Estimating the Effects of Soil Remediation on Children's Blood Lead near a Former Lead Smelter in Omaha, Nebraska, USA

Abstract

Background: Lead exposures from legacy sources threaten children's health. Soil in Omaha, Nebraska, was contaminated by emissions from a lead smelter and refinery. The U.S. Environmental Protection Agency excavated and replaced contaminated soil at the Omaha Lead Superfund Site between 1999 and 2016. Objectives: The goal of this study was to assess the association of soil lead level (SLL) and soil remediation status with blood lead levels (BLLs) in children living near or on the site. Methods: We linked information on SLL at residential properties with children's BLLs and assigned remediation status to children's BLL measure-ments based on whether their measurements occurred during residence at remediated or unremediated properties. We examined the association of SLL and remediation status with elevated BLL (EBLL). We distinguished the roles of temporal trend and the intervention with time-by-intervention-status interaction contrasts. All analyses estimated odds ratios (ORs) with a generalized estimating equations approach to ensure robustness under the complex correlations among BLL measurements. All analyses controlled for relevant covariates including children's characteristics. Results: EBLL (>5 lg=dL) was associated with both residential SLL [e.g., OR = 2:00; 95% confidence interval (CI): 1.83, 2.19; >400-800 vs. =200 ppm] and neighborhood SLL [e.g., OR = 1:85 (95% CI: 1.62, 2.11; >400-800 vs. =200 ppm)] before remediation but only with neighborhood SLL after remediation. The odds of EBLL were higher before remediation [OR 1.52 (95% CI: 1.34, 1.72)]. Similarly, EBLL was positively associated with preremediation status in our interaction analysis [interaction OR = 1:18 (95%CI: 1.02, 1.37)]. Discussion: Residential and neighborhood SLLs were important predictors of EBLLs in children residing near or on this Superfund site. Neighborhood SLL remained a strong predictor following remediation. Our data analyses showed the benefit of soil remediation. Results from the interaction analyses should be interpreted cautiously due to imperfect correspondence of remediation times between remediation and comparison groups.

Headnote

Background: Lead exposures from legacy sources threaten children's health. Soil in Omaha, Nebraska, was contaminated by emissions from a lead smelter and refinery. The U.S. Environmental Protection Agency excavated and replaced contaminated soil at the Omaha Lead Superfund Site between 1999 and 2016.

Objectives: The goal of this study was to assess the association of soil lead level (SLL) and soil remediation status with blood lead levels (BLLs) in children living near or on the site.

Methods: We linked information on SLL at residential properties with children's BLLs and assigned remediation status to children's BLL measure-ments based on whether their measurements occurred during residence at remediated or unremediated properties. We examined the association of SLL and remediation status with elevated BLL (EBLL). We distinguished the roles of temporal trend and the intervention with time-by-intervention-status interaction contrasts. All analyses estimated odds ratios (ORs) with a generalized estimating equations approach to ensure robustness under the complex correlations among BLL measurements. All analyses controlled for relevant covariates including children's characteristics.

Results: EBLL (>5 lg=dL) was associated with both residential SLL [e.g., OR = 2:00; 95% confidence interval (CI): 1.83, 2.19; >400-800 vs. =200 ppm] and neighborhood SLL [e.g., OR = 1:85 (95% CI: 1.62, 2.11; >400-800 vs. =200 ppm)] before remediation but only with neighborhood SLL after remediation. The odds of EBLL were higher before remediation [OR 1.52 (95% CI: 1.34, 1.72)]. Similarly, EBLL was positively associated with preremediation status in our interaction analysis [interaction OR = 1:18 (95%CI: 1.02, 1.37)].

Discussion: Residential and neighborhood SLLs were important predictors of EBLLs in children residing near or on this Superfund site. Neighborhood SLL remained a strong predictor following remediation. Our data analyses showed the benefit of soil remediation. Results from the interaction analyses should be interpreted cautiously due to imperfect correspondence of remediation times between remediation and comparison groups. https://doi.org/10.1289/EHP8657

Introduction

Children's blood lead levels (BLLs) in the United States have decreased steadily over the last several decades, due largely to the phaseout of lead in gasoline and consumer products such as paint. In October 2021, the U.S. Centers for Disease Control and Prevention (CDC) lowered the blood lead reference level (BLRV) for identifying children with BLLs that are much higher than that of most children to 3:5 lg=dL, which was the 97.Fifth percentile blood lead concentration of U.S. children (1-5 y old) during the period 2015-2018 (Ruckart et al. 2021). The previous BLRV of 5 lg=dL was set in 2012 based on the 97.Fifth percen-tile blood lead concentration of U.S. children (1-5 y old) during the period 2007-2010 (ACCLPP 2012). Before 2012, the CDC had designated a BLL of concern at 10 lg=dL.

Even low-level lead exposures are associated with decrements in cognitive function in young children; specifically, effects on cognition are observed in populations with group mean BLLs <5 lg=dL (ATSDR 2020; U.S. EPA 2013). Although contempo-rary BLLs in the United States are lower on average than they were in the past, lead from anthropogenic sources persists inthe environment (Kennedy et al. 2016; Raymond and Brown 2017), and chil-dren are potentially exposed from legacy sources. No threshold for lead-related cognitive effects in children has been identified.

The Omaha Lead Superfund Site (OLSS) was contaminated by emissions from a lead smelter and refining facility that operated for approximately 125 y until its closure in 1997. Other sources of lead contamination at the site include a former paint manufacturer, lead battery plant, and lead-based paint. The Agency for Toxic Substances and Disease Registry (ATSDR) conducted a public health assessment from July 2000 through August 2002 and found that 9.7% of the chil-dren 0-6 y old in Omaha had BLLs greater than 10 lg=dL (ATSDR 2005), whereas this proportion was 3.1% in U.S. children overall at the time (Meyer et al. 2003). In 2003, the OLSS was officially desig-nated a Superfund site under the Comprehensive Environmental Response Compensation and Liability Act and was included on the National Priorities List of contaminated sites.

In this study, we estimated the associations of soil lead levels (SLLs) and soil remediation status (i.e., soil excavation and replacement) with BLLs among children living on or near the OLSS. To answer our research questions, we leveraged large databases that were collected for surveillance and tracking purposes. Among the key challenges was to understand the potential biases stemming from use of these databases. We also sought to differen-tiate the effect of soil remediation from the temporal decline in BLLs that occurred for other reasons, as well as from the expected decline in BLLs as children grow older. Consistent with Steenland et al. (2020) and Savitz et al. (2019) who describe the merits of comparing results from different epidemiological methods that are vulnerable todifferent hypothesized biases, we adopted a modelling strategy that juxtaposed different epidemiological approaches, allowing us to tackle our research questions from several angles.

