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We greatly appreciate the alternative framing and call to action posed by Ivey, Pruitt, and Garcia (1) in response to our commentary. (2) We agree that the available evidence strongly [...]

BACKGROUND: Recently enacted environmental justice policies in the United States at the state and federal level emphasize addressing place-based inequities, including persistent disparities in air pollution exposure and associated health impacts. Advances in air quality measurement, models, and analytic methods have demonstrated the importance of finer-scale data and analysis in accurately quantifying the extent of inequity in intraurban pollution exposure, although the necessary degree of spatial resolution remains a complex and context-dependent question. OBJECTIVE: The objectives of this commentary were to a) discuss ways to maximize and evaluate the effectiveness of efforts to reduce air pollution disparities, and b) argue that environmental regulators must employ improved methods to project, measure, and track the distributional impacts of new policies at finer geographic and temporal scales. DISCUSSION: The historic federal investments from the Inflation Reduction Act, the Infrastructure Investment and Jobs Act, and the Biden Administration's commitment to Justice40 present an unprecedented opportunity to advance climate and energy policies that deliver real reductions in pollution-related health inequities. In our opinion, scientists, advocates, policymakers, and implementing agencies must work together to harness critical advances in air quality measurements, models, and analytic methods to ensure success. https://doi.org/ 10.1289/EHP13063, Introduction Twenty-three states and the federal government have enacted environmental justice policies since the 1990s, but only recently have significant resources been allocated at both state and federal levels. Currently, [...]

This paper was published by EHP on 8 December 2021. However, the article incorrectly listed the publication date as 15 December 2021. The article has been corrected. EHP regrets this [...]

BACKGROUND: Anemia and lead exposure remain significant public health issues in many parts of the world, often occurring together. Animal studies suggest that the dopamine D2 receptor (DRD2) mediates the [...]

BACKGROUND: Lead exposure has long been associated with deficits in IQ among children. However, few studies have assessed the impact of lead on specific domains of behavior and cognition. OBJECTIVE: [...]

BACKGROUND: Regulatory analyses of air pollution policies require the use of concentration-response functions and underlying health data to estimate the mortality and morbidity effects, as well as the resulting benefits, associated with policy-related changes in fine particulate matter ≤2.5 μm (PM2.5)]. Common practice by U.S. federal agencies involves using underlying health data and concentration-response functions that are not differentiated by racial/ethnic group. OBJECTIVES: We aim to explore the policy implications of using race/ethnicity-specific concentration–response functions and mortality data in comparison to standard approaches when estimating the impact of air pollution on non-White racial/ethnic subgroups. METHODS: Using new estimates from the epidemiological literature on race/ethnicity-specific concentration–response functions paired with race/ ethnicity-specific mortality rates, we estimated the mortality impacts of air pollution from all sources from a uniform increase in concentrations and from the regulations imposed by the Mercury Air Toxics Standards. RESULTS: Use of race/ethnicity-specific information increased PM2.5-related premature mortality estimates in older populations by 9% and among older Black Americans by 150% for all-source pollution exposure. Under a uniform degradation of air quality and race/ethnicity-specific information, older Black Americans were found to have approximately 3 times higher mortality relative to White Americans, which is obscured under a non–race/ ethnicity-specific modeling approach. Standard approaches of using non–racial/ethnic specific information underestimate the benefits of the Mercury Air Toxics Standards to older Black Americans by almost 60% and overestimate the benefits to older White Americans by 14% relative to using a race/ethnicity-specific modeling approach. DISCUSSION: Policy analyses incorporating race/ethnicity-specific concentration–response functions and mortality data relative to nondifferentiated inputs underestimate the overall magnitude of PM2.5 mortality burden and the disparity in impacts on older Black American populations. Based on our results, we recommend that the best available race/ethnicity-specific inputs are used in regulatory assessments to understand and reduce environmental injustices. [ABSTRACT FROM AUTHOR]

