OCTOBER 3, 2007 EPA 260-R-08-003



## FINAL REPORT FOR THE PILOT STUDY OF TARGETING ELEVATED BLOOD-LEAD LEVELS IN CHILDREN

U.S. Environmental Protection Agency 1200 Pennsylvania Avenue NW (7404T) Washington D.C. 20460

### Disclaimer

The U.S. Environmental Protection Agency through its Office of Environmental Information funded and managed and colla borated with the Office of Pollution Prevention and Toxics in the research described herein und er EPA Contract Number EPW-04-021 to Battelle. It has been subject to an administrative review but does not necessarily reflect the views of the Agency. No official endorsement should be inferred. EPA does not endorse the purchase or sale of any commercial pr oducts or services.



### Acknowledgements

The EPA acknowledges the organizations whose contributions made this report possible including the Lead Poisoning Prevention Branch at the Centers for Disease Control and Prevention, the Childhood Lead Poisoning Pr evention Program at the Massachusetts Department of Public Health, and the Offi ce of Healthy Homes and Lead Hazard Control at the U. S. Department of Housing and Urban Development.i TABLE OF CONTENTS

Page

EXECUTIVE SUMMA RY .............................................................................................................. ................ v

1.0 INTROD UCTION............................................................................................................... ............... 1 1.1 Background and Pu rpose of Study ..................................................................................... 1 1.2 Study Ob jectives ........................................................................................................... ...... 2 1.2.1 Objective 1 – Combine and Manage Multiple Da ta Sources ................................. 2 1.2.2 Objective 2 – Conduct Analyses to Identify Predictive Variables and Model Children’s Blood-Lead Levels ..................................................................... 2 1.2.3 Objective 3 – Develop Visualization Tool to Graphically Model Predicted Blood-Lead Levels ................................................................................................. 2

2.0 STUDY METHODOLOGY......................................................................................................... ....... 3 2.1 Genera l Approach.......................................................................................................... ..... 3 2.2 Data Management........................................................................................................... .... 3 2.3 Descriptive Data Analyses .................................................................................................. 4 2.4 Development of Multivar iate Statisti cal Model s ................................................................ 10 2.4.1 Statistical Models for the Broad Coverage – Low-Res olution Model................... 10 2.4.2 Statistical Models fo r the High-Resolution Model within Massachusetts............. 12

3.0 DATA SOURCES AND DATABASE DEVEL OPMENT ................................................................. 14 3.1 Children’s Blood- Lead Measur ements .............................................................................. 14 3.2 Demogr aphic Da ta .......................................................................................................... .. 16 3.3 Environm ental Data........................................................................................................ ... 21 3.3.1 Concentrations of Lead in Air............................................................................... 22 3.3.2 Toxics Releas e Inventor y Data............................................................................ 23 3.3.3 Water Q uality Da ta............................................................................................... 24 3.4 Progra mmatic Data ......................................................................................................... .. 24 3.4.1 Programmatic Funding Vari ables......................................................................... 25 3.4.2 EPA Region .......................................................................................................... 25 3.4.3 Housing Inspection Data (Massachusetts) .......................................................... 25 3.5 Data Linkages ............................................................................................................. ...... 27

4.0 EXPLORATORY DATA ANALYSES ............................................................................................. 29 4.1 Relationship between National Blood -Lead Data and Expl anatory Vari ables.................. 29 4.2 Relationship between Local Blood- Lead Data and Expl anatory Variables....................... 43

5.0 STATISTICAL MO DELING R ESULTS .......................................................................................... 47 5.1 Low-Resolution Modeling Re sults..................................................................................... 47 5.2 High-Resolution Modeling Re sults.................................................................................... 59

6.0 GRAPHICAL PRESENTATION OF MODELING RESULTS ......................................................... 70 6.1 Maps of Observed and Pr edicted Blood-Lea d Outcomes ................................................ 70 6.2 Visualization Tool Deve lopment........................................................................................ 70

7.0 DISCUSSION A ND FUTURE WORK ............................................................................................ 77 7.1 Major Findings............................................................................................................ ....... 77 7.2 Comparison of Nati onal Results and NHAN ES ................................................................ 78 7.3 Data Issues ............................................................................................................... ........ 80 7.3.1 Biases fr om Geocoding........................................................................................ 80 7.3.2 Reporting Limits in Surveill ance Da ta.................................................................. 80 7.3.3 Selection Bias in Surveilla nce Da ta ..................................................................... 81 7.3.4 Limitations of Ecological Models fo r Predicting Within-Area Relationships......... 81ii Page

7.3.5 Use of 2000 Census Data and Other Time Invariant Data as Predictors............ 82 7.4 Model Vali dation Issues ................................................................................................... . 82 7.5 Other Recommendations for Immediate Futu re Wo rk...................................................... 84

8.0 REFE RENCES ............................................................................................................... .............. 87

Appendix A Exploratory Analys is Summary Pages.............................................................................A-1

Appendix B Massachusetts Data: Explor atory Analysis Su mmary P ages.........................................B-1

Appendix C Detailed Exploratory Analyses of 95 th and 99 th Percentile Variables In National Models .......................................................................................................... C -1

Appendix D Detailed Discussion of Na tional Explorator y Analyses ................................................... D-1

Appendix E Detailed Discussi on of Massachusetts Expl oratory Anal yses .........................................E-1

Appendix F U.S. Counties and Massac husetts Census Trac ts with Highest Pr edicted BLLs ............F-1

Appendix G Detailed Maps of Nation al and State Model Output s...................................................... G-1

Appendix H Data Dictionaries for Natio nal and Massachusetts Database s....................................... H-1

LIST OF TABLES

Table 3-1 Initial Variables for Anal ysis Created From the 2000 C ensus .......................................... 17 Table 4-1 Summary of Exploratory Analysis Fi t as shown by -2 Log Likelihoods for Pr(PbB ≥ 5 μg/dL) Models, Na tional Data ........................................................................................... 31 Table 4-2 Summary of Exploratory Analysis Fi t as shown by -2 Log Likelihoods for Pr(PbB ≥ 10 μg/dL) Models, Na tional Data ........................................................................................... 34 Table 4-3 Summary of Exploratory Analysis Fi t as shown by -2 Log Likelihoods for Pr(PbB ≥ 15 μg/dL) Models, Na tional Data ........................................................................................... 37 Table 4-4 Summary of Exploratory Analysis Fi t as shown by -2 Log Likelihoods for Pr(PbB ≥ 25 μg/dL) Models, Na tional Data ........................................................................................... 40 Table 4-5 Summary of Log-likelihood Ratios from each Model Fit to all Potential Explanatory Variables, Massac husetts Data ........................................................................................ 44 Table 5-1 Summary of Variables Included in Final National Mult ivariate Model............................... 50 Table 5-2 Model 1 (Proportion ≥ 5 μ g/dL) Parameter Estimates for Mu ltivariate National Model ..... 51 Table 5-3 Model 2 (Proportion ≥ 10 μ g/dL) Parameter Estimates for Multivariate National Model ....................................................................................................................... ...... 53 Table 5-4 Model 3 (Proportion ≥ 15 μ g/dL) Parameter Estimates for Multivariate National Model ............................................................................................................... ... 55 Table 5-5 Model 4 (Proportion ≥ 25 μ g/dL) Parameter Estimates for Multivariate National Model ............................................................................................................... ... 57 Table 5-6 Summary of Variables Included in Final Massachusetts Mu ltivariate Model.................... 61 Table 5-7 Massachusetts Multiv ariate Model Estimate s................................................................... 62iii LIST OF FIGURES

Figure 5-1 Histograms of Residuals from Fitted National Multiv ariate M odel 1................................. 52 Figure 5-2a Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 5 μ g/dL. 52 Figure 5-2b Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 5 μ g/dL (Logit Scale). ............................................................................................. 52 Figure 5-3 Histograms of Residuals from Fitted National Multiv ariate M odel 2................................. 53 Figure 5-4a Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 10 μ g/dL. ................................................................................................................. 53 Figure 5-4b Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 10 μ g/dL (Logit Scale). ........................................................................................... 53 Figure 5-5 Histograms of Residuals from Fitted National Multiv ariate M odel 3................................. 56 Figure 5-6a Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 15 μ g/dL. ................................................................................................................. 56 Figure 5-6b Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 15 μ g/dL (Logit Scale). ........................................................................................... 56 Figure 5-7 Histograms of Residuals from Fitted National Multiv ariate M odel 4................................. 58 Figure 5-8a Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 25 μ g/dL. ................................................................................................................. 58 Figure 5-8b Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 25 μ g/dL (Logit Scale). ........................................................................................... 58 Figure 5-9 Histograms of Residuals from Fi tted Massachusetts Mult ivariate M odel 1 ...................... 65 Figure 5-10 Plots for Predicted versus Observed Va lues with 45° line from Fitted Massachusetts Multivariate Model 1.......................................................................................................... 6 5 Figure 5-11 Histograms of Residuals from Fi tted Massachusetts Mult ivariate M odel 2 ...................... 66 Figure 5-12 Plots for Predicted versus Observed Va lues with 45° line from Fitted Massachusetts Multivariate Model 2.......................................................................................................... 6 6 Figure 5-13 Histograms of Residuals from Fi tted Massachusetts Mult ivariate M odel 3 ...................... 67 Figure 5-14a Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 5 μ g/dL.................................................................................................................... 67 Figure 5-14b Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 5 μ g/dL (Logit Scale) .............................................................................................. 67 Figure 5-15 Histograms of Residuals from Fi tted Massachusetts Mult ivariate M odel 4 ...................... 68 Figure 5-16a Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 10 μ g/dL .................................................................................................................. 68 Figure 5-16b Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 10 μ g/dL (Logit Scale) ............................................................................................ 68 Figure 5-17 Histograms of Residuals from Fi tted Massachusetts Mult ivariate M odel 5 ...................... 69 Figure 5-18a Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 15 μ g/dL .................................................................................................................. 69iv Figure 5-18b Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 15 μ g/dL (Logit Scale) ............................................................................................ 69 Figure 6-1 Observed and Predicted Proporti on of Children with Blood-Lead Levels ≥ 10 μ g/dL in the U.S. by Coun ty, 2000 and 2005........................................................................................ 71 Figure 6-2 Observed and Predicted Proporti on of Children with Blood-Lead Levels ≥ 10 μ g/dL in Region V by County, 2000 and 2005 ............................................................................ 72 Figure 6-3 Observed and Predicted Proporti on of Children with Blood-Lead Levels ≥ 10 μ g/dL in Massachusetts by Census Tract, 2000 and 20 05......................................................... 73 Figure 6-4 Response Surface of Predicted Geom etric Mean Blood-Lead Concentration Across the State of Illinois from the Visualization Tool ................................................................. 75 Figure 6-5 Time Series Plot of Observed and Predicted Geometric Mean Blood-Lead Concentration in Cook County Illi nois from the Vis ualization Tool ................................... 76 Figure 7-1 Comparison of National Su rveillance Data to NHANES Data .......................................... 79v EXECUTIVE SUMMARY

This pilot study seeks to develop statistical models to predict risk of childhood lead poisoning within specified geographic areas based on a comb ination of demographic, environmental, and programmatic information sources. Exposure f actors associated with childhood lead poisoning were investigated within census tracts for a co mmunity-focused set of models in Massachusetts, as well as within counties across the United States in a series of national models. Aggregated summary measures of the pr oportion of children screened at or above 5, 10, 15 and 25 μ g/dL within defined geographic areas (census tracts and counties) were used as the response variable in the statistical models. These summary meas ures were constructed at 3-month (quarterly) intervals from 1995 through 2005, in counties across the nation using data from CDC’s National Surveillance Database, and from 2000-2006 in ce nsus tracts within the Commonwealth of Massachusetts based on data prov ided by the Massachusetts Depart ment of Public Health.

The results of this study suggest that longitudinal predictive m odels can be developed at the county level across the nation, based on the use of quarterly summary information from CDC’s National Surveillance Database, a nd at the census-tract level with in states that have a long history of universal screening a nd reporting, such as Massachusetts . These models can be used to describe how risk of childhood lead poisoning changes over time within different regions of the country, as well as within small geographic areas within states (e.g., counties) and even smaller geographic areas within counties (e.g., census tracts). They can be used to predict the risk of childhood lead poisoning in counties (or census tr acts) with little or no surveillance data, and also can be used to identify those counties (or census tracts) that are at highest risk at the end of the period of observation.

The statistical model chosen (a random effect s model with separate intercepts and slopes estimated within each county or census tract) also allows rank ing of geographic areas based on the rate of decline over time after accounting for the fixed-effects variables of the model (although only among those areas that provided ad equate surveillance data). These randomeffects models were fit to the exceedance proporti ons within the context of a logistic regression model. Within the context of the Broad-Base d National Model, these random effects allow EPA to identify those counties that are experiencing a more rapid reduction in risk of childhood lead poisoning over time (to identify best practices ) and those counties that are experiencing a significantly less rapid dec line over time (to identify areas in need of additional attention and resources for combating lead poisoning), af ter already accounting for the demographic, programmatic, and environmental factors included in the multivariate model.

Within the series of national models at the county level of geographi c specificity, the data suggest that there are signif icant differences in the dist ribution of childhood blood-lead concentrations among the different regions of th e country, and that the manner in which these distributions change over time and are impacted by seasonality also is regionally specific. The risk of childhood lead poisoning had a statistica lly significant downward trend over time in all areas of the country.

After accounting for these regional differences, a number of demographic, environmental, and programmatic variables were found to be highly predictive of childhood blood-leadvi concentrations among the different response variable s modeled within this project. The specific variables that were found to be predictive with in the multivariate models varied based on the response variable; however, there were certainly so me variables that were found to be predictive in multiple models. In addition to various census demographic variables that were identified in previous risk modeling efforts (e.g., age of housing, percent single parent families, race/ethnicity), air modeling data, variables constructed from EPA’ s Safe Drinking Water Information System, and programmatic funding information from HUD and CDC were found to be highly predictive in the multivariate models.

Within the context of the high-resolution mode l developed using data from the Commonwealth of Massachusetts, a highly signi ficant downward trend in the ri sk of childhood lead poisoning also was identified among the five models develo ped. Due to a very small number of children observed at or above 25 μ g/dL within Massachusetts over the 2000-2006 period of observation, we were unable to fit this sixth model. Afte r accounting for the long-term reduction over time and seasonality using similar methods that were employed in the Broad-Based National Model, only the demographic and programmatic variables were included in the multivariate models for risk of childhood lead poisoning at the census-tract level. Of particul ar interest were the variables that described the pr oportion of housing units within each census tract that were found to be in compliance and out of compliance with th e Massachusetts Standard of Care. In all five of the multivariate models, the risk of childhood lead poisoning was significantly reduced as the proportion of housing units in compliance increased within a census tract. In addition, for the last two models (which predicted propor tion of children at or above 10 and 15 μ g/dL), the risk of childhood lead poisoning increase d significantly as th e proportion of housing units out of compliance increased within a census tract.

The observed and predicted values from the multivariate models (including predicted values where there were no observed surv eillance data) were us ed to generate static maps using ArcView software, and were loaded into a customi zed dynamic visualization t ool that allows users to interact with the modeling results to asse ss how risk of childhood lead poisoning changes over time within specific regions of the country. This tool will help EPA and others identify areas that remain at risk for childhood lead poisoning as we approach the 2010 goal of elimination of this preventable adverse health outcome.1 1.0 INTRODUCTION

1.1 Background and Purpose of Study

Over the past 15 years, various childhood l ead poisoning prevention programs (CLPPPs) throughout the United States have conducted analyses of their screening data to develop “risk indices,” or mathematical models for predicti ng the prevalence of ch ildhood lead poisoning in different geographic areas within their regions of concern. These modeling efforts generally are intended to characterize the extent of the pr evalence of childhood lead poisoning within their geographic areas and to support the development of targeted screening and outreach plans in order to reach the 2010 goal of eliminating childhood lead poisoning throughout the United
States.

To date, the majority of modeling efforts ha ve focused on combining blood-lead testing information and demographic data available from the U.S. Census. Previous studies have combined childhood surveillance data (aggregated at the zip-code or censu s-tract level) with demographic predictor variables from the Ce nsus Bureau for the purposes of targeting geographic areas at higher ri sk of childhood lead poisoning (M iranda, Dolinoy, and Overstreet 2002; Miranda et al. 2005; Strauss et al. 2001a). These studies have led to recommendations for using age of housing and percent of populat ion below the poverty line for targeting neighborhoods that may be of increased ri sk for childhood lead poisoning (CDC 1997). Numerous studies also have been used to document the relationship between children’s bloodlead concentrations and measures of lead in residential environmental media (dust, soil, air, water, and food) (HUD 1995; Lanphear et al. 1998; Strauss et al. 2001b). These studies have contributed to EPA and HUD regul ations and policies for identif ying and reducing residential childhood lead exposures (24 CFR Part 35; 40 CFR Part 745; 40 CFR Part 745; U.S. Department of Housing and Urban Development September 15, 1999). Other studies have combined bloodlead surveillance data with programmatic inform ation on housing units treated to determine the positive impact of housing-based intervention programs (Strauss et al. 2006).

The goal of this study is to explore models base d on a hierarchical combin ation of demographic, environmental, and programmatic information sour ces in order to predict the number of children at risk of elevated blood-lead levels for a gi ven geographic area. While the models are highly dependent on available data, this study provi des a statistical methodology that combines each data source in an appropriate manner, adjusting for global and local tren ds over time. In doing so, the models build upon concepts of hierar chical modeling and longitudinal data analysis.

As EPA, CDC, and other federal and state agencies pr epare to meet the 2010 goal of eliminating childhood lead poisoning, this pilo t study of integrating several di fferent types of data sources hopefully improves the predictive power of models th at rely on a single information source. This allows for more efficient targeting of those geographic areas that n eed the most help in eliminating childhood lead poisoning.2 1.2 Study Objectives

1.2.1 Objective 1 – Combine and Ma nage Multiple Data Sources

The first objective of the study was to combine multiple sources of information in order to assess the impacts of various factors on children’s blood-lead levels . The study had to obtain and manage data relating to blood-lead levels, envi ronmental exposure, demo graphic characteristics, and programmatic support to state and local childhood lead-poisoning prevention efforts. Missing, incomplete, or error-prone data were identified for each data source and steps were taken to resolve data problems. Databases were developed to store and later combine each data source in a manner that supported the development of predictive models. Master databases that integrated multiple data sources were developed to enable efficient access to data required for statistical analyses. A data di ctionary was prepared to documen t the various study databases.

1.2.2 Objective 2 – Conduct Analyses to Iden tify Predictive Variables and Model Children’s Blood-Lead Levels

The second study objective was to conduct statistical analyses in order to develop models that are predictive of risk of childhood lead poisoning within defined geographic areas as a function of various different environmental, programmatic, and demographic factors. As part of this objective, a National model was developed for pr edicting risk at the county level based on surveillance data from the U.S. Centers for Dis ease Control and Prevention (CDC), and a local model was developed at the census-tract level usi ng blood-lead surveillance data from within the Commonwealth of Massachusetts. As part of the model building process at both the national and local levels, the various data sources underwent exploratory an alyses to investigate data distributions, identify relationshi ps between variables, and determine appropriate variables to include in subsequent statistical models. Part of the exploratory analyses included an effort to identify which environmental, programmatic, and demographic factors were most predictive of risk of childhood lead poisoning. Multivariate statistical models then were developed using appropriate statistical software to combine the various data sources with in a single model that accounted for trends in risk of childhood lead poisoning over time within defined geographic areas. Model diagnostics were reviewed, and models with the best fit were identified.

1.2.3 Objective 3 – Develop Visualization Tool to Graphically Model Predicted BloodLead Levels

The third study objective was to develop an approp riate visualization tool that allows users to interact with the result s of the statistical model predicting ch ildren’s blood-lead levels across the United States. This tool provides the user with the flexibility to visually compare the predicted blood-lead levels across areas of the country and also to drill down into individual counties or census tracts to assess the input data that generated the predicted value.3 2.0 STUDY METHODOLOGY

2.1 General Approach

This pilot study sought to develop models to pred ict the number of children at risk of elevated blood-lead levels for a given geographic ar ea based on a hierarchical combination of demographic, environmental, and programmatic in formation sources. Doing so required looking at both the mechanisms of childhood lead risk a ssessment and control activiti es at the local level as well as at broad trends acro ss the United States. The two ma in analysis goals correspond to developing predictive models at two different levels of geogra phic specificity, and appear as follows:

1. Broad Coverage (Low-Resolution) Model: This type of model is in tended to be able to characterize broad trends over time in the prevalence of childhood lead poisoning at the county level across the entire United States. This model was based on quarterly county-level aggregated surveillance da ta from the CDC and augmented with environmental data from a variety of s ources, demographic data from the U.S. Census, and programmatic (level of federal funding) information.

2. High-Resolution Model: This type of mode l represents the effort to assess the relative contribution of various exposure sources associated with elevated blood-lead concentrations within select communities. This type of model certainly reflects the idea that exposures that c ontribute to childhood lead poi soning are likely to be community specific. This analysis goal was met through modeling census-tract level surveillance data within Massachusetts as well as housing unit lead assessment and/or control activities. These data sources were augmented with all of the environmental, demographic, and programmatic information used in the national model with the addition of state programmatic funding levels.

The primary objective of this pi lot study was to utilize combined information from different sources at various levels of ge ographic and temporal specificity to more accurately target geographic areas at high ri sk for not meeting the 2010 goal of eliminating childhood lead-poisoning. As such, the study required careful integration of a variety of data sources with various characteristics and documentation. Data to support this study were gathered from multiple sources, including federal, state and lo cal lead poisoning prevention programs, as well as publicly available data that were download ed from the internet (e.g., census data, EPA’s Toxics Release Inventory).

2.2 Data Management

When each data source was received, the data and supporting documentation were reviewed to gain knowledge on the structure, relationship, an d quality of the data. Database managers worked with the project team to determine the fi nal format for each database, the desired uses of the databases; as well as the requirements for maintaining the databases. Based on this information, master databases were construc ted in SQL server for both the national lowresolution model and for the high-resolution model based on Massachusetts data that integrated the various environmental, demographic, and prog rammatic variables, and facilitated statistical4 analyses of the combined data. These datasets were translated directly to SAS datasets for statistical analysis, and also we re transferred to Microsoft Acce ss for delivery to EPA. The Microsoft Access database includes a compact versi on of each database utili zed in the statistical analysis, with any extraneous variables remove d. In addition, the Microsoft Access database includes a copy of the integrated longitudinal dataset used to support the final multivariate models developed with in this project.

Throughout the development process, checks fo r completeness were conducted on all study databases, and the project team worked with da ta-sharing collaborators and EPA to attempt to complete missing data as necessary to complete the proposed statistical analyses. Any changes to the databases (corrections, a dditions, deletions, etc.) were documented in appropriate metadata files, and reported to EPA w ithin the data dictionary attached to this report as Appendix H. As part of constructing and maintaining these databases, the project team will develop appropriate documentation of the combined master databases.