Evaluating the effectiveness of remediation actions and under-standing the factors that contribute to elevated BLLs (EBLLs) are crucial due to the costs associated with cleanup activities and the significant public health concern posed by EBLLs. Focusing on the OLSS allowed us to examine whether one of the largest cleanup efforts in the history of the Superfund program (U.S. EPA 2018) attained its goals. Literature on the effectiveness of soil remediation actions is sparse, and studies on interventions to mitigate exposure and detrimental health effects have produced mixed findings (Nussbaumer-Streit et al. 2016, 2020; Yeoh et al. 2012, 2008). Our study addresses an important research need by assessing soil-based environmental interventions on children's BLLs. In addition, our research advances understanding of factors that influence BLLs, potentially guiding future decisions about remediation efforts to protect public health.

Methods

Data Sources and Description

To conduct this study, we linked information from a U.S. EPA database on the SLLs measured between 1999 and 2016 at resi-dential properties in Omaha with blood lead surveillance data from the Douglas County Health Department (DCHD) for the same time period. The study protocol was reviewed and approved by the institutional review board (IRB) of the University of North Carolina at Chapel Hill. Because data were collected by another agency under their informed consent provisions and the risk of a privacy breach was considered extremely minimal, the U.S. EPA requested and received a waiver of informed consent in its en-tirety from the IRB. A Data Use Agreement between the DCHD and the U.S. EPA allowed a temporary use of the blood lead surveillance data for the time required to conduct the study.

The blood lead collection program was a public health screening effort; it was not designed for evaluating the effectiveness of soil remediation. In conformance with relevant guidelines (Nebraska DHHS 2012; CDC 1997; Council on Environmental Health 2016), blood lead screening using capillary tests was typi-cally conducted in children at 12 and 24 months of age and in chil-dren between 36 and 72 months of age if they were not tested at earlier ages. Children residing in Omaha tended to be screened with capillary samples more frequently than children with a lower risk of lead exposure were; hence, multiple capillary samples per child were available. Because capillary tests tend to overestimate BLL (Wang et al. 2019), confirmatory venous tests were scheduled when a capillary test showed an EBLL (>10 lg=dL before 2012 and >5 lg=dL from 2012 to 2016). Children with EBLLs con-firmed by venous tests were generally followed using venous tests until their BLLs were no longer elevated, at which point they might have been rescreened periodically using capillary samples. The children with venous samples were likely to include some with false positive elevated capillary BLLs and some whose guardians opted for venous BLLs. In general, BLLs between 5 and 10 lg=dL would be underrepresented in confirmatory venous samples until 2012 when the CDC reference value was lowered to 5 lg=dL.

The DCHD database maintained all capillary and venous blood lead test results, sampling dates, and clinics where the measure-ment was made, as well as children's residential addresses at sample collection, their birth dates, sexes, races, and ethnicities (Table 1). Blood samples were analyzed with varying limits of detection (LOD), and we did not have a separate LOD associated with each blood lead measurement. Consequently, we assumed an LOD of 3:3 lg=dL, which was the most common and the highest known LOD in our data set with 74.9% of capillary samples and 43.9% of venous samples below this LOD. We specified two binary outcome variables indicating EBLL, EBLL5, and EBLL10, by dichotomizing blood lead concentrations at thresholds BLL >5 ug=dLandBLL >10 ug=dL, respectively.

Residential soil sampling for lead contamination was per-formed at the OLSS since 1999 by a variety of different organiza-tions and contractors, all with U.S. EPA oversight and/or funding. Details of the field sampling and analysis methods are part of standard operating procedures described in U.S. EPA (2010). Briefly, yards were divided into quadrants (generally front, back, and two sides) and a "drip zone" that consisted of the area within approxi-mately 3 ft of the house or structure. Multiple soil samples were collected from each quadrant of the yard and from the drip zone. Composite samples representing each of the five areas were ana-lyzed in the field using hand-held X-ray fluorescence (XRF) tech-nology. No fewer than 5% of the samples were sent to a laboratory for confirmation using inductively coupled plasma (ICP) metal analysis. ICP measurements less than 1,000 parts per million (ppm) were 1.16 times greater than the corresponding XRF measurements but the data were well correlated (r2 = 0:83) (U.S. EPA 2010). Soils were air-dried in a controlled environment before sieving to provide bulk samples (particle size <2 mm) for standard soil lead concentration analyses (U.S. EPA 2009b).

Excavation and replacement actions at the OLSS began in 1999 and continued through 2016, the end of our study period. The approach to soil sampling and remediation, including which prop-erties were prioritized, was documented in the Record of Decision (U.S. EPA 2009a). Briefly, upon agreement of the property owner, soil from quadrants and the drip zone of eligible properties was excavated and replaced. Childcare centers and child-occupied resi-dences where the maximum midyard SLLs (i.e., soil samples >6 ft from the foundation) exceeded 400 ppm or where a child's BLL exceeded the CDC reference level (i.e., 10 Ug=dL until lowered to 5 Ug=dL in 2012) were treated as an immediate threat. Soil excavation and replacement was also initiated to address the most highly contaminated residential properties (i.e., with maximum midyard SLLs exceeding 1,200 ppm) regardless of whether a child resided there. Nonresidential-type properties such as schools, churches, parks, vacant lots in residential neighborhoods, and other noncom-mercial/industrial properties where exposure could occur were also remediated during this phase of the cleanup action. As clean-ups were completed at these highly contaminated properties, the SLL required to qualify a property for soil excavation and replacement was adjusted downward to 800 ppm in 2005 and to 400 ppm in 2009. Decisions regarding soil sampling priorities during later phases of OLSS cleanup, and in the absence of an immediate threat (i.e., approximately after 2005), were made in collaboration with community stakeholders. The drip zone, even if exceeding 400 ppm, was not remediated unless one of the yard quadrants also exceeded 400ppm. The U.S. EPA recorded measured SLLs and the dates of soil excavation and replacement. The replacement soil lead concentration (mean 14 ppm) was reported to the U.S. EPA.

We used two approaches to link the U.S. EPA and DCHD databases and assign a SLL to children with BLLs based on their reported address and the date of the BLL. First, we employed ArcMap (version 10.6) to geocode the residential addresses in the blood lead surveillance database so that their longitudes and lati-tudes could be projected onto the state plane coordinate system used to locate properties in the U.S. EPA database. This approach entailed obtaining a match based on the distance between a property and its geocoded address. For addresses that were not matched using this approach, the address format in multiple hand-entered U.S. EPA data tables was standardized to U.S. postal code norms for abbreviations and numbering and then matched to the address in the blood lead surveillance database.

