Abstract
For this state-of-science overview of geospatial approaches for identifying US communities with high lead-exposure risk, we compiled and summarized public data and national maps of lead indices and models, environmental lead indicators, and children’s blood lead surveillance data.
Currently available indices and models are primarily constructed from housing-age and sociodemographic data; differing methods, variables, data, weighting schemes, and geographic scales yield maps with different exposure risk profiles. Environmental lead indicators are available (e.g., air, drinking water, dust, soil) at different spatial scales, but key gaps remain. Blood lead level data have limitations as testing, reporting, and completeness vary across states.
Mapping tools and approaches developed by federal agencies and other groups for different purposes present an opportunity for greater collaboration. Maps, data visualization tools, and analyses that synthesize available geospatial efforts can be evaluated and improved with local knowledge and blood lead data to refine identification of high-risk locations for prioritizing prevention efforts and targeting risk-reduction strategies. Remaining challenges are discussed along with a work-in-progress systematic approach for cross-agency data integration, toward advancing “whole-of-government” public health protection from lead exposures. (Am J Public Health. 2022;112(S7):S658–S669. https://doi.org/10.2105/AJPH.2022.307051)
Lead is a toxic legacy contaminant. Significant progress has been made to reduce exposures over the past several decades, but it remains a public health priority. Despite progress in reducing environmental lead levels in the United States, many children and adults are still exposed, and there is no level of lead exposure known to be without risk of adverse health effects. Historically, lead has been addressed by various agencies with single media-specific regulations and policies.1 Maximizing risk reductions and public health protection requires coordinated efforts and approaches for considering multimedia (i.e., from multiple environmental media such as air, drinking water, dust, food, paint, soil) lead exposures, including the ability to identify high lead exposure geographies for taking effective actions. Federal agencies in the President’s Task Force on Environmental Health Risks and Safety Risks to Children have been collaborating on sharing approaches, data, and challenges for advancing primary and secondary lead-exposure prevention efforts.2,3
The Federal Lead Action Plan,3 the US Department of Housing and Urban Development (HUD) Lead Action plan,4 and the draft Environmental Protection Agency (EPA) Lead Strategy2 highlight the need for whole-of-government efforts to map high lead exposure locations and disparities for targeting exposure prevention and mitigation efforts in the United States. A common goal among federal agencies is to identify geographic locations and populations at risk for lead exposure so they can be addressed proactively. Identifying places for action involves scientific, programmatic, and communication coordination challenges.
Centers for Disease Control and Prevention (CDC), HUD, and EPA scientists have been working individually and collaboratively on “fit-for-purpose” lead mapping to support the related agencies’ goals: CDC to enhance and complement blood lead level (BLL) surveillance data efforts and focus primary prevention, HUD to target housing mitigation efforts, and EPA to target and prioritize environmental actions. The 3 agencies have been discussing and comparing lead mapping efforts, challenges, and data needs and moving toward more coordination since 2019.5–8 As public health practitioners rely increasingly on lead maps to inform risk identification and management, it is vital that the underlying science is sound and transparent. Summaries of lead mapping and data analysis approaches are provided in Table 1, and Tables A and B (available as supplements to the online version of this article at https://ajph.org).