Background: There is growing evidence that exposure to ultrafine particles (UFP; particles smaller than 100 nm) may play an underexplored role in the etiology of several illnesses, including cardiovascular disease (CVD). Objectives: We aimed o investigate the relationship between long-term exposure to ambient UFP and incident cardiovascular and cerebrovascular disease (CVA). As a secondary objective, we sought to compare effect estimates for UFP with those derived for other air pollutants, including estimates from two-pollutant models. Methods: Using a prospective cohort of 33,831 Dutch residents, we studied the association between long-term exposure to UFP (predicted via land use regression) and incident disease using Cox proportional hazard models. Hazard ratios (HR) for UFP were compared to HRs for more routinely monitored air pollutants, including particulate matter with aerodynamic diameter [less than or equal to] 10 [micro]m ([PM.sub.10]), PM with aerodynamic diameter [less than or equal to] 2.5 ([PM.sub.2.5]), and N[O.sub.2]. Results: Long-term UFP exposure was associated with an increased risk for all incident CVD [HR = 1.18 per 10,000 particles/[cm.sup.3]; 95% confidence interval (CI): 1.03, 1.34], myocardial infarction (MI) (HR = 1.34; 95% CI: 1.00, 1.79), and heart failure (HR = 1.76; 95% CI: 1.17, 2.66). Positive associations were also estimated for N[O.sub.2] (HR for heart failure = 1.22; 95% CI: 1.01, 1.48 per 20 [micro]g/[m.sup.3]) and coarse PM ([PM.sub.coarse]; HR for all CVD = 1.21; 95% CI: 1.01, 1.45 per 10 [micro]g/[m.sup.3]). CVD was not positively associated with [PM.sub.2.5] (HR for all CVD = 0.95; 95% CI: 0.75, 1.28 per 5 [micro]g/[m.sup.3]). HRs for UFP and CVAs were positive, but not significant. In two-pollutant models (UFP + N[O.sub.2] and UFP + [PM.sub.coarse]), positive associations tended to remain for UFP, while HRs for [PM.sub.coarse] and N[O.sub.2] generally attenuated towards the null. Conclusions: These findings strengthen the evidence that UFP exposure plays an important role in cardiovascular health and that risks of ambient air pollution may have been underestimated based on conventional air pollution metrics. https://doi.org/10.1289/EHP3047, Introduction Long-term exposure to ambient air pollution has been linked to multiple health outcomes, including mortality, malignant disease, and cardiovascular disease (CVD) (Beelen, Raaschou-Nielsen et al. 2014; Beelen et al. [...]

BACKGROUND: Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations. OBJECTIVES: We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California. METHODS: We estimated mortality and morbidity attributable to nitrogen dioxide (NO2), black carbon (BC), and fine particulate matter [PM =2.5 µm in aerodynamic diameter (PM2.5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO2 and BC; and b) models predicting annual NO2, BC and PM2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates. RESULTS: Estimated pollution-attributable deaths per 100,000 people at the 100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2 [mean = 30 (95% CI: 9, 50)], BC [mean = 2 (95% CI: 1, 2)], and PM2.5, [mean = 49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell–level NO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean = 64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean = 101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO2 and PM2.5, with more spatial heterogeneity at the grid-cell–level [NO2 CBG mean = 41 deaths per 100,000 people (95% CI: 12, 68); NO2 county mean = 38 (95% CI: 11, 64); PM2.5 CBG mean = 59 (95% CI: 40, 77); and PM2.5 county mean = 55 (95% CI: 37, 71)]. DISCUSSION: Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. [ABSTRACT FROM AUTHOR]

Background: Human milk is a potential source of lead exposure. Yet lactational transfer of lead from maternal blood into breast milk and its contribution to infant lead burden remains poorly understood. Objectives: We explored the dose-response relationships between maternal blood, plasma, and breast milk to better understand lactational transfer of lead from blood and plasma into milk and, ultimately, to the breastfeeding infant. Methods: We measured lead in 81 maternal blood, plasma, and breast milk samples at 1 month postpartum and in 60 infant blood samples at 3 months of age. Milk-to-plasma (M/P) lead ratios were calculated. Multivariate linear, piecewise, and generalized additive models were used to examine dose-response relationships between blood, plasma, and milk lead levels. Results: Maternal lead levels (mean ± SD) were as follows: blood: 7.7 ± 4.0 μg/dL; plasma: 0.1 ± 0.1 μg/L; milk: 0.8 ± 0.7 μg/L. The average M/P lead ratio was 7.7 (range, 0.6–39.8) with 97% of the ratios being > 1. The dose–response relationship between plasma lead and M/P ratio was nonlinear (empirical distribution function = 6.5, p = 0.0006) with the M/P ratio decreasing by 16.6 and 0.6 per 0.1 μg/L of plasma lead, respectively, below and above 0.1 μg/L plasma lead. Infant blood lead level (3.4 ± 2.2 μg/dL) increased by 1.8 μg/dL per 1 μg/L milk lead (p < 0.0001, R2 = 0.3). Conclusions: The M/P ratio for lead in humans is substantially higher than previously reported, and transfer of lead from plasma to milk may be higher at lower levels of plasma lead. Breast milk is an important determinant of lead burden among breastfeeding infants. [ABSTRACT FROM AUTHOR]