Standard Operating Procedures (SOPs) were follo wed to ensure the proper storage, backup, and retrieval of datasets created and analyzed for this study. Additional details of these SOPs can be found in the Quality Management Plan prep ared for this project (Battelle 2007).

2.3 Descriptive Data Analyses

The analysis began with an asse ssment of the study sample, i.e ., the proportion of counties and census tracts in the sample with complete data for both the response variab le and the explanatory variables. Prior to the fitting of any descriptive statistics to assess the predictive ability of any of the explanatory variables, the blood-lead respons e variables needed to be constructed based on the CDC and Massachusetts blood-lead survei llance data. These data sources contain information on individual blood-l ead testing results on childre n, and were aggregated into quarterly summary statistics (number of child ren observed, arithmetic and/or geometric mean 1

, and number of children observed at or above 5, 10, 15, and 25 μ g/dL) at the county level (for the CDC data) and the census-tract level (for the Mass achusetts data). An executable was developed to extract these quarterly summary statistics fr om each county from CDC’s SQL server database for children aged 6-36 months, and a similar exec utable was deployed to create parallel summary statistics at the census-tract level for the Massachusetts surveillanc e data. Because of confidentiality restrictions, count y/quarter (or censu s tract/quarter) combinations with fewer than 5 observations were automatically eliminated from the dataset. Data repo rted prior to 1995 also were eliminated from the analysis database prior to statistical analysis.

Once the aggregated summary datasets were co nstructed, they were reviewed for possible problems associated with childhood lead poisoning prevention programs not following universal reporting protocols (for some localities, da ta were only transmitted to the CDC National Surveillance Database for children with elevat ed blood-lead concentrations over certain periods of time). A screening algorithm was developed to remove these suspect data from the analysis dataset – resulting in the elimination of less th an 3 percent of the aggregate summary records from the National database. The screening algor ithm also was applied to the Massachusetts data – however no records were eliminated, as Mass achusetts was following universal screening and

1 The CDC reported only the arithmetic mean, while Massac husetts reported both arithmetic and geometric means.5 reporting guidelines over the 2000-2006 time period for which they provided data. Additional detail on the manner in which the blood-lead re sponse variables were co nstructed can be found in Section 3.1.

In preparation for developing longitudinal stat istical models, univariate summaries of each variable as a function of time were generated an d comparisons were made of these distributions using side-by-side box-plots for co ntinuous data or bar-charts for categorical data. This helped verify that the data were clean and ready for anal ysis and identified cells with sparse data. Such descriptive analyses were conducted on each databa se, to characterize the distributions of all observed variables using frequenc y distributions for categorical variables, and simple summary statistics (mean, median, mode, minimum, maxi mum, and select percentiles) for continuous variables. Distributional assumpti ons also were explored for certai n variables, as appropriate, in preparation for more sophisticated models. For example, some environmental concentration data may depart from normality, and follow a log-normal distribution. In these cases, we additionally reported the geometric mean and geometric standard deviation as part of the simple descriptive

summary.

The univariate descriptions then were followed by fitting a series of cross-sectional bivariate relationships between the blood-lead response variable(s) and each candidate explanatory variable. These cross-sectiona l relationships were explored as a function of time to better understand the stability of these re lationships, and whether they cha nge over time, so that they can be modeled appropriately in the more sophis ticated longitudinal analyses. These analyses also will help identify which explanatory va riables are most predic tive of the blood-lead response variable.

In preparation for more sophisticated statistical analyses, such as the Generalized Linear Mixed Logistical Regression Model outlined below, rele vant stratified analyses were performed to investigate interactions discussed in the data an alysis plan. For example, the population density variable was investigated in this manner, as de nsity may serve as a surrogate to differentiate between rural and urban geographic areas in th e analyses – and exposure variables may be different in these types of areas. Similarly, EPA regions were invest igated as a potential stratification variable. If vari ation in the measure of effect is not observed (e.g., odds ratios) across the levels of a third vari able; however, the third variable can likely be treated as a potential confounder in the multivariate model, rath er than as an effect modifier. If the odds ratios differ markedly—e.g., the effect appears to be protective in one subgroup and hazardous in another subgroup—the third va riable must be considered as an effect modifier.

Specific variables within each type were expl ored using four general approaches – (1) histograms or side-by-side box-plots of the candidate explanat ory variable, (2) simple regression line plots exploring the relationshi p between predicted risk of l ead poisoning and the explanatory variable for each of the four specified time periods, (3) distributional summaries of the explanatory variable across the th ree time periods, and (4) statisti cal modeling of the relationship between the explanatory variable and various blood-lead respons e variables after adjusting for the effects of time and seasonality within differe nt regions of the country for the National (LowResolution) model and for the effects of time in the Massachusetts (High-Resolution) Model.6 Histograms or Side-by-Side Box-Plots of Potential Explanatory Variables Using one record for each quarterly county- or census-tract -level da ta point, a histogram illustrating the distribution of the explanatory vari able is presented. A fitted line assists with assessing the distribution of each potential ex planatory variable (e.g., whether the data are approximately normally distributed). Histograms were plotted for potenti al predictor variables that were time invariant. For predictor variables that varied over time with in the analysis dataset, side-by-side box-plots were used to characterize the distribution over the time periods, using an average of the predictor variab le across the quarters in which blood-lead concentrations were observed within each time-period and area.

Logit Probability Plots for each Explanatory Variable The county-level quarterly proportion of screened children exceeding 10 μ g/dL reported by the CDC were modeled as a function of each candidate explanatory variable, with separate logit curves used to represent each of the time peri ods. This analysis allows comparison of the relationship between the explanatory variable an d predicted blood-lead level trends across time periods. If the relationship is st able across time, roughly parallel curv es are evident. If the effect of the variable on blood-lead varies over time, non-parallel (and perhaps intersecting) curves are observed. In this case, the longitudinal analyses ma y need to be adjusted to allow for the effect of the covariate to change over time.

Plots of Predicted GM Blood-Lead Levels and Explanatory Variables The census-tract-level quarterly blood-lead data available from Massachusetts were fit to each explanatory variable to generate predicted GM blood-lead levels ac ross the range of the explanatory variable for each of the time pe riods. A simple linear regression line plot summarizes this analysis with one line for each time period. This analysis allows comparison of the relationship between the expl anatory variable and predicted blood-lead level trends across time periods. If the relationship is stable across time, roughly parall el lines are evident. If the effect of the variable on blood l ead varies over time, non-parallel (and perhaps intersecting) lines are observed. In this case, separate slopes may need to be fit for these variables over different periods of time in the more sophi sticated longitu dinal analyses.

Distributional Summaries The first table presented for each explanatory variab le contains a series of summary statistics for each of the time periods including sample size, number missing, mean, and standard error. The sample size is relative to the number of quarters represented in the analysis dataset; therefore, these distributions correspond to the analysis data set (and not necessarily to the distribution of the variable across the nation or state). The dist ribution of the data for each time period also is presented (minimum, median, and maximum and 10 th , 25 th , 75 th , and 90 th percentiles). Comparing the summary data across time allows assessment of changes in the explanatory variable over time for the groups of tracts incl uded in the analysis for each time period. Generally, the mix of counties and Massachusetts census tracts included in each of the time periods is similar, so that the distribution of the data from each period also is similar.7 Statistical Modeling of Relationship between Explanatory Variables and Exceedance of Blood-Lead Thresholds for the Nati onal (Low-Resolution) Model: For each candidate predictor variable being c onsidered for the National (Low-Resolution) Model, the following generalized linear mixed models approach was used to model the proportion of children exceeding certain thresholds as a function of the predictor variable after adjusting for Region-specific intercepts, sl opes over time and effects of seasonality:

ij i i ij ij k ij k k ik ij ij t X Season t gion n Y E it

⋅ + + +
⋅ +
⋅ +
⋅ =

⋅ 1 0 3 2 1 0 ) ( Re ]) / [ ( log δ δ β β β β v

Where (i indexes county, j indexes time, a nd k indexes the region of the country), Y ij represents the number of ch ildren observed above the blood -lead threshold in the i th county at time j, n ij represents the number of children tested in the i th county at time j,

t ij and Season ij are fixed effects variables corresp onding to a time-trend (in years) and seasonality, X ij is the candidate predictor variab le being investigated, the beta parameters ( β ) represent a vector of fixed eff ects, and the delta parameters ( δ ) represent random effects that allow each county to have its own trend over time. The
X ij predictor variable is mean centered in th is series of models, allowing the intercept term to be relatively stable across the multiple predictor variables being investigated. In this model, it can be assumed that δ 0i and δ 1i jointly follow a multivariate normal

distribution with mean zero and covariance matrix ⎥ ⎥ ⎦ ⎤
⎢ ⎢ ⎣ ⎡

= Σ 2 11 2 10 2 01 2 00

σ σ σ σ

.

 Model 1 follows the above approach – where Y ij represents the number of children observed with blood-lead concentrations at or above 5 μ g/dL, and n ij represents the total number of children screened wi thin each record (county/quarter).

 Model 2 follows the above approach – where Y ij represents the number of children observed with blood-lead concentrations at or above 10 μ g/dL, and n ij represents the total number of children screened wi thin each record (county/quarter).

 Model 3 follows the above approach – where Y ij represents the number of children observed with blood-lead concentrations at or above 15 μ g/dL, and n ij represents the total number of children screened wi thin each record (county/quarter).

 Model 4 follows the above approach – where Y ij represents the number of children observed with blood-lead concentrations at or above 25 μ g/dL, and n ij represents the total number of children screened wi thin each record (county/quarter).

In addition to the above models, the project team explored whethe r the effect of each candidate predictor variable on the exceedence proportions va ried over time. This was done by exploring the interaction between each candida te predictor variable and (1) a linear effect of time, (2) a quadratic effect of time, and (3) a 4-level categorical effect of time.8 Statistical Modeling of Relationship between Explanatory Variables and Exceedance of Blood-Lead Thresholds for the Regi onal (High-Resolution) Model:

The Regional (High-Resolution) Models developed fo r the Massachusetts data at the census-tract level of geographic specificity included models for both continuous data (geometric mean) and binomial data (exceedence proportions). Therefor e, each explanatory variable being considered for these models were explored using models for both continuous and binomi al data as described below:

Continuous Data: The following mixed models analysis of variance (i.e., a random-effects model for continuous data) was used to m odel the geometric mean (GM) blood-lead concentration as a function of a candidate predictor variable:

Where (i indexes census tract, j indexes time), GM ij represents the geometric mean blood-lead concentration in the i th census tract at time j, t ij is a fixed-effects variable corresponding to a time-trend (in years), X ij is the candidate pred ictor variable being investigated, the beta parameters ( β ) represent a vector of fi xed effects, and the delta parameters ( δ ) represent random-effects that allow each county or census tract to have their own trend over time. The X ij variable typically is mean centered in this series of models, allowing the intercept term to be relatively stable across the multiple predictor variables be ing investigated. In this m odel, it can be assumed that δ 0i and δ 1i jointly follow a multivariate normal distribution with mean zero and covariance

matrix ⎥ ⎥ ⎦ ⎤
⎢ ⎢ ⎣ ⎡

= Σ 2 11 2 10 2 01 2 00

σ σ σ σ , and the residual error also is assumed to follow a normal

distribution with mean zero and variance

.

 Model 1 follows the above approach – wher e the responses are weighted equally.

 Model 2 follows the above approach – wher e the responses (GM) are weighted by the number of children observed (screened) with in each record (census tract/quarter).

Binomial Data: The following generalized linear mixe d model (i.e., a random-effects model for binomial data) was used to model the proportion of children exceeding certain thresholds as a function of a candidate predictor variable:

ij i i ij ij ij ij ij t X Season t n Y E it

⋅ + + +
⋅ +
⋅ + =

⋅ 1 0 3 2 1 0 ]) / [ ( log δ δ β β β β v

Where (i indexes census trac t and j indexes time), Y ij represents the number of children observed above the bl ood-lead threshold in the i th census tract at time j, n ij represents the number of children tested in the i th census tract at time j, t ij is a fixed effects variable corresponding to a time-trend (in years), X ij is the candidate predictor variable being investigated, the beta parameters ( β ) represent a vector of fixed effects, and the delta parameters ( δ ) represent random effects that allow each census tract to have its own trend over time. The X ij variable also is mean cen tered in this series of9 models, allowing the intercept term to be relatively stable across the multiple predictor variables be ing investigated. In this m odel, it can be assumed that δ 0i and δ 1i jointly follow a multivariate normal distribution with mean zero and covariance

matrix ⎥ ⎥ ⎦ ⎤
⎢ ⎢ ⎣ ⎡

= Σ 2 11 2 10 2 01 2 00

σ σ σ σ

.

 Model 3 follows the above approach – where Y ij represents the number of children observed with blood-lead concentrations at or above 5 μ g/dL, and n ij represents the total number of children screened within each record (census tract/quarter).

 Model 4 follows the above approach – where Y ij represents the number of children observed with blood-lead concentrations at or above 10 μ g/dL, and n ij represents the total number of children screened within each record (census tract /quarter).

 Model 5 follows the above approach – where Y ij represents the number of children observed with blood-lead concentrations at or above 15 μ g/dL, and n ij represents the total number of children screened within each record (census tract /quarter).

 Model 6 follows the above approach – where Y ij represents the number of children observed with blood-lead concentrations at or above 25 μ g/dL, and n ij represents the total number of children screened within each record (census tract /quarter).

To allow comparison of the different variables ex plored within each variable type, tables are included in Section 4 that pres ent the log-likelihood statistic fr om each model run and presented in Appendices A and B. Within each variable cate gory, the variable that provided the best fit for each of the six models is highlighted in yellow. To ensure compatibility in the likelihood-based statistics being used to make comparisons am ong the different candidate predictor variables, missing values for predictor variables were imput ed using the mean of the distribution. The number of imputed values that were necessary is provided by the nmiss column in the table of distributional summaries described above. The project team choose whether to adjust for changes in the slope for a candidate predictor va riable over time based on a comparison of the likelihood statistics after adjusting for the number of degrees of freedom used in the model for the effects of the explanatory variable (over tim e) on the response. Those variables highlighted in yellow have the largest like lihood statistic after ad justing for differences in the degrees of freedom, and were considered as strong candidate predictors for the multivariate statistical models.

Due to the iterative nature and complexity of the Mixed Models Analysis of Variance and Generalized Linear Mixed Mode ling Approaches, these models did not always converge. Models that failed to converge for a particular predictor variable are discussed in the results sections within Appendices D and E, and also are indicated in Tables 4-1 and 4-2, as well as in the summary pages of Appendices A and B by blank cells. Cases in which model convergence is not attained likely will translate to exclusion of that particular variable when building the multivariate model. Note that because of the sp arseness of data for children with blood-lead levels at or above 25 μ g/dL within the Massachusetts data, Model 6 failed to converge across all variables. Thus, Model 6 results are not presen ted or discussed for the Massachusetts models.10 2.4 Development of Multivariate Statistical Models

2.4.1 Statistical Models for the Broad Coverage – Low-Resolution Model

This model is being used to characterize broad trends over time in the prevalence of childhood lead poisoning across the entire Un ited States. The various survei llance, environmental sources, demographic characteristics, and programmatic support data sources were aggregated to the county level for all localities with universal screening and reporti ng. Quarterly estimates of each candidate predictor variable were created fo r each county within the United States, including those county/quarter combinations that did no t include observed blood-lead response variable information (allowing for the extrapolation of the model predictions to geographic areas and time-points that were not represented within CDC’s National Surveillance Database).

In addition to investigating the predictive abil ity of each potential envi ronmental, programmatic and demographic variable as described earlier, va rious different stratifica tion variables (region of the country, population density) and covariates (time trend and seas onality) were investigated. As a result, all four multivariate statistical m odels adjust for a categorical variable that differentiates among the risk of childhood lead poisoning within the 10 EPA regions. Within each EPA region, a separate intercept, trend ove r time, and seasonality term (based on fitting intercepts for each quarter of time) was included in the multivariate statistical model. For the purposes of discussion, it was assumed that th e modeling approach will focus on a logistic regression model for the proportion of children that have elevat ed blood-lead concentrations ( ≥ 10 μ g/dL). The temporal nature of declini ng childhood lead poisoning will be addressed via classic concepts of longitudinal data m odeling of the low resolution data. Let

Y ij represent the number of children that were detected with blood-lead concentration above
10 μ g/dL from the i th county and j th point in time (quarter),

n ij represent the number of childre n that had their blood-lead con centration tested from within the i th county and j th point in time (quarter),

Please note that we expect that n ij <N ij

, where N ij represents the total population of children in the i th county and j th point in time

.

t ij represent time (in year s) corresponding to the Y ij response variable,

Region ik represents the region of the country that the ith county is located within (where k==1,...,10 and is representative of the 10 established EPA regions),

Season ij represents a series of 4 i ndicator variables that differen tiate between the 4 different quarters captured by the j-index, and

X ij represent a series of predictor variables associat ed with the Y ij response variable. These predictor variables may repr esent air monitoring data, dr inking water data, census demographic data, programmatic data on fe deral financial support for lead poisoning prevention, and other related information as deta iled above that can potentially help predict the prevalence of lead poisoning at the county level.11 We introduce the following as a potential baseline model:

ij i i ij ij k ij k k ik ij ij t X Season t gion n Y E it

⋅ + + +
⋅ +
⋅ +
⋅ =

⋅ 1 0 3 2 1 0 ) ( Re ]) / [ ( log δ δ β β β β v v

Where the beta parameters ( β ) represent a vector of fixed effe cts, and the delta parameters ( δ ) represent random effects that allow each county to have thei r own trend over time. In this model, it can be assumed that δ 0i and δ 1i jointly follow a multivariate normal distribution with

mean zero and covariance matrix ⎥ ⎥ ⎦ ⎤
⎢ ⎢ ⎣ ⎡

= Σ 2 11 2 10 2 01 2 00

σ σ σ σ

.

Counties with larger δ 1 parameters estimates represent areas where lead-poisoning has not significantly decreased over time. Similarly, th e parameter estimates can be used to identify those counties with the highest predicted prev alence of childhood lead poisoning at various time points in the future.

In building the multivariate statistical model for the Broad-Based Modeling Objective, the project team first evaluated the predictive ability of each candidate predictor variable that was considered within the exploratory analyses. For the environmental predictor variables, in particular (information from EPA’s 1999 National Air Toxics Assessment, Safe Drinking Water Information System, and Toxics Release Inventory), the data were largely concentrated at zero. Therefore, a series of zero/one indicator variable s that represent county/qu arter combinations at or above the 95 th and 99 th percentile of observed values of these environmental predictor variables within the analysis dataset also were investigated.

Once the predictive ability of each candidate variable was established within the exploratory analyses described earlier, candidate predictor variables were classifi ed into groups (e.g., housing age, income, education, air modeling, pr ogrammatic financial support) and then the single best predictor variable within each group was selected fo r possible inclusion within each of the six multivariate statistical models being deve loped. If the selected variable demonstrated a relationship with risk of lead poisoning that changes over time (a s evidenced by intersecting lines in the plots generated in the exploratory anal yses), then this interaction was taken into consideration within the evaluation of the pred ictive ability of the candidate variable(s).

The approach to determining which environmen tal, programmatic, and demographic variables were included in the model followed a backward elimination process – in which each variable group’s best predictor variable id entified earlier was included in the first model – with variables being eliminated from the model when they were not deemed to be high ly significant. This model building process also was aided by inve stigation of the selected environmental, programmatic, and demographic variables for issues of potential colinearit y via investigation of correlation matrices and principal components anal ysis. The resulting multivariate statistical models were parsimonious – and in most cases only included variable s that were highly statistically significant. In a few cases, a variable was left in the model without being highly significant – because its elimination caused a la rge drop in the model log-likelihood (suggesting12 that the model is significantly improved with the addition of a variable whose slope is not significantly different from zero).

After the multivariate statistical models were developed, model fit diagnostics were evaluated and documented.

The parameter estimates for the four National Multivariate Statistical Models are provided in the results section. The results of these models also were explored in multiple ways. Maps were generated to demonstrate observed and predic ted proportion of children at or above 10 μ g/dL within each EPA region for the Years 2000 and 2005 (data were appropriately averaged across the four quarters in each of these years prior to mapping). List s also were generated to identify the 150 highest risk counties acro ss the United States at the end of the observation period (2006) as predicted by each of the six models, as well as the 10 highest risk counties within each state.

Finally, the predicted values from these multiv ariate statistical models (extrapolated to county/quarter combinations not represented in the CDC surveillance database) were integrated in a unique data visualization t ool. The product of this effort is a time-series of maps (or a movie) that spatially interpol ates risk of childhood lead poi soning as a function of various appropriate predictor variables. The visualization tool allows users to interact with the modeling results at different levels of temporal and geogr aphic specificity. The tool allows the user to select an appropriate respons e variable (e.g., proportion of children with blood-lead concentrations above 5 μ g/dL) and play a movie that displays a time-series of maps that displays how the predicted (or observed) risk changes over time across the various counties within a selected state. The user can zoom in on a rect angular area, to see these results with a higher degree of geographic specificity. The user also can stop the movie (or rewind, or fast-forward) to isolate specific points in time. By using the m ouse, the user also can select a specific county and the tool will display the observed and predicted data for that particular county in a separate window. The visualization tool was written in C++, and was built in a manner that will allow EPA to modify the model and for the project te am to quickly import the resulting data from a modification into the tool.

2.4.2 Statistical Models for the High-Reso lution Model within Massachusetts

High-resolution models will be utilized to identify the relative contribution of various types of exposure sources in elevated risk for childhood le ad poisoning within select communities within the Commonwealth of Massachusetts . These types of sources incl ude housing factors, broader environmental exposure, demographic compositi on, and programmatic resources. While this type of model reflects the idea that exposures contri buting to childhood lead poisoning likely are community-specific, analysis of the high-re solution models may ha ve certain limitations including selection bias and genera lizability to othe r geographic areas.

The Massachusetts Department of Public Health (MDPH) entered into a limited use data sharing agreement with the project team, allowing th em to provide blood-l ead testing results on individual children (aged 6-36 months) and housing inspection data in a format that preserves linkages through a housing unit identification variab le. These data will be utilized in two different modeling approaches. The first modeling approach will seek to develop census tract quarterly summary measures similar to the Na tional Model for blood-lead (e.g., exceedance13 proportions and geometric means), as well as summary measures for the proportion of housing units in each census tract that are known to be in (or out of) compliance with the Massachusetts standard of care (for use as a potential explanatory variable). MDPH also has provided the project team with summary in formation regarding HUD and stat e funding of residential housing interventions (lead hazard control and abatement) – which will be used to develop a longitudinal summary of current and cumulative per-capita sp ending on residential intervention within each census tract (using various assumptions on the allo cation of such dollars). Other explanatory variables, such as the U.S. Census, EPA Toxi cs Release Inventory, 1999 National Air Toxics Assessment, and water quality data will be available for use in these models.