We identified a set of covariates that are known to be associated with EBLL as potential confounders [i.e., year, season, age, sex, race, socioeconomic status (SES), and the presence of lead-based interior paint in the home] (Egan 2021; Raymond and Brown 2016, 2017; U.S. EPA 2013). Children's characteristics (date of birth, sex, and race) were reported by the caregiver and recorded with each BLL measurement. Age ateach BLL measurement was calcu-lated from date of birth. The other potential confounders, i.e., SES and the likely presence of lead-based paint in the home, were not directly reported with the blood lead surveillance data. To control for these covariates, we derived census tract-level variables for median income and percentage of housing built before 1940, using data from the 2010 American Community Survey (www.census.gov/programs-surveys/acs) and assigned those values to BLLs according to the child's current address. To evaluate the effect of season in our data and derive appropriate indicator variables, we examined the relationship between EBLL and month of the year of the measurement (see Figure S1). Based on this analysis and obser-vations in the published literature (Raymond and Brown 2016, 2017; U.S. EPA 2013), we defined a binary variable that indicated BLL measurement during the summer months (June-August) to adjust for season. We explored alternative functional forms for the year variable (i.e., categorical with each year as one category, con-tinuous, and continuous using a cubic function). We also consid-ered alternative control for season by specifying month as a categorical variable.

Because our data set was large, comprising BLLs for 74,537 children (Table 1), we did not expect that including covariates that were not demonstrated confounders (i.e., associated with both the outcome and the exposure) in our models would substantially increase variance inflation and thereby reduce the statistical power; hence, we included all aforementioned covariates in our models except race. We omitted race from our main models because it was missing for 58.2% of the children in our study, but we investigated whether that choice influenced our results. Observations with missing data for other model covariates were excluded.

Study Area and Cohort

Omaha, the largest city in Douglas County, encompasses approxi-mately 130 square miles ofeastern Nebraska bordering the Missouri River.We defined the study areatoinclude27sq miles and14postal codes in greater Omaha within Douglas County (Figure 1). The U.S. EPA sampled soils at residential properties and childcare facilities in the OLSS to define a "focus area" for cleanup operations; the final focus area (Figure 1) of approximately 13.8 sq miles was intended to delineate the area where soil concentrations for at least 1 in 20 homes exceeded 400 ppm; however, some residential properties that were outside the focus area boundary were also sampled and determined to be eligible for soil excavation and replacement (U.S. EPA 2009a). We constructed a cohort of children (0-7 y old) who resided within the study area and had one or more blood lead meas-urements between 1999 and 2016, comprising 165,124 blood samples from76,537 children (Figure 2).

We created a remediation status variable using the date of the blood draw to indicate whether a blood lead measurement was collected bef ore or after soil was excavated and replaced at the child's residence. For a child who never moved [i.e., had only one address recorded in the EPA database (79% of chil-dren)], this variable would take two values: pre- vs. postreme-diation. If a child moved from a remediated to a nonremediated property, however, the dichotomous classification seemed prob-lematic for measurements at the second residence. In such cases, the blood lead measurements at the second residence would nominally be classified as "preremediation" but could directly follow a measurement classified as postremediation at a previous residence. Whenever a BLL designated "preremedia-tion" was dated after the first postremediation BLL, we opted to change its remediation status to "missing," thus ensuring that children moving from a remediated property to an unremediated property would not contribute a "preremediation" BLL after a previous postremediation BLL. Such children could contribute postremediation BLLs at properties where they subsequently lived if those properties had recorded remediation dates. Because we lacked information on dates when children changed residences, we could not use the time residing at either resi-dence to inform the classification.

Overall, we excluded 3,410 blood lead samples with "missing" remediation status and 10,877 with missing values for BLL, sample type (i.e., venous or capillary), and/or venous blood samples potentially measured using an instrument with a defective detector (Mason et al. 2019). The final analytic data set contained 150,837 BLLs from 74,537 children residing in the study area, among which 113,923 BLLs came from children residing within the focus area (totals determined from Figure 2). The 44,060 addresses for these children (Table 2) spanned 71 census tracts and 48 school catchment areas; correspondingly, the focus area spanned 56 census tracts and 32 catchment areas.

To accommodate our planned analyses, we considered venous and capillary BLLs together to classify children into three groups based on whether or not they had pre- and/or post-remediation blood lead measurements (Figure 2). We defined the "remediation" group as children who had both pre- and postremediation BLLs [i.e., based on addresses reported at sample collection, they had samples taken at a property both before and after its remediation or at one property before its remedia-tion and another property after its remediation (n = 3,279 chil-dren, 9,898 capillary samples, 1,385 venous samples)]. The "comparison" group was defined as children who had prereme-diation blood lead measurements only [i.e., based on the address reported at sample collection, they never had samples collected at any property after its remediation or only lived at properties with no recorded remediation dates (n = 65,144 children, 110,158 capillary samples, 18,369 venous samples)]. We defined the "postremediation-only" group as children who had postremediation blood lead measurements only [i.e., based on the address reported at sample collection, all their samples were collected at properties after their respective remediations (6,114 children, 10,054 capillary samples, 973 venous samples)].

Epidemiological Analyses

To visualize changes in SLL over time, we calculated the daily average SLL at the residences for each group and fitted locally weighted smoothing (LOESS) curves. Using a similar approach for the proportion of EBLL, we calculated children's capillary EBLL status for each group by the month that the sample was taken and fitted LOESS curves.

For model fitting we used generalized estimating equation (GEE) with an exchangeable correlation structure to account sources of clustering among measurements (e.g., multiple measurements per child, multiple children in the same household, measurements taken at different clinics with different instru-ments, etc). We substituted an independent correlation structure when the exchangeable structure failed to provide estimates. Estimates of regression parameters from GEE models in large data sets are robust to possible misspecification of the correlation structure, though statistical efficiency improves with correct spec-ification (Zeger and Liang 1986); the exchangeable structure was useful for addressing the complex clustering in our data, despite its not capturing every nuance of the underlying correlation structure.

Estimating the Association between SLL and Children's EBLL

Study samples. To estimate associations between SLLs and child-ren's EBLL, we used capillary BLLs from all children in the study area (i.e., remediation, comparison, and postremediation-only groups; see Figures 1 and 2) because they were administered to most children regardless of their initial BLLs. Additionally, a large number of capillary measurements, including repeated measure-ments per child, were available. Our data set included 67,751 chil-dren contributing 130,110 capillary BLLs: 61,666 children contributed 115,405 preremediation BLLs, and 8,821 children contributed 14,705 postremediation BLLs. (Note that 3,218 chil-dren in the remediation group contributed both preremediation (n =5,247) and postremediation capillary BLLs [n = 4,651]).