TABLE 1— Summary of US National Lead Indices and Models (Produced by Federal Agencies, Published, or Both)
| Agency/Group or Author | Name of Approach (e.g., Indicator, Index, Model, GIS Mapping Tool, and Data Viewer) | Brief Approach Description | Data Years for Each Variable | Childhood Age(s) Considered | Geographic Scale |
| CDC (Egan and Courtney)6 | Lead Exposure Risk Index (LERI) | Composite indicator of lead exposures in the United States based on risk factors for 4 areas: sociodemographic, housing, environmental, and geographic. | American Community Survey (ACS): 2012–2016 | < 6 y | Census tract, state, national |
| Reuters (Pell and Schneyer)9 | Data viewer: national interactive map | National interactive map displaying child elevated BLLs from various US state health departments. | Varies by state (most fall within 2005–2015) | 0–5 y for most states | Zip code, county, census tract |
| Wheeler et al.10 | Multiple models: random forest, weighted quantile sum regression, and Bayesian socioeconomic status (SES) index model | “. . . we combined lead test result data over many states for a majority of the US ZIP Codes in order to estimate its association with many SES variables and predict lead exposure risk in all populated ZIP Codes in the US.” | BLLs: varies by state (most fall within 2005–2015) ACS: 2007–2011 | 0–5 y for most states | Zip code |
| HUD11 | Index, Web application: national interactive map | Deteriorated Paint Index developed to predict and identify high lead risk jurisdictions across the United States. | American Housing Survey: 2011 ACS: 2009–2013 | NA | Census tract, county, state |
| Vox12 | Pb Exposure Risk Score (index) | Lead exposure index produced by developing a weighted measure incorporating the proportion of the population below the federal poverty line and the age of housing for a given census tract. | ACS: 2010–2014 | NA | Census tract |
| EPA Report Final Report for the Pilot Study of Targeting Elevated Blood-Lead Levels in children—prepared by Battelle for the EPA13 | Broad coverage model (low-resolution) High-resolution model | “This pilot study sought to develop models to predict the number of children at risk of elevated blood-lead levels for a given geographic area based on a hierarchical combination of demographic, environmental, and programmatic information sources.” | Census 2000 BLL data: 1995–2005 NATA: 1999 TRI and SDWIS: (not specified—most likely a version either from 2000 or near it) | < 6 y | Census tract, county |
| EPA/Office of Environmental Justice14 | Index (EJSCREEN 2017 Pb Paint EJ Index) | Combining areas with the highest likelihood of lead paint with low income and minority populations to develop a national lead paint index. | ACS: 2011–2015 | NA | Census block group |
| Schultz et al.15 | Regression model | A regression model was developed to predict BLL values in children from every US census tract. | BLL data: MI (1999–2009), MA (2000–2009), TX (1999–2009) ACS data: 2005–2009 NHANES data: 2001–2010 NATA: 2011 | 1–2 y | Census tract |
Note. BLL = blood lead level; CDC = Centers for Disease Control and Prevention; EPA = Environmental Protection Agency; GIS = geographic information system; HUD = Department of Housing and Urban Development; NA = not applicable; NATA = National Air Toxics Assessment; NHANES = National Health and Nutrition Examination Survey; SDWIS = Safe Drinking Water Information System; TRI = Toxics Release Inventory.
The objectives of this analytic essay are to (1) summarize and present the state-of-the-science of publicly available methods, data, and maps for identifying US lead “hotspots” (i.e., locations at potential higher risk of lead exposure) and (2) discuss remaining challenges and a work-in-progress systematic approach for cross-agency data integration. We summarized and compiled publicly available data and maps at the national scale for the following 4 categories, and discussed remaining data needs, limitations, and challenges: (1) lead exposure indices, (2) lead predictive models, (3) environmental indicators of human lead exposures, and (4) BLL surveillance data.
Lead exposure and risk indices used as surrogates of complete BLL surveillance data are important for primary prevention efforts. Such indices, defined as composite indicators combining multiple variables to represent human vulnerability, exposure, or risk from lead6,7,10,14,16,17 have been developed and published by federal agencies and nongovernmental groups. Currently, all the indices include age of housing variables along with sociodemographic variables that contribute to lead paint exposure risks in overburdened communities. Several are intended to predict lead exposures more broadly. Figure 1, Table 1, Tables A and B, and Figure A (available as a supplement to the online version of this article at https://doi.org) provide an overview with details of the publicly available lead indices and models and underlying data used in them. High-level descriptions are provided here.
FIGURE 1— Lead Exposure Modeling Maps From the (a) Environmental Protection Agency, (b) US Department of Housing and Urban Development’s Office of Policy Development and Research, and (c) Centers for Disease Control and Prevention: United States
Note. EJ = environmental justice. Part a depicts the 2017 Lead Paint Environmental Justice Index; part b depicts homes with risk for deteriorated paint in 2021; part c depicts the 2021 Lead Exposure Risk Index. For additional maps and information pertaining to them, see Table 1, and Table A, Table B, and Figure A (available as supplements to the online version of this article at https://ajph.org).Source. Schultz et al.,15 Vox,12 and Wheeler et al.10
EPA’s EJSCREEN14 Environmental Justice Screening and Mapping Tool (https://www.epa.gov/ejscreen) enables the visualization and screening of a lead paint indicator (“Pb Paint Indicator”) and a lead paint environmental justice index (“Pb Paint EJ Index”) at census block group resolution via data tables and maps. EJSCREEN’s Pb Paint Indicator uses the percentage of occupied housing units built before 1960 as the indicator of potential lead paint exposure. The Pb Paint EJ Index combines pre-1960 housing along with low-income and minority population percentages and population count per block group. All demographic data are sourced from the American Community Survey (ACS).