These census-tract level summary data (both resp onse variable and explanatory variables) were modeled using a similar approach to what is be ing proposed for the National (Low-Resolution) Model – only the unit of clustering wa s census tract rather than county.14 3.0 DATA SOURCES AND DATABASE DEVELOPMENT

The main goal of the statistical an alysis were to develop a series of predictive models that help provide a better understanding of (1) the relative importance of various exposure sources in addition to leaded paint in housing and (2) the geographic areas across th e United States that remain at increased risk for ch ildhood lead poisoning. To do so, blood-lead data were combined with various environmental, demographic, and programmatic datasets at different levels of geographic specificity and coverage. A descript ion of each of these data sources, as well as discussion of how they were combin ed, is included in this section.

3.1 Children’s Blood-Lead Measurements

The statistical models are based upon blood-lead levels of children corresponding to the various geographic areas studied. To enable nationa l analyses, CDC’s Lead Poisoning Prevention Branch provided quarterly summary data from their national surveillance database for children aged 6-36 months within each county that ha d submitted data. These summary measures included the number of children screened, perc entage of children who exceeded certain bloodlead thresholds, and arithmetic mean blood-lead concentration for state/local grantees with a history of universal reporting.

The intention was to have the models reflect the annual prevalence of childhood lead poisoning over time. Thus, the data were summarized so that each child could only be reported once a year. An algorithm was developed to select re presentative screening test(s) for children with multiple results with an objective of having ch ildren represented in the analysis dataset maximally once a year. For a given patient with multiple testing results, the algorithm preferentially selected tests confirming elevated blood-lead levels and then selected follow-up tests taken beyond nine months of the previously selected test. Screening tests were selected when no confirming record was available.

The response variable consis ts of quarterly summary statistics from 1995-2005 on the distribution of observed blood-l ead concentrations in count ies across the nation, based on information from CDC’s national surveillance data base. It should be not ed that there likely exists significant vari ation and differences in the samp ling and analytical methodologies employed in performing childhood blood-lead te sting among the different counties that contributed to the CDC dataset, and within c ounties over time. Sampling methods include both capillary and venous tests, and diffe rent laboratory methods likely are represented within the data with varying reporting limits or limits of detect ion. Variation in reporti ng limits and limits of detection could introduce significant biases into statistical models of any continuous measures of blood-lead concentration that could be used in statistical models, such as the geometric or arithmetic mean blood-lead concentration. Alternatively, there was agreement among the research team and the CDC that measures wh ether a testing result was found above or below certain threshold values (5, 10, 15, and 25 μ g/dL) would be more r obust to these potential reporting and detection biases. Therefore, the National (Low-Res olution) Models focus on the proportion of screened children found above these threshold values using a logistic regression modeling approach.15 After summarizing the test-level data by year, quarter, and county, counties that contained less than five test records in a quarter were exclude d for confidentiality reasons. The time series of summary statistics within select counties were initially investigated to determine appropriate exclusion criteria to ensure th at the data retained for an alysis represented blood-lead concentrations that were univers ally reported (i.e., there were periods of time in which some state or local childhood lead poisoning preventi on programs only reported elevated blood-lead concentrations – and these data needed to be elim inated from the analysis). Thus, the number of quarterly summary statistics varied from count y to county within the analysis dataset.

As a prelude to developing the screening algorith m for elimination of data from counties that were not following universal repor ting protocols, a subset of da ta from counties with obvious non-universal reporting was identified from w ithin the National quarterly aggregate summary database. The algorithm was developed based on app lication to this subset of data prior to being utilized on the remainder of the National Survei llance database. The algorithm is based on the following:

Let

• n ij represent the number of children observed in the i th county during the j th quarter

• P90(n i ) represent the 90 th percentile of observed n ij within the i th county

• AM ij represent the arithmetic mean blood- lead concentration observed in the i th county during the j th quarter

• P50(AM i ) represent the 50 th percentile of observed AM ij within the i th county

• P10 ij represent the proportion of children with blood-lead concentra tion observed at or above 10 μ g/dL in the i th county during the j th quarter.

Then the following 3 exclusion/inclus ion criteria are applied sequentially:

Criterion #1: If n ij < Max(P90(n i )/5, 15) and (AM ij ≥ 2* P50(AM i ) or P10 ij ≥ 0.75), then exclude the data from the i th county during the j th quarter. This exclusion criterion essentially eliminates county/ quarter combinations with relatively lower screening penetration (compared to when peak sc reening was achieved) and high blood-lead concentrations. The rationale for this exclus ion criterion is that the periods of time in which a lead poisoning prevention program is not conducting universal reporting will involve fewer reported testing results th at have higher blood-lead concentrations.

Criterion #2: If n ij > 100 and AM ij <7, then include the data from the i th county during the j th quarter. This criterion was added to include a small number of county/quarter combinations within the testing subset of data that were e liminated by the first exclusion criteria but did not appear to be inconsistent with the rema inder of data that would be included in the analyses. This second criter ia was inspected carefully upon application to the entire set of quarterly county summary statistics from CDC’s National Surveillance database, to ensure that it wa s reintroducing data into the an alysis in a manner consistent with the data analysis goals.16 Criterion #3: If n ij < 100 and AM ij >10, then exclude the data from the i th county during the j th quarter. This third criteria was establis hed to exclude a small amount of data that was not captured by the first exclusion criter ia (mostly representing counties with a median observed blood-lead concentration slightly above 5 μ g/dL)

Within the quarterly county summary statisti cs from CDC’s National Surveillance database, there were 72,466 county/quarter comb ination-level records. Applic ation of Criterion 1-3 above eliminated an total of 2,351 records (3.25 %) from the final analysis dataset.

To enable analyses at a finer level of geogr aphic detail than the county level, the MDPH provided blood-lead surveillance data on specific testing results for individual children (with confidential identification information excluded) so that data could be summarized and reported by census tract. The Massachusetts blood-lead surv eillance data represents all children aged 6- 36 months tested from the period 2000-2006. As with the national data, quarterly census-tractlevel records were created for analysis.

Due to selection bias, it is expected that the CDC National Surveillance dataset as well as the Massachusetts surveillance data may show higher proportions of elevated blood-lead concentrations than found in the general popula tion. For this reason, th e proportion of children with elevated blood-lead concentrations as well as the distribution of the potential continuous summary measure derived from the surveillance da ta were compared with those reported by the most recent six years of available CDC Nationa l Health and Nutrition Examination Survey (NHANES). Results of this comparison are presen ted in Section 7.2. In the future, to account for differences between the surveillance and NHANE S data, modifications could be made to the models to calibrate the survei llance data to better match th e national distribution of childhood blood-lead concentrations as appropriate (Strauss, 2001a).

3.2 Demographic Data

Demographic information from the 2000 U.S. Cens us was utilized in both the high- and lowresolution models, with data bei ng acquired at the county level fo r the entire nation and at the census-tract level for Massachusetts. The Cens us 2000 data gathered by the Census Bureau includes over 1,000 variables. To narrow the scope of the project, 43 vari ables within 9 general categories were selected and expl ored, most of which had been used previously by the project team in a CDC-sponsored study to predict risk of elevated blood-lead concentrations at the census tract level (Strauss, 2001b). In many cases , the census variables are constructed from counts or summary statistics published in the de tailed Census 2000 tables. For example, within each geographic area, the Census Bureau reported the number of houses that were built before 1950 and the median income of all households. In order for the analysis to draw comparisons from tract to tract and/or c ounty to county, however, the Cens us variables needed to be manipulated in a fashion that depended upon the format of the variable. For example, count variables, such as the number of housing units built before 1950, were changed to percentages. Summary statistic variables describing income on the other hand, may be standardized within state to adjust for between-state differences in the cost of livi ng. Table 3-1 supp lies the list of the variables investigated within the nine ca tegories and notes how they were calculated.17 Table 3-1. Initial Variables for Analysis Created From the 2000 Census

Variable

Group Census Variable* Format Calculation Analyzed Variable

Persons Count Land Area (Units = .001 km 2 ) Population Density
Density Housing units Count Land Area (Units = .001 km 2 Housing Density

White population Count Persons Pct White Black population Count Persons Pct Black Indian, Eskimo, and Aleut population Count Persons Pct American Indian and Alaskan Native Asian Pacific population Count Persons Pct Asian Other Race population Count Persons Pct Other Race
Native Hawaiian and Other Pacific Islander population Count Persons Pct Native Hawaiian and Other Pacific Islander Multiple Race population Coun t Persons Pct Multiple Race Race

Hispanic population Count Persons Pct Hispanic Children Less than or Equal to 6 Years Old Count Persons Pct le 6 years

Median Age* Statistic Median age of persons Age Median Age of Children Less than or Equal to 6 Years Old* Statistic Median age of persons LE 6 years

Family Structure Single Parent* = Single Male with Children + Single Female with Children Count Household with Children Less than or equal to 18 years old = Married Couple with children + Single Male with Children + Single Female with Children Pct Single Parent

Less than a 9th grade Education Count Persons 18 years old and over Pct less than 9th grade

Less than high school* = #13 + persons with 9th to 12th grade education without obtaining a high school diploma Count Persons 18 years old and over Pct no HS degree

Less than college* = #14 + persons with high school diploma, but no college experience Count Persons 18 years old and over Pct no college Education

Less than college degree* = #15 + persons that attended college without obtaining a college diploma Count Persons 18 years old and over Pct no college degree18 Table 3-1. (continued)

Variable

Group Census Variable* Format Calculation Analyzed Variable Household Median Income Statistic Standardized Median Income for Households Family Median Income Statis tic Standardized Median Income of Families Per Capita Income Statistic Standardized per capita income of persons Households without earnings Count Households Pct No Earnings Households without wages Count Households Pct No Wage or Salary Income

Households that obtain public assistance Count Households Pct With Public Assistance

Persons below poverty level Count Persons for whom poverty status is determined Pct Persons Below Poverty

Persons who are less than or equal to five years old that are below poverty level* Count Persons who are less than or equal to five years old for whom poverty status is determined Pct Persons Below Poverty of Age LE 5 Below

Families with total income below the poverty level Count Families Pct Families Below Poverty Poverty

Level

Families with total income below the poverty level that have children under 5 years old. Count Families with children under five years old Pct Poverty of Families with Children LT 5

Vacant Count Housing Units Pct Vacant Housing Units Built before
1940 Count Housing Units Pct Pre 1940 Housing

Housing Units Built before
1950 Count Housing Units Pct Pre 1950 Housing

Housing Units Built before
1960 Count Housing Units Pct Pre 1960 Housing

Housing Units Built before
1970 Count Housing Units Pct Pre 1970 Housing

Housing Units Built before
1980 Count Housing Units Pct Pre 1980 Housing

Median Year that Housing Units were Built Statistic Median Year Built Housing

Units

Median Year that Housing Units were Built - Calculated by the Project Team Statistic Calculated Median Year Built19 Table 3-1. (continued)

Variable

Group Census Variable* Format Calculation Analyzed Variable Housing Units that are rented Count Occupi ed Housing Units Pct Renter Occupied Occupied Housing Units Built before 1940 Count Occupied Housing Units Pct Pre 1940 Occupied Housing Occupied Housing Units Built before 1950 Count Occupied Housing Units Pct Pre 1950 Occupied Housing Occupied Housing Units Built before 1960 Count Occupied Housing Units Pct Pre 1960 Occupied Housing Occupied Housing Units Built before 1970 Count Occupied Housing Units Pct Pre 1970 Occupied Housing Occupied Housing Units Built before 1980 Count Occupied Housing Units Pct Pre 1980 Occupied Housing Occupied Housing

Units

Median Year that Occupied Housing Units were Built Statistic Median Year Built - Occupied Only

Median Rent Statistic Standardized Median Gross Rent Housing

Value Value of Owner Occupied Housing Units Statistic Standardized Median Housing Unit Value *Variables that were created by combining diffe rent pieces of information from the 2000 Census

Income and Poverty Median income per household, family, and person were calculated. Additionally, the proportion of households that do not receive any wages, do not receive any earnings, and do receive public assistance were investigated. The census defines earnings and wages as follows:

• “Earnings ” represent the amount of income recei ved regularly before deductions for personal income taxes, Social Secur ity, bond purchases, union dues, Medicare deductions, etc.

• “Wages ” include total money earnings received for work performed as an employee during the calendar year 1999. It include s wages, salary, Armed Forces pay, commissions, tips, piece-rate payments, and cash bonuses earned before deductions were made for taxes, bonds, pensions, union dues, etc.

Similar to the income variables described above , the poverty level of individuals and families within each county were summarized as the va riables Percent Persons and Percent Families Below the Poverty Level. In order to focus on the poverty level of the children within each county, however, Percent Persons Five Years an d Under and Percent Families with Children Under Five Years Below Poverty Level variables were created. Note that in calculating the various percentages for each of th e variables, the denominator cha nges. Also note that for some of the multivariate models presented later in the report, some of the income variables may have been rescaled to represent income in thousands of dollars, to allow the parameter estimates for the regression models to be discernable within the first 3 significant digits.20 Race The Census Bureau presents five general race groups; (1) White, (2) Black, (3) Indian, Eskimo, and Aleut, (4) Asian Pacific and (5) Other, each of which was included and explored separately. Additional variables were includ ed on percent of Native Hawaiia ns and Other Pacific Islanders, percent of the population reporting multiple races (Percent Multiple Races), and percent of the population reporting that they are Hispanic (Percent Hispanic).

Housing Cost Two variables were constructed to investigate housing cost – Median Rent and Median Housing Value. Median Housing Value includes the value of all housing units (owned and rented). Both of these variables were standardi zed to account for state-to-state di fferences in the cost of living. Note that for some of the multiv ariate models presented later in the report, some of the housing cost variables may have been res caled to represent housing costs in thousands of dollars, to allow the parameter estimates for the regression models to be discernable within the first 3 significant digits.

Occupancy Occupied housing units are more likely to have le ad paint removed than vacant homes. Thus, the percent of housing units that are vacant potentially indica tes the level of care taken to maintain buildings within the area. Buildings that are not occupied are more likely to accumulate dust or debris to which the children of an area may be exposed upon reoccupancy. Percent of vacant housing units was explored for those reasons. Sim ilarly, the standard of care could be different between rental properties and owner-occupied propert ies. Thus, the percent of rental units in an area also was explored. The pe rcent of occupied housing units that are rented, rather than owned, was calculated by dividing the number of rented occupied housing units within an area by the total number of occupied housing units.

Family Structure The Census Bureau does not supply a unique variable that indicates the nu mber of single parent households within an area. Therefore, this va riable was created by combining Census variables as follows: M = Number of Households with a male householder (no wife present) whose own children are under 18 years old F = Number of Households with a female householder (no husband present) whose own children are under 18 years old T = M + F + Number of married couples with own children under 18 years.

The Percent of Single Parent Households variable used represented (M+F)/T.

Housing Age During the 1950s, as the United States started to become aware of the consequences associated with the exposure of lead in paint, the use of lead paint within homes began to decrease. In 1977, however, the use of lead paint in homes b ecame illegal. Thus, the years during which the housing units were built within each area is important to char acterize; older homes are more likely to contain lead paint than newer homes. A number of variables related to housing age by county were investigated to identify those that be st predict children’s bloo d-lead levels. Census21 data on the full population of housing units as we ll as the population of occupied housing units were investigated. Note that for some of the mu ltivariate models presented later in the report, the median age of house variable was centered at 1950 to provide stab ility to the inte rcept term in the models.

Children’s Age The Census Bureau does not report all data by singl e years of age. More typically the agency reports the total number of people that fall into various age categories. The variable, “Pct LE 6 years” was created to identify the number of ch ildren within each geogra phic area less than or equal to six years of age at the time of the 2000 Census. Addi tionally, the median age of the total population and of those less than or equal to six years old was calculated by taking a weighted average of the midpoint of each age ca tegory (the counts are used as the weights).

Education A series of variables pertaining to the proportion of adults with va rious levels of education were created as follows: L9 = Number of people older than 18, that have less than a 9th grade education L12 = Number of people older than 18, that have 9th though 12th grade experience, but do not have a high school diploma 12 = Number of people older than 18, that obtained a high school diploma or GED C = Number of people older than 18, that have some college experience but did not receive a college degree T = Number of People that are over than 18 years old

Percentage variables were creat ed from the L9 through C variable s by dividing them by the total number of people over 18 years old. Explor atory analyses were conducted upon the four percentage variables.

Population Variables Because both counties and census tracts vary with respect to spatial area and population, and previous work suggests that risk of childhood lead poisoning differs between rural and urban areas, a population density variable was used as a potential explanatory variable or effect modifier in the statistical mode ls. Population density was expl ored in two ways. The first divides the number of people within the tract by the amount of land area measured in .001 square kilometers. The second divides the number of housing units by the amount of land area measured in .001 square kilometers. Housing units include the following: a house, an apartment, a mobile home, a group of rooms, or single room that is occupied as se parate living quarters.

3.3 Environmental Data

Environmental data acquired for this projec t include air and groundwater monitoring data aggregated at the county level for the low-re solution model and at higher resolutions for the Massachusetts analyses. In cases where the da ta were available for a limited number of airmonitoring stations or drinking water samples ava ilable for the region(s) being investigated, geospatial modeling techniques might be used as ap propriate to develop pred ictions across the entire region. Existence of industrial sources of lead within each county and cen sus tract, as indicated22 by the Toxics Release Inventory (TRI), also were included as an environmental data source. Each of theses data sources are discussed in further detail below.

3.3.1 Concentrations of Lead in Air

EPA maintains a number of ongoing air monitori ng programs that collect data over time on concentrations of various criteria air pollutants, air toxics, constituents of particulate matter, and other airborne chemicals. Each of these m onitoring programs have multiple air monitoring stations that are deployed throughou t the country to meet various goals associated with the Clean Air Act and other federal and st ate regulations and programs. For example, some of the monitoring stations are placed in close proximity to industrial sources of pollution and major populations centers, while other stations are pl aced in remote areas to assess background chemical concentrations. While many of thes e monitoring sites provide information on the concentration of lead in air over time, a quick assessment of the spatial coverage of these monitoring networks suggested that making use of these data woul d be problematic for this study due to time and resource constraints. Lead c oncentrations in air from the monitoring networks are not available in the majority of counties that will be covered in the low-resolution model, or the census tracts that will be c overed in the high-resolution models – as shown at the following EPA Website ( http://www.epa.gov/airtrends/lead.html ).

Rather than using air monitoring data as descri bed above, the study used modeled predictions of concentrations of lead in air from EPA’s 1999 National Scale Air Toxics Assessment – in which county and census-tract-level pred ictions are available throughout the entire country based on the use of predictive models. Documentation for th e 1999 National Scale Air Toxics Assessment, as well as the predicted air concen tration data can be found at http://www.epa.gov/ttn/atw/nata1999/tables.html . The predictions were generated using the Assessment System for Population Exposure Nationw ide, or ASPEN. This model is based on the EPA’s Industrial Source Complex Long Term model (ISCLT), which simulates the behavior of the pollutants after they are emitted into the atmosphere. ASPEN uses estimates of toxic air pollutant emissions and meteorologi cal data from National Weather Service Stations to estimate air toxics concentrations nationwide.

The ASPEN model takes into account important de terminants of pollutant concentrations, such

as:

• rate of release

• location of release

• the height from which the pollutants are released

• wind speeds and directions from the meteorol ogical stations nearest to the release

• breakdown of the pollutants in the atmosphere after being released (i.e., reactive decay)

• settling of pollutants out of the atmosphere (i.e., deposition)

• transformation of one pollutant into another (i.e., secondary formation).

The model estimates toxic air pollu tant concentrations for every county and census tract in the continental United States; howev er, these data are only available for 1999. Both the BroadBased National Model and the High-Resolution Model within Massachusetts considered the integration of information from the ASPEN Model. The National Model investigated the23 median, average, and 95 th percentile predicted air lead conc entration within each county, while the High-Resolution Model only considered the aver age predicted air lead concentration within each census tract. Within the Nationa l Model, the median, average and 95 th percentile predicted air-lead concentrations were mostly distributed near zero. For this r eason, zero/one indicator variables were created to indicate that the observed value of these ASPEN Model predictions were observed at or above the 95 th and 99 th percentile within the analysis dataset for potential use within the predictive models. In addition, EPA collaborators identified a subset of 20 counties with observed elevated air-lead concentrations, and an indicator variab le was used to assess whether these 20 counties had higher risk of ch ildhood lead poisoning in the predictive models.

The second air-lead variable investigated is based on predictions from the HAPEM5 (Hazardous Air Pollutants Exposure Model, Version 5) m odel. According to the EPA website, “the HAPEM5 model has been designed to predict th e ‘apparent’ inhalation exposure for specified population groups and air toxics. Through a series of calculation routines , the model makes use of census data, human activity patterns, ambi ent air quality levels, climate data, and indoor/outdoor concentrati on relationships to estimate an exp ected range of ‘apparent’ inhalation exposure concentrations for groups of individuals.” 2 Because air quality concentrations in indoor environments can be quite different than those in the outdoor environment, an exposure model generally is employed to predict the appa rent inhalation exposure. The Air Exposure (HAPEM5) model variable captures the predicte d exposure data from this model.

The third air-lead variable considered, Air Hazard Quotient (HQ), is derived from the 1999 National Scale Air Toxics Assessment data. Th is variable represents lifetime exposure for children at the centroids of each census tract or county. Lifetime exposure is calculated based on considering annual exposures and yearly activ ity patterns. The HAPEM5 and HQ air-lead variables were only considered within the c ontext of the High-Res olution Model within Massachusetts.

3.3.2 Toxic Release Inventory Variables

EPA’s Toxics Release Inventory (TRI) catalogs various sources of lead, based on information provided by industrial facil ities. This data source was used to generate county- and census-tractlevel estimates of the total amount of lead and/ or lead-containing compounds that are released by industrial facilities into the environment via air, surface wate r, or underwater injection. Although the above-described ASPEN modeling resu lts are based on the (airborne) emissions data and how they would theoretic ally translate into average ambi ent air-lead concentrations, the data from the TRI are available for multiple years and for other types of emissions (such as surface water). Thus, this information has the po tential to add predictiv e power to the models.

Three types of TRI variables were utilized – total compounds, lead only, and total lead. Within each type, five pollution variables were explored – to tal lead in the air, lead in fugitive air, lead from smokestacks, lead in surface water, and lead in water by injection. Thus, 15 total TRI data variables were evaluated.

2 http://epa.gov/ttn/atw/nata1999/ted/teddraft.html24 Within the National Model, the distributions of the TRI emissions variables were mostly concentrated near zero. For this reason, additional zero /one indicator variab les were created to indicate that the observed valu e of these TRI emissions were observed at or above the 95 th and
99 th percentile within the analysis dataset for potential use within the predictive models.