We derived several residential soil lead metrics for each prop-erty before and after soil remediation: a) average concentration of the yard quadrants (excluding the drip zone); b) maximum concentration of the yard quadrants (excluding the drip zone; b) drip zone concentration; and d) maximum concentration among yard quadrants and drip zone (Table 2; Table S1). If soil from a yard quadrant was excavated and replaced, we assigned that quadrant a postremediation SLL of 14ppm, which was the average lead concentration in the backfill soil. The postremediation SLL for the yard was defined as the average of the SLLs across quadrants; quadrants assigned a postremediation SLL of 14 could be averaged with quadrants with measured SLLs that were below 400 ppm and never remediated. Among the children's addresses recorded from 1999 to 2016 in the DCHD database, 21,363 addresses in the study area were also in the U.S. EPA database and thus could be assigned SLLs and soil remediation dates, whereas 22,697 could not be assigned SLLs and remediation dates (Table 2). Properties in the DCHD database but not in the U.S. EPA database were assumed to have no soil lead measure-ment or remediation. This situation could arise if an incorrect address has been reported with a child's BLL or if the soil at a property had not been measured. For example, properties may not have been targeted for soil measurement if they were outside the focus area [as shown in Table S1, fewer properties within the focus area (n= 8,598) were without SLLs]. Properties missing remediation dates were assumed never remediated. We had no in-formation on the date that child moved into a house or how long they resided at that house. If a property's SLL was not measured, the property and any BLLs measured on its residents were excluded from our analysis. If a residence had no recorded remediation date in the U.S. EPA database, we assumed that its SLL remained the same over time. We matched BLLs to SLLs based on the SLL of the child's current residence on the date of blood sampling. For each blood lead observation, we also derived a neighborhood SLL, which was calculated as the mean of average yard SLL, excluding the drip zone, of all residences in an elemen-tary school catchment area on the day when the blood lead sample was collected.

Modeling strategy. In our main analysis, we estimated the associations of residential (average of the quadrants in the yard excluding the drip zone) and neighborhood SLLs with EBLL5. We performed separate analyses of preremediation blood samples and postremediation blood samples because we think that the estimates based on preremediation BLLs provide a better estimate of the association between SLL and EBLL, whereas the postremediation BLLs are more likely to be influenced by behavior and short-term kinetic factors that are dependent on the duration of exposure, which we could not derive from the available data. The preremediation analysis included preremediation samples from the remediation group plus samples from the comparison group (all of which were "preremediation"), whereas the postremediation analysis included postremediation samples from the remediation group plus samples from the postremediation-only group (Figure 2). We conducted sensitivity analyses in which we replaced the average SLL with the maximum SLL (including the drip zone concentration) and restricted the data to BLLs of children residing in the focus area.

We estimated the associations for residential and neighbor-hood SLLs separately in single-source models. We also included both SLL variables together in a two-source model to understand their relative contributions to EBLL5. We modeled SLL as a con-tinuous variable (log2-transformed) and as a categorical variable (0-200, >200-400, >400-800, >800-1,200, and >1,200, using 0-200 ppm as the reference). The current U.S. EPA guidance rec-ommends a SLL of 400 ppm as a residential screening level (see https://semspub.epa.gov/work/HQ/175347.pdf), which was used at the Omaha site to determine eligibility for soil removal and replacement. The SLL of 200 ppm limits the probability of child-ren's (age 1-5 y) BLLs exceeding 5 lg=dL to less than 5% based on the U.S. EPA's Integrated Exposure Uptake Biokinetic (IEUBK) (version 2.0) model (Brown et al. 2021).

Our logistic GEE model for estimating the association between EBLL5 and residential SLL (one-source model) was as follows: where i and j index the child and the repeated observations for each child, respectively; RSLL is residential soil lead level (ppm, log2-transformed continuous); Year is calendar year (continuous); Season is binary (1 for June-August, 0 for other months); Age is

categorical [0-1, 2-3, and 4-7 y old (the last as referent)], with k indexing the three levels respectively; Sex is binary (1 for male, 0 for female); Income is binary indicating whether the census tract level median household annual income was above $40,000 (= 1) or not ( = 0); House40 is binary indicating whether the census tract of children's residence had more than half of the houses built before 1940 (= 1) or not (= 0); and I( ×) is the indicator function. The OR for EBLL5 per unit increase in RSLL is e^1. We also fitted a one-source model using neighborhood SLL only and a two-source model that included both residential and neighborhood SLL with the same adjustments as in Equation 1.

EstimatingtheAssociationofSoilRemediationwith Children's EBLL

Study samples. We restricted our main analysis to children resid-ing in the focus area because soil excavation and removal actions were generally targeted within the focus area. We conducted separate analyses for capillary and venous BLLs. For our main analyses, we dichotomized capillary BLLs using a cut point of 5 Ug/dL and dichotomized venous BLLs using a cut point of 10 ug/dL. As noted previously, capillary tests were administered to most children, enabling us to include children regardless of their initial BLLs [i.e., 9,050 capillary BLLs from 3,135 children in the "reme-diation" group; 70,532 capillary BLLs from 38,667 children in the comparison group; and 9,910 capillary BLLs from 5,774 children in the postremediation-only group (Figure 2)]. Venous BLL meas-urements, however, were typically conducted in children with capillary BLLs greater than CDC reference levels; therefore, the pool of children with venous measurements tended to include the most highly exposed children, and confirmatory venous samples for cap-illary BLLs between 5 and 10 lg=dL were likely to be rare before 2012 when the CDC lowered the reference value to 5 lg=dL. There were 1,255 venous BLLs from 551 children in the remedia-tion group; 12,685 venous BLLs from 9,211 children in the com-parison group; and 960 venous BLLs from 593 children in the postremediation-only group (Figure 2).

Modeling strategy. We used two approaches to investigate the effect of soil remediation on children's blood lead. The first approach was directed primarily at comparing pre- vs. postremedia-tion EBLL among children in the remediation group. In addition, we included comparisons involving the comparison and the postreme-diation groups. This analysis useda four-category exposure variable: comparison group (all measurements from comparison-group chil-dren), preremediation measurements from remediation-group chil-dren, postremediation measurements from remediation-group children, postremediation-only group (all measurements from postremediation-only group children). We hypothesize that odds of postremediation EBLL will be lower than the odds of preremedia-tion EBLL for the remediation group children and that the odds of comparison-group EBLL and the odds of postremediation-only group EBLL will each be lower than the odds of postremediation EBLL in the remediation group because both comparison-group and postremediation-only group children generally lived at properties with residential SLLs below the threshold for remediation. In this approach, pre- vs. postremediationcontrasts may conflatea temporal trend toward lower EBLLs with any effect ofsoil remediation.