HUD developed a national, data-driven approach to identify occupied housing units with a high probability of lead dust exposure attributable to large areas of deteriorated paint. HUD’s Deteriorated Paint Index seeks to help HUD grantees better target home remediation and abatement efforts. The Deteriorated Paint Index uses microdata from the American Housing Survey, the nation’s largest housing survey, and the ACS to develop a household-level predicted risk metric that identifies housing units at risk for containing large areas of peeling paint. Predicted peeling paint risk is defined as the mean predicted percentage of occupied housing units built before 1980 within a given jurisdiction at risk for containing deteriorated paint. Results were summarized by state, county, and census tract.11
The purpose of the Lead Exposure Risk Index (LERI) is to provide a publicly available, interactive Web-based tool using national data to identify small geographic areas at high risk for potential lead exposure.6 The primary goal of this tool will be to assist health care providers, public health practitioners, and the public to better target blood lead screening in children and promote specificity in population-based interventions. The LERI, developed at the census tract scale, will use an overall composite index multivariate model based on 10 variables across 4 areas: sociodemographic, housing, geographic, and environmental. CDC will employ a model predicting nationally representative BLLs from the National Health and Nutrition Examination Survey (NHANES) to create weights for the covariates in the LERI model. CDC is planning further efforts to validate the model using BLL surveillance data from targeted state surveillance programs.6
The Vox national map of “lead exposure risk” and “Pb risk score”12 was developed by generalizing a method from the Washington State Department of Health, using age of housing and poverty factors. Vox generated a map for 72 241 census tracts in the United States with the objective to identify places where children would experience the highest and lowest risks of lead poisoning.
Researchers and government agencies have applied multivariate regression models to estimate BLLs for generating US lead risk maps. Examples with BLLs as the dependent variable and sociodemographic and environmental variables as independent variables include Wheeler et al.,18 Schultz et al.,15 and EPA.13 Although these lead models all have BLL as the dependent variable, their constructs differ, presumably because of differences in methods and choices made when selecting independent variables for the different analyses.
Wheeler et al. constructed indices for census tracts in Minnesota and risk scores for zip codes across the United States, respectively, and used them in models to identify the best socioeconomic measures for explaining risk of BLLs of 5 micrograms per deciliter (μg/dL) or higher.18 The results were compared against the Vox lead exposure risk score and several other approaches to determine goodness of fit. Key variables identified among the studies were percentage of houses built before 1940, median home value, percentage of renter-occupied housing, percentage unemployed, and percentage African American population.10,17
Schultz et al. published a multiple regression model approach to predict children’s BLLs at the census tract level and evaluated it against 3 states’ measured BLL data.15 Notable predictors included the percentage of pre-1960 housing, percentage below the federal poverty level, and percentage of non-Hispanic African Americans. Model performance varied widely by state.
EPA conducted multivariate regression modeling that included various census demographic variables identified in previous risk modeling efforts (e.g., age of housing, percentage of single-parent families, race/ethnicity) and reported that air-modeling data, variables constructed from EPA’s Safe Drinking Water Information System, and programmatic funding information from HUD and CDC were “highly predictive” of BLLs in several states.13(p.vi)
Identifying environmental sources in locations of high potential exposures or with many high BLLs is important for primary prevention efforts. EPA’s Office of Enforcement and Compliance Assurance developed a Lead Occurrence and Source Mapping tool19 to integrate publicly available lead environmental data into a single-analytics platform and assist in identifying geographic areas where there are potential exposure pathways in multiple media, including variables known to be indicators of lead exposures and BLLs based on published literature. In addition to including some of the indices described previously, the tool centralizes data layers on releases, transfers, and discharges reported to EPA data systems as well as monitoring data from multiple federal data systems (Figure 2).
