Warren Strauss

Exposure of U.S. children to residential dust lead, 1999-2004: II. The contribution of lead-contaminated dust to children's blood lead levels.

Background The U.S. Centers for Disease Control and Prevention collected health, housing, and environmental data in a single integrated national survey for the first time in the United States in 1999–2004. Objectives We aimed to determine how floor dust lead (PbD) loadings and other housing factors influence childhood blood lead (PbB) levels and lead poisoning. Methods We analyzed data from the 1999–2004 National Health and Nutrition Examination Survey (NHANES), including 2,155 children 12–60 months of age with PbB and PbD measurements. We used linear and logistic regression models to predict log-transformed PbB and the odds that PbB was ≥ 5 and ≥ 10 μg/dL at a range of floor PbD. Results The population-weighted geometric mean (GM) PbB was 2.0 μg/dL (geometric standard error = 1.0). Age of child, race/ethnicity, serum cotinine concentration, poverty-to-income ratio, country of birth, year of building construction, floor PbD by floor surface and condition, windowsill PbD, presence of deteriorated paint, home-apartment type, smoking in the home, and recent renovation were significant predictors in either the linear model [the proportion of variability in the dependent variable accounted for by the model (R2) = 40%] or logistic model for 10 μg/dL (R2 = 5%). At floor PbD = 12 μg/ft2, the models predict that 4.6% of children living in homes constructed before 1978 have PbB ≥ 10 μg/dL, 27% have PbB ≥ 5 μg/dL, and the GM PbB is 3.9 μg/dL. Conclusions Lowering the floor PbD standard below the current standard of 40 μg/ft2 would protect more children from elevated PbB.

Exposure of U.S. Children to Residential Dust Lead, 1999–2004: I. Housing and Demographic Factors

Background Lead-contaminated house dust is a major source of lead exposure for children in the United States. In 1999–2004, the National Health and Nutrition Examination Survey (NHANES) collected dust lead (PbD) loading samples from the homes of children 12–60 months of age. Objectives In this study we aimed to compare national PbD levels with existing health-based standards and to identify housing and demographic factors associated with floor and windowsill PbD. Methods We used NHANES PbD data (n = 2,065 from floors and n = 1,618 from windowsills) and covariates to construct linear and logistic regression models. Results The population-weighted geometric mean floor and windowsill PbD were 0.5 μg/ft2 [geometric standard error (GSE) = 1.0] and 7.6 μg/ft2 (GSE = 1.0), respectively. Only 0.16% of the floors and 4.0% of the sills had PbD at or above current federal standards of 40 and 250 μg/ft2, respectively. Income, race/ethnicity, floor surface/condition, windowsill PbD, year of construction, recent renovation, smoking, and survey year were significant predictors of floor PbD [the proportion of variability in the dependent variable accounted for by the model (R2) = 35%]. A similar set of predictors plus the presence of large areas of exterior deteriorated paint in pre-1950 homes and the presence of interior deteriorated paint explained 20% of the variability in sill PbD. A companion article [Dixon et al. Environ Health Perspect 117:468–474 (2009)] describes the relationship between children’s blood lead and PbD. Conclusion Most houses with children have PbD levels that comply with federal standards but may put children at risk. Factors associated with PbD in our population-based models are primarily the same as factors identified in smaller at-risk cohorts. PbD on floors and windowsills should be kept as low as possible to protect children.

Exposures of preschool children to chlorpyrifos, diazinon, pentachlorophenol, and 2,4-dichlorophenoxyacetic acid over 3 years from 2003 to 2005: A longitudinal model.

The impact of the US EPA-required phase-outs starting in 2000–2001 of residential uses of the organophosphate (OP) pesticides chlorpyrifos (CPF) and diazinon (DZN) on preschool childrens pesticide exposures was investigated over 2003–2005, in the Raleigh-Durham-Chapel Hill area of North Carolina. Data were collected from 50 homes, each with a child initially of age 3 years (OCh) and a younger child (YCh). Environmental samples (indoor and outdoor air, dust, soil) and child-specific samples (hand surface residue, urine, diet) were collected annually over 24-h periods at each home. Child time-activity diaries and household pesticide use information were also collected. Analytes included CPF and DZN; pentachlorophenol (PCP); 2,4-dichlorophenoxyacetic acid (2,4-D); the CPF metabolite 3,5,6-trichloro-2-pyridinol (TCP); and the DZN metabolite 2-isopropyl-6-methyl-4-pyrimidinol (IMP). Exposures (ng/day) through the inhalation, dietary ingestion, and indirect ingestion were calculated. Aggregate potential doses in ng/kg body weight per day (ng/kg/day) were obtained by summing the potential doses through the three routes of exposure. Geometric mean aggregate potential doses decreased from 2003 to 2005 for both OCh and YCh, with the exception of 2,4-D. Child-specific longitudinal modeling indicated significant declines across time of the potential doses of CPF, DZN, and PCP for both children; declines of IMP for both children, significant only for OCh; a decline of TCP for OCh but an increase of TCP for YCh; and no significant change of 2,4-D for either child. Age-adjusted modeling indicated significant effects of the childs age for all except CPF, and of time for all except PCP and 2,4-D. Within-home variability was small compared with that between homes; variability was smallest for 2,4-D, both within and between homes. The aggregate potential doses of CPF and DZN were well below published reference dose values. These findings show the success of the US EPA restrictions in reducing young childrens pesticide exposures.