Our second approach was designed to separate the component of the association due to temporal trend from potential effects due to soil remediation by comparing the OR of EBLL between preand postremediation measurements among children in the reme-diation group vs. OR of EBLL between early and late measure-ments among children in the comparison group. Our analysis has the spirit of, but differs in detail from, a typical difference-in-differences (DiD) analysis because our available data were incom-patible with the usual analysis. In a typical DiD analysis, the inter-vention is applied to all members of the treated group during the same circumscribed calendar period; then, the difference in mean outcome between before and after intervention in the treated group was compared with the differencein mean outcomein the untreated group before and after the same calendar period; such a difference of differences is statistically an interaction contrast. In our study, soil remediation was applied to individual subjects in the remedia-tion group at different dates over the study's course, making it con-ceptually difficult to assign matching times to subjects' measurements in our comparison group. The approach we adopted was to use the median date of soil remediation among the remedia-tion group as the boundary between pre- and postremediation measurements for all subjects in the comparison group. We com-puted a corresponding interaction contrast, which is expressed as a ratio of ORs or an interaction OR. We interpret this parameter as assessing the association of EBLL with remediation after accounting for nonremediation-related temporal trends.

For both these analyses, we adjusted for year, season, age, sex, income, and proportion of homes within the census tract of resi-dence built before 1940 (i.e., house40) as described for Equation 1; we also adjusted for remediation stage, a variable that describes when a property met the changing criteria for soil remediation (four categories: one category for properties that never underwent

remediation and three categories for remediated properties corre-sponding to the time periods when they became eligible for reme-diation, namely, 1999-2004, 2005-2009, and 2009 onward). Remediation phase was assigned to a BLL according to the house the child lived in at the time of blood sampling. Remediations that occurred during the earlier phases resulted in a large decrease in SLL because the most highly contaminated properties were priori-tized for soil excavation and replacement. We present details of these two approaches below.

Our logistic GEE model for analyzing the association of remediation status with EBLL was as follows:

where k indexes the levels of any categorical predictor; E has four categories as described earlier; remediation phase as described earlier has four categories; and all other variables are defined following Equation 1. The ORs for comparisons of EBLL among exposure categories, conditioning on covariates, were calculated by exponentiating contrasts among the fb1,kg.

Interaction Analysis to Adjust Association with Remediation for Temporal Trend

For this analysis, we used essentially the same model structure as that of Equation 2, although the data and the meaning of some terms changed. We no longer included the postexposure-only group, and we split the comparison group into pre- and postreme-diation subgroups based on the median date of remediation among remediated properties. We used a different four-category exposure variable: preremediation measurements in the remediation group, postremediation measurements in the remediation group, comparison group measurements taken before the median date when remediations occurred, and comparison group measurements taken after that median date. Explicitly, the term Pk=1 P1 kIðEk,ijÞ involved a different set of exposure categories than it did in for the previous analysis. Estimates of interest were calculated by exponentiating contrasts among the fP1 kg; in par-ticular, the interaction estimate, a ratio of ORs, involved exponentiating the prepost difference in the remediation group minus the pre-post difference in the comparison group.

All analyses were conducted using SAS (version 9.4; SAS Institute, Inc.).

Results

After excluding missing and potentially inaccurate venous blood lead data, BLLs for 74,537 children who lived in the study area from 1999 to 2016 were available for our analyses (Figure 2). The BLLs comprised 130,110 capillary and 20,727 venous measurements (n=150,837 total). Most children (59%) were 0-1 y old at their first blood lead measurement (Table 1). The children were approximately balanced with regard to sex. Race was missing for most children (58.2%), whereas 25.8% of caregivers reported their child was White and 12.5% reported that their child was Black. Most children (62.3%) lived in census tracts where the median income was less than $40,000 per year, and more than half of the children (54.8%) lived in census tracts where less than half of the housing was built before 1940. A large proportion of children (44.3%) had multiple capillary BLL measurements, whereas a much smaller proportion of children (4.3%) had multiple venous BLL measurements (Table 1; Table S2). Although the proportions for most variables remained constant, the proportion of Black race reported by the caregiver and the number of venous measurements per child declined over time.

The study area contained 44,060 residences (Table 2). Among the properties that were not remediated, 22,683 (61.9%) lacked SLLs. Soil was remediatedat 7,401 (16.8%) residences in the study area; among those, at most 226 (3.1%) were missing some SLL metric-primarily drip zone-but only 14 (0.2%) were missing average yard SLL (Table 2). Overall, SLLs were not measured at approximately half (51.5%) of the properties for which BLLs were available. SLLs at some of these properties were not measured because they were outside the focus area or because they were not targeted for sampling, whereas other properties may not have been matched to a BLL observation due to an incomplete or inaccurate address. Relatively few properties were remediated between 1999 and 2004 [n =711 (9.6%)], whereas the number of remediations substantially increased between 2005 and 2009 [3,135 (42.4%)] and between 2010 and 2016 [n= 3,555 (48.0%)]. SLLs were rela-tively high before remediation and were substantially reduced afterward [Table 2 and Table S1 (focus area only)].

The proportion of elevated BLLs (defined as capillary BLL >5 lg=dL) for children, and SLLs within the focus area declined through the study period (Figures 3 and 4; Tables S3 and S4). A relatively large proportion of children in the remediation group had elevated BLLs at the beginning of the study period. This group experienced a steeper decline in the proportion of elevated BLLs in comparison with the comparison group residing in the focus area and the comparison group residing outside the focus area. Specifically, the proportion of elevated BLL declined from approximately 76% in 1999 to 6% in 2016 for the remediation group residing within the focus area, and it declined from 31% in 1999 to <5% in 2016 for the comparison group. We also observed a seasonal trend, where the proportion of elevated BLLs was higher during warm months (June-August) in compar-ison with cooler months in each of the groups (Figure S1).

Estimated Associations between SLLs and Children's EBLL

In single-source models for preremediation BLLs, residential and neighborhood SLLs were each positively associated with child-ren's elevated BLL (Table 3; Tables S5 and S6). In two-source models with preremediation BLLs, ORs for the association of residential SLL with EBLL remained essentially unchanged from the single-source model, whereas ORs for neighborhood SLLs were closer to the null; in particular, the OR for continuous neighborhood SLL was essentially null.

Postremediation residential SLLs were not associated with EBLL, with ORs of approximately 1.0 before and after adjusting for neighborhood SLLs (Table 3). In contrast, postremediation neighborhood SLLs were positively associated with EBLL. We observed similar results when residential SLLs were based on the maximum of yard and drip zone SLLs (Table S5) and when the analysis was restricted to the focus area (Table S6).

