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HEALTH CONSULTATIONAnalysis of Risk Factors for Childhood Blood Lead Levels El Paso, Texas, 1997-2002EL PASO COUNTY METAL SURVEYEL PASO, EL PASO COUNTY, TEXASSUMMARY AND STATEMENT OF ISSUESThis report provides the results of our analysis of risk factors for elevated blood lead levels in children living in El Paso, Texas. To copy selected text, right click to Copy or choose the Copy option under your browser's Edit menu.
Text copied in this manner can be pasted directly into most documents with formatting maintained. Interpretation of a 95% confidence level for the proportion of the population that answered,"yes" to the above question. So if we gathered a variety of samples size n=100 95% should contain the true population mean. Environmental Protection Agency (EPA) Region 6, in cooperation with the Texas Department of Health (TDH), the Agency for Toxic Substances and Disease Registry (ATSDR), the Texas Commission on Environmental Quality (TCEQ), and local city and county officials have been investigating soil contamination in residential yards in El Paso, Texas.
So if we took any number of sample size of n=100 95% of the results would fall in this interval. People in the area have expressed concern that the levels of lead in the soil in the area may be associated with elevated blood lead levels in children. In response to these concerns, EPA, TCEQ, the City of El Paso, and various citizens' groups asked TDH and ATSDR to evaluate whether the soil lead level in El Paso is a risk factor for elevated blood lead levels in children.TDH performed two separate analyses to evaluate the significance of soil lead levels to childhood blood lead levels.
We obtained this information in the form of data sets from the Texas Childhood Lead Poisoning Prevention Program (CLPPP), the U.S.
In the second analysis, we matched the soil lead and blood lead data sets to assess whether soil lead levels significantly predict whether children (0-6 years of age) have elevated blood lead levels while controlling for the other risk factors.We found a significant association between soil lead and blood lead test results.
Although we found a significant association regardless of whether we modeled the blood lead as a binary or a continuous variable, there was a difference in the extent of soil lead's ability to predict blood lead levels in these models. Environmental Protection Agency (EPA) Region 6, in cooperation with the Texas Department of Health (TDH), the Agency for Toxic Substances and Disease Registry (ATSDR), the Texas Commission on Environmental Quality (TCEQ), and local city and county officials have been investigating soil contamination in residential yards in El Paso, Texas. Much of the history pertaining to the study of this contamination has focused on a smelter that occupies 123 acres of a 585-acre property along the Rio Grande, near the border between the United States and Mexico. In November 2002, EPA began a time-critical removal action to remove soil from yards in residential neighborhoods with the highest levels of lead contamination. The site is now being evaluated for listing on EPA's National Priorities List for Uncontrolled Hazardous Waste Sites (NPL). However, people in the area have expressed concerns that soil lead levels might affect childhood blood lead levels.
In response to these concerns, EPA, TCEQ, the City of El Paso, and various citizens' groups asked TDH and ATSDR to use available information to evaluate whether soil lead in El Paso is a risk factor for elevated blood lead levels in children.Rationale for the AnalysisBecause lead is ubiquitous in the environment, children may be exposed to lead from many different sources and through many different pathways. For many children with blood lead levels that are low but still elevated, identifying a single predominant source or pathway is not always possible. Some of the most common sources for lead exposure include lead-based paint, soil and dust, drinking water, occupations and hobbies of parents or care givers, air, and food. Other factors that have been associated with elevated blood lead levels include a child's age, socioeconomic status, and distance to a point source of lead (1-16). Because lead is believed to pose the greatest danger to young children, we focused our analyses on children ages 0 to 6 years old.To assess whether soil lead is a predictor of elevated blood lead levels in children, we conducted two separate analyses.
For the Greater El Paso analysis, we used multiple logistic regression analysis to identify the predictors or risk factors for elevated blood lead levels in the 1997-2002 Texas Childhood Lead Poisoning Prevention Program (CLPPP) data for the greater El Paso area.
We also used ordinary least squares regression analysis to model blood lead level as a continuous variable to evaluate whether soil lead levels contributed statistically to blood lead levels in this data set and, if so, to what extent.METHODOLOGYOverviewWe used Geographical Information System (GIS) mapping to bring together several sources of information to assess whether a statistical relationship exists between various risk factors and childhood lead poisoning in El Paso, Texas.
We obtained this information in the form of data sets from the Texas Childhood Lead Poisoning Prevention Program (CLPPP), the U.S.
Statistical Analysis System software was used for all the statistical calculations performed in our analyses.Data UsedThe Texas Childhood Lead Poisoning Prevention Program has maintained surveillance data for all blood lead test results reported on children throughout the state of Texas since 1996. From CLPPP, we obtained data for all blood lead results for children tested between 1997 and 2002 who had El Paso listed as their city of residence.
