 1.17), and 1.11 (1.06, 1.17) per one interquartile range increase of PM_2.5 (4.85 µg/m³), BC (0.88 · 10^-5 m^-1) and NO₂ (17.3 µg/m³) respectively. Significant and positive associations were also found for both AR and NAR. Results were similar in men and women, and in the different smoking strata, and were consistent with meta-analysis or after imputing missing covariates.

DISCUSSION

An increase of modeled annual average residential exposure to PM_2.5, BC, and NO₂ was significantly associated with an increase of prevalence of current rhinitis in adults in the French general population. The results suggest that among air pollutants, BC may be of special interest.

Leveraging open data to reconstruct the Singapore Housing Index and other building-level markers of socioeconomic status for health services research

BACKGROUND

Socioeconomic status (SES) is an important determinant of health, and SES data is an important confounder to control for in epidemiology and health services research. Individual level SES measures are cumbersome to collect and susceptible to biases, while area level SES measures may have insufficient granularity. The 'Singapore Housing Index' (SHI) is a validated, building level SES measure that bridges individual and area level measures. However, determination of the SHI has previously required periodic data purchase and manual parsing. In this study, we describe a means of SHI determination for public housing buildings with open government data, and validate this against the previous SHI determination method.

METHODS

Government open data sources (e.g.

DATA

gov.sg, Singapore Land Authority OneMAP API, Urban Redevelopment Authority API) were queried using custom Python scripts. Data on residential public housing block address and composition from the HDB Property Information dataset (data.gov.sg) was matched to postal code and geographical coordinates via OneMAP API calls. The SHI was calculated from open data, and compared to the original SHI dataset that was curated from non-open data sources in 2018.

RESULTS

Ten thousand seventy-seven unique residential buildings were identified from open data. OneMAP API calls generated valid geographical coordinates for all (100%) buildings, and valid postal code for 10,012 (99.36%) buildings. There was an overlap of 10,011 buildings between the open dataset and the original SHI dataset. Intraclass correlation coefficient was 0.999 for the two sources of SHI, indicating almost perfect agreement. A Bland-Altman plot analysis identified a small number of outliers, and this revealed 5 properties that had an incorrect SHI assigned by the original dataset. Information on recently transacted property prices was also obtained for 8599 (85.3%) of buildings.

CONCLUSION

SHI, a useful tool for health services research, can be accurately reconstructed using open datasets at no cost. This method is a convenient means for future researchers to obtain updated building-level markers of socioeconomic status for policy and research.