FIGURE 2— Environmental Protection Agency Office of Enforcement and Compliance Assurance Lead Occurrence and Source Map: United States
Note. AQS = Air Quality System = ATSDR = Agency for Toxic Substances and Disease Registry; CDC = Centers for Disease Control and Prevention; EJSCREEN = Environmental Justice Screening and Mapping Tool; EPA OAQPS = Enviromental Protection Agency Office of Air Quality Planning and Standards; ESRI = Environmental Systems Research Institute; HUD = Department of Housing and Urban Development; ICIS-NPDES = Integrated Compliance Information System National Pollutant Discharge Elimination System; NAA = Non-attainment areas (specifically for the Pb Criteria Air Pollutant); NATA = National Air Toxics Assessment; NeT = NPDES eReporting Tool; NPDES = National Pollutant Discharge Elimination System; RCRA = Resource Conservation and Recovery Act; SDWIS-Fed = Safe Drinking Water Information System-Federal Reporting Services; SEMS = Superfund Enterprise Management System; TRI = Toxic Release Inventory; USGS = United States Geological Survey; WQP = Water Quality Portal. Data on lead occurrence in natural resources, reporting of lead releases, lead indicators, and lead indexes are publicly available in national data systems or as downloadable data sets online. The model identifies data layers, automatically refreshed data, static data sets (shown as gray lines), and data sets where nationally consistent data are not yet available. The agency or data system from which the data were sourced is in parentheses.
Table C (available as a supplement to the online version of this article at https://ajph.org) presents an extensive list of environmental lead variable indicators and their respective publicly available data uniform resource locators (URLs) that are either directly linked to or correlated with BLLs and potential high BLLs based on existing published research and scientific works. The variable categories listed focus on media that are under the EPA’s regulatory purview, which is why other well-documented environmental lead indicators such as consumer goods (e.g., cosmetics, imported foods, and toys3,20) are not included. It should also be noted that we included housing variables because of their use in multiple EPA indices or models13,14 and significant consensus of their relevance among other notable published works.15,21–23 We used some of the data in Table C to generate the diagram of example environmental lead indicator data layers shown in Figure 2.
The data model shown in Figure 2 identifies data sets that are regularly updated from national data systems using Web services, static data sets that are manually updated (shown via gray lines), and data sets in which nationally consistent data are not yet available. The model also shows the agency or data system from which the data were sourced in brackets.
BLL surveillance data from blood lead test results of young children are important for secondary prevention efforts such as linking lead-exposed children to services, focusing primary prevention interventions, and tracking progress in addressing exposures over time. The CDC collects childhood blood lead surveillance data from state and local childhood lead poisoning prevention programs. CDC provides an interactive mapping tool for visualizing BLL data in the Environmental Public Health Tracking Network24 by county for some states (https://ephtracking.cdc.gov/DataExplorer).
A 2016 Reuters report included a map combining BLL data sets from various states to illustrate a national picture.9 It presents the caveats and uncertainties that arise when using available measured BLL data. We searched public state health department Web sites to look for available BLL data collected by Childhood Lead Poisoning Prevention programs and compiled the information in Table D (available as a supplement to the online version of this article at https://ajph.org), which includes for each state the data type, geographic scale (e.g., county, zip code, city or town, census tract), and comments on high-risk locations. A visual illustration of Table D BLL data and Environmental Public Health Tracking Network county-level BLL data can be seen in Figure B (available as a supplement to the online version of this article at https://ajph.org).
The following sections summarize geospatial lead mapping challenges, various data challenges and needs, a work-in-progress data integration roadmap, and anticipated impacts of coordinated lead mapping efforts.