We limited the data set to children between 0 and 6 years of age and identified those children who had an elevated blood lead level using the CDC's definition of a confirmed elevated blood lead level. If either 1) a child's first test was from a venous sample and was elevated or 2) if a child screened elevated with a capillary test and was then retested within 12 weeks was again elevated then the child was considered to have a confirmed elevated blood lead level. To have a single record for each child, we removed duplicate and subsequent repeated samples of the same child. The TDH GIS group successfully identified the geographic coordinates (geocoded) of the residences of 38,725 (98.9% of the original 39,145) of these children. Of the 38,725 children whose residences were geocoded, we identified a subset of 38,258 children who either screened negative or had a confirmed elevated blood lead level (this excludes unconfirmed positive tests).
Census, enabling them to obtain information on the neighborhoods in which the children lived. They were able to identify the percentage of people living below the national poverty level for the block group each child lived in at the time of testing. They were also able to identify for each child's census tract the percentage of the population that had emigrated from Mexico between 1990 and 2000.
The GIS group then added additional data fields to the CLPPP data for poverty, Mexican immigration, and distance to the smelter.We used age of housing, obtained from the El Paso County Appraisal District, as a surrogate for whether a child's residence might contain lead-based paint. The concentration of lead in paint decreased after the 1940s, and the use of interior lead-based paint decreased during the 1950s and 1960s (exterior lead-based paint was widely available until the mid-1970s). The TDH-GIS group geocoded El Paso County appraisal district data for the year 2000, matched that data to CLPPP data, and added a data field to the CLPPP data for the age of the child's residence at the time of blood lead testing. The TDH-GIS group matched that soil data to the 1997 to 2002 CLPPP data and added a field in the data set for the average soil lead level for each residence.
We were able to directly match 497 children from the CLPPP data set to the residential soil data; 484 had either screened negative or had a confirmed elevated blood lead test. The analytical data set for all of El Paso consisted of 38,258 children for the years 1997 through 2002.
The number of children in that data set totaled 484.Greater El Paso AnalysisFor the 38,258 children in the greater El Paso area in the augmented CLPPP data set, we calculated simple descriptive statistics for both the categorical and continuous variables in the data (Tables 2 and 3). We created crosstabulation tables for potential socioeconomic risk factors versus the percentage of elevated blood lead levels found in children within groupings (Table 4) and then created the associated graphs (Appendix B, Figures 1 through 7).
The potential risk factors for elevated blood lead levels in children assessed in this analysis included the following.
Census Percentage of population in census tract who immigrated from Mexico 1990-2000 2000 U.S.


Census Distance to smelter Calculated using GIS In preparation for the regression analysis, we created correlation and scatterplot matrices for the potential predictor variables for data visualization and assessment of predictor correlations (Table 5).In making use of statistical modeling to assess the relationship between an outcome and predictor variables, it is generally assumed that the subjects used in the model are a sample taken from some underlying population of subjects. Thus, we limited our use of statistical models to data on Medicaid children only (N=25,149). We compared descriptive statistics for the potential predictor variables in these two data sets to confirm their similarity and then performed a multiple logistic regression analysis to assess which potential risk factors were significant predictors of elevated blood lead levels in these children.We began with an initial full model for the multiple regression analysis complete with the five potential risk factors, suspected two-way interactions, and three-way interactions.
We modeled these predictor variables as grand-mean-centered continuous variables with the exception of the child-age variable. Child's age was modeled as a quadratic predictor because this and other studies demonstrate a peak near 3 years of age in the likelihood of a child having an elevated blood lead result. Rather than use an automated method of variable elimination; we sequentially assessed a series of models, which eliminated first the three-way interactions and then the two-way interactions before assessing the statistical significance of individual variables within the model. By doing this, we ensured that a hierarchically well-formulated model was maintained and assessed throughout the elimination procedure. The model produced using the model-development data set was then rerun using the model-validation data set, and the parameter estimates for the variables were compared for consistency. We then applied the final form of the multiple logistic regression model to the entire augmented data set to calculate parameter estimates, p-values, odds-ratios, and their 95% confidence intervals (Table 6). We calculated the odds ratios for the continuous variables by assessing what the odds ratio was given a specific change for each continuous variable. EPA and had either screened negative or confirmed positive for elevated blood lead, we calculated simple descriptive statistics for both the categorical and continuous variables in the data (Tables 7 and 8). We then created crosstabulation tables of the identified risk factors for the greater El Paso area versus the percentage of elevated child blood lead level status (Table 9) and produced their associated histograms (Appendix B, Figures 8 through 15).Using the contingency table for grouped soil lead levels versus elevated childhood blood lead levels, we calculated a Cochran-Mantel-Haenszel statistic to test for a linear trend of soil lead on elevated blood lead status. From this we calculated assessment-of-fit statistics and the unadjusted odds ratio for a 500-ppm change in soil lead along with 95% confidence intervals (Table 11).Using a multiple logistic regression model to control for covariates, we then tested whether a statistical association existed between residential soil lead levels and elevated blood lead status for children in the CLPPP data set matched to those residences.