3.3.3 Water Quality Data

The plumbing system inside a home and the servi ce line from the street to the home may contain lead and can contribute to drinki ng water contamination. To addr ess this potential source, EPA obtained data from their Safe Drinking Wate r Information System that includes the 90 th percentile result of tap wa ter lead levels for public water syst ems. Public water suppliers must monitor at customer’s taps every 6 months. Public water systems can reduce monitoring to annually, triennially, or every 9 years (i f granted a monitoring waiver) if the 90 th percentile value from previous monitoring is at or below the actio n level of 15 parts per billion. The number of customer’s taps, or monitoring sites, that a system is required to sample is based on the population served by the system. Fu rther, systems are required to se lect sites that are most likely to have the highest lead levels (i.e., older homes, homes with c opper pipes with lead solder or homes served by a lead service line). Therefore, the 90 th percentile value of samples collected during a monitoring period is not reflective of indi vidual exposure to lead in drinking water. Data available from this monitoring program include 90 th percentile water lead values for public drinking water systems serving greater than 3,300 persons (systems serving less than 3,300 persons are required to report the 90 th percentile level only if they exceed the action level), the population size served by each facility, the start a nd end date for the monitoring period, and the county in which the facility is located. These data were used to construct a population-size weighted average 90 th percentile water-lead concentration variable within each county/quarter combination. However, it is important to note that most public water systems do not remain within county lines. Large water systems may se rve multiple counties or a county may be served by several small public water systems.

Because there were some county/quarter combina tions with no observed data from EPA’s Safe Drinking Water Information System, an indicator variable was developed to indicate whether the county/quarter included a monitore d facility (or not) – allowing an intercept to be fit among those county/quarters with no drinking water mon itoring, and a slope estimate to be fit for the effect of the weighted average 90 th percentile drinking water-lead concentration among reported facilities.

EPA’s Safe Drinking Water Information System data were not geocoded to the census-tract level, and therefore th ese data were only available for use in supporting the Broad-Based National Model at this time.

3.4 Programmatic Data

Most of the explanatory variables being explored in this project are considered risk factors for childhood lead poisoning. Among fact ors that might mitigate these ri sks, it was anticipated that the level and characteristics of programmatic support from either federal, st ate, or local sponsors may contribute toward meaningful reductions in the prevalence of childhood lead poisoning. The level of financial support av ailable within each county serv ed as a proxy for programmatic25 support in the low-reso lution (National) models. In the high-resolution models run for Massachusetts, information from housing inspections also were explored within the statistical models. The following sections de tail the specific characteristics of the variables used within the models.

3.4.1 Programmatic Funding Variables

The goal of this variable is to construct a longitudinal hist ory of current and cumulative per-capita dollars allocated to each county and census tract to combat childhood lead poisoning. For use in both the national and state models, da ta were obtained from HUD’s Office of Healthy Homes and Lead Hazard Control on grants funded since the inception of the Lead-Based Paint Hazard Control Grant Program in 1992. Data also were obtained from CDC’s Lead Poisoning Prevention Branch on their program’s grant fundi ng approximately three weeks prior to the end of this project, and therefore these data were on ly able to be integrated into the Massachusetts models due to time constraints.

Four variables were generated from these data and analyzed – current and cumulative funding allocated to each county or census tract to co mbat childhood lead poisoning, both Standardized by number of children per tract and Not Standard ized. The Standardized variable is a funding per child variable while the Not Standardized vers ions are funding for geogra phic area variables. For the high-resolution model in the Commonwealth of Massachus etts, information on withinstate funding levels was obtained and analyzed. Within-state funding data were available down to the township level. The state, HUD, and CD C funding data also were combined to create Total Funding variables, including both current and cumulativ e levels and both Standardized and Not Standardized versions. The to tal funding variables also were onl y investigated as part of the Massachusetts analyses.

Becuase there may be delays in the effects of programmatic funding on risk of lead poisoning, time-lagged versions (at 6-, 12-, 18-, 24-, 30- , and 36-months) of the programmatic funding variables in the National Model also were investigated.

3.4.2 EPA Region

The EPA region was investigated as a potential predictor of children’s blood-lead levels to determine if that high-level geographic indicator s hould be included as a stra tification variable in the national multivariate models.

3.4.3 Housing Inspection Data (Massachusetts)

The Commonwealth of Massachusetts maintains an extensive database on all lead-based paint inspections conducted over time (dating back to the early 1990s). The MDPH provided a database that contains a singl e record for each inspection, with the following information: housing-unit id, census tract, date of inspection, and result of inspecti on (whether the housing unit was found to be in compliance with Massac husetts standards). Th e database contains records on over 200,000 housing units – with many housing units having multiple inspections26 over time. Note that for units with multiple reco rds, time periods in which the units were both in and out of compliance with the Massac husetts standards were identified.

These data can be used in the Massachusetts high-resolution models in two ways. First, a longitudinal summary measure of the proportion of housing within each census tract that was known to be in compliance with the Massachusetts standards was developed. It was anticipated that within a census tract, as this proporti on increases over time, th e risk of childhood lead poisoning will decrease. Second, due to the fact that individual blood-lead records from Massachusetts with linkable housin g-unit identification variables we re available, a determination could be made regarding whether a housing unit was in compliance at th e time of the blood-lead test for each child in the data base (with potential outcomes of the determination being yes, no, and unknown).

The first approach described above is consiste nt with the methods for exploring aggregated summary blood-lead information over time with in each census tract. The second approach allows utilization of some predictive information at the individual child level. This information may help improve prediction, and also may help assess what information might be lost when transitioning from individual-level data to aggregate summary data in the analyses. Unfortunately, due to time and resource constrai nts, only the first method was explored within this project. Thus, the three m easures listed below were calculate d using four different methods. The three measures are:

• P - represents the Proportion of Housing Units within a census tract that are assumed to Meet the Massachusetts Standard of Care at any given time

• F - represents the Proportion of Housing Units within a census tract that are assumed to Not Meet the Massachusetts Standa rd of Care at any given time

• N - represents the Proportion of Housing Units within a census tract with Housing Inspection Information at any given time.

As noted, the measures were generated in four different ways, each handling the longitudinal information in a slightly different manner. Th e four measures, numbered in the model results from 1 to 4, are listed below.

1. Naïve Method 1 – Create a longitudinal hi story for each housing unit inspected, and treat the first inspection observation as being representative for time periods preceding that inspection.
2. Naïve Method 2 – Create a longitudinal hi story for each housing unit inspected, and assume missing information for time peri od preceding the first test on each unit.
3. Naïve Method 3 – Create a longitudinal hi story for each housing unit inspected, and treat the first inspection observation as being representative for time periods preceding that inspection if the housing un it failed, and assume missing information for time period preceding the first test if the unit passed.
4. MDPH Approved Method – Create a longitudi nal history for each housing unit, with different rules for the treatment of the time- period preceding the first test based on (a) the housing inspection result and (b) th e reason for ordering the inspection.27 Note that for housing units with multiple inspect ions, each housing inspection result is assumed to be representative of the house (either pass or fail) until the next resu lt. The last result is carried forward over time (e.g., if the last obser ved inspection on a house passed in November of 1998 – that particular house is assumed to be mee ting the Massachusetts sta ndard of care over all subsequent time periods in the dataset). If mu ltiple inspections occur on the same house within a particular quarter (3-month interval), the maxi mum result (with pass being coded as a 1, and fail being coded as zero) is used to represent the hou se. The 0/1 results are then summed across all observed housing units within ea ch census tract over time (quart ers). The summed results are then divided by the number of housing units re ported within each census tract from the 2000
Census.

While all of the above described housing insp ection variables were investigated in the exploratory data analyses, only the P4, F4, a nd N4 variables associated with the MDPHapproved method of constructing th e longitudinal history within each housing unit observed was considered within the context of the multivariate models.

3.5 Data Linkages

The primary objective of this pi lot study was to utilize combined information from different sources at various levels of ge ographic and temporal specificity to more accurately target geographic areas at high risk for not meeting the 2010 goal of eliminating childhood lead poisoning. As such, work on the st udy required careful integration of a variety of data sources with various characteristics and documentation. Data to support this study were gathered from a variety of sources, including fede ral, state, and local lead pois oning prevention programs, as well as publicly available data downloaded from th e internet (e.g., Census data, EPA’s Toxics Release Inventory), as detailed in the previous sections.

Upon receipt of each data source, the data and supporting documentation was reviewed to gain knowledge on the structure, relati onship, and quality of the data. Database managers worked with the project team (including co llaborators providing data to the project, as well as EPA) to determine the final format for each database, de sired uses of the databases, as well as the requirements for maintaining the databases. Based on this information, separate master databases were constructed for the national mo del and for the high-resolution Massachusetts model that integrate the various environmental, demographic, and programmatic variables, and facilitate statistical analyses of the combined data. These databases were constructed by combining data from a variety of formats incl uding MS SQL Server, MS Access, Excel, ACSII, Access, ArcView, and SAS ® electronic databases. In order to combine the various data sets, they were merged on key fields, including state, county, census tract, and time period. The data being used for analyses of a particular geogra phic level (e.g., county) are comparable because they are representative of that geographic area.

Throughout the development process, checks fo r completeness were conducted on all study databases, and the project team worked with da ta-sharing collaborators and EPA to attempt to complete missing data as necessary to support the proposed statisti cal analyses. Any changes to the databases (corrections, additions, deletions, etc.) were documented in appropriate metadata files. Documentation of the combined ma ster databases is included in Appendix H.28 Standard Operating Procedures (SOPs) were follo wed to ensure the proper storage, backup, and retrieval of datasets created a nd analyzed for this study. The va rious databases were backed up to tape nightly via automated backup routines , and were only accessible to members of the project team. CD-ROM backups we re made on a regular basis to se rve as a safeguard in case the backup system failed for any reason.

Microsoft Access and SQL server were the primary software tools used for data management. The SAS ® System was the primary statistical data anal ysis tool used on this project. ArcView software was used to translate results into maps, as seen in Appendices F and G.

The data utilized for the study were maintained in a manner that preserved the confidentiality of all the data and prevented its unaut horized release. As data files were received from EPA, the original data (e.g., data with pers onal identifie rs) were handled as though they were classified as confidential business informati on (CBI) under the Toxic Substances Control Act (TSCA), even though EPA may not specifically classify these data as “CBI.” The data files were not shared with anyone outside of the project team.29 4.0 EXPLORATORY DATA ANALYSES

Because the goal of this study was to develop a series of statistical models th at predict the risk of childhood lead poisoning at the geographic leve l across multiple response variables (proportion of children screened at or above 5, 10, 15 and 25 ug/dL), all potential pr edictor variables first were explored individually to determine their pr edictive ability. Results from these bivariate analyses were assessed to identify the set of variables to include in the multivariate model that predicts how the risk of childhood lead poisoning changes over time among the various census tracts and counties included in the analysis.

This section of the report provides the results of the series of e xploratory analyses described in Section 2.2, which were performed to assess the pot ential predictive power of various candidate demographic, environmental, and programmatic va riables for potential use in the multivariate models. These exploratory analyses initiated wi th an assessment of the study sample, i.e., the proportion of counties in the sample with comp lete and reliable data for both the response variable and the explanatory variables.

Each candidate predictor variable was reviewed with particular attention focusing on the manner in which the county-level predic tor variables would be merged with the quarterly summary blood-lead information prior to fitting the stat istical models. In preparation for developing longitudinal statistical models, univariate summarie s of each predictor vari able as a function of time were produced. Comparisons of these distri butions were made usi ng side-by-side box-plots for continuous data or bar-charts for categorical data. This helps verify that the data are clean and ready for analysis and helps id entify cells with sparse data. Such descriptive analyses were conducted on each predictor variable database to characterize the distributions of all observed variables using frequency distribut ions for categorical variables, and simple summary statistics (mean, median, mode, minimum, maximum, and sele ct percentiles) for continuous variables.

The univariate descriptions then were followed by fitting a series of cross-sectional bivariate relationships between the blood-lead response variable(s) and each candidate explanatory variable. These cross-sectiona l relationships were explored as a function of time to better understand the stability of these re lationships, and whether they cha nge over time, so that they can be modeled appropriately in the more sophis ticated longitudinal analyses. These analyses also help identify which explan atory variables are most predic tive of the blood-lead response variable.

4.1 Relationship between National Blood-Lead Data and Explanatory Variables

The response variable for the nati onal data analysis consisted of quarterly summary statistics from 1995-2005 on the distribution of observed blood- lead concentrations in counties across the nation, based on information from CDC’s Na tional Childhood Lead Poisoning Surveillance Database. The time series of summary statistics w ithin select counties were initially investigated to determine appropriate exclus ion criteria to ensure that the data retained for analysis represented blood-lead con centrations that were universally re ported (i.e., there were periods of time in which some state or local childhood l ead poisoning prevention programs only reported elevated blood-lead concentrations – and these data needed to be eliminated from the analysis).30 Thus, the number of quarterly summary statisti cs varied from county to county within the analysis dataset.

The national blood-lead data were categorized into four time pe riods – (1) January 1, 1995 to December 31, 1999; (2) January 1, 2000 to Dece mber 31, 2001; (3) January 1, 2002 to December 31, 2003, and (4) January 1, 2004 to December 31, 2005 – so that change over time could be evaluated. Using the specified four time periods split the dataset of quarterly county-level records into roughly similar sizes. Presented belo w are the exploratory an alysis results for the demographic, environmental, and programmatic va riables investigated. Detailed figures and tables containing results are incl uded in Appendix A. A detailed discussion of the results seen in Appendix A is contained in Appendix D.

To allow comparison of the different variables ex plored within each variable type, Tables 4-1 through 4-4 present the log-likelihood statistic from each single covariate model presented in Appendix A for each of the four blood-lead thres hold values, respectively. Each explanatory variable was investigated in four different ways with respect to how the effect of the variable might vary over time within the longitudinal analysis dataset:

1. Investigate the explanatory variable on its own, assuming that the effect remains stable over time.
2. Investigate the explanatory variable with a li near interaction with time, assuming that the effect of the variable on risk of childhood lead poisoning either increases or decreases linearly over time (on the logit scale).
3. Investigate the explanatory variable with a qua dratic interaction with time, assuming that the effect of the variable on risk of childhood lead poisoning either increases or decreases as a quadratic function in time (on the logit scale).
4. Investigate the interaction between the explanat ory variable and four select time periods, which is helpful for diagnosing whether the eff ect remains stable (or changes) over time – but is not particularly useful for the final multivariate model where the application of the model might be to forecast how risk of l ead poisoning might extend into future years.

Within each variable category, th e variable that provided the best fit across the four time variables is indicated with a double asterisk (**) . For example, within the income category, the Categorical time variable achieved the best fit fo r seven of the eight income variables in the model of proportion of children wi th blood-lead levels above 5 μ g/dL. Within that category, Percent No Household Wage achie ved the best model fit across th e 8 income variables. Those variables (indicated w ith the double asterisk) were the mo st likely to become candidate predictors for the multivariate statistical models.31 Table 4-1. Summary of Exploratory Analysis Fit as shown by -2 Log Likelihoods for Pr(PbB >= 5 μg/dL) Models

Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time Median_Family_Income 235522.9 233514.6

Median_HH_Income 235495.9 233439 Median_Per_Capita_Income 235600.3 234626.6 233735.4 Pct_HH_No_Earnings 235611.3 235601.3 234404.8 233260.3

Pct_HH_No_Wage 235579.7 235567.8 234323.0** 233183.8*

Pct_LT_Poverty 235763.3 234363.2 233608.5 Pct_Family_Income_LT_Poverty 235761.5 235744.8 234446.2 233751.1 Income

Pct_LE_5Yrs_LT_Poverty 235794.2 234373.9 Pct_Asian 235769 235681.3 234627.7 234511.6 Pct_Black 235867.4 234612.0** Pct_White 235915.1 235259.3 233688.1 Pct_NHOPI 235888.9 235536.9 235918.5 Pct_Other_Race 235862.1 235848.1 235559.1 235250.1 Pct_Multi_Race 235817.2 234679.6 234595 Race

Pct_Hispanic 235851.8 233502.6* Median_Rent 235265.4 235189.1 234046.8** 233401.0* Housing

Cost Housing_Value 235571.9 235562.9 235128.2 234857.9 Pct_Rented 235913.4 235887.7 Occupancy Pct_Vacant 235884.8 235872.2 235427.2** 234479.4*

Single
Parent Pct_Single_Parent 235878.1 235884 234527.9** 233660.7*

Median_Yr_Built 235421.5 235483.4 234415.3 Median_Yr_Occ_Built 235412.7 235432.6 234214.3 233119.1

Pct_Built_Pre_1940 235268.9 235277.3 233803.4 233243.7 Pct_Built_Pre_1950 235241.8 233574.5 232990.3 Pct_Built_Pre_1960 235344.5 235357.7 233484.5** 232839.7* Pct_Built_Pre_1970 235449 235463.1 233650.7 232904.6 Pct_Built_Pre_1980 235487.1 235503 233834.3 232946.2 Pct_Occ_Built_Pre_1940 235277.5 235285.1 233834 233291 Pct_Occ_Built_Pre_1950 235233.3 235245.1 233601.1 233030.9 Pct_Occ_Built_Pre_1960 235359.2 233499.6 232866.4 Pct_Occ_Built_Pre_1970 235458.6 235471.3 233666 232928.4 Home Age

Pct_Occ_Built_Pre_1980 235495.4 235510.7 233840.8 232959.7 Pct_LE_Six 235827.7 235829.8 234448.4** 233449.9 Children Num_LE_Six 235905.5 235924.2 227590.8* Pct_LT_9th_Grade 235850.1 235848.4 234398.6 233744 Pct_No_HS_Degree 235715.8 234321.2 233411.6

Pct_No_College 235509.4 234237.9 Education

Pct_No_College_Degree 235556.5 235505.7 234226.0** 233106.0* Total_Housing_Units 235906.3** 235922.2 Total_Pop 235908.5 235928.6 228102.6* Population Housing_Density 235920.4 235925.8 235699.232 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time air_avg 235905.9 235906.7 235836 air_med 235905 235905.5 235921 air_p95 235907.6 235909.8 235815.6** 235735.7* air_avg_p95 235906.4 235909.6 233727.0* air_med_p95 235907.2 235135.4** air_med_p95 235914.4 air_p95_p95 235911.9 235918.5 236090.1 235244.7 air_avg_p99 235906.2 air_med_p99 235903.5 235766.3** 235718.3 Air Lead

air_p95_p99 235905.8 235695.8 TRI Compounds air_fug 235943.8 235684.6 235325.2 TRI Compounds air_tot 235922.9 235940.6 235199.3 TRI Compounds air_stk 235921.5 235940.7 235406.7 235355.6 TRI Compounds under_inj 235925.3 235968.5 235986.4 TRI Compounds water_surf 235921.3 235942.9 235910.3 235912.1

TRI Lead Only air_fug 235927.2 235312.9 235155.4 TRI Lead Only air_tot 235929.4 235955.8 235385.1 235169.4 TRI Lead Only air_stk 235928.7 235955.2 235437.5 TRI Lead Only under_inj 235928.5 235985.8 235997.1 TRI Lead Only water_surf 235926.6 235952.3 235942.9

TRI Lead Total air_fug 235927.3 235950.7 235244.9** 234850.4* TRI Lead Total air_tot 235930 235957.3 235036 TRI Lead Total air_stk 235955.9 235478.1 235321.1 TRI Lead Total under_inj 235928.4 235956.1 235984.3 235999.2 TRI Lead Total water_surf 235926.7 235933 tri_as1_p95 235909 234966.5 235011.7 tri_as2_p95 235912.6 235917.9 234262.7 tri_as3_p95 235907.5 235910.7 tri_af1_p95 235914 235919.2 235132.3 235125.1 tri_af2_p95 235910.9 235916.8 234837.1 233669.7 tri_af3_p95 235915.8 234396.5 233058.4 tri_at1_p95 234716.4 234606.5 tri_at2_p95 235911.7 235915.7 233993.9 232572.7 tri_at3_p95 235910.2 233685.3** 232564.5* tri_ws1_p95 235919.1 235370.9 235422 tri_ws2_p95 235908.1 235915.4 233861.9 233253.8 tri_ws3_p95 235908 tri_ui1_p95 235904.1 235904.1 234661.9 233559.3 tri_ui2_p95 235904.1 235904.1 234661.9 233559.3 tri_ui3_p95 235904.1 235904.1 234661.9 233559.3 tri_as1_p99 235908.5 235519.5 tri_as2_p99 235908.3 235914.4 235104.8 tri_as3_p99 235906.6 235912 tri_af1_p99 235906.1 235735.5 tri_af2_p99 235907.5 235912.6 234680 234116.8 tri_af3_p99 235905.7 235128.3 234668.4 tri_at1_p99 235909.1 235711.7 235364.2 TRI

tri_at2_p99 235915.6 234704.633 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time tri_at3_p99 235907.7 235913.8 235213.9 234780.2 tri_ws1_p99 235907 235912.3 235863.4 235847.8 tri_ws2_p99 235907.6 235913 235667.2 235712.8 tri_ws3_p99 235907.6 235913.9 235706.5 235748.5

tri_ui2_p99 235904.1 235904.1 234661.9 233559.3 tri_ui3_p99 235905 235910.1 235921.5 235887.1 CDC_cur_lag6 235415.6 234816.9 234090.5 CDC_cur_lag12 235427.3 235275.1 234678.5 233854.6 CDC_cur_lag18 235270.6 234599.8 233781.8* CDC_cur_lag24 235847 235039.4 234566.7 CDC_cur_lag30 235897.1 235246 CDC_cur_lag36 235727.6 235050.5 234702.5 234162.9 HUD_cur_lag6 235914 235833.5 235432.4 235494.8 HUD_cur_lag12 235761.9 235691.3 235358.3 235381.6 HUD_cur_lag18 235762.7 235673.8 235437.5 235352.7 HUD_cur_lag24 235707.6 235525.1 235434.3 235211.7 HUD_cur_lag30 235672.8 235487.1 235445.4 235175.9 HUD_cur_lag36 235626.3 235273 234912.4 CDC_cum_lag6 235611.1 234603.5 234200.7 233952.6 CDC_cum_lag12 235762.8 234668.8 234304.8 CDC_cum_lag18 235859.9 234760.3 234472.7 234162.5 CDC_cum_lag24 235898.2 234851.6 234646.1 CDC_cum_lag30 234933.1 234793.8 234433.2 CDC_cum_lag36 235883 234920.4 234824.7 234466.2 Funding HUD_cum_lag6 235908.5 234878.9 234725.3 234382.8 HUD_cum_lag12 235924.3 234413.4 HUD_cum_lag18 235961.9 235051 234650.3 HUD_cum_lag24 235952.8 235099.6 234558.3 234503.5 HUD_cum_lag30 235897.3 235132.9 234450.4 234499.3 HUD_cum_lag36 235794 235148.6 234334.5 tot_cur_lag6 235918.7 235841.5 235210 235306.5 tot_cur_lag12 235727.4 235169.7 235194.9 tot_cur_lag18 235799.2 235654.7 235245.5 235101.6 tot_cur_lag24 235742.9 235439.1 235226.8 234897.9 tot_cur_lag30 235657.8 235355.1 235216.4 234839.7 tot_cur_lag36 235546.3 235043.1 235054.3 234518.3 tot_cum_lag6 235928.9 234463 tot_cum_lag12 234735 234380.2 234067.2 tot_cum_lag18 235958 234825.3 234340.7 234123.9 tot_cum_lag24 235944.2 234876.7 234168.7 tot_cum_lag30 235888.1 234190.6 234192.1 tot_cum_lag36 235774.3 234937.9 234115.8** 234217.6 HUD_cur 252824.5 252845.5 252876.3 HUD_cum 252864.5 252670.1 252673.7 252679.3 CDC_cur 252679.7 252170.2 251830.5 251799.9 CDC_cum 252586.4 252213.5 252226.6 252202.334 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time
Current – HUD+CDC 235788 235296.1 235214.1 Funding Cumulative – HUD+CDC 235940.8 234583.8 234050.2 Screening screen_penetration 232188 232220.8 231442.3** 230769.8* ** Variable factor(s) showed best f it when adjusted for degrees of freedom and were thus chosen to represent parameter category in multivariate analysis. * Variable factors showed best fit; however, were not included in multivariate analysis because the time categorical variable had less th an ideal prediction properties.

