Estimation of pyrethroid pesticide intake using regression modeling of food groups based on composite dietary samples.

Population-based estimates of pesticide intake are needed to characterize exposure for particular demographic groups based on their dietary behaviors. Regression modeling performed on measurements of selected pesticides in composited duplicate diet samples allowed (1) estimation of pesticide intakes for a defined demographic community, and (2) comparison of dietary pesticide intakes between the composite and individual samples. Extant databases were useful for assigning individual samples to composites, but they could not provide the breadth of information needed to facilitate measurable levels in every composite. Composite sample measurements were found to be good predictors of pyrethroid pesticide levels in their individual sample constituents where sufficient measurements are available above the method detection limit. Statistical inference shows little evidence of differences between individual and composite measurements and suggests that regression modeling of food groups based on composite dietary samples may provide an effective tool for estimating dietary pesticide intake for a defined population.

Dietary intakes of pesticides based on community duplicate diet samples.

The calculation of dietary intake of selected pesticides was accomplished using food samples collected from individual representatives of a defined demographic community using a community duplicate diet approach. A community of nine participants was identified in Apopka, FL from which intake assessments of organophosphate (OP) and pyrethroid pesticides were made. From these nine participants, sixty-seven individual samples were collected and subsequently analyzed by gas chromatography/mass spectrometry. Measured concentrations were used to estimate dietary intakes for individuals and for the community. Individual intakes of total OP and pyrethroid pesticides ranged from 6.7 to 996 ng and 1.2 to 16,000 ng, respectively. The community intake was 256 ng for OPs and 3430 ng for pyrethroid pesticides. The most commonly detected pesticide was permethrin, but the highest overall intake was of bifenthrin followed by esfenvalerate. These data indicate that the community in Apopka, FL, as represented by the nine individuals, was potentially exposed to both OP and pyrethroid pesticides at levels consistent with a dietary model and other field studies in which standard duplicate diet samples were collected. Higher levels of pyrethroid pesticides were measured than OPs, which is consistent with decreased usage of OPs. The diversity of pyrethroid pesticides detected in food samples was greater than expected. Continually changing pesticide usage patterns need to be considered when determining analytes of interest for large scale epidemiology studies. The Community Duplicate Diet Methodology is a tool for researchers to meet emerging exposure measurement needs that will lead to more accurate assessments of intake which may enhance decisions for chemical regulation. Successfully determining the intake of pesticides through the dietary route will allow for accurate assessments of pesticide exposures to a community of individuals, thereby significantly enhancing the research benefit realized from epidemiological exposure studies.

Advancing infection control in dental care settings: factors associated with dentists' implementation of guidelines from the Centers for Disease Control and Prevention.

BACKGROUND AND OVERVIEW: The authors set out to identify factors associated with implementation by U.S. dentists of four practices first recommended in the Centers for Disease Control and Prevention's Guidelines for Infection Control in Dental Health-Care Settings-2003. METHODS: In 2008, the authors surveyed a stratified random sample of 6,825 U.S. dentists. The response rate was 49 percent. The authors gathered data regarding dentists' demographic and practice characteristics, attitudes toward infection control, sources of instruction regarding the guidelines and knowledge about the need to use sterile water for surgical procedures. Then they assessed the impact of those factors on the implementation of four recommendations: having an infection control coordinator, maintaining dental unit water quality, documenting percutaneous injuries and using safer medical devices, such as safer syringes and scalpels. The authors conducted bivariate analyses and proportional odds modeling. RESULTS: Responding dentists in 34 percent of practices had implemented none or one of the four recommendations, 40 percent had implemented two of the recommendations and 26 percent had implemented three or four of the recommendations. The likelihood of implementation was higher among dentists who acknowledged the importance of infection control, had practiced dentistry for less than 30 years, had received more continuing dental education credits in infection control, correctly identified more surgical procedures that require the use of sterile water, worked in larger practices and had at least three sources of instruction regarding the guidelines. Dentists with practices in the South Atlantic, Middle Atlantic or East South Central U.S. Census divisions were less likely to have complied. CONCLUSIONS: Implementation of the four recommendations varied among U.S. dentists. Strategies targeted at raising awareness of the importance of infection control, increasing continuing education requirements and developing multiple modes of instruction may increase implementation of current and future Centers for Disease Control and Prevention guidelines.