In analyses to explore alternatives to the functional forms for the year and season adjustments, we found that our results were insensitive to the particular form (Table S7). Similarly, these results did not change substantially when race was included in the model (Table S8).

Estimated Association of Soil Remediation with Children's EBLLs

Basedon capillary measurements from the focus area, the observed proportion of EBLL5 for children in the remediation group was lower after remediation in comparison with the proportion before remediation (Table 4). The proportion of elevated BLLs was also lower in 70,532 samples from the comparison group (11.7% of EBLL5) compared with 4,509 preremediation samples from the remediation group (18.9% of EBLL5) (Table 4). The lower propor-tion of postremediation EBLL held overall and for each covariate level except the 0-1 y age category, possibly because children exited the age group before having a postremediation measurement (i.e., there are only 128 postremediation but 2,204 preremediation samples). We also found that the proportion of EBLL5 was higher among those blood samples collected from children living at prop-erties that were remediated during earlier phases in comparison with those remediated more recently. This observation likely reflects the cleanup procedure that targeted children with an EBLL >10 lg=dL and the most contaminated properties first. We observed broadly similar patterns, though the observed proportions differed, when we raised the threshold for EBLL to 10 lg=dL for capillary samples (Table S9).

The percentage of venous EBLL10 was higher before reme-diation (42.8% of 725 BLLs) than after remediation considering the postremediation group (31.9% of 530 BLLs) and post-only group (24.6% of 960 BLLs) (Table 5). The lowest percentage of venous EBLL10 was found in the comparison group (12.25 of 1,548 BLLs). Similar to the pattern of capillary EBLL5, the percentage of venous EBLL10 changed with children's character-istics and was generally lower after remediation in the remedia-tion group (Table 5). The percentage of EBLL10 was higher among the 2- to 3-y-old children across all groups. Higher percentages of EBLL10 among males, Black children, and in the summer months that are expected based on the literature were observed for preremediation measurements in the remediation group but were not seen consistently across groups. We saw generally similar patterns with the lower threshold of 5 lg=dL applied to venous samples (Table S10).

In our regression analyses of the association between elevated BLL and remediation status, soil remediation was associated with reduced BLLs in children. The odds of EBLL in the remediation group were higher in samples collected before vs. after remedia-tion, resulting in positive associations between EBLL and prere-mediation status for capillary samples (Table 6). The odds of EBLL were also higher in samples collected after remediation in the remediation group vs. samples collected in the comparison group. This finding held for ORs comparing samples from chil-dren in the post-only group to the samples from children in the comparison group. As expected, the odds of EBLL in the postremediation-only samples were approximately the same as the odds of EBLL in the postremediation group samples. For ve-nous samples and a threshold of 1 0 lg=dL, we saw a similar pat-tern of associations, although the estimated ORs were larger in magnitude (Table 6). When we changed the thresholds for both sample types, patterns were again similar (Table S11).

In our interaction analyses, soil remediation was associated with lower BLLs, and the reduction was greater than would be expected based only on the general reduction in EBLL over time. When we partitioned comparison-group BLLs into those before and after the median date of remediation among properties that underwent remediation (i.e., 19 June 2009), the proportion of EBLLs before that date exceeded the proportion after that date both for capillary samples using the 5 lg=dL threshold and for ve-nous samples using the 10 lg=dL threshold (Table 7). In our inter-action models, for children residing within the focus area, using either capillary and or venous samples and the respective thresh-olds, the interaction OR, namely, the pre- vs. postremediation OR for EBLL in the remediation group divided by the comparable OR in the comparison group indicated a larger postremediation reduc-tion in elevated BLL for the remediation group (Table 8). We had similar findings for the capillary samples with the threshold for EBLL increased to 10 lg=dL, although the ORs were somewhat larger (Tables S12 and S13). For venous samples with the lower threshold of 5 lg=dL, the results in the comparison group changed. The OR comparing the EBLL5 using pre-2009 venous samples in the comparison group to EBLL5 using post-2009 samples in the comparison group was less than one, indicating alower oddsof ele-vated BLL in the earlier years ofthe study (Table S13).

Results of our regression analyses of the association between elevated BLL and remediation status were not sensitive to alter-native specification of year or season (Table S7). Similarly, the associations we report between remediation status and EBLL did not change substantially when race was included in the model (Tables S14 and S15).

Discussion

We examined the association between SLL and EBLL and the association between soil remediation and EBLL in children living near a former lead smelter. Consistent with prior studies (Egan 2021; Raymond and Brown 2016, 2017; U.S. EPA 2013), we found that proportion of EBLL was generally higher in males than in females and in June-August than in other months. The proportion of EBLL was higher in the lower income category in these data (Tables 4 and 5). Although this finding has been reported in previous studies (Pirkle et al. 1994), on a national level, the difference in BLLs reported between different income levels has been decreasing (U.S. EPA 2013). Although 2- to 3-y-old children typically have higher BLLs (Egan et al. 2021), we found a higher percentage of elevated capillary BLLs in the 0-1 y age group (Table 4).

Because BLLs of the U.S. population have been declining over the past decades due to various policies on lead reduction in the environment (Council on Environmental Health 2016), one of our key challenges was to differentiate the effect of soil lead and remediation on BLLs from the effects of other policies and inter-ventions that occurred over time. A strength of our study was that

we had information on the dates of the blood lead measurements and soil remediation that allowed us to employ two analytical strategies to control, at least in part, for temporal trends. Another strength of our study was its large sample size (n = 74,577 chil-dren 0-7 y old) and information on the children's addresses at the time of the blood draws, which we used derive variables to indi-cate neighborhood-level SES and the presence of lead-based paint in the home.

We found that EBLL was associated with SLL, that the risk of EBLL generally increased with increasing SLL before reme-diation, and that the rate of increase decreased at higher concen-trations (Table 3), though exposure misclassification of SLLs may have attenuated these observed associations. Our analyses suggested important roles for both the residential SLL and neigh-borhood SLL in predicting EBLL, with neighborhood SLL becom-ing more influential following remediation. We hypothesize that the observed greater importance of neighborhood SLL after reme-diation was influenced by behavior, i.e., efforts to educate that accompanied remediation would improve parents' ability to prevent exposure in their children. Our analysis of the association between EBLL and remediation status supported the conclusion that soil remediation is associated with reduced BLLs in children at the OLSS (Table 6; Table S11). Our interaction analysis further substantiated that the lower BLL that was associated with remedia-tion was greater than what would be expected due to the general temporal decline in BLLs (Table 8; Table S13).