We performed these calculations while controlling for the risk factors previously identified for the greater El Paso area.
Using this model, we calculated an adjusted odds ratio and 95% confidence intervals for a 500-ppm increase in residential soil lead for the outcome of elevated child blood lead status (Table 12). We performed a Hosmer and Lemeshow Goodness-of-Fit Test to assess the overall fit of the model.Additionally, although logistic regression is not amenable to a classic assessment for the coefficient of determination, or R-squared value, there is a statistic later referred to as the "maximum rescaled R-square" that can be applied to more general linear models. We calculated this generalized coefficient of determination for the models developed and used it to assess the predictive ability of the models.Modeling the natural logarithm of child blood lead levels, we used ordinary least squares regression to assess whether there was a statistical association between residential soil lead results and child blood lead levels as a continuous outcome.
We used this multiple linear regression to calculate both unadjusted and adjusted estimates of the change in child blood lead values with changes in soil lead level. We calculated 100 ppm, 500 ppm, and 1,000 ppm changes from an initial 250 ppm residential soil lead level while controlling for the risk factors identified for the greater El Paso area. We used diagnostic plots of model residuals to identify whether any serious problem in the linear model was apparent and performed multilevel modeling to determine whether this analytical approach altered the results of the linear modeling.RESULTSGreater El Paso ResultsBasic summary statistics for each of the potential risk factors used in the model are provided in Table 2.
The percentage of elevated child blood lead results was highest in the 3-year olds (Figure 1), and a slightly higher percentage of males than females had elevated blood lead levels (Figure 2). There appeared to be a trend of higher percentages of elevated child blood lead results with increasing housing age (Figure 3) and block-group poverty level (Figure 5). Census tracts with higher percentages of Mexican immigration in the previous decade seemed to have had higher rates of elevated blood lead levels (Figure 4).
The distance of residence from smelter versus percentage of elevated blood lead suggests children living closer to the smelter had higher percentages of elevated blood lead test results than children living farther away from the smelter (Figures 6 and 7).
The contribution of a child's sex was not statistically significant, but was retained in the model because of the believed clinical importance of sex on elevated blood lead levels. Including sex in the model neither changed the parameter estimates for the other variables nor increased their variance inflation factors.
The multiple logistic regression modeling did not suggest any significant interactions between the assessed predictor variables.
Thus, the odds ratios seen in Table 6 are all adjusted odds ratios for these predictors of elevated blood lead status. Because the predictor variables were grand-mean centered, the parameter estimates and odds ratios can be interpreted as what effect a given predictor variable has on elevated blood lead status for average measures of all other predictors. This indicates that for a child of average age, neutral sex, average poverty in block group, average Mexican immigration in census tract, and average distance from smelter for this data set of greater El Paso, there is a 26.9% increase in the odds of the child having an elevated blood lead result associated with that 20-year increase in the age of the child's housing.
As we see in Table 6, "distance from smelter" is the only parameter (or coefficient) estimate that has a negative value.
This indicates that as distance from smelter increases, the odds of a child having an elevated blood lead level decreases for this data set.
Even though all these variables were significant statistically, the R-squared value of the overall model was only 0.07 and indicates the model did not explain much of the total variation in specific blood lead levels in the children. Multilevel modeling for this same data set produced very similar results.Results for Children Matched to Soil Data AnalysisDescriptive statistics for those children whose residences matched EPA soil-sampled residences can be seen in Tables 7 and 8. One-year-olds were the largest single age group, and about 40% of the children were under age 2. The median age of housing was 66 years, and the median value for poverty in a child's block group was 40.6%. The average child lived 1.9 miles from the metals smelter and lived in a census tract where 13% of the population had emigrated from Mexico in the previous decade.
Twenty-three percent of these children lived at residences with soil lead levels above 500 ppm in 2003. The soil lead values of the residences of the 484 children in the Soil-Matched analysis ranged from 6 ppm to 2,180 ppm. The age group with the highest percentage of elevated blood lead results was 2-year olds with 6% of the children having a confirmed elevated blood lead status. No trend between either poverty in a block group or Mexican immigration in a census tract versus elevated blood lead status was readily apparent. Conversely, the percentage of children with elevated blood lead status did seem to increase with increasing soil lead levels. Of the children tested between 1997 and 2002 whose residences tested above 600 ppm for lead in 2003, 8.3% had elevated blood levels (see Figure 13). When viewing this comparison by sex, 5 of the 38 (or 13.2%) male children had elevated blood lead status (Figure 14).
We calculated a test for linear trend using the Cochran-Mantel-Haenszel Statistic between these soil lead ordered groups and elevated blood lead status.