Table 4-2. Summary of Exploratory Analysis Fit as shown by -2 Log Likelihoods for Pr(PbB >= 10 μg/dL) Models

Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time Median_Family_Income 252403.4 252372.3 251685 251406

Median_HH_Income 252382.8 251605.3 251222.3 Median_Per_Capita_Income 252486.2 252425 251792.5 251439.9 Pct_HH_No_Earnings 252446.7 252444 251506.4 251091

Pct_HH_No_Wage 252363.4 252336.2 251373.8** 250935.7 Pct_HH_Public_Assist 252779.8 252806.7 251403.6 250521.9 Pct_LT_Poverty 252743.7 252763.4 251392.3 250602.3 Pct_Family_Income_LT_Poverty 252715.7 252736.5 250516 Income

Pct_LE_5Yrs_LT_Poverty 252839.5 252848.3 250483.1* Pct_Asian 252668.4 252569.2 252369.5 250374.8 Pct_Black 253025.3 253012.9 252123.9 251966.4 Pct_White 252930.5 252888 252285.1 Pct_NHOPI 252837.9 253276.4 252846.7 Pct_Other_Race 252775.6 252727.4 252540.6 252403.1 Pct_Multi_Race 252788.2 252745.4 251886.2** Race

Pct_Hispanic 252813.9 248350.1* Median_Rent 252008.3 251790.4 251043.0** 250455.8* Housing Cost Housing_Value 252392.2 252324.9 252139.5 Pct_Rented 253049.2 252943.3** 251135.1* Occupancy Pct_Vacant 252968.6 252990.6 252368.7 Single Parent Pct_Single_Parent 253124 253084.3 251927.9** 251596.8* Median_Yr_Built 252361 252361 252263.4 252227.6 Median_Yr_Occ_Built 252391.1 252391.8 251439.8 250974.6

Pct_Built_Pre_1940 252030 251996.7 251206.9 Pct_Built_Pre_1950 252006.6 251977.8 251082.9 250788.7 Pct_Built_Pre_1960 252250.6 252241.6 251073.7** 250590.1* Pct_Built_Pre_1970 252433.4 252431.3 251254.4 250672.3 Pct_Built_Pre_1980 252481.8 251344.2 250756.3 Pct_Occ_Built_Pre_1940 252035.3 252001.6 251398 Pct_Occ_Built_Pre_1950 252020.3 251992 251106 250805.4 Pct_Occ_Built_Pre_1960 252267.3 251103.2 250611.8 Pct_Occ_Built_Pre_1970 252477.8 252477.2 250713.2 Home Age

Pct_Occ_Built_Pre_1980 252508.2 252502 251371.5 250782.6 Pct_LE_Six 252767 252725.9 251597.1** 251003.9* Children Num_LE_Six 252881.3 252835.735 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time Pct_LT_9th_Grade 252734.3 252691.3 251568.3 249792.4* Pct_No_HS_Degree 252574.1 252551.1 251197.8 250216.7

Pct_No_College 252279.4 252216.7 251090.4** 250602.5 Education

Pct_No_College_Degree 252237.3 251142.9 250691.1 Total_Housing_Units 252895.2 252818.7 251937 243207.7 Total_Pop 252890.7 252834.7 251907.9** 242397.1* Population Housing_Density 252854.6 252838.1 252786.9 252547.2 air_avg 252880.2 252861.4 252906.8 252015.3 air_med 252884.8 252867.1 253026.4 251966.2* air_p95 252876.9 252863.8 252740.9** 252187.9 air_avg_p95 252912.5 252878.3 249593.0* air_med_p95 252910 252728.4** 252883.7 air_p95_p95 252885 253156.2 air_avg_p99 252841.7 252844 252840.5 252814.5 air_med_p99 252839.7 252842.6 252819.6 252786.5 Air Lead

air_p95_p99 252848.7 252852.6 252832.5 TRI Compounds air_fug 252867.9 252889.1 252914 252314.2 TRI Compounds air_tot 252889.4 252901.7 252880.6 252313.3 TRI Compounds air_stk 252884.9 252894.2 252815.7 252454.4 TRI Compounds under_inj 252862.5 252885.6 252919.1 TRI Compounds water_surf 252864.1 252885.1 252884.7

TRI Lead Only air_fug 252869.4 252894.4 252747.2 252624.6 TRI Lead Only air_tot 252884.3 252905.2 252653.5** 252138.6 TRI Lead Only air_stk 252882.2 252900.4 252707.7 252298.7 TRI Lead Only under_inj 252866.5 252889.9 252907.5 252931.1 TRI Lead Only water_surf 252866.8 252892.3 252900.6

TRI Lead Total air_fug 252871.7 252896.1 252798.3 252475 TRI Lead Total air_tot 252891.5 252909.9 252702.6 252031.8* TRI Lead Total air_stk 252890.3 252905.6 252699.8 252203.4 TRI Lead Total under_inj 252867.1 252889.4 252904.5 252929.7 TRI Lead Total water_surf 252868.4 252893.9 252897.6 tri_as1_p95 252887.8 252864.5 252435.7 tri_as2_p95 252886.8 247577.4 tri_as3_p95 252922.4 252903.3 251601.7 248106.4 tri_af1_p95 252923.9 252910.9 252833 252941.9 tri_af2_p95 252864.3 252862.8 252237.8 248140.5 tri_af3_p95 252883.1 252872.3 251998.1 248650.8 tri_at1_p95 252886.9 252855.8 252210.2 tri_at2_p95 252911.6 251677.1 247510.1* tri_at3_p95 252893.2 251553.2 248305.6 tri_ws1_p95 252904.6 252880.2 253019.4 252996.8 tri_ws2_p95 252885.1 251407.9** tri_ws3_p95 252892.3 252895.8 251422.9 248164.1 tri_ui1_p95 252840.1 252840.1 251871.5 251377.4 tri_ui2_p95 252840.1 252840.1 251871.5 251377.4 tri_ui3_p95 252840.1 252840.1 251871.5 251377.4 Tri

tri_as1_p99 252856.436 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time tri_as2_p99 252864.7 252768.7 252011.1 tri_as3_p99 252867.3 252867.6 252781.5 tri_af1_p99 252855.1 252872.8 tri_af2_p99 252866.8 252862.5 252719 251667.2 tri_af3_p99 252851.4 252852.6 252743.1 251999.2 tri_at1_p99 252844.8 252850.4 252865.3 252235.8 tri_at2_p99 252869.5 252869.6 252035.9 tri_at3_p99 252863.2 252860.2 252780.2 252047.7 tri_ws1_p99 252864.6 252865.7 252817.4 252839.7 tri_ws2_p99 252853.8 252857.9 252679.5 252740.5 tri_ws3_p99 252858.5 252859.9 252756.3 tri_ui1_p99 252840.1 252840.1 251871.5 251377.4 tri_ui2_p99 252840.1 252840.1 251871.5 251377.4 tri_ui3_p99 252842.9 252851.7 252822.1 252824.4 CDC_cur_lag6 252593.4 252079 251795.6 CDC_cur_lag12 252468.6 251799 251576.9** 251723.5 CDC_cur_lag18 252652.5 252011.3 251869.8 CDC_cur_lag24 252698.2 252155.7 251983.6 251948.3 CDC_cur_lag30 252579.9 252298.4 252201.5 CDC_cur_lag36 252861.4 252659.6 HUD_cur_lag6 252848.9 252867.7 252599.3 252883.1 HUD_cur_lag12 252836 252839.2 252674.2 252823 HUD_cur_lag18 252857.7 252873.7 252693.1 252856.3 HUD_cur_lag24 252847.7 252847.4 252785.4 252804.8 HUD_cur_lag30 252821.7 252849.1 252709.2 HUD_cur_lag36 252800.2 252769.4 252762.3 252713.8 CDC_cum_lag6 252638.8 252230.7 252255.1 CDC_cum_lag12 252277.9 252311.3 252258.1 CDC_cum_lag18 252387.7 252319.1 CDC_cum_lag24 252817.8 252422.6 252451.6 252377.5 Funding CDC_cum_lag30 252840.3 252479.6 252498.3 252424.6 CDC_cum_lag36 252836.1 252501 252515.7 252446.4 HUD_cum_lag6 252859.8 252650.4 252640.5 HUD_cum_lag12 252856.9 252607.7 252639.5 252615.3 HUD_cum_lag18 252856.7 252636 252602.2 HUD_cum_lag24 252852.4 252583.7 252612.3 252582.4 HUD_cum_lag30 252853.1 252570.4 252586.8 252559.1 HUD_cum_lag36 252556.3 252544.6 252520.3 tot_cur_lag6 252840.8 252857 252532.5 252837.6 tot_cur_lag12 252848.3 252826.7 252615.3 252765.8 tot_cur_lag18 252863.6 252859.5 252627.1 252788.9 tot_cur_lag24 252855.8 252803.6 252686.2 252715.8 tot_cur_lag30 252838.7 252608.6 252773.5 tot_cur_lag36 252796.7 252732.3 252677.3 252653.3 tot_cum_lag6 252863.1 252551.2 252577.9 252560.4 tot_cum_lag12 252861 252531.9 252565.2 tot_cum_lag18 252858.7 252530.3 252565.1 252537.437 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time tot_cum_lag24 252518.4 252551.1 252528.4 tot_cum_lag30 252854.9 252510.3 252535.2 252515.6 tot_cum_lag36 252855.1 252503 252491.2 HUD_cur 252824.5 252845.5 252876.3 HUD_cum 252864.5 252670.1 252673.7 252679.3 CDC_cur 252679.7 252170.2 251830.5 251799.9 CDC_cum 252586.4 252213.5 252226.6 252202.3

Current – HUD+CDC 252812.9 252831.6 252573.5 252837 Funding

Cumulative – HUD+CDC 252861.2 252590.5 252602.3 252591.5 Screening screen_penetration 243704.7 243043.2** 243416.3 ** Variable factor(s) showed best f it when adjusted for degrees of freedom and were thus chosen to represent parameter category in multivariate analysis. * Variable factors showed best fit; however, were not included in multivariate analysis because the time categorical variable had less th an ideal prediction properties.

Table 4-3. Summary of Exploratory Analysis Fit as shown by -2 Log Likelihoods for Pr(PbB >= 15 μg/dL) Models

Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time Median_Family_Income 292772.5 292744.3 292659.9 292689.4

Median_HH_Income 292732.9 292590.8 292579.8 Median_Per_Capita_Income 292812.2 292732.2 292766.8 Pct_HH_No_Earnings 292772.9 292756 292428.7

Pct_HH_No_Wage 292673.9 292612.3 292449.8** 292310.0* Pct_HH_Public_Assist 293171.5 293226 292889.5 292588 Pct_LT_Poverty 293148.6 293226.2 292933.3 292609.9 Pct_Family_Income_LT_Poverty 293112.9 292898.4 292547.8 Income

Pct_LE_5Yrs_LT_Poverty 293290.6 293338.3 292997.4 292700.3 Pct_Asian 292966.2** 293176.7 292343.9 Pct_Black 293610.6 293556.2 293324.7 293411.1 Pct_White 293448.5 293343.9 293334.4 293311.7 Pct_NHOPI 293217.4 293230 293216.3 Pct_Other_Race 293165.6 293130.1 293088.7 293085.7 Pct_Multi_Race 293181.3 293035.3 292946.7 Race

Pct_Hispanic 293237.8 293524.2 292091.3* Median_Rent 292358.3 292076.9 291971.3** 292091.4 Housing

Cost Housing_Value 292727.1 292691.2 292732.6 292573.7 Pct_Rented 293905.5 293533.3** Occupancy Pct_Vacant 293703.2 293721.5 293765 293491.3* Single
Parent Pct_Single_Parent 293863 293781.9**

Median_Yr_Built 292980.6 292980.6 293008.9 292998.6 Median_Yr_Occ_Built 292999.8 293001.5 292856.3 292749.7 Home Age Pct_Built_Pre_1940 292381.9 292335.9 292195.2 292161 Pct_Built_Pre_1950 292404.2 292364.6 292078.8 Pct_Built_Pre_1960 292815.6 292525.3 292400.738 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time Pct_Built_Pre_1970 293100 293103.4 292826.6 292669 Pct_Built_Pre_1980 293086.5 293089.8 292851.2 292684.6 Pct_Occ_Built_Pre_1940 292356.8 292313.7 292177 292138.7 Home Age Pct_Occ_Built_Pre_1950 292357.3 292139.4** 292071.1* Pct_Occ_Built_Pre_1960 292842.2 292546.1 292418.1 Pct_Occ_Built_Pre_1970 293160.9 292884 292725.2 Pct_Occ_Built_Pre_1980 293111.9 292711 Pct_LE_Six 293214.5 292911.5** 292859.9 Children Num_LE_Six 293389 293281.3 293584.6 290311.1* Pct_LT_9th_Grade 293199.4 293131.7 293000.2 292491.7 Pct_No_HS_Degree 292960 292951 292701.2 292376

Pct_No_College 292557.4 292434.4 292260.2** 292152.4* Education

Pct_No_College_Degree 292613.7 292501.1 292328.1 292254.9 Total_Housing_Units 293444.1 293298.7 293466.2 Total_Pop 293417.4 293297.9 293561.7 290521.5* Population Housing_Density 293273.5 293267.2** 293232.3 air_avg 293392.7 293361.7 293366.1 air_med 293410.8 293382.5 293418.4 292995.5* air_p95 293378.4 293353.2 293316.8** 293112.1 air_avg_p95 293522.6 293462.9 293467.7 292547.1* air_med_p95 293524.1 293509.8** 292580.3* air_p95_p95 293421.4 293566.7 292731.7 air_med_p99 293289.2** 293293.8 293304.7 293262.7 air_avg_p99 293312.1 293284.2 air_med_p99 293289.2** 293293.8 293304.7 293262.7 Air Lead

air_p95_p99 293307 293306.7 TRI Compounds air_fug 293312 293329.5 293358.7 TRI Compounds air_tot 293391.4 293416.4 TRI Compounds air_stk 293379.4 293373.4 293382.6 293324 TRI Compounds under_inj 293280.9** 293325.9 TRI Compounds water_surf 293302.9 293322.7 293339.6

TRI Lead Only air_fug 293303.9 293327.8 293305.4 293302.9 TRI Lead Only air_tot 293317.6 293193.9* TRI Lead Only air_stk 293339.1 293351.3 293337.7 293240.5 TRI Lead Only under_inj 293317.4 293358.1 TRI Lead Only water_surf 293295.9 293321.1 293362.7

TRI Lead Total air_fug 293334.7 293331 TRI Lead Total air_tot 293361.2 293369.8 293351.5 293196.7 TRI Lead Total air_stk 293364.2 293370.5 293355.6 293251.3 TRI Lead Total under_inj 293315.8 293344.4 293356.7 TRI Lead Total water_surf 293300.6 293326 293350.3 293369 tri_as1_p95 293431.9 293374.3 293326.2 293354.1 tri_as2_p95 293447.9 tri_as3_p95 293468.1 293184.4 292325.9 tri_af1_p95 293511.4 293486.3 293488.2 293535.3 tri_af2_p95 293346.9 293341.6 292046.2* Tri

tri_af3_p95 293406.8 293388.9 293217 292195.839 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time tri_at1_p95 293445.5 293386 293309.2 tri_at2_p95 293490.2 293434.4 293188.8 tri_at3_p95 293524.3 293473.4 293159 292314 tri_ws1_p95 293384.9 293682.5 tri_ws2_p95 293397.5 293388.1 293081.1** 292172.3 tri_ws3_p95 293449.3 293433.9 293122.9 292238.7 tri_ui1_p95 293260.7 293260.7 293101.5 tri_ui2_p95 293260.7 293260.7 293101.5 tri_ui3_p95 293260.7 293260.7 293101.5 tri_as1_p99 293300.7 293299.9 293278.9 tri_as2_p99 293334.1 293325.5 tri_as3_p99 293352.7 293342.5 293339.9 293153 tri_af1_p99 293317.7 293328.2 293169 tri_af2_p99 293341.5 293329 293303.8 293016 tri_af3_p99 293319.2 293311.8 293303.8 293101.5 tri_at1_p99 293295.1 293296.7 293129.5 tri_at2_p99 293359.2 293347.3 293169.3 tri_at3_p99 293332.6 293315.4 293315.4 293153.9 tri_ws1_p99 293338.4 293329.3 293345.2 tri_ws2_p99 293304.9 293222.9 293282 tri_ws3_p99 293308.9 tri_ui1_p99 293260.7 293260.7 293101.5 tri_ui2_p99 293260.7 293260.7 293101.5 tri_ui3_p99 293269.5 293293.4 293282.9 CDC_cur_lag6 293205.3 292977.5 292865.8 292890.8 CDC_cur_lag12 293218.4 292865.8 292774.0** 292863.8 CDC_cur_lag18 293255.1 292878.1 292799.5 292857.9 CDC_cur_lag24 293274.7 292961.8 292890.1 292900.2 CDC_cur_lag30 293176.8 293015.4 293003.6 CDC_cur_lag36 293290.1 293206.1 293076.1 293048.7 HUD_cur_lag6 293284.6 293128.1 293287.2 HUD_cur_lag12 293253.2 293249.9 293149.3 293242.7 HUD_cur_lag18 293258.5 293268.9 293117.4 Funding HUD_cur_lag24 293253.7 293257.1 293203.1 293251.9 HUD_cur_lag30 293238.4 293251.9 293106.6 293268.9 HUD_cur_lag36 293208.4 293194.5 293190.6 293186.5 CDC_cum_lag6 293422.1 293214.4 293231.5 293238 CDC_cum_lag12 293393.4 293207.1 CDC_cum_lag18 293368.7 293212.7 293218.9 CDC_cum_lag24 293218.2 293214.6 CDC_cum_lag30 293218 293228.4 293207.6 CDC_cum_lag36 293310.8 293205.4 293218.2 293193.2 HUD_cum_lag6 293239.4 293145.3 HUD_cum_lag12 293234.4 293120.8 293146.5 293150.1 HUD_cum_lag18 293243.7 293134.7 293163.9 293150.3 HUD_cum_lag24 293257.1 293145.3 293148.9 HUD_cum_lag30 293267.1 293164.4 293133.940 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time HUD_cum_lag36 293278.3 293149.3 293146.1 293110.8 tot_cur_lag6 293267.9 293280.2 293102.1 293268.2 tot_cur_lag12 293260.9 293241.6 293127.1 293218.6 tot_cur_lag18 293262.4 293261.9 293237.1 tot_cur_lag24 293257.8 293239.6 293160.8 293215.4 tot_cur_lag30 293245.2 293258.1 293049.9 293252.3 tot_cur_lag36 293206.8 293151.9 293164.7 tot_cum_lag6 293257.7 293107.8 293124.1 tot_cum_lag12 293100 293123.9 293133.9 Funding tot_cum_lag18 293257.7 293114.6 293142.1 293138.5 tot_cum_lag24 293267.9 293154.5 293141.8 tot_cum_lag30 293275.4 293128.8 293153.2 tot_cum_lag36 293284.2 293130.5 293142.3 293117.5 HUD_cur 293269.1 293287.6 293172.6 293293 HUD_cum 293156.9 293156.8 293193.2 CDC_cur 293240.8 292881.8 292906.9 CDC_cum 293450.7 293231.1 293246.3 293256.8

Current – HUD+CDC 293267.6 293284.9
Cumulative – HUD+CDC 293135.6 293170.4 Screening screen_penetration 286744.8** ** Variable factor(s) showed best f it when adjusted for degrees of freedom and were thus chosen to represent parameter category in multivariate analysis. * Variable factors showed best fit; however, were not included in multivariate analysis because the time categorical variable had less than ideal prediction properties.

