ObjectiveWe reviewed the sources of lead in the environments of U.S. children, contributions to children’s blood lead levels, source elimination and control efforts, and existing federal authorities. Our context is the U.S. public health goal to eliminate pediatric elevated blood lead levels (EBLs) by 2010.Data sourcesNational, state, and local exposure assessments over the past half century have identified risk factors for EBLs among U.S. children, including age, race, income, age and location of housing, parental occupation, and season.Data extraction and synthesisRecent national policies have greatly reduced lead exposure among U.S. children, but even very low exposure levels compromise children’s later intellectual development and lifetime achievement. No threshold for these effects has been demonstrated. Although lead paint and dust may still account for up to 70% of EBLs in U.S. children, the U.S. Centers for Disease Control and Prevention estimates that ≥30% of current EBLs do not have an immediate lead paint source, and numerous studies indicate that lead exposures result from multiple sources. EBLs and even deaths have been associated with inadequately controlled sources including ethnic remedies and goods, consumer products, and food-related items such as ceramics. Lead in public drinking water and in older urban centers remain exposure sources in many areas.ConclusionsAchieving the 2010 goal requires maintaining current efforts, especially programs addressing lead paint, while developing interventions that prevent exposure before children are poisoned. It also requires active collaboration across all levels of government to identify and control all potential sources of lead exposure, as well as primary prevention.

In this study we estimated the number of housing units in the United States with lead-based paint and lead-based paint hazards. We included measurements of lead in intact and deteriorated paint, interior dust, and bare soil. A nationally representative, random sample of 831 housing units was evaluated in a survey between 1998 and 2000; the units and their occupants did not differ significantly from nationwide characteristics. Results indicate that 38 million housing units had lead-based paint, down from the 1990 estimate of 64 million. Twenty-four million had significant lead-based paint hazards. Of those with hazards, 1.2 million units housed low-income families (< 30,000 US dollars/year) with children under 6 years of age. Although 17% of government-supported, low-income housing had hazards, 35% of all low-income housing had hazards. For households with incomes greater than or equal to 30,000 US dollars/year, 19% had hazards. Fourteen percent of all houses had significantly deteriorated lead-based paint, and 16% and 7%, respectively, had dust lead and soil lead levels above current standards of the U.S. Department of Housing and Urban Development and the U.S. Environmental Protection Agency. The prevalence of lead-based paint and hazards increases with age of housing, but most painted surfaces, even in older housing, do not have lead-based paint. Between 2% and 25% of painted building components were coated with lead-based paint. Housing in the Northeast and Midwest had about twice the prevalence of hazards compared with housing in the South and West. The greatest risk occurs in older units with lead-based paint hazards that either will be or are currently occupied by families with children under 6 years of age and are low-income and/or are undergoing renovation or maintenance that disturbs lead-based paint. This study also confirms projections made in 2000 by the President's Task Force on Environmental Health Risks and Safety Risks to Children of the number of houses with lead-based paint hazards. Public- and private-sector resources should be directed to units posing the greatest risk if future lead poisoning is to be prevented.

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a) Input image with detected face (d) Our output synthetic shallow depth-of-eld image (b) Person segmentation mask (c) Mask + disparity from DP Fig. 1. We present a system that uses a person segmentation mask (b) and a noisy depth map computed using the camera's dual-pixel (DP) auto-focus hardware (c) to produce a synthetic shallow depth-of-field image (d) with a depth-dependent blur on a mobile phone. Our system is marketed as "Portrait Mode" on several Google-branded phones. Shallow depth-of-field is commonly used by photographers to isolate a subject from a distracting background. However, standard cell phone cameras cannot produce such images optically, as their short focal lengths and small apertures capture nearly all-in-focus images. We present a system to computationally synthesize shallow depth-of-field images with a single mobile camera and a single button press. If the image is of a person, we use a person segmentation network to separate the person and their accessories from the background. If available, we also use dense dual-pixel auto-focus hardware, effectively a 2-sample light field with an approximately 1 millimeter baseline, to compute a dense depth map. These two signals are combined and used to render a defocused image. Our system can process a 5.4 megapixel image in 4 seconds on a mobile phone, is fully automatic, and is robust enough to be used by non-experts. The modular nature of our system allows it to degrade naturally in the absence of a dual-pixel sensor or a human subject.

The physical infrastructure and housing make human interaction possible and provide shelter. How well that infrastructure performs and which groups it serves have important implications for social equity and health. Populations in inadequate housing are more likely to have environmental diseases and injuries. Substantial disparities in housing have remained largely unchanged. Approximately 2.6 million (7.5%) non-Hispanic Blacks and 5.9 million Whites (2.8%) live in substandard housing. Segregation, lack of housing mobility, and homelessness are all associated with adverse health outcomes. Yet the experience with childhood lead poisoning in the United States has shown that housing-related disparities can be reduced. Effective interventions should be implemented to reduce environmental health disparities related to housing.