Soil lead abatement and children's blood lead levels in an urban setting.

OBJECTIVES The effect of abating soil lead was assessed among Baltimore children. The hypothesis was that a reduction of 1000 parts per million would reduce childrens blood lead levels by 0.14 to 0.29 mumol/L (3-6 micrograms/dL). METHODS In 2 neighborhoods (study and control), 187 children completed the protocol. In the study area, contaminated soil was replaced with clean soil. RESULTS Soil lead abatement in this study did not lower childrens blood lead. CONCLUSIONS Although it did not show an effect in this study, soil lead abatement may be useful in certain areas.

A Bayesian Hierarchical Approach for Relating PM2.5 Exposure to Cardiovascular Mortality in North Carolina

Considerable attention has been given to the relationship between levels of fine particulate matter (particulate matter ≤ 2.5 μm in aerodynamic diameter; PM2.5) in the atmosphere and health effects in human populations. Since the U.S. Environmental Protection Agency began widespread monitoring of PM2.5 levels in 1999, the epidemiologic community has performed numerous observational studies modeling mortality and morbidity responses to PM2.5 levels using Poisson generalized additive models (GAMs). Although these models are useful for relating ambient PM2.5 levels to mortality, they cannot directly measure the strength of the effect of exposure to PM2.5 on mortality. In order to assess this effect, we propose a three-stage Bayesian hierarchical model as an alternative to the classical Poisson GAM. Fitting our model to data collected in seven North Carolina counties from 1999 through 2001, we found that an increase in PM2.5 exposure is linked to increased risk of cardiovascular mortality in the same day and next 2 days. Specifically, a 10-μg/m3 increase in average PM2.5 exposure is associated with a 2.5% increase in the relative risk of current-day cardiovascular mortality, a 4.0% increase in the relative risk of cardiovascular mortality the next day, and an 11.4% increase in the relative risk of cardiovascular mortality 2 days later. Because of the small sample size of our study, only the third effect was found to have > 95% posterior probability of being > 0. In addition, we compared the results obtained from our model to those obtained by applying frequentist (or classical, repeated sampling-based) and Bayesian versions of the classical Poisson GAM to our study population.

Statistical Design Features of the Healthy Communities Study

The Healthy Communities Study is designed to assess relationships between characteristics of community programs and policies targeting childhood obesity and childrens BMI, diet, and physical activity. The study involved a complex data collection protocol implemented over a 2-year period (2013-2015) across a diverse sample of 130 communities, defined as public high school catchment areas. The protocol involved baseline assessment within each community that included in-person or telephone interviews regarding community programs and policies and in-home collection of BMI, nutritional, and physical activity outcomes from a sample of up to 81 children enrolled in kindergarten through eighth grade in public schools. The protocol also involved medical record reviews to establish a longitudinal trajectory of BMI for an estimated 70% of participating children. Staged sampling was used to collect less detailed measures of physical activity and nutrition across the entire sample of children, with a subset assessed using more costly, burdensome, and detailed measures. Data from the Healthy Community Study will be analyzed using both cross-sectional and longitudinal models that account for the complex design and correct for measurement error and bias using a likelihood-based Markov-chain Monte Carlo methodology. This methods paper provides insights into the complex design features of the Healthy Communities Study and may serve as an example for future large-scale studies that assess the relationship between community-based programs and policies and health outcomes of community residents.

Operational Implementation of the Healthy Communities Study: How Communities Shape Children’s Health

The Healthy Communities Study (HCS) is examining how characteristics of community programs and policies targeting childhood obesity are related to childhood diet, physical activity, and obesity outcomes. The study involves selected districts and public schools in 130 communities; families recruited through schools; and data collected at the community, school, household, and child levels. Data collection took place in two waves-Wave 1 in Spring 2012 and Wave 2 from 2013 to 2015-with analysis to be completed by August 2016. This paper describes operational elements of the HCS, including recruitment activities, field operations, training of data collectors, human subjects protection, and quality assurance and quality control procedures. Experienced trainers oversaw and conducted all training, including training of (1) district and school recruitment staff; (2) telephone interviewers for household screening and recruitment; (3) field data collectors for conducting household data collection; and (4) community liaisons for conducting key informant interviews, document abstraction, and community observations. The study team developed quality assurance and quality control procedures that were implemented for all aspects of the study. Planning and operationalizing a study of this complexity and magnitude, with multiple functional teams, required frequent communication and strong collaboration among all study partners to ensure timely and effective decision making.