Table 4-4. Summary of Exploratory Analysis Fit as shown by -2 Log Likelihoods for Pr(PbB >= 25 μg/dL) Models

Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time Median_Family_Income 364225.4 364192.5 364302.8 364426

Median_HH_Income 364098.6 364048 364139.1 364252.5 Median_Per_Capita_Income 364504.4 364426.7 364532.3 364681.7 Pct_HH_No_Earnings 363967.7 363893.9** 364071.9 364002.6

Pct_HH_No_Wage 363920.7 363960.8 363949.4 Pct_HH_Public_Assist 364638.9 364705.3 364637.4 Pct_LT_Poverty 364611.6 364741.3 364847.1 364616.8 Pct_Family_Income_LT_Poverty 364541.7 364696 364777.3 364527.4 Income

Pct_LE_5Yrs_LT_Poverty 364863.6 364929.7 Pct_Asian 364527.2 364399.3** 364394.5* Pct_Black 365490.4 365511.8 Pct_White 365248.9 365038.9 365365.2 Pct_NHOPI 364794.8 364856 364770.1 Pct_Other_Race 364698 364678.7 Pct_Multi_Race 364801.9 364772.8 364831.2 364838.6 Race

Pct_Hispanic 364789.2 364703.1 Housing Median_Rent 363769.6 363524.9** 363876.341 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time
Cost Housing_Value 364228.4 364217.5 364266.9 364326.7 Pct_Rented 366003.6** 366069.6 366361.5 Occupancy Pct_Vacant 366381.1 366351.7 366647.4 366332.7 Single
Parent Pct_Single_Parent 365919.4 365878.4** 365993.3 366022.9

Median_Yr_Built 364858.6 364893.4 364874.1 364997.8 Median_Yr_Occ_Built 364890.1 364927.1 365077.9 365041.9

Pct_Built_Pre_1940 363573.4 363524.8 363636.7 363617.9 Pct_Built_Pre_1950 363716.9 363694.8 363795.8 363754.9 Pct_Built_Pre_1960 364677.4 364715.4 364720.3 Pct_Built_Pre_1970 365255 365322.4 365404.2 365310.9 Pct_Built_Pre_1980 365157.7 365231.3 365333.9 365231.9 Pct_Occ_Built_Pre_1940 363502.8** 363615.6 363591.1 Pct_Occ_Built_Pre_1950 363710.5 363694.8 363795.4 Pct_Occ_Built_Pre_1960 364710.4 364754.6 Pct_Occ_Built_Pre_1970 365363.2 365429.2 365509.7 365420.4 Home Age

Pct_Occ_Built_Pre_1980 365200.7 365373.4 365276 Pct_LE_Six 364874.1 364787.6** 365003.5 Children Num_LE_Six 365297.1 Pct_LT_9th_Grade 364694.7 364636.7 364676.4 364634.3 Pct_No_HS_Degree 364365.8 364490.3 364359.9

Pct_No_College 363888.4 363710.1** 363897.3 363941.3 Education

Pct_No_College_Degree 364172.5 363983.5 364160.7 Total_Housing_Units 365469.1 365116.6 Total_Pop 365377.3 365172.2 Population Housing_Density 364890.0** 364908.1 364921.6 364925.2 air_avg 365109.3 365098.9 365098.8 365022.8* air_med 365149.3 365142.8 air_p95 365066.7 365050.9** 365053.1 365028.1 air_avg_p95 365506.4 365342.9 air_med_p95 365605.6** 365444.5* air_p95_p95 365231.4 365186.4 365205.3 air_med_p99 364910.2** 364934.1 364912.5* air_avg_p99 364950.2 364949.7 364959 364957.4 air_p95_p99 364974.5 364974.2 364984.1 TRI Compounds air_fug 365029.5 365019.8 365019.4 TRI Compounds air_tot 365254.2 365215.5 365259.6 TRI Compounds air_stk 365170.3 365147.7 365176.2 365183.3 TRI Compounds under_inj 364946 364964.8 365107.4 TRI Compounds water_surf 364940.3 364958 364980.3 364994 Tri TRI Lead Only air_fug 364920.3 365037.8

TRI Lead Only air_tot 364918.4 364960.3 364976.7 TRI Lead Only air_stk 364950.5 364975.5 364992.6 365001.8 TRI Lead Only under_inj 364931.6 364947.7 364984.2 364995.3 TRI Lead Only water_surf 364934.6 364960.9 365008.7 TRI Lead Total air_fug 364905.3** 364928.5 364977.9 TRI Lead Total air_tot 364996.6 365013.8 365014.842 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time TRI Lead Total air_stk 365012.9 365029.9 365049.1 365054.7 TRI Lead Total under_inj 364926.7 364942.8 364980 364990.4 TRI Lead Total water_surf 364946.9 364973.4 365004.6 365022.5 tri_as1_p95 365325.7 365249.4 365249.7 tri_as2_p95 365223.1 365193.4 365152.7 tri_as3_p95 365559.3 365489.7 365459.1 365423.2 tri_af1_p95 365498 365506.5 365620 tri_af2_p95 365113.2 365113.5 365005.7 tri_af3_p95 365283.2 365267 365225.7 365145.6 tri_at1_p95 365406.5 365320.9 365306.8 365423.7 tri_at2_p95 365435.5 365358.5 365319.4 365270.2 tri_ws1_p95 365307.8 365270.9 365266.6 tri_ws2_p95 365239.4 365230.3 365195 tri_ws3_p95 365338.3 365316.1 365285.3

Tri tri_ui1_p95 364871.7 364871.7 365011.6 364990 tri_ui2_p95 364871.7 364871.7 365011.6 364990 tri_ui3_p95 364871.7 364871.7 365011.6 364990 tri_as1_p99 364965.4 364961.3 364961.2 364964.3 tri_as2_p99 365027.6 365021.2 365024.1 364974.7 tri_as3_p99 365079.1 365059.8 365067.5 365020.9 tri_af1_p99 365068 365051.1 tri_af2_p99 365053.7 365042.3 365038.6 364985.1 tri_af3_p99 365022.6 365006.5 365010.2 364974 tri_at1_p99 365048.8 365036.8 365050 364994.8 tri_at2_p99 365034.9 365011.9 365019.8 365005.5 tri_at3_p99 364999.7 364972.1 364983 tri_ws1_p99 364994.9 364977.9 364985.8 364990.4 tri_ws2_p99 364991.4 364981.6 364976.5 tri_ws3_p99 364951.1 364993.6 tri_ui1_p99 364871.7 364871.7 365011.6 364990 tri_ui2_p99 364871.7 364871.7 365011.6 364990 tri_ui3_p99 364867.2 364864.5** 364857.2* CDC_cur_lag6 365029.5 364956.9 364956.9 364945.6 CDC_cur_lag12 365008.6 364858.7 364871.8 364857.2 CDC_cur_lag18 364967 364840.3 364881.1 364852 CDC_cur_lag24 364989 364897 364960.1 CDC_cur_lag30 364937.9 364919.4 364963.5 364906.9 CDC_cur_lag36 364893.3 364894.9 364991.3 364904.2 HUD_cur_lag6 364916 364927.7 364808.6 364920 Funding HUD_cur_lag12 364889.1 364804.6 364900.9 HUD_cur_lag18 364869.8 364768.1** HUD_cur_lag24 364850.4 364863.1 364824 364891.8 HUD_cur_lag30 364824.3 364841.4 364789.4 HUD_cur_lag36 364796.1 364782.8 364802.3 364768.8* CDC_cum_lag6 365040.7 365009.7 365084.8 365040 CDC_cum_lag12 365006.7 365000.8 365059.1 365027.3 CDC_cum_lag18 364981.8 365047.7 365021.543 Parameter Category Variable Name X Only Linear Time Quadratic Time Categorical Time CDC_cum_lag24 364957 364993.3 365033.1 365011.9 CDC_cum_lag30 364934.4 364984.2 365017.3 CDC_cum_lag36 364920.3 364978 365004.7 HUD_cum_lag6 364860.6 364793.5 364808.3 364816.4 HUD_cum_lag12 364850.7 364788 364807.5 HUD_cum_lag18 364851.8 364791.1 364810.2 364794.2 HUD_cum_lag24 364867.1 364816.1 364790.5 HUD_cum_lag30 364877.2 364807.6 364803.6 364772.6 HUD_cum_lag36 364892.6 364826.6 364809.5 364775.8 tot_cur_lag6 364930.2 364941.5 364842.7 364926.8 tot_cur_lag12 364840.7 364900 tot_cur_lag18 364862.5 364872.6 364795.3 364887.3 Funding tot_cur_lag24 364862 364873 364853.4 tot_cur_lag30 364830.9 364848.9 364794.7 364868.3 tot_cur_lag36 364793.7 364783.3 364816.2 364776.2 tot_cum_lag6 364890.4 364819.5 364839.4 364845.2 tot_cum_lag12 364876.1 364814 364833.9 364836.1 tot_cum_lag18 364873.5 364819 364837.1 364830.7 tot_cum_lag24 364884.2 364833.1 364846 364832.5 tot_cum_lag30 364889.6 364839.5 364821.7 tot_cum_lag36 364902.3 364849.2 364829.3 HUD_cur 364930.7 364948 364886.1 364936.8 HUD_cum 364883.2 364815.7 364835.9 CDC_cur 365027.8 364962.7 364969.9 364980.6 CDC_cum 365081.4 365028.2 365131.3 365060.7

Current – HUD+CDC 364946.5 364966.1 364919.2
Cumulative – HUD+CDC 364843 364865.1 364864.6 Screening screen_penetration 360376 360256.7** 360321.8 ** Variable factor(s) showed best f it when adjusted for degrees of freedom and were thus chosen to represent parameter category in multivariate analysis. * Variable factors showed best fit; however, were not included in multivariate analysis because the time categorical variable had less th an ideal prediction properties.

4.2 Relationship between Local Blood-Lead Data and Explanatory Variables

Many of the variables inve stigated for the National (Low Resolution) model also were explored for the local modeling usi ng Massachusetts data. All of the census data were used in both models, although at the census-tr act level rather than at th e county level. The various demographic, environmental, and programmatic variables were explored using the same techniques as the national data, which were desc ribed in Section 2.2. Detailed figures and tables containing exploratory resu lts are included in Appendix B. A detailed discussion of the results seen in Appendix B is contained in Appendix E. Table 4-5 presen ts the log-likelihood statistics that resulted from the bivariate modeling. Vari ables presenting the best model fit within each variable category are highlighted in yellow.44Table 4-5. Summary of Log-likelihood Ratios from each Model Fit to all Potential Explanatory Variables, Massachusetts DataVariable CategoryVariableModel 1Model 2Model 3Model 4Model 5Income Median Family Income ($) 51727.2 48154.7 86501.8 139627.0 178071.4IncomeMedian Household Income ($)51689.248114.686375.5139433.1177919.9IncomeMedian Per Capita Income ($) 51917.2 48345.5 86812.3 140036.2 178467.2IncomePercent No Household Earnings 52020.1 48445.8 86853.4 139531.9 177346.7IncomePercent No Household Wage 52039.6 48467.3 86858.3 139459.8177184.8IncomePercent Household on Public Assistance 51963.8 48385.7 86854.4 139836.6 178218.1IncomePercent Below Poverty Line 51974.1 48389.9 86778.4 139558.4 177838.6IncomePercent Family Income Below Poverty Line 51991.3 48412.1 86847.5 139686.4 177952.4IncomePercent Less than 5 Years in Poverty 52025.2 48446.0 86924.8 . 178034.4Race Percent Amer. Indian and Alaskan Native Alone 52259.2 48692.5 87120.6 139739.5 177392.1RacePercent Asian Alone 52264.2 48699.0 87139.8 . 177384.8RacePercent Black Alone 52170.1 48599.1 87051.0 139781.5 177691.4RacePercent White Alone 52123.5 48547.4 86960.9 139784.2 178066.6RacePercent Native Hawaiian and Other Pacific Islander Alone 52273.4 48706.6 87135.6 139740.3177378.7RacePercent Other Race Alone 52202.6 48633.4 87055.1 139688.4 177606.3RacePercent Multiple Races52042.248470.686889.1139661.3178104.6RacePercent Hispanic 52181.3 48609.2 87019.6 139808.6 177754.4Housing Costs Median Rent ($)52004.048440.186942.8139972.2177952.2Housing CostsHousing Value ($) 52094.5 48520.9 87032.6 140053.9 178202.0Occupancy Percent Rented52006.748421.186681.5139426.7177818.5OccupencyPercent Vacant 52186.5 48617.5 87003.7 139451.9177021.2Single Parent Percent Single Parent51747.748155.686542.8139654.1178338.5Home Age Year Built 51949.7 48360.5 86621.1 139739.2 178275.2Home AgeYear Occupied Unit Built 51966.8 48377.9 86641.4 139748.6 178258.9Home AgePercent Built Before 1940 51923.9 48335.3 86505.1 139476.8 178110.9Home AgePercent Built Before 195051897.948308.186483.1139547.1 178188.5Home AgePercent Built Before 1960 51959.8 48374.0 86653.5 139697.8 178061.5Home AgePercent Built Before 1970 52052.3 48469.4 86806.3 139775.8 177977.1Home AgePercent Built Before 1980 52073.4 48490.9 86850.4 139771.3 177896.0Home AgePercent Occupied Units Built Before 1940 51931.0 48342.4 86512.2139475.5178078.2Home AgePercent Occupied Units Built Before 1950 51910.8 48321.2 86497.8 139549.3 178149.3Home AgePercent Occupied Units Built Before 1960 51975.9 48389.9 86675.0 139713.0 178044.545Variable CategoryVariableModel 1Model 2Model 3Model 4Model 5Home AgePercent Occupied Units Built Before 1970 52070.4 48486.2 86829.5 139799.3 177979.1Home AgePercent Occupied Units Built Before 1980 52083.6 48501.0 86860.0 139776.7177867.1Children Percent Less than 6 Years of Age 52280.4 48713.7 87126.9 .177615.0ChildrenNumber Less than 6 Years of Age52243.248678.286913.2138995.8.Education Percent Less than 9thGrade 52006.5 48435.7 86828.7139726.7177859.2EducationPercent without High School Degree 51852.7 48279.986709.8139857.7 178346.7EducationPercent without any College51787.248218.286729.0 140104.0 178689.0EducationPercent without College Degree 51822.1 48251.2 86759.6 140084.9 178625.6Population Total Housing Units 52286.9 48720.8 87090.7 139461.7176701.5PopulationTotal Population52223.048660.686909.7138948.0.PopulationHousing Density 52272.6 48697.2 87068.2 139579.8 177260.2Air Air Dispersion (ASPEN) Model 52275.1 48708.3 87136.9 139740.1 177377.1AirAir Exposure (HAPEM5) Model 52273.1 48706.3 87134.9 139737.8 177375.0AirAir Hazard Quotient (HQ)52272.348705.587134.0139737.0177374.2HUD Funding Current HUD Funding ($ per Child) 52290.9 48722.4 87140.4 139755.6 177426.7HUD FundingCumulative HUD Funding ($ per Child) 52287.3 48723.3 87163.4 139804.9 177444.3HUD FundingCurrent State Funding ($ per Child)52162.448582.787014.3139706.6 177503.7HUD FundingCumulative State Funding ($ per Child) 52200.7 48617.7 87044.0 139740.2 177459.9HUD FundingCurrent CDC Funding ($ per Child) 52288.3 48720.8 87151.7 139760.5 177456.0HUD FundingCumulative CDC Funding ($ per Child) 52292.6 48725.0 87136.0 139706.1 177330.9HUD FundingCurrent Total Funding ($ per Child) 52282.8 48711.9 . . 177377.3HUD FundingCumulative Total Funding ($ per Child) 52292.2 48721.9 87145.9 . .HUD FundingCurrent HUD Funding ($ per Census Tract) 52302.9 48737.1 87167.5 139723.5 177172.3HUD FundingCumulative HUD Funding ($ per Census Tract) 52306.8 48740.4 87097.6 . 177127.9HUD FundingCurrent State Funding ($ per Census Tract) 52232.2 48659.5 87178.1 140076.2 178005.1HUD FundingCumulative State Funding ($ per Census Tract) 52210.6 48638.3 87222.7 140117.0 178018.7HUD FundingCurrent CDC Funding ($ per Census Tract) 52297.5 48729.5 87162.7 . 177462.6HUD FundingCumulative CDC Funding ($ per Census Tract) 52303.7 48737.2 87121.6139570.8176972.0HUD FundingCurrent Total Funding ($ per Census Tract) 52303.1 48735.6 87173.9 139790.6 177370.8HUD FundingCumulative Total Funding ($ per Census Tract) 52300.1 48732.3 87179.7 139780.5 177447.946Variable CategoryVariableModel 1Model 2Model 3Model 4Model 5TRI TRI Compounds (Total Air) 52295.5 48728.8 87153.5 139761.4 177395.8TRITRI Compounds (Fugitive Air) 52287.7 48720.7 87146.1 139750.0 177399.8TRITRI Compounds (Stacks) 52295.6 48728.9 87154.3 139761.8 177395.5TRITRI Compounds (Water Surface) 52285.5 48718.8 87147.0 139753.0 177391.9TRITRI Lead Only (Total Air) 52291.0 48724.0 87153.1 139762.9 177405.1TRI Lead Only (Fugitive Air) 52289.6 48722.6 87151.4 139759.1 177404.2TRITRI Lead Only (Stacks) 52291.7 48725.0 87153.7 139757.2 177390.5TRITRI Lead Only (Water Surface)52274.448708.587138.1139746.5177386.0TRITRI Total Lead (Total Air) 52295.8 48729.0 87155.2 139759.7 177392.4TRITRI Total Lead (Fugitive Air) 52291.5 48724.6 87154.0 139761.5 177401.9TRITRI Total Lead (Stacks) 52295.6 48728.9 87154.3 139760.6 177393.4TRITRI Total Lead (Water Surface) 52285.4 48718.6 87147.4 139753.3 177393.0Housing Inspection P1: Proportion of Housing Units Passing MA Standard of Care:Naïve Method 152214.0 48643.0 87070.2 139774.6 177816.1F1: Proportion of Housing Units Failing MA Standard of Care:Naïve Method 152108.5 48548.2 86916.6 139803.2 178520.9N1: Proportion of Housing Units Assessed: Naïve Method 1 52131.7 48554.8 86963.0 139849.2 178224.8P2: Proportion of Housing Units Passing MA Standard of Care:Naïve Method 252240.8 48671.5 87101.3 139767.8 177800.8F2: Proportion of Housing Units Failing MA Standard of Care:Naïve Method 252199.5 48645.5 87046.4 139861.5 178375.2N2: Proportion of Housing Units Assessed: Naïve Method 2 52208.4 48641.6 87066.2 139826.1 178110.9P3: Proportion of Housing Units Passing MA Standard of Care:Naïve Method 352240.8 48671.5 87101.3139767.8177800.8F3: Proportion of Housing Units Failing MA Standard of Care:Naïve Method 352108.5 48548.286916.6139803.2 178520.9N3: Proportion of Housing Units Assessed: Naïve Method 3 52160.8 48586.2 86996.1 139865.4 178257.4P4: Proportion of Housing Units Passing MA Standard of Care:MDPH Method52240.5 48671.1 87098.8 139769.1 177809.5F4: Proportion of Housing Units Failing MA Standard of Care:MDPH Method52106.548545.886919.8 139808.9 178518.4N4: Proportion of Housing Units Assessed: MDPH Method 52160.3 48585.5 86994.8 . 178259.847 5.0 STATISTICAL MODELING RESULTS

As described in Section 2.3, for each statistical model within each of the two broad model types (Low and High Resolution) the variab les that led to the best model fits were initially included in a multivariate statistical model and assessed jointl y to determine which variables were predictive of children’s blood-lead levels. If higher order in teractions with time were not significant within the multivariate model and did not negatively im pact the fit of the model upon removal, they were subsequently removed. As results of each model run were reviewed, some variables were dropped from the model if they were not significant predictors of the outcome variable and were not improving the fit of the model by being included. Thus, each model was run and results were assessed multiple times until a final model was r eached. The sections below present the final model results for the national risk models (Section 5.1) and the lo cal risk models for Massachusetts (Section 5.2). Maps of the predicted results are included in Section 6 and in Appendix G.

5.1 Low-Resolution Modeling Results

Table 5-1 presents the full set of variables includ ed in the final multivariate models for Models 1 through 4. Across all four models, the time and sp ace variables were import ant predictors of the various outcomes. The same three variables rela ted to time and space were included in all four models:

• EPA region

• the interaction between EPA region and a continuous measure of time (in years, centered at the year 2000)

• the interaction of EPA region and quarter of the year with the 3 rd quarter (JulySeptember) associated with the highest predicted lead levels.

Notes on the other variable types explored and a summarization of the se t of variables included in the final models are presented below.

• Income – Percent of Units with No Household Wages was included in Models 1 to 3 with all interactions with time included. Percen t of Units with No Household Earnings was included in all Model 4 although the inte raction with time squared was dropped.

• Race – Percent Black was included in Model 1, Percent Multiple Races in Model 2, and Percent Asian in Models 3 and 4, although the interaction with time squared was dropped in Model 3 and both interactions were dr opped in Model 4. The best-fitting race variables were included in Models 1 through 5.

• Housing Cost – Median Rent was only included in a ll models and all interaction terms appeared to be strong predictors.

• Occupancy – Percent Vacant was included in Mode l 1 and Percent Rented in the other three. The interaction with time s quared was dropped in Models 2 and 4.

• Single Parent Status – The percent of single parent households was included in all models with the interacti on with time squared was dropped in Models 3 and 4.

• Housing Age – Percent Built Pre-1960 was included in Models 1 and 2, Percent Built Pre1950 in Model 3, and Percent Built Pre-1940 in Model 4.48 • Children’s Age – The percent of children less than six years old was included in all models, although the p-values for each term in Model 2 are high.

• Education Level – Percent Without a College De gree was included in all models, although the interaction with time squared wa s dropped in Model 3 a nd both interaction terms were dropped in Model 4.

• Population – Total Housing Units was included in Model 1 without e ither interaction with time. Total Population was included in Model 2 with both in teractions. Housing Density was included in Models 3 and 4, a lthough the interaction terms were dropped from Model 4.

• Air Lead – Median Air Lead, 99 th Percentile was included in Models 1, 2, and 4, although the interaction terms were dropped from Models 3 and 4. Air Lead 9 th Percentile was included in Model 2.

• TRI –TRI Lead Total Air, 95 th Percentile was included in Model 1 with all interaction terms. TRI Lead Water Surface 95 th Percentile was included in Models 2 and 3 with both interaction terms. TRI L ead Underwater Injection 95 th Percentile was included in Model 4 but the interaction with time squared was dropped.

• Drinking Water – The two Mean Water Lead Concen tration variables were included in each model, although the interaction with tim e squared was dropped in Model 3 and both interactions were dropped in Model 4.

• Funding – Total Cumulative Funding 36-month Ti me Lag was included in Model 1 with both interactions with time. Current CD C Funding 12-month Time Lag was included in Models 2 and 3 with both interaction terms. Current HUD Funding 12-month Time Lag was included in Model 4 with all terms being significant.

• Screening – Screening penetration was included in each model, although the interaction with time squared was dropped in Models 2 and 4.

Thus, in Model 4 for probab ility of blood-lead level ≥ 25, most of the interactions with time squared were dropped from the model and a number of the interactions w ith time were dropped as well. Note that when the interaction with time and/or time squared were significant or improved the model, the lower order terms were kept in the model even if a particular term had a p-value above 0.05.

Tables 5-2 through 5-7 present the parameter es timates from each of the four multivariate national models. The standard error and p-value a ssociated with each predictor also is included. Estimates also are presented for the three varian ce components that were included in the national
models – 2

0 δ σ

, 2

, 1 0 δ δ σ

, and 2

1 δ σ (related to the random intercept ( δ 0i ) and slope ( δ 1i ) terms.

Following each table are two figures that provide information on the fit of the final models. The first, a histogram of the residuals from the fina l model fit, helps determine whether or not it is reasonable to assume that the random errors in a statistical process can be assumed to be drawn from a normal distribution. Figures 5-1, 5-3, 5-5, and 5-7 contain the residual histograms of the observed-predicted probabilities from each of the four logistic regressi on models. Please note that for these four histograms – the model was actually applied on the logit scale. However, because the logit is undefined for observed propor tions at zero and one, the histograms were applied to the original scale of measure.49 The second set of figures plot the observed values versus the predic ted values for each model. If the multivariate model fitted is appropriate, predicted values obtained from regressing the observed values on the multivariate model’s predicted values when plotted against observed values, one would expect all the po ints to be very close to the 45° line. Figures 5-2, 5-4, 5-6, and 5-8 contain these comparison plots for each of th e four national models, respectively. The plots were conducted on both the observed probability and logit probability scales, with observed data points at zero and one censored in the logit scale plots.