Housing age versus soil lead level showed a coefficient of correlation, or R-value, of 0.51. The parameter estimate for soil lead was highly significant upon statistical testing with a p-value of 0.0002. Because the model controlled for all the identified confounders, it obtained by definition the most valid, or accurate, point estimate of soil lead's coefficient and corresponding odds ratio for these data.
Each of these covariates could potentially be dropped from the model, but the point estimate obtained from the full model was considered to be the "gold-standard" for soil lead's association with elevated blood lead. In fact, poverty and Mexican immigration could be dropped from the model without changing the point estimate for soil lead, but no improvement in precision was obtained. Housing age could not be dropped from the model without changing the point estimate toward its unadjusted value. We attempted to address this by modeling the male children and female children separately, and the results can be seen in Table 13. These results suggest a difference existed between sexes for the adjusted odds ratios of soil lead values versus elevated blood lead status in this data set. Similar to the results in the larger data set, the R-squared value for this model was low, equaling 0.08.
Multilevel modeling for this same data set produced nearly identical results.DISCUSSIONTo properly interpret the results of these analyses, it is important to put the findings into a larger context and consider what has been previously shown in other studies in other locations. Although this tenet applies to the interpretation of individual studies in general, it is especially important for these findings because the statistical analyses conducted in this report were performed on data sets that were originally collected for other purposes. Reviews or meta-analyses of epidemiologic studies of soil lead's influence on children's blood lead levels came to the same conclusions (28;29).
The only recent study that identified a population of children whose blood lead levels were seemingly unaffected by soil lead levels suggested this lack of association might be attributable to a low bioavailability of lead in soil in a mining community in Sweden. Therefore the interpretation of the predictor variables is accomplished using odds ratios.
An odds ratio of one (1.0) would indicate that the data do not suggest a relationship between the variable and the outcome. In addition, because a certain amount of chance or variation can be expected in relationships between variables and outcomes, statisticians have developed methods to address this uncertainty. One method is to calculate the 95% confidence intervals (95% CI) to help determine whether the odds ratio is significantly different than one. The 95% CI is the range of estimated odds ratio values that has a 95% probability of including the true odds ratio for the population. In the analysis of the soil-matched data, we found a statistically significant association between soil lead and blood lead test results regardless of whether we modeled the blood lead data as a binary or a continuous outcome.
It is clear that many of the important components of soil lead's contribution to a child's blood lead status were not included in our models.
The time difference between the time that the blood lead tests were performed (1997 to 2002) and when the soil lead levels were measured (2003) could lead to exposure misclassification because assumptions are made on the status of soil lead levels measured in the yards for the time period when the children would have come into contact with the soil. For these analyses, we assumed that the soil samples collected in 2003 were representative of the soil levels to which the children would have been exposed at the time that the blood lead tests were performed.CONCLUSIONSBased on the analysis of the soil-matched data, there was a significant association between soil lead and blood lead test results regardless of whether we modeled the blood lead data as a binary or a continuous outcome. Shire, MS Senior Staff Epidemiologist Environmental Epidemiology and Toxicology Division Texas Department of HealthJohn F. Relationship of sociodemographic factors to blood lead concentrations in New Haven children.
Tijuana childhood lead risk assessment revisited: validating a GIS model with environmental data. Contributions of risk factors to elevated blood and dentine lead levels in preschool children.
Demographic risk factors associated with elevated lead levels in Texas children covered by Medicaid. An investigation of environmental racism claims: testing environmental management approaches with a geographic information system.
Mapping for prevention: GIS models for directing childhood lead poisoning prevention programs. Geographic distribution of mean blood lead levels by year in children residing in communities near the Bunker Hill lead smelter site, 1974-1983.
Lead and cadmium exposure from contaminated soil among residents of a farm area near an industrial site.
The impact of soil lead abatement on urban children's blood lead levels: phase II results from the Boston Lead-In-Soil Demonstration Project.
The effect of soil abatement on blood lead levels in children living near a former smelting and milling operation. Lead sources, behaviors, and socioeconomic factors in relation to blood lead of Native American and white children: a community-based assessment of a former mining area.
Associations between soil lead and childhood blood lead in urban New Orleans and rural Lafourche Parish of Louisiana.
The contribution of lead-contaminated house dust and residential soil to children's blood lead levels.
The effect of interior lead hazard controls on children's blood lead concentrations: a systematic evaluation [Record supplied by publisher]. Interrelations of lead levels in bone, venous blood, and umbilical cord blood with exogenous lead exposure through maternal plasma lead in peripartum women. Iron deficiency associated with higher blood lead in children living in contaminated environments.
The association of lead-contaminated house dust and blood lead levels of children living on a former landfill in Puerto Rico.
Influence of nutrient intake on blood lead levels of young children at risk for lead poisoning.



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