A small number of studies have employed similar methods to examine the association between SLL and children's BLL (Lanphear et al. 1998; Lewin et al. 1999; von Lindern et al. 2003; Mielke et al. 2007; Zahran et al. 2011; Gulson et al. 2009; Mielke et al. 2019), including one study conducted in Omaha (Angle and McIntire 1979). Consistent with our find-ings, these studies generally report an increase in BLL with increasing soil concentrations. The quantitative results from the study of the SLL and BLL in Omaha (Angle and McIntire 1979) and other earlier studies may not inform the current rela-tionship between soil lead concentration and BLL at the OLSS, however, because the relationship between lead concentrations in environmental media and BLLs has changed as lead concen-trations have declined (U.S. EPA 2013; Richmond-Bryant et al. 2013; Richmond-Bryant et al. 2014). Nonetheless, observations from previous studies are useful to contextualize our findings. For example, Lanphear et al. (1998) reported that floor dust lead loading and soil lead concentration were the most significant predictors of BLLs in children 6 months to 3 y old. Child's age, mouthing behaviors, and race were also significant predictors in that analysis. Lewin et al. (1999) and von Lindern et al. (2003) reported increases in BLL with increasing soil concentrations at Superfund sites. In a study of the decline in SLLs in New Orleans, Louisiana, following Hurricane Katrina and the blood lead reductions in children during the same period, Mielke et al. (2019) reported that the reductions in census tract-level SLLs from 99 ppm in 2001 to 54 ppm in 2017 influenced the concurrent decline in median BLL from 3.6 to 1:3 lg=dL. This finding is consistent with our analysis showing the importance of neighbor-hood SLL, calculated as the average of residential SLLs across the elementary school catchment area, on pre- and postremedia-tion BLLs.

The von Lindern et al. (2003) study found that a large propor-tion of the estimated effect of lowering BLLs at the Bunker Hill Superfund site (BHSS) was associated with community and neighborhood soil lead reductions and consequent dust lead decreases, with a lesser amount associated with soil excavation and replacement in the individual yard. This finding led to the creation of a Box Model (40% indoor dust, 30% yard soil, 30% community-wide) partitioning of children's ingestion of dust and soils for estimates of predicted blood leads at the BHSS site. More recently, structural equations modeling by von Lindern et al. (2016) using BHSS data resulted in a new recommended dust and soil partition scenario of 50/25/10/15 (dust/yard soil/neigh-borhood soil mean/community soil mean) for the BHSS. Thus, these studies also highlight the important contribution of SLLs in neighborhoods and communities to children's BLLs. Notably, the reductions in neighborhood and community SLLs follow from residential soil remediation.

The association between living near a source of lead exposure such as a smelter and elevated BLLs is well documented (U.S. EPA 2013). We identified only a handful of studies examining the effectiveness of interventions to reduce exposure from conta-minated soil on reducing BLLs, however. Yeoh et al. (2008) con-ducted a systematic review and meta-analysis of randomized controlled trials (RCTs) and quasi-RCTs, which was subse-quently updated (Yeoh et al. 2014; Nussbaumer-Streit et al. 2016; Nussbaumer-Streit et al. 2020). The studies included in the review were conducted to evaluate the effectiveness interventions including dust control actions, such as soil excavation and replacement, that were intended reduce children's BLLs. Overall, these reviews found no statistical evidence that these interven-tions were effective in reducing children's BLL. The authors also noted that the evidence specifically pertaining to the effect of soil remediation was limited to two studies reporting contradictory findings (Weitzman et al. 1993; Farrell et al. 1998). The authors also found that the current evidence was insufficient to clarify whether a combination of interventions reduces BLLs.

The effectiveness of soil excavation and replacement actions likely depends on whether ongoing sources of contamination or recontamination are present (Bowers et al. 2014). In a cross-sectional analysis that was not included in the aforementioned systematic review, Lanphear et al. (2003) described a faster decline in BLL among children (6 months to 6 y old) who lived in residences where soil was removed and replaced in compari-son with children who lived in housing where lead-contaminated soil was not remediated. The von Lindern et al. (2003) study, also observational, used mixed-effects models to assess the soil cleanup effort at the BHSS and reported a large reduction in a typical 2-y-old child's mean BLL, i.e., 7:5 lg=dL over the course of the 10-y cleanup. Klemick et al. (2020) used a DiD model to compare the change in the risk of elevated BLL among children living closer vs. farther from lead-contaminated Superfund sites before, during, and after cleanup actions. These authors found that Superfund cleanups reduced the risk of elevated BLL by 13% to 26% among children in their study. In contrast, a study of children living in Philadelphia neighborhoods found that lead in floor dust was associated with children's BLL, but proximity to lead-emitting industries was not (Dignam et al. 2019).

Some covariate information in our data set was lacking or incomplete. We opted to drop the race variable from our models because it was missing for more than half of the children in our study. We evaluated the impact of this decision by comparing our results with and without including race and found that the overall pattern of results, considering magnitude and direction, were sim-ilar across models (Tables S8, S14, and S15). Most housing in our study (98%) was built before 1978, when interior lead-based paint was widely used. We created a variable to indicate the pro-portion of housing built before 1940 within a census tract, which has been demonstrated to be a predictor of lead paint exposure (Klemick et al. 2020); based on a representative sample of U.S. housing units, a relatively large proportion (68%) of units built before 1940 have significant lead hazards from interior paint and dust (Jacobs et al. 2002). Despite the predictive ability of this variable, we recognize that it does not capture the behaviors, extent of interior lead paint damage, or lead paint abatement activities that together ultimately determine exposure in young children. Although the U.S. EPA database included some infor-mation on indoor dust lead loadings, on paint assessment and sta-bilization, and whether a high-efficiency particulate air (HEPA) vacuum was provided to the residents, the incompleteness of these data precluded analysis of the association between of indoor dust lead and SLLs. Lead from soil can infiltrate indoors combin-ing with house dust, which may be composed of lead from multiple sources, including paint and consumer products. Exposure to soil lead depends on activity patterns and the age of the child; i.e., very young children are exposed to relatively more indoor house dust, whereas older, more mobile children may be exposed to soil lead directly in their yards and neighborhoods (U.S. EPA 2013). In addition, children exhibiting pica behaviors may ingest more lead than others at a given SLL. The U.S. EPA (2009a), however, reported that, "the percent of lead from soil [contribut-ing to indoor dust] is not a constant but may range from less than 50% when soil levels are low [<60ppm] to over 90% when soil levels are high [>500ppm]." This evidence generally indicates that soil was the dominant source of lead exposure at the OLSS before remediation. At low postremediation SLLs, the contribu-tion of lead-based paint to indoor dust lead at the OLSS may equal or exceed the contribution of soil to dust lead. However, based on Figure F-11 of the U.S. EPA (2009b) report, just over 10% of homes with yard SLL of <500ppm had indoor dust lead concentrations of >750ppm. This statistic suggests that other sources such as lead-based paint were dominate sources of indoor dust lead even before in remediation some homes.