Reducing childhood lead exposure requires science-based, validated, and consistent approaches to effectively identify high lead exposure locations. Spatially defining and identifying high BLL locations using geographic information systems, a highly complex process in and of itself, can be challenging and subjective.25–27 Akkus and Ozdenerol25 identified literature that demonstrate how the resolution at which the data are processed (e.g., census tract, census block, counties, zip codes),26 how the data are aggregated and geocoded,27 and the selected BLL reference level (personal interview with Betsy Shockley, Shelby County Health Department, Memphis, TN, September 27, 2013, by Akkus and Ozdenerol25) can greatly affect the practical utility of a study’s geospatial results. This observation that scale matters, illustrated by case studies, was also discussed by CDC and HUD in their presentations to CDC’s December 2021 Lead Exposure and Prevention Advisory Committee.6,7
The science of lead risk identification has evolved, but challenges remain because of limitations of available BLL data, the need for a systematic approach for selecting sociodemographic and housing variables, and more complete environmental lead data (e.g., lead in drinking water; lead concentrations in air and soil near airports with leaded aviation gasoline).
There are significant gaps and inconsistencies in available BLL data across states, as illustrated in Table D. Mapping BLL data is challenging because of limitations in available and uniform data and differences in testing and reporting by jurisdiction.6,28
BLL undertesting remains an important public health issue: individual-level and neighborhood-level disparities have a significant impact on the rate of blood lead testing.29 Screening and testing approaches also vary across states (targeted or universal). Using national surveys across a 16-year period, CDC researchers evaluated trends in BLL and BLL testing. Despite testing disparities, the researchers concluded that mean BLLs are higher for children living in low-income households, non-Hispanic Black children, and children living in housing units built before 1950.30 Egan et al.31 analyzed 40 years of NHANES data and reported higher geometric mean BLLs among children aged 1 to 11 years to be associated with non-Hispanic Black race/ethnicity, lower family income-to-poverty-ratio, and older housing age. To account for testing disparities, Roberts and English23 employed NHANES data (1999–2010) with a Bayesian model and found increased odds of BLL greater than 10 μg/dL among low-income households, non-Hispanic Black children, and children living in housing units built before 1978. BLLs of 5 μg/dL or higher were significantly associated with pre-1950 housing and higher poverty quintiles in studies by McClure et al.22 (2009‒2015 period) and Hauptman et al.32 (2018‒2020 period).
At the national level, the CDC’s childhood BLL surveillance system annually receives approximately 4 million test results reported by state and local health departments. CDC publishes surveillance data on its Web site (https://www.cdc.gov/nceh/lead/data/surveillance-data.htm), but note that data cannot be compared across states or counties because data collection methods and state requirements for blood lead testing and reporting vary. Furthermore, individuals tested may not be representative of the population of the larger geographic unit.33
Additional challenges include accessibility and availability of state-level census tract BLL data. Furthermore, there are differences in how states currently define “elevated BLLs” (Table D). CDC recently lowered the blood lead reference value to 3.5 μg/dL,34 which may have an impact on identification of hotspots and evaluation of lead exposure indices. Lastly, obtaining up-to-date BLL data at a fine geographic spatial scale can be difficult given privacy concerns, and there are statistical sampling biases, different analytical methods and detection limits, and other issues that make using the data for modeling or mapping purposes challenging.23,28
In originally developing lead indices and models for differing agency mandates, EPA, HUD, and CDC used different constructs. Through the President’s Task Force on Environmental Health Risks and Safety Risks to Children, the agencies have been sharing publicly available data and working to understand how to utilize and synergize the collective mapping efforts as appropriate. Proxies for quantifying lead exposure and risk yield varying maps because they are constructed with different methods, variables, and geographic scales. Available lead indices (Figure 1, Table 1, Tables A and B) use sociodemographic variables from the ACS and age of housing from the ACS and American Housing Survey, but there are differences across surveys for categorizing variables such as age of housing and family income. As shown in Figures 1 and 2 and Table B, while there are overlaps among available indices with age of housing and sociodemographic variables, differences in selected variables can lead to different visualization of and different correlations with measured BLL data.35
The current public-facing maps use various color classification schemes, and are not always “predicting” the same thing (e.g., predicting deteriorated paint vs lead exposure). Nonetheless, collectively utilizing the current indices could inform selection of locations for action even while research to improve and evaluate them is ongoing. More details and examples are provided in the Approach for Cross-Agency Data Integration section.