In general, these plots suggest that the models are performing well. A weighted regression line (blue line) fit to the observed versus predicted plots shows a very high R 2 value in most of the models that mirrors the 45° line (shown in red) for the majority of the data. One trend observed in these plots that is important to consider is that the Broad-Based National Models tend to under-predict for county/quarter combinations wi th higher proportions th at exceed the 5, 10, 15 and 25 μ g/dL threshold values. Further exploration may be necessary to determine whether these higher values represent county/quarter combina tions with fairly sparse data (i.e., few observations) – which might explain why they w ould have been less influential because the models are influenced by the number of observati ons associated with each observed value. For the higher blood-lead threshold categories, the m odel appears to over-predict the lower observed proportions – suggesting the possi bility of a regression to th e mean effect.50 Table 5-1. Summary of Variables Included in Final National Multivariate Model

Variable Type Model 1 Model 2 Model 3 Model 4 Region Region Region Region Time*Region Time*Region Time*Region Time*Region Area and Time Region*Quarter Region*Quarter Region*Quarter Region*Quarter

Income Percent of Units No HH Wages Percent of Units No HH Wages Percent of Units No HH Wages Percent of Units No HH Earnings

Race Percent Black Percent Multiple Races Percent Asian Percent Asian Housing Cost Median Rent Median Rent Median Rent Median Rent Occupancy Percent Vacant Percent Rented Percent Rented Percent Rented Family
Structure Percent Single Parent Percent Single
Parent Percent Single
Parent Percent Single
Parent

Housing Age Percent Built Pre1960 Percent Built Pre1960 Percent Occupied Built Pre-1950 Percent Occupied Built Pre-1940

Children’s Age Percent < Six Years Old Percent < Six Years Old Percent < Six Years Old Percent < Six Years Old

Education Percent without College Degree Percent No College Percent No College Percent No College

Population Total Housing Units Total Popul ation Housing Density Housing Density

Air Lead Median Air Lead,
99 th percentile Air Lead, 95 th percentile Median Air 99 th percentile Median Air 99 th percentile

TRI TRI Lead Total Air,
95 th percentile TRI Lead Water Surface 95 th

, percentile TRI Lead Water Surface 95 th

, percentile TRI Lead UI 95 th

, percentile
Mean Water Lead (water=1) Mean Water Lead (water=1) Mean Water Lead (water=1) Mean Water Lead (water=1) Drinking Water Lead Mean Water Lead (water=2) Mean Water Lead (water=2) Mean Water Lead (water=2) Mean Water Lead (water=2)

Funding Total Cumulative Funding 36-month Time Lag Current CDC Funding 12-month Time Lag Current CDC Funding 12-month Time Lag Current HUD Funding, 12-month Time Lag

Screening Screening Penetration Screening Penetration Screening Penetration Screening Penetration52 -0.84 -0.72 -0.6 -0.48 -0.36 -0.24 -0.12 0 0.12 0.24 0.36 0.48 0.6 0.72 0.84 0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5Percent

Residuals from Model-3 (P5)

Figure 5-1. Histograms of Residuals from Fitted National Multivariate Model 1Predicted (P5)

0.0 0.2 0.4 0.6 0.8 1.0

Observed (P5) 0.0 0.2 0.4 0.6 0.8 1.0

Figure 5-2a. Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 5 μ g/dLPredicted (P5 - Logit Scale)

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

Observed (P5 - Logit Scale)

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

Figure 5-2b. Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 5 μ g/dL (Logit Scale) R 2 from Fitted Regression= 0.840

R 2 from Fitted Regression=0.85754 -0.2775 -0.1875 -0.0975 -0.0075 0.0825 0.1725 0.2625 0.3525 0.4425 0.5325 0.6225 0 5 10 15 20 25 30Percent

Residuals from Model-4 (P10)

Figure 5-3. Histograms of Residuals from Fitted National Multivariate Model 2Predicted (P10)

0.0 0.2 0.4 0.6 0.8

Observed (P10) 0.0 0.2 0.4 0.6 0.8

Figure 5-4a. Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 10 μ g/dL.Predicted (P10 - Logit Scale)

-8 -6 -4 -2 0 2

Observed (P10 - Logit Scale)

-8 -6 -4 -2 0 2

Figure 5-4b. Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 10 μ g/dL (Logit Scale) R 2 from Fitted Regression= 0.867 R 2 from Fitted Regression= 0.89656 -0.148 -0.108 -0.068 -0.028 0.012 0.052 0.092 0.132 0.172 0.212 0.252 0.292 0.332 0 10 20 30 40 50Percent

Residuals from Model-5 (P15)

Figure 5-5. Histograms of Residuals from Fitted National Multivariate Model 3Predicted (P15)

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Observed (P15) 0.0 0.1 0.2 0.3 0.4 0.5 0.6

Figure 5-6a. Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 15 μ g/dL.Predicted (P15 - Logit Scale)

-9 -7 -5 -3 -1 1

Observed (P15 - Logit Scale)

-9 -7 -5 -3 -1 1

Figure 5-6b. Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 15 μ g/dL (Logit Scale) R 2 from Fitted Regression= 0.867

R 2 from Fitted Regression= 0.82158 -0.0525 -0.0275 -0.0025 0.0225 0.0475 0.0725 0.0975 0.1225 0.1475 0.1725 0.1975 0 10 20 30 40 50 60 70 80 90Percent

Residuals from Model-6 (P25)

Figure 5-7. Histograms of Residuals from Fitted National Multivariate Model 4Predicted (P25)

0.00 0.07 0.14 0.21 0.28 0.35

Observed (P25) 0.00 0.07 0.14 0.21 0.28 0.35

Figure 5-8a. Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 25 μ g/dL

.Predicted (P25 - Logit Scale)

-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0

Observed (P25 - Logit Scale)

-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0

Figure 5-8b. Plot of National Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 25 μ g/dL (Logit Scale ) R 2 from Fitted Regression= 0.726 R 2 from Fitted Regression= 0.74459 5.2 High-Resolution Modeling Results

The Massachusetts final multivariate models were constructed similarly to the national models. One basic difference in the Massachusetts models is that there was no EPA region. Thus, there was no area variable included othe r than census tract. As in th e national model, time period and quarter were significant predictors ; however, in the Massachusetts model they are not interacted with an area variable. Table 5-6 presents th e full set of variable s included in the final multivariate models for Models 1 through 5. M odel 6 was not fit for the Massachusetts data because of the scarcity of data above 25 μ g/dL.

Among the demographic variables, housing cost, o ccupancy, family structure, and housing age were significant predictors in all five models . Median Rent was the selected housing cost variable in all five models. For occupancy, Percen t Rented was the selected variable in four of the five models. Percent of Single Parent Househ olds is the family structure variable in all models. Three housing age variables were incl uded across the five models, but Percent Built Pre-1950 was the included vari able in Models 1 to 3.

Race and Income variables were included in four of the five final models. Median Household Income and Percent Multiple Races were the two variables used in all four models. Children’s Age, Education, and Population each had a variable included in one of the final models. Number of Children less than or equal to six years old and Total Population were included in Model 3. Percent Without 9 th Grade Education was included in Model 4.

Unlike the national models, none of the environm ental variables were included in the final multivariate models for Massachusetts. On th e other hand, the housing inspection data from Massachusetts were predictive a nd included in all of the final m odels. The percentage of units passing the Massachusetts standard of care (calculated using the MDPH method) was included in all five models. Additionally, the percentage of units failing the Massach usetts standard of care (calculated using the MDPH method) wa s included in Models 4 and 5.

The selected programmatic funding variable was in cluded in Models 1, 2, and 5. Current State Funding ($ per Child) was used in the GM models and Cumulative CDC Funding ($ per tract) was used in Model 5.

As with the national models, parameter estimates a nd associated standard errors and p-values are presented for all models in Table 5-7. Figures 5-9 to 5-18 contain the histograms of residuals and plots of observed versus predic ted values that allow assessment of the various model fits.

These plots suggest that mode ls 1-3 are performing well, wi th Models 4 and 5 providing a somewhat suboptimal fit (perhaps due to fewe r children being observe d above the 10 and 15

μ g/dL threshold values in Massachusetts). Th e weighted regression lin e fit to the observed versus predicted plots (shown in blue) also de monstrates a systematic degradation in model performance from Models 3 through 5, with the R 2 value diminishing as the blood-lead threshold value increases. Similar to the National M odels, the High-Resolution Multivariate Models in Massachusetts tend to under-predict for census-tr act/quarter combinations with higher geometric mean blood-lead concentrations and higher exceedance proportions. Further exploration may be necessary to determine whether these higher values represent c ounty/quarter combinations with60 fairly sparse data (i.e., few observations) – whic h might explain why they would have been less influential in Models 2 through 6, which are infl uenced by the number of observations associated with each observed value.

Appendix F presents predictions of areas of the country estimated to have the highest children’s blood-lead levels. These predictions were gene rated by averaging predic ted values across the four quarters of 2006. Table F-1 lists the 150 counties/townships in the United States with the highest predicted GM blood-lead levels (using Model 2) and proportion of children above 5, 10, 15, and 25 μ g/dL. Table F-2 lists the 10 counties in each state with the highest levels of those same five outcomes. Table F-3 lists the 150 Massachusetts census tracts with the highest predicted GM blood-lead levels. Figure F-1 prov ides a map of these 150 Massachusetts census

tracts.61Table 5-6. Summary of Variables Included in Final Massachusetts Multivariate ModelVariable TypeModel 1Model 2Model 3Model 4Model 5TimeTime Period, Quarter Time Period, Quarter Time Period,Quarter Time Period, Quarter Time Period, QuarterIncomeMedian HouseholdIncomeMedian HouseholdIncomeMedian HouseholdIncomeMedian HouseholdIncomeRacePercent Multiple Race Percent Multiple Race Percent Multiple Race Percent Multiple RaceHousing CostMedian Rent Median Rent Median Rent Median Rent Median RentOccupancyPercent Rented Percent Rented Percent Rented Percent Rented Percent VacantFamily StructurePercent Single Parent Percent Single Parent Percent Single Parent Percent Single Parent Percent Single ParentHousing AgePercent Built Pre-1950 Percent Built Pre-1950Percent Built Pre-1950Percent OccupiedBuilt Pre-1940Percent OccupiedBuilt Pre-1980Children's AgeNumber less than 6years oldEducationPercent without 9thGrade educationPopulationTotal PopulationP4 - % PassingStandard of Care,MDPH MethodP4 - % PassingStandard of Care,MDPH MethodP4 - % PassingStandard of Care,MDPH MethodP4 - % PassingStandard of Care,MDPH MethodP4 - % PassingStandard of Care,MDPH MethodHousing InspectionF4 - % failingstandard of care,MDPH MethodF4 - % failing standardof care, MDPHMethodFundingCurrent State Funding($ per Child)Current State Funding($ per Child)Cumulative CDCFunding ($ per tract)62 Table 5-7. Massachusetts Multivariate Model Estimates

Model Effect Levels Estimate Standard Error PValue Intercept _ 2.290 0.033 <.0001 Time _ -0.088 0.002 <.0001

1 -0.187 0.007 <.0001

2 -0.143 0.007 <.0001

3 0.130 0.006 <.0001 Quarter (Season)

4 0.000 . . Median Household Income _ -0.008 0.001 <.0001 Percent Multiple Races _ 3.909 0.536 <.0001 Median Rent ($): _ -0.032 0.005 <.0001

Percent Rented Units _ -0.568 0.069 <.0001 Percent Single Parent Households _ 0.702 0.096 <.0001

Percent Units Built Before 1950 _ 0.849 0.047 <.0001 p4 _ -0.983 0.166 <.0001 Current State Funding _ 0.028 0.006 <.0001

2

0 δ σ 0.229 .

2

, 1 0 δ δ σ -0.026 .

2

1 δ σ 0.004 . 1 (Geometric

Mean)

2
Error σ 0.191 .

Intercept _ 2.249 0.033 <.0001 Time _ -0.087 0.002 <.0001

1 -0.185 0.006 <.0001

2 -0.137 0.006 <.0001

3 0.127 0.006 <.0001 Quarter (Season)

4 0.000 . . Median Household Income _ -0.008 0.001 <.0001 Percent Multiple Races _ 3.826 0.531 <.0001 Median Rent ($): _ -0.032 0.005 <.0001

Percent Rented Units _ -0.561 0.069 <.0001 Percent Single Parent Households _ 0.735 0.096 <.0001

Percent Units Built Before 1950 _ 0.866 0.046 <.0001 p4 _ -0.933 0.164 <.0001 Current State Funding _ 0.031 0.006 <.0001

2

0 δ σ 0.218 .

2

, 1 0 δ δ σ -0.024 .

2

1 δ σ 0.004 . 2 (Weighted Geometric

Mean)

2
Error σ 3.986 .63 Model Effect Levels Estimate Standard
Error PValue Intercept _ -2.312 0.054 <.0001 Time _ -0.146 0.003 <.0001

1 -0.195 0.010 <.0001

2 -0.117 0.010 <.0001

3 0.187 0.009 <.0001 Quarter (Season)

4 0.000 . . Median Household Income _ -0.010 0.001 <.0001 Percent Multiple Races _ 3.420 0.610 <.0001 Median Rent ($): _ -0.044 0.006 <.0001

Percent Rented Units _ -0.724 0.083 <.0001 Percent Single Parent Households _ 0.817 0.119 <.0001

Percent Units Built Before 1950 _ 1.468 0.056 <.0001 Number Residents Less than Six Years of Age _ 0.000 0.000 0.0114 Total Population _ 0.000 0.000 0.0292
p4 _ -0.649 0.218 0.0029

2

0 δ σ 0.129 0.007

2

, 1 0 δ δ σ -0.008 0.001 3 (Proportion of Children with Blood

Lead ≥ 5

μ g/dL)

2

1 δ σ 0.004 0.000

Intercept _ -4.235 0.052 <.0001 Time _ -0.136 0.005 <.0001

1 -0.282 0.022 <.0001

2 -0.130 0.021 <.0001

3 0.247 0.020 <.0001 Quarter (Season)

4 0.000 . . Median Household Income _ -0.010 0.001 <.0001 Percent Multiple Races _ 4.326 0.781 <.0001 Median Rent ($): _ -0.057 0.009 <.0001

Percent Rented Units _ -0.562 0.119 <.0001 Percent Single Parent Households _ 0.697 0.159 <.0001 Percent Occupied Units Built Before 1980 _ 1.758 0.083 <.0001
Percent Residents with Less than Ninth Grade Education _ -0.581 0.279 0.0372

f4 _ 1.802 0.504 0.0004 p4 _ -1.339 0.321 <.0001

2

0 δ σ 0.175 0.016

2

, 1 0 δ δ σ -0.013 0.003 4 (Proportion of Children with Blood Lead ≥ 10

μ g/dL)

2

1 δ σ 0.004 0.00164 Model Effect Levels Estimate Standard
Error PValue Intercept _ -6.278 0.144 <.0001 Time _ -0.093 0.008 <.0001

1 -0.307 0.042 <.0001

2 -0.093 0.040 0.019

3 0.332 0.036 <.0001 Quarter (Season)

4 0.000 . . Median Rent ($): _ -0.049 0.010 <.0001

Percent Vacant Units _ 1.039 0.335 0.0019 Percent Single Parent Households _ 1.002 0.169 <.0001 Percent Occupied Units Built Before 1980 _ 1.187 0.168 <.0001 f4 _ 4.047 0.677 <.0001 p4 _ -1.476 0.440 0.0008 Cumulative CDC Funding _ 0.000 0.000 0.0127

2

0 δ σ 0.249 0.035

2

, 1 0 δ δ σ -0.020 0.008 5 (Proportion of Children with Blood Lead ≥ 15

μ g/dL)

2

1 δ σ 0.004 0.00265 -1.875 -1.275 -0.675 -0.075 0.525 1.125 1.725 2.325 2.925 3.525 4.125 4.725 0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0Percent

Residuals from Model-1 (GM)

Figure 5-9. Histograms of Residuals from Fitted Massachusetts Multivariate Model 1Predicted (GM)

0 1 2 3 4 5 6 7 8

Observed (GM) 012345678

R 2 from Fitted Regression = 0.697

Figure 5-10. Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Unweighted Geometric Mean Response66 -1.8 -1.35 -0.9 -0.45 0 0.45 0.9 1.35 1.8 2.25 2.7 3.15 3.6 4.05 4.5 4.95 0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0Percent

Residuals from Model-2 (Weighted GM)

Figure 5-11. Histograms of Residuals from Fitted Massachusetts Multivariate Model 2Predicted (Weighted GM)

0 1 2 3 4 5 6 7 8

Observed (Weighted GM) 012345678

R 2 from Fitted Regression = 0.700

Figure 5-12. Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Weighted Geometric Mean Response67 -0.34 -0.26 -0.18 -0.1 -0.02 0.06 0.14 0.22 0.3 0.38 0.46 0.54 0.62 0 2.5 5.0 7.5 10.0 12.5 15.0Percent

Residuals from Model-3 (P5)

Figure 5-13. Histograms of Residuals from Fitted Massachusetts Multivariate Model 3Predicted (P5)

0.00 0.12 0.24 0.36 0.48 0.60 0.72 0.84

Observed (P5) 0.00 0.12 0.24 0.36 0.48 0.60 0.72 0.84

Figure 5-14a. Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 5 μ g/dL.Predicted (P5 - Logit Scale)

-5 -4 -3 -2 -1 0 1 2

Observed (P5 - Logit Scale)

-5 -4 -3 -2 -1 0 1 2

Figure 5-14b. Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ μ g/dL (Logit Scale) R 2 from Fitted Regression = 0.579

R 2 from Fitted Regression = 0.53168 -0.12 -0.09 -0.06 -0.03 0 0.03 0.06 0.09 0.12 0.15 0.18 0.21 0.24 0.27 0.3 0.33 0.36 0 5 10 15 20 25 30 35Percent

Residuals from Model-4 (P10)

Figure 5-15. Histograms of Residuals from Fitted Massachusetts Multivariate Model 4Predicted (P10)

0.0 0.1 0.2 0.3 0.4

Observed (P10) 0.0 0.1 0.2 0.3 0.4

Figure 5-16a. Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 10 μ g/dLPredicted (P10 - Logit Scale)

-6 -5 -4 -3 -2 -1 0

Observed (P10 - Logit Scale)

-6 -5 -4 -3 -2 -1 0

Figure 5-16b. Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 10 μ g/dL (Logit Scale) R 2 from Fitted Regression = 0.269

R 2 from Fitted Regression = 0.31269 -0.039 -0.015 0.009 0.033 0.057 0.081 0.105 0.129 0.153 0.177 0.201 0.225 0.249 0.273 0 10 20 30 40 50 60Percent

Residuals from Model-5 (P15)

Figure 5-17. Histograms of Residuals from Fitted Massachusetts Multivariate Model 5Predicted (P15)

0.0 0.1 0.2 0.3

Observed (P15) 0.0 0.1 0.2 0.3

Figure 5-18a. Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 15 μ g/dL.Predicted (P15 - Logit Scale)

-5 -4 -3 -2 -1 0

Observed (P15 - Logit Scale)

-5 -4 -3 -2 -1 0

Figure 5-18b. Plot of Massachusetts Multivariate Model Predicted Values versus Observed with Fitted Regression Line and 45° Reference Line for Proportion of Children with BLL ≥ 15 μ g/dL (Logit Scale) R 2 from Fitted Regression = 0.113

R 2 from Fitted Regression = 0.11970 6.0 GRAPHICAL PRESENTAT ION OF MODELING RESULTS

In addition to the discussion of the final mu ltivariate modeling results in Section 5, it is informative to be able to view the results visu ally. Two methods were utilized for graphical presentation of the results – mapping and via us e of an interactive software tool. Section 6.1 presents a subset of the maps generated, wh ile Sections 6.2 and 6.3 di scuss the interactive software tool.

6.1 Maps of Observed and Predicted Blood-Lead Outcomes

Mapping is an informative method to graphically pr esent the results of the multivariate models. Figure 6-1 contains maps displa ying the observed levels of GM blood-lead levels in 2000 and 2005 based on CDC’s national survei llance data, and the comparable predicted GM blood-lead levels in 2000 and 2005. A key difference is that maps of observed levels contain many counties with missing data either because they do not su bmit childhood lead surveillance data to CDC or they have too few test records to be included in the analysis, while the maps of predicted levels covers all counties in the country. Appendix G c ontains detailed maps from the national level models of GM blood-lead levels a nd proportion of children with BLLs ≥ 10 μ g/dL.

Because it is difficult to view many of the i ndividual counties within the U.S.-level maps, regional-level maps also were produced. Figure 6-2 contains examples of these for EPA Region V. Comparable maps for all regions are in cluded in Appendix G. With darker colors representing areas of higher le ad levels, it appears that lead levels are declining across EPA Region V from the 2000 to 2005 time period. Figure 6-3 contains maps of observed and predicted proportion of children’s bl ood-lead levels in Massachusetts at the census-tract level. The Boston area is enlarged to better show the tracts in that area.

6.2 Visualization Tool Development

In addition to generating maps, a software tool wa s developed to provide a flexible way for users to quickly view data for particul ar areas and to obtain informati on that led to the results being viewed. To do this, the projec t team utilized ex isting technology developed through internal research and modified this technology to meet the needs of this study. The software sews together a series of static maps so that they can be viewed dynamically. This allows users to view a movie of changes in surfaces over time and space.