Community duplicate diet methodology: a new tool for estimating dietary exposures to pesticides.

An observational field study was conducted to assess the feasibility of a community duplicate diet collection method; a dietary monitoring tool that is population-based. The purpose was to establish an alternative procedure to duplicate diet sampling that would be more efficient for a large, defined population, e.g., in the National Children's Study (NCS). Questionnaire data and food samples were collected in a residence so as not to lose the important component of storage, preparation, and handling in a contaminated microenvironment. The participants included nine Hispanic women of child bearing age living in Apopka, FL, USA. Foods highly consumed by Hispanic women were identified based on national food frequency questionnaires and prioritized by permethrin residue concentrations as measured for the Pesticide Data Program. Participants filled out questionnaires to determine if highly consumed foods were commonly eaten by them and to assess the collection protocol for the food samples. Measureable levels of permethrin were found in 54% of the samples. Questionnaire responses indicated that the collection of the community duplicate diet was feasible for a defined population.

Pesticides on household surfaces may influence dietary intake of children.

The physical and chemical environment influences children's exposures to pesticides in and around the home. Children's activities, which increase their potential for exposure especially during eating, have been captured in the Children's Dietary Intake Model (CDIM). In addition to the chemical exposure associated with the food itself, this model incorporates excess dietary exposures due to handling of food during consumption. To stochastically evaluate CDIM, distributions of measured, and in some cases estimated, model factors were determined from measurements of permethrin, chlorpyrifos, and diazinon derived from assembled databases and laboratory experiments. Using the distributions of these factors, Monte Carlo simulations were performed to obtain distributions of total dietary intake of pesticides. To target the sources of pesticide contamination that were influencing total dietary intake, each factor was evaluated. We found pesticide surface concentration to be highly influential. By excluding surface concentration, we were also able to determine the influence of the other factors based on the F-statistic. Transfer efficiencies, followed by pesticide residue in consumed foods and amount of food consumed, were the next most influential factors within the model. With these distributions for model inputs, CDIM has the potential to more accurately predict total dietary intake of a contaminant by a child.

A model to predict the breathing zone concentrations of particles emitted from surfaces.

Activity based sampling (ABS) is typically performed to assess inhalation exposure to particulate contaminants known to have low, heterogeneous concentrations on a surface. Activity based sampling determines the contaminant concentration in a person's breathing zone as they perform a scripted activity, such as raking a specified area of soil, while wearing appropriate sample collection instrumentation. As an alternative approach, a probabilistic model based on aerosol physics and fluid dynamics was developed to predict the breathing zone concentration of a particulate contaminant emitted from a surface during activities of variable intensity. The model predicted the particle emission rate, tracked particle transport to the breathing zone, and calculated the breathing zone concentration for two scenarios. One scenario used an Eulerian model based on a Gaussian concentration distribution to quantify aerosol exposure in the trailing wake of a moving object. The second scenario modeled exposure in a quiescent environment. A Lagrangian model tracked the cumulative number of individual particles entering the breathing zone volume at a particular time. A Monte Carlo simulation calculated the breathing zone concentration probability distribution for each scenario. Both models predicted probability distributions of asbestos breathing zone concentrations that bracketed experimentally measured personal exposure concentrations. Modeled breathing zone concentrations were statistically correlated (p-value < 0.001) with independently collected ABS concentrations. The linear regression slope of 0.70 and intercept of 0.03 were influenced by the quantity of ABS data collected and model parameter input distributions at a site broader than those at other sites.

Field turbidity method for the determination of lead in acid extracts of dried paint.

