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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: 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: 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 =10 lm (PM10), PM with aerodynamic diameter =2:5 (PM2:5), and NO2. RESULTS: Long-term UFP exposure was associated with an increased risk for all incident CVD [HR=1:18 per 10,000 particles=cm3; 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 NO2 (HR for heart failure=1:22; 95% CI: 1.01, 1.48 per 20 lg=m3) and coarse PM (PMcoarse; HR for all CVD=1:21; 95% CI: 1.01, 1.45 per 10 lg=m3). CVD was not positively associated with PM2:5 (HR for all CVD=0:95; 95% CI: 0.75, 1.28 per 5 lg=m3). HRs for UFP and CVAs were positive, but not significant. In two-pollutant models (UFP+NO2 and UFP+PMcoarse), positive associations tended to remain for UFP, while HRs for PMcoarse and NO2 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. [ABSTRACT FROM AUTHOR]