The software is written in C++. Users intera ct with the software via a Windows GUI that is implemented using Microsoft Foundations Classes (MFC). The 3-dimensional graphics within the tool were implemented using an Open Graphics Library (OpenGL).71Figure 6-1. Observed and Predicted Proportion of Children with Blood-Lead Levels≥10μg/dL in the United Statesby County, 2000 and 2005Predicted Proportion of Blood-Lead Levels >/= 10 ug/dL2005United StatesSeptember 2007LEGEND0 - 0.00990.01 - 0.02490.025 - 0.04990.05 - 0.07490.075 - 0.09990.10 - 0.24990.25 +No dataProportion of PbB Levels >/= 10 ug/dL0 180 360 54090Miles0 490 980245Miles0 130 26065MilesPredicted Proportion of Blood-Lead Levels >/= 10 ug/dL2000United StatesSeptember 2007LEGEND0 - 0.00990.01 - 0.02490.025 - 0.04990.05 - 0.07490.075 - 0.09990.10 - 0.24990.25 +No dataProportion of PbB Levels >/= 10 ug/dL0 180 360 54090Miles0 475 950237.5Miles0 130 26065MilesObserved Proportion of Blood-Lead Levels >/= 10 ug/dL2005United StatesSeptember 2007LEGEND0 - 0.00990.01 - 0.02490.025 - 0.04990.05 - 0.07490.075 - 0.09990.10 - 0.24990.25 +No dataProportion of PbB Levels >/= 10 ug/dL0 175 350 52587.5Miles0 460 920230Miles0 125 25062.5MilesObserved Proportion of Blood-Lead Levels >/= 10 ug/dL2000United StatesSeptember 2007LEGEND0 - 0.00990.01 - 0.02490.025 - 0.04990.05 - 0.07490.075 - 0.09990.10 - 0.24990.25 +No dataProportion of PbB Levels >/= 10 ug/dL0 170 340 51085Miles0 470 940235Miles0 130 26065Miles72 Figure 6-2. Observed and Predicted Proportion of Children with Blood-Lead Levels ≥ 10
μ g/dL in Region V by County, 2000 and 2005 Predicted Proportion of Blood-Lead Levels >/= 10 ug/dL 2005

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No data Proportion of PbB Levels >/= 10 ug/dL73Figure 6-3. Observed and Predicted Proportion of Children with Blood-Lead Levels≥10μg/dL in Massachusetts by CensusTract, 2000 and 2005Observed Proportion of Blood-Lead Levels >/= 10 ug/dL2000MassachusettsFebruary 2007LEGEND0 - 0.00990.01 - 0.02490.025 - 0.04990.05 - 0.07490.075 - 0.09990.10 - 0.24990.25 +No dataProportion of PbB Levels >/= 10 ug/dL0 10 20 305MilesObserved Proportion of Blood-Lead Levels >/= 10 ug/dL2005MassachusettsFebruary 2007LEGEND0 - 0.00990.01 - 0.02490.025 - 0.04990.05 - 0.07490.075 - 0.09990.10 - 0.24990.25 +No dataProportion of PbB Levels >/= 10 ug/dL0 10 20 305MilesPredicted Proportion of Blood-Lead Levels >/= 10 ug/dL2005MassachusettsFebruary 2007LEGEND0 - 0.00990.01 - 0.02490.025 - 0.04990.05 - 0.07490.075 - 0.09990.10 - 0.24990.25 +No dataProportion of PbB Levels >/= 10 ug/dL0 10 20 305MilesPredicted Proportion of Blood-Lead Levels >/= 10 ug/dL2000MassachusettsFebruary 2007LEGEND0 - 0.00990.01 - 0.02490.025 - 0.04990.05 - 0.07490.075 - 0.09990.10 - 0.24990.25 +No dataProportion of PbB Levels >/= 10 ug/dL0 10 20 305Miles74 The software visualizes the obser ved values and the predicted valu es for the response variable of each model (6 national models, 5 Massachusetts models). The software interpolates the predicted values spatially within each state using a squared inverse distance algorithm; it interpolates linearly in time. The predicted values are defined for each county. There are two visualization modes: (1) a spatia l surface moving in time, and (2) a time series. The tool was built in a flexible way so that it can be easily adapted to accept updated data.

Figures 6-4 and 6-5 are screen sh ots from the visualization tool. Figure 6-4 provides an example of a response surface generated by the tool to illustrate predic ted blood-lead levels across a geographic area. In this example, the area is the state of Illinois. Figure 6-5 provides an example of a method the visualization provides to plot predicted blood-lead levels in a given geographic area over time.75 Figure 6-4. Response Surface of Predicted Geometric Mean Blood-Lead Concentration Across the State of Illinois from the Visualization Tool76 Figure 6-5. Time Series Plot of Observed and Predicted Geometric Mean Blood-Lead Concentration in Cook County Illinois from the Visualization Tool77 7.0 DISCUSSION AND FUTURE WORK

The goal of this study was to determine whethe r tools could be devel oped to differentiate geographic areas (counties and census tracts), based on their predic ted risk of containing children with elevated blood-lead levels . Statistical models were developed that link CDC’s childhood blood-lead surveillance data to demographic pr edictor variables available in the 2000 U.S. Census. While earlier chapters of this report focus on the development and performance of these statistical models, this chapter provides a discus sion of the factors that should be considered when using the models, and some preliminary ideas for improvement.

7.1 Major Findings

The results of this study suggest that longitudinal predictive m odels can be developed at the county level across the nation based on the use of quarterly summary information from CDC’s National Surveillance Database, a nd at the census-tract level with in states that have a long history of universal screening a nd reporting, such as Massachusetts . These models can be used to describe how risk of childhood lead poisoning changes over time within different regions of the country, as well as within small geographic areas within states (e.g., counties) and even smaller geographic areas within counties (e.g., census tracts). They can be used to predict the risk of childhood lead poisoning in counties (or census trac ts) with little or no surveillance data, and also can be used to identify those counties (or census tracts) that are at highest risk at the end of the period of observation (see Appendix F for a list of the 150 counties across the country at highest risk predicted by each of the six models, as well as the top 10 counties within each state).

The statistical model chosen (a random-effect s model with separate intercepts and slopes estimated within each county or census tract) also allows rank ing of geographic areas based on the rate of decline over time after accounting for the fixed-effects variables of the model (although only among those areas that provided adequa te surveillance data). Within the context of the Broad-Based National Model, these rando m effects would allow us to identify those counties that are experiencing a more rapid redu ction in risk of chil dhood lead poisoning over time (to identify best practices) and those counties that are experiencing a si gnificantly less rapid decline over time (to identify areas in need of additional attention and resources for combating lead poisoning), after already accounting for the demographic, programmatic, and environmental factors included in the multivariate model.

Within the context of the series of Broad-Based National Models, the data suggest that there are significant differences in the distribution of childhood blood-lead concentrations among the different regions of the country, and that the manner in which these distributions change over time and are impacted by seasonality also is re gionally specific. After accounting for these regional differences, a number of demographic, environmental, and programmatic variables were found to be highly predictive of childhood blood- lead concentrations among the different response variables modeled within this project. The specific va riables that were found to be predictive within the multivariate models varied based on the response variable; however, there were certainly some variables that were found to be selected in multiple models. In addition to various census demographic variable s that were identified in prev ious risk modeling efforts (e.g., age of housing, percent single parent families, race/ethnicity), it was found that variables constructed from EPA’s Safe Drinking Water Information System, time-lagged programmatic78 funding information from HUD and/or CDC, and va riables associated with high lead emmisions or predicted air concentrations were selected within the National (Low Resolution) multivariate statistical models.

Within the context of the High-Resolution Mode l developed using data from the Commonwealth of Massachusetts, the project team also found a highly significant downward trend in the risk of childhood lead poisoning among the five models de veloped. Due to a very small number of children observed at or above 25 μ g/dL within Massachusetts over the 2000-2006 period of observation – this sixth model was not included. After accounting for the long-term reduction over time and seasonality using similar met hods that were employed in the Broad-Based National Model, we found that only the dem ographic and programmatic variables were predictive of the risk of childhood lead poisoning at the census-tract level. Of particular interest were the variables that describe d the proportion of housing units w ithin each census tract that were found to be in compliance and out of compli ance with the Massachusetts Standard of Care. In all five of the multivariate models, the ri sk of childhood lead poisoning was significantly reduced as the proportion of housing units in comp liance increased within a census tract. In addition, for the last two models (which predic ted proportion of children at or above 10 and 15

μ g/dL), the risk of childhood lead poisoning incr eased significantly as the proportion of housing units out of compliance increased within a census tract.

7.2 Comparison Between Results and NHANES

Due to selection bias associated with surveilla nce data, it is expected that the CDC National Surveillance dataset as well as the Massachuse tts surveillance data may show higher proportions of elevated blood-lead concentrations than f ound in the general population. For this reason, the proportion of children with elevated blood-lead con centrations as well as the distribution of the potential continuous summary meas ure derived from the surveillanc e data were compared with those reported by the most recent six years of available CDC National Health and Nutrition Examination Survey (NHANES). Results of this comparison are presented graphically in Figure 7-1 – suggesting that there is a highly significant differen ce between the NHANES and CDC’s National Surveillance Database with respect to th e proportion of children observed at or above 5

μ g/dL (with lesser differences observed for the proportion of children observed at or above 10, 15, and 25 μ g/dL). In future work on this project, EPA might consider methods for calibrating the Surveillance data to better match the National Distribution of childhood blood-lead concentrations using methods similar to those employed by Strauss, et. al. 2001a.79 Legend: NHANES National SurveillanceProb(Blood Lead >= 15)

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Figure 7-1. Comparison of National Surveillance Data to NHANES Data80 7.3 Data Issues

The models that were developed as part of this project are based on data sources that have both strengths and limitations. In this section, f our potentially limiting asp ects of the data are considered – biases from the geocoding proce ss, biases inherent in the surveillance data, reporting limits in surveillance da ta, predicting within-area relationships with ecological models, and use of Census data from 2000.

7.3.1 Biases from Geocoding

The quarterly summary statistics from CDC’s Na tional Surveillance Database utilized in these analyses were available at the county level of geographic specificity. CDC based this summarization on county FIPS codes reported by its gran tees. This field is quite well reported in CDC’s CBLS database. The Massachusetts surv eillance data was summari zed and analyzed at the census-tract level, with the geocoding of a ddress data within the Massachusetts data being conducted by MDPH staff. While there is no reas on to suspect lack of data quality within the Massachusetts surveillance data, experience show s that the process of geocoding can introduce some subtle biases into surveillance data. Thus , the following section is offered as a guide for EPA to consider for future modeling efforts in which state or local surveillance data are geocoded to the census-tract level:

The geocoding process is highly dependent on the qua lity of address data recorded by the local lead poisoning prevention programs with whom th e blood-lead information originated. Several factors could prevent an address from being successfully geocoded, such as:

• Erroneous, illegible, or purposefully misl eading address information being provided to the childhood lead poisoning prevention program

• Address data that contain either a P.O. Box or Rural Route as part of the street address, which typically cannot be successfully geocoded

• Errors in data entry.

While these problems with address data are likely to occur in all progr ams with a non-trivial frequency, there may be a systematic bias that programs introduce (albeit unintentionally) when correcting address data. It is likely that address data errors are identified and corrected with higher frequency for children who have an el evated blood-lead level and require follow up.

Given the potential bias introduced through the geocoding process, further research may be worthwhile to determine whether there are reasonable approaches that could be used to adjust the models for this bias.

7.3.2 Reporting Limits in Surveillance Data

Other naturally occurrin g biases in the surveillance data may influence the degree to which models are representative of th e true trends in childhood lead poi soning. For example, within the context of the Broad-Based National Model, th ere may be differences between states and localities in the manner in which childhood blood-l ead testing results are reported to CDC. Sections 2 and 3 included a discussion about a screening algorithm that was applied to the81 surveillance data to supress count y/quarter data combinations fo r areas that were not conducting universal reporting of blood-lead te sting results. Additional scru tiny of these data by CDC and other members of the lead poisoning prevention community may reveal other county/quarter combinations that were not identified through the screening algorithm th at should be excluded from these analyses. We are confident that the overall impact of includi ng these data in the current work will not severely bias the fixed- effects parameter estimates in the series of generalized linear mixed models developed in this project.

7.3.3 Selection Bias in Surveillance Data

Selection bias is perhaps the most serious bias th at is yet left unaccounted for in the models that have been developed, and may have severe impact on their predictive ability. Surveillance data are observational by nature, and are not designed to be representati ve of the general population. There are many competing forces that influence whether or not a child is screened at an appropriate age, and recorded in the blood-lead surveillance da tabase. Some have hypothesized that surveillance data in the urban environment are representa tive of the affluent (who have private health insurance) and the poor (who receiv e Medicaid or other medical assistance), while under-representing the working poor (who may have no health insurance, and no mechanism for receiving appropriate preventive medical testing) . While this may be true in general, many outstanding lead poisoning prevention programs cu rrently are extending outreach, education, and screening services to areas with historically high incidence of childhood lead poisoning. These programs generally provide assistance to al l members of the community, regardless of entitlement status. While these services are typi cally offered in high-risk urban areas with the infrastructure of a federally funded (CDC and/or HUD) or state-funded lead poisoning prevention program, they typically are less availa ble in similar high-risk rural areas without similar infrastructure. In a ddition to outreach, education a nd screening activ ities, many childhood lead poisoning prevention programs (o r partnering housing agen cies) receive funding from HUD’s Office of Lead Hazard Control to conduct environmental investigations and reduce lead hazards in the residential environment. Many of these activities generate targeted screening of children living in deteriorate d, older housing – which also is a non-trivial source of selection bias in the surveillance data.

An important question for EPA to address is how se lection bias is likely to influence the relative rankings of counties within a regi on or census tracts within a more localized area, as well as the predictive ability of the models themselves.

7.3.4 Limitations of Ecological Models for Predicting Within-Area Relationships

The models that were developed within this proj ect are ecological models that describe quarterly distributional summary statistics within geograp hic areas as a function of predictor variables assessed within those same geographic areas. It also may be the case that some of these predictor variables have significan t variation within a county (or census tract) – and that this within-area variation is highly predictive of risk of childhood lead poisoning within these geographic areas. Unfortunately, the data limitations w ithin this study (for both the blood-lead response variables as well as many of the predicto r variables) prohibit us from ascertaining these important person-level relationships . This type of relationship ca n be established only by linking82 individual blood-lead concentra tion data with individual-level environmental, demographic, and/or programmatic information (w hich usually is not available).

Within the context of the High-Resolution Mass achusetts Model – it may be possible to link individual blood-lead records w ith the longitudinal housing inspect ion information to assess the loss of information associated with going from an individual-level model to an area-based ecological model. This type of assessment could be introduced in later stages of this project.

7.3.5 Use of 2000 Census Data and Other Ti me Invariant Data as Predictors

One potential criticism of the modeling effort is that we are linking blood -lead surveillance data collected between 1995 and 2006 to census data that were collected in 2000. Is the demographic information collected in 2000 likely to remain unc hanged over the course of time? The answer probably depends on the variable under consideration. For exampl e, age of housing in census tracts or proportion of housing bui lt prior to 1950 is not likely to change dramatically in census tracts, unless there is a lot of demolition or new construction o ccurring. On the flip side, average income is likely to chan ge substantively over time.

Even though the demographic information containe d in the 2000 Census is likely to change over time, the more important question is what effect will that change have on our model predictions? While the models likely would be improved with th e use of more current census data for use as predictors, we do not believe that the use of older (less current) informati on will result in poor or inaccurate prediction. In fact, for the purpose of predicting current or future trends in childhood lead poisoning, we are more concerned with the ag e of the surveillance data that are being used as the response variable in this modeling exercise than with the age of the predictor variables.

Similar arguments can be made for the use of st atic air modeling data, and averaged information from EPA’s Toxics Release Inventory.

7.4 Model Validation Issues

The risk index models developed as part of this project may requi re validation before being used by childhood lead poisoning prevention programs throughout the country. The following four issues might be considered by EPA as being impor tant to address as pa rt of this validation exercise:

1. Within counties and/or census tracts that contribute blood-lead information to the models, how representative is the screened population of children (on which the models are based) of the ge neral population of children?

2. Within counties and/or census tracts that do not contribute much information to the models (e.g., counties with low screeni ng penetration), how well does the model perform at predicting relative ri sk and blood-lead distributions?

3. Can risk index models based on histor ical blood-lead data from 1995 through 2005 accurately predict risk and blood-lead distribut ions in future years (e.g., can it be used to forecast towards the federal 2010 goal)?83 4. Can the High-Resolution Model developed in Massachusetts be gene ralized to predict risk and blood-lead distributi ons in other states across th e Nation (or even within EPA Region 1)?

If EPA is to provide childhood l ead poisoning prevention programs with a risk characterization tool based on these models, a comprehensive vali dation should be pursued to address the above four issues.

Validation of the Surveillance Data The first issue is related to th e quality of the data supporting m odel development. For example, if CDC’s surveillance data are biased toward in clusion of high-risk children (as shown in the comparisons to NHANES), the risk index models al so will be biased a nd tend to over-predict children at high risk. Note that if the bias is consistent among all coun ties and census tracts (i.e., it over-represents high risk children everywhere), the model predictions for the proportion of children in each blood-lead categor y likely will be biased, while the ability for risk indices to differentiate between high- and low- risk areas wi ll be preserved. If the biases occur differently in different areas, non-trivial ad justments to the model would need to be pursued prior to use by childhood lead poisoning prevention programs.

Because the unit of analysis in the development of the Broad- Based National Model is at the county level, the goal of a valida tion exercise would be to determ ine whether the distribution of children’s blood-lead concentrations that are included in the surv eillance data for a sample of census tracts are representative of the general popul ation of children found within those census tracts. One possible approach, would be to deve lop a field testing validation survey, in which a stratified random sample of c ounties are selected for a short-term outreach campaign in which eligible children are sampled in a representa tive manner. Stratific ation variables to be considered would be Rural/Suburba n/Urban, predicted level of risk from the model, and possibly levels of socio-economic status. Obviously, de velopment of such a survey would be costly, difficult to implement, and likely beyond the scope of this project. Alternatively, CDC might be able to reveal the specific counties that par ticipated in various waves of NHANES – with comparisons being made in those specific counties. Access to the identific ation of the specific counties from which NHANES study subjects we re sampled (within the NHANES analysis dataset) would provide this project with the best foundation to addre ss the serious biases identified in Section 7.2 and calibrate the model to ensure that it is more reflective of the U.S. population.

Validation of the Models in Areas with Low Screening Penetration This second issue relates to the performance of the risk index m odels in predicting both relative risk and the number of children in different blood-lead categor ies in the census tracts that historically had low screening penetr ation. Due to the fact that there is little to no data in these geographic areas to determine the fit of the risk index models, so me field studies similar to the one described in the previous se ction would need to be conducted to address this issue. The major difference between the two field studies is th at the census tracts chosen for this validation exercise would be tracts in which th e screening penetration is low.84 A similar approach could be used to conduct this field validation exercise in which a stratified sample of counties would be identified for the study, and a representative sample of children’s blood-lead levels would be obtaine d within those census tracts us ing an intense, brief outreach effort. The counties again would be chosen using a stratified random sampling approach, to obtain a sample of tracts that re presents a combination of high-, medium- and low-risk areas in the rural, suburban, and urban environments. This is an area for potential future collaboration with CDC and perhaps some of their lead poisoning prevention grantees.

Validation of the Models in Predic ting Future Blood-Lead Concentrations Validation of this third issue can be performed to a certain extent using data that are already available as part of the modeling process. Fo r example, in the national model where data are available from 1995 through 2005, data for a state or set of states can be removed for one or more years and the missing data predicted by the model. If all 2005 data were removed, models would be developed using the data from 1996 through 2004, and then the “future” predictive ability of those models can be assessed by applying them to the data from 2005.

Validation of the Models in Predicting Blood-Le ad Concentrations in Other Geographic Areas The last type of validation involves the determina tion of synergies (or lack thereof) in prediction between the Broad-Based National Model and th e High-Resolution Model. Conceptually, we should be able to aggregate the modeling predicti ons from multiple census tracts within a county from the low-resolution model and match the county-level predictions from the Broad-Based National Model. Due to the fact that the National Model and Massachusetts Models were developed independently, using different data sources for th e surveillance data (CDC and MDPH), and utilizing different predictor va riables – these synergies may not exist.

Further work on integrating the Broad-Based Na tional Model with the High-Resolution Model (or multiple high resolution models if EPA is su ccessful at expanding this project to include multiple additional programs) can be done by fitti ng these two types of models jointly under the concept of hierarchical linear modeling. This type of model, while more sophisticated and computer-intense, can be developed using sp ecialized software unde r a Monte-Carlo Markov Chain Bayesian formulation.

7.5 Other Recommendations for Immediate Future Work

The previous sections within Chapter 7 focu s on various important issues related to the development of models to predict risk of ch ildhood lead poisoning at the geographic level, including calibration to the na tionally representative trends over time observed in NHANES, assessment of the potential impact of a variety of important biases and other data quality issues, and various model validation exerci ses that can be explored. EPA also has been including other state and local lead poisoning prevention programs as part of the project conference calls in anticipation of developing additi onal High-Resolution Models as part of follow-up work to this project. While these are all worthy tasks to pur sue as part of future work, there are some additional analyses that the project team would recommend pursuing on the Broad-Based National Model as well as the High-Resolution M odel within Massachusetts prior to approval of this report as a final report. These activities include the following:85 • Broad-Based National Model

o CDC grantee relationship managers may ha ve insight into data quality issues (such as the previously discussed labo ratory minimum reporting values, and not following universal reporting guidelin es) for specific geographic areas and periods of time. Additional scrutiny of these data could be used to improve the quality of the blood-lead response data that serves as a basis for these models. The maps and visualization tool should he lp foster this review of the data.

o In addition to the above data review –fur ther investigation into using urban vs. rural status as a potential e ffect modifier in the analys es also is recommended. Differentiating between urban and rura l areas can be conducting in numerous ways, including:

 Determining whether the county is part of a Metropolitan Statistical Area within the 2000 US Census

 Identification of the counties that co ntain the U.S. top 100 (or 200) cities based on population size

 Use of a population density score (with a cut-off value).

Use of this variable as a potential eff ect modifier might include fitting separate intercepts and slopes for the effects of time and seasonality within the different regions of the country, as we ll as the potential for usi ng different environmental, programmatic, and demographic predictor va riables in these two area types in the multivariate predictive models.

o Once the proper way of handling the potenti al effect modifier for rural versus urban areas – the exploratory analyses that assess the predictive ability of each candidate environmental, programmatic, a nd demographic variab le could be refit in a manner consistent with the baseline eff ects that will be incl uded in the model. Thus – rather than assessing the predictiv e ability of a candida te variable after adjusting for the downward trend of time, it should be assessed after adjusting it for region, region*time, region*seasonalit y, and potentially region*urban/rural.

High-Resolution Model in Massachusetts

• Due to the fact that we know that Mass achusetts followed universal screening and reporting guidelines during the entire period of observation (2000-2006), and the fact that these data have been used previously to s upport federally funded rese arch projects – there is less concern about some of the previously mentioned data quality issues. This does not mean that the Massachusetts data are not poten tially biased or flawed, as there are still probable selection biases and potential geocod ing biases that were introduced into the analysis dataset that suppor ts the High-Resolution Model. Our collaborators at the Massachusetts Department of Public Health are invited to review and comment on this work, and add their insight and experien ce in making recommendations on additional ways of handling the various data sources that were integrated into this model.

• It also is recommended that comparisons be made between the observed and predicted data from the Broad-Based National Model fo r counties in Massachusetts (based on the input data received from CDC) with the observed and predicted data from the High-86 Resolution Model (based on the input data received from MDPH) by aggregating the observed and predicted census tr act data within Massachusetts to the county level.

• Finally, pursuit of some additional analyses of the individual-level data from MDPH is recommended – by linking individual blood-lead testing results on children over time to the housing inspection results (as well as other census-tract level pr edictors that were used in the current High-Resolution Model). This will help identify the degree of information loss experienced by pursuit of th e ecological models of aggregate summary data.87 8.0 REFERENCES

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