Relating Ambient Particulate Matter Concentration Levels to Mortality Using an Exposure Simulator

Since the U.S. Environmental Protection Agency began widespread monitoring of PM2.5 (particulate matter <2.5 μ in diameter) concentration levels in the late 1990s, the epidemiological community has performed several observational studies directly relating PM2.5 concentration to various health endpoints including mortality and morbidity. However, recent research suggests that human exposure to the constituents of PM2.5 may differ significantly from ambient (or outdoor) PM2.5 concentration measured by monitors because people spend a great deal of time in environments, such as various indoor environments, where they are partially shielded from ambient sources of PM and are exposed to nonambient sources of PM. Recent research has provided some ways to include exposure information, but little has been done to determine the impact of including such information in a statistical model. To address this concern, we develop a three-stage Bayesian hierarchical model based on the Poisson regression model that is traditionally used to characterize the relationship between PM2.5 concentration and health endpoints. Our approach includes a spatial model relating monitor readings to average county PM2.5 concentration and an exposure simulator that links average ambient PM2.5 concentration to average personal exposure using activity pattern data. We apply our model to a study population in North Carolina and explore the impact of various exposure modeling assumptions on the conclusions that can be drawn about the link between PM2.5 exposure and cardiovascular mortality.

Improving cost‐effectiveness of epidemiological studies via designed missingness strategies

Modern epidemiological studies face opportunities and challenges posed by an ever-expanding capacity to measure a wide range of environmental exposures, along with sophisticated biomarkers of exposure and response at the individual level. The challenge of deciding what to measure is further complicated for longitudinal studies, where logistical and cost constraints preclude the collection of all possible measurements on all participants at every follow-up time. This is true for the National Childrens Study (NCS), a large-scale longitudinal study that will enroll women both prior to conception and during pregnancy and collect information on their environment, their pregnancies, and their childrens development through early adulthood-with a goal of assessing key exposure/outcome relationships among a cohort of approximately 100 000 children. The success of the NCS will significantly depend on the accurate, yet cost-effective, characterization of environmental exposures thought to be related to the health outcomes of interest. The purpose of this paper is to explore the use of cost saving, yet valid and adequately powered statistical approaches for gathering exposure information within epidemiological cohort studies. The proposed approach involves the collection of detailed exposure assessment information on a specially selected subset of the study population, and collection of less-costly, and presumably less-detailed and less-burdensome, surrogate measures across the entire cohort. We show that large-scale efficiency in costs and burden may be achieved without making substantive sacrifices on the ability to draw reliable inferences concerning the relationship between exposure and health outcome. Several detailed scenarios are provided that document how the targeted sub-sampling design strategy can benefit large cohort studies like the NCS, as well as other more focused environmental epidemiologic studies.

Community Policies and Programs to Prevent Obesity and Child Adiposity

INTRODUCTION Evidence regarding impact of community policies and programs (CPPs) to prevent child obesity is limited, and which combinations of strategies and components are most important is not understood. The Healthy Communities Study was an observational study to assess relationships of characteristics and intensity of CPPs with adiposity, diet, and physical activity in children, taking advantage of variation across the U.S. in community actions to prevent child obesity. The study examined the association of CPPs to prevent child obesity with measured BMI and waist circumference, hypothesizing that communities with more-comprehensive CPPs would have children with lower adiposity. METHODS The study included 130 communities selected by probability-based sampling or because of known CPPs targeting child obesity. Data were collected at home visits on 5,138 children during 2013-2015. CPPs were scored for multiple attributes to create a CPP intensity score. A CPP target behavior score reflected the number of distinct target behaviors addressed. Scores were standardized with the smallest observed score across communities being 0 and the largest 1. Multilevel regression analysis in 2016 adjusted for community, household, and individual characteristics. RESULTS Higher CPP target behavior score was significantly associated with lower BMI and waist circumference in a dose-response relationship, with magnitude for the past 3 years of CPPs of 0.843 (p=0.013) for BMI and 1.783 cm (p=0.020) for waist circumference. CONCLUSIONS This study provides plausible evidence that comprehensive CPPs targeting a greater number of distinct physical activity and nutrition behaviors were associated with lower child adiposity.