Another challenge in estimating the association between EBLL and SLL was that not all properties that were linked to a child's BLL had a corresponding SLL. Because properties without SLLs were dropped from the analyses, our inferences about the association between EBLL and SLL are applicable only to properties that met criteria for receiving soil measurements. Nevertheless, the U.S. EPA's faceted approach of working in collaboration with the DCHD, media sources, and even local physicians was developed to assure the successful identification of at-risk children; thus, inference based on our study population is relevant for informing policy decisions to assure protection of children at Superfund sites. A related issue stems from the fact that properties were not selected for soil excavation and replacement at random; instead, priority was given to properties with the highest SLLs and/or with resident children showing elevated BLL. Bias could be introduced because children compared with one another might be differentially selected based on preremediation soil lead and BLLs. To answer our primary research question (i.e., was remediation associated with lowered BLLs), we compared pre- and postremediation ele-vated BLLs on children in the remediation group; children in the comparison group tended to live farther from former smelter and likely differed from those in the remediation group. Accordingly, we also compared the odds of elevated BLL among postremedia-tion samples in the remediation group to corresponding odds in the comparison group (Table 6). The OR for this contrast was positive for both venous and capillary samples, although the association using venous samples was larger in magnitude. This finding indi-cated that the odds of EBLL in postremediation samples among children in the remediation group may not decline to meet the odds among children in the comparison group and was consistent with our understanding that lead is stored in bone and may be released to the blood in the absence of ongoing exposure. The contrasts involving the post-only group indicated that children who lived only in remediated housing had odds of elevated BLL similar to those of children who lived in both unremediated and remediated housing after remediation but higher odds than those of children in the comparison group. We did not have data on duration of resi-dence in the home (i.e., proxy for exposure duration) that would allow us to explore these findings in the context of the predicted decline in BLL after exposure ceases.

In an effort to better separate temporal trends unrelated to reme-diation from the influence of remediation itself, we proposed an analysis that, like a DiD analysis, examines an interaction contrast; but our analysis is not a true DiD and should not be interpreted as implying causal associations or as offering protection from unmeas-ured confounding. Unlike typical DiD data sets, the intervention was distributed over calendar time among subjects in the remedia-tion group. Because we saw no obvious way to mimic that distribu-tion closely among comparison group children, we opted to apply a single temporal cut point, set at the median date of remediation among remediated properties, to all unremediated properties. That date then separated nominal pre- and postremediation BLLs for chil-dren in the comparison group. The interaction analysis that resulted from this assignment indicated that the remediation was associated with a larger reduction in the proportion of children with elevated BLL than expected from temporal trends alone, an association that lends further supporttothe benefits ofsoil remediation.

Our findings lend support to our decision to use EBLL10 for analyses of venous samples, because an inverse association com-paring the odds of EBLL5 before and after 2009 was observed in our data (Table S13). We think that this finding is explained by the relative rarity of confirmatory venous BLLs between 5 and 10 lg=dL before 2012, when the CDC lowered its reference level. Results from our interaction analysis must be interpreted with some caution, however. The single remediation time assumed in the unremediated properties may not appropriately reflect the distribution of remediation times actually experienced by remediated properties. Nevertheless, we believe our approach offers a defensible, though not conclusive, way to probe whether remediation reduces BLLs to a greater extent than would be expected based on a general downward drift over time.

Several other factors present challenges when examining the effect of environmental interventions generally. First, dust gener-ated during soil excavation could temporarily increase the children's blood lead. Second, the effect of remediation could also be mediated via neighborhood exposure. In fact, we documented that neighborhood soil lead was associated with children's BLLs both pre- and postremediation (Table 3; Table S5 and S6). Consistent with von Lindern et al. (2003), our results suggested that after remediation the influence of neighborhood lead contami-nation was a more important risk factor for EBLL than residual yard soil contamination, which was between 200 and 400 ppm in some yard quadrants (and/or drip zones) relative to yards where all areas were <200 ppm. As properties were remediated in the study area, the neighborhood SLLs decreased. This decrease was experi-enced by all children, including those children who did not receive soil remediation at home (i.e., children in the comparison group); their actual lead exposures couldbe decreasing over time as a result of the remediation actions at nearby residences.

The proportion of elevated BLL among children residing in Omaha has declined dramatically since 1999, when the U.S. EPA began soil excavation and replacement actions at the OLSS (U.S. EPA 2009a). We observed an association between SLL and the odds of EBLL after adjustment for year, season, the likely pres-ence of interior lead paint, and children's characteristics (i.e., age and sex). Our findings point to the importance of reducing residential and neighborhood soil concentrations to achieve associated decreases in BLL. Analyses comparing pre- to postreme-diation BLLs showed that the odds of EBLL were reduced after soil excavation and replacement. Our results strongly implicate soil as an important source of lead exposure, support soil reme-diation actions as helpful in reducing highly exposed children's lead burden, and present methodological approaches that address some of the challenges in evaluating the effectiveness of household-based interventions for lead exposure. Although we hypothesize that the pattern of associations with postremediation BLLs may reflect successful efforts to educate parents regarding preventing lead exposure, we did not formally test that hypothesis. Efforts to reduce missing and inaccurate data collected for surveillance purposes, to document soil lead sampling strategies (including those that evolve with community engagement), and to record information about concurrent exposure-reducing inter-ventions coordinated across federal, state, and local agencies will enhance the ability of researchers to evaluate the success of mul-tifaceted programs that rely on more than one strategy to lower children's BLLs.

Acknowledgments

The authors thank C. Yund for her efforts in managing this study prior to her retirement in 2017. The authors also thank S. Deflorio-Barker, K. Sullivan, and H. Klemick for comments on drafts of this paper. In addition, the authors would like to thank K. Park for his assistance addressing reviewer comments and producing the final figures for inclusion in the manuscript. Finally, the authors thank U.S. EPA Region 7 and the Douglas County Health Department for providing environmental and blood lead surveillance data for this analysis.

This work was supported, in part, by an appointment to the Internship/Research Participation Program at Office of Research and Development, U.S. EPA, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. EPA, and, in part, by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences (ZIA-ES103126).

This manuscript has been reviewed by the U.S. EPA and approved for publication. The views expressed in this manuscript are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA.

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