Background: Human milk is a potential source of lead exposure. Yet lactational transfer of lead from maternal blood into breast milk and its contribution to infant lead burden remains poorly understood. Objectives: We explored the dose–response relationships between maternal blood, plasma, and breast milk to better understand lactational transfer of lead from blood and plasma into milk and, ultimately, to the breastfeeding infant. Methods: We measured lead in 81 maternal blood, plasma, and breast milk samples at 1 month postpartum and in 60 infant blood samples at 3 months of age. Milk-to-plasma (M/P) lead ratios were calculated. Multivariate linear, piecewise, and generalized additive models were used to examine dose–response relationships between blood, plasma, and milk lead levels. Results: Maternal lead levels (mean ± SD) were as follows: blood: 7.7 ± 4.0 μg/dL; plasma: 0.1 ± 0.1 μg/L; milk: 0.8 ± 0.7 μg/L. The average M/P lead ratio was 7.7 (range, 0.6–39.8) with 97% of the ratios being > 1. The dose–response relationship between plasma lead and M/P ratio was nonlinear (empirical distribution function = 6.5, p = 0.0006) with the M/P ratio decreasing by 16.6 and 0.6 per 0.1 μg/L of plasma lead, respectively, below and above 0.1 μg/L plasma lead. Infant blood lead level (3.4 ± 2.2 μg/dL) increased by 1.8 μg/dL per 1 μg/L milk lead (p < 0.0001, R2 = 0.3). Conclusions: The M/P ratio for lead in humans is substantially higher than previously reported, and transfer of lead from plasma to milk may be higher at lower levels of plasma lead. Breast milk is an important determinant of lead burden among breastfeeding infants. Citation: Ettinger AS, Roy A, Amarasiriwardena CJ, Smith DR, Lupoli N, Mercado-García A, Lamadrid-Figueroa H, Tellez-Rojo MM, Hu H, Hernández-Avila M. 2014. Maternal blood, plasma, and breast milk lead: lactational transfer and contribution to infant exposure. Environ Health Perspect 122:87–92; http://dx.doi.org/10.1289/ehp.1307187

Background: Regulatory analyses of air pollution policies require the use of concentration-response functions and underlying health data to estimate the mortality and morbidity effects, as well as the resulting benefits, associated with policy-related changes in fine particulate matter ≤ 2.5 μ m ( PM 2.5 )]. Common practice by U.S. federal agencies involves using underlying health data and concentration-response functions that are not differentiated by racial/ethnic group., Objectives: We aim to explore the policy implications of using race/ethnicity-specific concentration-response functions and mortality data in comparison to standard approaches when estimating the impact of air pollution on non-White racial/ethnic subgroups., Methods: Using new estimates from the epidemiological literature on race/ethnicity-specific concentration-response functions paired with race/ethnicity-specific mortality rates, we estimated the mortality impacts of air pollution from all sources from a uniform increase in concentrations and from the regulations imposed by the Mercury Air Toxics Standards., Results: Use of race/ethnicity-specific information increased PM 2.5 -related premature mortality estimates in older populations by 9% and among older Black Americans by 150% for all-source pollution exposure. Under a uniform degradation of air quality and race/ethnicity-specific information, older Black Americans were found to have approximately 3 times higher mortality relative to White Americans, which is obscured under a non-race/ethnicity-specific modeling approach. Standard approaches of using non-racial/ethnic specific information underestimate the benefits of the Mercury Air Toxics Standards to older Black Americans by almost 60% and overestimate the benefits to older White Americans by 14% relative to using a race/ethnicity-specific modeling approach., Discussion: Policy analyses incorporating race/ethnicity-specific concentration-response functions and mortality data relative to nondifferentiated inputs underestimate the overall magnitude of PM 2.5 mortality burden and the disparity in impacts on older Black American populations. Based on our results, we recommend that the best available race/ethnicity-specific inputs are used in regulatory assessments to understand and reduce environmental injustices. https://doi.org/10.1289/EHP9001.