Available indices and multivariate models for estimating BLLs do not generally include certain environmental lead sources (e.g., historically contaminated sites, metals industries, airports using leaded aviation fuel, drinking water, or multimedia lead concentrations). There is a need to systematically include and appropriately weight important predictors of exposure and BLLs in lead indices used to identify geographic locations for action. Challenges include obtaining, extrapolating, and modeling local or site-specific environmental data, and significant differences in data availability, quality, and geographic scale among indicators of BLLs. For example, representation of air emissions sources (e.g., metals industries or leaded aviation fuel usage) might need to augment emissions estimates and locations with additional variables that influence potential for local exposures.36
Data gaps exist for another important exposure pathway—drinking water. Public Water Systems service areas are not available nationally, which otherwise would aid in associating federally required, nationally available lead monitoring data to populations potentially exposed. Some states have these boundaries available for some or all of their permitted public water systems,37 but at the national level, zip codes served remains the finest available geographic resolution, and only for a select number of systems. Current mapping applications of some drinking water providers also demonstrate uneven occurrence of lead service lines (e.g., DC Water Service Information;38 Deignan39). Fortunately, the EPA National Primary Drinking Water Regulations: Lead and Copper Rule Revisions will require that initial lead service line inventories be completed in the next 3 years,40 so national data are forthcoming that will help improve evolving lead mapping efforts.
Note that Table C is also lacking lead concentrations in other media such as soil and house dust, which can be potential routes of human lead exposure. It does include the US Geological Survey background soil lead survey,41 which represents background soil lead concentrations and does not provide the spatial resolution to reflect local heterogeneity. No surrogates or predictive models are available at the national level for estimating soil lead or dust lead at high resolution. EPA, HUD, academia, and private-sector groups have been collecting multimedia environmental lead data, such as through federal field studies,42 environmental cleanups of hazardous waste,43 citizen science efforts,44 and more efforts that are under way or planned.
Figures 1, 2, A, and B show available housing, environmental, and BLL data being collectively assessed and analyzed through federal collaborations, but there are gaps in identifying some drivers of lead hotspots not explained by housing age and available environmental data.35 There could be other sources such as those summarized in Frank et al.,20 the EPA Integrated Science Assessment for Lead,45 and the Federal Lead Action Plan.3 Examples include ceramic cookware, food, toys, cosmetics, herbal and traditional medicines, and leaded ammunition, all of which are difficult to accurately pinpoint using demographic and environmental variables alone.
Temporally, environmental data can have a large range of reporting intervals (e.g., National Air Toxics Assessment 2014 modeled data on ambient air vs quarterly data in the Safe Drinking Water Information System46) and reported changes in environmental levels over time (e.g., Mielke et al.47). More nationally representative and local-scale data are needed, and there are challenges with using data from different studies, locations, and timeframes. It may also be important to account for human mobility48 as well as the effect of environmental education and outreach.49
Identifying high lead exposure risk locations is a key step in CDC’s Lead-Free Communities Toolkit50 and regional interagency lead task forces that are under way to address lead exposure hazards. EPA is using EJSCREEN as one approach to target and prioritize lead management actions for addressing sources of lead exposure (e.g., outreach, education, compliance assurance, enforcement). HUD is using the Deteriorated Paint Index to better target home lead exposure prevention, remediation, and abatement efforts. CDC is developing the LERI to identify geographic areas at higher risk of lead exposure to focus and improve blood lead testing efforts and target innovative primary prevention strategies. While the agencies started lead mapping efforts independently for mission-specific purposes, the agencies are moving toward an integrated assessment approach through data sharing and collaborative case studies. The aim is consistent with the Organisation for Economic Co-operation and Development methodology, which outlines key considerations in selection of variables into a fit-for-purpose composite indicator.51 Following is a work-in-progress data integration roadmap8 for stakeholders to identify and prioritize lead exposure risk locations to target actions and maximize investments: 1. To identify places for action and inform decisions, one could use the available BLL data, surrogate lead indices and models, and environmental lead data summarized in this article—the type of analysis and data needed depends on the questions to be addressed. a. For national scale, one could use lead indices and evaluate identified geographic locations by available BLL data and local knowledge. b. For state or local scale, one could use BLL data directly (depending on availability and robustness), either alone or together with lead indices as surrogates, to identify statistical hotspots with methods described in EPA’s generalizable Michigan lead data mapping and analysis case study.35