Lead, which can be found in old paint, soil, and dust, has been clearly shown to have adverse health effects on the neurological systems of both children and adults. As part of an ongoing effort to reduce childhood lead poisoning, the US Environmental Protection Agency promulgated the Lead Renovation, Repair, and Painting Program (RRP) rule requiring that paint in target housing built prior to 1978 be tested for lead before any renovation, repair, or painting activities are initiated. This rule has led to a need for a rapid, relatively easy, and an inexpensive method for measuring lead in paint. This paper presents a new method for measuring lead extracted from paint that is based on turbidimetry. This method is applicable to paint that has been collected from a surface and extracted into 25% (v/v) of nitric acid. An aliquot of the filtered extract is mixed with an aliquot of solid potassium molybdate in 1 M ammonium acetate to form a turbid suspension of lead molybdate. The lead concentration is determined using a portable turbidity meter. This turbidimetric method has a response of approximately 0.9 NTU per microg lead per mL extract, with a range of 1-1000 Nephelometric Turbidity Units (NTUs). Precision at a concentration corresponding to the EPA-mandated decision point of 1 mg of lead per cm(2) is <2%. This method is insensitive to the presence of other metals common to paint, including Ba(2+), Ca(2+), Mg(2+), Fe(3+), Co(2+), Cu(2+), and Cd(2+), at concentrations of 10 mg mL(-1) or to Zn(2+) at 50 mg mL(-1). Analysis of 14 samples from six reference materials with lead concentrations near 1 mg cm(-2) yielded a correlation to inductively coupled plasma-atomic emission spectroscopy (ICP-AES) analysis of 0.97, with an average bias of 2.8%. Twenty-four sets of either 6 or 10 paint samples each were collected from different locations in old houses, a hospital, tobacco factory, and power station. Half of each set was analyzed using rotor/stator-25% (v/v) nitric acid extraction with measurement using the new turbidimetric method, and the other half was analyzed using microwave extraction and measurement by ICP-AES. The average relative percent difference between the turbidimetric method and the ICP-AES method for the 24 sets measured as milligrams of lead per cm(2) is -0.63 +/- 32.5%; the mean difference is -2.1 +/- 7.0 mg lead per cm(2). Non-parametric and parametric statistical tests on these data showed no difference in the results for the two procedures. At the federal regulated level of 1 mg of lead per cm(2) paint, this turbidimetric method meets the performance requirements for EPA's National Lead Laboratory Accreditation Program (NLLAP) of accuracy within +/-20% and has the potential to meet the performance specifications of EPA's RRP rule.

Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data.

There is increasing interest in estimating and drawing inferences about risk or prevalence ratios and differences instead of odds ratios in the regression setting. Recent publications have shown how the GENMOD procedure in SAS (SAS Institute Inc., Cary, North Carolina) can be used to estimate these parameters in non-population-based studies. In this paper, the authors show how model-adjusted risks, risk differences, and risk ratio estimates can be obtained directly from logistic regression models in the complex sample survey setting to yield population-based inferences. Complex sample survey designs typically involve some combination of weighting, stratification, multistage sampling, clustering, and perhaps finite population adjustments. Point estimates of model-adjusted risks, risk differences, and risk ratios are obtained from average marginal predictions in the fitted logistic regression model. The model can contain both continuous and categorical covariates, as well as interaction terms. The authors use the SUDAAN software package (Research Triangle Institute, Research Triangle Park, North Carolina) to obtain point estimates, standard errors (via linearization or a replication method), confidence intervals, and P values for the parameters and contrasts of interest. Data from the 2006 National Health Interview Survey are used to illustrate these concepts.

Voluntary disenrollment from Medicare managed care: market factors and disabled beneficiaries.

Disenrollment rates from Medicare managed care plans have been reported to the public as an indicator of health plan quality. Previous studies have shown that voluntary disenrollment rates differ among vulnerable subgroups, and that these rates can reflect patient care experiences. We hypothesized that disabled beneficiaries may be affected differently than other beneficiaries by competitive market factors, due to higher expected expenditures and impaired mobility. Findings suggest that disabled beneficiaries are more likely to experience multiple problems with managed care.