While the science is evolving to compare available lead indices and models against each other and evaluate or ground-truth them against states’-measured surveillance BLL data,52 an analyst or decision-maker may want to use them collectively to identify high-risk locations to cast a wider net or to use their intersecting locations for a more focused list. 2. Once high-risk lead areas are identified, they can be evaluated against local-scale BLL data and local knowledge (e.g., communities identified in state health department reports summarized in Table D). When using surveillance data to evaluate the indices, it is important to select jurisdictions with complete and accurate information that screen a high proportion of their young children. 3. To prioritize exposure hazards and inform targeted exposure reduction actions for populations within an identified location, one can use available environmental lead data and human exposure models that quantify relative exposure contributions and key factors.53
Further enhancing and evaluating the various indices with BLL data and local knowledge, and analyzing them collectively in different states, is under way. Other research, collaboration, and implementation needs include the following: • developing data-sharing agreements for easier access to BLL data at finer resolution, while recognizing that specific address information such as street address or geocoded locations reside at the state and local health department level; • expanding lead-focused environmental justice partnerships with local and state public health agencies to both obtain data and communicate risks based on lead hotspots mapping; • addressing multimedia data gaps at the local scale to evaluate and apply lead indices and models and prioritizing exposure pathways for targeting actions to maximize investments; • incorporating new data sources as they become available; and • coordinating risk communication around public lead maps.
Federal collaborations on lead risk maps being used for identifying places for action and high-impact decisions are critical for targeting hundreds of millions of dollars in HUD remediation grants, EPA environmental cleanup actions, CDC blood lead level surveillance programs for children, and primary and secondary prevention interventions at the state and local level. Consistency and coordination of science, data visualization, and risk communications is important for optimal targeting, education, and outreach. Collaborations among federal, state, and local partners are critical for more strategically identifying places for action to holistically address aggregate multimedia impacts from lead. Interagency collaborations can help harmonize approaches and foster data sharing for improving accuracy of lead indices, models, and maps for guiding effective risk-reduction actions. With coordinated lead mapping efforts, federal agencies will be able to better support regulations and guide lead prevention and mitigation efforts, promote environmental justice, partner with state and local governments, and implement the Federal Lead Action Plan. In addition, with these efforts, communities will be able to more effectively target and prioritize lead risk reduction in the most vulnerable locations and engage in broad dissemination of the information for risk communication.54
Currently available data inform actionable insight by various agencies (Breysse et al., p. S640), for example, addressing old housing‒related lead sources and expanding BLL surveillance efforts; however, a number of gaps—and coordination challenges—remain for addressing multimedia lead exposures and reducing uncertainties in identifying locations for actions at local, state, and federal levels. Scientists, policymakers, risk communicators, and the public need to work together so that approaches for identifying communities with high lead exposure risk can be advanced to meet the whole-of-government goals outlined in the Federal Lead Action Plan.
See also Breysse et al., p.
ACKNOWLEDGMENTS
The work in this article was funded in part by the EPA under the East Coast Help Desk Operations (ECHO)—Multi Region Information Technology (IT) Services Support Contract and the EPA Region 1 IT Services Support Task Order; specifically, LinTech Global Inc’s ECHO Contract No. GS-35F-0343W/68HE0319F0020 with the EPA, Regions 1, 2, and 3.
This article has been reviewed by EPA, HUD, and CDC and approved for publication. The coauthors appreciate technical reviewers and managers from our agencies who supported this article and related research. We gratefully acknowledge the EPA’s Office of Research and Development, Office of Enforcement and Compliance Assurance, and EPA Lead Coordinating Committee Mapping Subgroup; the CDC Lead Exposure Risk Index Working Group, Agency for Toxic Substances and Disease Registry, Office of Community Health and Hazard Assessment and the National Center for Environmental Health, Division of Environmental Health Science and Practice; and the HUD Office of Policy Development and Research.
Note. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the EPA, HUD, and CDC. In addition, contractor’s role did not include establishing agency policy.
CONFLICTS OF INTEREST
There are no known potential or actual conflicts of interest.
HUMAN PARTICIPANT PROTECTION
Human participants were not involved in this study, and, therefore, a statement on human participation protection is not applicable.
